High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 47	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 73	99.27 1	99.54 1

UniMVSNet_ETH3D		89.12 68	90.72 49	84.31 189	97.00 2	64.33 315	89.67 80	88.38 259	88.84 16	94.29 22	97.57 7	90.48 14	91.26 213	72.57 278	97.65 69	97.34 15

PMVS	80.48 6	90.08 44	90.66 50	88.34 87	96.71 3	92.97 1	90.31 65	89.57 234	88.51 20	90.11 116	95.12 53	90.98 7	88.92 296	77.55 183	97.07 93	83.13 455
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MTAPA		91.52 18	91.60 23	91.29 29	96.59 4	86.29 29	92.02 39	91.81 154	84.07 57	92.00 78	94.40 82	86.63 61	95.28 62	88.59 10	98.31 25	92.30 239

PEN-MVS		90.03 48	91.88 19	84.48 180	96.57 5	58.88 420	88.95 96	93.19 94	91.62 4	96.01 6	96.16 26	87.02 56	95.60 42	78.69 160	98.72 8	98.97 3

PS-CasMVS		90.06 46	91.92 16	84.47 181	96.56 6	58.83 423	89.04 95	92.74 120	91.40 5	96.12 4	96.06 28	87.23 53	95.57 43	79.42 151	98.74 5	99.00 2

DTE-MVSNet		89.98 50	91.91 18	84.21 192	96.51 7	57.84 434	88.93 97	92.84 115	91.92 3	96.16 3	96.23 23	86.95 57	95.99 12	79.05 156	98.57 14	98.80 6

CP-MVSNet		89.27 65	90.91 46	84.37 182	96.34 8	58.61 426	88.66 104	92.06 143	90.78 6	95.67 7	95.17 51	81.80 140	95.54 46	79.00 157	98.69 9	98.95 4

WR-MVS_H		89.91 53	91.31 35	85.71 144	96.32 9	62.39 347	89.54 85	93.31 88	90.21 11	95.57 10	95.66 36	81.42 145	95.90 17	80.94 130	98.80 2	98.84 5

MP-MVS		91.14 28	90.91 46	91.83 19	96.18 10	86.88 23	92.20 32	93.03 106	82.59 75	88.52 164	94.37 84	86.74 59	95.41 56	86.32 49	98.21 33	93.19 180
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
FOURS1					96.08 11	87.41 18	96.19 2	95.83 4	92.95 2	96.57 2

mPP-MVS		91.69 15	91.47 28	92.37 5	96.04 12	88.48 11	92.72 19	92.60 127	83.09 70	91.54 86	94.25 89	87.67 48	95.51 49	87.21 36	98.11 39	93.12 185

MP-MVS-pluss		90.81 31	91.08 39	89.99 49	95.97 13	79.88 104	88.13 111	94.51 19	75.79 163	92.94 53	94.96 55	88.36 32	95.01 73	90.70 2	98.40 21	95.09 74
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		93.52 2	93.39 4	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 56	96.29 21	88.16 37	94.17 108	86.07 55	98.48 17	97.22 18

ACMMP_NAP		90.65 34	91.07 41	89.42 61	95.93 15	79.54 110	89.95 72	93.68 68	77.65 138	91.97 79	94.89 57	88.38 31	95.45 54	89.27 5	97.87 55	93.27 174

HPM-MVS_fast		92.50 7	92.54 9	92.37 5	95.93 15	85.81 42	92.99 12	94.23 28	85.21 45	92.51 66	95.13 52	90.65 10	95.34 59	88.06 15	98.15 38	95.95 45

MSP-MVS		89.08 69	88.16 88	91.83 19	95.76 17	86.14 33	92.75 18	93.90 49	78.43 127	89.16 148	92.25 187	72.03 287	96.36 4	88.21 12	90.93 357	92.98 195
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
region2R		91.44 22	91.30 36	91.87 18	95.75 18	85.90 38	92.63 23	93.30 89	81.91 81	90.88 105	94.21 91	87.75 45	95.87 20	87.60 26	97.71 64	93.83 142

ACMMPR		91.49 19	91.35 32	91.92 15	95.74 19	85.88 39	92.58 24	93.25 91	81.99 79	91.40 88	94.17 95	87.51 49	95.87 20	87.74 21	97.76 61	93.99 130

ZNCC-MVS		91.26 24	91.34 33	91.01 33	95.73 20	83.05 72	92.18 33	94.22 30	80.14 102	91.29 92	93.97 106	87.93 43	95.87 20	88.65 9	97.96 50	94.12 126

TSAR-MVS + MP.		88.14 80	87.82 92	89.09 68	95.72 21	76.74 146	92.49 27	91.19 177	67.85 316	86.63 228	94.84 59	79.58 166	95.96 15	87.62 24	94.50 217	94.56 97
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		91.20 26	90.95 45	91.93 14	95.67 22	85.85 40	90.00 68	93.90 49	80.32 99	91.74 85	94.41 81	88.17 36	95.98 13	86.37 48	97.99 45	93.96 133

XVS		91.54 17	91.36 30	92.08 8	95.64 23	86.25 30	92.64 21	93.33 85	85.07 46	89.99 120	94.03 103	86.57 62	95.80 30	87.35 32	97.62 72	94.20 118

X-MVStestdata		85.04 149	82.70 227	92.08 8	95.64 23	86.25 30	92.64 21	93.33 85	85.07 46	89.99 120	16.05 555	86.57 62	95.80 30	87.35 32	97.62 72	94.20 118

HPM-MVS		92.13 11	92.20 13	91.91 16	95.58 25	84.67 56	93.51 8	94.85 15	82.88 73	91.77 84	93.94 112	90.55 13	95.73 37	88.50 11	98.23 32	95.33 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		91.91 14	91.87 20	92.03 11	95.53 26	85.91 37	93.35 11	94.16 33	82.52 76	92.39 69	94.14 96	89.15 26	95.62 41	87.35 32	98.24 31	94.56 97
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
GST-MVS		90.96 30	91.01 42	90.82 36	95.45 27	82.73 75	91.75 44	93.74 58	80.98 92	91.38 89	93.80 116	87.20 54	95.80 30	87.10 39	97.69 66	93.93 134

HFP-MVS		91.30 23	91.39 29	91.02 32	95.43 28	84.66 57	92.58 24	93.29 90	81.99 79	91.47 87	93.96 109	88.35 33	95.56 44	87.74 21	97.74 63	92.85 201

SMA-MVS		90.31 40	90.48 53	89.83 54	95.31 29	79.52 111	90.98 52	93.24 92	75.37 173	92.84 57	95.28 48	85.58 80	96.09 8	87.92 17	97.76 61	93.88 137
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CP-MVS		91.67 16	91.58 24	91.96 13	95.29 30	87.62 16	93.38 9	93.36 81	83.16 69	91.06 97	94.00 105	88.26 34	95.71 39	87.28 35	98.39 22	92.55 219

VDDNet		84.35 170	85.39 149	81.25 290	95.13 31	59.32 408	85.42 172	81.11 390	86.41 35	87.41 205	96.21 24	73.61 257	90.61 247	66.33 347	96.85 100	93.81 146

CPTT-MVS		89.39 61	88.98 73	90.63 39	95.09 32	86.95 21	92.09 38	92.30 136	79.74 107	87.50 203	92.38 178	81.42 145	93.28 150	83.07 101	97.24 89	91.67 269

RA-MVS			89.67 60		95.08 33	87.07 19	92.84 16	92.84 115	69.58 280	92.71 62	94.23 90	87.28 51	96.46 2	84.88 80	97.57 78

ACMM	79.39 9	90.65 34	90.99 43	89.63 57	95.03 34	83.53 66	89.62 82	93.35 84	79.20 116	93.83 33	93.60 127	90.81 8	92.96 161	85.02 76	98.45 18	92.41 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		91.49 19	91.53 25	91.39 26	94.98 35	82.95 74	93.52 7	92.79 118	88.22 22	88.53 163	97.64 6	83.45 103	94.55 91	86.02 59	98.60 12	96.67 30

HPM-MVS++		88.93 71	88.45 84	90.38 43	94.92 36	85.85 40	89.70 77	91.27 174	78.20 130	86.69 227	92.28 186	80.36 159	95.06 72	86.17 54	96.49 115	90.22 313

XVG-ACMP-BASELINE		89.98 50	89.84 57	90.41 42	94.91 37	84.50 58	89.49 87	93.98 44	79.68 108	92.09 75	93.89 114	83.80 98	93.10 157	82.67 109	98.04 40	93.64 155

EGC-MVSNET		74.79 383	69.99 443	89.19 66	94.89 38	87.00 20	91.89 43	86.28 303	1.09 557	2.23 561	95.98 29	81.87 138	89.48 283	79.76 143	95.96 142	91.10 283

SR-MVS		92.23 10	92.34 11	91.91 16	94.89 38	87.85 13	92.51 26	93.87 52	88.20 23	93.24 44	94.02 104	90.15 17	95.67 40	86.82 42	97.34 86	92.19 247

OPM-MVS		89.80 54	89.97 55	89.27 63	94.76 40	79.86 105	86.76 139	92.77 119	78.78 122	92.51 66	93.64 126	88.13 38	93.84 123	84.83 83	97.55 79	94.10 127
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LPG-MVS_test		91.47 21	91.68 21	90.82 36	94.75 41	81.69 84	90.00 68	94.27 25	82.35 77	93.67 39	94.82 62	91.18 5	95.52 47	85.36 68	98.73 6	95.23 67

LGP-MVS_train				90.82 36	94.75 41	81.69 84		94.27 25	82.35 77	93.67 39	94.82 62	91.18 5	95.52 47	85.36 68	98.73 6	95.23 67

XVG-OURS-SEG-HR		89.59 58	89.37 65	90.28 45	94.47 43	85.95 36	86.84 135	93.91 48	80.07 103	86.75 223	93.26 139	93.64 2	90.93 230	84.60 86	90.75 367	93.97 132

NormalMVS		86.47 110	85.32 151	89.94 50	94.43 44	80.42 99	88.63 105	93.59 73	74.56 183	85.12 273	90.34 270	66.19 327	94.20 103	76.57 199	98.44 19	95.19 69

lecture		92.43 8	93.50 2	89.21 65	94.43 44	79.31 112	92.69 20	95.72 7	88.48 21	94.43 19	95.73 33	91.34 4	94.68 83	90.26 3	98.44 19	93.63 156

reproduce-ours		92.86 5	93.22 5	91.76 22	94.39 46	87.71 14	92.40 29	94.38 20	89.82 12	95.51 11	95.49 42	89.64 22	95.82 28	89.13 6	98.26 29	91.76 263

our_new_method		92.86 5	93.22 5	91.76 22	94.39 46	87.71 14	92.40 29	94.38 20	89.82 12	95.51 11	95.49 42	89.64 22	95.82 28	89.13 6	98.26 29	91.76 263

aaatest				88.50 80	94.38 48	76.12 157	92.12 34	93.85 53	77.53 142	93.24 44	93.18 142		95.85 24	84.99 77	97.69 66	93.54 166

MED-MVS		90.78 32	91.50 26	88.60 78	94.38 48	76.12 157	92.12 34	93.85 53	85.28 43	93.24 44	94.84 59	87.06 55	95.85 24	84.99 77	97.78 58	93.84 139

TestfortrainingZip a		91.12 29	92.04 14	88.36 86	94.38 48	76.05 160	92.12 34	93.73 59	85.28 43	93.85 32	94.84 59	88.66 29	95.18 67	87.89 18	97.59 77	93.84 139

ACMP	79.16 10	90.54 37	90.60 52	90.35 44	94.36 51	80.98 93	89.16 93	94.05 42	79.03 119	92.87 55	93.74 121	90.60 12	95.21 65	82.87 105	98.76 3	94.87 80
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS		89.18 66	88.83 79	90.23 46	94.28 52	86.11 34	85.91 157	93.60 72	80.16 101	89.13 150	93.44 129	83.82 97	90.98 227	83.86 93	95.30 177	93.60 159

test_0728_SECOND				86.79 114	94.25 53	72.45 202	90.54 58	94.10 40					95.88 18	86.42 46	97.97 48	92.02 255

reproduce_model		92.89 4	93.18 7	92.01 12	94.20 54	88.23 12	92.87 13	94.32 22	90.25 10	95.65 8	95.74 32	87.75 45	95.72 38	89.60 4	98.27 27	92.08 252

SED-MVS		90.46 39	91.64 22	86.93 111	94.18 55	72.65 192	90.47 61	93.69 64	83.77 60	94.11 27	94.27 85	90.28 15	95.84 26	86.03 56	97.92 51	92.29 241

IU-MVS					94.18 55	72.64 194		90.82 189	56.98 462	89.67 132				85.78 64	97.92 51	93.28 173

test_241102_ONE					94.18 55	72.65 192		93.69 64	83.62 63	94.11 27	93.78 118	90.28 15	95.50 51

DVP-MVS		90.06 46	91.32 34	86.29 124	94.16 58	72.56 198	90.54 58	91.01 182	83.61 64	93.75 36	94.65 67	89.76 19	95.78 34	86.42 46	97.97 48	90.55 306
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					94.16 58	72.56 198	90.63 55	93.90 49	83.61 64	93.75 36	94.49 75	89.76 19

SR-MVS-dyc-post		92.41 9	92.41 10	92.39 4	94.13 60	88.95 7	92.87 13	94.16 33	88.75 17	93.79 34	94.43 78	88.83 27	95.51 49	87.16 37	97.60 74	92.73 204

RE-MVS-def			92.61 8		94.13 60	88.95 7	92.87 13	94.16 33	88.75 17	93.79 34	94.43 78	90.64 11		87.16 37	97.60 74	92.73 204

MIMVSNet1		83.63 199	84.59 173	80.74 302	94.06 62	62.77 334	82.72 260	84.53 343	77.57 140	90.34 113	95.92 30	76.88 210	85.83 385	61.88 395	97.42 84	93.62 157

TranMVSNet+NR-MVSNet		87.86 87	88.76 82	85.18 157	94.02 63	64.13 316	84.38 200	91.29 170	84.88 49	92.06 76	93.84 115	86.45 65	93.73 126	73.22 269	98.66 10	97.69 9

新几何1				82.95 235	93.96 64	78.56 120		80.24 397	55.45 473	83.93 316	91.08 236	71.19 295	88.33 320	65.84 354	93.07 279	81.95 470

SteuartSystems-ACMMP		91.16 27	91.36 30	90.55 40	93.91 65	80.97 94	91.49 46	93.48 78	82.82 74	92.60 64	93.97 106	88.19 35	96.29 6	87.61 25	98.20 35	94.39 112
Skip Steuart: Steuart Systems R&D Blog.
test_part2					93.86 66	77.77 131				92.84 57

test_one_0601					93.85 67	73.27 184		94.11 39	86.57 33	93.47 43	94.64 70	88.42 30

save fliter					93.75 68	77.44 137	86.31 148	89.72 228	70.80 263

LTVRE_ROB	86.10 1	93.04 3	93.44 3	91.82 21	93.73 69	85.72 43	96.79 1	95.51 9	88.86 15	95.63 9	96.99 12	84.81 88	93.16 154	91.10 1	97.53 82	96.58 33
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
COLMAP_ROB	83.01 3	91.97 13	91.95 15	92.04 10	93.68 70	86.15 32	93.37 10	95.10 13	90.28 9	92.11 74	95.03 54	89.75 21	94.93 75	79.95 141	98.27 27	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	82.31 4	89.15 67	89.08 70	89.37 62	93.64 71	79.07 115	88.54 107	94.20 31	73.53 204	89.71 130	94.82 62	85.09 84	95.77 36	84.17 90	98.03 42	93.26 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tt0320		86.63 107	88.36 86	81.41 288	93.57 72	60.73 385	84.37 201	88.61 253	87.00 30	90.75 107	97.98 2	85.54 81	86.45 365	69.75 310	97.70 65	97.06 22

mvs_tets		89.78 55	89.27 67	91.30 28	93.51 73	84.79 54	89.89 74	90.63 194	70.00 275	94.55 18	96.67 16	87.94 42	93.59 136	84.27 89	95.97 141	95.52 57

sc_t1		87.70 91	88.94 74	83.99 198	93.47 74	67.15 277	85.05 181	88.21 267	86.81 31	91.87 81	97.65 5	85.51 82	87.91 329	74.22 237	97.63 70	96.92 25

tt0320-xc		86.67 105	88.41 85	81.44 287	93.45 75	60.44 388	83.96 211	88.50 254	87.26 28	90.90 104	97.90 3	85.61 79	86.40 368	70.14 305	98.01 44	97.47 14

HQP_MVS		87.75 90	87.43 98	88.70 76	93.45 75	76.42 150	89.45 88	93.61 70	79.44 112	86.55 229	92.95 156	74.84 233	95.22 63	80.78 133	95.83 153	94.46 104

plane_prior7					93.45 75	77.31 140

WR-MVS		83.56 202	84.40 182	81.06 296	93.43 78	54.88 465	78.67 365	85.02 331	81.24 88	90.74 108	91.56 213	72.85 273	91.08 224	68.00 332	98.04 40	97.23 17

DPE-MVS		90.53 38	91.08 39	88.88 70	93.38 79	78.65 119	89.15 94	94.05 42	84.68 51	93.90 29	94.11 98	88.13 38	96.30 5	84.51 87	97.81 57	91.70 267
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
jajsoiax		89.41 60	88.81 81	91.19 31	93.38 79	84.72 55	89.70 77	90.29 212	69.27 285	94.39 20	96.38 20	86.02 73	93.52 141	83.96 91	95.92 147	95.34 61

test-260524					93.36 81	75.43 167		93.68 68		91.87 81		86.66 60	95.37 57	85.83 63	97.78 58

PS-MVSNAJss		88.31 78	87.90 91	89.56 59	93.31 82	77.96 129	87.94 116	91.97 146	70.73 264	94.19 26	96.67 16	76.94 204	94.57 89	83.07 101	96.28 124	96.15 37

test222					93.31 82	76.54 147	79.38 348	77.79 415	52.59 492	82.36 353	90.84 250	66.83 324			91.69 336	81.25 478

tt0805		88.09 83	89.79 58	82.98 233	93.26 84	63.94 319	91.10 51	89.64 231	85.07 46	90.91 102	91.09 235	89.16 25	91.87 192	82.03 117	95.87 151	93.13 182

DU-MVS		86.80 102	86.99 108	86.21 129	93.24 85	67.02 282	83.16 247	92.21 137	81.73 83	90.92 99	91.97 194	77.20 198	93.99 114	74.16 241	98.35 23	97.61 10

NR-MVSNet		86.00 120	86.22 124	85.34 154	93.24 85	64.56 309	82.21 281	90.46 200	80.99 91	88.42 167	91.97 194	77.56 189	93.85 121	72.46 279	98.65 11	97.61 10

OurMVSNet-221017-0		90.01 49	89.74 59	90.83 35	93.16 87	80.37 101	91.91 42	93.11 99	81.10 90	95.32 13	97.24 9	72.94 271	94.85 77	85.07 73	97.78 58	97.26 16

UniMVSNet (Re)		86.87 99	86.98 109	86.55 119	93.11 88	68.48 264	83.80 219	92.87 113	80.37 97	89.61 137	91.81 204	77.72 186	94.18 106	75.00 228	98.53 15	96.99 24

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 24	93.02 89	85.17 49	92.47 28	95.05 14	87.65 27	93.21 47	94.39 83	90.09 18	95.08 71	86.67 44	97.60 74	94.18 121

ACMH+	77.89 11	90.73 33	91.50 26	88.44 82	93.00 90	76.26 153	89.65 81	95.55 8	87.72 26	93.89 31	94.94 56	91.62 3	93.44 145	78.35 164	98.76 3	95.61 56

APDe-MVS		91.22 25	91.92 16	89.14 67	92.97 91	78.04 126	92.84 16	94.14 37	83.33 67	93.90 29	95.73 33	88.77 28	96.41 3	87.60 26	97.98 47	92.98 195
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
114514_t		83.10 218	82.54 233	84.77 169	92.90 92	69.10 256	86.65 141	90.62 195	54.66 479	81.46 378	90.81 251	76.98 203	94.38 96	72.62 277	96.18 130	90.82 294

testdata				79.54 334	92.87 93	72.34 203		80.14 399	59.91 440	85.47 264	91.75 208	67.96 315	85.24 392	68.57 329	92.18 318	81.06 483

CNVR-MVS		87.81 89	87.68 93	88.21 89	92.87 93	77.30 141	85.25 176	91.23 175	77.31 144	87.07 216	91.47 218	82.94 109	94.71 82	84.67 85	96.27 126	92.62 212

SF-MVS		90.27 41	90.80 48	88.68 77	92.86 95	77.09 142	91.19 50	95.74 5	81.38 87	92.28 71	93.80 116	86.89 58	94.64 86	85.52 67	97.51 83	94.30 117

UniMVSNet_NR-MVSNet		86.84 101	87.06 104	86.17 131	92.86 95	67.02 282	82.55 266	91.56 160	83.08 71	90.92 99	91.82 203	78.25 178	93.99 114	74.16 241	98.35 23	97.49 13

plane_prior1					92.83 97

aaEdge-Enhanced		90.09 43	90.66 50	88.38 84	92.82 98	76.12 157	89.40 91	93.70 61	83.72 62	92.39 69	93.18 142	88.02 41	95.47 52	84.99 77	97.69 66	93.54 166

原ACMM1				84.60 176	92.81 99	74.01 176		91.50 162	62.59 394	82.73 348	90.67 260	76.53 213	94.25 100	69.24 314	95.69 161	85.55 415

plane_prior6					92.61 100	76.54 147						74.84 233

APD-MVS		89.54 59	89.63 62	89.26 64	92.57 101	81.34 91	90.19 67	93.08 102	80.87 94	91.13 95	93.19 141	86.22 69	95.97 14	82.23 115	97.18 91	90.45 308
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 74	89.58 64	85.88 139	92.55 102	72.22 206	84.01 209	89.44 237	88.63 19	94.38 21	95.77 31	86.38 68	93.59 136	79.84 142	95.21 178	91.82 261

SixPastTwentyTwo		87.20 96	87.45 97	86.45 121	92.52 103	69.19 254	87.84 118	88.05 268	81.66 84	94.64 17	96.53 19	65.94 330	94.75 81	83.02 103	96.83 102	95.41 59

ACMH	76.49 14	89.34 62	91.14 37	83.96 200	92.50 104	70.36 236	89.55 83	93.84 55	81.89 82	94.70 16	95.44 44	90.69 9	88.31 321	83.33 97	98.30 26	93.20 179
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 290	81.68 248	75.94 412	92.46 105	47.98 508	76.70 402	81.67 383	73.45 206	84.87 285	92.82 162	74.66 239	86.51 363	61.66 398	96.85 100	93.33 170

SymmetryMVS		84.79 157	83.54 199	88.55 79	92.44 106	80.42 99	88.63 105	82.37 374	74.56 183	85.12 273	90.34 270	66.19 327	94.20 103	76.57 199	95.68 162	91.03 286

F-COLMAP		84.97 153	83.42 205	89.63 57	92.39 107	83.40 67	88.83 99	91.92 148	73.19 216	80.18 403	89.15 312	77.04 202	93.28 150	65.82 355	92.28 313	92.21 246

test_djsdf		89.62 57	89.01 71	91.45 25	92.36 108	82.98 73	91.98 40	90.08 218	71.54 249	94.28 25	96.54 18	81.57 143	94.27 98	86.26 50	96.49 115	97.09 20

TEST9					92.34 109	79.70 107	83.94 212	90.32 207	65.41 358	84.49 296	90.97 240	82.03 133	93.63 131

train_agg		85.98 121	85.28 152	88.07 93	92.34 109	79.70 107	83.94 212	90.32 207	65.79 346	84.49 296	90.97 240	81.93 135	93.63 131	81.21 124	96.54 113	90.88 292

NCCC		87.36 94	86.87 111	88.83 71	92.32 111	78.84 118	86.58 143	91.09 180	78.77 123	84.85 286	90.89 246	80.85 151	95.29 60	81.14 125	95.32 174	92.34 236

FC-MVSNet-test		85.93 124	87.05 105	82.58 251	92.25 112	56.44 447	85.75 163	93.09 101	77.33 143	91.94 80	94.65 67	74.78 235	93.41 147	75.11 227	98.58 13	97.88 7

CDPH-MVS		86.17 118	85.54 143	88.05 94	92.25 112	75.45 166	83.85 216	92.01 144	65.91 344	86.19 240	91.75 208	83.77 99	94.98 74	77.43 187	96.71 107	93.73 149

test1111		78.53 320	78.85 312	77.56 377	92.22 114	47.49 511	82.61 262	69.24 489	72.43 231	85.28 269	94.20 92	51.91 441	90.07 270	65.36 359	96.45 118	95.11 73

ZD-MVS					92.22 114	80.48 98		91.85 150	71.22 257	90.38 112	92.98 152	86.06 72	96.11 7	81.99 119	96.75 106

pmmvs6		86.52 109	88.06 89	81.90 271	92.22 114	62.28 350	84.66 191	89.15 243	83.54 66	89.85 126	97.32 8	88.08 40	86.80 357	70.43 302	97.30 88	96.62 31

EG-PatchMatch MVS		84.08 180	84.11 189	83.98 199	92.22 114	72.61 197	82.20 283	87.02 294	72.63 229	88.86 152	91.02 238	78.52 174	91.11 223	73.41 263	91.09 351	88.21 370

test_8					92.09 118	78.87 117	83.82 217	90.31 209	65.79 346	84.36 300	90.96 242	81.93 135	93.44 145

Vis-MVSNet		86.86 100	86.58 114	87.72 97	92.09 118	77.43 138	87.35 124	92.09 142	78.87 121	84.27 308	94.05 102	78.35 177	93.65 129	80.54 137	91.58 340	92.08 252
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		86.66 106	86.82 113	86.17 131	92.05 120	66.87 286	91.21 49	88.64 251	86.30 36	89.60 138	92.59 169	69.22 308	94.91 76	73.89 249	97.89 54	96.72 29

MVSMamba_PlusPlus		87.53 93	88.86 78	83.54 218	92.03 121	62.26 351	91.49 46	92.62 124	88.07 24	88.07 178	96.17 25	72.24 282	95.79 33	84.85 82	94.16 234	92.58 216

旧先验1					91.97 122	71.77 211		81.78 380			91.84 201	73.92 252			93.65 255	83.61 443

v7n		90.13 42	90.96 44	87.65 99	91.95 123	71.06 226	89.99 70	93.05 103	86.53 34	94.29 22	96.27 22	82.69 113	94.08 111	86.25 52	97.63 70	97.82 8

NP-MVS					91.95 123	74.55 173					90.17 283

OMC-MVS		88.19 79	87.52 95	90.19 47	91.94 125	81.68 86	87.49 123	93.17 95	76.02 155	88.64 160	91.22 229	84.24 94	93.37 148	77.97 178	97.03 94	95.52 57

OPU-MVS				88.27 88	91.89 126	77.83 130	90.47 61				91.22 229	81.12 148	94.68 83	74.48 232	95.35 172	92.29 241

FIs		85.35 139	86.27 123	82.60 250	91.86 127	57.31 439	85.10 180	93.05 103	75.83 162	91.02 98	93.97 106	73.57 258	92.91 165	73.97 248	98.02 43	97.58 12

test2506		74.12 390	73.39 391	76.28 408	91.85 128	44.20 526	84.06 208	48.20 553	72.30 237	81.90 364	94.20 92	27.22 549	89.77 279	64.81 365	96.02 138	94.87 80

ECVR-MVS		78.44 324	78.63 316	77.88 371	91.85 128	48.95 504	83.68 223	69.91 484	72.30 237	84.26 309	94.20 92	51.89 442	89.82 276	63.58 376	96.02 138	94.87 80

9.14			89.29 66		91.84 130		88.80 100	95.32 12	75.14 175	91.07 96	92.89 158	87.27 52	93.78 125	83.69 96	97.55 79

MSLP-MVS++		85.00 152	86.03 130	81.90 271	91.84 130	71.56 219	86.75 140	93.02 107	75.95 158	87.12 210	89.39 301	77.98 181	89.40 290	77.46 185	94.78 207	84.75 424

h-mvs33		84.25 174	82.76 226	88.72 74	91.82 132	82.60 76	84.00 210	84.98 333	71.27 253	86.70 225	90.55 266	63.04 355	93.92 119	78.26 167	94.20 232	89.63 331

DKM-HiRes		83.22 214	82.10 238	86.59 117	91.79 133	88.73 10	82.92 255	77.76 416	69.00 293	91.15 94	89.69 295	63.65 350	81.20 431	76.19 207	96.70 108	89.86 325

DP-MVS Recon		84.05 183	83.22 210	86.52 120	91.73 134	75.27 168	83.23 244	92.40 130	72.04 241	82.04 361	88.33 330	77.91 183	93.95 118	66.17 348	95.12 186	90.34 312

RoMa-HiRes		85.97 122	85.47 145	87.48 100	91.66 135	89.37 4	87.18 127	83.89 350	71.47 252	94.29 22	91.35 222	75.59 221	81.39 427	76.88 195	96.92 98	91.68 268

SD-MVS		88.96 70	89.88 56	86.22 128	91.63 136	77.07 143	89.82 75	93.77 57	78.90 120	92.88 54	92.29 185	86.11 71	90.22 258	86.24 53	97.24 89	91.36 278
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
AllTest		87.97 86	87.40 99	89.68 55	91.59 137	83.40 67	89.50 86	95.44 10	79.47 110	88.00 181	93.03 150	82.66 114	91.47 203	70.81 293	96.14 132	94.16 123

TestCases				89.68 55	91.59 137	83.40 67		95.44 10	79.47 110	88.00 181	93.03 150	82.66 114	91.47 203	70.81 293	96.14 132	94.16 123

MCST-MVS		84.36 169	83.93 194	85.63 146	91.59 137	71.58 217	83.52 232	92.13 140	61.82 409	83.96 315	89.75 293	79.93 164	93.46 144	78.33 165	94.34 226	91.87 260

agg_prior					91.58 140	77.69 133		90.30 210		84.32 303			93.18 153

PVSNet_Blended_VisFu		81.55 259	80.49 280	84.70 173	91.58 140	73.24 185	84.21 204	91.67 157	62.86 391	80.94 385	87.16 361	67.27 319	92.87 166	69.82 309	88.94 411	87.99 377

DVP-MVS++		90.07 45	91.09 38	87.00 108	91.55 142	72.64 194	96.19 2	94.10 40	85.33 41	93.49 41	94.64 70	81.12 148	95.88 18	87.41 30	95.94 145	92.48 222

MSC_two_6792asdad				88.81 72	91.55 142	77.99 127		91.01 182					96.05 9	87.45 28	98.17 36	92.40 230

No_MVS				88.81 72	91.55 142	77.99 127		91.01 182					96.05 9	87.45 28	98.17 36	92.40 230

EPP-MVSNet		85.47 132	85.04 157	86.77 115	91.52 145	69.37 249	91.63 45	87.98 271	81.51 86	87.05 217	91.83 202	66.18 329	95.29 60	70.75 296	96.89 99	95.64 54

DeepPCF-MVS	81.24 5	87.28 95	86.21 125	90.49 41	91.48 146	84.90 52	83.41 236	92.38 132	70.25 272	89.35 144	90.68 257	82.85 112	94.57 89	79.55 148	95.95 144	92.00 256

Baseline_NR-MVSNet		84.00 187	85.90 133	78.29 363	91.47 147	53.44 477	82.29 277	87.00 297	79.06 118	89.55 139	95.72 35	77.20 198	86.14 375	72.30 280	98.51 16	95.28 64

HyFIR lowres test		75.12 374	72.66 406	82.50 255	91.44 148	65.19 304	72.47 468	87.31 281	46.79 520	80.29 398	84.30 413	52.70 437	92.10 186	51.88 491	86.73 452	90.22 313

usedtu_dtu_shiyan2		78.92 308	78.15 324	81.25 290	91.33 149	73.10 187	80.75 322	79.00 407	74.19 191	79.17 416	92.04 192	67.17 320	81.33 428	42.86 529	96.81 104	89.31 339

DP-MVS		88.60 75	89.01 71	87.36 103	91.30 150	77.50 135	87.55 120	92.97 111	87.95 25	89.62 135	92.87 159	84.56 89	93.89 120	77.65 181	96.62 110	90.70 298

DeepC-MVS_fast	80.27 8	86.23 113	85.65 142	87.96 95	91.30 150	76.92 144	87.19 126	91.99 145	70.56 265	84.96 281	90.69 255	80.01 162	95.14 69	78.37 163	95.78 157	91.82 261
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	83.92 2	89.97 52	89.66 61	90.92 34	91.27 152	81.66 88	91.25 48	94.13 38	88.89 14	88.83 154	94.26 88	77.55 190	95.86 23	84.88 80	95.87 151	95.24 66

Elysia		88.71 72	88.89 75	88.19 90	91.26 153	72.96 188	88.10 112	93.59 73	84.31 53	90.42 110	94.10 99	74.07 247	94.82 78	88.19 13	95.92 147	96.80 27

StellarMVS		88.71 72	88.89 75	88.19 90	91.26 153	72.96 188	88.10 112	93.59 73	84.31 53	90.42 110	94.10 99	74.07 247	94.82 78	88.19 13	95.92 147	96.80 27

HQP-NCC					91.19 155		84.77 184		73.30 212	80.55 392

ACMP_Plane					91.19 155		84.77 184		73.30 212	80.55 392

HQP-MVS		84.61 161	84.06 190	86.27 125	91.19 155	70.66 229	84.77 184	92.68 121	73.30 212	80.55 392	90.17 283	72.10 283	94.61 87	77.30 189	94.47 219	93.56 163

VDD-MVS		84.23 176	84.58 174	83.20 226	91.17 158	65.16 305	83.25 241	84.97 334	79.79 106	87.18 209	94.27 85	74.77 236	90.89 233	69.24 314	96.54 113	93.55 165

K. test v3		85.14 145	84.73 164	86.37 122	91.13 159	69.63 246	85.45 171	76.68 430	84.06 58	92.44 68	96.99 12	62.03 358	94.65 85	80.58 136	93.24 272	94.83 89

lessismore_v0				85.95 136	91.10 160	70.99 227		70.91 480		91.79 83	94.42 80	61.76 359	92.93 163	79.52 150	93.03 280	93.93 134

hse-mvs2		83.47 208	81.81 247	88.46 81	91.03 161	82.27 80	82.61 262	83.69 354	71.27 253	86.70 225	86.05 382	63.04 355	92.41 175	78.26 167	93.62 257	90.71 297

TransMVSNet (Re)		84.02 186	85.74 140	78.85 348	91.00 162	55.20 463	82.29 277	87.26 283	79.65 109	88.38 169	95.52 40	83.00 108	86.88 353	67.97 333	96.60 111	94.45 106

AUN-MVS		81.18 268	78.78 313	88.39 83	90.93 163	82.14 81	82.51 268	83.67 355	64.69 370	80.29 398	85.91 385	51.07 449	92.38 176	76.29 206	93.63 256	90.65 302

PAPM_NR		83.23 213	83.19 212	83.33 222	90.90 164	65.98 296	88.19 110	90.78 190	78.13 132	80.87 388	87.92 340	73.49 261	92.42 174	70.07 306	88.40 419	91.60 271

CSCG		86.26 112	86.47 116	85.60 147	90.87 165	74.26 175	87.98 115	91.85 150	80.35 98	89.54 141	88.01 335	79.09 169	92.13 183	75.51 219	95.06 188	90.41 309

PLC	73.85 16	82.09 243	80.31 282	87.45 101	90.86 166	80.29 102	85.88 158	90.65 193	68.17 308	76.32 450	86.33 375	73.12 269	92.61 171	61.40 402	90.02 389	89.44 335
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RoMa-SfM		83.52 205	82.69 228	86.00 135	90.77 167	89.30 5	85.98 156	81.47 387	65.77 349	92.99 51	89.25 307	69.55 305	78.65 451	72.01 282	96.45 118	90.04 321

test12				86.57 118	90.74 168	72.63 196		90.69 192		82.76 346		79.20 167	94.80 80		95.32 174	92.27 243

ITE_SJBPF				90.11 48	90.72 169	84.97 51		90.30 210	81.56 85	90.02 119	91.20 231	82.40 119	90.81 237	73.58 261	94.66 213	94.56 97

DPM-MVS		80.10 296	79.18 306	82.88 241	90.71 170	69.74 243	78.87 361	90.84 188	60.29 437	75.64 462	85.92 384	67.28 318	93.11 156	71.24 290	91.79 331	85.77 413

DKM		82.99 220	82.10 238	85.66 145	90.69 171	88.83 9	82.94 254	78.86 408	66.54 336	92.02 77	88.74 321	67.79 316	78.28 453	74.39 233	96.96 96	89.85 326

TAMVS		78.08 328	76.36 352	83.23 225	90.62 172	72.87 190	79.08 357	80.01 400	61.72 412	81.35 380	86.92 366	63.96 346	88.78 302	50.61 493	93.01 281	88.04 375

test_prior				86.32 123	90.59 173	71.99 210		92.85 114					94.17 108			92.80 202

ambc				82.98 233	90.55 174	64.86 306	88.20 109	89.15 243		89.40 143	93.96 109	71.67 292	91.38 209	78.83 158	96.55 112	92.71 207

SSC-MVS		77.55 334	81.64 250	65.29 504	90.46 175	20.33 559	73.56 454	68.28 493	85.44 40	88.18 176	94.64 70	70.93 296	81.33 428	71.25 289	92.03 322	94.20 118

Anonymous20231211		88.40 76	89.62 63	84.73 171	90.46 175	65.27 302	88.86 98	93.02 107	87.15 29	93.05 50	97.10 10	82.28 126	92.02 187	76.70 196	97.99 45	96.88 26

Test_1112_low_res		73.90 394	73.08 397	76.35 406	90.35 177	55.95 449	73.40 459	86.17 305	50.70 508	73.14 482	85.94 383	58.31 385	85.90 381	56.51 439	83.22 494	87.20 394

VPA-MVSNet		83.47 208	84.73 164	79.69 329	90.29 178	57.52 437	81.30 304	88.69 250	76.29 151	87.58 202	94.44 77	80.60 156	87.20 346	66.60 345	96.82 103	94.34 114

FMVSNet1		84.55 165	85.45 147	81.85 273	90.27 179	61.05 375	86.83 136	88.27 264	78.57 126	89.66 133	95.64 37	75.43 223	90.68 242	69.09 318	95.33 173	93.82 143

DenseAffine		81.00 272	79.38 302	85.84 140	90.25 180	87.48 17	81.47 296	78.40 412	65.68 352	89.63 134	86.45 371	58.79 381	82.05 422	67.78 335	95.99 140	87.99 377

Anonymous20240529		86.20 115	87.13 102	83.42 220	90.19 181	64.55 310	84.55 194	90.71 191	85.85 39	89.94 123	95.24 50	82.13 129	90.40 253	69.19 317	96.40 121	95.31 63

MVS_111021_HR		84.63 160	84.34 185	85.49 152	90.18 182	75.86 163	79.23 355	87.13 288	73.35 209	85.56 262	89.34 303	83.60 102	90.50 249	76.64 198	94.05 239	90.09 320

SSM_0404		85.16 144	85.09 155	85.36 153	90.14 183	69.52 247	86.17 152	91.58 158	74.41 186	86.55 229	91.49 215	78.54 172	93.97 116	73.71 253	93.21 275	92.59 215

GeoE		85.45 133	85.81 136	84.37 182	90.08 184	67.07 281	85.86 160	91.39 167	72.33 236	87.59 200	90.25 277	84.85 87	92.37 177	78.00 176	91.94 326	93.66 151

RPSCF		88.00 85	86.93 110	91.22 30	90.08 184	89.30 5	89.68 79	91.11 178	79.26 115	89.68 131	94.81 65	82.44 117	87.74 334	76.54 201	88.74 414	96.61 32

nrg030		87.85 88	88.49 83	85.91 137	90.07 186	69.73 244	87.86 117	94.20 31	74.04 192	92.70 63	94.66 66	85.88 74	91.50 201	79.72 144	97.32 87	96.50 34

AdaColmap		83.66 197	83.69 198	83.57 216	90.05 187	72.26 205	86.29 149	90.00 220	78.19 131	81.65 373	87.16 361	83.40 104	94.24 101	61.69 397	94.76 210	84.21 435

pm-mvs1		83.69 196	84.95 160	79.91 324	90.04 188	59.66 402	82.43 272	87.44 279	75.52 169	87.85 188	95.26 49	81.25 147	85.65 389	68.74 325	96.04 137	94.42 110

CHOSEN 1792x2688		72.45 414	70.56 434	78.13 365	90.02 189	63.08 328	68.72 497	83.16 362	42.99 536	75.92 458	85.46 392	57.22 399	85.18 394	49.87 499	81.67 504	86.14 407

WB-MVS		76.06 361	80.01 294	64.19 508	89.96 190	20.58 558	72.18 471	68.19 494	83.21 68	86.46 237	93.49 128	70.19 302	78.97 447	65.96 349	90.46 383	93.02 189

anonymousdsp		89.73 56	88.88 77	92.27 7	89.82 191	86.67 25	90.51 60	90.20 215	69.87 276	95.06 14	96.14 27	84.28 93	93.07 158	87.68 23	96.34 122	97.09 20

LuminaMVS		83.94 190	83.51 200	85.23 155	89.78 192	71.74 212	84.76 187	87.27 282	72.60 230	89.31 145	90.60 265	64.04 343	90.95 228	79.08 155	94.11 235	92.99 193

fmvsm_s_conf0.5_n_9		87.04 97	87.02 106	87.08 106	89.67 193	75.87 162	84.60 192	89.74 226	74.40 188	89.92 124	93.41 130	80.45 157	90.63 245	86.66 45	94.37 225	94.73 94

1112_ss		74.82 381	73.74 385	78.04 368	89.57 194	60.04 393	76.49 408	87.09 293	54.31 480	73.66 480	79.80 480	60.25 368	86.76 359	58.37 421	84.15 486	87.32 392

CS-MVS		88.14 80	87.67 94	89.54 60	89.56 195	79.18 114	90.47 61	94.77 16	79.37 114	84.32 303	89.33 304	83.87 96	94.53 93	82.45 111	94.89 196	94.90 78

MM		87.64 92	87.15 101	89.09 68	89.51 196	76.39 152	88.68 103	86.76 298	84.54 52	83.58 324	93.78 118	73.36 266	96.48 1	87.98 16	96.21 128	94.41 111

APD_test1		88.40 76	87.91 90	89.88 51	89.50 197	86.65 27	89.98 71	91.91 149	84.26 55	90.87 106	93.92 113	82.18 128	89.29 291	73.75 252	94.81 206	93.70 150

SPE-MVS-test		87.00 98	86.43 117	88.71 75	89.46 198	77.46 136	89.42 90	95.73 6	77.87 136	81.64 374	87.25 359	82.43 118	94.53 93	77.65 181	96.46 117	94.14 125

PCF-MVS	74.62 15	82.15 242	80.92 272	85.84 140	89.43 199	72.30 204	80.53 326	91.82 152	57.36 458	87.81 189	89.92 290	77.67 187	93.63 131	58.69 419	95.08 187	91.58 272
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 366	73.51 389	82.71 243	89.35 200	73.62 178	80.06 331	85.20 325	60.30 436	73.96 477	87.94 337	57.89 394	89.45 286	52.02 486	74.87 531	85.06 421
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		83.55 203	83.10 216	84.90 164	89.34 201	83.87 62	84.54 196	88.77 246	79.09 117	83.54 326	88.66 325	74.87 231	81.73 425	66.84 342	92.29 312	89.11 348

EC-MVSNet		88.01 84	88.32 87	87.09 105	89.28 202	72.03 209	90.31 65	96.31 3	80.88 93	85.12 273	89.67 296	84.47 91	95.46 53	82.56 110	96.26 127	93.77 148

TSAR-MVS + GP.		83.95 189	82.69 228	87.72 97	89.27 203	81.45 90	83.72 221	81.58 385	74.73 180	85.66 257	86.06 381	72.56 278	92.69 169	75.44 221	95.21 178	89.01 355

PMatch-SfM		81.28 264	79.37 303	87.00 108	89.23 204	85.40 46	81.27 306	81.28 389	65.97 340	92.13 72	90.30 276	44.94 496	85.43 390	74.06 246	95.14 183	90.18 318

MVS_111021_LR		84.28 173	83.76 197	85.83 142	89.23 204	83.07 71	80.99 314	83.56 356	72.71 228	86.07 243	89.07 314	81.75 142	86.19 373	77.11 191	93.36 266	88.24 369

LFMVS		80.15 294	80.56 278	78.89 345	89.19 206	55.93 450	85.22 177	73.78 452	82.96 72	84.28 307	92.72 167	57.38 396	90.07 270	63.80 375	95.75 158	90.68 299

Casviewmamba		88.12 82	88.82 80	86.03 134	89.14 207	68.35 265	86.40 147	94.70 17	79.80 105	90.92 99	93.72 123	87.83 44	93.81 124	81.09 126	95.75 158	95.92 47

mamba_0408		83.44 211	82.88 223	85.11 158	89.13 208	68.97 257	72.73 466	91.28 171	72.90 222	85.68 254	90.61 263	76.78 211	93.97 116	73.37 265	93.47 259	92.38 233

SSM_04072		81.44 261	82.88 223	77.10 389	89.13 208	68.97 257	72.73 466	91.28 171	72.90 222	85.68 254	90.61 263	76.78 211	69.94 490	73.37 265	93.47 259	92.38 233

SSM_0407		84.89 154	84.85 161	85.01 163	89.13 208	68.97 257	85.60 167	91.58 158	74.41 186	85.68 254	91.49 215	78.54 172	93.69 128	73.71 253	93.47 259	92.38 233

PMatch-Up-SfM		81.93 251	80.09 292	87.42 102	89.08 211	86.10 35	81.31 301	83.35 359	67.64 321	92.96 52	90.69 255	45.71 484	85.82 386	75.20 225	94.89 196	90.35 311

FE-MVSNET2		82.80 224	83.51 200	80.67 308	89.08 211	58.46 427	82.40 274	89.26 239	71.25 256	88.24 173	94.07 101	75.75 219	89.56 282	65.91 353	95.67 164	93.98 131

CLD-MVS		83.18 215	82.64 230	84.79 168	89.05 213	67.82 273	77.93 377	92.52 128	68.33 305	85.07 277	81.54 463	82.06 132	92.96 161	69.35 313	97.91 53	93.57 162
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		90.60 36	90.34 54	91.38 27	89.03 214	84.23 59	93.58 6	94.68 18	90.65 7	90.33 114	93.95 111	84.50 90	95.37 57	80.87 131	95.50 169	94.53 101

CDS-MVSNet		77.32 337	75.40 362	83.06 230	89.00 215	72.48 201	77.90 378	82.17 376	60.81 429	78.94 419	83.49 430	59.30 376	88.76 303	54.64 462	92.37 308	87.93 381
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tttt0517		81.07 270	79.58 299	85.52 149	88.99 216	66.45 291	87.03 131	75.51 439	73.76 196	88.32 171	90.20 279	37.96 517	94.16 110	79.36 153	95.13 184	95.93 46

BridgeMVS		84.80 155	85.40 148	83.00 232	88.95 217	61.44 365	90.42 64	92.37 134	71.48 251	88.72 159	93.13 146	70.16 303	95.15 68	79.26 154	94.11 235	92.41 228

testing3-2		70.72 439	70.97 429	69.95 470	88.93 218	34.80 548	69.85 492	66.59 506	78.42 128	77.58 440	85.55 388	31.83 533	82.08 421	46.28 520	93.73 252	92.98 195

tfpnnormal		81.79 255	82.95 221	78.31 361	88.93 218	55.40 459	80.83 319	82.85 366	76.81 147	85.90 252	94.14 96	74.58 240	86.51 363	66.82 343	95.68 162	93.01 192

testing3		71.53 429	70.79 431	73.77 439	88.89 220	41.86 534	76.60 407	59.12 540	72.83 225	80.97 383	82.08 454	19.80 556	87.33 344	65.12 361	91.68 337	92.13 251

TestfortrainingZip				84.49 179	88.84 221	70.49 232	92.12 34	91.01 182	84.70 50	82.82 345	89.25 307	74.30 243	94.06 112		90.73 372	88.92 356

Vis-MVSNet (Re-imp)		77.82 330	77.79 330	77.92 370	88.82 222	51.29 494	83.28 239	71.97 472	74.04 192	82.23 355	89.78 292	57.38 396	89.41 289	57.22 432	95.41 170	93.05 188

SDMVSNet		81.90 254	83.17 214	78.10 366	88.81 223	62.45 346	76.08 416	86.05 309	73.67 197	83.41 328	93.04 148	82.35 120	80.65 435	70.06 307	95.03 189	91.21 280

sd_testset		79.95 299	81.39 261	75.64 419	88.81 223	58.07 431	76.16 415	82.80 367	73.67 197	83.41 328	93.04 148	80.96 150	77.65 456	58.62 420	95.03 189	91.21 280

TAPA-MVS	77.73 12	85.71 127	84.83 162	88.37 85	88.78 225	79.72 106	87.15 129	93.50 77	69.17 286	85.80 253	89.56 297	80.76 153	92.13 183	73.21 274	95.51 168	93.25 177
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
testf1		89.30 63	89.12 68	89.84 52	88.67 226	85.64 44	90.61 56	93.17 95	86.02 37	93.12 48	95.30 46	84.94 85	89.44 287	74.12 243	96.10 135	94.45 106

APD_test2		89.30 63	89.12 68	89.84 52	88.67 226	85.64 44	90.61 56	93.17 95	86.02 37	93.12 48	95.30 46	84.94 85	89.44 287	74.12 243	96.10 135	94.45 106

GDP-MVS		82.17 240	80.85 275	86.15 133	88.65 228	68.95 260	85.65 166	93.02 107	68.42 303	83.73 319	89.54 298	45.07 494	94.31 97	79.66 146	93.87 244	95.19 69

FPMVS		72.29 418	72.00 413	73.14 444	88.63 229	85.00 50	74.65 436	67.39 498	71.94 243	77.80 433	87.66 349	50.48 455	75.83 465	49.95 497	79.51 514	58.58 545

dcpmvs_2		84.23 176	85.14 154	81.50 284	88.61 230	61.98 355	82.90 257	93.11 99	68.66 299	92.77 60	92.39 177	78.50 175	87.63 337	76.99 193	92.30 310	94.90 78

dtuonlycased		77.13 340	76.99 341	77.55 380	88.60 231	57.48 438	74.18 443	81.70 381	55.62 471	85.10 276	88.40 327	74.87 231	82.26 420	56.73 437	87.66 436	92.90 200

ETV-MVS		84.31 171	83.91 196	85.52 149	88.58 232	70.40 234	84.50 199	93.37 80	78.76 124	84.07 312	78.72 491	80.39 158	95.13 70	73.82 251	92.98 282	91.04 285

BH-untuned		80.96 273	80.99 270	80.84 301	88.55 233	68.23 266	80.33 330	88.46 256	72.79 227	86.55 229	86.76 367	74.72 237	91.77 195	61.79 396	88.99 409	82.52 463

Anonymous202405211		80.51 281	81.19 268	78.49 356	88.48 234	57.26 440	76.63 404	82.49 370	81.21 89	84.30 306	92.24 188	67.99 314	86.24 370	62.22 387	95.13 184	91.98 258

ab-mvs		79.67 301	80.56 278	76.99 391	88.48 234	56.93 442	84.70 190	86.06 308	68.95 294	80.78 389	93.08 147	75.30 225	84.62 398	56.78 436	90.90 358	89.43 337

PHI-MVS		86.38 111	85.81 136	88.08 92	88.44 236	77.34 139	89.35 92	93.05 103	73.15 217	84.76 290	87.70 348	78.87 171	94.18 106	80.67 135	96.29 123	92.73 204

xiu_mvs_v1_base_debu		80.84 275	80.14 288	82.93 238	88.31 237	71.73 213	79.53 340	87.17 285	65.43 355	79.59 405	82.73 448	76.94 204	90.14 265	73.22 269	88.33 421	86.90 399

xiu_mvs_v1_base		80.84 275	80.14 288	82.93 238	88.31 237	71.73 213	79.53 340	87.17 285	65.43 355	79.59 405	82.73 448	76.94 204	90.14 265	73.22 269	88.33 421	86.90 399

xiu_mvs_v1_base_debi		80.84 275	80.14 288	82.93 238	88.31 237	71.73 213	79.53 340	87.17 285	65.43 355	79.59 405	82.73 448	76.94 204	90.14 265	73.22 269	88.33 421	86.90 399

E6new		85.44 134	86.37 118	82.66 245	88.23 240	61.86 357	83.59 226	93.69 64	73.64 199	87.61 198	93.30 135	85.85 75	91.26 213	78.02 172	93.40 262	94.86 84

E6		85.44 134	86.37 118	82.66 245	88.23 240	61.86 357	83.59 226	93.69 64	73.64 199	87.61 198	93.30 135	85.85 75	91.26 213	78.02 172	93.40 262	94.86 84

E5new		85.44 134	86.37 118	82.66 245	88.22 242	61.86 357	83.59 226	93.70 61	73.64 199	87.62 196	93.30 135	85.85 75	91.26 213	78.02 172	93.40 262	94.86 84

E5		85.44 134	86.37 118	82.66 245	88.22 242	61.86 357	83.59 226	93.70 61	73.64 199	87.62 196	93.30 135	85.85 75	91.26 213	78.02 172	93.40 262	94.86 84

MG-MVS		80.32 288	80.94 271	78.47 357	88.18 244	52.62 484	82.29 277	85.01 332	72.01 242	79.24 414	92.54 174	69.36 307	93.36 149	70.65 298	89.19 404	89.45 334

E4		84.75 158	85.46 146	82.61 249	88.17 245	61.55 364	81.39 299	93.55 76	73.13 219	86.83 220	92.83 161	84.17 95	91.48 202	76.92 194	92.19 317	94.80 91

PM-MVS		80.20 292	79.00 307	83.78 206	88.17 245	86.66 26	81.31 301	66.81 504	69.64 278	88.33 170	90.19 280	64.58 337	83.63 412	71.99 283	90.03 388	81.06 483

v10		86.54 108	87.10 103	84.84 165	88.16 247	63.28 326	86.64 142	92.20 138	75.42 172	92.81 59	94.50 74	74.05 250	94.06 112	83.88 92	96.28 124	97.17 19

mvsmamba		80.30 289	78.87 309	84.58 177	88.12 248	67.55 274	92.35 31	84.88 337	63.15 388	85.33 267	90.91 245	50.71 452	95.20 66	66.36 346	87.98 428	90.99 287

sasdasda		85.50 129	86.14 126	83.58 214	87.97 249	67.13 278	87.55 120	94.32 22	73.44 207	88.47 165	87.54 351	86.45 65	91.06 225	75.76 215	93.76 248	92.54 220

canonicalmvs		85.50 129	86.14 126	83.58 214	87.97 249	67.13 278	87.55 120	94.32 22	73.44 207	88.47 165	87.54 351	86.45 65	91.06 225	75.76 215	93.76 248	92.54 220

EIA-MVS		82.19 239	81.23 266	85.10 159	87.95 251	69.17 255	83.22 245	93.33 85	70.42 267	78.58 423	79.77 482	77.29 195	94.20 103	71.51 288	88.96 410	91.93 259

fmvsm_s_conf0.5_n_5		84.56 163	84.71 167	84.11 196	87.92 252	72.09 208	84.80 183	88.64 251	64.43 373	88.77 156	91.78 206	78.07 180	87.95 328	85.85 62	92.18 318	92.30 239

VNet		79.31 303	80.27 283	76.44 405	87.92 252	53.95 473	75.58 424	84.35 345	74.39 189	82.23 355	90.72 253	72.84 274	84.39 403	60.38 408	93.98 240	90.97 288

BP-MVS1		82.81 223	81.67 249	86.23 126	87.88 254	68.53 263	86.06 155	84.36 344	75.65 165	85.14 272	90.19 280	45.84 482	94.42 95	85.18 71	94.72 211	95.75 49

v8		86.21 114	86.83 112	84.36 184	87.82 255	62.35 349	86.42 146	91.33 169	76.78 148	92.73 61	94.48 76	73.41 263	93.72 127	83.10 100	95.41 170	97.01 23

ArgMatch-SfM		79.08 304	77.37 335	84.22 191	87.80 256	86.73 24	79.32 349	78.45 410	56.81 464	89.54 141	84.95 404	55.35 420	79.21 445	68.89 321	95.21 178	86.73 402

alignmvs		83.94 190	83.98 192	83.80 204	87.80 256	67.88 272	84.54 196	91.42 166	73.27 215	88.41 168	87.96 336	72.32 280	90.83 236	76.02 212	94.11 235	92.69 208

hybridcas		86.07 119	87.02 106	83.19 228	87.76 258	62.85 332	84.53 198	93.42 79	75.52 169	89.88 125	93.31 134	86.15 70	91.68 197	77.76 180	94.89 196	95.05 75

fmvsm_s_conf0.5_n_6		84.05 183	84.14 188	83.81 203	87.75 259	71.17 224	83.42 235	91.10 179	67.90 315	84.53 294	90.70 254	73.01 270	88.73 304	85.09 72	93.72 253	91.53 275

v1192		84.57 162	84.69 169	84.21 192	87.75 259	62.88 330	83.02 250	91.43 164	69.08 290	89.98 122	90.89 246	72.70 276	93.62 134	82.41 112	94.97 193	96.13 38

PatchMatch-RL		74.48 385	73.22 395	78.27 364	87.70 261	85.26 48	75.92 418	70.09 482	64.34 375	76.09 454	81.25 465	65.87 331	78.07 454	53.86 466	83.82 490	71.48 527

fmvsm_s_conf0.1_n_a		82.58 229	81.93 245	84.50 178	87.68 262	73.35 181	86.14 154	77.70 417	61.64 414	85.02 278	91.62 210	77.75 184	86.24 370	82.79 107	87.07 445	93.91 136

v1144		84.54 166	84.72 166	84.00 197	87.67 263	62.55 339	82.97 252	90.93 186	70.32 270	89.80 127	90.99 239	73.50 259	93.48 143	81.69 123	94.65 214	95.97 43

v1240		84.30 172	84.51 178	83.65 211	87.65 264	61.26 371	82.85 258	91.54 161	67.94 313	90.68 109	90.65 261	71.71 291	93.64 130	82.84 106	94.78 207	96.07 40

v1921920		84.23 176	84.37 183	83.79 205	87.64 265	61.71 362	82.91 256	91.20 176	67.94 313	90.06 117	90.34 270	72.04 286	93.59 136	82.32 113	94.91 194	96.07 40

ArgMatch-Sym		78.58 319	76.86 344	83.71 209	87.61 266	86.40 28	78.19 371	77.45 419	55.72 469	88.82 155	82.01 456	59.68 374	78.75 450	67.43 338	94.86 202	85.98 408

v144192		84.24 175	84.41 181	83.71 209	87.59 267	61.57 363	82.95 253	91.03 181	67.82 317	89.80 127	90.49 267	73.28 267	93.51 142	81.88 122	94.89 196	96.04 42

KinetiMVS		85.95 123	86.10 128	85.50 151	87.56 268	69.78 242	83.70 222	89.83 225	80.42 96	87.76 192	93.24 140	73.76 256	91.54 200	85.03 75	93.62 257	95.19 69

MGCFI-Net		85.04 149	85.95 131	82.31 261	87.52 269	63.59 322	86.23 151	93.96 45	73.46 205	88.07 178	87.83 346	86.46 64	90.87 235	76.17 209	93.89 243	92.47 224

Fast-Effi-MVS+		81.04 271	80.57 277	82.46 257	87.50 270	63.22 327	78.37 369	89.63 232	68.01 310	81.87 365	82.08 454	82.31 122	92.65 170	67.10 339	88.30 425	91.51 276

casdiffseed414692147		85.64 128	86.08 129	84.32 187	87.49 271	65.55 301	85.81 162	93.00 110	75.85 161	87.50 203	93.40 131	83.10 106	91.71 196	73.70 257	94.84 205	95.69 51

E2		84.06 181	84.61 171	82.40 259	87.49 271	61.31 368	81.03 312	93.36 81	71.83 244	86.02 245	91.87 196	82.91 110	91.37 210	75.66 217	91.33 344	94.53 101

E3		84.06 181	84.61 171	82.40 259	87.49 271	61.30 369	81.03 312	93.36 81	71.83 244	86.01 247	91.87 196	82.91 110	91.36 211	75.66 217	91.33 344	94.53 101

fmvsm_l_conf0.5_n_3		85.11 148	84.96 159	85.56 148	87.49 271	75.69 164	84.71 189	90.61 196	67.64 321	84.88 284	92.05 191	82.30 123	88.36 319	83.84 94	91.10 350	92.62 212

pmmvs-eth3d		78.42 325	77.04 340	82.57 253	87.44 275	74.41 174	80.86 318	79.67 401	55.68 470	84.69 291	90.31 275	60.91 363	85.42 391	62.20 388	91.59 339	87.88 382

IterMVS-LS		84.73 159	84.98 158	83.96 200	87.35 276	63.66 320	83.25 241	89.88 224	76.06 153	89.62 135	92.37 181	73.40 265	92.52 172	78.16 169	94.77 209	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres100view900		75.45 370	75.05 371	76.66 400	87.27 277	51.88 489	81.07 311	73.26 457	75.68 164	83.25 333	86.37 374	45.54 485	88.80 299	51.98 487	90.99 353	89.31 339

viewdifsd2359ckpt09		83.64 198	83.18 213	85.03 161	87.26 278	66.99 284	85.32 174	93.83 56	65.57 354	84.99 280	89.40 300	77.30 194	93.57 139	71.16 292	93.80 246	94.54 100

fmvsm_s_conf0.5_n_4		84.38 168	84.27 186	84.74 170	87.25 279	70.84 228	83.55 231	88.45 257	68.64 300	86.29 239	91.31 225	74.97 230	88.42 317	87.87 19	90.07 387	94.95 77

MIMVSNet		71.09 433	71.59 417	69.57 475	87.23 280	50.07 501	78.91 359	71.83 473	60.20 439	71.26 492	91.76 207	55.08 423	76.09 463	41.06 533	87.02 448	82.54 462

Effi-MVS+		83.90 192	84.01 191	83.57 216	87.22 281	65.61 300	86.55 144	92.40 130	78.64 125	81.34 381	84.18 419	83.65 101	92.93 163	74.22 237	87.87 430	92.17 249

BH-RMVSNet		80.53 280	80.22 286	81.49 285	87.19 282	66.21 293	77.79 380	86.23 304	74.21 190	83.69 321	88.50 326	73.25 268	90.75 239	63.18 382	87.90 429	87.52 389

thisisatest0530		79.07 305	77.33 336	84.26 190	87.13 283	64.58 308	83.66 224	75.95 434	68.86 295	85.22 270	87.36 357	38.10 514	93.57 139	75.47 220	94.28 229	94.62 95

Effi-MVS+-dtu		85.82 126	83.38 207	93.14 3	87.13 283	91.15 2	87.70 119	88.42 258	74.57 182	83.56 325	85.65 387	78.49 176	94.21 102	72.04 281	92.88 285	94.05 129

v2v482		84.09 179	84.24 187	83.62 212	87.13 283	61.40 366	82.71 261	89.71 229	72.19 239	89.55 139	91.41 219	70.70 298	93.20 152	81.02 129	93.76 248	96.25 36

fmvsm_s_conf0.5_n_8		85.48 131	85.75 139	84.68 174	87.10 286	69.98 240	84.28 203	92.68 121	74.77 179	87.90 185	92.36 183	73.94 251	90.41 252	85.95 61	92.74 291	93.66 151

jason		77.42 336	75.75 358	82.43 258	87.10 286	69.27 250	77.99 375	81.94 378	51.47 502	77.84 431	85.07 402	60.32 367	89.00 294	70.74 297	89.27 402	89.03 353
jason: jason.
PS-MVSNAJ		77.04 343	76.53 349	78.56 354	87.09 288	61.40 366	75.26 427	87.13 288	61.25 421	74.38 475	77.22 507	76.94 204	90.94 229	64.63 368	84.83 481	83.35 450

casdiffmvs_mvg		86.72 103	87.51 96	84.36 184	87.09 288	65.22 303	84.16 205	94.23 28	77.89 134	91.28 93	93.66 125	84.35 92	92.71 167	80.07 138	94.87 201	95.16 72
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SD_0403		76.08 360	76.77 345	73.98 433	87.08 290	49.45 503	83.62 225	84.68 342	63.31 385	75.13 469	87.47 354	71.85 288	84.56 399	49.97 496	87.86 431	87.94 380

AstraMVS		81.67 256	81.40 260	82.48 256	87.06 291	66.47 290	81.41 298	81.68 382	68.78 296	88.00 181	90.95 244	65.70 332	87.86 333	76.66 197	92.38 307	93.12 185

xiu_mvs_v2_base		77.19 339	76.75 346	78.52 355	87.01 292	61.30 369	75.55 425	87.12 292	61.24 422	74.45 473	78.79 490	77.20 198	90.93 230	64.62 369	84.80 482	83.32 451

thres600view7		75.97 364	75.35 365	77.85 374	87.01 292	51.84 490	80.45 328	73.26 457	75.20 174	83.10 336	86.31 377	45.54 485	89.05 293	55.03 457	92.24 314	92.66 210

fmvsm_s_conf0.5_n_7		82.04 246	82.05 242	82.01 269	86.98 294	71.07 225	78.70 363	89.45 236	68.07 309	78.14 427	91.61 211	74.19 245	85.92 378	79.61 147	91.73 334	89.05 352

viewcassd2359sk11		83.53 204	83.96 193	82.25 262	86.97 295	61.13 373	80.80 321	93.22 93	70.97 261	85.36 266	91.08 236	81.84 139	91.29 212	74.79 230	90.58 379	94.33 115

fmvsm_s_conf0.5_n_3		86.19 116	87.27 100	82.95 235	86.91 296	70.38 235	85.31 175	92.61 126	75.59 167	88.32 171	92.87 159	82.22 127	88.63 310	88.80 8	92.82 289	89.83 327

CL-MVSNet_self_test		76.81 346	77.38 334	75.12 424	86.90 297	51.34 492	73.20 460	80.63 396	68.30 306	81.80 369	88.40 327	66.92 323	80.90 432	55.35 453	94.90 195	93.12 185

BH-w/o		76.57 350	76.07 356	78.10 366	86.88 298	65.92 297	77.63 383	86.33 302	65.69 351	80.89 387	79.95 479	68.97 311	90.74 240	53.01 476	85.25 470	77.62 513

fmvsm_s_conf0.1_n		82.17 240	81.59 253	83.94 202	86.87 299	71.57 218	85.19 178	77.42 421	62.27 405	84.47 298	91.33 223	76.43 214	85.91 380	83.14 98	87.14 443	94.33 115

MAR-MVS		80.24 291	78.74 315	84.73 171	86.87 299	78.18 125	85.75 163	87.81 276	65.67 353	77.84 431	78.50 492	73.79 255	90.53 248	61.59 399	90.87 360	85.49 417
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
fmvsm_s_conf0.5_n_a		82.21 238	81.51 258	84.32 187	86.56 301	73.35 181	85.46 170	77.30 423	61.81 410	84.51 295	90.88 248	77.36 192	86.21 372	82.72 108	86.97 450	93.38 168

fmvsm_s_conf0.5_n_11		84.56 163	84.69 169	84.15 195	86.53 302	71.29 222	85.53 168	92.62 124	70.54 266	82.75 347	91.20 231	77.33 193	88.55 315	83.80 95	91.93 327	92.61 214

FE-MVSNET		78.46 321	79.36 304	75.75 415	86.53 302	54.53 467	78.03 372	85.35 322	69.01 292	85.41 265	90.68 257	64.27 339	85.73 387	62.59 385	92.35 309	87.00 397

E3new		83.08 219	83.39 206	82.14 266	86.49 304	61.00 378	80.64 323	93.12 98	70.30 271	84.78 289	90.34 270	80.85 151	91.24 218	74.20 240	89.83 392	94.17 122

FE-MVS		79.98 298	78.86 310	83.36 221	86.47 305	66.45 291	89.73 76	84.74 341	72.80 226	84.22 310	91.38 220	44.95 495	93.60 135	63.93 373	91.50 341	90.04 321

balanced_ft_v1		83.49 206	83.93 194	82.19 263	86.46 306	59.61 404	90.81 53	90.92 187	71.78 246	88.08 177	92.56 172	66.97 321	94.54 92	75.34 223	92.42 306	92.42 226

QAPM		82.59 228	82.59 232	82.58 251	86.44 307	66.69 287	89.94 73	90.36 205	67.97 312	84.94 283	92.58 171	72.71 275	92.18 182	70.63 299	87.73 433	88.85 357

viewmacassd2359aftdt		84.04 185	84.78 163	81.81 276	86.43 308	60.32 390	81.95 286	92.82 117	71.56 248	86.06 244	92.98 152	81.79 141	90.28 254	76.18 208	93.24 272	94.82 90

guyue		81.57 258	81.37 262	82.15 265	86.39 309	66.13 294	81.54 295	83.21 361	69.79 277	87.77 191	89.95 287	65.36 335	87.64 336	75.88 213	92.49 304	92.67 209

PAPM		71.77 423	70.06 441	76.92 394	86.39 309	53.97 472	76.62 405	86.62 300	53.44 486	63.97 533	84.73 408	57.79 395	92.34 178	39.65 536	81.33 508	84.45 429

GBi-Net		82.02 247	82.07 240	81.85 273	86.38 311	61.05 375	86.83 136	88.27 264	72.43 231	86.00 248	95.64 37	63.78 347	90.68 242	65.95 350	93.34 267	93.82 143

test1		82.02 247	82.07 240	81.85 273	86.38 311	61.05 375	86.83 136	88.27 264	72.43 231	86.00 248	95.64 37	63.78 347	90.68 242	65.95 350	93.34 267	93.82 143

FMVSNet2		81.31 263	81.61 252	80.41 314	86.38 311	58.75 424	83.93 214	86.58 301	72.43 231	87.65 195	92.98 152	63.78 347	90.22 258	66.86 340	93.92 242	92.27 243

3Dnovator	80.37 7	84.80 155	84.71 167	85.06 160	86.36 314	74.71 171	88.77 101	90.00 220	75.65 165	84.96 281	93.17 144	74.06 249	91.19 220	78.28 166	91.09 351	89.29 342

Anonymous20231206		71.38 431	71.88 414	69.88 471	86.31 315	54.37 468	70.39 488	74.62 443	52.57 493	76.73 445	88.76 319	59.94 370	72.06 479	44.35 527	93.23 274	83.23 453

baseline		85.20 142	85.93 132	83.02 231	86.30 316	62.37 348	84.55 194	93.96 45	74.48 185	87.12 210	92.03 193	82.30 123	91.94 188	78.39 162	94.21 230	94.74 93

API-MVS		82.28 235	82.61 231	81.30 289	86.29 317	69.79 241	88.71 102	87.67 277	78.42 128	82.15 357	84.15 420	77.98 181	91.59 199	65.39 358	92.75 290	82.51 464

tfpn200view9		74.86 380	74.23 380	76.74 399	86.24 318	52.12 486	79.24 353	73.87 450	73.34 210	81.82 367	84.60 410	46.02 476	88.80 299	51.98 487	90.99 353	89.31 339

thres400		75.14 372	74.23 380	77.86 373	86.24 318	52.12 486	79.24 353	73.87 450	73.34 210	81.82 367	84.60 410	46.02 476	88.80 299	51.98 487	90.99 353	92.66 210

UGNet		82.78 225	81.64 250	86.21 129	86.20 320	76.24 154	86.86 134	85.68 317	77.07 146	73.76 479	92.82 162	69.64 304	91.82 194	69.04 320	93.69 254	90.56 305
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
CANet		83.79 195	82.85 225	86.63 116	86.17 321	72.21 207	83.76 220	91.43 164	77.24 145	74.39 474	87.45 355	75.36 224	95.42 55	77.03 192	92.83 288	92.25 245

casdiffmvs		85.21 141	85.85 135	83.31 223	86.17 321	62.77 334	83.03 249	93.93 47	74.69 181	88.21 174	92.68 168	82.29 125	91.89 191	77.87 179	93.75 251	95.27 65
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
FA-MVS(test-final)		83.13 217	83.02 218	83.43 219	86.16 323	66.08 295	88.00 114	88.36 260	75.55 168	85.02 278	92.75 166	65.12 336	92.50 173	74.94 229	91.30 346	91.72 265

fmvsm_l_conf0.5_n_9		83.98 188	84.46 179	82.53 254	86.11 324	70.65 231	82.45 271	89.17 242	67.72 320	86.74 224	91.49 215	79.20 167	85.86 384	84.71 84	92.60 300	91.07 284

TR-MVS		76.77 347	75.79 357	79.72 328	86.10 325	65.79 298	77.14 393	83.02 364	65.20 365	81.40 379	82.10 452	66.30 325	90.73 241	55.57 449	85.27 469	82.65 458

fmvsm_s_conf0.5_n		81.91 253	81.30 263	83.75 207	86.02 326	71.56 219	84.73 188	77.11 426	62.44 402	84.00 314	90.68 257	76.42 215	85.89 382	83.14 98	87.11 444	93.81 146

fmvsm_s_conf0.5_n_10		85.20 142	85.25 153	85.02 162	86.01 327	71.31 221	84.96 182	91.76 156	69.10 288	88.90 151	92.56 172	73.84 254	90.63 245	86.88 40	93.26 271	93.13 182

fmvsm_s_conf0.1_n_2		83.82 193	83.49 202	84.84 165	85.99 328	70.19 238	80.93 316	87.58 278	67.26 328	87.94 184	92.37 181	71.40 294	88.01 325	86.03 56	91.87 330	96.31 35

test_fmvsmconf0.01_n		86.68 104	86.52 115	87.18 104	85.94 329	78.30 122	86.93 132	92.20 138	65.94 342	89.16 148	93.16 145	83.10 106	89.89 275	87.81 20	94.43 221	93.35 169

LCM-MVSNet-Re		83.48 207	85.06 156	78.75 351	85.94 329	55.75 454	80.05 332	94.27 25	76.47 149	96.09 5	94.54 73	83.31 105	89.75 281	59.95 410	94.89 196	90.75 295

testing915		74.11 391	74.71 374	72.32 456	85.86 331	47.86 509	81.27 306	75.02 441	67.81 318	76.45 447	86.10 380	55.88 412	84.39 403	52.09 484	91.92 328	84.70 425

viewdifsd2359ckpt07		83.41 212	84.35 184	80.56 310	85.84 332	58.93 419	79.47 344	91.28 171	73.01 221	87.59 200	92.07 190	85.24 83	88.68 307	73.59 260	91.11 349	94.09 128

test_fmvsmvis_n_1920		85.22 140	85.36 150	84.81 167	85.80 333	76.13 156	85.15 179	92.32 135	61.40 416	91.33 90	90.85 249	83.76 100	86.16 374	84.31 88	93.28 270	92.15 250

icg_test_0407_2		78.46 321	79.68 297	74.78 428	85.76 334	62.46 341	68.51 498	87.91 272	65.23 361	82.12 358	87.92 340	77.27 196	72.67 477	71.67 284	90.74 368	89.20 343

IMVS_0407		81.08 269	81.23 266	80.62 309	85.76 334	62.46 341	82.46 269	87.91 272	65.23 361	82.12 358	87.92 340	77.27 196	90.18 260	71.67 284	90.74 368	89.20 343

IMVS_0404		77.24 338	77.75 331	75.73 416	85.76 334	62.46 341	70.84 484	87.91 272	65.23 361	72.21 488	87.92 340	67.48 317	75.53 467	71.67 284	90.74 368	89.20 343

IMVS_0403		80.93 274	81.00 269	80.72 304	85.76 334	62.46 341	81.82 289	87.91 272	65.23 361	82.07 360	87.92 340	75.91 218	90.50 249	71.67 284	90.74 368	89.20 343

Fast-Effi-MVS+-dtu		82.54 230	81.41 259	85.90 138	85.60 338	76.53 149	83.07 248	89.62 233	73.02 220	79.11 417	83.51 429	80.74 154	90.24 257	68.76 324	89.29 400	90.94 289

v148		82.31 234	82.48 234	81.81 276	85.59 339	59.66 402	81.47 296	86.02 310	72.85 224	88.05 180	90.65 261	70.73 297	90.91 232	75.15 226	91.79 331	94.87 80

MVSFormer		82.23 236	81.57 255	84.19 194	85.54 340	69.26 251	91.98 40	90.08 218	71.54 249	76.23 451	85.07 402	58.69 383	94.27 98	86.26 50	88.77 412	89.03 353

lupinMVS		76.37 357	74.46 378	82.09 267	85.54 340	69.26 251	76.79 400	80.77 394	50.68 509	76.23 451	82.82 445	58.69 383	88.94 295	69.85 308	88.77 412	88.07 372

viewdifsd2359ckpt13		82.22 237	81.98 244	82.95 235	85.48 342	64.44 312	83.17 246	92.11 141	65.97 340	83.72 320	89.73 294	77.60 188	90.80 238	70.61 300	89.42 398	93.59 160

fmvsm_s_conf0.5_n_2		83.62 200	83.29 209	84.62 175	85.43 343	70.18 239	80.61 325	87.24 284	67.14 329	87.79 190	91.87 196	71.79 290	87.98 327	86.00 60	91.77 333	95.71 50

TinyColmap		81.25 265	82.34 236	77.99 369	85.33 344	60.68 386	82.32 276	88.33 261	71.26 255	86.97 218	92.22 189	77.10 201	86.98 351	62.37 386	95.17 181	86.31 406

MGCNet		85.37 138	84.58 174	87.75 96	85.28 345	73.36 180	86.54 145	85.71 316	77.56 141	81.78 372	92.47 176	70.29 301	96.02 11	85.59 66	95.96 142	93.87 138

test_fmvsmconf0.1_n		86.18 117	85.88 134	87.08 106	85.26 346	78.25 123	85.82 161	91.82 152	65.33 359	88.55 162	92.35 184	82.62 116	89.80 277	86.87 41	94.32 227	93.18 181

test_fmvsm_n_1920		83.60 201	82.89 222	85.74 143	85.22 347	77.74 132	84.12 207	90.48 198	59.87 441	86.45 238	91.12 234	75.65 220	85.89 382	82.28 114	90.87 360	93.58 161

viewmanbaseed2359cas		82.95 222	83.43 204	81.52 283	85.18 348	60.03 395	81.36 300	92.38 132	69.55 281	84.84 287	91.38 220	79.85 165	90.09 268	74.22 237	92.09 320	94.43 109

PAPR		78.84 312	78.10 327	81.07 295	85.17 349	60.22 391	82.21 281	90.57 197	62.51 395	75.32 466	84.61 409	74.99 229	92.30 180	59.48 413	88.04 427	90.68 299

RRT-MVS		82.97 221	83.44 203	81.57 281	85.06 350	58.04 432	87.20 125	90.37 204	77.88 135	88.59 161	93.70 124	63.17 352	93.05 159	76.49 202	88.47 418	93.62 157

fmvsm_l_mol_unc0.5_1		82.09 243	83.08 217	79.12 342	85.01 351	56.67 446	77.48 389	81.51 386	68.49 302	92.60 64	95.50 41	72.88 272	88.10 323	81.09 126	93.20 276	92.58 216

ALIKED-LG		78.19 326	77.07 338	81.54 282	84.95 352	86.95 21	86.16 153	83.96 349	56.64 466	87.21 207	90.05 286	51.36 446	78.05 455	57.73 429	95.60 167	79.63 495

pmmvs4		74.92 379	72.98 399	80.73 303	84.95 352	71.71 216	76.23 413	77.59 418	52.83 491	77.73 435	86.38 373	56.35 404	84.97 395	57.72 430	87.05 446	85.51 416

baseline1		73.26 402	73.54 388	72.43 453	84.92 354	47.79 510	79.89 335	74.00 448	65.93 343	78.81 420	86.28 378	56.36 403	81.63 426	56.63 438	79.04 520	87.87 383

FBQ-MVS		71.59 428	69.67 446	77.34 384	84.84 355	56.41 448	81.26 308	76.51 431	62.70 392	73.28 481	75.95 514	36.93 519	88.04 324	48.28 510	87.27 440	87.56 388

Patchmatch-RL test		74.48 385	73.68 386	76.89 396	84.83 356	66.54 288	72.29 469	69.16 490	57.70 453	86.76 222	86.33 375	45.79 483	82.59 416	69.63 311	90.65 377	81.54 474

patch_mono-2		78.89 310	79.39 301	77.41 383	84.78 357	68.11 269	75.60 421	83.11 363	60.96 427	79.36 411	89.89 291	75.18 226	72.97 476	73.32 267	92.30 310	91.15 282

test_fmvsmconf_n		85.88 125	85.51 144	86.99 110	84.77 358	78.21 124	85.40 173	91.39 167	65.32 360	87.72 194	91.81 204	82.33 121	89.78 278	86.68 43	94.20 232	92.99 193

KD-MVS_self_test		81.93 251	83.14 215	78.30 362	84.75 359	52.75 481	80.37 329	89.42 238	70.24 273	90.26 115	93.39 132	74.55 242	86.77 358	68.61 327	96.64 109	95.38 60

mmtdpeth		85.13 146	85.78 138	83.17 229	84.65 360	74.71 171	85.87 159	90.35 206	77.94 133	83.82 317	96.96 14	77.75 184	80.03 441	78.44 161	96.21 128	94.79 92

XXY-MVS		74.44 387	76.19 354	69.21 477	84.61 361	52.43 485	71.70 475	77.18 425	60.73 431	80.60 390	90.96 242	75.44 222	69.35 495	56.13 442	88.33 421	85.86 412

ALIKED-MNN		76.42 356	75.39 364	79.52 335	84.57 362	84.06 61	84.33 202	82.48 371	49.85 513	80.53 395	88.35 329	54.52 425	77.10 460	56.89 435	96.96 96	77.39 514

cascas		76.29 358	74.81 373	80.72 304	84.47 363	62.94 329	73.89 449	87.34 280	55.94 467	75.16 468	76.53 512	63.97 345	91.16 221	65.00 362	90.97 356	88.06 374

PVSNet_BlendedMVS		78.80 313	77.84 329	81.65 280	84.43 364	63.41 323	79.49 343	90.44 201	61.70 413	75.43 463	87.07 364	69.11 309	91.44 205	60.68 406	92.24 314	90.11 319

PVSNet_Blended		76.49 354	75.40 362	79.76 327	84.43 364	63.41 323	75.14 429	90.44 201	57.36 458	75.43 463	78.30 495	69.11 309	91.44 205	60.68 406	87.70 435	84.42 430

OpenMVS	76.72 13	81.98 249	82.00 243	81.93 270	84.42 366	68.22 267	88.50 108	89.48 235	66.92 332	81.80 369	91.86 199	72.59 277	90.16 262	71.19 291	91.25 347	87.40 391

OpenMVS_ROB	70.19 17	77.77 332	77.46 332	78.71 352	84.39 367	61.15 372	81.18 310	82.52 369	62.45 401	83.34 331	87.37 356	66.20 326	88.66 309	64.69 367	85.02 475	86.32 405

test_yl		78.71 317	78.51 318	79.32 339	84.32 368	58.84 421	78.38 367	85.33 323	75.99 156	82.49 349	86.57 369	58.01 390	90.02 272	62.74 383	92.73 292	89.10 349

DCV-MVSNet		78.71 317	78.51 318	79.32 339	84.32 368	58.84 421	78.38 367	85.33 323	75.99 156	82.49 349	86.57 369	58.01 390	90.02 272	62.74 383	92.73 292	89.10 349

DELS-MVS		81.44 261	81.25 264	82.03 268	84.27 370	62.87 331	76.47 409	92.49 129	70.97 261	81.64 374	83.83 423	75.03 227	92.70 168	74.29 234	92.22 316	90.51 307
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
Gipuma		84.44 167	86.33 122	78.78 350	84.20 371	73.57 179	89.55 83	90.44 201	84.24 56	84.38 299	94.89 57	76.35 217	80.40 438	76.14 210	96.80 105	82.36 465
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UWE-MVS		66.43 475	65.56 481	69.05 478	84.15 372	40.98 536	73.06 464	64.71 515	54.84 477	76.18 453	79.62 483	29.21 542	80.50 437	38.54 540	89.75 393	85.66 414

SSC-MVS3.2		73.90 394	75.67 360	68.61 485	84.11 373	41.28 535	64.17 522	72.83 462	72.09 240	79.08 418	87.94 337	70.31 300	73.89 474	55.99 443	94.49 218	90.67 301

usedtu_dtu_shiyan1		75.70 368	75.08 369	77.56 377	84.10 374	55.50 457	73.58 452	84.89 335	62.48 396	78.16 425	84.24 415	58.14 388	87.47 339	59.35 414	90.82 363	89.72 328

FE-MVSNET3		75.70 368	75.08 369	77.56 377	84.10 374	55.50 457	73.58 452	84.89 335	62.48 396	78.16 425	84.24 415	58.14 388	87.47 339	59.34 415	90.82 363	89.72 328

EI-MVSNet-Vis-set		85.12 147	84.53 177	86.88 112	84.01 376	72.76 191	83.91 215	85.18 326	80.44 95	88.75 157	85.49 391	80.08 161	91.92 189	82.02 118	90.85 362	95.97 43

fmvsm_l_conf0.5_n		82.06 245	81.54 257	83.60 213	83.94 377	73.90 177	83.35 238	86.10 306	58.97 443	83.80 318	90.36 269	74.23 244	86.94 352	82.90 104	90.22 385	89.94 323

IterMVS-SCA-FT		80.64 279	79.41 300	84.34 186	83.93 378	69.66 245	76.28 412	81.09 391	72.43 231	86.47 236	90.19 280	60.46 365	93.15 155	77.45 186	86.39 457	90.22 313

MSDG		80.06 297	79.99 295	80.25 317	83.91 379	68.04 271	77.51 386	89.19 241	77.65 138	81.94 363	83.45 432	76.37 216	86.31 369	63.31 381	86.59 454	86.41 404

EI-MVSNet-UG-set		85.04 149	84.44 180	86.85 113	83.87 380	72.52 200	83.82 217	85.15 327	80.27 100	88.75 157	85.45 393	79.95 163	91.90 190	81.92 121	90.80 366	96.13 38

PRO-TEST		78.77 316	78.12 326	80.70 306	83.83 381	62.76 336	82.20 283	88.77 246	64.67 372	75.01 470	83.52 428	70.67 299	89.92 274	67.67 337	86.75 451	89.44 335

testing91		69.94 449	68.99 455	72.80 447	83.81 382	45.89 519	71.57 478	73.64 455	68.24 307	70.77 499	77.82 497	34.37 525	84.44 402	53.64 469	87.00 449	88.07 372

fmvsm_l_conf0.5_n_a		81.46 260	80.87 274	83.25 224	83.73 383	73.21 186	83.00 251	85.59 319	58.22 449	82.96 338	90.09 285	72.30 281	86.65 360	81.97 120	89.95 390	89.88 324

viewdifsd2359ckpt11		82.46 232	82.98 220	80.88 299	83.53 384	61.00 378	79.46 346	85.97 312	69.48 283	87.89 186	91.31 225	82.10 130	88.61 311	74.28 235	92.86 286	93.02 189

viewmsd2359difaftdt		82.46 232	82.99 219	80.88 299	83.52 385	61.00 378	79.46 346	85.97 312	69.48 283	87.89 186	91.31 225	82.10 130	88.61 311	74.28 235	92.86 286	93.02 189

UBG		64.34 488	63.35 492	67.30 492	83.50 386	40.53 537	67.46 505	65.02 512	54.77 478	67.54 518	74.47 525	32.99 529	78.50 452	40.82 534	83.58 491	82.88 457

thres200		72.34 417	71.55 420	74.70 430	83.48 387	51.60 491	75.02 432	73.71 453	70.14 274	78.56 424	80.57 473	46.20 474	88.20 322	46.99 517	89.29 400	84.32 431

USDC		76.63 349	76.73 347	76.34 407	83.46 388	57.20 441	80.02 333	88.04 269	52.14 498	83.65 322	91.25 228	63.24 351	86.65 360	54.66 461	94.11 235	85.17 419

ETVMVS		64.67 484	63.34 493	68.64 482	83.44 389	41.89 533	69.56 495	61.70 532	61.33 419	68.74 509	75.76 516	28.76 543	79.35 442	34.65 545	86.16 462	84.67 426

myMVS_eth3d28		65.83 480	65.85 476	65.78 500	83.42 390	35.71 546	67.29 507	68.01 495	67.58 323	69.80 505	77.72 500	32.29 530	74.30 473	37.49 542	89.06 408	87.32 392

testing222		66.93 467	65.30 482	71.81 459	83.38 391	45.83 520	72.06 472	67.50 497	64.12 377	69.68 506	76.37 513	27.34 548	83.00 414	38.88 537	88.38 420	86.62 403

testing11		67.38 465	65.93 475	71.73 460	83.37 392	46.60 516	70.95 483	69.40 486	62.47 399	66.14 520	76.66 510	31.22 534	84.10 407	49.10 504	84.10 488	84.49 427

LoFTR		76.52 353	76.53 349	76.49 403	83.36 393	80.97 94	80.82 320	68.96 491	62.47 399	92.13 72	89.95 287	51.45 445	74.61 472	64.97 364	94.67 212	73.87 522

VortexMVS		80.51 281	80.63 276	80.15 320	83.36 393	61.82 361	80.63 324	88.00 270	67.11 330	87.23 206	89.10 313	63.98 344	88.00 326	73.63 259	92.63 294	90.64 303

HY-MVS	64.64 18	73.03 407	72.47 411	74.71 429	83.36 393	54.19 471	82.14 285	81.96 377	56.76 465	69.57 507	86.21 379	60.03 369	84.83 397	49.58 501	82.65 500	85.11 420

WBMVS		68.76 460	68.43 460	69.75 473	83.29 396	40.30 538	67.36 506	72.21 469	57.09 461	77.05 444	85.53 390	33.68 527	80.51 436	48.79 506	90.90 358	88.45 365

testing99		69.27 455	68.15 463	72.63 449	83.29 396	45.45 521	71.15 480	71.08 478	67.34 326	70.43 501	77.77 499	32.24 531	84.35 405	53.72 467	86.33 458	88.10 371

EI-MVSNet		82.61 227	82.42 235	83.20 226	83.25 398	63.66 320	83.50 233	85.07 328	76.06 153	86.55 229	85.10 399	73.41 263	90.25 255	78.15 171	90.67 374	95.68 53

CVMVSNet		72.62 412	71.41 421	76.28 408	83.25 398	60.34 389	83.50 233	79.02 406	37.77 548	76.33 449	85.10 399	49.60 461	87.41 342	70.54 301	77.54 526	81.08 481

WB-MVSnew		68.72 461	69.01 454	67.85 487	83.22 400	43.98 527	74.93 433	65.98 507	55.09 474	73.83 478	79.11 485	65.63 333	71.89 481	38.21 541	85.04 474	87.69 387

V42		83.47 208	83.37 208	83.75 207	83.16 401	63.33 325	81.31 301	90.23 214	69.51 282	90.91 102	90.81 251	74.16 246	92.29 181	80.06 139	90.22 385	95.62 55

Anonymous20240521		80.18 293	81.25 264	76.95 393	83.15 402	60.84 383	82.46 269	85.99 311	68.76 297	86.78 221	93.73 122	59.13 378	77.44 457	73.71 253	97.55 79	92.56 218

EU-MVSNet		75.12 374	74.43 379	77.18 388	83.11 403	59.48 406	85.71 165	82.43 373	39.76 543	85.64 258	88.76 319	44.71 498	87.88 331	73.86 250	85.88 465	84.16 436

ET-MVSNet_ETH3D		75.28 371	72.77 402	82.81 242	83.03 404	68.11 269	77.09 394	76.51 431	60.67 432	77.60 439	80.52 474	38.04 515	91.15 222	70.78 295	90.68 373	89.17 347

ALIKED-NN		74.80 382	73.22 395	79.55 333	82.93 405	83.79 63	81.84 288	82.56 368	47.43 518	74.33 476	88.03 334	53.21 431	76.31 462	54.08 464	94.57 216	78.54 506

FMVSNet3		78.80 313	78.55 317	79.57 332	82.89 406	56.89 444	81.76 290	85.77 315	69.04 291	86.00 248	90.44 268	51.75 444	90.09 268	65.95 350	93.34 267	91.72 265

MVS_Test		82.47 231	83.22 210	80.22 318	82.62 407	57.75 436	82.54 267	91.96 147	71.16 258	82.89 342	92.52 175	77.41 191	90.50 249	80.04 140	87.84 432	92.40 230

SIFT-MNN		74.38 388	73.27 393	77.72 375	82.37 408	83.68 65	76.29 411	67.76 496	64.16 376	84.33 302	84.30 413	50.36 457	68.84 501	57.79 428	92.07 321	80.66 487

mvs5depth		83.82 193	84.54 176	81.68 279	82.23 409	68.65 262	86.89 133	89.90 223	80.02 104	87.74 193	97.86 4	64.19 342	82.02 423	76.37 203	95.63 166	94.35 113

LF4IMVS		82.75 226	81.93 245	85.19 156	82.08 410	80.15 103	85.53 168	88.76 248	68.01 310	85.58 261	87.75 347	71.80 289	86.85 355	74.02 247	93.87 244	88.58 362

SIFT-NCM-Cal		73.77 396	72.70 405	76.99 391	82.03 411	83.73 64	75.59 423	63.01 525	63.50 383	84.80 288	83.94 422	55.86 413	67.80 511	52.94 477	92.62 295	79.44 497

PVSNet	58.17 21	66.41 476	65.63 480	68.75 481	81.96 412	49.88 502	62.19 527	72.51 466	51.03 505	68.04 513	75.34 522	50.84 451	74.77 469	45.82 524	82.96 495	81.60 473

GA-MVS		75.83 365	74.61 375	79.48 336	81.87 413	59.25 410	73.42 458	82.88 365	68.68 298	79.75 404	81.80 458	50.62 453	89.46 285	66.85 341	85.64 466	89.72 328

MS-PatchMatch		70.93 436	70.22 439	73.06 445	81.85 414	62.50 340	73.82 450	77.90 414	52.44 494	75.92 458	81.27 464	55.67 416	81.75 424	55.37 452	77.70 524	74.94 520

ELoFTR		73.12 406	73.47 390	72.08 457	81.84 415	77.60 134	80.51 327	66.79 505	49.99 512	89.23 147	88.83 317	47.19 467	65.24 531	61.99 392	94.85 204	73.39 523

blended_shiyan8		76.05 362	75.11 367	78.86 347	81.76 416	59.18 413	75.09 430	83.81 351	64.70 369	79.37 409	78.35 494	58.30 386	88.68 307	62.03 391	92.56 301	88.73 360

blended_shiyan6		76.05 362	75.11 367	78.87 346	81.74 417	59.15 414	75.08 431	83.79 352	64.69 370	79.37 409	78.37 493	58.30 386	88.69 306	61.99 392	92.61 296	88.77 358

Syy-MVS		69.40 454	70.03 442	67.49 490	81.72 418	38.94 540	71.00 481	61.99 527	61.38 417	70.81 496	72.36 531	61.37 361	79.30 443	64.50 372	85.18 471	84.22 433

myMVS_eth3d		64.66 485	63.89 487	66.97 494	81.72 418	37.39 543	71.00 481	61.99 527	61.38 417	70.81 496	72.36 531	20.96 555	79.30 443	49.59 500	85.18 471	84.22 433

SIFT-NN-NCMNet		72.70 410	71.25 424	77.06 390	81.65 420	84.07 60	75.19 428	63.15 523	61.29 420	78.74 421	83.21 436	53.60 429	69.25 496	53.99 465	90.47 381	77.86 512

SCA		73.32 401	72.57 409	75.58 420	81.62 421	55.86 452	78.89 360	71.37 477	61.73 411	74.93 471	83.42 433	60.46 365	87.01 348	58.11 425	82.63 502	83.88 437

FMVSNet5		72.10 420	71.69 416	73.32 441	81.57 422	53.02 480	76.77 401	78.37 413	63.31 385	76.37 448	91.85 200	36.68 520	78.98 446	47.87 513	92.45 305	87.95 379

thisisatest0515		73.00 408	70.52 435	80.46 312	81.45 423	59.90 398	73.16 461	74.31 447	57.86 452	76.08 455	77.78 498	37.60 518	92.12 185	65.00 362	91.45 342	89.35 338

eth_miper_zixun_eth		80.84 275	80.22 286	82.71 243	81.41 424	60.98 381	77.81 379	90.14 217	67.31 327	86.95 219	87.24 360	64.26 340	92.31 179	75.23 224	91.61 338	94.85 88

CANet_DTU		77.81 331	77.05 339	80.09 322	81.37 425	59.90 398	83.26 240	88.29 263	69.16 287	67.83 516	83.72 425	60.93 362	89.47 284	69.22 316	89.70 394	90.88 292

ANet_high		83.17 216	85.68 141	75.65 418	81.24 426	45.26 523	79.94 334	92.91 112	83.83 59	91.33 90	96.88 15	80.25 160	85.92 378	68.89 321	95.89 150	95.76 48

new-patchmatchnet		70.10 444	73.37 392	60.29 521	81.23 427	16.95 561	59.54 532	74.62 443	62.93 389	80.97 383	87.93 339	62.83 357	71.90 480	55.24 454	95.01 192	92.00 256

test20.03		73.75 397	74.59 377	71.22 462	81.11 428	51.12 496	70.15 490	72.10 471	70.42 267	80.28 400	91.50 214	64.21 341	74.72 471	46.96 518	94.58 215	87.82 385

blend_shiyan4		70.82 437	68.15 463	78.83 349	81.06 429	59.77 400	74.58 437	83.79 352	64.94 367	77.34 442	75.47 521	29.39 540	88.89 297	58.91 417	67.86 546	87.84 384

MVS		73.21 404	72.59 408	75.06 425	80.97 430	60.81 384	81.64 293	85.92 314	46.03 525	71.68 491	77.54 501	68.47 312	89.77 279	55.70 447	85.39 467	74.60 521

N_pmnet		70.20 442	68.80 458	74.38 431	80.91 431	84.81 53	59.12 534	76.45 433	55.06 475	75.31 467	82.36 451	55.74 415	54.82 546	47.02 516	87.24 442	83.52 445

IterMVS		76.91 344	76.34 353	78.64 353	80.91 431	64.03 317	76.30 410	79.03 405	64.88 368	83.11 335	89.16 311	59.90 371	84.46 401	68.61 327	85.15 473	87.42 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dtuplus		78.46 321	78.13 325	79.45 337	80.90 433	59.52 405	77.65 382	86.72 299	61.21 423	82.91 341	89.26 306	73.46 262	87.27 345	63.53 378	87.49 438	91.55 273

c3_l		81.64 257	81.59 253	81.79 278	80.86 434	59.15 414	78.61 366	90.18 216	68.36 304	87.20 208	87.11 363	69.39 306	91.62 198	78.16 169	94.43 221	94.60 96

WTY-MVS		67.91 464	68.35 461	66.58 496	80.82 435	48.12 507	65.96 513	72.60 464	53.67 485	71.20 493	81.68 460	58.97 379	69.06 498	48.57 507	81.67 504	82.55 461

IB-MVS	62.13 19	71.64 426	68.97 456	79.66 330	80.80 436	62.26 351	73.94 448	76.90 427	63.27 387	68.63 511	76.79 509	33.83 526	91.84 193	59.28 416	87.26 441	84.88 422
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
our_test_3		71.85 422	71.59 417	72.62 450	80.71 437	53.78 474	69.72 493	71.71 476	58.80 445	78.03 428	80.51 475	56.61 402	78.84 448	62.20 388	86.04 463	85.23 418

ppachtmachnet_test		74.73 384	74.00 382	76.90 395	80.71 437	56.89 444	71.53 479	78.42 411	58.24 448	79.32 413	82.92 443	57.91 393	84.26 406	65.60 357	91.36 343	89.56 333

diffmvs_AUTHOR		81.24 266	81.55 256	80.30 316	80.61 439	60.22 391	77.98 376	90.48 198	67.77 319	83.34 331	89.50 299	74.69 238	87.42 341	78.78 159	90.81 365	93.27 174

testgi		72.36 415	74.61 375	65.59 501	80.56 440	42.82 532	68.29 499	73.35 456	66.87 333	81.84 366	89.93 289	72.08 285	66.92 519	46.05 523	92.54 302	87.01 396

D2MVS		76.84 345	75.67 360	80.34 315	80.48 441	62.16 354	73.50 456	84.80 340	57.61 455	82.24 354	87.54 351	51.31 447	87.65 335	70.40 303	93.19 277	91.23 279

SIFT-ConvMatch		74.17 389	72.94 400	77.87 372	80.47 442	83.15 70	74.56 438	63.87 519	63.44 384	85.61 259	83.95 421	53.15 432	69.97 489	57.21 433	94.21 230	80.48 488

viewmamba		81.97 250	82.13 237	81.47 286	80.43 443	62.46 341	79.31 350	89.99 222	71.08 259	83.39 330	90.21 278	78.08 179	88.73 304	77.55 183	89.16 405	93.23 178

1314		73.22 403	72.56 410	75.20 423	80.41 444	57.84 434	81.64 293	85.36 321	51.68 501	73.10 483	76.65 511	61.45 360	85.19 393	63.54 377	79.21 518	82.59 459

SP-DiffGlue		78.90 309	78.86 310	79.02 343	80.36 445	79.68 109	81.86 287	80.17 398	71.69 247	86.02 245	83.77 424	57.33 398	69.38 492	79.38 152	89.12 406	88.02 376

wanda-best-256-512		74.97 377	73.85 383	78.35 359	80.36 445	58.13 428	73.10 462	83.53 357	64.04 378	77.62 436	75.71 517	56.22 406	88.60 313	61.42 400	92.61 296	88.32 366

FE-blended-shiyan7		74.97 377	73.85 383	78.35 359	80.36 445	58.13 428	73.10 462	83.53 357	64.03 379	77.62 436	75.71 517	56.22 406	88.60 313	61.42 400	92.61 296	88.32 366

usedtu_blend_shiyan5		77.07 342	76.43 351	78.99 344	80.36 445	59.77 400	83.25 241	88.32 262	74.91 177	77.62 436	75.71 517	56.22 406	88.89 297	58.91 417	92.61 296	88.32 366

SIFT-NN		71.05 434	69.58 447	75.45 421	80.35 449	81.93 82	74.31 440	63.57 521	61.17 426	75.98 456	81.67 461	46.63 472	65.25 530	53.44 472	89.09 407	79.18 500

gbinet_0.2-2-1-0.02		76.14 359	74.88 372	79.92 323	80.33 450	60.02 396	75.80 419	82.44 372	66.36 339	79.24 414	75.07 523	56.11 409	90.17 261	64.60 370	93.95 241	89.58 332

viewmambaseed2359dif		78.80 313	78.47 320	79.78 325	80.26 451	59.28 409	77.31 392	87.13 288	60.42 434	82.37 352	88.67 324	74.58 240	87.87 332	67.78 335	87.73 433	92.19 247

SP-LightGlue		79.92 300	79.74 296	80.46 312	80.22 452	81.52 89	81.28 305	81.81 379	75.89 160	81.60 376	84.90 405	55.82 414	71.10 485	85.62 65	90.47 381	88.76 359

SIFT-UMatch		73.61 398	72.65 407	76.46 404	80.19 453	82.31 79	74.23 442	64.86 513	64.03 379	84.69 291	84.19 418	50.89 450	67.79 512	57.03 434	93.79 247	79.28 499

onestephybrid01		81.22 267	80.90 273	82.18 264	80.05 454	64.49 311	79.47 344	89.23 240	69.10 288	81.96 362	89.27 305	75.02 228	89.12 292	73.71 253	90.24 384	92.92 199

cl____		80.42 284	80.23 284	81.02 297	79.99 455	59.25 410	77.07 395	87.02 294	67.37 325	86.18 242	89.21 310	63.08 354	90.16 262	76.31 205	95.80 155	93.65 154

DIV-MVS_self_test		80.43 283	80.23 284	81.02 297	79.99 455	59.25 410	77.07 395	87.02 294	67.38 324	86.19 240	89.22 309	63.09 353	90.16 262	76.32 204	95.80 155	93.66 151

SP-SuperGlue		80.13 295	80.14 288	80.11 321	79.95 457	80.97 94	80.94 315	80.77 394	76.46 150	82.92 340	85.73 386	58.75 382	70.83 486	85.20 70	90.50 380	88.53 363

MonoMVSNet		76.66 348	77.26 337	74.86 426	79.86 458	54.34 469	86.26 150	86.08 307	71.08 259	85.59 260	88.68 322	53.95 427	85.93 377	63.86 374	80.02 513	84.32 431

miper_ehance_all_eth		80.34 287	80.04 293	81.24 293	79.82 459	58.95 418	77.66 381	89.66 230	65.75 350	85.99 251	85.11 398	68.29 313	91.42 207	76.03 211	92.03 322	93.33 170

SIFT-CM-Cal		73.20 405	71.85 415	77.25 387	79.80 460	82.49 78	73.51 455	64.83 514	62.27 405	83.49 327	82.81 447	51.79 443	69.71 491	53.70 468	94.43 221	79.53 496

CR-MVSNet		74.00 393	73.04 398	76.85 398	79.58 461	62.64 337	82.58 264	76.90 427	50.50 510	75.72 460	92.38 178	48.07 465	84.07 408	68.72 326	82.91 497	83.85 440

RPMNet		78.88 311	78.28 322	80.68 307	79.58 461	62.64 337	82.58 264	94.16 33	74.80 178	75.72 460	92.59 169	48.69 462	95.56 44	73.48 262	82.91 497	83.85 440

baseline2		69.77 450	66.89 470	78.41 358	79.51 463	58.09 430	76.23 413	69.57 485	57.50 456	64.82 531	77.45 503	46.02 476	88.44 316	53.08 473	77.83 522	88.70 361

UnsupCasMVSNet_bld		69.21 456	69.68 445	67.82 488	79.42 464	51.15 495	67.82 503	75.79 435	54.15 482	77.47 441	85.36 397	59.26 377	70.64 487	48.46 508	79.35 516	81.66 472

PatchT		70.52 440	72.76 403	63.79 510	79.38 465	33.53 549	77.63 383	65.37 511	73.61 203	71.77 490	92.79 165	44.38 499	75.65 466	64.53 371	85.37 468	82.18 467

Patchmtry		76.56 352	77.46 332	73.83 436	79.37 466	46.60 516	82.41 273	76.90 427	73.81 195	85.56 262	92.38 178	48.07 465	83.98 409	63.36 380	95.31 176	90.92 290

mvs_anonymous		78.13 327	78.76 314	76.23 410	79.24 467	50.31 500	78.69 364	84.82 339	61.60 415	83.09 337	92.82 162	73.89 253	87.01 348	68.33 331	86.41 456	91.37 277

SIFT-UM-Cal		73.50 400	72.76 403	75.71 417	79.21 468	81.68 86	72.85 465	68.91 492	62.93 389	85.31 268	83.39 435	52.88 434	67.56 515	54.97 458	94.42 224	77.89 511

MVS-HIRNet		61.16 500	62.92 495	55.87 526	79.09 469	35.34 547	71.83 473	57.98 544	46.56 522	59.05 543	91.14 233	49.95 460	76.43 461	38.74 538	71.92 538	55.84 546

MDA-MVSNet-bldmvs		77.47 335	76.90 343	79.16 341	79.03 470	64.59 307	66.58 511	75.67 437	73.15 217	88.86 152	88.99 315	66.94 322	81.23 430	64.71 366	88.22 426	91.64 270

diffmvs		80.40 285	80.48 281	80.17 319	79.02 471	60.04 393	77.54 385	90.28 213	66.65 335	82.40 351	87.33 358	73.50 259	87.35 343	77.98 177	89.62 395	93.13 182
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpm2		68.45 462	66.83 471	73.30 443	78.93 472	48.50 505	79.76 336	71.76 474	47.50 517	69.92 504	83.60 427	42.07 507	88.40 318	48.44 509	79.51 514	83.01 456

SIFT-NN-CMatch		72.68 411	71.28 423	76.88 397	78.79 473	82.59 77	73.68 451	61.02 535	60.35 435	81.79 371	83.09 438	52.94 433	68.88 500	57.28 431	92.53 303	79.16 501

tpm		67.95 463	68.08 465	67.55 489	78.74 474	43.53 529	75.60 421	67.10 503	54.92 476	72.23 487	88.10 333	42.87 506	75.97 464	52.21 483	80.95 512	83.15 454

SIFT-NN-UMatch		72.46 413	71.25 424	76.08 411	78.57 475	81.88 83	74.36 439	61.59 533	61.99 408	80.24 402	83.46 431	51.20 448	68.08 510	57.95 427	91.91 329	78.28 508

SP-MNN		77.71 333	77.85 328	77.29 385	78.48 476	75.90 161	79.14 356	79.46 402	69.61 279	81.56 377	84.60 410	54.98 424	69.02 499	81.08 128	91.72 335	86.95 398

hybridnocas07		79.65 302	79.65 298	79.63 331	78.06 477	59.34 407	77.00 399	88.72 249	66.51 337	81.08 382	89.36 302	72.35 279	87.12 347	74.56 231	89.20 403	92.44 225

MDTV_nov1_ep13			68.29 462		78.03 478	43.87 528	74.12 445	72.22 468	52.17 496	67.02 519	85.54 389	45.36 489	80.85 433	55.73 445	84.42 484

hybrid		79.06 306	78.94 308	79.40 338	77.99 479	59.05 416	77.07 395	88.49 255	64.42 374	80.52 396	88.78 318	71.45 293	86.82 356	73.23 268	88.52 417	92.34 236

SIFT-PointCN		72.17 419	71.14 428	75.23 422	77.93 480	79.30 113	72.22 470	64.71 515	62.60 393	84.13 311	81.00 467	46.91 469	67.69 514	55.17 455	95.64 165	78.70 505

cl22		78.97 307	78.21 323	81.24 293	77.74 481	59.01 417	77.46 390	87.13 288	65.79 346	84.32 303	85.10 399	58.96 380	90.88 234	75.36 222	92.03 322	93.84 139

EPNet_dtu		72.87 409	71.33 422	77.49 382	77.72 482	60.55 387	82.35 275	75.79 435	66.49 338	58.39 546	81.06 466	53.68 428	85.98 376	53.55 470	92.97 283	85.95 410
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SIFT-NN-PointCN		72.35 416	71.17 427	75.90 413	77.68 483	80.93 97	73.48 457	63.14 524	60.88 428	80.94 385	82.91 444	52.54 438	67.74 513	55.98 444	92.95 284	79.05 503

PatchmatchNet		69.71 451	68.83 457	72.33 455	77.66 484	53.60 475	79.29 351	69.99 483	57.66 454	72.53 486	82.93 442	46.45 473	80.08 440	60.91 405	72.09 537	83.31 452
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_vis1_n_1920		71.30 432	71.58 419	70.47 466	77.58 485	59.99 397	74.25 441	84.22 347	51.06 504	74.85 472	79.10 486	55.10 422	68.83 502	68.86 323	79.20 519	82.58 460

SIFT-NCMNet		71.70 425	70.97 429	73.90 434	77.55 486	81.03 92	71.58 477	63.31 522	63.91 382	87.12 210	81.00 467	50.00 458	64.64 534	49.37 502	94.86 202	76.04 517

SIFT-PCN-Cal		71.86 421	71.21 426	73.82 437	77.43 487	78.37 121	71.75 474	65.73 508	62.15 407	84.04 313	81.59 462	50.59 454	64.96 532	52.46 482	95.15 182	78.14 510

SP-NN		76.57 350	76.54 348	76.66 400	77.40 488	75.50 165	78.02 373	78.77 409	68.60 301	75.98 456	83.71 426	55.56 417	66.71 520	82.06 116	88.74 414	87.76 386

dmvs_testset		60.59 504	62.54 497	54.72 528	77.26 489	27.74 554	74.05 446	61.00 536	60.48 433	65.62 525	67.03 539	55.93 411	68.23 508	32.07 549	69.46 544	68.17 532

sss		66.92 468	67.26 467	65.90 499	77.23 490	51.10 497	64.79 517	71.72 475	52.12 499	70.13 503	80.18 477	57.96 392	65.36 529	50.21 495	81.01 510	81.25 478

CostFormer		69.98 448	68.68 459	73.87 435	77.14 491	50.72 498	79.26 352	74.51 445	51.94 500	70.97 495	84.75 407	45.16 493	87.49 338	55.16 456	79.23 517	83.40 449

tpm cat1		66.76 472	65.21 483	71.42 461	77.09 492	50.62 499	78.01 374	73.68 454	44.89 528	68.64 510	79.00 487	45.51 487	82.42 419	49.91 498	70.15 540	81.23 480

pmmvs5		70.73 438	70.07 440	72.72 448	77.03 493	52.73 482	74.14 444	75.65 438	50.36 511	72.17 489	85.37 396	55.42 419	80.67 434	52.86 478	87.59 437	84.77 423

dmvs_re		66.81 471	66.98 469	66.28 497	76.87 494	58.68 425	71.66 476	72.24 467	60.29 437	69.52 508	73.53 527	52.38 439	64.40 535	44.90 525	81.44 507	75.76 518

EPNet		80.37 286	78.41 321	86.23 126	76.75 495	73.28 183	87.18 127	77.45 419	76.24 152	68.14 512	88.93 316	65.41 334	93.85 121	69.47 312	96.12 134	91.55 273
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		76.45 355	76.10 355	77.51 381	76.72 496	60.97 382	64.69 518	85.04 330	63.98 381	83.20 334	88.22 331	56.67 401	78.79 449	73.22 269	93.12 278	92.78 203

reproduce_monomvs		74.09 392	73.23 394	76.65 402	76.52 497	54.54 466	77.50 387	81.40 388	65.85 345	82.86 344	86.67 368	27.38 547	84.53 400	70.24 304	90.66 376	90.89 291

CHOSEN 280x420		59.08 506	56.52 514	66.76 495	76.51 498	64.39 314	49.62 546	59.00 541	43.86 532	55.66 551	68.41 538	35.55 524	68.21 509	43.25 528	76.78 529	67.69 534

UnsupCasMVSNet_eth		71.63 427	72.30 412	69.62 474	76.47 499	52.70 483	70.03 491	80.97 392	59.18 442	79.36 411	88.21 332	60.50 364	69.12 497	58.33 423	77.62 525	87.04 395

test-LLR		67.21 466	66.74 472	68.63 483	76.45 500	55.21 461	67.89 500	67.14 501	62.43 403	65.08 528	72.39 529	43.41 502	69.37 493	61.00 403	84.89 479	81.31 476

test-mter		65.00 483	63.79 489	68.63 483	76.45 500	55.21 461	67.89 500	67.14 501	50.98 506	65.08 528	72.39 529	28.27 545	69.37 493	61.00 403	84.89 479	81.31 476

MatchFormer		68.98 458	69.54 449	67.33 491	76.37 502	74.77 170	79.54 339	57.73 545	46.87 519	89.77 129	86.43 372	41.98 508	65.54 527	52.83 480	94.31 228	61.67 541

miper_enhance_ethall		77.83 329	76.93 342	80.51 311	76.15 503	58.01 433	75.47 426	88.82 245	58.05 451	83.59 323	80.69 470	64.41 338	91.20 219	73.16 275	92.03 322	92.33 238

gg-mvs-nofinetune		68.96 459	69.11 452	68.52 486	76.12 504	45.32 522	83.59 226	55.88 547	86.68 32	64.62 532	97.01 11	30.36 537	83.97 410	44.78 526	82.94 496	76.26 516

test_vis1_n		70.29 441	69.99 443	71.20 463	75.97 505	66.50 289	76.69 403	80.81 393	44.22 531	75.43 463	77.23 506	50.00 458	68.59 503	66.71 344	82.85 499	78.52 507

CMPMVS	59.41 20	75.12 374	73.57 387	79.77 326	75.84 506	67.22 276	81.21 309	82.18 375	50.78 507	76.50 446	87.66 349	55.20 421	82.99 415	62.17 390	90.64 378	89.09 351
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
nomal-1		66.61 473	65.11 484	71.13 465	75.60 507	61.96 356	65.47 515	69.28 487	57.45 457	70.78 498	77.26 505	35.65 523	73.16 475	50.42 494	84.07 489	78.25 509

wuyk23d		75.13 373	79.30 305	62.63 511	75.56 508	75.18 169	80.89 317	73.10 459	75.06 176	94.76 15	95.32 45	87.73 47	52.85 548	34.16 546	97.11 92	59.85 543

Patchmatch-test		65.91 478	67.38 466	61.48 518	75.51 509	43.21 531	68.84 496	63.79 520	62.48 396	72.80 485	83.42 433	44.89 497	59.52 541	48.27 511	86.45 455	81.70 471

new_pmnet		55.69 511	57.66 512	49.76 529	75.47 510	30.59 552	59.56 531	51.45 550	43.62 534	62.49 535	75.48 520	40.96 510	49.15 552	37.39 543	72.52 535	69.55 530

gm-plane-assit					75.42 511	44.97 525			52.17 496		72.36 531		87.90 330	54.10 463

MVSTER		77.09 341	75.70 359	81.25 290	75.27 512	61.08 374	77.49 388	85.07 328	60.78 430	86.55 229	88.68 322	43.14 505	90.25 255	73.69 258	90.67 374	92.42 226

PVSNet_0	51.08 22	56.10 510	54.97 516	59.48 523	75.12 513	53.28 479	55.16 542	61.89 529	44.30 530	59.16 542	62.48 542	54.22 426	65.91 526	35.40 544	47.01 551	59.25 544

test0.0.03 1		64.66 485	64.36 485	65.57 502	75.03 514	46.89 515	64.69 518	61.58 534	62.43 403	71.18 494	77.54 501	43.41 502	68.47 506	40.75 535	82.65 500	81.35 475

test_fmvs3		75.72 367	75.20 366	77.27 386	75.01 515	69.47 248	78.93 358	84.88 337	46.67 521	87.08 215	87.84 345	50.44 456	71.62 482	77.42 188	88.53 416	90.72 296

tpmvs		70.16 443	69.56 448	71.96 458	74.71 516	48.13 506	79.63 337	75.45 440	65.02 366	70.26 502	81.88 457	45.34 490	85.68 388	58.34 422	75.39 530	82.08 469

0.4-1-1-0.1		64.02 490	60.59 502	74.31 432	73.99 517	55.62 455	67.66 504	72.78 463	55.53 472	60.35 540	58.45 544	29.26 541	86.88 353	52.84 479	74.42 532	80.42 489

test_fmvs1_n		70.94 435	70.41 438	72.53 452	73.92 518	66.93 285	75.99 417	84.21 348	43.31 535	79.40 408	79.39 484	43.47 501	68.55 504	69.05 319	84.91 478	82.10 468

MDA-MVSNet_test_wron		70.05 446	70.44 436	68.88 480	73.84 519	53.47 476	58.93 536	67.28 499	58.43 446	87.09 214	85.40 394	59.80 373	67.25 517	59.66 412	83.54 492	85.92 411

YYNet1		70.06 445	70.44 436	68.90 479	73.76 520	53.42 478	58.99 535	67.20 500	58.42 447	87.10 213	85.39 395	59.82 372	67.32 516	59.79 411	83.50 493	85.96 409

test_cas_vis1_n_1920		69.20 457	69.12 451	69.43 476	73.68 521	62.82 333	70.38 489	77.21 424	46.18 524	80.46 397	78.95 488	52.03 440	65.53 528	65.77 356	77.45 527	79.95 492

UWE-MVS-28		58.44 508	57.71 511	60.65 520	73.58 522	31.23 551	69.68 494	48.80 552	53.12 490	61.79 536	78.83 489	30.98 535	68.40 507	21.58 552	80.99 511	82.33 466

GG-mvs-BLEND				67.16 493	73.36 523	46.54 518	84.15 206	55.04 548		58.64 545	61.95 543	29.93 538	83.87 411	38.71 539	76.92 528	71.07 528

JIA-IIPM		69.41 453	66.64 474	77.70 376	73.19 524	71.24 223	75.67 420	65.56 510	70.42 267	65.18 527	92.97 155	33.64 528	83.06 413	53.52 471	69.61 543	78.79 504

ADS-MVSNet2		65.87 479	63.64 491	72.55 451	73.16 525	56.92 443	67.10 508	74.81 442	49.74 514	66.04 522	82.97 440	46.71 470	77.26 458	42.29 530	69.96 541	83.46 447

ADS-MVSNet		61.90 496	62.19 498	61.03 519	73.16 525	36.42 545	67.10 508	61.75 530	49.74 514	66.04 522	82.97 440	46.71 470	63.21 536	42.29 530	69.96 541	83.46 447

ttmdpeth		71.72 424	70.67 432	74.86 426	73.08 527	55.88 451	77.41 391	69.27 488	55.86 468	78.66 422	93.77 120	38.01 516	75.39 468	60.12 409	89.87 391	93.31 172

DSMNet-mixed		60.98 502	61.61 500	59.09 525	72.88 528	45.05 524	74.70 435	46.61 554	26.20 551	65.34 526	90.32 274	55.46 418	63.12 537	41.72 532	81.30 509	69.09 531

tpmrst		66.28 477	66.69 473	65.05 505	72.82 529	39.33 539	78.20 370	70.69 481	53.16 489	67.88 515	80.36 476	48.18 464	74.75 470	58.13 424	70.79 539	81.08 481

test_fmvs2		73.57 399	72.80 401	75.90 413	72.74 530	68.84 261	77.07 395	84.32 346	45.14 527	82.89 342	84.22 417	48.37 463	70.36 488	73.40 264	87.03 447	88.52 364

TESTMET0.1,1		61.29 499	60.32 504	64.19 508	72.06 531	51.30 493	67.89 500	62.09 526	45.27 526	60.65 539	69.01 536	27.93 546	64.74 533	56.31 440	81.65 506	76.53 515

dp		60.70 503	60.29 505	61.92 515	72.04 532	38.67 542	70.83 485	64.08 517	51.28 503	60.75 538	77.28 504	36.59 521	71.58 483	47.41 515	62.34 548	75.52 519

0.3-1-1-0.015		62.57 492	58.82 509	73.82 437	71.85 533	54.96 464	65.63 514	72.97 461	54.16 481	56.95 549	55.43 545	26.76 551	86.59 362	52.05 485	73.55 534	79.92 493

pmmvs3		62.47 493	60.02 506	69.80 472	71.58 534	64.00 318	70.52 487	58.44 543	39.77 542	66.05 521	75.84 515	27.10 550	72.28 478	46.15 522	84.77 483	73.11 525

dongtai		41.90 514	42.65 518	39.67 531	70.86 535	21.11 556	61.01 530	21.42 562	57.36 458	57.97 547	50.06 549	16.40 558	58.73 543	21.03 553	27.69 555	39.17 549

0.4-1-1-0.2		62.43 495	58.81 510	73.31 442	70.85 536	54.20 470	64.36 520	72.99 460	53.70 484	57.51 548	54.59 546	29.52 539	86.44 366	51.70 492	74.02 533	79.30 498

EPMVS		62.47 493	62.63 496	62.01 513	70.63 537	38.74 541	74.76 434	52.86 549	53.91 483	67.71 517	80.01 478	39.40 512	66.60 521	55.54 451	68.81 545	80.68 485

mvsany_test3		65.48 482	62.97 494	73.03 446	69.99 538	76.17 155	64.83 516	43.71 555	43.68 533	80.25 401	87.05 365	52.83 436	63.09 538	51.92 490	72.44 536	79.84 494

test_vis3_rt		71.42 430	70.67 432	73.64 440	69.66 539	70.46 233	66.97 510	89.73 227	42.68 538	88.20 175	83.04 439	43.77 500	60.07 539	65.35 360	86.66 453	90.39 310

dtuonly		66.56 474	67.23 468	64.55 506	69.44 540	43.53 529	66.34 512	72.11 470	48.23 516	68.04 513	83.21 436	55.95 410	66.59 522	55.55 450	86.17 461	83.53 444

test_fmvs1		69.57 452	69.05 453	71.14 464	69.15 541	65.77 299	73.98 447	83.32 360	42.83 537	77.77 434	78.27 496	43.39 504	68.50 505	68.39 330	84.38 485	79.15 502

KD-MVS_2432*1600		66.87 469	65.81 478	70.04 468	67.50 542	47.49 511	62.56 525	79.16 403	61.21 423	77.98 429	80.61 471	25.29 552	82.48 417	53.02 474	84.92 476	80.16 490

miper_refine_blended		66.87 469	65.81 478	70.04 468	67.50 542	47.49 511	62.56 525	79.16 403	61.21 423	77.98 429	80.61 471	25.29 552	82.48 417	53.02 474	84.92 476	80.16 490

E-PMN		61.59 498	61.62 499	61.49 517	66.81 544	55.40 459	53.77 543	60.34 537	66.80 334	58.90 544	65.50 540	40.48 511	66.12 524	55.72 446	86.25 459	62.95 540

test_f		64.31 489	65.85 476	59.67 522	66.54 545	62.24 353	57.76 538	70.96 479	40.13 541	84.36 300	82.09 453	46.93 468	51.67 549	61.99 392	81.89 503	65.12 537

test_vis1_rt		65.64 481	64.09 486	70.31 467	66.09 546	70.20 237	61.16 529	81.60 384	38.65 545	72.87 484	69.66 534	52.84 435	60.04 540	56.16 441	77.77 523	80.68 485

EMVS		61.10 501	60.81 501	61.99 514	65.96 547	55.86 452	53.10 544	58.97 542	67.06 331	56.89 550	63.33 541	40.98 509	67.03 518	54.79 460	86.18 460	63.08 539

mvsany_test1		58.48 507	56.47 515	64.50 507	65.90 548	68.21 268	56.95 539	42.11 556	38.30 546	65.69 524	77.19 508	56.96 400	59.35 542	46.16 521	58.96 550	65.93 535

PMMVS		61.65 497	60.38 503	65.47 503	65.40 549	69.26 251	63.97 523	61.73 531	36.80 550	60.11 541	68.43 537	59.42 375	66.35 523	48.97 505	78.57 521	60.81 542

PMMVS2		55.64 512	59.27 507	44.74 530	64.30 550	12.32 563	40.60 547	49.79 551	53.19 488	65.06 530	84.81 406	53.60 429	49.76 551	32.68 548	89.41 399	72.15 526

MASt3R-SfM		63.18 491	63.70 490	61.64 516	63.57 551	67.13 278	64.25 521	57.31 546	37.50 549	82.96 338	80.95 469	45.96 479	49.82 550	54.93 459	85.89 464	67.95 533

XFeat-NN		59.92 505	59.04 508	62.58 512	63.37 552	64.42 313	55.18 541	60.26 538	41.73 539	77.26 443	69.20 535	31.98 532	58.40 544	48.23 512	84.12 487	64.93 538

MVE	40.22 23	51.82 513	50.47 517	55.87 526	62.66 553	51.91 488	31.61 550	39.28 557	40.65 540	50.76 552	74.98 524	56.24 405	44.67 553	33.94 547	64.11 547	71.04 529
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
XFeat-MNN		64.44 487	63.82 488	66.28 497	61.83 554	67.23 275	61.52 528	63.95 518	44.72 529	85.19 271	74.40 526	36.05 522	66.04 525	55.58 448	91.14 348	65.57 536

MVStest1		70.05 446	69.26 450	72.41 454	58.62 555	55.59 456	76.61 406	65.58 509	53.44 486	89.28 146	93.32 133	22.91 554	71.44 484	74.08 245	89.52 396	90.21 317

PDCNetPlus		57.49 509	56.93 513	59.15 524	56.36 556	47.35 514	52.32 545	77.34 422	39.50 544	63.50 534	73.19 528	13.19 560	56.86 545	47.51 514	89.48 397	73.22 524

kuosan		30.83 516	32.17 520	26.83 534	53.36 557	19.02 560	57.90 537	20.44 563	38.29 547	38.01 553	37.82 551	15.18 559	33.45 556	7.74 558	20.76 558	28.03 550

DeepMVS_CX				24.13 535	32.95 558	29.49 553		21.63 561	12.07 553	37.95 554	45.07 550	30.84 536	19.21 557	17.94 554	33.06 554	23.69 551

GLUNet-SfM		36.71 515	36.32 519	37.87 532	23.81 559	32.04 550	38.61 548	29.05 559	18.10 552	70.60 500	50.66 548	18.79 557	40.81 555	17.68 555	59.57 549	40.74 548

MVS_clip		14.31 520	16.37 524	8.11 537	18.08 560	12.42 562	12.95 552	3.12 565	3.73 555	28.79 556	35.98 552	8.84 561	4.85 560	12.31 556	23.54 556	7.07 552

test_method		30.46 517	29.60 521	33.06 533	17.99 561	3.84 566	13.62 551	73.92 449	2.79 556	18.29 558	53.41 547	28.53 544	43.25 554	22.56 550	35.27 553	52.11 547

VLMVS_CLIP		13.55 521	14.55 525	10.53 536	11.59 562	10.03 564	11.68 553	18.47 564	4.20 554	20.50 557	24.42 553	8.69 562	16.48 558	8.18 557	23.25 557	5.10 553

tmp_tt		20.25 519	24.50 523	7.49 538	4.47 563	8.70 565	34.17 549	25.16 560	1.00 558	32.43 555	18.49 554	39.37 513	9.21 559	21.64 551	43.75 552	4.57 554

MVS_baseline		4.35 526	5.47 530	0.99 540	3.75 564	0.34 570	2.10 554	0.79 568	0.13 562	12.26 559	14.40 556	2.36 564	0.00 564	1.87 559	11.56 559	2.62 556

VLMVS		3.03 527	3.34 531	2.13 539	3.00 565	1.87 567	1.95 555	1.16 566	0.16 561	5.10 560	6.49 557	5.23 563	1.51 561	1.34 560	5.59 560	3.02 555

testmvs		5.91 525	7.65 529	0.72 542	1.20 566	0.37 569	59.14 533	0.67 569	0.49 560	1.11 562	2.76 560	0.94 566	0.24 563	1.02 562	1.47 561	1.55 558

test123		6.27 524	8.08 528	0.84 541	1.11 567	0.57 568	62.90 524	0.82 567	0.54 559	1.07 563	2.75 561	1.26 565	0.30 562	1.04 561	1.26 562	1.66 557

PatchmatchNet2					0.00 568	20.88 557	55.62 540	59.13 539	52.38 495
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mmdepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

monomultidepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

test_blank		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

eth-test2					0.00 568

eth-test					0.00 568

uanet_test		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

DCPMVS		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

cdsmvs_eth3d_5k		20.81 518	27.75 522	0.00 543	0.00 568	0.00 571	0.00 556	85.44 320	0.00 563	0.00 564	82.82 445	81.46 144	0.00 564	0.00 563	0.00 563	0.00 559

pcd_1.5k_mvsjas		6.41 523	8.55 527	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	76.94 204	0.00 564	0.00 563	0.00 563	0.00 559

sosnet-low-res		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

sosnet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uncertanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

Regformer		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

ab-mvs-re		6.65 522	8.87 526	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	79.80 480	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

uanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.00 562	0.00 567	0.00 564	0.00 563	0.00 563	0.00 559

Meshroom													0.00 564
: In preparation.
AliceVision / Meshro													0.00 564
: In preparation.
AliceVision_Meshroom													0.00 564
: In preparation.
PatchmatchNet1														46.85 519	87.28 439	83.48 446
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													54.72 547
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						37.39 543								52.61 481

PC_three_1452									58.96 444	90.06 117	91.33 223	80.66 155	93.03 160	75.78 214	95.94 145	92.48 222

test_241102_TWO								93.71 60	83.77 60	93.49 41	94.27 85	89.27 24	95.84 26	86.03 56	97.82 56	92.04 254

test_0728_THIRD									85.33 41	93.75 36	94.65 67	87.44 50	95.78 34	87.41 30	98.21 33	92.98 195

GSMVS																83.88 437

sam_mvs1												46.11 475				83.88 437

sam_mvs												45.92 481

MTGPA								91.81 154

test_post1							78.85 362				3.13 558	45.19 492	80.13 439	58.11 425

test_post											3.10 559	45.43 488	77.22 459

patchmatchnet-post											81.71 459	45.93 480	87.01 348

MTMP							90.66 54	33.14 558

test9_res														80.83 132	96.45 118	90.57 304

agg_prior2														79.68 145	96.16 131	90.22 313

test_prior4						78.97 116	84.59 193

test_prior2							83.37 237		75.43 171	84.58 293	91.57 212	81.92 137		79.54 149	96.97 95

旧先验2							81.73 291		56.88 463	86.54 235			84.90 396	72.81 276

新几何2							81.72 292

无先验							82.81 259	85.62 318	58.09 450				91.41 208	67.95 334		84.48 428

原ACMM2							82.26 280

testdata2													86.43 367	63.52 379

segment_acmp												81.94 134

testdata1							79.62 338		73.95 194

plane_prior5								93.61 70					95.22 63	80.78 133	95.83 153	94.46 104

plane_prior4											92.95 156

plane_prior3						76.85 145			77.79 137	86.55 229

plane_prior2							89.45 88		79.44 112

plane_prior						76.42 150	87.15 129		75.94 159						95.03 189

n2								0.00 570

nn								0.00 570

door-mid								74.45 446

test11								91.46 163

door								72.57 465

HQP5-MVS						70.66 229

BP-MVS														77.30 189

HQP4-MVS										80.56 391			94.61 87			93.56 163

HQP3-MVS								92.68 121							94.47 219

HQP2-MVS												72.10 283

MDTV_nov1_ep13_2view						27.60 555	70.76 486		46.47 523	61.27 537		45.20 491		49.18 503		83.75 442

ACMMP++_ref															95.74 160

ACMMP++															97.35 85

Test By Simon												79.09 169

High-resolution multi-view benchmark