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 36	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 15	85.07 70	99.27 1	99.54 1

UniMVSNet_ETH3D		89.12 68	90.72 49	84.31 179	97.00 2	64.33 272	89.67 79	88.38 248	88.84 16	94.29 22	97.57 7	90.48 14	91.26 207	72.57 258	97.65 69	97.34 15

PMVS	80.48 6	90.08 43	90.66 50	88.34 86	96.71 3	92.97 1	90.31 64	89.57 227	88.51 20	90.11 105	95.12 52	90.98 7	88.92 287	77.55 173	97.07 91	83.13 424
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MTAPA		91.52 18	91.60 22	91.29 29	96.59 4	86.29 20	92.02 38	91.81 149	84.07 54	92.00 70	94.40 79	86.63 58	95.28 61	88.59 10	98.31 25	92.30 230

PEN-MVS		90.03 47	91.88 18	84.48 171	96.57 5	58.88 369	88.95 95	93.19 91	91.62 4	96.01 6	96.16 26	87.02 54	95.60 42	78.69 151	98.72 8	98.97 3

PS-CasMVS		90.06 45	91.92 15	84.47 172	96.56 6	58.83 372	89.04 94	92.74 115	91.40 5	96.12 4	96.06 28	87.23 50	95.57 43	79.42 143	98.74 5	99.00 2

DTE-MVSNet		89.98 49	91.91 17	84.21 181	96.51 7	57.84 383	88.93 96	92.84 111	91.92 3	96.16 3	96.23 23	86.95 55	95.99 11	79.05 147	98.57 14	98.80 6

CP-MVSNet		89.27 65	90.91 45	84.37 173	96.34 8	58.61 375	88.66 103	92.06 138	90.78 6	95.67 7	95.17 50	81.80 135	95.54 46	79.00 148	98.69 9	98.95 4

WR-MVS_H		89.91 53	91.31 33	85.71 137	96.32 9	62.39 301	89.54 84	93.31 85	90.21 11	95.57 10	95.66 36	81.42 140	95.90 16	80.94 122	98.80 2	98.84 5

MP-MVS		91.14 28	90.91 45	91.83 19	96.18 10	86.88 16	92.20 31	93.03 103	82.59 73	88.52 147	94.37 81	86.74 57	95.41 56	86.32 49	98.21 33	93.19 176
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
FOURS1					96.08 11	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

mPP-MVS		91.69 15	91.47 26	92.37 5	96.04 12	88.48 7	92.72 18	92.60 122	83.09 68	91.54 77	94.25 86	87.67 46	95.51 49	87.21 36	98.11 39	93.12 181

MP-MVS-pluss		90.81 30	91.08 38	89.99 49	95.97 13	79.88 76	88.13 110	94.51 18	75.79 158	92.94 50	94.96 54	88.36 31	95.01 71	90.70 2	98.40 21	95.09 72
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 53	96.29 21	88.16 36	94.17 106	86.07 55	98.48 17	97.22 18

ACMMP_NAP		90.65 32	91.07 40	89.42 61	95.93 15	79.54 81	89.95 71	93.68 67	77.65 135	91.97 71	94.89 56	88.38 30	95.45 54	89.27 5	97.87 55	93.27 171

HPM-MVS_fast		92.50 7	92.54 9	92.37 5	95.93 15	85.81 32	92.99 12	94.23 27	85.21 43	92.51 61	95.13 51	90.65 10	95.34 58	88.06 15	98.15 38	95.95 45

MSP-MVS		89.08 69	88.16 86	91.83 19	95.76 17	86.14 24	92.75 17	93.90 48	78.43 124	89.16 132	92.25 182	72.03 274	96.36 3	88.21 12	90.93 320	92.98 191
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 34	91.87 18	95.75 18	85.90 28	92.63 22	93.30 86	81.91 79	90.88 94	94.21 87	87.75 43	95.87 19	87.60 26	97.71 62	93.83 137

ACMMPR		91.49 19	91.35 30	91.92 15	95.74 19	85.88 29	92.58 23	93.25 88	81.99 77	91.40 79	94.17 91	87.51 47	95.87 19	87.74 21	97.76 59	93.99 127

ZNCC-MVS		91.26 24	91.34 31	91.01 33	95.73 20	83.05 55	92.18 32	94.22 29	80.14 100	91.29 83	93.97 102	87.93 42	95.87 19	88.65 9	97.96 50	94.12 123

TSAR-MVS + MP.		88.14 80	87.82 90	89.09 68	95.72 21	76.74 114	92.49 26	91.19 172	67.85 301	86.63 208	94.84 58	79.58 161	95.96 14	87.62 24	94.50 195	94.56 94
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 44	91.93 14	95.67 22	85.85 30	90.00 67	93.90 48	80.32 97	91.74 76	94.41 78	88.17 35	95.98 12	86.37 48	97.99 45	93.96 130

XVS		91.54 17	91.36 28	92.08 8	95.64 23	86.25 21	92.64 20	93.33 82	85.07 44	89.99 109	94.03 99	86.57 59	95.80 30	87.35 32	97.62 72	94.20 115

X-MVStestdata		85.04 145	82.70 221	92.08 8	95.64 23	86.25 21	92.64 20	93.33 82	85.07 44	89.99 109	16.05 494	86.57 59	95.80 30	87.35 32	97.62 72	94.20 115

HPM-MVS		92.13 11	92.20 13	91.91 16	95.58 25	84.67 45	93.51 8	94.85 15	82.88 71	91.77 75	93.94 108	90.55 13	95.73 37	88.50 11	98.23 32	95.33 60
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 19	92.03 11	95.53 26	85.91 27	93.35 11	94.16 32	82.52 74	92.39 64	94.14 92	89.15 26	95.62 41	87.35 32	98.24 31	94.56 94
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 29	91.01 41	90.82 36	95.45 27	82.73 58	91.75 43	93.74 58	80.98 90	91.38 80	93.80 112	87.20 51	95.80 30	87.10 39	97.69 64	93.93 131

HFP-MVS		91.30 23	91.39 27	91.02 32	95.43 28	84.66 46	92.58 23	93.29 87	81.99 77	91.47 78	93.96 105	88.35 32	95.56 44	87.74 21	97.74 61	92.85 195

SMA-MVS		90.31 39	90.48 53	89.83 54	95.31 29	79.52 82	90.98 51	93.24 89	75.37 167	92.84 54	95.28 47	85.58 76	96.09 7	87.92 17	97.76 59	93.88 134
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 23	91.96 13	95.29 30	87.62 12	93.38 9	93.36 78	83.16 67	91.06 87	94.00 101	88.26 33	95.71 39	87.28 35	98.39 22	92.55 212

VDDNet		84.35 166	85.39 144	81.25 274	95.13 31	59.32 358	85.42 166	81.11 367	86.41 35	87.41 187	96.21 24	73.61 247	90.61 241	66.33 322	96.85 95	93.81 141

CPTT-MVS		89.39 61	88.98 72	90.63 39	95.09 32	86.95 15	92.09 37	92.30 131	79.74 104	87.50 186	92.38 173	81.42 140	93.28 146	83.07 97	97.24 87	91.67 259

ACMM	79.39 9	90.65 32	90.99 42	89.63 57	95.03 33	83.53 50	89.62 81	93.35 81	79.20 113	93.83 31	93.60 122	90.81 8	92.96 157	85.02 73	98.45 18	92.41 220
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		91.49 19	91.53 24	91.39 26	94.98 34	82.95 57	93.52 7	92.79 113	88.22 22	88.53 146	97.64 6	83.45 99	94.55 89	86.02 59	98.60 12	96.67 30

HPM-MVS++		88.93 71	88.45 82	90.38 43	94.92 35	85.85 30	89.70 76	91.27 169	78.20 127	86.69 207	92.28 181	80.36 154	95.06 70	86.17 54	96.49 109	90.22 301

XVG-ACMP-BASELINE		89.98 49	89.84 57	90.41 42	94.91 36	84.50 47	89.49 86	93.98 43	79.68 105	92.09 68	93.89 110	83.80 94	93.10 153	82.67 105	98.04 40	93.64 151

EGC-MVSNET		74.79 353	69.99 397	89.19 66	94.89 37	87.00 14	91.89 42	86.28 291	1.09 495	2.23 497	95.98 29	81.87 133	89.48 275	79.76 135	95.96 134	91.10 272

SR-MVS		92.23 10	92.34 11	91.91 16	94.89 37	87.85 9	92.51 25	93.87 51	88.20 23	93.24 42	94.02 100	90.15 17	95.67 40	86.82 42	97.34 84	92.19 238

OPM-MVS		89.80 54	89.97 55	89.27 63	94.76 39	79.86 77	86.76 137	92.78 114	78.78 119	92.51 61	93.64 121	88.13 37	93.84 120	84.83 79	97.55 77	94.10 124
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LPG-MVS_test		91.47 21	91.68 20	90.82 36	94.75 40	81.69 62	90.00 67	94.27 24	82.35 75	93.67 37	94.82 59	91.18 5	95.52 47	85.36 66	98.73 6	95.23 65

LGP-MVS_train				90.82 36	94.75 40	81.69 62		94.27 24	82.35 75	93.67 37	94.82 59	91.18 5	95.52 47	85.36 66	98.73 6	95.23 65

XVG-OURS-SEG-HR		89.59 58	89.37 64	90.28 45	94.47 42	85.95 26	86.84 133	93.91 47	80.07 101	86.75 203	93.26 132	93.64 2	90.93 224	84.60 82	90.75 330	93.97 129

NormalMVS		86.47 109	85.32 146	89.94 50	94.43 43	80.42 71	88.63 104	93.59 71	74.56 177	85.12 249	90.34 263	66.19 310	94.20 101	76.57 187	98.44 19	95.19 67

lecture		92.43 8	93.50 2	89.21 65	94.43 43	79.31 83	92.69 19	95.72 7	88.48 21	94.43 19	95.73 33	91.34 4	94.68 81	90.26 3	98.44 19	93.63 152

reproduce-ours		92.86 5	93.22 5	91.76 22	94.39 45	87.71 10	92.40 28	94.38 19	89.82 12	95.51 11	95.49 41	89.64 22	95.82 28	89.13 6	98.26 29	91.76 254

our_new_method		92.86 5	93.22 5	91.76 22	94.39 45	87.71 10	92.40 28	94.38 19	89.82 12	95.51 11	95.49 41	89.64 22	95.82 28	89.13 6	98.26 29	91.76 254

MED-MVS test				88.50 79	94.38 47	76.12 125	92.12 33	93.85 52	77.53 139	93.24 42	93.18 135		95.85 23	84.99 74	97.69 64	93.54 162

MED-MVS		90.48 37	91.14 35	88.50 79	94.38 47	76.12 125	92.12 33	93.85 52	83.72 59	93.24 42	93.18 135	87.06 52	95.85 23	84.99 74	97.69 64	93.54 162

TestfortrainingZip a		89.97 51	90.77 48	87.58 99	94.38 47	73.21 149	92.12 33	93.85 52	77.53 139	93.24 42	93.18 135	87.06 52	95.85 23	87.89 18	97.69 64	93.68 146

ACMP	79.16 10	90.54 35	90.60 52	90.35 44	94.36 50	80.98 68	89.16 92	94.05 41	79.03 116	92.87 52	93.74 117	90.60 12	95.21 64	82.87 101	98.76 3	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS		89.18 66	88.83 78	90.23 46	94.28 51	86.11 25	85.91 152	93.60 70	80.16 99	89.13 134	93.44 124	83.82 93	90.98 221	83.86 89	95.30 166	93.60 155

test_0728_SECOND				86.79 111	94.25 52	72.45 166	90.54 57	94.10 39					95.88 17	86.42 46	97.97 48	92.02 246

reproduce_model		92.89 4	93.18 7	92.01 12	94.20 53	88.23 8	92.87 13	94.32 21	90.25 10	95.65 8	95.74 32	87.75 43	95.72 38	89.60 4	98.27 27	92.08 243

SED-MVS		90.46 38	91.64 21	86.93 108	94.18 54	72.65 156	90.47 60	93.69 63	83.77 57	94.11 26	94.27 82	90.28 15	95.84 26	86.03 56	97.92 51	92.29 232

IU-MVS					94.18 54	72.64 158		90.82 183	56.98 419	89.67 119				85.78 63	97.92 51	93.28 170

test_241102_ONE					94.18 54	72.65 156		93.69 63	83.62 61	94.11 26	93.78 114	90.28 15	95.50 51

DVP-MVS		90.06 45	91.32 32	86.29 120	94.16 57	72.56 162	90.54 57	91.01 177	83.61 62	93.75 34	94.65 64	89.76 19	95.78 34	86.42 46	97.97 48	90.55 295
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 57	72.56 162	90.63 54	93.90 48	83.61 62	93.75 34	94.49 72	89.76 19

SR-MVS-dyc-post		92.41 9	92.41 10	92.39 4	94.13 59	88.95 5	92.87 13	94.16 32	88.75 17	93.79 32	94.43 75	88.83 27	95.51 49	87.16 37	97.60 74	92.73 198

RE-MVS-def			92.61 8		94.13 59	88.95 5	92.87 13	94.16 32	88.75 17	93.79 32	94.43 75	90.64 11		87.16 37	97.60 74	92.73 198

MIMVSNet1		83.63 195	84.59 168	80.74 286	94.06 61	62.77 290	82.72 250	84.53 331	77.57 137	90.34 102	95.92 30	76.88 204	85.83 368	61.88 366	97.42 82	93.62 153

TranMVSNet+NR-MVSNet		87.86 86	88.76 80	85.18 149	94.02 62	64.13 273	84.38 193	91.29 165	84.88 47	92.06 69	93.84 111	86.45 62	93.73 122	73.22 249	98.66 10	97.69 9

新几何1				82.95 222	93.96 63	78.56 90		80.24 373	55.45 425	83.93 286	91.08 230	71.19 281	88.33 310	65.84 329	93.07 250	81.95 439

SteuartSystems-ACMMP		91.16 27	91.36 28	90.55 40	93.91 64	80.97 69	91.49 45	93.48 76	82.82 72	92.60 60	93.97 102	88.19 34	96.29 5	87.61 25	98.20 35	94.39 109
Skip Steuart: Steuart Systems R&D Blog.
test_part2					93.86 65	77.77 100				92.84 54

test_one_0601					93.85 66	73.27 147		94.11 38	86.57 33	93.47 41	94.64 67	88.42 29

save fliter					93.75 67	77.44 105	86.31 145	89.72 221	70.80 254

LTVRE_ROB	86.10 1	93.04 3	93.44 3	91.82 21	93.73 68	85.72 33	96.79 1	95.51 9	88.86 15	95.63 9	96.99 12	84.81 84	93.16 150	91.10 1	97.53 80	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 14	92.04 10	93.68 69	86.15 23	93.37 10	95.10 13	90.28 9	92.11 67	95.03 53	89.75 21	94.93 73	79.95 133	98.27 27	95.04 73
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 69	89.37 62	93.64 70	79.07 85	88.54 106	94.20 30	73.53 198	89.71 117	94.82 59	85.09 80	95.77 36	84.17 86	98.03 42	93.26 173
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tt0320		86.63 106	88.36 84	81.41 272	93.57 71	60.73 338	84.37 194	88.61 243	87.00 30	90.75 96	97.98 2	85.54 77	86.45 348	69.75 289	97.70 63	97.06 22

mvs_tets		89.78 55	89.27 66	91.30 28	93.51 72	84.79 43	89.89 73	90.63 188	70.00 266	94.55 18	96.67 16	87.94 41	93.59 132	84.27 85	95.97 133	95.52 55

sc_t1		87.70 90	88.94 73	83.99 187	93.47 73	67.15 238	85.05 175	88.21 256	86.81 31	91.87 73	97.65 5	85.51 78	87.91 317	74.22 221	97.63 70	96.92 25

tt0320-xc		86.67 104	88.41 83	81.44 271	93.45 74	60.44 341	83.96 203	88.50 244	87.26 28	90.90 93	97.90 3	85.61 75	86.40 351	70.14 284	98.01 44	97.47 14

HQP_MVS		87.75 89	87.43 96	88.70 76	93.45 74	76.42 118	89.45 87	93.61 68	79.44 109	86.55 209	92.95 151	74.84 224	95.22 62	80.78 125	95.83 145	94.46 101

plane_prior7					93.45 74	77.31 108

WR-MVS		83.56 198	84.40 177	81.06 280	93.43 77	54.88 409	78.67 338	85.02 319	81.24 86	90.74 97	91.56 208	72.85 261	91.08 218	68.00 310	98.04 40	97.23 17

DPE-MVS		90.53 36	91.08 38	88.88 70	93.38 78	78.65 89	89.15 93	94.05 41	84.68 48	93.90 28	94.11 94	88.13 37	96.30 4	84.51 83	97.81 57	91.70 258
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 79	91.19 31	93.38 78	84.72 44	89.70 76	90.29 206	69.27 274	94.39 20	96.38 20	86.02 69	93.52 137	83.96 87	95.92 139	95.34 59

PS-MVSNAJss		88.31 78	87.90 89	89.56 59	93.31 80	77.96 98	87.94 115	91.97 141	70.73 255	94.19 25	96.67 16	76.94 198	94.57 87	83.07 97	96.28 117	96.15 37

test222					93.31 80	76.54 115	79.38 324	77.79 385	52.59 443	82.36 317	90.84 244	66.83 307			91.69 300	81.25 447

tt0805		88.09 82	89.79 58	82.98 220	93.26 82	63.94 276	91.10 50	89.64 224	85.07 44	90.91 91	91.09 229	89.16 25	91.87 188	82.03 112	95.87 143	93.13 178

DU-MVS		86.80 101	86.99 105	86.21 125	93.24 83	67.02 242	83.16 239	92.21 132	81.73 81	90.92 89	91.97 189	77.20 192	93.99 111	74.16 225	98.35 23	97.61 10

NR-MVSNet		86.00 118	86.22 121	85.34 146	93.24 83	64.56 268	82.21 271	90.46 194	80.99 89	88.42 150	91.97 189	77.56 183	93.85 118	72.46 259	98.65 11	97.61 10

OurMVSNet-221017-0		90.01 48	89.74 59	90.83 35	93.16 85	80.37 73	91.91 41	93.11 96	81.10 88	95.32 13	97.24 9	72.94 260	94.85 75	85.07 70	97.78 58	97.26 16

UniMVSNet (Re)		86.87 98	86.98 106	86.55 115	93.11 86	68.48 227	83.80 211	92.87 109	80.37 95	89.61 123	91.81 199	77.72 180	94.18 104	75.00 214	98.53 15	96.99 24

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 24	93.02 87	85.17 38	92.47 27	95.05 14	87.65 27	93.21 46	94.39 80	90.09 18	95.08 69	86.67 44	97.60 74	94.18 118

ACMH+	77.89 11	90.73 31	91.50 25	88.44 82	93.00 88	76.26 121	89.65 80	95.55 8	87.72 26	93.89 30	94.94 55	91.62 3	93.44 141	78.35 155	98.76 3	95.61 54

APDe-MVS		91.22 25	91.92 15	89.14 67	92.97 89	78.04 95	92.84 16	94.14 36	83.33 65	93.90 28	95.73 33	88.77 28	96.41 2	87.60 26	97.98 47	92.98 191
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
114514_t		83.10 212	82.54 226	84.77 161	92.90 90	69.10 219	86.65 139	90.62 189	54.66 431	81.46 338	90.81 245	76.98 197	94.38 94	72.62 257	96.18 123	90.82 283

testdata				79.54 312	92.87 91	72.34 167		80.14 374	59.91 398	85.47 242	91.75 203	67.96 299	85.24 373	68.57 307	92.18 286	81.06 452

CNVR-MVS		87.81 88	87.68 91	88.21 88	92.87 91	77.30 109	85.25 170	91.23 170	77.31 142	87.07 196	91.47 213	82.94 104	94.71 80	84.67 81	96.27 119	92.62 206

SF-MVS		90.27 40	90.80 47	88.68 77	92.86 93	77.09 110	91.19 49	95.74 5	81.38 85	92.28 66	93.80 112	86.89 56	94.64 84	85.52 65	97.51 81	94.30 114

UniMVSNet_NR-MVSNet		86.84 100	87.06 102	86.17 127	92.86 93	67.02 242	82.55 256	91.56 155	83.08 69	90.92 89	91.82 198	78.25 173	93.99 111	74.16 225	98.35 23	97.49 13

plane_prior1					92.83 95

ME-MVS		90.09 42	90.66 50	88.38 84	92.82 96	76.12 125	89.40 90	93.70 60	83.72 59	92.39 64	93.18 135	88.02 40	95.47 52	84.99 74	97.69 64	93.54 162

原ACMM1				84.60 168	92.81 97	74.01 139		91.50 157	62.59 361	82.73 312	90.67 253	76.53 207	94.25 98	69.24 293	95.69 152	85.55 387

plane_prior6					92.61 98	76.54 115						74.84 224

APD-MVS		89.54 59	89.63 61	89.26 64	92.57 99	81.34 67	90.19 66	93.08 99	80.87 92	91.13 85	93.19 134	86.22 66	95.97 13	82.23 111	97.18 89	90.45 297
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 74	89.58 63	85.88 133	92.55 100	72.22 170	84.01 201	89.44 230	88.63 19	94.38 21	95.77 31	86.38 65	93.59 132	79.84 134	95.21 167	91.82 252

SixPastTwentyTwo		87.20 95	87.45 95	86.45 117	92.52 101	69.19 217	87.84 117	88.05 257	81.66 82	94.64 17	96.53 19	65.94 313	94.75 79	83.02 99	96.83 97	95.41 57

ACMH	76.49 14	89.34 62	91.14 35	83.96 189	92.50 102	70.36 199	89.55 82	93.84 55	81.89 80	94.70 16	95.44 43	90.69 9	88.31 311	83.33 93	98.30 26	93.20 175
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 277	81.68 238	75.94 372	92.46 103	47.98 452	76.70 369	81.67 363	73.45 200	84.87 260	92.82 157	74.66 230	86.51 346	61.66 369	96.85 95	93.33 167

SymmetryMVS		84.79 153	83.54 194	88.55 78	92.44 104	80.42 71	88.63 104	82.37 356	74.56 177	85.12 249	90.34 263	66.19 310	94.20 101	76.57 187	95.68 153	91.03 275

F-COLMAP		84.97 149	83.42 200	89.63 57	92.39 105	83.40 51	88.83 98	91.92 143	73.19 210	80.18 358	89.15 295	77.04 196	93.28 146	65.82 330	92.28 281	92.21 237

test_djsdf		89.62 57	89.01 70	91.45 25	92.36 106	82.98 56	91.98 39	90.08 212	71.54 242	94.28 24	96.54 18	81.57 138	94.27 96	86.26 50	96.49 109	97.09 20

TEST9					92.34 107	79.70 79	83.94 204	90.32 201	65.41 335	84.49 269	90.97 234	82.03 128	93.63 127

train_agg		85.98 119	85.28 147	88.07 92	92.34 107	79.70 79	83.94 204	90.32 201	65.79 325	84.49 269	90.97 234	81.93 130	93.63 127	81.21 119	96.54 107	90.88 281

NCCC		87.36 93	86.87 108	88.83 71	92.32 109	78.84 88	86.58 141	91.09 175	78.77 120	84.85 261	90.89 240	80.85 146	95.29 59	81.14 120	95.32 163	92.34 228

FC-MVSNet-test		85.93 121	87.05 103	82.58 238	92.25 110	56.44 394	85.75 157	93.09 98	77.33 141	91.94 72	94.65 64	74.78 226	93.41 143	75.11 213	98.58 13	97.88 7

CDPH-MVS		86.17 117	85.54 139	88.05 93	92.25 110	75.45 131	83.85 208	92.01 139	65.91 323	86.19 220	91.75 203	83.77 95	94.98 72	77.43 176	96.71 102	93.73 144

test1111		78.53 299	78.85 293	77.56 348	92.22 112	47.49 454	82.61 252	69.24 450	72.43 225	85.28 246	94.20 88	51.91 404	90.07 263	65.36 334	96.45 112	95.11 71

ZD-MVS					92.22 112	80.48 70		91.85 145	71.22 249	90.38 101	92.98 147	86.06 68	96.11 6	81.99 114	96.75 101

pmmvs6		86.52 108	88.06 87	81.90 257	92.22 112	62.28 304	84.66 185	89.15 235	83.54 64	89.85 114	97.32 8	88.08 39	86.80 341	70.43 281	97.30 86	96.62 31

EG-PatchMatch MVS		84.08 176	84.11 184	83.98 188	92.22 112	72.61 161	82.20 273	87.02 283	72.63 223	88.86 136	91.02 232	78.52 169	91.11 217	73.41 244	91.09 314	88.21 349

test_8					92.09 116	78.87 87	83.82 209	90.31 203	65.79 325	84.36 273	90.96 236	81.93 130	93.44 141

Vis-MVSNet		86.86 99	86.58 111	87.72 96	92.09 116	77.43 106	87.35 123	92.09 137	78.87 118	84.27 280	94.05 98	78.35 172	93.65 125	80.54 129	91.58 304	92.08 243
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		86.66 105	86.82 110	86.17 127	92.05 118	66.87 246	91.21 48	88.64 241	86.30 36	89.60 124	92.59 164	69.22 292	94.91 74	73.89 232	97.89 54	96.72 29

MVSMamba_PlusPlus		87.53 92	88.86 77	83.54 206	92.03 119	62.26 305	91.49 45	92.62 119	88.07 24	88.07 161	96.17 25	72.24 269	95.79 33	84.85 78	94.16 208	92.58 210

旧先验1					91.97 120	71.77 175		81.78 361			91.84 196	73.92 242			93.65 227	83.61 414

v7n		90.13 41	90.96 43	87.65 98	91.95 121	71.06 190	89.99 69	93.05 100	86.53 34	94.29 22	96.27 22	82.69 108	94.08 109	86.25 52	97.63 70	97.82 8

NP-MVS					91.95 121	74.55 136					90.17 274

OMC-MVS		88.19 79	87.52 93	90.19 47	91.94 123	81.68 64	87.49 122	93.17 92	76.02 152	88.64 143	91.22 223	84.24 90	93.37 144	77.97 169	97.03 92	95.52 55

OPU-MVS				88.27 87	91.89 124	77.83 99	90.47 60				91.22 223	81.12 143	94.68 81	74.48 217	95.35 161	92.29 232

FIs		85.35 135	86.27 120	82.60 237	91.86 125	57.31 387	85.10 174	93.05 100	75.83 157	91.02 88	93.97 102	73.57 248	92.91 161	73.97 231	98.02 43	97.58 12

test2506		74.12 358	73.39 359	76.28 369	91.85 126	44.20 468	84.06 200	48.20 493	72.30 231	81.90 327	94.20 88	27.22 492	89.77 271	64.81 339	96.02 131	94.87 77

ECVR-MVS		78.44 302	78.63 297	77.88 344	91.85 126	48.95 448	83.68 215	69.91 446	72.30 231	84.26 281	94.20 88	51.89 405	89.82 268	63.58 349	96.02 131	94.87 77

9.14			89.29 65		91.84 128		88.80 99	95.32 12	75.14 169	91.07 86	92.89 153	87.27 49	93.78 121	83.69 92	97.55 77

MSLP-MVS++		85.00 148	86.03 126	81.90 257	91.84 128	71.56 183	86.75 138	93.02 104	75.95 155	87.12 191	89.39 289	77.98 175	89.40 282	77.46 174	94.78 187	84.75 396

h-mvs33		84.25 170	82.76 220	88.72 74	91.82 130	82.60 59	84.00 202	84.98 321	71.27 245	86.70 205	90.55 259	63.04 337	93.92 116	78.26 158	94.20 206	89.63 315

DP-MVS Recon		84.05 179	83.22 205	86.52 116	91.73 131	75.27 132	83.23 236	92.40 125	72.04 235	82.04 325	88.33 308	77.91 177	93.95 115	66.17 323	95.12 172	90.34 300

SD-MVS		88.96 70	89.88 56	86.22 124	91.63 132	77.07 111	89.82 74	93.77 57	78.90 117	92.88 51	92.29 180	86.11 67	90.22 252	86.24 53	97.24 87	91.36 267
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 85	87.40 97	89.68 55	91.59 133	83.40 51	89.50 85	95.44 10	79.47 107	88.00 164	93.03 145	82.66 109	91.47 197	70.81 272	96.14 125	94.16 120

TestCases				89.68 55	91.59 133	83.40 51		95.44 10	79.47 107	88.00 164	93.03 145	82.66 109	91.47 197	70.81 272	96.14 125	94.16 120

MCST-MVS		84.36 165	83.93 189	85.63 138	91.59 133	71.58 181	83.52 224	92.13 135	61.82 372	83.96 285	89.75 282	79.93 159	93.46 140	78.33 156	94.34 202	91.87 251

agg_prior					91.58 136	77.69 102		90.30 204		84.32 275			93.18 149

PVSNet_Blended_VisFu		81.55 249	80.49 269	84.70 165	91.58 136	73.24 148	84.21 196	91.67 152	62.86 360	80.94 344	87.16 338	67.27 302	92.87 162	69.82 288	88.94 364	87.99 355

DVP-MVS++		90.07 44	91.09 37	87.00 106	91.55 138	72.64 158	96.19 2	94.10 39	85.33 41	93.49 39	94.64 67	81.12 143	95.88 17	87.41 30	95.94 137	92.48 215

MSC_two_6792asdad				88.81 72	91.55 138	77.99 96		91.01 177					96.05 8	87.45 28	98.17 36	92.40 222

No_MVS				88.81 72	91.55 138	77.99 96		91.01 177					96.05 8	87.45 28	98.17 36	92.40 222

EPP-MVSNet		85.47 128	85.04 152	86.77 112	91.52 141	69.37 212	91.63 44	87.98 260	81.51 84	87.05 197	91.83 197	66.18 312	95.29 59	70.75 275	96.89 94	95.64 52

DeepPCF-MVS	81.24 5	87.28 94	86.21 122	90.49 41	91.48 142	84.90 41	83.41 228	92.38 127	70.25 263	89.35 129	90.68 250	82.85 107	94.57 87	79.55 140	95.95 136	92.00 247

Baseline_NR-MVSNet		84.00 183	85.90 129	78.29 336	91.47 143	53.44 421	82.29 267	87.00 286	79.06 115	89.55 125	95.72 35	77.20 192	86.14 358	72.30 260	98.51 16	95.28 62

HyFIR lowres test		75.12 345	72.66 369	82.50 242	91.44 144	65.19 263	72.47 422	87.31 270	46.79 465	80.29 354	84.30 383	52.70 401	92.10 182	51.88 438	86.73 398	90.22 301

usedtu_dtu_shiyan2		78.92 290	78.15 305	81.25 274	91.33 145	73.10 151	80.75 301	79.00 381	74.19 185	79.17 370	92.04 187	67.17 303	81.33 405	42.86 470	96.81 99	89.31 321

DP-MVS		88.60 75	89.01 70	87.36 101	91.30 146	77.50 103	87.55 119	92.97 107	87.95 25	89.62 121	92.87 154	84.56 85	93.89 117	77.65 171	96.62 104	90.70 287

DeepC-MVS_fast	80.27 8	86.23 112	85.65 138	87.96 94	91.30 146	76.92 112	87.19 125	91.99 140	70.56 256	84.96 256	90.69 249	80.01 157	95.14 67	78.37 154	95.78 149	91.82 252
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 51	89.66 60	90.92 34	91.27 148	81.66 65	91.25 47	94.13 37	88.89 14	88.83 138	94.26 85	77.55 184	95.86 22	84.88 77	95.87 143	95.24 64

Elysia		88.71 72	88.89 74	88.19 89	91.26 149	72.96 152	88.10 111	93.59 71	84.31 50	90.42 99	94.10 95	74.07 237	94.82 76	88.19 13	95.92 139	96.80 27

StellarMVS		88.71 72	88.89 74	88.19 89	91.26 149	72.96 152	88.10 111	93.59 71	84.31 50	90.42 99	94.10 95	74.07 237	94.82 76	88.19 13	95.92 139	96.80 27

HQP-NCC					91.19 151		84.77 178		73.30 206	80.55 350

ACMP_Plane					91.19 151		84.77 178		73.30 206	80.55 350

HQP-MVS		84.61 157	84.06 185	86.27 121	91.19 151	70.66 193	84.77 178	92.68 116	73.30 206	80.55 350	90.17 274	72.10 270	94.61 85	77.30 178	94.47 197	93.56 159

VDD-MVS		84.23 172	84.58 169	83.20 214	91.17 154	65.16 264	83.25 233	84.97 322	79.79 103	87.18 190	94.27 82	74.77 227	90.89 227	69.24 293	96.54 107	93.55 161

K. test v3		85.14 141	84.73 159	86.37 118	91.13 155	69.63 209	85.45 165	76.68 397	84.06 55	92.44 63	96.99 12	62.03 340	94.65 83	80.58 128	93.24 244	94.83 86

lessismore_v0				85.95 130	91.10 156	70.99 191		70.91 442		91.79 74	94.42 77	61.76 341	92.93 159	79.52 142	93.03 251	93.93 131

hse-mvs2		83.47 203	81.81 237	88.47 81	91.03 157	82.27 60	82.61 252	83.69 340	71.27 245	86.70 205	86.05 356	63.04 337	92.41 171	78.26 158	93.62 229	90.71 286

TransMVSNet (Re)		84.02 182	85.74 136	78.85 321	91.00 158	55.20 408	82.29 267	87.26 272	79.65 106	88.38 152	95.52 40	83.00 103	86.88 339	67.97 311	96.60 105	94.45 103

AUN-MVS		81.18 256	78.78 294	88.39 83	90.93 159	82.14 61	82.51 258	83.67 341	64.69 347	80.29 354	85.91 359	51.07 408	92.38 172	76.29 194	93.63 228	90.65 291

PAPM_NR		83.23 208	83.19 207	83.33 210	90.90 160	65.98 256	88.19 109	90.78 184	78.13 129	80.87 346	87.92 317	73.49 251	92.42 170	70.07 285	88.40 370	91.60 261

CSCG		86.26 111	86.47 113	85.60 139	90.87 161	74.26 138	87.98 114	91.85 145	80.35 96	89.54 127	88.01 312	79.09 164	92.13 179	75.51 206	95.06 174	90.41 298

PLC	73.85 16	82.09 236	80.31 271	87.45 100	90.86 162	80.29 74	85.88 153	90.65 187	68.17 293	76.32 401	86.33 350	73.12 258	92.61 167	61.40 373	90.02 347	89.44 318
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test12				86.57 114	90.74 163	72.63 160		90.69 186		82.76 310		79.20 162	94.80 78		95.32 163	92.27 234

ITE_SJBPF				90.11 48	90.72 164	84.97 40		90.30 204	81.56 83	90.02 108	91.20 225	82.40 114	90.81 231	73.58 242	94.66 192	94.56 94

DPM-MVS		80.10 282	79.18 289	82.88 228	90.71 165	69.74 206	78.87 334	90.84 182	60.29 395	75.64 411	85.92 358	67.28 301	93.11 152	71.24 269	91.79 296	85.77 385

TAMVS		78.08 305	76.36 324	83.23 213	90.62 166	72.87 154	79.08 330	80.01 375	61.72 375	81.35 340	86.92 343	63.96 329	88.78 293	50.61 440	93.01 252	88.04 354

test_prior				86.32 119	90.59 167	71.99 174		92.85 110					94.17 106			92.80 196

ambc				82.98 220	90.55 168	64.86 265	88.20 108	89.15 235		89.40 128	93.96 105	71.67 279	91.38 203	78.83 149	96.55 106	92.71 201

SSC-MVS		77.55 310	81.64 240	65.29 451	90.46 169	20.33 498	73.56 411	68.28 452	85.44 40	88.18 159	94.64 67	70.93 282	81.33 405	71.25 268	92.03 289	94.20 115

Anonymous20231211		88.40 76	89.62 62	84.73 163	90.46 169	65.27 261	88.86 97	93.02 104	87.15 29	93.05 49	97.10 10	82.28 121	92.02 183	76.70 184	97.99 45	96.88 26

Test_1112_low_res		73.90 361	73.08 363	76.35 367	90.35 171	55.95 395	73.40 414	86.17 293	50.70 458	73.14 428	85.94 357	58.31 364	85.90 364	56.51 402	83.22 436	87.20 369

VPA-MVSNet		83.47 203	84.73 159	79.69 309	90.29 172	57.52 386	81.30 289	88.69 240	76.29 148	87.58 185	94.44 74	80.60 151	87.20 333	66.60 320	96.82 98	94.34 111

FMVSNet1		84.55 161	85.45 142	81.85 259	90.27 173	61.05 328	86.83 134	88.27 253	78.57 123	89.66 120	95.64 37	75.43 216	90.68 236	69.09 297	95.33 162	93.82 138

Anonymous20240529		86.20 114	87.13 100	83.42 208	90.19 174	64.55 269	84.55 188	90.71 185	85.85 39	89.94 112	95.24 49	82.13 124	90.40 247	69.19 296	96.40 114	95.31 61

MVS_111021_HR		84.63 156	84.34 180	85.49 144	90.18 175	75.86 129	79.23 329	87.13 277	73.35 203	85.56 240	89.34 290	83.60 98	90.50 243	76.64 186	94.05 213	90.09 307

SSM_0404		85.16 140	85.09 150	85.36 145	90.14 176	69.52 210	86.17 149	91.58 153	74.41 180	86.55 209	91.49 210	78.54 167	93.97 113	73.71 236	93.21 247	92.59 209

GeoE		85.45 129	85.81 132	84.37 173	90.08 177	67.07 241	85.86 155	91.39 162	72.33 230	87.59 183	90.25 269	84.85 83	92.37 173	78.00 167	91.94 293	93.66 147

RPSCF		88.00 84	86.93 107	91.22 30	90.08 177	89.30 4	89.68 78	91.11 173	79.26 112	89.68 118	94.81 62	82.44 112	87.74 322	76.54 189	88.74 367	96.61 32

nrg030		87.85 87	88.49 81	85.91 131	90.07 179	69.73 207	87.86 116	94.20 30	74.04 186	92.70 59	94.66 63	85.88 70	91.50 195	79.72 136	97.32 85	96.50 34

AdaColmap		83.66 193	83.69 193	83.57 204	90.05 180	72.26 169	86.29 146	90.00 214	78.19 128	81.65 335	87.16 338	83.40 100	94.24 99	61.69 368	94.76 190	84.21 406

pm-mvs1		83.69 192	84.95 155	79.91 304	90.04 181	59.66 354	82.43 262	87.44 268	75.52 164	87.85 171	95.26 48	81.25 142	85.65 371	68.74 303	96.04 130	94.42 107

CHOSEN 1792x2688		72.45 373	70.56 388	78.13 338	90.02 182	63.08 285	68.72 448	83.16 347	42.99 480	75.92 407	85.46 365	57.22 377	85.18 375	49.87 445	81.67 446	86.14 380

WB-MVS		76.06 332	80.01 281	64.19 454	89.96 183	20.58 497	72.18 424	68.19 453	83.21 66	86.46 217	93.49 123	70.19 287	78.97 422	65.96 324	90.46 342	93.02 185

anonymousdsp		89.73 56	88.88 76	92.27 7	89.82 184	86.67 17	90.51 59	90.20 209	69.87 267	95.06 14	96.14 27	84.28 89	93.07 154	87.68 23	96.34 115	97.09 20

LuminaMVS		83.94 186	83.51 195	85.23 147	89.78 185	71.74 176	84.76 181	87.27 271	72.60 224	89.31 130	90.60 258	64.04 326	90.95 222	79.08 146	94.11 209	92.99 189

fmvsm_s_conf0.5_n_9		87.04 96	87.02 104	87.08 104	89.67 186	75.87 128	84.60 186	89.74 219	74.40 182	89.92 113	93.41 125	80.45 152	90.63 239	86.66 45	94.37 201	94.73 91

1112_ss		74.82 352	73.74 354	78.04 341	89.57 187	60.04 346	76.49 375	87.09 282	54.31 432	73.66 427	79.80 430	60.25 350	86.76 343	58.37 392	84.15 430	87.32 367

CS-MVS		88.14 80	87.67 92	89.54 60	89.56 188	79.18 84	90.47 60	94.77 16	79.37 111	84.32 275	89.33 291	83.87 92	94.53 91	82.45 107	94.89 182	94.90 75

MM		87.64 91	87.15 99	89.09 68	89.51 189	76.39 120	88.68 102	86.76 287	84.54 49	83.58 294	93.78 114	73.36 255	96.48 1	87.98 16	96.21 121	94.41 108

APD_test1		88.40 76	87.91 88	89.88 51	89.50 190	86.65 19	89.98 70	91.91 144	84.26 52	90.87 95	93.92 109	82.18 123	89.29 283	73.75 235	94.81 186	93.70 145

SPE-MVS-test		87.00 97	86.43 114	88.71 75	89.46 191	77.46 104	89.42 89	95.73 6	77.87 133	81.64 336	87.25 336	82.43 113	94.53 91	77.65 171	96.46 111	94.14 122

PCF-MVS	74.62 15	82.15 235	80.92 262	85.84 134	89.43 192	72.30 168	80.53 305	91.82 147	57.36 415	87.81 172	89.92 279	77.67 181	93.63 127	58.69 390	95.08 173	91.58 262
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 337	73.51 358	82.71 230	89.35 193	73.62 141	80.06 309	85.20 313	60.30 394	73.96 424	87.94 314	57.89 373	89.45 278	52.02 433	74.87 473	85.06 393
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		83.55 199	83.10 211	84.90 156	89.34 194	83.87 49	84.54 190	88.77 238	79.09 114	83.54 296	88.66 305	74.87 223	81.73 403	66.84 317	92.29 280	89.11 330

EC-MVSNet		88.01 83	88.32 85	87.09 103	89.28 195	72.03 173	90.31 64	96.31 3	80.88 91	85.12 249	89.67 284	84.47 87	95.46 53	82.56 106	96.26 120	93.77 143

TSAR-MVS + GP.		83.95 185	82.69 222	87.72 96	89.27 196	81.45 66	83.72 213	81.58 365	74.73 174	85.66 236	86.06 355	72.56 266	92.69 165	75.44 208	95.21 167	89.01 337

MVS_111021_LR		84.28 169	83.76 192	85.83 135	89.23 197	83.07 54	80.99 295	83.56 342	72.71 222	86.07 223	89.07 297	81.75 137	86.19 356	77.11 180	93.36 238	88.24 348

LFMVS		80.15 281	80.56 267	78.89 318	89.19 198	55.93 396	85.22 171	73.78 417	82.96 70	84.28 279	92.72 162	57.38 375	90.07 263	63.80 348	95.75 150	90.68 288

mamba_0408		83.44 206	82.88 217	85.11 150	89.13 199	68.97 220	72.73 420	91.28 166	72.90 216	85.68 233	90.61 256	76.78 205	93.97 113	73.37 246	93.47 231	92.38 225

SSM_04072		81.44 251	82.88 217	77.10 356	89.13 199	68.97 220	72.73 420	91.28 166	72.90 216	85.68 233	90.61 256	76.78 205	69.94 454	73.37 246	93.47 231	92.38 225

SSM_0407		84.89 150	84.85 156	85.01 155	89.13 199	68.97 220	85.60 161	91.58 153	74.41 180	85.68 233	91.49 210	78.54 167	93.69 124	73.71 236	93.47 231	92.38 225

FE-MVSNET2		82.80 217	83.51 195	80.67 291	89.08 202	58.46 376	82.40 264	89.26 232	71.25 248	88.24 156	94.07 97	75.75 213	89.56 274	65.91 328	95.67 155	93.98 128

CLD-MVS		83.18 209	82.64 223	84.79 160	89.05 203	67.82 235	77.93 348	92.52 123	68.33 290	85.07 252	81.54 416	82.06 127	92.96 157	69.35 292	97.91 53	93.57 158
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 34	90.34 54	91.38 27	89.03 204	84.23 48	93.58 6	94.68 17	90.65 7	90.33 103	93.95 107	84.50 86	95.37 57	80.87 123	95.50 158	94.53 98

CDS-MVSNet		77.32 313	75.40 334	83.06 217	89.00 205	72.48 165	77.90 349	82.17 358	60.81 388	78.94 373	83.49 393	59.30 357	88.76 294	54.64 419	92.37 276	87.93 358
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tttt0517		81.07 258	79.58 284	85.52 141	88.99 206	66.45 251	87.03 129	75.51 405	73.76 190	88.32 154	90.20 270	37.96 465	94.16 108	79.36 144	95.13 170	95.93 46

balanced_conf03		84.80 151	85.40 143	83.00 219	88.95 207	61.44 318	90.42 63	92.37 129	71.48 244	88.72 142	93.13 141	70.16 288	95.15 66	79.26 145	94.11 209	92.41 220

testing3-2		70.72 392	70.97 384	69.95 419	88.93 208	34.80 489	69.85 443	66.59 463	78.42 125	77.58 393	85.55 361	31.83 477	82.08 400	46.28 461	93.73 224	92.98 191

tfpnnormal		81.79 245	82.95 215	78.31 334	88.93 208	55.40 404	80.83 299	82.85 351	76.81 145	85.90 231	94.14 92	74.58 231	86.51 346	66.82 318	95.68 153	93.01 188

testing3		71.53 383	70.79 385	73.77 391	88.89 210	41.86 475	76.60 374	59.12 482	72.83 219	80.97 342	82.08 410	19.80 498	87.33 332	65.12 336	91.68 301	92.13 242

Vis-MVSNet (Re-imp)		77.82 307	77.79 308	77.92 343	88.82 211	51.29 438	83.28 231	71.97 434	74.04 186	82.23 319	89.78 281	57.38 375	89.41 281	57.22 399	95.41 159	93.05 184

SDMVSNet		81.90 244	83.17 209	78.10 339	88.81 212	62.45 300	76.08 382	86.05 297	73.67 191	83.41 297	93.04 143	82.35 115	80.65 411	70.06 286	95.03 175	91.21 269

sd_testset		79.95 285	81.39 251	75.64 377	88.81 212	58.07 380	76.16 381	82.81 352	73.67 191	83.41 297	93.04 143	80.96 145	77.65 427	58.62 391	95.03 175	91.21 269

TAPA-MVS	77.73 12	85.71 124	84.83 157	88.37 85	88.78 214	79.72 78	87.15 127	93.50 75	69.17 275	85.80 232	89.56 285	80.76 148	92.13 179	73.21 254	95.51 157	93.25 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
testf1		89.30 63	89.12 67	89.84 52	88.67 215	85.64 34	90.61 55	93.17 92	86.02 37	93.12 47	95.30 45	84.94 81	89.44 279	74.12 227	96.10 128	94.45 103

APD_test2		89.30 63	89.12 67	89.84 52	88.67 215	85.64 34	90.61 55	93.17 92	86.02 37	93.12 47	95.30 45	84.94 81	89.44 279	74.12 227	96.10 128	94.45 103

GDP-MVS		82.17 233	80.85 264	86.15 129	88.65 217	68.95 223	85.65 160	93.02 104	68.42 288	83.73 289	89.54 286	45.07 444	94.31 95	79.66 138	93.87 217	95.19 67

FPMVS		72.29 376	72.00 375	73.14 396	88.63 218	85.00 39	74.65 399	67.39 456	71.94 237	77.80 386	87.66 326	50.48 412	75.83 434	49.95 443	79.51 456	58.58 487

dcpmvs_2		84.23 172	85.14 149	81.50 269	88.61 219	61.98 309	82.90 247	93.11 96	68.66 286	92.77 57	92.39 172	78.50 170	87.63 325	76.99 182	92.30 278	94.90 75

ETV-MVS		84.31 167	83.91 191	85.52 141	88.58 220	70.40 197	84.50 192	93.37 77	78.76 121	84.07 283	78.72 441	80.39 153	95.13 68	73.82 234	92.98 253	91.04 274

BH-untuned		80.96 260	80.99 260	80.84 285	88.55 221	68.23 228	80.33 308	88.46 245	72.79 221	86.55 209	86.76 344	74.72 228	91.77 191	61.79 367	88.99 362	82.52 432

Anonymous202405211		80.51 268	81.19 258	78.49 329	88.48 222	57.26 388	76.63 371	82.49 354	81.21 87	84.30 278	92.24 183	67.99 298	86.24 353	62.22 359	95.13 170	91.98 249

ab-mvs		79.67 286	80.56 267	76.99 357	88.48 222	56.93 390	84.70 184	86.06 296	68.95 281	80.78 347	93.08 142	75.30 218	84.62 379	56.78 400	90.90 321	89.43 319

PHI-MVS		86.38 110	85.81 132	88.08 91	88.44 224	77.34 107	89.35 91	93.05 100	73.15 211	84.76 264	87.70 325	78.87 166	94.18 104	80.67 127	96.29 116	92.73 198

xiu_mvs_v1_base_debu		80.84 262	80.14 277	82.93 225	88.31 225	71.73 177	79.53 317	87.17 274	65.43 332	79.59 360	82.73 404	76.94 198	90.14 258	73.22 249	88.33 372	86.90 373

xiu_mvs_v1_base		80.84 262	80.14 277	82.93 225	88.31 225	71.73 177	79.53 317	87.17 274	65.43 332	79.59 360	82.73 404	76.94 198	90.14 258	73.22 249	88.33 372	86.90 373

xiu_mvs_v1_base_debi		80.84 262	80.14 277	82.93 225	88.31 225	71.73 177	79.53 317	87.17 274	65.43 332	79.59 360	82.73 404	76.94 198	90.14 258	73.22 249	88.33 372	86.90 373

E6new		85.44 130	86.37 115	82.66 232	88.23 228	61.86 310	83.59 218	93.69 63	73.64 193	87.61 181	93.30 128	85.85 71	91.26 207	78.02 163	93.40 234	94.86 81

E6		85.44 130	86.37 115	82.66 232	88.23 228	61.86 310	83.59 218	93.69 63	73.64 193	87.61 181	93.30 128	85.85 71	91.26 207	78.02 163	93.40 234	94.86 81

E5new		85.44 130	86.37 115	82.66 232	88.22 230	61.86 310	83.59 218	93.70 60	73.64 193	87.62 179	93.30 128	85.85 71	91.26 207	78.02 163	93.40 234	94.86 81

E5		85.44 130	86.37 115	82.66 232	88.22 230	61.86 310	83.59 218	93.70 60	73.64 193	87.62 179	93.30 128	85.85 71	91.26 207	78.02 163	93.40 234	94.86 81

MG-MVS		80.32 275	80.94 261	78.47 330	88.18 232	52.62 428	82.29 267	85.01 320	72.01 236	79.24 369	92.54 169	69.36 291	93.36 145	70.65 277	89.19 360	89.45 317

E4		84.75 154	85.46 141	82.61 236	88.17 233	61.55 317	81.39 285	93.55 74	73.13 213	86.83 200	92.83 156	84.17 91	91.48 196	76.92 183	92.19 285	94.80 88

PM-MVS		80.20 279	79.00 290	83.78 195	88.17 233	86.66 18	81.31 287	66.81 462	69.64 269	88.33 153	90.19 271	64.58 320	83.63 392	71.99 262	90.03 346	81.06 452

v10		86.54 107	87.10 101	84.84 157	88.16 235	63.28 283	86.64 140	92.20 133	75.42 166	92.81 56	94.50 71	74.05 240	94.06 110	83.88 88	96.28 117	97.17 19

mvsmamba		80.30 276	78.87 291	84.58 169	88.12 236	67.55 236	92.35 30	84.88 325	63.15 358	85.33 245	90.91 239	50.71 410	95.20 65	66.36 321	87.98 379	90.99 276

sasdasda		85.50 125	86.14 123	83.58 202	87.97 237	67.13 239	87.55 119	94.32 21	73.44 201	88.47 148	87.54 328	86.45 62	91.06 219	75.76 202	93.76 220	92.54 213

canonicalmvs		85.50 125	86.14 123	83.58 202	87.97 237	67.13 239	87.55 119	94.32 21	73.44 201	88.47 148	87.54 328	86.45 62	91.06 219	75.76 202	93.76 220	92.54 213

EIA-MVS		82.19 232	81.23 256	85.10 151	87.95 239	69.17 218	83.22 237	93.33 82	70.42 258	78.58 376	79.77 432	77.29 189	94.20 101	71.51 267	88.96 363	91.93 250

fmvsm_s_conf0.5_n_5		84.56 159	84.71 162	84.11 185	87.92 240	72.09 172	84.80 177	88.64 241	64.43 349	88.77 139	91.78 201	78.07 174	87.95 316	85.85 62	92.18 286	92.30 230

VNet		79.31 287	80.27 272	76.44 366	87.92 240	53.95 417	75.58 388	84.35 333	74.39 183	82.23 319	90.72 247	72.84 262	84.39 384	60.38 379	93.98 214	90.97 277

BP-MVS1		82.81 216	81.67 239	86.23 122	87.88 242	68.53 226	86.06 151	84.36 332	75.65 160	85.14 248	90.19 271	45.84 433	94.42 93	85.18 68	94.72 191	95.75 48

v8		86.22 113	86.83 109	84.36 175	87.82 243	62.35 303	86.42 144	91.33 164	76.78 146	92.73 58	94.48 73	73.41 252	93.72 123	83.10 96	95.41 159	97.01 23

alignmvs		83.94 186	83.98 187	83.80 193	87.80 244	67.88 234	84.54 190	91.42 161	73.27 209	88.41 151	87.96 313	72.33 267	90.83 230	76.02 199	94.11 209	92.69 202

fmvsm_s_conf0.5_n_6		84.05 179	84.14 183	83.81 192	87.75 245	71.17 188	83.42 227	91.10 174	67.90 300	84.53 267	90.70 248	73.01 259	88.73 295	85.09 69	93.72 225	91.53 264

v1192		84.57 158	84.69 164	84.21 181	87.75 245	62.88 287	83.02 242	91.43 159	69.08 278	89.98 111	90.89 240	72.70 264	93.62 130	82.41 108	94.97 179	96.13 38

PatchMatch-RL		74.48 355	73.22 362	78.27 337	87.70 247	85.26 37	75.92 384	70.09 444	64.34 350	76.09 405	81.25 418	65.87 314	78.07 426	53.86 421	83.82 432	71.48 473

fmvsm_s_conf0.1_n_a		82.58 222	81.93 235	84.50 170	87.68 248	73.35 144	86.14 150	77.70 386	61.64 377	85.02 253	91.62 205	77.75 178	86.24 353	82.79 103	87.07 392	93.91 133

v1144		84.54 162	84.72 161	84.00 186	87.67 249	62.55 294	82.97 244	90.93 180	70.32 261	89.80 115	90.99 233	73.50 249	93.48 139	81.69 118	94.65 193	95.97 43

v1240		84.30 168	84.51 173	83.65 199	87.65 250	61.26 324	82.85 248	91.54 156	67.94 298	90.68 98	90.65 254	71.71 278	93.64 126	82.84 102	94.78 187	96.07 40

v1921920		84.23 172	84.37 178	83.79 194	87.64 251	61.71 315	82.91 246	91.20 171	67.94 298	90.06 106	90.34 263	72.04 273	93.59 132	82.32 109	94.91 180	96.07 40

v144192		84.24 171	84.41 176	83.71 198	87.59 252	61.57 316	82.95 245	91.03 176	67.82 302	89.80 115	90.49 260	73.28 256	93.51 138	81.88 117	94.89 182	96.04 42

KinetiMVS		85.95 120	86.10 125	85.50 143	87.56 253	69.78 205	83.70 214	89.83 218	80.42 94	87.76 175	93.24 133	73.76 246	91.54 194	85.03 72	93.62 229	95.19 67

MGCFI-Net		85.04 145	85.95 127	82.31 248	87.52 254	63.59 279	86.23 148	93.96 44	73.46 199	88.07 161	87.83 323	86.46 61	90.87 229	76.17 196	93.89 216	92.47 217

Fast-Effi-MVS+		81.04 259	80.57 266	82.46 244	87.50 255	63.22 284	78.37 342	89.63 225	68.01 295	81.87 328	82.08 410	82.31 117	92.65 166	67.10 314	88.30 376	91.51 265

E2		84.06 177	84.61 166	82.40 246	87.49 256	61.31 321	81.03 293	93.36 78	71.83 238	86.02 225	91.87 191	82.91 105	91.37 204	75.66 204	91.33 308	94.53 98

E3		84.06 177	84.61 166	82.40 246	87.49 256	61.30 322	81.03 293	93.36 78	71.83 238	86.01 226	91.87 191	82.91 105	91.36 205	75.66 204	91.33 308	94.53 98

fmvsm_l_conf0.5_n_3		85.11 144	84.96 154	85.56 140	87.49 256	75.69 130	84.71 183	90.61 190	67.64 305	84.88 259	92.05 186	82.30 118	88.36 309	83.84 90	91.10 313	92.62 206

pmmvs-eth3d		78.42 303	77.04 316	82.57 240	87.44 259	74.41 137	80.86 298	79.67 376	55.68 424	84.69 265	90.31 268	60.91 345	85.42 372	62.20 360	91.59 303	87.88 359

IterMVS-LS		84.73 155	84.98 153	83.96 189	87.35 260	63.66 277	83.25 233	89.88 217	76.06 150	89.62 121	92.37 176	73.40 254	92.52 168	78.16 160	94.77 189	95.69 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres100view900		75.45 341	75.05 342	76.66 364	87.27 261	51.88 433	81.07 292	73.26 422	75.68 159	83.25 301	86.37 349	45.54 435	88.80 290	51.98 434	90.99 316	89.31 321

viewdifsd2359ckpt09		83.64 194	83.18 208	85.03 153	87.26 262	66.99 244	85.32 168	93.83 56	65.57 331	84.99 255	89.40 288	77.30 188	93.57 135	71.16 271	93.80 219	94.54 97

fmvsm_s_conf0.5_n_4		84.38 164	84.27 181	84.74 162	87.25 263	70.84 192	83.55 223	88.45 246	68.64 287	86.29 219	91.31 219	74.97 222	88.42 307	87.87 19	90.07 345	94.95 74

MIMVSNet		71.09 387	71.59 378	69.57 424	87.23 264	50.07 445	78.91 332	71.83 435	60.20 397	71.26 438	91.76 202	55.08 394	76.09 432	41.06 474	87.02 395	82.54 431

Effi-MVS+		83.90 188	84.01 186	83.57 204	87.22 265	65.61 260	86.55 142	92.40 125	78.64 122	81.34 341	84.18 387	83.65 97	92.93 159	74.22 221	87.87 381	92.17 240

BH-RMVSNet		80.53 267	80.22 275	81.49 270	87.19 266	66.21 253	77.79 351	86.23 292	74.21 184	83.69 291	88.50 306	73.25 257	90.75 233	63.18 354	87.90 380	87.52 364

thisisatest0530		79.07 288	77.33 313	84.26 180	87.13 267	64.58 267	83.66 216	75.95 400	68.86 282	85.22 247	87.36 334	38.10 462	93.57 135	75.47 207	94.28 204	94.62 92

Effi-MVS+-dtu		85.82 123	83.38 202	93.14 3	87.13 267	91.15 2	87.70 118	88.42 247	74.57 176	83.56 295	85.65 360	78.49 171	94.21 100	72.04 261	92.88 255	94.05 126

v2v482		84.09 175	84.24 182	83.62 200	87.13 267	61.40 319	82.71 251	89.71 222	72.19 233	89.55 125	91.41 214	70.70 284	93.20 148	81.02 121	93.76 220	96.25 36

fmvsm_s_conf0.5_n_8		85.48 127	85.75 135	84.68 166	87.10 270	69.98 203	84.28 195	92.68 116	74.77 173	87.90 168	92.36 178	73.94 241	90.41 246	85.95 61	92.74 261	93.66 147

jason		77.42 312	75.75 330	82.43 245	87.10 270	69.27 213	77.99 346	81.94 360	51.47 452	77.84 384	85.07 375	60.32 349	89.00 285	70.74 276	89.27 359	89.03 335
jason: jason.
PS-MVSNAJ		77.04 318	76.53 322	78.56 327	87.09 272	61.40 319	75.26 391	87.13 277	61.25 383	74.38 423	77.22 456	76.94 198	90.94 223	64.63 342	84.83 425	83.35 419

casdiffmvs_mvg		86.72 102	87.51 94	84.36 175	87.09 272	65.22 262	84.16 197	94.23 27	77.89 131	91.28 84	93.66 120	84.35 88	92.71 163	80.07 130	94.87 185	95.16 70
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		76.08 331	76.77 319	73.98 388	87.08 274	49.45 447	83.62 217	84.68 330	63.31 355	75.13 418	87.47 331	71.85 275	84.56 380	49.97 442	87.86 382	87.94 357

AstraMVS		81.67 246	81.40 250	82.48 243	87.06 275	66.47 250	81.41 284	81.68 362	68.78 283	88.00 164	90.95 238	65.70 315	87.86 321	76.66 185	92.38 275	93.12 181

xiu_mvs_v2_base		77.19 315	76.75 320	78.52 328	87.01 276	61.30 322	75.55 389	87.12 281	61.24 384	74.45 421	78.79 440	77.20 192	90.93 224	64.62 343	84.80 426	83.32 420

thres600view7		75.97 335	75.35 336	77.85 346	87.01 276	51.84 434	80.45 306	73.26 422	75.20 168	83.10 304	86.31 352	45.54 435	89.05 284	55.03 416	92.24 282	92.66 204

fmvsm_s_conf0.5_n_7		82.04 238	82.05 232	82.01 255	86.98 278	71.07 189	78.70 336	89.45 229	68.07 294	78.14 380	91.61 206	74.19 235	85.92 361	79.61 139	91.73 299	89.05 334

viewcassd2359sk11		83.53 200	83.96 188	82.25 249	86.97 279	61.13 326	80.80 300	93.22 90	70.97 252	85.36 244	91.08 230	81.84 134	91.29 206	74.79 216	90.58 341	94.33 112

fmvsm_s_conf0.5_n_3		86.19 115	87.27 98	82.95 222	86.91 280	70.38 198	85.31 169	92.61 121	75.59 162	88.32 154	92.87 154	82.22 122	88.63 300	88.80 8	92.82 259	89.83 311

CL-MVSNet_self_test		76.81 321	77.38 312	75.12 380	86.90 281	51.34 436	73.20 415	80.63 372	68.30 291	81.80 332	88.40 307	66.92 306	80.90 408	55.35 413	94.90 181	93.12 181

BH-w/o		76.57 325	76.07 328	78.10 339	86.88 282	65.92 257	77.63 353	86.33 290	65.69 329	80.89 345	79.95 429	68.97 295	90.74 234	53.01 429	85.25 414	77.62 464

fmvsm_s_conf0.1_n		82.17 233	81.59 243	83.94 191	86.87 283	71.57 182	85.19 172	77.42 389	62.27 371	84.47 271	91.33 217	76.43 208	85.91 363	83.14 94	87.14 390	94.33 112

MAR-MVS		80.24 278	78.74 296	84.73 163	86.87 283	78.18 94	85.75 157	87.81 265	65.67 330	77.84 384	78.50 442	73.79 245	90.53 242	61.59 370	90.87 323	85.49 389
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		82.21 231	81.51 248	84.32 178	86.56 285	73.35 144	85.46 164	77.30 390	61.81 373	84.51 268	90.88 242	77.36 186	86.21 355	82.72 104	86.97 397	93.38 165

fmvsm_s_conf0.5_n_11		84.56 159	84.69 164	84.15 184	86.53 286	71.29 186	85.53 162	92.62 119	70.54 257	82.75 311	91.20 225	77.33 187	88.55 305	83.80 91	91.93 294	92.61 208

FE-MVSNET		78.46 300	79.36 287	75.75 374	86.53 286	54.53 411	78.03 344	85.35 310	69.01 280	85.41 243	90.68 250	64.27 322	85.73 369	62.59 357	92.35 277	87.00 372

E3new		83.08 213	83.39 201	82.14 252	86.49 288	61.00 331	80.64 302	93.12 95	70.30 262	84.78 263	90.34 263	80.85 146	91.24 212	74.20 224	89.83 350	94.17 119

FE-MVS		79.98 284	78.86 292	83.36 209	86.47 289	66.45 251	89.73 75	84.74 329	72.80 220	84.22 282	91.38 215	44.95 445	93.60 131	63.93 346	91.50 305	90.04 308

balanced_ft_v1		83.49 201	83.93 189	82.19 250	86.46 290	59.61 356	90.81 52	90.92 181	71.78 240	88.08 160	92.56 167	66.97 304	94.54 90	75.34 210	92.42 274	92.42 218

QAPM		82.59 221	82.59 225	82.58 238	86.44 291	66.69 247	89.94 72	90.36 199	67.97 297	84.94 258	92.58 166	72.71 263	92.18 178	70.63 278	87.73 384	88.85 338

viewmacassd2359aftdt		84.04 181	84.78 158	81.81 262	86.43 292	60.32 343	81.95 275	92.82 112	71.56 241	86.06 224	92.98 147	81.79 136	90.28 248	76.18 195	93.24 244	94.82 87

guyue		81.57 248	81.37 252	82.15 251	86.39 293	66.13 254	81.54 282	83.21 346	69.79 268	87.77 174	89.95 277	65.36 318	87.64 324	75.88 200	92.49 272	92.67 203

PAPM		71.77 379	70.06 395	76.92 359	86.39 293	53.97 416	76.62 372	86.62 288	53.44 437	63.97 476	84.73 379	57.79 374	92.34 174	39.65 477	81.33 450	84.45 400

GBi-Net		82.02 239	82.07 230	81.85 259	86.38 295	61.05 328	86.83 134	88.27 253	72.43 225	86.00 227	95.64 37	63.78 330	90.68 236	65.95 325	93.34 239	93.82 138

test1		82.02 239	82.07 230	81.85 259	86.38 295	61.05 328	86.83 134	88.27 253	72.43 225	86.00 227	95.64 37	63.78 330	90.68 236	65.95 325	93.34 239	93.82 138

FMVSNet2		81.31 253	81.61 242	80.41 296	86.38 295	58.75 373	83.93 206	86.58 289	72.43 225	87.65 178	92.98 147	63.78 330	90.22 252	66.86 315	93.92 215	92.27 234

3Dnovator	80.37 7	84.80 151	84.71 162	85.06 152	86.36 298	74.71 134	88.77 100	90.00 214	75.65 160	84.96 256	93.17 139	74.06 239	91.19 214	78.28 157	91.09 314	89.29 324

Anonymous20231206		71.38 385	71.88 376	69.88 420	86.31 299	54.37 412	70.39 439	74.62 408	52.57 444	76.73 397	88.76 300	59.94 352	72.06 446	44.35 468	93.23 246	83.23 422

baseline		85.20 138	85.93 128	83.02 218	86.30 300	62.37 302	84.55 188	93.96 44	74.48 179	87.12 191	92.03 188	82.30 118	91.94 184	78.39 153	94.21 205	94.74 90

API-MVS		82.28 228	82.61 224	81.30 273	86.29 301	69.79 204	88.71 101	87.67 266	78.42 125	82.15 321	84.15 388	77.98 175	91.59 193	65.39 333	92.75 260	82.51 433

tfpn200view9		74.86 351	74.23 349	76.74 363	86.24 302	52.12 430	79.24 327	73.87 415	73.34 204	81.82 330	84.60 381	46.02 428	88.80 290	51.98 434	90.99 316	89.31 321

thres400		75.14 343	74.23 349	77.86 345	86.24 302	52.12 430	79.24 327	73.87 415	73.34 204	81.82 330	84.60 381	46.02 428	88.80 290	51.98 434	90.99 316	92.66 204

UGNet		82.78 218	81.64 240	86.21 125	86.20 304	76.24 122	86.86 132	85.68 305	77.07 144	73.76 426	92.82 157	69.64 289	91.82 190	69.04 299	93.69 226	90.56 294
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		83.79 191	82.85 219	86.63 113	86.17 305	72.21 171	83.76 212	91.43 159	77.24 143	74.39 422	87.45 332	75.36 217	95.42 55	77.03 181	92.83 258	92.25 236

casdiffmvs		85.21 137	85.85 131	83.31 211	86.17 305	62.77 290	83.03 241	93.93 46	74.69 175	88.21 157	92.68 163	82.29 120	91.89 187	77.87 170	93.75 223	95.27 63
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)		83.13 211	83.02 212	83.43 207	86.16 307	66.08 255	88.00 113	88.36 249	75.55 163	85.02 253	92.75 161	65.12 319	92.50 169	74.94 215	91.30 310	91.72 256

fmvsm_l_conf0.5_n_9		83.98 184	84.46 174	82.53 241	86.11 308	70.65 195	82.45 261	89.17 234	67.72 304	86.74 204	91.49 210	79.20 162	85.86 367	84.71 80	92.60 269	91.07 273

TR-MVS		76.77 322	75.79 329	79.72 308	86.10 309	65.79 258	77.14 362	83.02 349	65.20 342	81.40 339	82.10 408	66.30 308	90.73 235	55.57 410	85.27 413	82.65 427

fmvsm_s_conf0.5_n		81.91 243	81.30 253	83.75 196	86.02 310	71.56 183	84.73 182	77.11 393	62.44 368	84.00 284	90.68 250	76.42 209	85.89 365	83.14 94	87.11 391	93.81 141

fmvsm_s_conf0.5_n_10		85.20 138	85.25 148	85.02 154	86.01 311	71.31 185	84.96 176	91.76 151	69.10 277	88.90 135	92.56 167	73.84 244	90.63 239	86.88 40	93.26 243	93.13 178

fmvsm_s_conf0.1_n_2		83.82 189	83.49 197	84.84 157	85.99 312	70.19 201	80.93 296	87.58 267	67.26 311	87.94 167	92.37 176	71.40 280	88.01 313	86.03 56	91.87 295	96.31 35

test_fmvsmconf0.01_n		86.68 103	86.52 112	87.18 102	85.94 313	78.30 91	86.93 130	92.20 133	65.94 321	89.16 132	93.16 140	83.10 102	89.89 267	87.81 20	94.43 199	93.35 166

LCM-MVSNet-Re		83.48 202	85.06 151	78.75 324	85.94 313	55.75 400	80.05 310	94.27 24	76.47 147	96.09 5	94.54 70	83.31 101	89.75 273	59.95 381	94.89 182	90.75 284

viewdifsd2359ckpt07		83.41 207	84.35 179	80.56 293	85.84 315	58.93 368	79.47 321	91.28 166	73.01 215	87.59 183	92.07 185	85.24 79	88.68 297	73.59 241	91.11 312	94.09 125

test_fmvsmvis_n_1920		85.22 136	85.36 145	84.81 159	85.80 316	76.13 124	85.15 173	92.32 130	61.40 379	91.33 81	90.85 243	83.76 96	86.16 357	84.31 84	93.28 242	92.15 241

icg_test_0407_2		78.46 300	79.68 283	74.78 384	85.76 317	62.46 296	68.51 449	87.91 261	65.23 338	82.12 322	87.92 317	77.27 190	72.67 444	71.67 263	90.74 331	89.20 325

IMVS_0407		81.08 257	81.23 256	80.62 292	85.76 317	62.46 296	82.46 259	87.91 261	65.23 338	82.12 322	87.92 317	77.27 190	90.18 254	71.67 263	90.74 331	89.20 325

IMVS_0404		77.24 314	77.75 309	75.73 375	85.76 317	62.46 296	70.84 435	87.91 261	65.23 338	72.21 434	87.92 317	67.48 300	75.53 436	71.67 263	90.74 331	89.20 325

IMVS_0403		80.93 261	81.00 259	80.72 288	85.76 317	62.46 296	81.82 276	87.91 261	65.23 338	82.07 324	87.92 317	75.91 212	90.50 243	71.67 263	90.74 331	89.20 325

Fast-Effi-MVS+-dtu		82.54 223	81.41 249	85.90 132	85.60 321	76.53 117	83.07 240	89.62 226	73.02 214	79.11 371	83.51 392	80.74 149	90.24 251	68.76 302	89.29 357	90.94 278

v148		82.31 227	82.48 227	81.81 262	85.59 322	59.66 354	81.47 283	86.02 298	72.85 218	88.05 163	90.65 254	70.73 283	90.91 226	75.15 212	91.79 296	94.87 77

MVSFormer		82.23 229	81.57 245	84.19 183	85.54 323	69.26 214	91.98 39	90.08 212	71.54 242	76.23 402	85.07 375	58.69 362	94.27 96	86.26 50	88.77 365	89.03 335

lupinMVS		76.37 329	74.46 347	82.09 253	85.54 323	69.26 214	76.79 367	80.77 371	50.68 459	76.23 402	82.82 402	58.69 362	88.94 286	69.85 287	88.77 365	88.07 351

viewdifsd2359ckpt13		82.22 230	81.98 234	82.95 222	85.48 325	64.44 270	83.17 238	92.11 136	65.97 320	83.72 290	89.73 283	77.60 182	90.80 232	70.61 279	89.42 355	93.59 156

fmvsm_s_conf0.5_n_2		83.62 196	83.29 204	84.62 167	85.43 326	70.18 202	80.61 304	87.24 273	67.14 312	87.79 173	91.87 191	71.79 277	87.98 315	86.00 60	91.77 298	95.71 49

TinyColmap		81.25 254	82.34 229	77.99 342	85.33 327	60.68 339	82.32 266	88.33 250	71.26 247	86.97 198	92.22 184	77.10 195	86.98 337	62.37 358	95.17 169	86.31 379

MGCNet		85.37 134	84.58 169	87.75 95	85.28 328	73.36 143	86.54 143	85.71 304	77.56 138	81.78 334	92.47 171	70.29 286	96.02 10	85.59 64	95.96 134	93.87 135

test_fmvsmconf0.1_n		86.18 116	85.88 130	87.08 104	85.26 329	78.25 92	85.82 156	91.82 147	65.33 336	88.55 145	92.35 179	82.62 111	89.80 269	86.87 41	94.32 203	93.18 177

test_fmvsm_n_1920		83.60 197	82.89 216	85.74 136	85.22 330	77.74 101	84.12 199	90.48 192	59.87 399	86.45 218	91.12 228	75.65 214	85.89 365	82.28 110	90.87 323	93.58 157

viewmanbaseed2359cas		82.95 215	83.43 199	81.52 268	85.18 331	60.03 348	81.36 286	92.38 127	69.55 270	84.84 262	91.38 215	79.85 160	90.09 261	74.22 221	92.09 288	94.43 106

PAPR		78.84 293	78.10 306	81.07 279	85.17 332	60.22 344	82.21 271	90.57 191	62.51 362	75.32 415	84.61 380	74.99 221	92.30 176	59.48 384	88.04 378	90.68 288

RRT-MVS		82.97 214	83.44 198	81.57 267	85.06 333	58.04 381	87.20 124	90.37 198	77.88 132	88.59 144	93.70 119	63.17 334	93.05 155	76.49 190	88.47 369	93.62 153

pmmvs4		74.92 350	72.98 365	80.73 287	84.95 334	71.71 180	76.23 379	77.59 387	52.83 442	77.73 388	86.38 348	56.35 382	84.97 376	57.72 398	87.05 393	85.51 388

baseline1		73.26 366	73.54 357	72.43 405	84.92 335	47.79 453	79.89 313	74.00 413	65.93 322	78.81 374	86.28 353	56.36 381	81.63 404	56.63 401	79.04 462	87.87 360

Patchmatch-RL test		74.48 355	73.68 355	76.89 361	84.83 336	66.54 248	72.29 423	69.16 451	57.70 411	86.76 202	86.33 350	45.79 434	82.59 396	69.63 290	90.65 339	81.54 443

patch_mono-2		78.89 291	79.39 286	77.41 353	84.78 337	68.11 231	75.60 386	83.11 348	60.96 387	79.36 366	89.89 280	75.18 219	72.97 443	73.32 248	92.30 278	91.15 271

test_fmvsmconf_n		85.88 122	85.51 140	86.99 107	84.77 338	78.21 93	85.40 167	91.39 162	65.32 337	87.72 177	91.81 199	82.33 116	89.78 270	86.68 43	94.20 206	92.99 189

KD-MVS_self_test		81.93 242	83.14 210	78.30 335	84.75 339	52.75 425	80.37 307	89.42 231	70.24 264	90.26 104	93.39 126	74.55 233	86.77 342	68.61 305	96.64 103	95.38 58

mmtdpeth		85.13 142	85.78 134	83.17 216	84.65 340	74.71 134	85.87 154	90.35 200	77.94 130	83.82 287	96.96 14	77.75 178	80.03 417	78.44 152	96.21 121	94.79 89

XXY-MVS		74.44 357	76.19 326	69.21 426	84.61 341	52.43 429	71.70 427	77.18 392	60.73 390	80.60 348	90.96 236	75.44 215	69.35 457	56.13 405	88.33 372	85.86 384

cascas		76.29 330	74.81 343	80.72 288	84.47 342	62.94 286	73.89 407	87.34 269	55.94 422	75.16 417	76.53 461	63.97 328	91.16 215	65.00 337	90.97 319	88.06 353

PVSNet_BlendedMVS		78.80 294	77.84 307	81.65 266	84.43 343	63.41 280	79.49 320	90.44 195	61.70 376	75.43 412	87.07 341	69.11 293	91.44 199	60.68 377	92.24 282	90.11 306

PVSNet_Blended		76.49 327	75.40 334	79.76 307	84.43 343	63.41 280	75.14 392	90.44 195	57.36 415	75.43 412	78.30 445	69.11 293	91.44 199	60.68 377	87.70 386	84.42 401

OpenMVS	76.72 13	81.98 241	82.00 233	81.93 256	84.42 345	68.22 229	88.50 107	89.48 228	66.92 315	81.80 332	91.86 194	72.59 265	90.16 255	71.19 270	91.25 311	87.40 366

OpenMVS_ROB	70.19 17	77.77 309	77.46 310	78.71 325	84.39 346	61.15 325	81.18 291	82.52 353	62.45 367	83.34 299	87.37 333	66.20 309	88.66 299	64.69 341	85.02 419	86.32 378

test_yl		78.71 297	78.51 299	79.32 314	84.32 347	58.84 370	78.38 340	85.33 311	75.99 153	82.49 313	86.57 346	58.01 369	90.02 265	62.74 355	92.73 262	89.10 331

DCV-MVSNet		78.71 297	78.51 299	79.32 314	84.32 347	58.84 370	78.38 340	85.33 311	75.99 153	82.49 313	86.57 346	58.01 369	90.02 265	62.74 355	92.73 262	89.10 331

DELS-MVS		81.44 251	81.25 254	82.03 254	84.27 349	62.87 288	76.47 376	92.49 124	70.97 252	81.64 336	83.83 389	75.03 220	92.70 164	74.29 218	92.22 284	90.51 296
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		84.44 163	86.33 119	78.78 323	84.20 350	73.57 142	89.55 82	90.44 195	84.24 53	84.38 272	94.89 56	76.35 211	80.40 414	76.14 197	96.80 100	82.36 434
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UWE-MVS		66.43 425	65.56 431	69.05 427	84.15 351	40.98 477	73.06 419	64.71 469	54.84 429	76.18 404	79.62 433	29.21 485	80.50 413	38.54 481	89.75 351	85.66 386

SSC-MVS3.2		73.90 361	75.67 332	68.61 434	84.11 352	41.28 476	64.17 468	72.83 426	72.09 234	79.08 372	87.94 314	70.31 285	73.89 442	55.99 406	94.49 196	90.67 290

usedtu_dtu_shiyan1		75.70 339	75.08 340	77.56 348	84.10 353	55.50 402	73.58 409	84.89 323	62.48 363	78.16 378	84.24 384	58.14 367	87.47 327	59.35 385	90.82 326	89.72 312

FE-MVSNET3		75.70 339	75.08 340	77.56 348	84.10 353	55.50 402	73.58 409	84.89 323	62.48 363	78.16 378	84.24 384	58.14 367	87.47 327	59.34 386	90.82 326	89.72 312

EI-MVSNet-Vis-set		85.12 143	84.53 172	86.88 109	84.01 355	72.76 155	83.91 207	85.18 314	80.44 93	88.75 140	85.49 364	80.08 156	91.92 185	82.02 113	90.85 325	95.97 43

fmvsm_l_conf0.5_n		82.06 237	81.54 247	83.60 201	83.94 356	73.90 140	83.35 230	86.10 294	58.97 401	83.80 288	90.36 262	74.23 234	86.94 338	82.90 100	90.22 343	89.94 309

IterMVS-SCA-FT		80.64 266	79.41 285	84.34 177	83.93 357	69.66 208	76.28 378	81.09 368	72.43 225	86.47 216	90.19 271	60.46 347	93.15 151	77.45 175	86.39 403	90.22 301

MSDG		80.06 283	79.99 282	80.25 299	83.91 358	68.04 233	77.51 356	89.19 233	77.65 135	81.94 326	83.45 394	76.37 210	86.31 352	63.31 353	86.59 400	86.41 377

EI-MVSNet-UG-set		85.04 145	84.44 175	86.85 110	83.87 359	72.52 164	83.82 209	85.15 315	80.27 98	88.75 140	85.45 366	79.95 158	91.90 186	81.92 116	90.80 329	96.13 38

testing91		69.94 402	68.99 406	72.80 399	83.81 360	45.89 461	71.57 429	73.64 420	68.24 292	70.77 444	77.82 447	34.37 470	84.44 383	53.64 423	87.00 396	88.07 351

fmvsm_l_conf0.5_n_a		81.46 250	80.87 263	83.25 212	83.73 361	73.21 149	83.00 243	85.59 307	58.22 407	82.96 306	90.09 276	72.30 268	86.65 344	81.97 115	89.95 348	89.88 310

viewdifsd2359ckpt11		82.46 225	82.98 214	80.88 283	83.53 362	61.00 331	79.46 322	85.97 300	69.48 272	87.89 169	91.31 219	82.10 125	88.61 301	74.28 219	92.86 256	93.02 185

viewmsd2359difaftdt		82.46 225	82.99 213	80.88 283	83.52 363	61.00 331	79.46 322	85.97 300	69.48 272	87.89 169	91.31 219	82.10 125	88.61 301	74.28 219	92.86 256	93.02 185

UBG		64.34 437	63.35 439	67.30 440	83.50 364	40.53 478	67.46 455	65.02 468	54.77 430	67.54 461	74.47 472	32.99 474	78.50 425	40.82 475	83.58 433	82.88 426

thres200		72.34 375	71.55 381	74.70 386	83.48 365	51.60 435	75.02 395	73.71 418	70.14 265	78.56 377	80.57 423	46.20 426	88.20 312	46.99 459	89.29 357	84.32 402

USDC		76.63 324	76.73 321	76.34 368	83.46 366	57.20 389	80.02 311	88.04 258	52.14 448	83.65 292	91.25 222	63.24 333	86.65 344	54.66 418	94.11 209	85.17 391

ETVMVS		64.67 434	63.34 440	68.64 431	83.44 367	41.89 474	69.56 446	61.70 478	61.33 382	68.74 453	75.76 464	28.76 486	79.35 418	34.65 486	86.16 407	84.67 397

myMVS_eth3d28		65.83 430	65.85 426	65.78 447	83.42 368	35.71 487	67.29 457	68.01 454	67.58 306	69.80 449	77.72 450	32.29 475	74.30 441	37.49 483	89.06 361	87.32 367

testing222		66.93 419	65.30 432	71.81 409	83.38 369	45.83 462	72.06 425	67.50 455	64.12 351	69.68 450	76.37 462	27.34 491	83.00 394	38.88 478	88.38 371	86.62 376

testing11		67.38 417	65.93 425	71.73 410	83.37 370	46.60 458	70.95 434	69.40 448	62.47 366	66.14 463	76.66 459	31.22 478	84.10 387	49.10 449	84.10 431	84.49 398

VortexMVS		80.51 268	80.63 265	80.15 302	83.36 371	61.82 314	80.63 303	88.00 259	67.11 313	87.23 188	89.10 296	63.98 327	88.00 314	73.63 240	92.63 264	90.64 292

HY-MVS	64.64 18	73.03 369	72.47 373	74.71 385	83.36 371	54.19 415	82.14 274	81.96 359	56.76 421	69.57 451	86.21 354	60.03 351	84.83 378	49.58 447	82.65 442	85.11 392

WBMVS		68.76 412	68.43 411	69.75 422	83.29 373	40.30 479	67.36 456	72.21 432	57.09 418	77.05 396	85.53 363	33.68 472	80.51 412	48.79 451	90.90 321	88.45 344

testing99		69.27 408	68.15 414	72.63 401	83.29 373	45.45 463	71.15 431	71.08 440	67.34 309	70.43 445	77.77 449	32.24 476	84.35 385	53.72 422	86.33 404	88.10 350

EI-MVSNet		82.61 220	82.42 228	83.20 214	83.25 375	63.66 277	83.50 225	85.07 316	76.06 150	86.55 209	85.10 372	73.41 252	90.25 249	78.15 162	90.67 336	95.68 51

CVMVSNet		72.62 372	71.41 382	76.28 369	83.25 375	60.34 342	83.50 225	79.02 380	37.77 490	76.33 400	85.10 372	49.60 416	87.41 330	70.54 280	77.54 468	81.08 450

WB-MVSnew		68.72 413	69.01 405	67.85 436	83.22 377	43.98 469	74.93 396	65.98 464	55.09 426	73.83 425	79.11 435	65.63 316	71.89 448	38.21 482	85.04 418	87.69 363

V42		83.47 203	83.37 203	83.75 196	83.16 378	63.33 282	81.31 287	90.23 208	69.51 271	90.91 91	90.81 245	74.16 236	92.29 177	80.06 131	90.22 343	95.62 53

Anonymous20240521		80.18 280	81.25 254	76.95 358	83.15 379	60.84 336	82.46 259	85.99 299	68.76 284	86.78 201	93.73 118	59.13 359	77.44 428	73.71 236	97.55 77	92.56 211

EU-MVSNet		75.12 345	74.43 348	77.18 355	83.11 380	59.48 357	85.71 159	82.43 355	39.76 486	85.64 237	88.76 300	44.71 447	87.88 319	73.86 233	85.88 409	84.16 407

ET-MVSNet_ETH3D		75.28 342	72.77 367	82.81 229	83.03 381	68.11 231	77.09 363	76.51 398	60.67 391	77.60 392	80.52 424	38.04 463	91.15 216	70.78 274	90.68 335	89.17 329

FMVSNet3		78.80 294	78.55 298	79.57 311	82.89 382	56.89 392	81.76 277	85.77 303	69.04 279	86.00 227	90.44 261	51.75 406	90.09 261	65.95 325	93.34 239	91.72 256

MVS_Test		82.47 224	83.22 205	80.22 300	82.62 383	57.75 385	82.54 257	91.96 142	71.16 250	82.89 307	92.52 170	77.41 185	90.50 243	80.04 132	87.84 383	92.40 222

mvs5depth		83.82 189	84.54 171	81.68 265	82.23 384	68.65 225	86.89 131	89.90 216	80.02 102	87.74 176	97.86 4	64.19 325	82.02 401	76.37 191	95.63 156	94.35 110

LF4IMVS		82.75 219	81.93 235	85.19 148	82.08 385	80.15 75	85.53 162	88.76 239	68.01 295	85.58 239	87.75 324	71.80 276	86.85 340	74.02 230	93.87 217	88.58 342

PVSNet	58.17 21	66.41 426	65.63 430	68.75 430	81.96 386	49.88 446	62.19 473	72.51 429	51.03 455	68.04 457	75.34 470	50.84 409	74.77 438	45.82 465	82.96 437	81.60 442

GA-MVS		75.83 336	74.61 344	79.48 313	81.87 387	59.25 360	73.42 413	82.88 350	68.68 285	79.75 359	81.80 413	50.62 411	89.46 277	66.85 316	85.64 410	89.72 312

MS-PatchMatch		70.93 389	70.22 393	73.06 397	81.85 388	62.50 295	73.82 408	77.90 384	52.44 445	75.92 407	81.27 417	55.67 389	81.75 402	55.37 412	77.70 466	74.94 469

blended_shiyan8		76.05 333	75.11 338	78.86 320	81.76 389	59.18 363	75.09 393	83.81 337	64.70 346	79.37 364	78.35 444	58.30 365	88.68 297	62.03 363	92.56 270	88.73 340

blended_shiyan6		76.05 333	75.11 338	78.87 319	81.74 390	59.15 364	75.08 394	83.79 338	64.69 347	79.37 364	78.37 443	58.30 365	88.69 296	61.99 364	92.61 265	88.77 339

Syy-MVS		69.40 407	70.03 396	67.49 439	81.72 391	38.94 481	71.00 432	61.99 473	61.38 380	70.81 442	72.36 476	61.37 343	79.30 419	64.50 345	85.18 415	84.22 404

myMVS_eth3d		64.66 435	63.89 436	66.97 442	81.72 391	37.39 484	71.00 432	61.99 473	61.38 380	70.81 442	72.36 476	20.96 497	79.30 419	49.59 446	85.18 415	84.22 404

SCA		73.32 365	72.57 371	75.58 378	81.62 393	55.86 398	78.89 333	71.37 439	61.73 374	74.93 419	83.42 395	60.46 347	87.01 334	58.11 396	82.63 444	83.88 408

FMVSNet5		72.10 377	71.69 377	73.32 393	81.57 394	53.02 424	76.77 368	78.37 383	63.31 355	76.37 399	91.85 195	36.68 467	78.98 421	47.87 456	92.45 273	87.95 356

thisisatest0515		73.00 370	70.52 389	80.46 295	81.45 395	59.90 350	73.16 416	74.31 412	57.86 410	76.08 406	77.78 448	37.60 466	92.12 181	65.00 337	91.45 306	89.35 320

eth_miper_zixun_eth		80.84 262	80.22 275	82.71 230	81.41 396	60.98 334	77.81 350	90.14 211	67.31 310	86.95 199	87.24 337	64.26 323	92.31 175	75.23 211	91.61 302	94.85 85

CANet_DTU		77.81 308	77.05 315	80.09 303	81.37 397	59.90 350	83.26 232	88.29 252	69.16 276	67.83 459	83.72 390	60.93 344	89.47 276	69.22 295	89.70 352	90.88 281

ANet_high		83.17 210	85.68 137	75.65 376	81.24 398	45.26 465	79.94 312	92.91 108	83.83 56	91.33 81	96.88 15	80.25 155	85.92 361	68.89 300	95.89 142	95.76 47

new-patchmatchnet		70.10 397	73.37 360	60.29 465	81.23 399	16.95 500	59.54 477	74.62 408	62.93 359	80.97 342	87.93 316	62.83 339	71.90 447	55.24 414	95.01 178	92.00 247

test20.03		73.75 363	74.59 346	71.22 412	81.11 400	51.12 440	70.15 441	72.10 433	70.42 258	80.28 356	91.50 209	64.21 324	74.72 440	46.96 460	94.58 194	87.82 362

blend_shiyan4		70.82 390	68.15 414	78.83 322	81.06 401	59.77 352	74.58 400	83.79 338	64.94 344	77.34 395	75.47 469	29.39 484	88.89 288	58.91 388	67.86 486	87.84 361

MVS		73.21 368	72.59 370	75.06 381	80.97 402	60.81 337	81.64 280	85.92 302	46.03 470	71.68 437	77.54 451	68.47 296	89.77 271	55.70 409	85.39 411	74.60 470

N_pmnet		70.20 395	68.80 409	74.38 387	80.91 403	84.81 42	59.12 479	76.45 399	55.06 427	75.31 416	82.36 407	55.74 388	54.82 488	47.02 458	87.24 389	83.52 415

IterMVS		76.91 319	76.34 325	78.64 326	80.91 403	64.03 274	76.30 377	79.03 379	64.88 345	83.11 303	89.16 294	59.90 353	84.46 382	68.61 305	85.15 417	87.42 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		81.64 247	81.59 243	81.79 264	80.86 405	59.15 364	78.61 339	90.18 210	68.36 289	87.20 189	87.11 340	69.39 290	91.62 192	78.16 160	94.43 199	94.60 93

WTY-MVS		67.91 416	68.35 412	66.58 444	80.82 406	48.12 451	65.96 462	72.60 427	53.67 436	71.20 439	81.68 415	58.97 360	69.06 459	48.57 452	81.67 446	82.55 430

IB-MVS	62.13 19	71.64 381	68.97 407	79.66 310	80.80 407	62.26 305	73.94 406	76.90 394	63.27 357	68.63 455	76.79 458	33.83 471	91.84 189	59.28 387	87.26 388	84.88 394
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		71.85 378	71.59 378	72.62 402	80.71 408	53.78 418	69.72 444	71.71 438	58.80 403	78.03 381	80.51 425	56.61 380	78.84 423	62.20 360	86.04 408	85.23 390

ppachtmachnet_test		74.73 354	74.00 351	76.90 360	80.71 408	56.89 392	71.53 430	78.42 382	58.24 406	79.32 368	82.92 401	57.91 372	84.26 386	65.60 332	91.36 307	89.56 316

diffmvs_AUTHOR		81.24 255	81.55 246	80.30 298	80.61 410	60.22 344	77.98 347	90.48 192	67.77 303	83.34 299	89.50 287	74.69 229	87.42 329	78.78 150	90.81 328	93.27 171

testgi		72.36 374	74.61 344	65.59 448	80.56 411	42.82 473	68.29 450	73.35 421	66.87 316	81.84 329	89.93 278	72.08 272	66.92 471	46.05 464	92.54 271	87.01 371

D2MVS		76.84 320	75.67 332	80.34 297	80.48 412	62.16 308	73.50 412	84.80 328	57.61 413	82.24 318	87.54 328	51.31 407	87.65 323	70.40 282	93.19 248	91.23 268

1314		73.22 367	72.56 372	75.20 379	80.41 413	57.84 383	81.64 280	85.36 309	51.68 451	73.10 429	76.65 460	61.45 342	85.19 374	63.54 350	79.21 460	82.59 428

wanda-best-256-512		74.97 348	73.85 352	78.35 332	80.36 414	58.13 377	73.10 417	83.53 343	64.04 352	77.62 389	75.71 465	56.22 384	88.60 303	61.42 371	92.61 265	88.32 345

FE-blended-shiyan7		74.97 348	73.85 352	78.35 332	80.36 414	58.13 377	73.10 417	83.53 343	64.03 353	77.62 389	75.71 465	56.22 384	88.60 303	61.42 371	92.61 265	88.32 345

usedtu_blend_shiyan5		77.07 317	76.43 323	78.99 317	80.36 414	59.77 352	83.25 233	88.32 251	74.91 171	77.62 389	75.71 465	56.22 384	88.89 288	58.91 388	92.61 265	88.32 345

viewmambaseed2359dif		78.80 294	78.47 301	79.78 305	80.26 417	59.28 359	77.31 361	87.13 277	60.42 393	82.37 316	88.67 304	74.58 231	87.87 320	67.78 313	87.73 384	92.19 238

cl____		80.42 271	80.23 273	81.02 281	79.99 418	59.25 360	77.07 364	87.02 283	67.37 308	86.18 222	89.21 293	63.08 336	90.16 255	76.31 193	95.80 147	93.65 150

DIV-MVS_self_test		80.43 270	80.23 273	81.02 281	79.99 418	59.25 360	77.07 364	87.02 283	67.38 307	86.19 220	89.22 292	63.09 335	90.16 255	76.32 192	95.80 147	93.66 147

MonoMVSNet		76.66 323	77.26 314	74.86 382	79.86 420	54.34 413	86.26 147	86.08 295	71.08 251	85.59 238	88.68 302	53.95 396	85.93 360	63.86 347	80.02 455	84.32 402

miper_ehance_all_eth		80.34 274	80.04 280	81.24 277	79.82 421	58.95 367	77.66 352	89.66 223	65.75 328	85.99 230	85.11 371	68.29 297	91.42 201	76.03 198	92.03 289	93.33 167

CR-MVSNet		74.00 360	73.04 364	76.85 362	79.58 422	62.64 292	82.58 254	76.90 394	50.50 460	75.72 409	92.38 173	48.07 420	84.07 388	68.72 304	82.91 439	83.85 411

RPMNet		78.88 292	78.28 303	80.68 290	79.58 422	62.64 292	82.58 254	94.16 32	74.80 172	75.72 409	92.59 164	48.69 417	95.56 44	73.48 243	82.91 439	83.85 411

baseline2		69.77 403	66.89 420	78.41 331	79.51 424	58.09 379	76.23 379	69.57 447	57.50 414	64.82 474	77.45 453	46.02 428	88.44 306	53.08 426	77.83 464	88.70 341

UnsupCasMVSNet_bld		69.21 409	69.68 399	67.82 437	79.42 425	51.15 439	67.82 454	75.79 401	54.15 433	77.47 394	85.36 370	59.26 358	70.64 452	48.46 453	79.35 458	81.66 441

PatchT		70.52 393	72.76 368	63.79 456	79.38 426	33.53 490	77.63 353	65.37 467	73.61 197	71.77 436	92.79 160	44.38 448	75.65 435	64.53 344	85.37 412	82.18 436

Patchmtry		76.56 326	77.46 310	73.83 390	79.37 427	46.60 458	82.41 263	76.90 394	73.81 189	85.56 240	92.38 173	48.07 420	83.98 389	63.36 352	95.31 165	90.92 279

mvs_anonymous		78.13 304	78.76 295	76.23 371	79.24 428	50.31 444	78.69 337	84.82 327	61.60 378	83.09 305	92.82 157	73.89 243	87.01 334	68.33 309	86.41 402	91.37 266

MVS-HIRNet		61.16 446	62.92 442	55.87 469	79.09 429	35.34 488	71.83 426	57.98 486	46.56 467	59.05 484	91.14 227	49.95 415	76.43 431	38.74 479	71.92 478	55.84 488

MDA-MVSNet-bldmvs		77.47 311	76.90 318	79.16 316	79.03 430	64.59 266	66.58 461	75.67 403	73.15 211	88.86 136	88.99 298	66.94 305	81.23 407	64.71 340	88.22 377	91.64 260

diffmvs		80.40 272	80.48 270	80.17 301	79.02 431	60.04 346	77.54 355	90.28 207	66.65 318	82.40 315	87.33 335	73.50 249	87.35 331	77.98 168	89.62 353	93.13 178
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		68.45 414	66.83 421	73.30 395	78.93 432	48.50 449	79.76 314	71.76 436	47.50 464	69.92 448	83.60 391	42.07 456	88.40 308	48.44 454	79.51 456	83.01 425

tpm		67.95 415	68.08 416	67.55 438	78.74 433	43.53 471	75.60 386	67.10 461	54.92 428	72.23 433	88.10 311	42.87 455	75.97 433	52.21 432	80.95 454	83.15 423

MDTV_nov1_ep13			68.29 413		78.03 434	43.87 470	74.12 403	72.22 431	52.17 446	67.02 462	85.54 362	45.36 439	80.85 409	55.73 407	84.42 428

cl22		78.97 289	78.21 304	81.24 277	77.74 435	59.01 366	77.46 359	87.13 277	65.79 325	84.32 275	85.10 372	58.96 361	90.88 228	75.36 209	92.03 289	93.84 136

EPNet_dtu		72.87 371	71.33 383	77.49 352	77.72 436	60.55 340	82.35 265	75.79 401	66.49 319	58.39 487	81.06 419	53.68 397	85.98 359	53.55 424	92.97 254	85.95 382
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 404	68.83 408	72.33 407	77.66 437	53.60 419	79.29 325	69.99 445	57.66 412	72.53 432	82.93 400	46.45 425	80.08 416	60.91 376	72.09 477	83.31 421
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_vis1_n_1920		71.30 386	71.58 380	70.47 415	77.58 438	59.99 349	74.25 401	84.22 335	51.06 454	74.85 420	79.10 436	55.10 393	68.83 460	68.86 301	79.20 461	82.58 429

dmvs_testset		60.59 450	62.54 444	54.72 471	77.26 439	27.74 494	74.05 404	61.00 480	60.48 392	65.62 468	67.03 483	55.93 387	68.23 466	32.07 490	69.46 484	68.17 478

sss		66.92 420	67.26 418	65.90 446	77.23 440	51.10 441	64.79 464	71.72 437	52.12 449	70.13 447	80.18 427	57.96 371	65.36 477	50.21 441	81.01 452	81.25 447

CostFormer		69.98 401	68.68 410	73.87 389	77.14 441	50.72 442	79.26 326	74.51 410	51.94 450	70.97 441	84.75 378	45.16 443	87.49 326	55.16 415	79.23 459	83.40 418

tpm cat1		66.76 424	65.21 433	71.42 411	77.09 442	50.62 443	78.01 345	73.68 419	44.89 473	68.64 454	79.00 437	45.51 437	82.42 399	49.91 444	70.15 480	81.23 449

pmmvs5		70.73 391	70.07 394	72.72 400	77.03 443	52.73 426	74.14 402	75.65 404	50.36 461	72.17 435	85.37 369	55.42 391	80.67 410	52.86 430	87.59 387	84.77 395

dmvs_re		66.81 423	66.98 419	66.28 445	76.87 444	58.68 374	71.66 428	72.24 430	60.29 395	69.52 452	73.53 473	52.38 402	64.40 479	44.90 466	81.44 449	75.76 467

EPNet		80.37 273	78.41 302	86.23 122	76.75 445	73.28 146	87.18 126	77.45 388	76.24 149	68.14 456	88.93 299	65.41 317	93.85 118	69.47 291	96.12 127	91.55 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		76.45 328	76.10 327	77.51 351	76.72 446	60.97 335	64.69 465	85.04 318	63.98 354	83.20 302	88.22 309	56.67 379	78.79 424	73.22 249	93.12 249	92.78 197

reproduce_monomvs		74.09 359	73.23 361	76.65 365	76.52 447	54.54 410	77.50 357	81.40 366	65.85 324	82.86 309	86.67 345	27.38 490	84.53 381	70.24 283	90.66 338	90.89 280

CHOSEN 280x420		59.08 451	56.52 457	66.76 443	76.51 448	64.39 271	49.62 488	59.00 483	43.86 476	55.66 491	68.41 482	35.55 469	68.21 467	43.25 469	76.78 471	67.69 479

UnsupCasMVSNet_eth		71.63 382	72.30 374	69.62 423	76.47 449	52.70 427	70.03 442	80.97 369	59.18 400	79.36 366	88.21 310	60.50 346	69.12 458	58.33 394	77.62 467	87.04 370

test-LLR		67.21 418	66.74 422	68.63 432	76.45 450	55.21 406	67.89 451	67.14 459	62.43 369	65.08 471	72.39 474	43.41 451	69.37 455	61.00 374	84.89 423	81.31 445

test-mter		65.00 433	63.79 437	68.63 432	76.45 450	55.21 406	67.89 451	67.14 459	50.98 456	65.08 471	72.39 474	28.27 488	69.37 455	61.00 374	84.89 423	81.31 445

miper_enhance_ethall		77.83 306	76.93 317	80.51 294	76.15 452	58.01 382	75.47 390	88.82 237	58.05 409	83.59 293	80.69 420	64.41 321	91.20 213	73.16 255	92.03 289	92.33 229

gg-mvs-nofinetune		68.96 411	69.11 403	68.52 435	76.12 453	45.32 464	83.59 218	55.88 487	86.68 32	64.62 475	97.01 11	30.36 481	83.97 390	44.78 467	82.94 438	76.26 466

test_vis1_n		70.29 394	69.99 397	71.20 413	75.97 454	66.50 249	76.69 370	80.81 370	44.22 475	75.43 412	77.23 455	50.00 414	68.59 461	66.71 319	82.85 441	78.52 463

CMPMVS	59.41 20	75.12 345	73.57 356	79.77 306	75.84 455	67.22 237	81.21 290	82.18 357	50.78 457	76.50 398	87.66 326	55.20 392	82.99 395	62.17 362	90.64 340	89.09 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
wuyk23d		75.13 344	79.30 288	62.63 457	75.56 456	75.18 133	80.89 297	73.10 424	75.06 170	94.76 15	95.32 44	87.73 45	52.85 489	34.16 487	97.11 90	59.85 485

Patchmatch-test		65.91 428	67.38 417	61.48 462	75.51 457	43.21 472	68.84 447	63.79 471	62.48 363	72.80 431	83.42 395	44.89 446	59.52 485	48.27 455	86.45 401	81.70 440

new_pmnet		55.69 455	57.66 456	49.76 472	75.47 458	30.59 492	59.56 476	51.45 490	43.62 478	62.49 477	75.48 468	40.96 458	49.15 492	37.39 484	72.52 475	69.55 476

gm-plane-assit					75.42 459	44.97 467			52.17 446		72.36 476		87.90 318	54.10 420

MVSTER		77.09 316	75.70 331	81.25 274	75.27 460	61.08 327	77.49 358	85.07 316	60.78 389	86.55 209	88.68 302	43.14 454	90.25 249	73.69 239	90.67 336	92.42 218

PVSNet_0	51.08 22	56.10 454	54.97 459	59.48 467	75.12 461	53.28 423	55.16 485	61.89 475	44.30 474	59.16 483	62.48 486	54.22 395	65.91 475	35.40 485	47.01 490	59.25 486

test0.0.03 1		64.66 435	64.36 434	65.57 449	75.03 462	46.89 457	64.69 465	61.58 479	62.43 369	71.18 440	77.54 451	43.41 451	68.47 464	40.75 476	82.65 442	81.35 444

test_fmvs3		75.72 338	75.20 337	77.27 354	75.01 463	69.47 211	78.93 331	84.88 325	46.67 466	87.08 195	87.84 322	50.44 413	71.62 449	77.42 177	88.53 368	90.72 285

tpmvs		70.16 396	69.56 400	71.96 408	74.71 464	48.13 450	79.63 315	75.45 406	65.02 343	70.26 446	81.88 412	45.34 440	85.68 370	58.34 393	75.39 472	82.08 438

test_fmvs1_n		70.94 388	70.41 392	72.53 404	73.92 465	66.93 245	75.99 383	84.21 336	43.31 479	79.40 363	79.39 434	43.47 450	68.55 462	69.05 298	84.91 422	82.10 437

MDA-MVSNet_test_wron		70.05 399	70.44 390	68.88 429	73.84 466	53.47 420	58.93 481	67.28 457	58.43 404	87.09 194	85.40 367	59.80 355	67.25 469	59.66 383	83.54 434	85.92 383

YYNet1		70.06 398	70.44 390	68.90 428	73.76 467	53.42 422	58.99 480	67.20 458	58.42 405	87.10 193	85.39 368	59.82 354	67.32 468	59.79 382	83.50 435	85.96 381

test_cas_vis1_n_1920		69.20 410	69.12 402	69.43 425	73.68 468	62.82 289	70.38 440	77.21 391	46.18 469	80.46 353	78.95 438	52.03 403	65.53 476	65.77 331	77.45 469	79.95 458

UWE-MVS-28		58.44 453	57.71 455	60.65 464	73.58 469	31.23 491	69.68 445	48.80 492	53.12 441	61.79 478	78.83 439	30.98 479	68.40 465	21.58 493	80.99 453	82.33 435

GG-mvs-BLEND				67.16 441	73.36 470	46.54 460	84.15 198	55.04 488		58.64 486	61.95 487	29.93 482	83.87 391	38.71 480	76.92 470	71.07 474

JIA-IIPM		69.41 406	66.64 424	77.70 347	73.19 471	71.24 187	75.67 385	65.56 466	70.42 258	65.18 470	92.97 150	33.64 473	83.06 393	53.52 425	69.61 483	78.79 462

ADS-MVSNet2		65.87 429	63.64 438	72.55 403	73.16 472	56.92 391	67.10 458	74.81 407	49.74 462	66.04 465	82.97 398	46.71 423	77.26 429	42.29 471	69.96 481	83.46 416

ADS-MVSNet		61.90 442	62.19 445	61.03 463	73.16 472	36.42 486	67.10 458	61.75 476	49.74 462	66.04 465	82.97 398	46.71 423	63.21 480	42.29 471	69.96 481	83.46 416

ttmdpeth		71.72 380	70.67 386	74.86 382	73.08 474	55.88 397	77.41 360	69.27 449	55.86 423	78.66 375	93.77 116	38.01 464	75.39 437	60.12 380	89.87 349	93.31 169

DSMNet-mixed		60.98 448	61.61 447	59.09 468	72.88 475	45.05 466	74.70 398	46.61 494	26.20 492	65.34 469	90.32 267	55.46 390	63.12 481	41.72 473	81.30 451	69.09 477

tpmrst		66.28 427	66.69 423	65.05 452	72.82 476	39.33 480	78.20 343	70.69 443	53.16 440	67.88 458	80.36 426	48.18 419	74.75 439	58.13 395	70.79 479	81.08 450

test_fmvs2		73.57 364	72.80 366	75.90 373	72.74 477	68.84 224	77.07 364	84.32 334	45.14 472	82.89 307	84.22 386	48.37 418	70.36 453	73.40 245	87.03 394	88.52 343

TESTMET0.1,1		61.29 445	60.32 450	64.19 454	72.06 478	51.30 437	67.89 451	62.09 472	45.27 471	60.65 481	69.01 480	27.93 489	64.74 478	56.31 403	81.65 448	76.53 465

dp		60.70 449	60.29 451	61.92 460	72.04 479	38.67 483	70.83 436	64.08 470	51.28 453	60.75 480	77.28 454	36.59 468	71.58 450	47.41 457	62.34 488	75.52 468

pmmvs3		62.47 439	60.02 452	69.80 421	71.58 480	64.00 275	70.52 438	58.44 485	39.77 485	66.05 464	75.84 463	27.10 493	72.28 445	46.15 463	84.77 427	73.11 471

dongtai		41.90 458	42.65 461	39.67 474	70.86 481	21.11 496	61.01 475	21.42 501	57.36 415	57.97 488	50.06 490	16.40 499	58.73 487	21.03 494	27.69 494	39.17 490

0.4-1-1-0.2		62.43 441	58.81 454	73.31 394	70.85 482	54.20 414	64.36 467	72.99 425	53.70 435	57.51 489	54.59 488	29.52 483	86.44 349	51.70 439	74.02 474	79.30 460

EPMVS		62.47 439	62.63 443	62.01 458	70.63 483	38.74 482	74.76 397	52.86 489	53.91 434	67.71 460	80.01 428	39.40 460	66.60 472	55.54 411	68.81 485	80.68 454

mvsany_test3		65.48 432	62.97 441	73.03 398	69.99 484	76.17 123	64.83 463	43.71 495	43.68 477	80.25 357	87.05 342	52.83 400	63.09 482	51.92 437	72.44 476	79.84 459

test_vis3_rt		71.42 384	70.67 386	73.64 392	69.66 485	70.46 196	66.97 460	89.73 220	42.68 482	88.20 158	83.04 397	43.77 449	60.07 483	65.35 335	86.66 399	90.39 299

test_fmvs1		69.57 405	69.05 404	71.14 414	69.15 486	65.77 259	73.98 405	83.32 345	42.83 481	77.77 387	78.27 446	43.39 453	68.50 463	68.39 308	84.38 429	79.15 461

KD-MVS_2432*1600		66.87 421	65.81 428	70.04 417	67.50 487	47.49 454	62.56 471	79.16 377	61.21 385	77.98 382	80.61 421	25.29 494	82.48 397	53.02 427	84.92 420	80.16 456

miper_refine_blended		66.87 421	65.81 428	70.04 417	67.50 487	47.49 454	62.56 471	79.16 377	61.21 385	77.98 382	80.61 421	25.29 494	82.48 397	53.02 427	84.92 420	80.16 456

E-PMN		61.59 444	61.62 446	61.49 461	66.81 489	55.40 404	53.77 486	60.34 481	66.80 317	58.90 485	65.50 484	40.48 459	66.12 474	55.72 408	86.25 405	62.95 483

test_f		64.31 438	65.85 426	59.67 466	66.54 490	62.24 307	57.76 483	70.96 441	40.13 484	84.36 273	82.09 409	46.93 422	51.67 490	61.99 364	81.89 445	65.12 481

test_vis1_rt		65.64 431	64.09 435	70.31 416	66.09 491	70.20 200	61.16 474	81.60 364	38.65 487	72.87 430	69.66 479	52.84 399	60.04 484	56.16 404	77.77 465	80.68 454

EMVS		61.10 447	60.81 448	61.99 459	65.96 492	55.86 398	53.10 487	58.97 484	67.06 314	56.89 490	63.33 485	40.98 457	67.03 470	54.79 417	86.18 406	63.08 482

mvsany_test1		58.48 452	56.47 458	64.50 453	65.90 493	68.21 230	56.95 484	42.11 496	38.30 488	65.69 467	77.19 457	56.96 378	59.35 486	46.16 462	58.96 489	65.93 480

PMMVS		61.65 443	60.38 449	65.47 450	65.40 494	69.26 214	63.97 469	61.73 477	36.80 491	60.11 482	68.43 481	59.42 356	66.35 473	48.97 450	78.57 463	60.81 484

PMMVS2		55.64 456	59.27 453	44.74 473	64.30 495	12.32 501	40.60 489	49.79 491	53.19 439	65.06 473	84.81 377	53.60 398	49.76 491	32.68 489	89.41 356	72.15 472

MVE	40.22 23	51.82 457	50.47 460	55.87 469	62.66 496	51.91 432	31.61 491	39.28 497	40.65 483	50.76 492	74.98 471	56.24 383	44.67 493	33.94 488	64.11 487	71.04 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MVStest1		70.05 399	69.26 401	72.41 406	58.62 497	55.59 401	76.61 373	65.58 465	53.44 437	89.28 131	93.32 127	22.91 496	71.44 451	74.08 229	89.52 354	90.21 305

kuosan		30.83 459	32.17 462	26.83 476	53.36 498	19.02 499	57.90 482	20.44 502	38.29 489	38.01 493	37.82 492	15.18 500	33.45 495	7.74 496	20.76 495	28.03 491

DeepMVS_CX				24.13 477	32.95 499	29.49 493		21.63 500	12.07 493	37.95 494	45.07 491	30.84 480	19.21 496	17.94 495	33.06 493	23.69 492

test_method		30.46 460	29.60 463	33.06 475	17.99 500	3.84 503	13.62 492	73.92 414	2.79 494	18.29 496	53.41 489	28.53 487	43.25 494	22.56 491	35.27 492	52.11 489

tmp_tt		20.25 462	24.50 465	7.49 478	4.47 501	8.70 502	34.17 490	25.16 499	1.00 496	32.43 495	18.49 493	39.37 461	9.21 497	21.64 492	43.75 491	4.57 493

testmvs		5.91 466	7.65 469	0.72 480	1.20 502	0.37 505	59.14 478	0.67 504	0.49 498	1.11 498	2.76 497	0.94 502	0.24 499	1.02 498	1.47 496	1.55 495

test123		6.27 465	8.08 468	0.84 479	1.11 503	0.57 504	62.90 470	0.82 503	0.54 497	1.07 499	2.75 498	1.26 501	0.30 498	1.04 497	1.26 497	1.66 494

mmdepth		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

monomultidepth		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

test_blank		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

eth-test2					0.00 504

eth-test					0.00 504

uanet_test		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

DCPMVS		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

cdsmvs_eth3d_5k		20.81 461	27.75 464	0.00 481	0.00 504	0.00 506	0.00 493	85.44 308	0.00 499	0.00 500	82.82 402	81.46 139	0.00 500	0.00 499	0.00 498	0.00 496

pcd_1.5k_mvsjas		6.41 464	8.55 467	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	76.94 198	0.00 500	0.00 499	0.00 498	0.00 496

sosnet-low-res		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

sosnet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

uncertanet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

Regformer		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

ab-mvs-re		6.65 463	8.87 466	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	79.80 430	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

uanet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

TestfortrainingZip							92.12 33

WAC-MVS						37.39 484								52.61 431

PC_three_1452									58.96 402	90.06 106	91.33 217	80.66 150	93.03 156	75.78 201	95.94 137	92.48 215

test_241102_TWO								93.71 59	83.77 57	93.49 39	94.27 82	89.27 24	95.84 26	86.03 56	97.82 56	92.04 245

test_0728_THIRD									85.33 41	93.75 34	94.65 64	87.44 48	95.78 34	87.41 30	98.21 33	92.98 191

GSMVS																83.88 408

sam_mvs1												46.11 427				83.88 408

sam_mvs												45.92 432

MTGPA								91.81 149

test_post1							78.85 335				3.13 495	45.19 442	80.13 415	58.11 396

test_post											3.10 496	45.43 438	77.22 430

patchmatchnet-post											81.71 414	45.93 431	87.01 334

MTMP							90.66 53	33.14 498

test9_res														80.83 124	96.45 112	90.57 293

agg_prior2														79.68 137	96.16 124	90.22 301

test_prior4						78.97 86	84.59 187

test_prior2							83.37 229		75.43 165	84.58 266	91.57 207	81.92 132		79.54 141	96.97 93

旧先验2							81.73 278		56.88 420	86.54 215			84.90 377	72.81 256

新几何2							81.72 279

无先验							82.81 249	85.62 306	58.09 408				91.41 202	67.95 312		84.48 399

原ACMM2							82.26 270

testdata2													86.43 350	63.52 351

segment_acmp												81.94 129

testdata1							79.62 316		73.95 188

plane_prior5								93.61 68					95.22 62	80.78 125	95.83 145	94.46 101

plane_prior4											92.95 151

plane_prior3						76.85 113			77.79 134	86.55 209

plane_prior2							89.45 87		79.44 109

plane_prior						76.42 118	87.15 127		75.94 156						95.03 175

n2								0.00 505

nn								0.00 505

door-mid								74.45 411

test11								91.46 158

door								72.57 428

HQP5-MVS						70.66 193

BP-MVS														77.30 178

HQP4-MVS										80.56 349			94.61 85			93.56 159

HQP3-MVS								92.68 116							94.47 197

HQP2-MVS												72.10 270

MDTV_nov1_ep13_2view						27.60 495	70.76 437		46.47 468	61.27 479		45.20 441		49.18 448		83.75 413

ACMMP++_ref															95.74 151

ACMMP++															97.35 83

Test By Simon												79.09 164

High-resolution multi-view benchmark