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 3	98.67 1	85.39 37	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 64	99.27 1	99.54 1

UniMVSNet_ETH3D		89.12 65	90.72 47	84.31 162	97.00 2	64.33 247	89.67 74	88.38 212	88.84 17	94.29 22	97.57 4	90.48 13	91.26 189	72.57 219	97.65 62	97.34 14

PMVS	80.48 6	90.08 41	90.66 48	88.34 81	96.71 3	92.97 2	90.31 59	89.57 195	88.51 21	90.11 96	95.12 49	90.98 6	88.92 254	77.55 155	97.07 83	83.13 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MTAPA		91.52 18	91.60 22	91.29 30	96.59 4	86.29 21	92.02 33	91.81 127	84.07 49	92.00 66	94.40 76	86.63 54	95.28 58	88.59 10	98.31 24	92.30 189

PEN-MVS		90.03 45	91.88 18	84.48 154	96.57 5	58.88 316	88.95 89	93.19 75	91.62 5	96.01 7	96.16 24	87.02 50	95.60 40	78.69 137	98.72 9	98.97 3

PS-CasMVS		90.06 43	91.92 15	84.47 155	96.56 6	58.83 319	89.04 88	92.74 97	91.40 6	96.12 5	96.06 26	87.23 48	95.57 41	79.42 131	98.74 6	99.00 2

DTE-MVSNet		89.98 47	91.91 17	84.21 164	96.51 7	57.84 327	88.93 90	92.84 94	91.92 4	96.16 4	96.23 21	86.95 51	95.99 12	79.05 134	98.57 15	98.80 6

CP-MVSNet		89.27 62	90.91 44	84.37 156	96.34 8	58.61 322	88.66 97	92.06 116	90.78 7	95.67 8	95.17 47	81.80 117	95.54 44	79.00 135	98.69 10	98.95 4

WR-MVS_H		89.91 50	91.31 33	85.71 128	96.32 9	62.39 272	89.54 79	93.31 70	90.21 12	95.57 11	95.66 33	81.42 121	95.90 17	80.94 110	98.80 3	98.84 5

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 46	97.23 2	95.32 2	99.01 2	97.26 6	80.16 135	98.99 1	95.15 1	99.14 2	96.47 30

MP-MVS		91.14 28	90.91 44	91.83 20	96.18 11	86.88 17	92.20 30	93.03 86	82.59 66	88.52 135	94.37 78	86.74 53	95.41 53	86.32 44	98.21 32	93.19 147
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
FOURS1					96.08 12	87.41 14	96.19 2	95.83 5	92.95 3	96.57 3

mPP-MVS		91.69 15	91.47 26	92.37 6	96.04 13	88.48 8	92.72 18	92.60 102	83.09 61	91.54 72	94.25 83	87.67 44	95.51 47	87.21 33	98.11 38	93.12 151

MP-MVS-pluss		90.81 30	91.08 37	89.99 50	95.97 14	79.88 75	88.13 102	94.51 18	75.79 148	92.94 47	94.96 51	88.36 30	95.01 68	90.70 3	98.40 20	95.09 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		93.52 3	93.39 4	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 50	96.29 19	88.16 35	94.17 97	86.07 50	98.48 18	97.22 17

ACMMP_NAP		90.65 32	91.07 39	89.42 61	95.93 16	79.54 80	89.95 66	93.68 58	77.65 127	91.97 67	94.89 53	88.38 29	95.45 51	89.27 5	97.87 53	93.27 143

HPM-MVS_fast		92.50 8	92.54 9	92.37 6	95.93 16	85.81 33	92.99 12	94.23 27	85.21 40	92.51 58	95.13 48	90.65 9	95.34 55	88.06 13	98.15 37	95.95 41

MSP-MVS		89.08 66	88.16 78	91.83 20	95.76 18	86.14 25	92.75 17	93.90 48	78.43 116	89.16 121	92.25 161	72.03 234	96.36 4	88.21 12	90.93 273	92.98 157
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 19	95.75 19	85.90 29	92.63 21	93.30 71	81.91 72	90.88 88	94.21 84	87.75 41	95.87 20	87.60 23	97.71 60	93.83 116

ACMMPR		91.49 19	91.35 30	91.92 16	95.74 20	85.88 30	92.58 22	93.25 73	81.99 70	91.40 74	94.17 88	87.51 45	95.87 20	87.74 18	97.76 57	93.99 107

ZNCC-MVS		91.26 24	91.34 31	91.01 34	95.73 21	83.05 56	92.18 31	94.22 29	80.14 92	91.29 78	93.97 96	87.93 40	95.87 20	88.65 9	97.96 48	94.12 104

TSAR-MVS + MP.		88.14 75	87.82 82	89.09 67	95.72 22	76.74 112	92.49 25	91.19 144	67.85 259	86.63 180	94.84 55	79.58 140	95.96 15	87.62 21	94.50 182	94.56 81
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 43	91.93 15	95.67 23	85.85 31	90.00 62	93.90 48	80.32 89	91.74 71	94.41 75	88.17 34	95.98 13	86.37 43	97.99 43	93.96 109

XVS		91.54 17	91.36 28	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	94.03 93	86.57 55	95.80 28	87.35 29	97.62 64	94.20 97

X-MVStestdata		85.04 127	82.70 183	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	16.05 433	86.57 55	95.80 28	87.35 29	97.62 64	94.20 97

HPM-MVS		92.13 11	92.20 13	91.91 17	95.58 26	84.67 46	93.51 8	94.85 15	82.88 64	91.77 70	93.94 102	90.55 12	95.73 35	88.50 11	98.23 31	95.33 56
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 12	95.53 27	85.91 28	93.35 11	94.16 32	82.52 67	92.39 61	94.14 89	89.15 25	95.62 39	87.35 29	98.24 30	94.56 81
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 40	90.82 37	95.45 28	82.73 59	91.75 38	93.74 54	80.98 83	91.38 75	93.80 106	87.20 49	95.80 28	87.10 36	97.69 61	93.93 110

HFP-MVS		91.30 23	91.39 27	91.02 33	95.43 29	84.66 47	92.58 22	93.29 72	81.99 70	91.47 73	93.96 99	88.35 31	95.56 42	87.74 18	97.74 59	92.85 161

SMA-MVS		90.31 38	90.48 50	89.83 54	95.31 30	79.52 81	90.98 47	93.24 74	75.37 157	92.84 51	95.28 44	85.58 67	96.09 8	87.92 15	97.76 57	93.88 113
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 14	95.29 31	87.62 13	93.38 9	93.36 65	83.16 60	91.06 82	94.00 95	88.26 32	95.71 37	87.28 32	98.39 21	92.55 175

VDDNet		84.35 144	85.39 128	81.25 237	95.13 32	59.32 309	85.42 153	81.11 308	86.41 32	87.41 162	96.21 22	73.61 207	90.61 214	66.33 274	96.85 87	93.81 120

CPTT-MVS		89.39 58	88.98 69	90.63 40	95.09 33	86.95 16	92.09 32	92.30 110	79.74 96	87.50 161	92.38 153	81.42 121	93.28 133	83.07 85	97.24 79	91.67 217

ACMM	79.39 9	90.65 32	90.99 41	89.63 57	95.03 34	83.53 51	89.62 76	93.35 66	79.20 105	93.83 31	93.60 116	90.81 7	92.96 144	85.02 66	98.45 19	92.41 182
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 27	94.98 35	82.95 58	93.52 7	92.79 95	88.22 22	88.53 134	97.64 3	83.45 86	94.55 83	86.02 54	98.60 13	96.67 25

HPM-MVS++		88.93 68	88.45 76	90.38 44	94.92 36	85.85 31	89.70 71	91.27 141	78.20 119	86.69 179	92.28 160	80.36 133	95.06 67	86.17 49	96.49 100	90.22 256

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 43	94.91 37	84.50 48	89.49 81	93.98 43	79.68 97	92.09 64	93.89 104	83.80 81	93.10 140	82.67 93	98.04 39	93.64 128

EGC-MVSNET		74.79 295	69.99 339	89.19 65	94.89 38	87.00 15	91.89 37	86.28 246	1.09 434	2.23 436	95.98 27	81.87 116	89.48 242	79.76 123	95.96 125	91.10 230

SR-MVS		92.23 10	92.34 11	91.91 17	94.89 38	87.85 10	92.51 24	93.87 51	88.20 23	93.24 42	94.02 94	90.15 16	95.67 38	86.82 38	97.34 76	92.19 197

OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 40	79.86 76	86.76 127	92.78 96	78.78 111	92.51 58	93.64 115	88.13 36	93.84 109	84.83 69	97.55 69	94.10 105
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 37	94.75 41	81.69 63	90.00 62	94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 60	98.73 7	95.23 61

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 60	98.73 7	95.23 61

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 46	94.47 43	85.95 27	86.84 123	93.91 47	80.07 93	86.75 176	93.26 121	93.64 2	90.93 200	84.60 71	90.75 280	93.97 108

reproduce-ours		92.86 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 212

our_new_method		92.86 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 212

ACMP	79.16 10	90.54 35	90.60 49	90.35 45	94.36 46	80.98 69	89.16 86	94.05 41	79.03 108	92.87 49	93.74 111	90.60 11	95.21 61	82.87 89	98.76 4	94.87 71
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS		89.18 63	88.83 72	90.23 47	94.28 47	86.11 26	85.91 141	93.60 61	80.16 91	89.13 123	93.44 118	83.82 80	90.98 198	83.86 78	95.30 153	93.60 131

test_0728_SECOND				86.79 102	94.25 48	72.45 156	90.54 52	94.10 39					95.88 18	86.42 41	97.97 46	92.02 204

reproduce_model		92.89 5	93.18 7	92.01 13	94.20 49	88.23 9	92.87 13	94.32 21	90.25 11	95.65 9	95.74 30	87.75 41	95.72 36	89.60 4	98.27 26	92.08 201

SED-MVS		90.46 37	91.64 21	86.93 99	94.18 50	72.65 146	90.47 55	93.69 56	83.77 52	94.11 26	94.27 79	90.28 14	95.84 24	86.03 51	97.92 49	92.29 191

IU-MVS					94.18 50	72.64 148		90.82 154	56.98 359	89.67 109				85.78 57	97.92 49	93.28 142

test_241102_ONE					94.18 50	72.65 146		93.69 56	83.62 54	94.11 26	93.78 108	90.28 14	95.50 49

DVP-MVS		90.06 43	91.32 32	86.29 111	94.16 53	72.56 152	90.54 52	91.01 149	83.61 55	93.75 34	94.65 61	89.76 18	95.78 32	86.42 41	97.97 46	90.55 250
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 53	72.56 152	90.63 49	93.90 48	83.61 55	93.75 34	94.49 69	89.76 18

SR-MVS-dyc-post		92.41 9	92.41 10	92.39 5	94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	88.83 26	95.51 47	87.16 34	97.60 66	92.73 164

RE-MVS-def			92.61 8		94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	90.64 10		87.16 34	97.60 66	92.73 164

MIMVSNet1		83.63 167	84.59 144	80.74 246	94.06 57	62.77 265	82.72 221	84.53 280	77.57 129	90.34 93	95.92 28	76.88 175	85.83 313	61.88 314	97.42 74	93.62 129

TranMVSNet+NR-MVSNet		87.86 81	88.76 74	85.18 137	94.02 58	64.13 248	84.38 175	91.29 140	84.88 44	92.06 65	93.84 105	86.45 58	93.73 111	73.22 210	98.66 11	97.69 9

新几何1				82.95 203	93.96 59	78.56 88		80.24 314	55.45 365	83.93 244	91.08 198	71.19 240	88.33 267	65.84 280	93.07 223	81.95 379

SteuartSystems-ACMMP		91.16 27	91.36 28	90.55 41	93.91 60	80.97 70	91.49 40	93.48 63	82.82 65	92.60 57	93.97 96	88.19 33	96.29 6	87.61 22	98.20 34	94.39 92
Skip Steuart: Steuart Systems R&D Blog.
test_part2					93.86 61	77.77 98				92.84 51

test_one_0601					93.85 62	73.27 141		94.11 38	86.57 30	93.47 41	94.64 64	88.42 28

save fliter					93.75 63	77.44 103	86.31 135	89.72 189	70.80 221

LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 22	93.73 64	85.72 34	96.79 1	95.51 9	88.86 16	95.63 10	96.99 10	84.81 72	93.16 137	91.10 2	97.53 72	96.58 28
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 11	93.68 65	86.15 24	93.37 10	95.10 13	90.28 10	92.11 63	95.03 50	89.75 20	94.93 70	79.95 121	98.27 26	95.04 67
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 64	89.08 66	89.37 62	93.64 66	79.07 83	88.54 98	94.20 30	73.53 176	89.71 107	94.82 56	85.09 68	95.77 34	84.17 75	98.03 41	93.26 144
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mvs_tets		89.78 52	89.27 63	91.30 29	93.51 67	84.79 44	89.89 68	90.63 159	70.00 231	94.55 19	96.67 14	87.94 39	93.59 120	84.27 74	95.97 124	95.52 51

HQP_MVS		87.75 84	87.43 88	88.70 75	93.45 68	76.42 116	89.45 82	93.61 59	79.44 101	86.55 181	92.95 134	74.84 191	95.22 59	80.78 113	95.83 134	94.46 85

plane_prior7					93.45 68	77.31 106

WR-MVS		83.56 170	84.40 152	81.06 242	93.43 70	54.88 351	78.67 291	85.02 271	81.24 79	90.74 90	91.56 183	72.85 221	91.08 195	68.00 263	98.04 39	97.23 16

DPE-MVS		90.53 36	91.08 37	88.88 69	93.38 71	78.65 87	89.15 87	94.05 41	84.68 45	93.90 28	94.11 91	88.13 36	96.30 5	84.51 72	97.81 55	91.70 216
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
jajsoiax		89.41 57	88.81 73	91.19 32	93.38 71	84.72 45	89.70 71	90.29 176	69.27 235	94.39 20	96.38 18	86.02 65	93.52 124	83.96 76	95.92 130	95.34 55

PS-MVSNAJss		88.31 73	87.90 81	89.56 59	93.31 73	77.96 96	87.94 105	91.97 119	70.73 222	94.19 25	96.67 14	76.94 169	94.57 81	83.07 85	96.28 108	96.15 33

test222					93.31 73	76.54 113	79.38 277	77.79 325	52.59 382	82.36 271	90.84 210	66.83 263			91.69 256	81.25 387

tt0805		88.09 77	89.79 55	82.98 201	93.26 75	63.94 251	91.10 45	89.64 192	85.07 41	90.91 86	91.09 197	89.16 24	91.87 175	82.03 100	95.87 132	93.13 149

DU-MVS		86.80 94	86.99 96	86.21 116	93.24 76	67.02 221	83.16 210	92.21 111	81.73 74	90.92 84	91.97 166	77.20 163	93.99 102	74.16 192	98.35 22	97.61 10

NR-MVSNet		86.00 108	86.22 108	85.34 135	93.24 76	64.56 244	82.21 239	90.46 164	80.99 82	88.42 138	91.97 166	77.56 158	93.85 107	72.46 220	98.65 12	97.61 10

OurMVSNet-221017-0		90.01 46	89.74 56	90.83 36	93.16 78	80.37 72	91.91 36	93.11 79	81.10 81	95.32 14	97.24 7	72.94 220	94.85 72	85.07 64	97.78 56	97.26 15

UniMVSNet (Re)		86.87 91	86.98 97	86.55 106	93.11 79	68.48 207	83.80 190	92.87 92	80.37 87	89.61 113	91.81 174	77.72 156	94.18 95	75.00 187	98.53 16	96.99 22

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 25	93.02 80	85.17 39	92.47 26	95.05 14	87.65 27	93.21 43	94.39 77	90.09 17	95.08 66	86.67 40	97.60 66	94.18 100

ACMH+	77.89 11	90.73 31	91.50 25	88.44 78	93.00 81	76.26 119	89.65 75	95.55 8	87.72 26	93.89 30	94.94 52	91.62 3	93.44 128	78.35 141	98.76 4	95.61 50

APDe-MVS		91.22 25	91.92 15	89.14 66	92.97 82	78.04 93	92.84 16	94.14 36	83.33 58	93.90 28	95.73 31	88.77 27	96.41 3	87.60 23	97.98 45	92.98 157
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
114514_t		83.10 180	82.54 188	84.77 145	92.90 83	69.10 202	86.65 129	90.62 160	54.66 371	81.46 289	90.81 211	76.98 168	94.38 87	72.62 218	96.18 114	90.82 239

testdata				79.54 265	92.87 84	72.34 157		80.14 315	59.91 338	85.47 207	91.75 178	67.96 258	85.24 317	68.57 260	92.18 244	81.06 392

CNVR-MVS		87.81 83	87.68 83	88.21 83	92.87 84	77.30 107	85.25 156	91.23 142	77.31 132	87.07 170	91.47 185	82.94 91	94.71 75	84.67 70	96.27 110	92.62 171

SF-MVS		90.27 39	90.80 46	88.68 76	92.86 86	77.09 108	91.19 44	95.74 6	81.38 78	92.28 62	93.80 106	86.89 52	94.64 78	85.52 59	97.51 73	94.30 96

UniMVSNet_NR-MVSNet		86.84 93	87.06 94	86.17 118	92.86 86	67.02 221	82.55 227	91.56 130	83.08 62	90.92 84	91.82 173	78.25 149	93.99 102	74.16 192	98.35 22	97.49 13

plane_prior1					92.83 88

原ACMM1				84.60 151	92.81 89	74.01 133		91.50 132	62.59 304	82.73 267	90.67 218	76.53 176	94.25 91	69.24 246	95.69 141	85.55 327

plane_prior6					92.61 90	76.54 113						74.84 191

APD-MVS		89.54 56	89.63 58	89.26 64	92.57 91	81.34 68	90.19 61	93.08 82	80.87 85	91.13 80	93.19 122	86.22 62	95.97 14	82.23 99	97.18 81	90.45 252
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 69	89.58 60	85.88 124	92.55 92	72.22 160	84.01 181	89.44 198	88.63 20	94.38 21	95.77 29	86.38 61	93.59 120	79.84 122	95.21 154	91.82 210

SixPastTwentyTwo		87.20 89	87.45 87	86.45 108	92.52 93	69.19 200	87.84 107	88.05 219	81.66 75	94.64 18	96.53 17	65.94 267	94.75 74	83.02 87	96.83 89	95.41 53

ACMH	76.49 14	89.34 59	91.14 35	83.96 170	92.50 94	70.36 185	89.55 77	93.84 52	81.89 73	94.70 17	95.44 40	90.69 8	88.31 268	83.33 81	98.30 25	93.20 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 232	81.68 199	75.94 316	92.46 95	47.98 391	76.70 319	81.67 304	73.45 178	84.87 220	92.82 139	74.66 196	86.51 295	61.66 317	96.85 87	93.33 139

F-COLMAP		84.97 131	83.42 168	89.63 57	92.39 96	83.40 52	88.83 92	91.92 121	73.19 188	80.18 309	89.15 250	77.04 167	93.28 133	65.82 281	92.28 240	92.21 196

test_djsdf		89.62 54	89.01 67	91.45 26	92.36 97	82.98 57	91.98 34	90.08 182	71.54 211	94.28 24	96.54 16	81.57 119	94.27 89	86.26 45	96.49 100	97.09 19

TEST9					92.34 98	79.70 78	83.94 183	90.32 171	65.41 288	84.49 227	90.97 201	82.03 111	93.63 115

train_agg		85.98 109	85.28 130	88.07 85	92.34 98	79.70 78	83.94 183	90.32 171	65.79 279	84.49 227	90.97 201	81.93 113	93.63 115	81.21 107	96.54 98	90.88 237

NCCC		87.36 87	86.87 99	88.83 70	92.32 100	78.84 86	86.58 131	91.09 147	78.77 112	84.85 221	90.89 206	80.85 127	95.29 56	81.14 108	95.32 150	92.34 187

FC-MVSNet-test		85.93 110	87.05 95	82.58 213	92.25 101	56.44 338	85.75 146	93.09 81	77.33 131	91.94 68	94.65 61	74.78 193	93.41 130	75.11 186	98.58 14	97.88 7

CDPH-MVS		86.17 107	85.54 124	88.05 86	92.25 101	75.45 125	83.85 187	92.01 117	65.91 277	86.19 191	91.75 178	83.77 82	94.98 69	77.43 158	96.71 93	93.73 123

test1111		78.53 252	78.85 246	77.56 295	92.22 103	47.49 393	82.61 223	69.24 389	72.43 198	85.28 209	94.20 85	51.91 346	90.07 231	65.36 285	96.45 103	95.11 65

ZD-MVS					92.22 103	80.48 71		91.85 123	71.22 217	90.38 92	92.98 131	86.06 64	96.11 7	81.99 102	96.75 92

pmmvs6		86.52 99	88.06 79	81.90 224	92.22 103	62.28 275	84.66 168	89.15 201	83.54 57	89.85 104	97.32 5	88.08 38	86.80 290	70.43 236	97.30 78	96.62 26

EG-PatchMatch MVS		84.08 154	84.11 158	83.98 169	92.22 103	72.61 151	82.20 241	87.02 238	72.63 197	88.86 124	91.02 199	78.52 145	91.11 194	73.41 207	91.09 267	88.21 292

test_8					92.09 107	78.87 85	83.82 188	90.31 173	65.79 279	84.36 231	90.96 203	81.93 113	93.44 128

Vis-MVSNet		86.86 92	86.58 102	87.72 89	92.09 107	77.43 104	87.35 113	92.09 115	78.87 110	84.27 238	94.05 92	78.35 148	93.65 113	80.54 117	91.58 260	92.08 201
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		86.66 97	86.82 101	86.17 118	92.05 109	66.87 224	91.21 43	88.64 207	86.30 33	89.60 114	92.59 146	69.22 251	94.91 71	73.89 199	97.89 52	96.72 24

MVSMamba_PlusPlus		87.53 86	88.86 71	83.54 187	92.03 110	62.26 276	91.49 40	92.62 100	88.07 24	88.07 147	96.17 23	72.24 229	95.79 31	84.85 68	94.16 194	92.58 173

旧先验1					91.97 111	71.77 165		81.78 303			91.84 171	73.92 204			93.65 211	83.61 354

v7n		90.13 40	90.96 42	87.65 91	91.95 112	71.06 177	89.99 64	93.05 83	86.53 31	94.29 22	96.27 20	82.69 93	94.08 100	86.25 47	97.63 63	97.82 8

NP-MVS					91.95 112	74.55 130					90.17 233

OMC-MVS		88.19 74	87.52 85	90.19 48	91.94 114	81.68 65	87.49 112	93.17 76	76.02 142	88.64 131	91.22 192	84.24 78	93.37 131	77.97 151	97.03 84	95.52 51

OPU-MVS				88.27 82	91.89 115	77.83 97	90.47 55				91.22 192	81.12 124	94.68 76	74.48 189	95.35 148	92.29 191

FIs		85.35 119	86.27 107	82.60 212	91.86 116	57.31 331	85.10 160	93.05 83	75.83 147	91.02 83	93.97 96	73.57 208	92.91 148	73.97 198	98.02 42	97.58 12

test2506		74.12 300	73.39 301	76.28 313	91.85 117	44.20 407	84.06 180	48.20 432	72.30 204	81.90 278	94.20 85	27.22 432	89.77 239	64.81 290	96.02 122	94.87 71

ECVR-MVS		78.44 253	78.63 250	77.88 291	91.85 117	48.95 387	83.68 193	69.91 385	72.30 204	84.26 239	94.20 85	51.89 347	89.82 236	63.58 300	96.02 122	94.87 71

9.14			89.29 62		91.84 119		88.80 93	95.32 12	75.14 159	91.07 81	92.89 136	87.27 47	93.78 110	83.69 80	97.55 69

MSLP-MVS++		85.00 130	86.03 112	81.90 224	91.84 119	71.56 172	86.75 128	93.02 87	75.95 145	87.12 165	89.39 244	77.98 151	89.40 249	77.46 156	94.78 174	84.75 336

h-mvs33		84.25 148	82.76 182	88.72 73	91.82 121	82.60 60	84.00 182	84.98 273	71.27 214	86.70 177	90.55 221	63.04 286	93.92 105	78.26 144	94.20 192	89.63 268

DP-MVS Recon		84.05 155	83.22 172	86.52 107	91.73 122	75.27 126	83.23 208	92.40 105	72.04 208	82.04 276	88.33 261	77.91 153	93.95 104	66.17 275	95.12 159	90.34 255

SD-MVS		88.96 67	89.88 53	86.22 115	91.63 123	77.07 109	89.82 69	93.77 53	78.90 109	92.88 48	92.29 159	86.11 63	90.22 222	86.24 48	97.24 79	91.36 225
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 80	87.40 89	89.68 55	91.59 124	83.40 52	89.50 80	95.44 10	79.47 99	88.00 150	93.03 129	82.66 94	91.47 182	70.81 228	96.14 116	94.16 101

TestCases				89.68 55	91.59 124	83.40 52		95.44 10	79.47 99	88.00 150	93.03 129	82.66 94	91.47 182	70.81 228	96.14 116	94.16 101

MCST-MVS		84.36 143	83.93 162	85.63 129	91.59 124	71.58 170	83.52 197	92.13 114	61.82 313	83.96 243	89.75 240	79.93 139	93.46 127	78.33 142	94.34 188	91.87 209

agg_prior					91.58 127	77.69 100		90.30 174		84.32 233			93.18 136

PVSNet_Blended_VisFu		81.55 209	80.49 224	84.70 149	91.58 127	73.24 142	84.21 176	91.67 129	62.86 303	80.94 295	87.16 286	67.27 260	92.87 149	69.82 242	88.94 307	87.99 298

DVP-MVS++		90.07 42	91.09 36	87.00 97	91.55 129	72.64 148	96.19 2	94.10 39	85.33 38	93.49 39	94.64 64	81.12 124	95.88 18	87.41 27	95.94 128	92.48 178

MSC_two_6792asdad				88.81 71	91.55 129	77.99 94		91.01 149					96.05 9	87.45 25	98.17 35	92.40 184

No_MVS				88.81 71	91.55 129	77.99 94		91.01 149					96.05 9	87.45 25	98.17 35	92.40 184

EPP-MVSNet		85.47 116	85.04 133	86.77 103	91.52 132	69.37 195	91.63 39	87.98 221	81.51 77	87.05 171	91.83 172	66.18 266	95.29 56	70.75 231	96.89 86	95.64 48

DeepPCF-MVS	81.24 5	87.28 88	86.21 109	90.49 42	91.48 133	84.90 42	83.41 201	92.38 107	70.25 228	89.35 119	90.68 216	82.85 92	94.57 81	79.55 128	95.95 127	92.00 205

Baseline_NR-MVSNet		84.00 158	85.90 115	78.29 283	91.47 134	53.44 361	82.29 235	87.00 241	79.06 107	89.55 115	95.72 32	77.20 163	86.14 304	72.30 221	98.51 17	95.28 58

HyFIR lowres test		75.12 289	72.66 311	82.50 216	91.44 135	65.19 239	72.47 363	87.31 227	46.79 404	80.29 305	84.30 331	52.70 343	92.10 169	51.88 380	86.73 339	90.22 256

DP-MVS		88.60 70	89.01 67	87.36 93	91.30 136	77.50 101	87.55 109	92.97 90	87.95 25	89.62 111	92.87 137	84.56 73	93.89 106	77.65 153	96.62 95	90.70 243

DeepC-MVS_fast	80.27 8	86.23 102	85.65 123	87.96 87	91.30 136	76.92 110	87.19 115	91.99 118	70.56 223	84.96 216	90.69 215	80.01 137	95.14 64	78.37 140	95.78 138	91.82 210
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 49	89.66 57	90.92 35	91.27 138	81.66 66	91.25 42	94.13 37	88.89 15	88.83 126	94.26 82	77.55 159	95.86 23	84.88 67	95.87 132	95.24 60

HQP-NCC					91.19 139		84.77 162		73.30 184	80.55 301

ACMP_Plane					91.19 139		84.77 162		73.30 184	80.55 301

HQP-MVS		84.61 136	84.06 159	86.27 112	91.19 139	70.66 180	84.77 162	92.68 98	73.30 184	80.55 301	90.17 233	72.10 230	94.61 79	77.30 160	94.47 184	93.56 134

VDD-MVS		84.23 150	84.58 145	83.20 195	91.17 142	65.16 240	83.25 206	84.97 274	79.79 95	87.18 164	94.27 79	74.77 194	90.89 203	69.24 246	96.54 98	93.55 136

K. test v3		85.14 123	84.73 138	86.37 109	91.13 143	69.63 193	85.45 152	76.68 337	84.06 50	92.44 60	96.99 10	62.03 289	94.65 77	80.58 116	93.24 219	94.83 76

lessismore_v0				85.95 121	91.10 144	70.99 178		70.91 381		91.79 69	94.42 74	61.76 290	92.93 146	79.52 130	93.03 224	93.93 110

hse-mvs2		83.47 173	81.81 198	88.47 77	91.03 145	82.27 61	82.61 223	83.69 286	71.27 214	86.70 177	86.05 304	63.04 286	92.41 158	78.26 144	93.62 213	90.71 242

TransMVSNet (Re)		84.02 157	85.74 121	78.85 271	91.00 146	55.20 350	82.29 235	87.26 228	79.65 98	88.38 140	95.52 37	83.00 90	86.88 288	67.97 264	96.60 96	94.45 87

AUN-MVS		81.18 214	78.78 247	88.39 79	90.93 147	82.14 62	82.51 229	83.67 287	64.69 294	80.29 305	85.91 307	51.07 350	92.38 159	76.29 172	93.63 212	90.65 247

PAPM_NR		83.23 176	83.19 174	83.33 191	90.90 148	65.98 232	88.19 101	90.78 155	78.13 121	80.87 297	87.92 270	73.49 211	92.42 157	70.07 239	88.40 313	91.60 219

CSCG		86.26 101	86.47 104	85.60 130	90.87 149	74.26 132	87.98 104	91.85 123	80.35 88	89.54 117	88.01 265	79.09 142	92.13 166	75.51 180	95.06 161	90.41 253

PLC	73.85 16	82.09 198	80.31 226	87.45 92	90.86 150	80.29 73	85.88 142	90.65 158	68.17 251	76.32 343	86.33 298	73.12 218	92.61 154	61.40 319	90.02 292	89.44 271
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test12				86.57 105	90.74 151	72.63 150		90.69 157		82.76 266		79.20 141	94.80 73		95.32 150	92.27 193

ITE_SJBPF				90.11 49	90.72 152	84.97 41		90.30 174	81.56 76	90.02 99	91.20 194	82.40 99	90.81 207	73.58 205	94.66 179	94.56 81

DPM-MVS		80.10 237	79.18 242	82.88 208	90.71 153	69.74 190	78.87 287	90.84 153	60.29 335	75.64 353	85.92 306	67.28 259	93.11 139	71.24 226	91.79 252	85.77 325

TAMVS		78.08 256	76.36 272	83.23 194	90.62 154	72.87 144	79.08 283	80.01 316	61.72 316	81.35 291	86.92 291	63.96 278	88.78 258	50.61 381	93.01 225	88.04 297

test_prior				86.32 110	90.59 155	71.99 164		92.85 93					94.17 97			92.80 162

ambc				82.98 201	90.55 156	64.86 241	88.20 100	89.15 201		89.40 118	93.96 99	71.67 238	91.38 188	78.83 136	96.55 97	92.71 167

SSC-MVS		77.55 261	81.64 201	65.29 390	90.46 157	20.33 437	73.56 356	68.28 391	85.44 37	88.18 146	94.64 64	70.93 241	81.33 348	71.25 225	92.03 246	94.20 97

Anonymous20231211		88.40 71	89.62 59	84.73 147	90.46 157	65.27 237	88.86 91	93.02 87	87.15 28	93.05 46	97.10 8	82.28 106	92.02 170	76.70 165	97.99 43	96.88 23

Test_1112_low_res		73.90 303	73.08 305	76.35 311	90.35 159	55.95 339	73.40 359	86.17 248	50.70 397	73.14 369	85.94 305	58.31 313	85.90 310	56.51 344	83.22 377	87.20 310

VPA-MVSNet		83.47 173	84.73 138	79.69 262	90.29 160	57.52 330	81.30 251	88.69 206	76.29 138	87.58 160	94.44 71	80.60 131	87.20 282	66.60 272	96.82 90	94.34 94

FMVSNet1		84.55 139	85.45 126	81.85 226	90.27 161	61.05 290	86.83 124	88.27 216	78.57 115	89.66 110	95.64 34	75.43 183	90.68 211	69.09 250	95.33 149	93.82 117

Anonymous20240529		86.20 104	87.13 92	83.42 189	90.19 162	64.55 245	84.55 170	90.71 156	85.85 36	89.94 103	95.24 46	82.13 109	90.40 218	69.19 249	96.40 105	95.31 57

MVS_111021_HR		84.63 135	84.34 154	85.49 134	90.18 163	75.86 123	79.23 282	87.13 233	73.35 181	85.56 205	89.34 245	83.60 85	90.50 216	76.64 166	94.05 198	90.09 262

GeoE		85.45 117	85.81 118	84.37 156	90.08 164	67.07 220	85.86 144	91.39 137	72.33 203	87.59 159	90.25 228	84.85 71	92.37 160	78.00 149	91.94 250	93.66 125

RPSCF		88.00 79	86.93 98	91.22 31	90.08 164	89.30 5	89.68 73	91.11 145	79.26 104	89.68 108	94.81 59	82.44 97	87.74 275	76.54 167	88.74 310	96.61 27

nrg030		87.85 82	88.49 75	85.91 122	90.07 166	69.73 191	87.86 106	94.20 30	74.04 168	92.70 56	94.66 60	85.88 66	91.50 181	79.72 124	97.32 77	96.50 29

AdaColmap		83.66 166	83.69 165	83.57 185	90.05 167	72.26 159	86.29 136	90.00 184	78.19 120	81.65 286	87.16 286	83.40 87	94.24 92	61.69 316	94.76 177	84.21 346

pm-mvs1		83.69 165	84.95 136	79.91 258	90.04 168	59.66 306	82.43 231	87.44 225	75.52 154	87.85 153	95.26 45	81.25 123	85.65 315	68.74 256	96.04 121	94.42 90

CHOSEN 1792x2688		72.45 315	70.56 330	78.13 285	90.02 169	63.08 260	68.72 388	83.16 290	42.99 419	75.92 349	85.46 313	57.22 322	85.18 319	49.87 385	81.67 387	86.14 320

WB-MVS		76.06 280	80.01 236	64.19 393	89.96 170	20.58 436	72.18 365	68.19 392	83.21 59	86.46 188	93.49 117	70.19 246	78.97 364	65.96 276	90.46 287	93.02 154

anonymousdsp		89.73 53	88.88 70	92.27 8	89.82 171	86.67 18	90.51 54	90.20 179	69.87 232	95.06 15	96.14 25	84.28 77	93.07 141	87.68 20	96.34 106	97.09 19

1112_ss		74.82 294	73.74 296	78.04 288	89.57 172	60.04 301	76.49 325	87.09 237	54.31 372	73.66 368	79.80 376	60.25 299	86.76 292	58.37 334	84.15 371	87.32 308

CS-MVS		88.14 75	87.67 84	89.54 60	89.56 173	79.18 82	90.47 55	94.77 16	79.37 103	84.32 233	89.33 246	83.87 79	94.53 84	82.45 95	94.89 169	94.90 69

MM		87.64 85	87.15 91	89.09 67	89.51 174	76.39 118	88.68 96	86.76 242	84.54 46	83.58 251	93.78 108	73.36 215	96.48 2	87.98 14	96.21 112	94.41 91

APD_test1		88.40 71	87.91 80	89.88 51	89.50 175	86.65 20	89.98 65	91.91 122	84.26 47	90.87 89	93.92 103	82.18 108	89.29 250	73.75 202	94.81 173	93.70 124

SPE-MVS-test		87.00 90	86.43 105	88.71 74	89.46 176	77.46 102	89.42 84	95.73 7	77.87 125	81.64 287	87.25 284	82.43 98	94.53 84	77.65 153	96.46 102	94.14 103

PCF-MVS	74.62 15	82.15 197	80.92 218	85.84 125	89.43 177	72.30 158	80.53 261	91.82 125	57.36 355	87.81 154	89.92 237	77.67 157	93.63 115	58.69 332	95.08 160	91.58 220
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 283	73.51 300	82.71 210	89.35 178	73.62 135	80.06 265	85.20 265	60.30 334	73.96 365	87.94 267	57.89 318	89.45 245	52.02 375	74.87 414	85.06 333
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		83.55 171	83.10 177	84.90 140	89.34 179	83.87 50	84.54 172	88.77 204	79.09 106	83.54 253	88.66 258	74.87 190	81.73 346	66.84 269	92.29 239	89.11 278

EC-MVSNet		88.01 78	88.32 77	87.09 95	89.28 180	72.03 163	90.31 59	96.31 4	80.88 84	85.12 212	89.67 241	84.47 75	95.46 50	82.56 94	96.26 111	93.77 122

TSAR-MVS + GP.		83.95 159	82.69 184	87.72 89	89.27 181	81.45 67	83.72 192	81.58 306	74.73 162	85.66 201	86.06 303	72.56 226	92.69 152	75.44 182	95.21 154	89.01 285

MVS_111021_LR		84.28 147	83.76 164	85.83 126	89.23 182	83.07 55	80.99 255	83.56 288	72.71 196	86.07 194	89.07 251	81.75 118	86.19 302	77.11 162	93.36 214	88.24 291

LFMVS		80.15 236	80.56 222	78.89 270	89.19 183	55.93 340	85.22 157	73.78 357	82.96 63	84.28 237	92.72 144	57.38 320	90.07 231	63.80 299	95.75 139	90.68 244

CLD-MVS		83.18 177	82.64 185	84.79 144	89.05 184	67.82 215	77.93 299	92.52 103	68.33 248	85.07 213	81.54 362	82.06 110	92.96 144	69.35 245	97.91 51	93.57 133
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 51	91.38 28	89.03 185	84.23 49	93.58 6	94.68 17	90.65 8	90.33 94	93.95 101	84.50 74	95.37 54	80.87 111	95.50 145	94.53 84

CDS-MVSNet		77.32 264	75.40 282	83.06 198	89.00 186	72.48 155	77.90 300	82.17 300	60.81 329	78.94 321	83.49 339	59.30 306	88.76 259	54.64 361	92.37 236	87.93 300
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tttt0517		81.07 215	79.58 238	85.52 132	88.99 187	66.45 228	87.03 119	75.51 345	73.76 172	88.32 142	90.20 229	37.96 407	94.16 99	79.36 132	95.13 157	95.93 42

balanced_conf03		84.80 132	85.40 127	83.00 200	88.95 188	61.44 283	90.42 58	92.37 108	71.48 213	88.72 130	93.13 125	70.16 247	95.15 63	79.26 133	94.11 195	92.41 182

testing3-2		70.72 333	70.97 326	69.95 358	88.93 189	34.80 428	69.85 383	66.59 402	78.42 117	77.58 336	85.55 309	31.83 419	82.08 343	46.28 401	93.73 208	92.98 157

tfpnnormal		81.79 207	82.95 179	78.31 281	88.93 189	55.40 346	80.83 259	82.85 294	76.81 135	85.90 199	94.14 89	74.58 197	86.51 295	66.82 270	95.68 142	93.01 155

testing3		71.53 325	70.79 327	73.77 331	88.89 191	41.86 414	76.60 324	59.12 421	72.83 193	80.97 293	82.08 356	19.80 438	87.33 281	65.12 287	91.68 257	92.13 200

Vis-MVSNet (Re-imp)		77.82 258	77.79 259	77.92 290	88.82 192	51.29 378	83.28 204	71.97 373	74.04 168	82.23 273	89.78 239	57.38 320	89.41 248	57.22 341	95.41 146	93.05 153

SDMVSNet		81.90 206	83.17 175	78.10 286	88.81 193	62.45 271	76.08 332	86.05 252	73.67 173	83.41 254	93.04 127	82.35 100	80.65 353	70.06 240	95.03 162	91.21 227

sd_testset		79.95 240	81.39 210	75.64 319	88.81 193	58.07 324	76.16 331	82.81 295	73.67 173	83.41 254	93.04 127	80.96 126	77.65 369	58.62 333	95.03 162	91.21 227

TAPA-MVS	77.73 12	85.71 113	84.83 137	88.37 80	88.78 195	79.72 77	87.15 117	93.50 62	69.17 236	85.80 200	89.56 242	80.76 128	92.13 166	73.21 215	95.51 144	93.25 145
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
testf1		89.30 60	89.12 64	89.84 52	88.67 196	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 246	74.12 194	96.10 119	94.45 87

APD_test2		89.30 60	89.12 64	89.84 52	88.67 196	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 246	74.12 194	96.10 119	94.45 87

GDP-MVS		82.17 195	80.85 220	86.15 120	88.65 198	68.95 203	85.65 149	93.02 87	68.42 246	83.73 247	89.54 243	45.07 386	94.31 88	79.66 126	93.87 202	95.19 63

FPMVS		72.29 318	72.00 317	73.14 335	88.63 199	85.00 40	74.65 347	67.39 395	71.94 210	77.80 332	87.66 275	50.48 354	75.83 376	49.95 383	79.51 397	58.58 426

dcpmvs_2		84.23 150	85.14 131	81.50 234	88.61 200	61.98 280	82.90 218	93.11 79	68.66 244	92.77 54	92.39 152	78.50 146	87.63 277	76.99 164	92.30 237	94.90 69

ETV-MVS		84.31 145	83.91 163	85.52 132	88.58 201	70.40 183	84.50 174	93.37 64	78.76 113	84.07 241	78.72 387	80.39 132	95.13 65	73.82 201	92.98 226	91.04 231

BH-untuned		80.96 217	80.99 216	80.84 245	88.55 202	68.23 208	80.33 264	88.46 209	72.79 195	86.55 181	86.76 292	74.72 195	91.77 178	61.79 315	88.99 305	82.52 372

Anonymous202405211		80.51 224	81.19 215	78.49 278	88.48 203	57.26 332	76.63 321	82.49 297	81.21 80	84.30 236	92.24 162	67.99 257	86.24 299	62.22 309	95.13 157	91.98 207

ab-mvs		79.67 241	80.56 222	76.99 301	88.48 203	56.93 334	84.70 167	86.06 251	68.95 240	80.78 298	93.08 126	75.30 185	84.62 323	56.78 342	90.90 274	89.43 272

PHI-MVS		86.38 100	85.81 118	88.08 84	88.44 205	77.34 105	89.35 85	93.05 83	73.15 189	84.76 222	87.70 274	78.87 144	94.18 95	80.67 115	96.29 107	92.73 164

xiu_mvs_v1_base_debu		80.84 218	80.14 232	82.93 205	88.31 206	71.73 166	79.53 273	87.17 230	65.43 285	79.59 311	82.73 350	76.94 169	90.14 227	73.22 210	88.33 315	86.90 313

xiu_mvs_v1_base		80.84 218	80.14 232	82.93 205	88.31 206	71.73 166	79.53 273	87.17 230	65.43 285	79.59 311	82.73 350	76.94 169	90.14 227	73.22 210	88.33 315	86.90 313

xiu_mvs_v1_base_debi		80.84 218	80.14 232	82.93 205	88.31 206	71.73 166	79.53 273	87.17 230	65.43 285	79.59 311	82.73 350	76.94 169	90.14 227	73.22 210	88.33 315	86.90 313

MG-MVS		80.32 230	80.94 217	78.47 279	88.18 209	52.62 368	82.29 235	85.01 272	72.01 209	79.24 318	92.54 149	69.36 250	93.36 132	70.65 233	89.19 303	89.45 270

PM-MVS		80.20 234	79.00 243	83.78 176	88.17 210	86.66 19	81.31 249	66.81 401	69.64 233	88.33 141	90.19 230	64.58 272	83.63 335	71.99 223	90.03 291	81.06 392

v10		86.54 98	87.10 93	84.84 141	88.16 211	63.28 258	86.64 130	92.20 112	75.42 156	92.81 53	94.50 68	74.05 203	94.06 101	83.88 77	96.28 108	97.17 18

mvsmamba		80.30 231	78.87 244	84.58 152	88.12 212	67.55 216	92.35 29	84.88 275	63.15 301	85.33 208	90.91 205	50.71 352	95.20 62	66.36 273	87.98 322	90.99 232

sasdasda		85.50 114	86.14 110	83.58 183	87.97 213	67.13 218	87.55 109	94.32 21	73.44 179	88.47 136	87.54 277	86.45 58	91.06 196	75.76 178	93.76 204	92.54 176

canonicalmvs		85.50 114	86.14 110	83.58 183	87.97 213	67.13 218	87.55 109	94.32 21	73.44 179	88.47 136	87.54 277	86.45 58	91.06 196	75.76 178	93.76 204	92.54 176

EIA-MVS		82.19 194	81.23 214	85.10 138	87.95 215	69.17 201	83.22 209	93.33 67	70.42 224	78.58 324	79.77 378	77.29 162	94.20 94	71.51 224	88.96 306	91.93 208

fmvsm_s_conf0.5_n_5		84.56 138	84.71 141	84.11 167	87.92 216	72.09 162	84.80 161	88.64 207	64.43 295	88.77 127	91.78 176	78.07 150	87.95 272	85.85 56	92.18 244	92.30 189

VNet		79.31 242	80.27 227	76.44 310	87.92 216	53.95 357	75.58 338	84.35 282	74.39 166	82.23 273	90.72 213	72.84 222	84.39 327	60.38 325	93.98 199	90.97 233

BP-MVS1		82.81 182	81.67 200	86.23 113	87.88 218	68.53 206	86.06 140	84.36 281	75.65 150	85.14 211	90.19 230	45.84 375	94.42 86	85.18 62	94.72 178	95.75 44

v8		86.22 103	86.83 100	84.36 158	87.82 219	62.35 274	86.42 134	91.33 139	76.78 136	92.73 55	94.48 70	73.41 212	93.72 112	83.10 84	95.41 146	97.01 21

alignmvs		83.94 160	83.98 161	83.80 174	87.80 220	67.88 214	84.54 172	91.42 136	73.27 187	88.41 139	87.96 266	72.33 227	90.83 206	76.02 176	94.11 195	92.69 168

fmvsm_s_conf0.5_n_6		84.05 155	84.14 157	83.81 173	87.75 221	71.17 175	83.42 200	91.10 146	67.90 258	84.53 225	90.70 214	73.01 219	88.73 260	85.09 63	93.72 209	91.53 222

v1192		84.57 137	84.69 143	84.21 164	87.75 221	62.88 262	83.02 213	91.43 134	69.08 238	89.98 102	90.89 206	72.70 224	93.62 118	82.41 96	94.97 166	96.13 34

PatchMatch-RL		74.48 297	73.22 304	78.27 284	87.70 223	85.26 38	75.92 334	70.09 383	64.34 296	76.09 347	81.25 364	65.87 268	78.07 368	53.86 363	83.82 373	71.48 412

fmvsm_s_conf0.1_n_a		82.58 187	81.93 196	84.50 153	87.68 224	73.35 138	86.14 139	77.70 326	61.64 318	85.02 214	91.62 180	77.75 154	86.24 299	82.79 91	87.07 333	93.91 112

v1144		84.54 140	84.72 140	84.00 168	87.67 225	62.55 269	82.97 215	90.93 152	70.32 227	89.80 105	90.99 200	73.50 209	93.48 126	81.69 106	94.65 180	95.97 39

v1240		84.30 146	84.51 149	83.65 180	87.65 226	61.26 287	82.85 219	91.54 131	67.94 256	90.68 91	90.65 219	71.71 237	93.64 114	82.84 90	94.78 174	96.07 36

v1921920		84.23 150	84.37 153	83.79 175	87.64 227	61.71 281	82.91 217	91.20 143	67.94 256	90.06 97	90.34 225	72.04 233	93.59 120	82.32 97	94.91 167	96.07 36

v144192		84.24 149	84.41 151	83.71 179	87.59 228	61.57 282	82.95 216	91.03 148	67.82 260	89.80 105	90.49 222	73.28 216	93.51 125	81.88 105	94.89 169	96.04 38

MGCFI-Net		85.04 127	85.95 113	82.31 219	87.52 229	63.59 254	86.23 138	93.96 44	73.46 177	88.07 147	87.83 272	86.46 57	90.87 205	76.17 173	93.89 201	92.47 180

Fast-Effi-MVS+		81.04 216	80.57 221	82.46 217	87.50 230	63.22 259	78.37 295	89.63 193	68.01 253	81.87 279	82.08 356	82.31 102	92.65 153	67.10 266	88.30 319	91.51 223

fmvsm_l_conf0.5_n_3		85.11 126	84.96 135	85.56 131	87.49 231	75.69 124	84.71 166	90.61 161	67.64 261	84.88 219	92.05 164	82.30 103	88.36 266	83.84 79	91.10 266	92.62 171

pmmvs-eth3d		78.42 254	77.04 266	82.57 215	87.44 232	74.41 131	80.86 258	79.67 317	55.68 364	84.69 223	90.31 227	60.91 294	85.42 316	62.20 310	91.59 259	87.88 301

IterMVS-LS		84.73 134	84.98 134	83.96 170	87.35 233	63.66 252	83.25 206	89.88 187	76.06 140	89.62 111	92.37 156	73.40 214	92.52 155	78.16 146	94.77 176	95.69 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres100view900		75.45 285	75.05 286	76.66 308	87.27 234	51.88 373	81.07 254	73.26 362	75.68 149	83.25 257	86.37 297	45.54 377	88.80 255	51.98 376	90.99 269	89.31 274

fmvsm_s_conf0.5_n_4		84.38 142	84.27 155	84.74 146	87.25 235	70.84 179	83.55 196	88.45 210	68.64 245	86.29 190	91.31 190	74.97 189	88.42 264	87.87 16	90.07 290	94.95 68

MIMVSNet		71.09 329	71.59 320	69.57 363	87.23 236	50.07 385	78.91 285	71.83 374	60.20 337	71.26 378	91.76 177	55.08 336	76.09 374	41.06 413	87.02 336	82.54 371

Effi-MVS+		83.90 161	84.01 160	83.57 185	87.22 237	65.61 236	86.55 132	92.40 105	78.64 114	81.34 292	84.18 333	83.65 84	92.93 146	74.22 191	87.87 324	92.17 198

BH-RMVSNet		80.53 223	80.22 230	81.49 235	87.19 238	66.21 230	77.79 302	86.23 247	74.21 167	83.69 248	88.50 259	73.25 217	90.75 208	63.18 305	87.90 323	87.52 305

thisisatest0530		79.07 243	77.33 263	84.26 163	87.13 239	64.58 243	83.66 194	75.95 340	68.86 241	85.22 210	87.36 282	38.10 404	93.57 123	75.47 181	94.28 190	94.62 79

Effi-MVS+-dtu		85.82 112	83.38 169	93.14 4	87.13 239	91.15 3	87.70 108	88.42 211	74.57 164	83.56 252	85.65 308	78.49 147	94.21 93	72.04 222	92.88 228	94.05 106

v2v482		84.09 153	84.24 156	83.62 181	87.13 239	61.40 284	82.71 222	89.71 190	72.19 206	89.55 115	91.41 186	70.70 243	93.20 135	81.02 109	93.76 204	96.25 32

jason		77.42 263	75.75 278	82.43 218	87.10 242	69.27 196	77.99 298	81.94 302	51.47 391	77.84 330	85.07 323	60.32 298	89.00 252	70.74 232	89.27 302	89.03 283
jason: jason.
PS-MVSNAJ		77.04 267	76.53 271	78.56 276	87.09 243	61.40 284	75.26 341	87.13 233	61.25 324	74.38 364	77.22 400	76.94 169	90.94 199	64.63 293	84.83 366	83.35 359

casdiffmvs_mvg		86.72 95	87.51 86	84.36 158	87.09 243	65.22 238	84.16 177	94.23 27	77.89 123	91.28 79	93.66 114	84.35 76	92.71 150	80.07 118	94.87 172	95.16 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
xiu_mvs_v2_base		77.19 265	76.75 269	78.52 277	87.01 245	61.30 286	75.55 339	87.12 236	61.24 325	74.45 362	78.79 386	77.20 163	90.93 200	64.62 294	84.80 367	83.32 360

thres600view7		75.97 281	75.35 284	77.85 293	87.01 245	51.84 374	80.45 262	73.26 362	75.20 158	83.10 260	86.31 300	45.54 377	89.05 251	55.03 358	92.24 241	92.66 169

fmvsm_s_conf0.5_n_7		82.04 200	82.05 194	82.01 222	86.98 247	71.07 176	78.70 289	89.45 197	68.07 252	78.14 326	91.61 181	74.19 200	85.92 307	79.61 127	91.73 255	89.05 282

fmvsm_s_conf0.5_n_3		86.19 105	87.27 90	82.95 203	86.91 248	70.38 184	85.31 155	92.61 101	75.59 152	88.32 142	92.87 137	82.22 107	88.63 262	88.80 8	92.82 230	89.83 266

CL-MVSNet_self_test		76.81 270	77.38 262	75.12 322	86.90 249	51.34 376	73.20 360	80.63 313	68.30 249	81.80 283	88.40 260	66.92 262	80.90 350	55.35 355	94.90 168	93.12 151

BH-w/o		76.57 274	76.07 276	78.10 286	86.88 250	65.92 233	77.63 304	86.33 245	65.69 283	80.89 296	79.95 375	68.97 254	90.74 209	53.01 371	85.25 355	77.62 403

fmvsm_s_conf0.1_n		82.17 195	81.59 204	83.94 172	86.87 251	71.57 171	85.19 158	77.42 329	62.27 312	84.47 229	91.33 188	76.43 177	85.91 309	83.14 82	87.14 331	94.33 95

MAR-MVS		80.24 233	78.74 249	84.73 147	86.87 251	78.18 92	85.75 146	87.81 222	65.67 284	77.84 330	78.50 388	73.79 206	90.53 215	61.59 318	90.87 276	85.49 329
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		82.21 193	81.51 208	84.32 161	86.56 253	73.35 138	85.46 151	77.30 330	61.81 314	84.51 226	90.88 208	77.36 161	86.21 301	82.72 92	86.97 338	93.38 137

FE-MVS		79.98 239	78.86 245	83.36 190	86.47 254	66.45 228	89.73 70	84.74 279	72.80 194	84.22 240	91.38 187	44.95 387	93.60 119	63.93 297	91.50 261	90.04 263

QAPM		82.59 186	82.59 187	82.58 213	86.44 255	66.69 225	89.94 67	90.36 169	67.97 255	84.94 218	92.58 148	72.71 223	92.18 165	70.63 234	87.73 326	88.85 286

PAPM		71.77 321	70.06 337	76.92 303	86.39 256	53.97 356	76.62 322	86.62 243	53.44 376	63.97 416	84.73 327	57.79 319	92.34 161	39.65 416	81.33 391	84.45 340

GBi-Net		82.02 201	82.07 192	81.85 226	86.38 257	61.05 290	86.83 124	88.27 216	72.43 198	86.00 195	95.64 34	63.78 279	90.68 211	65.95 277	93.34 215	93.82 117

test1		82.02 201	82.07 192	81.85 226	86.38 257	61.05 290	86.83 124	88.27 216	72.43 198	86.00 195	95.64 34	63.78 279	90.68 211	65.95 277	93.34 215	93.82 117

FMVSNet2		81.31 212	81.61 203	80.41 252	86.38 257	58.75 320	83.93 185	86.58 244	72.43 198	87.65 158	92.98 131	63.78 279	90.22 222	66.86 267	93.92 200	92.27 193

3Dnovator	80.37 7	84.80 132	84.71 141	85.06 139	86.36 260	74.71 128	88.77 94	90.00 184	75.65 150	84.96 216	93.17 123	74.06 202	91.19 191	78.28 143	91.09 267	89.29 276

Anonymous20231206		71.38 327	71.88 318	69.88 359	86.31 261	54.37 353	70.39 379	74.62 348	52.57 383	76.73 339	88.76 254	59.94 301	72.06 386	44.35 408	93.23 220	83.23 362

baseline		85.20 122	85.93 114	83.02 199	86.30 262	62.37 273	84.55 170	93.96 44	74.48 165	87.12 165	92.03 165	82.30 103	91.94 171	78.39 139	94.21 191	94.74 78

API-MVS		82.28 191	82.61 186	81.30 236	86.29 263	69.79 189	88.71 95	87.67 223	78.42 117	82.15 275	84.15 334	77.98 151	91.59 180	65.39 284	92.75 231	82.51 373

tfpn200view9		74.86 293	74.23 293	76.74 307	86.24 264	52.12 370	79.24 280	73.87 355	73.34 182	81.82 281	84.60 329	46.02 370	88.80 255	51.98 376	90.99 269	89.31 274

thres400		75.14 287	74.23 293	77.86 292	86.24 264	52.12 370	79.24 280	73.87 355	73.34 182	81.82 281	84.60 329	46.02 370	88.80 255	51.98 376	90.99 269	92.66 169

UGNet		82.78 183	81.64 201	86.21 116	86.20 266	76.24 120	86.86 122	85.68 258	77.07 134	73.76 367	92.82 139	69.64 248	91.82 177	69.04 252	93.69 210	90.56 249
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		83.79 164	82.85 181	86.63 104	86.17 267	72.21 161	83.76 191	91.43 134	77.24 133	74.39 363	87.45 280	75.36 184	95.42 52	77.03 163	92.83 229	92.25 195

casdiffmvs		85.21 121	85.85 117	83.31 192	86.17 267	62.77 265	83.03 212	93.93 46	74.69 163	88.21 144	92.68 145	82.29 105	91.89 174	77.87 152	93.75 207	95.27 59
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)		83.13 179	83.02 178	83.43 188	86.16 269	66.08 231	88.00 103	88.36 213	75.55 153	85.02 214	92.75 143	65.12 271	92.50 156	74.94 188	91.30 264	91.72 214

TR-MVS		76.77 271	75.79 277	79.72 261	86.10 270	65.79 234	77.14 312	83.02 292	65.20 291	81.40 290	82.10 354	66.30 264	90.73 210	55.57 352	85.27 354	82.65 367

fmvsm_s_conf0.5_n		81.91 205	81.30 211	83.75 177	86.02 271	71.56 172	84.73 165	77.11 333	62.44 309	84.00 242	90.68 216	76.42 178	85.89 311	83.14 82	87.11 332	93.81 120

fmvsm_s_conf0.1_n_2		83.82 162	83.49 166	84.84 141	85.99 272	70.19 187	80.93 256	87.58 224	67.26 267	87.94 152	92.37 156	71.40 239	88.01 270	86.03 51	91.87 251	96.31 31

test_fmvsmconf0.01_n		86.68 96	86.52 103	87.18 94	85.94 273	78.30 89	86.93 120	92.20 112	65.94 275	89.16 121	93.16 124	83.10 89	89.89 235	87.81 17	94.43 186	93.35 138

LCM-MVSNet-Re		83.48 172	85.06 132	78.75 273	85.94 273	55.75 344	80.05 266	94.27 24	76.47 137	96.09 6	94.54 67	83.31 88	89.75 241	59.95 327	94.89 169	90.75 240

test_fmvsmvis_n_1920		85.22 120	85.36 129	84.81 143	85.80 275	76.13 122	85.15 159	92.32 109	61.40 320	91.33 76	90.85 209	83.76 83	86.16 303	84.31 73	93.28 218	92.15 199

Fast-Effi-MVS+-dtu		82.54 188	81.41 209	85.90 123	85.60 276	76.53 115	83.07 211	89.62 194	73.02 191	79.11 319	83.51 338	80.74 129	90.24 221	68.76 255	89.29 300	90.94 234

v148		82.31 190	82.48 189	81.81 229	85.59 277	59.66 306	81.47 248	86.02 253	72.85 192	88.05 149	90.65 219	70.73 242	90.91 202	75.15 185	91.79 252	94.87 71

MVSFormer		82.23 192	81.57 206	84.19 166	85.54 278	69.26 197	91.98 34	90.08 182	71.54 211	76.23 344	85.07 323	58.69 311	94.27 89	86.26 45	88.77 308	89.03 283

lupinMVS		76.37 278	74.46 291	82.09 220	85.54 278	69.26 197	76.79 317	80.77 312	50.68 398	76.23 344	82.82 348	58.69 311	88.94 253	69.85 241	88.77 308	88.07 294

fmvsm_s_conf0.5_n_2		83.62 168	83.29 171	84.62 150	85.43 280	70.18 188	80.61 260	87.24 229	67.14 268	87.79 155	91.87 168	71.79 236	87.98 271	86.00 55	91.77 254	95.71 45

TinyColmap		81.25 213	82.34 191	77.99 289	85.33 281	60.68 297	82.32 234	88.33 214	71.26 216	86.97 172	92.22 163	77.10 166	86.98 286	62.37 308	95.17 156	86.31 319

MVS_0304		85.37 118	84.58 145	87.75 88	85.28 282	73.36 137	86.54 133	85.71 257	77.56 130	81.78 285	92.47 151	70.29 245	96.02 11	85.59 58	95.96 125	93.87 114

test_fmvsmconf0.1_n		86.18 106	85.88 116	87.08 96	85.26 283	78.25 90	85.82 145	91.82 125	65.33 289	88.55 133	92.35 158	82.62 96	89.80 237	86.87 37	94.32 189	93.18 148

test_fmvsm_n_1920		83.60 169	82.89 180	85.74 127	85.22 284	77.74 99	84.12 179	90.48 163	59.87 339	86.45 189	91.12 196	75.65 181	85.89 311	82.28 98	90.87 276	93.58 132

PAPR		78.84 247	78.10 257	81.07 241	85.17 285	60.22 300	82.21 239	90.57 162	62.51 305	75.32 357	84.61 328	74.99 188	92.30 163	59.48 330	88.04 321	90.68 244

RRT-MVS		82.97 181	83.44 167	81.57 233	85.06 286	58.04 325	87.20 114	90.37 168	77.88 124	88.59 132	93.70 113	63.17 283	93.05 142	76.49 168	88.47 312	93.62 129

pmmvs4		74.92 292	72.98 307	80.73 247	84.95 287	71.71 169	76.23 329	77.59 327	52.83 381	77.73 334	86.38 296	56.35 327	84.97 320	57.72 340	87.05 334	85.51 328

baseline1		73.26 308	73.54 299	72.43 344	84.92 288	47.79 392	79.89 269	74.00 353	65.93 276	78.81 322	86.28 301	56.36 326	81.63 347	56.63 343	79.04 403	87.87 302

Patchmatch-RL test		74.48 297	73.68 297	76.89 305	84.83 289	66.54 226	72.29 364	69.16 390	57.70 351	86.76 175	86.33 298	45.79 376	82.59 339	69.63 243	90.65 285	81.54 383

patch_mono-2		78.89 245	79.39 240	77.41 298	84.78 290	68.11 211	75.60 336	83.11 291	60.96 328	79.36 315	89.89 238	75.18 186	72.97 384	73.32 209	92.30 237	91.15 229

test_fmvsmconf_n		85.88 111	85.51 125	86.99 98	84.77 291	78.21 91	85.40 154	91.39 137	65.32 290	87.72 157	91.81 174	82.33 101	89.78 238	86.68 39	94.20 192	92.99 156

KD-MVS_self_test		81.93 204	83.14 176	78.30 282	84.75 292	52.75 365	80.37 263	89.42 199	70.24 229	90.26 95	93.39 119	74.55 198	86.77 291	68.61 258	96.64 94	95.38 54

mmtdpeth		85.13 124	85.78 120	83.17 197	84.65 293	74.71 128	85.87 143	90.35 170	77.94 122	83.82 245	96.96 12	77.75 154	80.03 359	78.44 138	96.21 112	94.79 77

XXY-MVS		74.44 299	76.19 274	69.21 365	84.61 294	52.43 369	71.70 368	77.18 332	60.73 331	80.60 299	90.96 203	75.44 182	69.35 396	56.13 347	88.33 315	85.86 324

cascas		76.29 279	74.81 287	80.72 248	84.47 295	62.94 261	73.89 354	87.34 226	55.94 362	75.16 359	76.53 405	63.97 277	91.16 192	65.00 288	90.97 272	88.06 296

PVSNet_BlendedMVS		78.80 248	77.84 258	81.65 232	84.43 296	63.41 255	79.49 276	90.44 165	61.70 317	75.43 354	87.07 289	69.11 252	91.44 184	60.68 323	92.24 241	90.11 261

PVSNet_Blended		76.49 276	75.40 282	79.76 260	84.43 296	63.41 255	75.14 342	90.44 165	57.36 355	75.43 354	78.30 389	69.11 252	91.44 184	60.68 323	87.70 327	84.42 341

OpenMVS	76.72 13	81.98 203	82.00 195	81.93 223	84.42 298	68.22 209	88.50 99	89.48 196	66.92 270	81.80 283	91.86 169	72.59 225	90.16 224	71.19 227	91.25 265	87.40 307

OpenMVS_ROB	70.19 17	77.77 260	77.46 260	78.71 274	84.39 299	61.15 288	81.18 253	82.52 296	62.45 308	83.34 256	87.37 281	66.20 265	88.66 261	64.69 292	85.02 360	86.32 318

test_yl		78.71 250	78.51 252	79.32 267	84.32 300	58.84 317	78.38 293	85.33 263	75.99 143	82.49 268	86.57 294	58.01 314	90.02 233	62.74 306	92.73 232	89.10 279

DCV-MVSNet		78.71 250	78.51 252	79.32 267	84.32 300	58.84 317	78.38 293	85.33 263	75.99 143	82.49 268	86.57 294	58.01 314	90.02 233	62.74 306	92.73 232	89.10 279

DELS-MVS		81.44 211	81.25 212	82.03 221	84.27 302	62.87 263	76.47 326	92.49 104	70.97 220	81.64 287	83.83 335	75.03 187	92.70 151	74.29 190	92.22 243	90.51 251
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		84.44 141	86.33 106	78.78 272	84.20 303	73.57 136	89.55 77	90.44 165	84.24 48	84.38 230	94.89 53	76.35 180	80.40 356	76.14 174	96.80 91	82.36 374
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UWE-MVS		66.43 366	65.56 372	69.05 366	84.15 304	40.98 416	73.06 362	64.71 408	54.84 369	76.18 346	79.62 379	29.21 425	80.50 355	38.54 420	89.75 295	85.66 326

SSC-MVS3.2		73.90 303	75.67 280	68.61 373	84.11 305	41.28 415	64.17 406	72.83 365	72.09 207	79.08 320	87.94 267	70.31 244	73.89 383	55.99 348	94.49 183	90.67 246

EI-MVSNet-Vis-set		85.12 125	84.53 148	86.88 100	84.01 306	72.76 145	83.91 186	85.18 266	80.44 86	88.75 128	85.49 312	80.08 136	91.92 172	82.02 101	90.85 278	95.97 39

fmvsm_l_conf0.5_n		82.06 199	81.54 207	83.60 182	83.94 307	73.90 134	83.35 203	86.10 249	58.97 341	83.80 246	90.36 224	74.23 199	86.94 287	82.90 88	90.22 288	89.94 264

IterMVS-SCA-FT		80.64 222	79.41 239	84.34 160	83.93 308	69.66 192	76.28 328	81.09 309	72.43 198	86.47 187	90.19 230	60.46 296	93.15 138	77.45 157	86.39 344	90.22 256

MSDG		80.06 238	79.99 237	80.25 254	83.91 309	68.04 213	77.51 307	89.19 200	77.65 127	81.94 277	83.45 340	76.37 179	86.31 298	63.31 304	86.59 341	86.41 317

EI-MVSNet-UG-set		85.04 127	84.44 150	86.85 101	83.87 310	72.52 154	83.82 188	85.15 267	80.27 90	88.75 128	85.45 314	79.95 138	91.90 173	81.92 104	90.80 279	96.13 34

testing91		69.94 343	68.99 348	72.80 338	83.81 311	45.89 400	71.57 370	73.64 360	68.24 250	70.77 384	77.82 391	34.37 412	84.44 326	53.64 365	87.00 337	88.07 294

fmvsm_l_conf0.5_n_a		81.46 210	80.87 219	83.25 193	83.73 312	73.21 143	83.00 214	85.59 260	58.22 347	82.96 262	90.09 235	72.30 228	86.65 293	81.97 103	89.95 293	89.88 265

UBG		64.34 378	63.35 380	67.30 379	83.50 313	40.53 417	67.46 394	65.02 407	54.77 370	67.54 401	74.47 412	32.99 416	78.50 367	40.82 414	83.58 374	82.88 366

thres200		72.34 317	71.55 323	74.70 327	83.48 314	51.60 375	75.02 343	73.71 358	70.14 230	78.56 325	80.57 369	46.20 368	88.20 269	46.99 399	89.29 300	84.32 342

USDC		76.63 273	76.73 270	76.34 312	83.46 315	57.20 333	80.02 267	88.04 220	52.14 387	83.65 249	91.25 191	63.24 282	86.65 293	54.66 360	94.11 195	85.17 331

ETVMVS		64.67 375	63.34 381	68.64 370	83.44 316	41.89 413	69.56 386	61.70 417	61.33 323	68.74 393	75.76 408	28.76 426	79.35 360	34.65 425	86.16 348	84.67 337

myMVS_eth3d28		65.83 371	65.85 367	65.78 386	83.42 317	35.71 426	67.29 396	68.01 393	67.58 262	69.80 389	77.72 394	32.29 417	74.30 382	37.49 422	89.06 304	87.32 308

testing222		66.93 360	65.30 373	71.81 348	83.38 318	45.83 401	72.06 366	67.50 394	64.12 297	69.68 390	76.37 406	27.34 431	83.00 337	38.88 417	88.38 314	86.62 316

testing11		67.38 358	65.93 366	71.73 349	83.37 319	46.60 397	70.95 375	69.40 387	62.47 307	66.14 403	76.66 403	31.22 420	84.10 330	49.10 389	84.10 372	84.49 338

HY-MVS	64.64 18	73.03 311	72.47 315	74.71 326	83.36 320	54.19 355	82.14 242	81.96 301	56.76 361	69.57 391	86.21 302	60.03 300	84.83 322	49.58 387	82.65 383	85.11 332

WBMVS		68.76 353	68.43 353	69.75 361	83.29 321	40.30 418	67.36 395	72.21 371	57.09 358	77.05 338	85.53 311	33.68 414	80.51 354	48.79 391	90.90 274	88.45 290

testing99		69.27 349	68.15 356	72.63 340	83.29 321	45.45 402	71.15 372	71.08 379	67.34 265	70.43 385	77.77 393	32.24 418	84.35 328	53.72 364	86.33 345	88.10 293

EI-MVSNet		82.61 185	82.42 190	83.20 195	83.25 323	63.66 252	83.50 198	85.07 268	76.06 140	86.55 181	85.10 320	73.41 212	90.25 219	78.15 148	90.67 282	95.68 47

CVMVSNet		72.62 314	71.41 324	76.28 313	83.25 323	60.34 299	83.50 198	79.02 321	37.77 429	76.33 342	85.10 320	49.60 358	87.41 279	70.54 235	77.54 409	81.08 390

WB-MVSnew		68.72 354	69.01 347	67.85 375	83.22 325	43.98 408	74.93 344	65.98 403	55.09 366	73.83 366	79.11 381	65.63 269	71.89 388	38.21 421	85.04 359	87.69 304

V42		83.47 173	83.37 170	83.75 177	83.16 326	63.33 257	81.31 249	90.23 178	69.51 234	90.91 86	90.81 211	74.16 201	92.29 164	80.06 119	90.22 288	95.62 49

Anonymous20240521		80.18 235	81.25 212	76.95 302	83.15 327	60.84 295	82.46 230	85.99 254	68.76 242	86.78 174	93.73 112	59.13 308	77.44 370	73.71 203	97.55 69	92.56 174

EU-MVSNet		75.12 289	74.43 292	77.18 300	83.11 328	59.48 308	85.71 148	82.43 298	39.76 425	85.64 202	88.76 254	44.71 389	87.88 274	73.86 200	85.88 350	84.16 347

ET-MVSNet_ETH3D		75.28 286	72.77 309	82.81 209	83.03 329	68.11 211	77.09 313	76.51 338	60.67 332	77.60 335	80.52 370	38.04 405	91.15 193	70.78 230	90.68 281	89.17 277

FMVSNet3		78.80 248	78.55 251	79.57 264	82.89 330	56.89 336	81.76 243	85.77 256	69.04 239	86.00 195	90.44 223	51.75 348	90.09 230	65.95 277	93.34 215	91.72 214

MVS_Test		82.47 189	83.22 172	80.22 255	82.62 331	57.75 329	82.54 228	91.96 120	71.16 218	82.89 263	92.52 150	77.41 160	90.50 216	80.04 120	87.84 325	92.40 184

mvs5depth		83.82 162	84.54 147	81.68 231	82.23 332	68.65 205	86.89 121	89.90 186	80.02 94	87.74 156	97.86 2	64.19 276	82.02 344	76.37 169	95.63 143	94.35 93

LF4IMVS		82.75 184	81.93 196	85.19 136	82.08 333	80.15 74	85.53 150	88.76 205	68.01 253	85.58 204	87.75 273	71.80 235	86.85 289	74.02 197	93.87 202	88.58 288

PVSNet	58.17 21	66.41 367	65.63 371	68.75 369	81.96 334	49.88 386	62.19 411	72.51 368	51.03 394	68.04 397	75.34 410	50.84 351	74.77 379	45.82 405	82.96 378	81.60 382

GA-MVS		75.83 282	74.61 288	79.48 266	81.87 335	59.25 310	73.42 358	82.88 293	68.68 243	79.75 310	81.80 359	50.62 353	89.46 244	66.85 268	85.64 351	89.72 267

MS-PatchMatch		70.93 331	70.22 335	73.06 336	81.85 336	62.50 270	73.82 355	77.90 324	52.44 384	75.92 349	81.27 363	55.67 331	81.75 345	55.37 354	77.70 407	74.94 408

Syy-MVS		69.40 348	70.03 338	67.49 378	81.72 337	38.94 420	71.00 373	61.99 412	61.38 321	70.81 382	72.36 416	61.37 292	79.30 361	64.50 296	85.18 356	84.22 344

myMVS_eth3d		64.66 376	63.89 377	66.97 381	81.72 337	37.39 423	71.00 373	61.99 412	61.38 321	70.81 382	72.36 416	20.96 437	79.30 361	49.59 386	85.18 356	84.22 344

SCA		73.32 307	72.57 313	75.58 320	81.62 339	55.86 342	78.89 286	71.37 378	61.73 315	74.93 360	83.42 341	60.46 296	87.01 283	58.11 338	82.63 385	83.88 348

FMVSNet5		72.10 319	71.69 319	73.32 333	81.57 340	53.02 364	76.77 318	78.37 323	63.31 299	76.37 341	91.85 170	36.68 409	78.98 363	47.87 396	92.45 235	87.95 299

thisisatest0515		73.00 312	70.52 331	80.46 251	81.45 341	59.90 304	73.16 361	74.31 352	57.86 350	76.08 348	77.78 392	37.60 408	92.12 168	65.00 288	91.45 262	89.35 273

eth_miper_zixun_eth		80.84 218	80.22 230	82.71 210	81.41 342	60.98 293	77.81 301	90.14 181	67.31 266	86.95 173	87.24 285	64.26 274	92.31 162	75.23 184	91.61 258	94.85 75

CANet_DTU		77.81 259	77.05 265	80.09 257	81.37 343	59.90 304	83.26 205	88.29 215	69.16 237	67.83 399	83.72 336	60.93 293	89.47 243	69.22 248	89.70 296	90.88 237

ANet_high		83.17 178	85.68 122	75.65 318	81.24 344	45.26 404	79.94 268	92.91 91	83.83 51	91.33 76	96.88 13	80.25 134	85.92 307	68.89 253	95.89 131	95.76 43

new-patchmatchnet		70.10 338	73.37 302	60.29 404	81.23 345	16.95 439	59.54 415	74.62 348	62.93 302	80.97 293	87.93 269	62.83 288	71.90 387	55.24 356	95.01 165	92.00 205

test20.03		73.75 305	74.59 290	71.22 351	81.11 346	51.12 380	70.15 381	72.10 372	70.42 224	80.28 307	91.50 184	64.21 275	74.72 381	46.96 400	94.58 181	87.82 303

MVS		73.21 310	72.59 312	75.06 323	80.97 347	60.81 296	81.64 246	85.92 255	46.03 409	71.68 377	77.54 395	68.47 255	89.77 239	55.70 351	85.39 352	74.60 409

N_pmnet		70.20 336	68.80 351	74.38 328	80.91 348	84.81 43	59.12 417	76.45 339	55.06 367	75.31 358	82.36 353	55.74 330	54.82 427	47.02 398	87.24 330	83.52 355

IterMVS		76.91 268	76.34 273	78.64 275	80.91 348	64.03 249	76.30 327	79.03 320	64.88 293	83.11 259	89.16 249	59.90 302	84.46 325	68.61 258	85.15 358	87.42 306
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		81.64 208	81.59 204	81.79 230	80.86 350	59.15 313	78.61 292	90.18 180	68.36 247	87.20 163	87.11 288	69.39 249	91.62 179	78.16 146	94.43 186	94.60 80

WTY-MVS		67.91 357	68.35 354	66.58 383	80.82 351	48.12 390	65.96 401	72.60 366	53.67 375	71.20 379	81.68 361	58.97 309	69.06 398	48.57 392	81.67 387	82.55 370

IB-MVS	62.13 19	71.64 323	68.97 349	79.66 263	80.80 352	62.26 276	73.94 353	76.90 334	63.27 300	68.63 395	76.79 402	33.83 413	91.84 176	59.28 331	87.26 329	84.88 334
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		71.85 320	71.59 320	72.62 341	80.71 353	53.78 358	69.72 384	71.71 377	58.80 343	78.03 327	80.51 371	56.61 325	78.84 365	62.20 310	86.04 349	85.23 330

ppachtmachnet_test		74.73 296	74.00 295	76.90 304	80.71 353	56.89 336	71.53 371	78.42 322	58.24 346	79.32 317	82.92 347	57.91 317	84.26 329	65.60 283	91.36 263	89.56 269

testgi		72.36 316	74.61 288	65.59 387	80.56 355	42.82 412	68.29 389	73.35 361	66.87 271	81.84 280	89.93 236	72.08 232	66.92 410	46.05 404	92.54 234	87.01 312

D2MVS		76.84 269	75.67 280	80.34 253	80.48 356	62.16 279	73.50 357	84.80 278	57.61 353	82.24 272	87.54 277	51.31 349	87.65 276	70.40 237	93.19 221	91.23 226

1314		73.22 309	72.56 314	75.20 321	80.41 357	57.84 327	81.64 246	85.36 262	51.68 390	73.10 370	76.65 404	61.45 291	85.19 318	63.54 301	79.21 401	82.59 368

cl____		80.42 226	80.23 228	81.02 243	79.99 358	59.25 310	77.07 314	87.02 238	67.37 264	86.18 193	89.21 248	63.08 285	90.16 224	76.31 171	95.80 136	93.65 127

DIV-MVS_self_test		80.43 225	80.23 228	81.02 243	79.99 358	59.25 310	77.07 314	87.02 238	67.38 263	86.19 191	89.22 247	63.09 284	90.16 224	76.32 170	95.80 136	93.66 125

MonoMVSNet		76.66 272	77.26 264	74.86 324	79.86 360	54.34 354	86.26 137	86.08 250	71.08 219	85.59 203	88.68 256	53.95 338	85.93 306	63.86 298	80.02 396	84.32 342

miper_ehance_all_eth		80.34 229	80.04 235	81.24 239	79.82 361	58.95 315	77.66 303	89.66 191	65.75 282	85.99 198	85.11 319	68.29 256	91.42 186	76.03 175	92.03 246	93.33 139

CR-MVSNet		74.00 302	73.04 306	76.85 306	79.58 362	62.64 267	82.58 225	76.90 334	50.50 399	75.72 351	92.38 153	48.07 362	84.07 331	68.72 257	82.91 380	83.85 351

RPMNet		78.88 246	78.28 255	80.68 249	79.58 362	62.64 267	82.58 225	94.16 32	74.80 161	75.72 351	92.59 146	48.69 359	95.56 42	73.48 206	82.91 380	83.85 351

baseline2		69.77 344	66.89 361	78.41 280	79.51 364	58.09 323	76.23 329	69.57 386	57.50 354	64.82 414	77.45 397	46.02 370	88.44 263	53.08 368	77.83 405	88.70 287

UnsupCasMVSNet_bld		69.21 350	69.68 341	67.82 376	79.42 365	51.15 379	67.82 393	75.79 341	54.15 373	77.47 337	85.36 318	59.26 307	70.64 392	48.46 393	79.35 399	81.66 381

PatchT		70.52 334	72.76 310	63.79 395	79.38 366	33.53 429	77.63 304	65.37 406	73.61 175	71.77 376	92.79 142	44.38 390	75.65 377	64.53 295	85.37 353	82.18 376

Patchmtry		76.56 275	77.46 260	73.83 330	79.37 367	46.60 397	82.41 232	76.90 334	73.81 171	85.56 205	92.38 153	48.07 362	83.98 332	63.36 303	95.31 152	90.92 235

mvs_anonymous		78.13 255	78.76 248	76.23 315	79.24 368	50.31 384	78.69 290	84.82 277	61.60 319	83.09 261	92.82 139	73.89 205	87.01 283	68.33 262	86.41 343	91.37 224

MVS-HIRNet		61.16 386	62.92 383	55.87 408	79.09 369	35.34 427	71.83 367	57.98 425	46.56 406	59.05 424	91.14 195	49.95 357	76.43 373	38.74 418	71.92 418	55.84 427

MDA-MVSNet-bldmvs		77.47 262	76.90 268	79.16 269	79.03 370	64.59 242	66.58 400	75.67 343	73.15 189	88.86 124	88.99 252	66.94 261	81.23 349	64.71 291	88.22 320	91.64 218

diffmvs		80.40 227	80.48 225	80.17 256	79.02 371	60.04 301	77.54 306	90.28 177	66.65 273	82.40 270	87.33 283	73.50 209	87.35 280	77.98 150	89.62 297	93.13 149
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		68.45 355	66.83 362	73.30 334	78.93 372	48.50 388	79.76 270	71.76 375	47.50 403	69.92 388	83.60 337	42.07 398	88.40 265	48.44 394	79.51 397	83.01 365

tpm		67.95 356	68.08 357	67.55 377	78.74 373	43.53 410	75.60 336	67.10 400	54.92 368	72.23 374	88.10 264	42.87 397	75.97 375	52.21 374	80.95 395	83.15 363

MDTV_nov1_ep13			68.29 355		78.03 374	43.87 409	74.12 350	72.22 370	52.17 385	67.02 402	85.54 310	45.36 381	80.85 351	55.73 349	84.42 369

cl22		78.97 244	78.21 256	81.24 239	77.74 375	59.01 314	77.46 310	87.13 233	65.79 279	84.32 233	85.10 320	58.96 310	90.88 204	75.36 183	92.03 246	93.84 115

EPNet_dtu		72.87 313	71.33 325	77.49 297	77.72 376	60.55 298	82.35 233	75.79 341	66.49 274	58.39 427	81.06 365	53.68 339	85.98 305	53.55 366	92.97 227	85.95 322
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 345	68.83 350	72.33 346	77.66 377	53.60 359	79.29 278	69.99 384	57.66 352	72.53 373	82.93 346	46.45 367	80.08 358	60.91 322	72.09 417	83.31 361
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_vis1_n_1920		71.30 328	71.58 322	70.47 354	77.58 378	59.99 303	74.25 348	84.22 284	51.06 393	74.85 361	79.10 382	55.10 335	68.83 399	68.86 254	79.20 402	82.58 369

dmvs_testset		60.59 390	62.54 385	54.72 410	77.26 379	27.74 433	74.05 351	61.00 419	60.48 333	65.62 408	67.03 423	55.93 329	68.23 405	32.07 429	69.46 424	68.17 417

sss		66.92 361	67.26 359	65.90 385	77.23 380	51.10 381	64.79 403	71.72 376	52.12 388	70.13 387	80.18 373	57.96 316	65.36 416	50.21 382	81.01 393	81.25 387

CostFormer		69.98 342	68.68 352	73.87 329	77.14 381	50.72 382	79.26 279	74.51 350	51.94 389	70.97 381	84.75 326	45.16 385	87.49 278	55.16 357	79.23 400	83.40 358

tpm cat1		66.76 365	65.21 374	71.42 350	77.09 382	50.62 383	78.01 297	73.68 359	44.89 412	68.64 394	79.00 383	45.51 379	82.42 342	49.91 384	70.15 420	81.23 389

pmmvs5		70.73 332	70.07 336	72.72 339	77.03 383	52.73 366	74.14 349	75.65 344	50.36 400	72.17 375	85.37 317	55.42 333	80.67 352	52.86 372	87.59 328	84.77 335

dmvs_re		66.81 364	66.98 360	66.28 384	76.87 384	58.68 321	71.66 369	72.24 369	60.29 335	69.52 392	73.53 413	52.38 344	64.40 418	44.90 406	81.44 390	75.76 406

EPNet		80.37 228	78.41 254	86.23 113	76.75 385	73.28 140	87.18 116	77.45 328	76.24 139	68.14 396	88.93 253	65.41 270	93.85 107	69.47 244	96.12 118	91.55 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		76.45 277	76.10 275	77.51 296	76.72 386	60.97 294	64.69 404	85.04 270	63.98 298	83.20 258	88.22 262	56.67 324	78.79 366	73.22 210	93.12 222	92.78 163

reproduce_monomvs		74.09 301	73.23 303	76.65 309	76.52 387	54.54 352	77.50 308	81.40 307	65.85 278	82.86 265	86.67 293	27.38 430	84.53 324	70.24 238	90.66 284	90.89 236

CHOSEN 280x420		59.08 391	56.52 397	66.76 382	76.51 388	64.39 246	49.62 426	59.00 422	43.86 415	55.66 430	68.41 422	35.55 411	68.21 406	43.25 409	76.78 412	67.69 418

UnsupCasMVSNet_eth		71.63 324	72.30 316	69.62 362	76.47 389	52.70 367	70.03 382	80.97 310	59.18 340	79.36 315	88.21 263	60.50 295	69.12 397	58.33 336	77.62 408	87.04 311

test-LLR		67.21 359	66.74 363	68.63 371	76.45 390	55.21 348	67.89 390	67.14 398	62.43 310	65.08 411	72.39 414	43.41 393	69.37 394	61.00 320	84.89 364	81.31 385

test-mter		65.00 374	63.79 378	68.63 371	76.45 390	55.21 348	67.89 390	67.14 398	50.98 395	65.08 411	72.39 414	28.27 428	69.37 394	61.00 320	84.89 364	81.31 385

miper_enhance_ethall		77.83 257	76.93 267	80.51 250	76.15 392	58.01 326	75.47 340	88.82 203	58.05 349	83.59 250	80.69 366	64.41 273	91.20 190	73.16 216	92.03 246	92.33 188

gg-mvs-nofinetune		68.96 352	69.11 345	68.52 374	76.12 393	45.32 403	83.59 195	55.88 426	86.68 29	64.62 415	97.01 9	30.36 423	83.97 333	44.78 407	82.94 379	76.26 405

test_vis1_n		70.29 335	69.99 339	71.20 352	75.97 394	66.50 227	76.69 320	80.81 311	44.22 414	75.43 354	77.23 399	50.00 356	68.59 400	66.71 271	82.85 382	78.52 402

CMPMVS	59.41 20	75.12 289	73.57 298	79.77 259	75.84 395	67.22 217	81.21 252	82.18 299	50.78 396	76.50 340	87.66 275	55.20 334	82.99 338	62.17 312	90.64 286	89.09 281
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
wuyk23d		75.13 288	79.30 241	62.63 396	75.56 396	75.18 127	80.89 257	73.10 364	75.06 160	94.76 16	95.32 41	87.73 43	52.85 428	34.16 426	97.11 82	59.85 424

Patchmatch-test		65.91 369	67.38 358	61.48 401	75.51 397	43.21 411	68.84 387	63.79 410	62.48 306	72.80 372	83.42 341	44.89 388	59.52 424	48.27 395	86.45 342	81.70 380

new_pmnet		55.69 395	57.66 396	49.76 411	75.47 398	30.59 431	59.56 414	51.45 429	43.62 417	62.49 417	75.48 409	40.96 400	49.15 431	37.39 423	72.52 415	69.55 415

gm-plane-assit					75.42 399	44.97 406			52.17 385		72.36 416		87.90 273	54.10 362

MVSTER		77.09 266	75.70 279	81.25 237	75.27 400	61.08 289	77.49 309	85.07 268	60.78 330	86.55 181	88.68 256	43.14 396	90.25 219	73.69 204	90.67 282	92.42 181

PVSNet_0	51.08 22	56.10 394	54.97 399	59.48 406	75.12 401	53.28 363	55.16 423	61.89 414	44.30 413	59.16 423	62.48 426	54.22 337	65.91 414	35.40 424	47.01 429	59.25 425

test0.0.03 1		64.66 376	64.36 375	65.57 388	75.03 402	46.89 396	64.69 404	61.58 418	62.43 310	71.18 380	77.54 395	43.41 393	68.47 403	40.75 415	82.65 383	81.35 384

test_fmvs3		75.72 284	75.20 285	77.27 299	75.01 403	69.47 194	78.93 284	84.88 275	46.67 405	87.08 169	87.84 271	50.44 355	71.62 389	77.42 159	88.53 311	90.72 241

tpmvs		70.16 337	69.56 342	71.96 347	74.71 404	48.13 389	79.63 271	75.45 346	65.02 292	70.26 386	81.88 358	45.34 382	85.68 314	58.34 335	75.39 413	82.08 378

test_fmvs1_n		70.94 330	70.41 334	72.53 343	73.92 405	66.93 223	75.99 333	84.21 285	43.31 418	79.40 314	79.39 380	43.47 392	68.55 401	69.05 251	84.91 363	82.10 377

MDA-MVSNet_test_wron		70.05 340	70.44 332	68.88 368	73.84 406	53.47 360	58.93 419	67.28 396	58.43 344	87.09 168	85.40 315	59.80 304	67.25 408	59.66 329	83.54 375	85.92 323

YYNet1		70.06 339	70.44 332	68.90 367	73.76 407	53.42 362	58.99 418	67.20 397	58.42 345	87.10 167	85.39 316	59.82 303	67.32 407	59.79 328	83.50 376	85.96 321

test_cas_vis1_n_1920		69.20 351	69.12 344	69.43 364	73.68 408	62.82 264	70.38 380	77.21 331	46.18 408	80.46 304	78.95 384	52.03 345	65.53 415	65.77 282	77.45 410	79.95 398

UWE-MVS-28		58.44 393	57.71 395	60.65 403	73.58 409	31.23 430	69.68 385	48.80 431	53.12 380	61.79 418	78.83 385	30.98 421	68.40 404	21.58 432	80.99 394	82.33 375

GG-mvs-BLEND				67.16 380	73.36 410	46.54 399	84.15 178	55.04 427		58.64 426	61.95 427	29.93 424	83.87 334	38.71 419	76.92 411	71.07 413

JIA-IIPM		69.41 347	66.64 365	77.70 294	73.19 411	71.24 174	75.67 335	65.56 405	70.42 224	65.18 410	92.97 133	33.64 415	83.06 336	53.52 367	69.61 423	78.79 401

ADS-MVSNet2		65.87 370	63.64 379	72.55 342	73.16 412	56.92 335	67.10 397	74.81 347	49.74 401	66.04 405	82.97 344	46.71 365	77.26 371	42.29 410	69.96 421	83.46 356

ADS-MVSNet		61.90 382	62.19 386	61.03 402	73.16 412	36.42 425	67.10 397	61.75 415	49.74 401	66.04 405	82.97 344	46.71 365	63.21 419	42.29 410	69.96 421	83.46 356

ttmdpeth		71.72 322	70.67 328	74.86 324	73.08 414	55.88 341	77.41 311	69.27 388	55.86 363	78.66 323	93.77 110	38.01 406	75.39 378	60.12 326	89.87 294	93.31 141

DSMNet-mixed		60.98 388	61.61 388	59.09 407	72.88 415	45.05 405	74.70 346	46.61 433	26.20 431	65.34 409	90.32 226	55.46 332	63.12 420	41.72 412	81.30 392	69.09 416

tpmrst		66.28 368	66.69 364	65.05 391	72.82 416	39.33 419	78.20 296	70.69 382	53.16 379	67.88 398	80.36 372	48.18 361	74.75 380	58.13 337	70.79 419	81.08 390

test_fmvs2		73.57 306	72.80 308	75.90 317	72.74 417	68.84 204	77.07 314	84.32 283	45.14 411	82.89 263	84.22 332	48.37 360	70.36 393	73.40 208	87.03 335	88.52 289

TESTMET0.1,1		61.29 385	60.32 391	64.19 393	72.06 418	51.30 377	67.89 390	62.09 411	45.27 410	60.65 421	69.01 420	27.93 429	64.74 417	56.31 345	81.65 389	76.53 404

dp		60.70 389	60.29 392	61.92 399	72.04 419	38.67 422	70.83 376	64.08 409	51.28 392	60.75 420	77.28 398	36.59 410	71.58 390	47.41 397	62.34 427	75.52 407

pmmvs3		62.47 380	60.02 393	69.80 360	71.58 420	64.00 250	70.52 378	58.44 424	39.77 424	66.05 404	75.84 407	27.10 433	72.28 385	46.15 403	84.77 368	73.11 410

dongtai		41.90 398	42.65 401	39.67 413	70.86 421	21.11 435	61.01 413	21.42 440	57.36 355	57.97 428	50.06 429	16.40 439	58.73 426	21.03 433	27.69 433	39.17 429

EPMVS		62.47 380	62.63 384	62.01 397	70.63 422	38.74 421	74.76 345	52.86 428	53.91 374	67.71 400	80.01 374	39.40 402	66.60 411	55.54 353	68.81 425	80.68 394

mvsany_test3		65.48 373	62.97 382	73.03 337	69.99 423	76.17 121	64.83 402	43.71 434	43.68 416	80.25 308	87.05 290	52.83 342	63.09 421	51.92 379	72.44 416	79.84 399

test_vis3_rt		71.42 326	70.67 328	73.64 332	69.66 424	70.46 182	66.97 399	89.73 188	42.68 421	88.20 145	83.04 343	43.77 391	60.07 422	65.35 286	86.66 340	90.39 254

test_fmvs1		69.57 346	69.05 346	71.14 353	69.15 425	65.77 235	73.98 352	83.32 289	42.83 420	77.77 333	78.27 390	43.39 395	68.50 402	68.39 261	84.38 370	79.15 400

KD-MVS_2432*1600		66.87 362	65.81 369	70.04 356	67.50 426	47.49 393	62.56 409	79.16 318	61.21 326	77.98 328	80.61 367	25.29 434	82.48 340	53.02 369	84.92 361	80.16 396

miper_refine_blended		66.87 362	65.81 369	70.04 356	67.50 426	47.49 393	62.56 409	79.16 318	61.21 326	77.98 328	80.61 367	25.29 434	82.48 340	53.02 369	84.92 361	80.16 396

E-PMN		61.59 384	61.62 387	61.49 400	66.81 428	55.40 346	53.77 424	60.34 420	66.80 272	58.90 425	65.50 424	40.48 401	66.12 413	55.72 350	86.25 346	62.95 422

test_f		64.31 379	65.85 367	59.67 405	66.54 429	62.24 278	57.76 421	70.96 380	40.13 423	84.36 231	82.09 355	46.93 364	51.67 429	61.99 313	81.89 386	65.12 420

test_vis1_rt		65.64 372	64.09 376	70.31 355	66.09 430	70.20 186	61.16 412	81.60 305	38.65 426	72.87 371	69.66 419	52.84 341	60.04 423	56.16 346	77.77 406	80.68 394

EMVS		61.10 387	60.81 389	61.99 398	65.96 431	55.86 342	53.10 425	58.97 423	67.06 269	56.89 429	63.33 425	40.98 399	67.03 409	54.79 359	86.18 347	63.08 421

mvsany_test1		58.48 392	56.47 398	64.50 392	65.90 432	68.21 210	56.95 422	42.11 435	38.30 427	65.69 407	77.19 401	56.96 323	59.35 425	46.16 402	58.96 428	65.93 419

PMMVS		61.65 383	60.38 390	65.47 389	65.40 433	69.26 197	63.97 407	61.73 416	36.80 430	60.11 422	68.43 421	59.42 305	66.35 412	48.97 390	78.57 404	60.81 423

PMMVS2		55.64 396	59.27 394	44.74 412	64.30 434	12.32 440	40.60 427	49.79 430	53.19 378	65.06 413	84.81 325	53.60 340	49.76 430	32.68 428	89.41 299	72.15 411

MVE	40.22 23	51.82 397	50.47 400	55.87 408	62.66 435	51.91 372	31.61 429	39.28 436	40.65 422	50.76 431	74.98 411	56.24 328	44.67 432	33.94 427	64.11 426	71.04 414
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MVStest1		70.05 340	69.26 343	72.41 345	58.62 436	55.59 345	76.61 323	65.58 404	53.44 376	89.28 120	93.32 120	22.91 436	71.44 391	74.08 196	89.52 298	90.21 260

kuosan		30.83 399	32.17 402	26.83 415	53.36 437	19.02 438	57.90 420	20.44 441	38.29 428	38.01 432	37.82 431	15.18 440	33.45 434	7.74 435	20.76 434	28.03 430

DeepMVS_CX				24.13 416	32.95 438	29.49 432		21.63 439	12.07 432	37.95 433	45.07 430	30.84 422	19.21 435	17.94 434	33.06 432	23.69 431

test_method		30.46 400	29.60 403	33.06 414	17.99 439	3.84 442	13.62 430	73.92 354	2.79 433	18.29 435	53.41 428	28.53 427	43.25 433	22.56 430	35.27 431	52.11 428

tmp_tt		20.25 402	24.50 405	7.49 417	4.47 440	8.70 441	34.17 428	25.16 438	1.00 435	32.43 434	18.49 432	39.37 403	9.21 436	21.64 431	43.75 430	4.57 432

testmvs		5.91 406	7.65 409	0.72 419	1.20 441	0.37 444	59.14 416	0.67 443	0.49 437	1.11 437	2.76 436	0.94 442	0.24 438	1.02 437	1.47 435	1.55 434

test123		6.27 405	8.08 408	0.84 418	1.11 442	0.57 443	62.90 408	0.82 442	0.54 436	1.07 438	2.75 437	1.26 441	0.30 437	1.04 436	1.26 436	1.66 433

mmdepth		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

monomultidepth		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

test_blank		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

eth-test2					0.00 443

eth-test					0.00 443

uanet_test		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

DCPMVS		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

cdsmvs_eth3d_5k		20.81 401	27.75 404	0.00 420	0.00 443	0.00 445	0.00 431	85.44 261	0.00 438	0.00 439	82.82 348	81.46 120	0.00 439	0.00 438	0.00 437	0.00 435

pcd_1.5k_mvsjas		6.41 404	8.55 407	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	76.94 169	0.00 439	0.00 438	0.00 437	0.00 435

sosnet-low-res		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

sosnet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

uncertanet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

Regformer		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

ab-mvs-re		6.65 403	8.87 406	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	79.80 376	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

uanet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	0.00 438	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

WAC-MVS						37.39 423								52.61 373

PC_three_1452									58.96 342	90.06 97	91.33 188	80.66 130	93.03 143	75.78 177	95.94 128	92.48 178

test_241102_TWO								93.71 55	83.77 52	93.49 39	94.27 79	89.27 23	95.84 24	86.03 51	97.82 54	92.04 203

test_0728_THIRD									85.33 38	93.75 34	94.65 61	87.44 46	95.78 32	87.41 27	98.21 32	92.98 157

GSMVS																83.88 348

sam_mvs1												46.11 369				83.88 348

sam_mvs												45.92 374

MTGPA								91.81 127

test_post1							78.85 288				3.13 434	45.19 384	80.13 357	58.11 338

test_post											3.10 435	45.43 380	77.22 372

patchmatchnet-post											81.71 360	45.93 373	87.01 283

MTMP							90.66 48	33.14 437

test9_res														80.83 112	96.45 103	90.57 248

agg_prior2														79.68 125	96.16 115	90.22 256

test_prior4						78.97 84	84.59 169

test_prior2							83.37 202		75.43 155	84.58 224	91.57 182	81.92 115		79.54 129	96.97 85

旧先验2							81.73 244		56.88 360	86.54 186			84.90 321	72.81 217

新几何2							81.72 245

无先验							82.81 220	85.62 259	58.09 348				91.41 187	67.95 265		84.48 339

原ACMM2							82.26 238

testdata2													86.43 297	63.52 302

segment_acmp												81.94 112

testdata1							79.62 272		73.95 170

plane_prior5								93.61 59					95.22 59	80.78 113	95.83 134	94.46 85

plane_prior4											92.95 134

plane_prior3						76.85 111			77.79 126	86.55 181

plane_prior2							89.45 82		79.44 101

plane_prior						76.42 116	87.15 117		75.94 146						95.03 162

n2								0.00 444

nn								0.00 444

door-mid								74.45 351

test11								91.46 133

door								72.57 367

HQP5-MVS						70.66 180

BP-MVS														77.30 160

HQP4-MVS										80.56 300			94.61 79			93.56 134

HQP3-MVS								92.68 98							94.47 184

HQP2-MVS												72.10 230

MDTV_nov1_ep13_2view						27.60 434	70.76 377		46.47 407	61.27 419		45.20 383		49.18 388		83.75 353

ACMMP++_ref															95.74 140

ACMMP++															97.35 75

Test By Simon												79.09 142

High-resolution multi-view benchmark