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.

DeepPCF-MVS	95.94 2	97.71 83	98.98 10	93.92 265	99.63 88	81.76 340	99.96 23	98.56 77	99.47 1	99.19 74	99.99 1	94.16 87	100.00 1	99.92 9	99.93 63	100.00 1

CNVR-MVS		99.40 1	99.26 1	99.84 4	99.98 2	99.51 4	99.98 8	98.69 55	98.20 3	99.93 1	99.98 2	96.82 19	100.00 1	99.75 22	100.00 1	99.99 20

NCCC		99.37 2	99.25 2	99.71 10	99.96 8	99.15 16	99.97 16	98.62 67	98.02 6	99.90 2	99.95 3	97.33 13	100.00 1	99.54 33	100.00 1	100.00 1

SED-MVS		99.28 5	99.11 6	99.77 6	99.93 26	99.30 8	99.96 23	98.43 116	97.27 20	99.80 16	99.94 4	96.71 20	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								98.43 116	97.27 20	99.80 16	99.94 4	97.18 17	100.00 1	100.00 1	100.00 1	100.00 1

test0726					99.93 26	99.29 10	99.96 23	98.42 127	97.28 18	99.86 4	99.94 4	97.22 15

DPM-MVS		98.83 21	98.46 30	99.97 1	99.33 106	99.92 1	99.96 23	98.44 108	97.96 7	99.55 43	99.94 4	97.18 17	100.00 1	93.81 189	99.94 57	99.98 51

SMA-MVS		98.76 26	98.48 29	99.62 15	99.87 52	98.87 27	99.86 99	98.38 139	93.19 149	99.77 23	99.94 4	95.54 40	100.00 1	99.74 24	99.99 20	100.00 1
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
DPE-MVS		99.26 6	99.10 7	99.74 7	99.89 45	99.24 14	99.87 88	98.44 108	97.48 15	99.64 35	99.94 4	96.68 22	99.99 36	99.99 5	100.00 1	99.99 20
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSLP-MVS++		99.13 7	99.01 9	99.49 31	99.94 14	98.46 59	99.98 8	98.86 45	97.10 25	99.80 16	99.94 4	95.92 33	100.00 1	99.51 34	100.00 1	100.00 1

ETH3 D test6400		98.81 22	98.54 26	99.59 18	99.93 26	98.93 22	99.93 62	98.46 105	94.56 95	99.84 8	99.92 11	94.32 80	99.86 90	99.96 8	99.98 33	100.00 1

MCST-MVS		99.32 3	99.14 4	99.86 3	99.97 3	99.59 3	99.97 16	98.64 63	98.47 2	99.13 76	99.92 11	96.38 26	100.00 1	99.74 24	100.00 1	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 8	99.93 26	99.29 10	99.95 41	98.32 151	97.28 18	99.83 10	99.91 13	97.22 15	100.00 1	99.99 5	100.00 1	99.89 90
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
test_0728_THIRD									96.48 40	99.83 10	99.91 13	97.87 4	100.00 1	99.92 9	100.00 1	100.00 1

SteuartSystems-ACMMP		99.02 11	98.97 11	99.18 54	98.72 138	97.71 83	99.98 8	98.44 108	96.85 29	99.80 16	99.91 13	97.57 6	99.85 94	99.44 38	99.99 20	99.99 20
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	96.59 1	98.81 22	98.54 26	99.62 15	99.90 42	98.85 29	99.24 217	98.47 103	98.14 4	99.08 77	99.91 13	93.09 116	100.00 1	99.04 54	99.99 20	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETH3D-3000-0.1		98.68 29	98.42 31	99.47 34	99.83 63	98.57 51	99.90 74	98.37 142	93.81 131	99.81 12	99.90 17	94.34 76	99.86 90	99.84 13	99.98 33	99.97 63

tmp_tt		65.23 327	62.94 330	72.13 340	44.90 367	50.03 363	81.05 357	89.42 362	38.45 359	48.51 361	99.90 17	54.09 353	78.70 360	91.84 218	18.26 362	87.64 352

xxxxxxxxxxxxxcwj		98.98 14	98.79 15	99.54 23	99.82 65	98.79 33	99.96 23	97.52 234	97.66 10	99.81 12	99.89 19	94.70 65	99.86 90	99.84 13	99.93 63	99.96 70

SF-MVS		98.67 30	98.40 35	99.50 29	99.77 73	98.67 44	99.90 74	98.21 168	93.53 140	99.81 12	99.89 19	94.70 65	99.86 90	99.84 13	99.93 63	99.96 70

9.14			98.38 38		99.87 52		99.91 70	98.33 149	93.22 148	99.78 22	99.89 19	94.57 68	99.85 94	99.84 13	99.97 44

test_241102_ONE					99.93 26	99.30 8		98.43 116	97.26 22	99.80 16	99.88 22	96.71 20	100.00 1

MSP-MVS		99.09 8	99.12 5	98.98 80	99.93 26	97.24 104	99.95 41	98.42 127	97.50 14	99.52 48	99.88 22	97.43 12	99.71 129	99.50 35	99.98 33	100.00 1
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
zzz-MVS		98.33 55	98.00 62	99.30 47	99.85 55	97.93 78	99.80 120	98.28 158	95.76 62	97.18 139	99.88 22	92.74 124	100.00 1	98.67 77	99.88 76	99.99 20

MTAPA		98.29 58	97.96 67	99.30 47	99.85 55	97.93 78	99.39 198	98.28 158	95.76 62	97.18 139	99.88 22	92.74 124	100.00 1	98.67 77	99.88 76	99.99 20

ETH3D cwj APD-0.16		98.40 51	98.07 59	99.40 41	99.59 90	98.41 60	99.86 99	98.24 164	92.18 188	99.73 26	99.87 26	93.47 103	99.85 94	99.74 24	99.95 51	99.93 81

CDPH-MVS		98.65 31	98.36 42	99.49 31	99.94 14	98.73 41	99.87 88	98.33 149	93.97 123	99.76 24	99.87 26	94.99 58	99.75 121	98.55 84	100.00 1	99.98 51

CP-MVS		98.45 46	98.32 44	98.87 86	99.96 8	96.62 124	99.97 16	98.39 135	94.43 100	98.90 86	99.87 26	94.30 81	100.00 1	99.04 54	99.99 20	99.99 20

xiu_mvs_v2_base		98.23 63	97.97 64	99.02 77	98.69 139	98.66 46	99.52 178	98.08 185	97.05 26	99.86 4	99.86 29	90.65 161	99.71 129	99.39 41	98.63 128	98.69 201

TEST9					99.92 35	98.92 23	99.96 23	98.43 116	93.90 128	99.71 30	99.86 29	95.88 34	99.85 94

train_agg		98.88 19	98.65 20	99.59 18	99.92 35	98.92 23	99.96 23	98.43 116	94.35 105	99.71 30	99.86 29	95.94 31	99.85 94	99.69 31	99.98 33	99.99 20

LS3D		95.84 144	95.11 154	98.02 135	99.85 55	95.10 178	98.74 266	98.50 101	87.22 280	93.66 197	99.86 29	87.45 196	99.95 60	90.94 232	99.81 87	99.02 188

MP-MVS-pluss		98.07 68	97.64 74	99.38 44	99.74 77	98.41 60	99.74 138	98.18 173	93.35 144	96.45 156	99.85 33	92.64 127	99.97 51	98.91 62	99.89 74	99.77 104
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_8					99.92 35	98.88 26	99.96 23	98.43 116	94.35 105	99.69 32	99.85 33	95.94 31	99.85 94

agg_prior1		98.88 19	98.66 19	99.54 23	99.93 26	98.77 36	99.96 23	98.43 116	94.63 94	99.63 36	99.85 33	95.79 37	99.85 94	99.72 28	99.99 20	99.99 20

HFP-MVS		98.56 37	98.37 40	99.14 63	99.96 8	97.43 99	99.95 41	98.61 69	94.77 86	99.31 64	99.85 33	94.22 83	100.00 1	98.70 75	99.98 33	99.98 51

region2R		98.54 39	98.37 40	99.05 73	99.96 8	97.18 107	99.96 23	98.55 83	94.87 84	99.45 51	99.85 33	94.07 89	100.00 1	98.67 77	100.00 1	99.98 51

PS-MVSNAJ		98.44 47	98.20 50	99.16 59	98.80 135	98.92 23	99.54 176	98.17 174	97.34 16	99.85 6	99.85 33	91.20 150	99.89 79	99.41 40	99.67 95	98.69 201

#test#		98.59 35	98.41 33	99.14 63	99.96 8	97.43 99	99.95 41	98.61 69	95.00 80	99.31 64	99.85 33	94.22 83	100.00 1	98.78 72	99.98 33	99.98 51

HPM-MVS++		99.07 9	98.88 13	99.63 12	99.90 42	99.02 19	99.95 41	98.56 77	97.56 13	99.44 52	99.85 33	95.38 45	100.00 1	99.31 43	99.99 20	99.87 93

旧先验1					99.76 74	97.52 91		98.64 63			99.85 33	95.63 39			99.94 57	99.99 20

原ACMM1				98.96 82	99.73 81	96.99 114		98.51 97	94.06 119	99.62 38	99.85 33	94.97 59	99.96 53	95.11 155	99.95 51	99.92 87

testdata				98.42 119	99.47 100	95.33 170		98.56 77	93.78 133	99.79 21	99.85 33	93.64 101	99.94 68	94.97 157	99.94 57	100.00 1

APDe-MVS		99.06 10	98.91 12	99.51 28	99.94 14	98.76 40	99.91 70	98.39 135	97.20 24	99.46 50	99.85 33	95.53 42	99.79 109	99.86 12	100.00 1	99.99 20

API-MVS		97.86 74	97.66 73	98.47 114	99.52 96	95.41 168	99.47 187	98.87 44	91.68 203	98.84 87	99.85 33	92.34 134	99.99 36	98.44 87	99.96 48	100.00 1

testtj		98.89 18	98.69 18	99.52 26	99.94 14	98.56 53	99.90 74	98.55 83	95.14 78	99.72 29	99.84 46	95.46 43	100.00 1	99.65 32	99.99 20	99.99 20

ACMMPR		98.50 42	98.32 44	99.05 73	99.96 8	97.18 107	99.95 41	98.60 71	94.77 86	99.31 64	99.84 46	93.73 98	100.00 1	98.70 75	99.98 33	99.98 51

DP-MVS Recon		98.41 49	98.02 61	99.56 21	99.97 3	98.70 43	99.92 66	98.44 108	92.06 193	98.40 110	99.84 46	95.68 38	100.00 1	98.19 93	99.71 93	99.97 63

ZD-MVS					99.92 35	98.57 51		98.52 90	92.34 184	99.31 64	99.83 49	95.06 52	99.80 106	99.70 30	99.97 44

ACMMP_NAP		98.49 43	98.14 54	99.54 23	99.66 87	98.62 50	99.85 102	98.37 142	94.68 91	99.53 45	99.83 49	92.87 120	100.00 1	98.66 80	99.84 80	99.99 20

test222					99.55 94	97.41 102	99.34 204	98.55 83	91.86 197	99.27 69	99.83 49	93.84 96			99.95 51	99.99 20

1121		98.03 69	97.57 78	99.40 41	99.74 77	98.21 66	98.31 289	98.62 67	92.78 161	99.53 45	99.83 49	95.08 50	100.00 1	94.36 176	99.92 67	99.99 20

ZNCC-MVS		98.31 56	98.03 60	99.17 57	99.88 49	97.59 87	99.94 56	98.44 108	94.31 108	98.50 105	99.82 53	93.06 117	99.99 36	98.30 92	99.99 20	99.93 81

新几何1				99.42 38	99.75 76	98.27 65		98.63 66	92.69 166	99.55 43	99.82 53	94.40 71	100.00 1	91.21 223	99.94 57	99.99 20

CSCG		97.10 102	97.04 96	97.27 164	99.89 45	91.92 246	99.90 74	99.07 31	88.67 260	95.26 179	99.82 53	93.17 115	99.98 42	98.15 96	99.47 109	99.90 89

MAR-MVS		97.43 89	97.19 90	98.15 131	99.47 100	94.79 187	99.05 237	98.76 51	92.65 169	98.66 98	99.82 53	88.52 189	99.98 42	98.12 97	99.63 97	99.67 117
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
MP-MVS		98.23 63	97.97 64	99.03 75	99.94 14	97.17 110	99.95 41	98.39 135	94.70 90	98.26 117	99.81 57	91.84 144	100.00 1	98.85 66	99.97 44	99.93 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test1172		98.38 53	98.25 47	98.77 90	99.88 49	96.56 127	99.80 120	98.36 144	94.68 91	99.20 71	99.80 58	93.28 110	99.78 111	99.34 42	99.92 67	99.98 51

OPU-MVS				99.93 2	99.89 45	99.80 2	99.96 23				99.80 58	97.44 11	100.00 1	100.00 1	99.98 33	100.00 1

SR-MVS		98.46 45	98.30 46	98.93 84	99.88 49	97.04 112	99.84 106	98.35 146	94.92 81	99.32 63	99.80 58	93.35 105	99.78 111	99.30 44	99.95 51	99.96 70

mPP-MVS		98.39 52	98.20 50	98.97 81	99.97 3	96.92 117	99.95 41	98.38 139	95.04 79	98.61 101	99.80 58	93.39 104	100.00 1	98.64 81	100.00 1	99.98 51

Regformer-1		98.79 24	98.60 23	99.36 45	99.85 55	98.34 62	99.87 88	98.52 90	96.05 53	99.41 55	99.79 62	94.93 60	99.76 118	99.07 49	99.90 72	99.99 20

Regformer-2		98.78 25	98.59 24	99.36 45	99.85 55	98.32 63	99.87 88	98.52 90	96.04 54	99.41 55	99.79 62	94.92 61	99.76 118	99.05 50	99.90 72	99.98 51

CPTT-MVS		97.64 85	97.32 88	98.58 105	99.97 3	95.77 157	99.96 23	98.35 146	89.90 240	98.36 111	99.79 62	91.18 153	99.99 36	98.37 89	99.99 20	99.99 20

MVS_111021_LR		98.42 48	98.38 38	98.53 111	99.39 103	95.79 156	99.87 88	99.86 2	96.70 36	98.78 90	99.79 62	92.03 140	99.90 75	99.17 46	99.86 79	99.88 92

XVS		98.70 28	98.55 25	99.15 61	99.94 14	97.50 94	99.94 56	98.42 127	96.22 49	99.41 55	99.78 66	94.34 76	99.96 53	98.92 60	99.95 51	99.99 20

PHI-MVS		98.41 49	98.21 49	99.03 75	99.86 54	97.10 111	99.98 8	98.80 50	90.78 227	99.62 38	99.78 66	95.30 46	100.00 1	99.80 18	99.93 63	99.99 20

Regformer-3		98.58 36	98.41 33	99.10 69	99.84 60	97.57 88	99.66 154	98.52 90	95.79 59	99.01 81	99.77 68	94.40 71	99.75 121	98.82 67	99.83 81	99.98 51

Regformer-4		98.56 37	98.39 37	99.08 71	99.84 60	97.52 91	99.66 154	98.52 90	95.76 62	99.01 81	99.77 68	94.33 79	99.75 121	98.80 70	99.83 81	99.98 51

APD-MVS_3200maxsize		98.25 62	98.08 58	98.78 89	99.81 68	96.60 125	99.82 113	98.30 156	93.95 125	99.37 61	99.77 68	92.84 121	99.76 118	98.95 57	99.92 67	99.97 63

MVS_111021_HR		98.72 27	98.62 22	99.01 78	99.36 105	97.18 107	99.93 62	99.90 1	96.81 33	98.67 97	99.77 68	93.92 92	99.89 79	99.27 45	99.94 57	99.96 70

EI-MVSNet-Vis-set		98.27 59	98.11 57	98.75 92	99.83 63	96.59 126	99.40 194	98.51 97	95.29 75	98.51 104	99.76 72	93.60 102	99.71 129	98.53 85	99.52 106	99.95 78

test_prior3		98.99 13	98.84 14	99.43 35	99.94 14	98.49 57	99.95 41	98.65 60	95.78 60	99.73 26	99.76 72	96.00 29	99.80 106	99.78 20	100.00 1	99.99 20

test_prior2							99.95 41		95.78 60	99.73 26	99.76 72	96.00 29		99.78 20	100.00 1

SD-MVS		98.92 16	98.70 17	99.56 21	99.70 85	98.73 41	99.94 56	98.34 148	96.38 44	99.81 12	99.76 72	94.59 67	99.98 42	99.84 13	99.96 48	99.97 63
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
PGM-MVS		98.34 54	98.13 55	98.99 79	99.92 35	97.00 113	99.75 135	99.50 16	93.90 128	99.37 61	99.76 72	93.24 113	100.00 1	97.75 118	99.96 48	99.98 51

SR-MVS-dyc-post		98.31 56	98.17 52	98.71 93	99.79 70	96.37 134	99.76 132	98.31 153	94.43 100	99.40 59	99.75 77	93.28 110	99.78 111	98.90 63	99.92 67	99.97 63

RE-MVS-def			98.13 55		99.79 70	96.37 134	99.76 132	98.31 153	94.43 100	99.40 59	99.75 77	92.95 119		98.90 63	99.92 67	99.97 63

EI-MVSNet-UG-set		98.14 65	97.99 63	98.60 102	99.80 69	96.27 136	99.36 203	98.50 101	95.21 77	98.30 114	99.75 77	93.29 109	99.73 128	98.37 89	99.30 115	99.81 98

PAPR		98.52 41	98.16 53	99.58 20	99.97 3	98.77 36	99.95 41	98.43 116	95.35 73	98.03 122	99.75 77	94.03 90	99.98 42	98.11 98	99.83 81	99.99 20

GST-MVS		98.27 59	97.97 64	99.17 57	99.92 35	97.57 88	99.93 62	98.39 135	94.04 121	98.80 89	99.74 81	92.98 118	100.00 1	98.16 95	99.76 89	99.93 81

TSAR-MVS + MP.		98.93 15	98.77 16	99.41 39	99.74 77	98.67 44	99.77 127	98.38 139	96.73 35	99.88 3	99.74 81	94.89 62	99.59 140	99.80 18	99.98 33	99.97 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
APD-MVS		98.62 32	98.35 43	99.41 39	99.90 42	98.51 56	99.87 88	98.36 144	94.08 116	99.74 25	99.73 83	94.08 88	99.74 125	99.42 39	99.99 20	99.99 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MG-MVS		98.91 17	98.65 20	99.68 11	99.94 14	99.07 18	99.64 161	99.44 18	97.33 17	99.00 83	99.72 84	94.03 90	99.98 42	98.73 74	100.00 1	100.00 1

AdaColmap		97.23 99	96.80 103	98.51 112	99.99 1	95.60 164	99.09 226	98.84 47	93.32 145	96.74 149	99.72 84	86.04 209	100.00 1	98.01 103	99.43 112	99.94 80

CANet		98.27 59	97.82 70	99.63 12	99.72 83	99.10 17	99.98 8	98.51 97	97.00 28	98.52 103	99.71 86	87.80 192	99.95 60	99.75 22	99.38 113	99.83 96

ACMMP		97.74 82	97.44 81	98.66 97	99.92 35	96.13 145	99.18 221	99.45 17	94.84 85	96.41 159	99.71 86	91.40 147	99.99 36	97.99 105	98.03 144	99.87 93
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
abl_6		97.67 84	97.34 86	98.66 97	99.68 86	96.11 148	99.68 151	98.14 180	93.80 132	99.27 69	99.70 88	88.65 188	99.98 42	97.46 122	99.72 92	99.89 90

PAPM_NR		98.12 66	97.93 68	98.70 94	99.94 14	96.13 145	99.82 113	98.43 116	94.56 95	97.52 132	99.70 88	94.40 71	99.98 42	97.00 133	99.98 33	99.99 20

OMC-MVS		97.28 96	97.23 89	97.41 157	99.76 74	93.36 216	99.65 157	97.95 195	96.03 55	97.41 135	99.70 88	89.61 173	99.51 143	96.73 140	98.25 138	99.38 161

xiu_mvs_v1_base_debu		97.43 89	97.06 93	98.55 106	97.74 188	98.14 67	99.31 208	97.86 205	96.43 41	99.62 38	99.69 91	85.56 213	99.68 133	99.05 50	98.31 135	97.83 210

xiu_mvs_v1_base		97.43 89	97.06 93	98.55 106	97.74 188	98.14 67	99.31 208	97.86 205	96.43 41	99.62 38	99.69 91	85.56 213	99.68 133	99.05 50	98.31 135	97.83 210

xiu_mvs_v1_base_debi		97.43 89	97.06 93	98.55 106	97.74 188	98.14 67	99.31 208	97.86 205	96.43 41	99.62 38	99.69 91	85.56 213	99.68 133	99.05 50	98.31 135	97.83 210

CNLPA		97.76 81	97.38 82	98.92 85	99.53 95	96.84 118	99.87 88	98.14 180	93.78 133	96.55 154	99.69 91	92.28 135	99.98 42	97.13 129	99.44 111	99.93 81

cdsmvs_eth3d_5k		23.43 335	31.24 338	0.00 350	0.00 371	0.00 372	0.00 362	98.09 183	0.00 367	0.00 368	99.67 95	83.37 231	0.00 368	0.00 366	0.00 366	0.00 364

lupinMVS		97.85 75	97.60 76	98.62 100	97.28 214	97.70 85	99.99 4	97.55 228	95.50 71	99.43 53	99.67 95	90.92 157	98.71 175	98.40 88	99.62 98	99.45 154

114514_t		97.41 93	96.83 101	99.14 63	99.51 98	97.83 80	99.89 82	98.27 161	88.48 264	99.06 78	99.66 97	90.30 165	99.64 139	96.32 143	99.97 44	99.96 70

PAPM		98.60 33	98.42 31	99.14 63	96.05 248	98.96 20	99.90 74	99.35 23	96.68 37	98.35 112	99.66 97	96.45 25	98.51 185	99.45 37	99.89 74	99.96 70

CANet_DTU		96.76 115	96.15 119	98.60 102	98.78 136	97.53 90	99.84 106	97.63 217	97.25 23	99.20 71	99.64 99	81.36 246	99.98 42	92.77 209	98.89 122	98.28 204

XVG-OURS		94.82 164	94.74 161	95.06 220	98.00 170	89.19 293	99.08 228	97.55 228	94.10 115	94.71 183	99.62 100	80.51 257	99.74 125	96.04 146	93.06 216	96.25 223

MVS		96.60 123	95.56 142	99.72 9	96.85 231	99.22 15	98.31 289	98.94 36	91.57 206	90.90 221	99.61 101	86.66 204	99.96 53	97.36 124	99.88 76	99.99 20

EIA-MVS		97.53 87	97.46 80	97.76 145	98.04 169	94.84 184	99.98 8	97.61 222	94.41 103	97.90 125	99.59 102	92.40 132	98.87 163	98.04 102	99.13 120	99.59 130

XVG-OURS-SEG-HR		94.79 165	94.70 162	95.08 219	98.05 168	89.19 293	99.08 228	97.54 230	93.66 137	94.87 182	99.58 103	78.78 270	99.79 109	97.31 125	93.40 212	96.25 223

HPM-MVS		97.96 70	97.72 72	98.68 95	99.84 60	96.39 133	99.90 74	98.17 174	92.61 171	98.62 100	99.57 104	91.87 143	99.67 136	98.87 65	99.99 20	99.99 20
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		98.60 33	98.51 28	98.86 87	99.73 81	96.63 123	99.97 16	97.92 199	98.07 5	98.76 93	99.55 105	95.00 57	99.94 68	99.91 11	97.68 148	99.99 20

DP-MVS		94.54 175	93.42 190	97.91 140	99.46 102	94.04 198	98.93 249	97.48 239	81.15 330	90.04 230	99.55 105	87.02 201	99.95 60	88.97 255	98.11 139	99.73 108

MVSFormer		96.94 107	96.60 108	97.95 137	97.28 214	97.70 85	99.55 174	97.27 260	91.17 216	99.43 53	99.54 107	90.92 157	96.89 280	94.67 170	99.62 98	99.25 175

jason		97.24 98	96.86 100	98.38 122	95.73 260	97.32 103	99.97 16	97.40 249	95.34 74	98.60 102	99.54 107	87.70 193	98.56 182	97.94 108	99.47 109	99.25 175
jason: jason.
HPM-MVS_fast		97.80 79	97.50 79	98.68 95	99.79 70	96.42 130	99.88 85	98.16 177	91.75 202	98.94 85	99.54 107	91.82 145	99.65 138	97.62 120	99.99 20	99.99 20

DeepC-MVS	94.51 4	96.92 109	96.40 115	98.45 116	99.16 110	95.90 153	99.66 154	98.06 186	96.37 47	94.37 188	99.49 110	83.29 232	99.90 75	97.63 119	99.61 101	99.55 139
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
alignmvs		97.81 78	97.33 87	99.25 49	98.77 137	98.66 46	99.99 4	98.44 108	94.40 104	98.41 108	99.47 111	93.65 100	99.42 151	98.57 83	94.26 204	99.67 117

TAPA-MVS	92.12 8	94.42 179	93.60 183	96.90 172	99.33 106	91.78 250	99.78 124	98.00 189	89.89 241	94.52 185	99.47 111	91.97 141	99.18 155	69.90 344	99.52 106	99.73 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETV-MVS		97.92 73	97.80 71	98.25 126	98.14 165	96.48 128	99.98 8	97.63 217	95.61 68	99.29 68	99.46 113	92.55 129	98.82 165	99.02 56	98.54 129	99.46 152

ET-MVSNet_ETH3D		94.37 181	93.28 196	97.64 149	98.30 151	97.99 74	99.99 4	97.61 222	94.35 105	71.57 349	99.45 114	96.23 27	95.34 324	96.91 138	85.14 266	99.59 130

canonicalmvs		97.09 104	96.32 116	99.39 43	98.93 123	98.95 21	99.72 146	97.35 252	94.45 98	97.88 126	99.42 115	86.71 203	99.52 142	98.48 86	93.97 208	99.72 110

VDD-MVS		93.77 193	92.94 199	96.27 193	98.55 142	90.22 280	98.77 265	97.79 210	90.85 225	96.82 147	99.42 115	61.18 346	99.77 115	98.95 57	94.13 205	98.82 196

1112_ss		96.01 141	95.20 151	98.42 119	97.80 183	96.41 131	99.65 157	96.66 311	92.71 164	92.88 207	99.40 117	92.16 137	99.30 152	91.92 216	93.66 209	99.55 139

ab-mvs-re		8.28 337	11.04 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	99.40 117	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

LFMVS		94.75 168	93.56 186	98.30 124	99.03 114	95.70 162	98.74 266	97.98 192	87.81 273	98.47 106	99.39 119	67.43 328	99.53 141	98.01 103	95.20 197	99.67 117

WTY-MVS		98.10 67	97.60 76	99.60 17	98.92 125	99.28 12	99.89 82	99.52 13	95.58 69	98.24 118	99.39 119	93.33 106	99.74 125	97.98 107	95.58 191	99.78 103

PMMVS		96.76 115	96.76 104	96.76 176	98.28 154	92.10 241	99.91 70	97.98 192	94.12 114	99.53 45	99.39 119	86.93 202	98.73 173	96.95 136	97.73 146	99.45 154

EPNet		98.49 43	98.40 35	98.77 90	99.62 89	96.80 120	99.90 74	99.51 15	97.60 12	99.20 71	99.36 122	93.71 99	99.91 74	97.99 105	98.71 127	99.61 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CS-MVS		97.52 88	97.36 85	98.00 136	97.47 204	96.11 148	100.00 1	97.08 276	94.74 88	99.65 33	99.33 123	89.89 170	98.22 215	98.79 71	99.25 116	99.68 114

VDDNet		93.12 206	91.91 221	96.76 176	96.67 241	92.65 231	98.69 271	98.21 168	82.81 324	97.75 129	99.28 124	61.57 344	99.48 149	98.09 100	94.09 206	98.15 206

diffmvs		97.00 105	96.64 107	98.09 132	97.64 195	96.17 144	99.81 115	97.19 264	94.67 93	98.95 84	99.28 124	86.43 206	98.76 171	98.37 89	97.42 154	99.33 168

baseline		96.43 128	95.98 125	97.76 145	97.34 208	95.17 177	99.51 180	97.17 267	93.92 127	96.90 145	99.28 124	85.37 216	98.64 179	97.50 121	96.86 168	99.46 152

UA-Net		96.54 124	95.96 130	98.27 125	98.23 159	95.71 161	98.00 303	98.45 107	93.72 136	98.41 108	99.27 127	88.71 187	99.66 137	91.19 224	97.69 147	99.44 156

RPSCF		91.80 237	92.79 202	88.83 322	98.15 164	69.87 353	98.11 299	96.60 313	83.93 317	94.33 189	99.27 127	79.60 264	99.46 150	91.99 214	93.16 215	97.18 219

PLC	95.54 3	97.93 72	97.89 69	98.05 134	99.82 65	94.77 188	99.92 66	98.46 105	93.93 126	97.20 138	99.27 127	95.44 44	99.97 51	97.41 123	99.51 108	99.41 159
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
casdiffmvs		96.42 129	95.97 128	97.77 144	97.30 212	94.98 180	99.84 106	97.09 275	93.75 135	96.58 152	99.26 130	85.07 219	98.78 168	97.77 116	97.04 163	99.54 143

BH-RMVSNet		95.18 157	94.31 168	97.80 141	98.17 163	95.23 175	99.76 132	97.53 232	92.52 178	94.27 190	99.25 131	76.84 281	98.80 166	90.89 234	99.54 105	99.35 166

DELS-MVS		98.54 39	98.22 48	99.50 29	99.15 111	98.65 48	100.00 1	98.58 73	97.70 9	98.21 119	99.24 132	92.58 128	99.94 68	98.63 82	99.94 57	99.92 87
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
PCF-MVS	94.20 5	95.18 157	94.10 172	98.43 118	98.55 142	95.99 151	97.91 305	97.31 257	90.35 233	89.48 246	99.22 133	85.19 218	99.89 79	90.40 243	98.47 131	99.41 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet	91.05 13	97.13 101	96.69 106	98.45 116	99.52 96	95.81 155	99.95 41	99.65 10	94.73 89	99.04 79	99.21 134	84.48 223	99.95 60	94.92 158	98.74 126	99.58 136

MSDG		94.37 181	93.36 194	97.40 158	98.88 130	93.95 202	99.37 201	97.38 250	85.75 301	90.80 222	99.17 135	84.11 227	99.88 85	86.35 284	98.43 132	98.36 203

F-COLMAP		96.93 108	96.95 99	96.87 173	99.71 84	91.74 251	99.85 102	97.95 195	93.11 152	95.72 172	99.16 136	92.35 133	99.94 68	95.32 153	99.35 114	98.92 190

Vis-MVSNet (Re-imp)		96.32 132	95.98 125	97.35 162	97.93 174	94.82 185	99.47 187	98.15 179	91.83 198	95.09 180	99.11 137	91.37 148	97.47 245	93.47 198	97.43 152	99.74 107

CHOSEN 280x420		99.01 12	99.03 8	98.95 83	99.38 104	98.87 27	98.46 282	99.42 20	97.03 27	99.02 80	99.09 138	99.35 1	98.21 216	99.73 27	99.78 88	99.77 104

PVSNet_Blended		97.94 71	97.64 74	98.83 88	99.59 90	96.99 114	100.00 1	99.10 28	95.38 72	98.27 115	99.08 139	89.00 183	99.95 60	99.12 47	99.25 116	99.57 137

sss		97.57 86	97.03 97	99.18 54	98.37 149	98.04 72	99.73 143	99.38 21	93.46 142	98.76 93	99.06 140	91.21 149	99.89 79	96.33 142	97.01 164	99.62 125

thisisatest0515		97.41 93	97.02 98	98.59 104	97.71 194	97.52 91	99.97 16	98.54 87	91.83 198	97.45 134	99.04 141	97.50 8	99.10 157	94.75 166	96.37 175	99.16 180

EI-MVSNet		93.73 195	93.40 193	94.74 230	96.80 234	92.69 228	99.06 233	97.67 215	88.96 253	91.39 216	99.02 142	88.75 186	97.30 253	91.07 226	87.85 245	94.22 255

CVMVSNet		94.68 171	94.94 156	93.89 267	96.80 234	86.92 314	99.06 233	98.98 34	94.45 98	94.23 191	99.02 142	85.60 212	95.31 325	90.91 233	95.39 194	99.43 157

EPP-MVSNet		96.69 120	96.60 108	96.96 170	97.74 188	93.05 220	99.37 201	98.56 77	88.75 258	95.83 170	99.01 144	96.01 28	98.56 182	96.92 137	97.20 160	99.25 175

COLMAP_ROB	90.47 14	92.18 228	91.49 230	94.25 252	99.00 117	88.04 309	98.42 287	96.70 310	82.30 327	88.43 268	99.01 144	76.97 279	99.85 94	86.11 287	96.50 172	94.86 228
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
3Dnovator	91.47 12	96.28 136	95.34 147	99.08 71	96.82 233	97.47 97	99.45 190	98.81 48	95.52 70	89.39 247	99.00 146	81.97 238	99.95 60	97.27 126	99.83 81	99.84 95

test_yl		97.83 76	97.37 83	99.21 51	99.18 108	97.98 75	99.64 161	99.27 25	91.43 212	97.88 126	98.99 147	95.84 35	99.84 103	98.82 67	95.32 195	99.79 100

DCV-MVSNet		97.83 76	97.37 83	99.21 51	99.18 108	97.98 75	99.64 161	99.27 25	91.43 212	97.88 126	98.99 147	95.84 35	99.84 103	98.82 67	95.32 195	99.79 100

1314		96.84 111	95.96 130	99.48 33	96.74 238	98.52 55	98.31 289	98.86 45	95.82 58	89.91 233	98.98 149	87.49 195	99.96 53	97.80 111	99.73 91	99.96 70

3Dnovator+	91.53 11	96.31 133	95.24 149	99.52 26	96.88 230	98.64 49	99.72 146	98.24 164	95.27 76	88.42 270	98.98 149	82.76 234	99.94 68	97.10 131	99.83 81	99.96 70

thisisatest0530		97.10 102	96.72 105	98.22 127	97.60 197	96.70 121	99.92 66	98.54 87	91.11 219	97.07 142	98.97 151	97.47 9	99.03 158	93.73 194	96.09 178	98.92 190

baseline2		96.71 119	96.49 112	97.37 160	95.63 267	95.96 152	99.74 138	98.88 43	92.94 154	91.61 214	98.97 151	97.72 5	98.62 180	94.83 162	98.08 143	97.53 218

gm-plane-assit					96.97 224	93.76 206			91.47 210		98.96 153		98.79 167	94.92 158

IS-MVSNet		96.29 135	95.90 134	97.45 155	98.13 166	94.80 186	99.08 228	97.61 222	92.02 194	95.54 175	98.96 153	90.64 162	98.08 220	93.73 194	97.41 155	99.47 151

OpenMVS	90.15 15	94.77 167	93.59 184	98.33 123	96.07 247	97.48 96	99.56 172	98.57 75	90.46 230	86.51 294	98.95 155	78.57 272	99.94 68	93.86 185	99.74 90	97.57 217

GeoE		94.36 183	93.48 188	96.99 169	97.29 213	93.54 210	99.96 23	96.72 309	88.35 267	93.43 198	98.94 156	82.05 237	98.05 223	88.12 266	96.48 173	99.37 163

Vis-MVSNet		95.72 146	95.15 153	97.45 155	97.62 196	94.28 195	99.28 214	98.24 164	94.27 111	96.84 146	98.94 156	79.39 265	98.76 171	93.25 200	98.49 130	99.30 171
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tttt0517		96.85 110	96.49 112	97.92 139	97.48 203	95.89 154	99.85 102	98.54 87	90.72 228	96.63 151	98.93 158	97.47 9	99.02 159	93.03 207	95.76 187	98.85 194

DWT-MVSNet_test		97.31 95	97.19 90	97.66 148	98.24 158	94.67 189	98.86 258	98.20 172	93.60 139	98.09 120	98.89 159	97.51 7	98.78 168	94.04 183	97.28 157	99.55 139

QAPM		95.40 154	94.17 170	99.10 69	96.92 225	97.71 83	99.40 194	98.68 56	89.31 245	88.94 259	98.89 159	82.48 235	99.96 53	93.12 206	99.83 81	99.62 125

VNet		97.21 100	96.57 110	99.13 68	98.97 119	97.82 81	99.03 239	99.21 27	94.31 108	99.18 75	98.88 161	86.26 208	99.89 79	98.93 59	94.32 203	99.69 113

thres200		96.96 106	96.21 118	99.22 50	98.97 119	98.84 30	99.85 102	99.71 5	93.17 150	96.26 162	98.88 161	89.87 171	99.51 143	94.26 180	94.91 198	99.31 170

tfpn200view9		96.79 113	95.99 123	99.19 53	98.94 121	98.82 31	99.78 124	99.71 5	92.86 155	96.02 165	98.87 163	89.33 177	99.50 145	93.84 186	94.57 199	99.27 173

thres400		96.78 114	95.99 123	99.16 59	98.94 121	98.82 31	99.78 124	99.71 5	92.86 155	96.02 165	98.87 163	89.33 177	99.50 145	93.84 186	94.57 199	99.16 180

thres100view900		96.74 117	95.92 133	99.18 54	98.90 128	98.77 36	99.74 138	99.71 5	92.59 173	95.84 168	98.86 165	89.25 179	99.50 145	93.84 186	94.57 199	99.27 173

thres600view7		96.69 120	95.87 136	99.14 63	98.90 128	98.78 35	99.74 138	99.71 5	92.59 173	95.84 168	98.86 165	89.25 179	99.50 145	93.44 199	94.50 202	99.16 180

CHOSEN 1792x2688		96.81 112	96.53 111	97.64 149	98.91 127	93.07 218	99.65 157	99.80 3	95.64 67	95.39 176	98.86 165	84.35 225	99.90 75	96.98 134	99.16 119	99.95 78

CLD-MVS		94.06 187	93.90 177	94.55 239	96.02 249	90.69 269	99.98 8	97.72 212	96.62 39	91.05 220	98.85 168	77.21 277	98.47 186	98.11 98	89.51 225	94.48 233
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-w/o		95.71 148	95.38 146	96.68 179	98.49 146	92.28 237	99.84 106	97.50 237	92.12 190	92.06 212	98.79 169	84.69 221	98.67 178	95.29 154	99.66 96	99.09 186

Anonymous202405211		93.10 207	91.99 219	96.40 189	99.10 112	89.65 290	98.88 254	97.93 197	83.71 319	94.00 193	98.75 170	68.79 320	99.88 85	95.08 156	91.71 218	99.68 114

mvs-test1		95.53 151	95.97 128	94.20 253	97.77 185	85.44 322	99.95 41	97.06 279	94.92 81	96.58 152	98.72 171	85.81 210	98.98 160	94.80 163	98.11 139	98.18 205

TR-MVS		94.54 175	93.56 186	97.49 154	97.96 172	94.34 194	98.71 269	97.51 236	90.30 235	94.51 186	98.69 172	75.56 292	98.77 170	92.82 208	95.99 180	99.35 166

BH-untuned		95.18 157	94.83 158	96.22 194	98.36 150	91.22 263	99.80 120	97.32 256	90.91 223	91.08 219	98.67 173	83.51 229	98.54 184	94.23 181	99.61 101	98.92 190

OPM-MVS		93.21 204	92.80 201	94.44 246	93.12 306	90.85 268	99.77 127	97.61 222	96.19 51	91.56 215	98.65 174	75.16 297	98.47 186	93.78 192	89.39 226	93.99 281

NP-MVS					95.77 257	91.79 249					98.65 174

HQP-MVS		94.61 173	94.50 164	94.92 225	95.78 254	91.85 247	99.87 88	97.89 201	96.82 30	93.37 199	98.65 174	80.65 255	98.39 197	97.92 109	89.60 220	94.53 229

baseline1		95.78 145	94.86 157	98.54 109	98.47 147	98.07 70	99.06 233	97.99 190	92.68 167	94.13 192	98.62 177	93.28 110	98.69 177	93.79 191	85.76 259	98.84 195

HQP_MVS		94.49 178	94.36 166	94.87 226	95.71 263	91.74 251	99.84 106	97.87 203	96.38 44	93.01 203	98.59 178	80.47 259	98.37 202	97.79 114	89.55 223	94.52 231

plane_prior4											98.59 178

Anonymous20240529		92.10 230	90.65 241	96.47 184	98.82 133	90.61 272	98.72 268	98.67 59	75.54 345	93.90 195	98.58 180	66.23 331	99.90 75	94.70 169	90.67 219	98.90 193

Effi-MVS+		96.30 134	95.69 139	98.16 128	97.85 180	96.26 137	97.41 311	97.21 263	90.37 232	98.65 99	98.58 180	86.61 205	98.70 176	97.11 130	97.37 156	99.52 146

EPNet_dtu		95.71 148	95.39 145	96.66 180	98.92 125	93.41 214	99.57 170	98.90 41	96.19 51	97.52 132	98.56 182	92.65 126	97.36 248	77.89 328	98.33 134	99.20 178
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test0.0.03 1		93.86 188	93.61 181	94.64 234	95.02 276	92.18 240	99.93 62	98.58 73	94.07 117	87.96 275	98.50 183	93.90 94	94.96 329	81.33 314	93.17 214	96.78 220

LPG-MVS_test		92.96 210	92.71 203	93.71 271	95.43 269	88.67 299	99.75 135	97.62 219	92.81 158	90.05 228	98.49 184	75.24 295	98.40 195	95.84 150	89.12 227	94.07 273

LGP-MVS_train				93.71 271	95.43 269	88.67 299		97.62 219	92.81 158	90.05 228	98.49 184	75.24 295	98.40 195	95.84 150	89.12 227	94.07 273

PVSNet_Blended_VisFu		97.27 97	96.81 102	98.66 97	98.81 134	96.67 122	99.92 66	98.64 63	94.51 97	96.38 160	98.49 184	89.05 182	99.88 85	97.10 131	98.34 133	99.43 157

testmvs		40.60 333	44.45 336	29.05 348	19.49 370	14.11 371	99.68 151	18.47 369	20.74 364	64.59 352	98.48 187	10.95 369	17.09 367	56.66 357	11.01 363	55.94 360

AllTest		92.48 221	91.64 224	95.00 222	99.01 115	88.43 303	98.94 248	96.82 303	86.50 289	88.71 261	98.47 188	74.73 299	99.88 85	85.39 290	96.18 176	96.71 221

TestCases				95.00 222	99.01 115	88.43 303		96.82 303	86.50 289	88.71 261	98.47 188	74.73 299	99.88 85	85.39 290	96.18 176	96.71 221

hse-mvs3		94.92 163	94.36 166	96.59 183	98.85 132	91.29 262	98.93 249	98.94 36	95.90 56	98.77 91	98.42 190	90.89 159	99.77 115	97.80 111	70.76 337	98.72 200

PatchMatch-RL		96.04 140	95.40 144	97.95 137	99.59 90	95.22 176	99.52 178	99.07 31	93.96 124	96.49 155	98.35 191	82.28 236	99.82 105	90.15 246	99.22 118	98.81 197

CDS-MVSNet		96.34 131	96.07 120	97.13 166	97.37 206	94.96 181	99.53 177	97.91 200	91.55 207	95.37 177	98.32 192	95.05 53	97.13 264	93.80 190	95.75 188	99.30 171
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMP	92.05 9	92.74 215	92.42 212	93.73 269	95.91 253	88.72 298	99.81 115	97.53 232	94.13 113	87.00 288	98.23 193	74.07 303	98.47 186	96.22 144	88.86 232	93.99 281
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testgi		89.01 287	88.04 288	91.90 300	93.49 299	84.89 325	99.73 143	95.66 332	93.89 130	85.14 308	98.17 194	59.68 347	94.66 333	77.73 329	88.88 230	96.16 226

ITE_SJBPF				92.38 293	95.69 265	85.14 323		95.71 330	92.81 158	89.33 250	98.11 195	70.23 317	98.42 192	85.91 288	88.16 243	93.59 305

HyFIR lowres test		96.66 122	96.43 114	97.36 161	99.05 113	93.91 203	99.70 148	99.80 3	90.54 229	96.26 162	98.08 196	92.15 138	98.23 214	96.84 139	95.46 192	99.93 81

TESTMET0.1,1		96.74 117	96.26 117	98.16 128	97.36 207	96.48 128	99.96 23	98.29 157	91.93 195	95.77 171	98.07 197	95.54 40	98.29 207	90.55 238	98.89 122	99.70 111

TAMVS		95.85 143	95.58 141	96.65 181	97.07 218	93.50 211	99.17 222	97.82 209	91.39 215	95.02 181	98.01 198	92.20 136	97.30 253	93.75 193	95.83 185	99.14 183

hse-mvs2		94.38 180	94.08 173	95.31 213	98.27 156	90.02 284	99.29 213	98.56 77	95.90 56	98.77 91	98.00 199	90.89 159	98.26 213	97.80 111	69.20 343	97.64 215

AUN-MVS		93.28 203	92.60 205	95.34 211	98.29 152	90.09 283	99.31 208	98.56 77	91.80 201	96.35 161	98.00 199	89.38 176	98.28 209	92.46 210	69.22 342	97.64 215

ACMM	91.95 10	92.88 212	92.52 210	93.98 264	95.75 259	89.08 296	99.77 127	97.52 234	93.00 153	89.95 232	97.99 201	76.17 289	98.46 189	93.63 197	88.87 231	94.39 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+		95.02 161	94.19 169	97.52 153	97.88 176	94.55 190	99.97 16	97.08 276	88.85 257	94.47 187	97.96 202	84.59 222	98.41 193	89.84 249	97.10 161	99.59 130

GG-mvs-BLEND				98.54 109	98.21 160	98.01 73	93.87 341	98.52 90		97.92 124	97.92 203	99.02 2	97.94 231	98.17 94	99.58 103	99.67 117

Fast-Effi-MVS+-dtu		93.72 196	93.86 179	93.29 280	97.06 219	86.16 316	99.80 120	96.83 301	92.66 168	92.58 210	97.83 204	81.39 245	97.67 238	89.75 250	96.87 167	96.05 227

RRT_MVS		95.23 156	94.77 160	96.61 182	98.28 154	98.32 63	99.81 115	97.41 247	92.59 173	91.28 218	97.76 205	95.02 54	97.23 259	93.65 196	87.14 252	94.28 251

ACMH+	89.98 16	90.35 265	89.54 262	92.78 291	95.99 250	86.12 317	98.81 262	97.18 266	89.38 244	83.14 317	97.76 205	68.42 324	98.43 191	89.11 254	86.05 258	93.78 297

ACMH	89.72 17	90.64 258	89.63 259	93.66 275	95.64 266	88.64 301	98.55 277	97.45 240	89.03 249	81.62 324	97.61 207	69.75 318	98.41 193	89.37 251	87.62 249	93.92 287
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		94.64 172	93.61 181	97.74 147	97.82 182	96.26 137	99.96 23	97.78 211	85.76 299	94.00 193	97.54 208	76.95 280	99.21 154	97.23 127	95.43 193	97.76 214

nrg030		93.51 199	92.53 209	96.45 186	94.36 284	97.20 106	99.81 115	97.16 269	91.60 205	89.86 235	97.46 209	86.37 207	97.68 237	95.88 149	80.31 304	94.46 234

VPNet		91.81 234	90.46 243	95.85 203	94.74 279	95.54 165	98.98 243	98.59 72	92.14 189	90.77 223	97.44 210	68.73 322	97.54 242	94.89 161	77.89 318	94.46 234

UniMVSNet_ETH3D		90.06 274	88.58 280	94.49 243	94.67 281	88.09 308	97.81 307	97.57 227	83.91 318	88.44 266	97.41 211	57.44 350	97.62 240	91.41 221	88.59 238	97.77 213

HY-MVS	92.50 7	97.79 80	97.17 92	99.63 12	98.98 118	99.32 6	97.49 310	99.52 13	95.69 66	98.32 113	97.41 211	93.32 107	99.77 115	98.08 101	95.75 188	99.81 98

PVSNet_0	88.03 19	91.80 237	90.27 249	96.38 191	98.27 156	90.46 276	99.94 56	99.61 11	93.99 122	86.26 301	97.39 213	71.13 315	99.89 79	98.77 73	67.05 347	98.79 198

FIs		94.10 186	93.43 189	96.11 196	94.70 280	96.82 119	99.58 168	98.93 40	92.54 177	89.34 249	97.31 214	87.62 194	97.10 267	94.22 182	86.58 255	94.40 241

OurMVSNet-221017-0		89.81 277	89.48 266	90.83 308	91.64 327	81.21 341	98.17 297	95.38 338	91.48 209	85.65 306	97.31 214	72.66 307	97.29 256	88.15 264	84.83 267	93.97 283

FC-MVSNet-test		93.81 191	93.15 198	95.80 204	94.30 286	96.20 142	99.42 193	98.89 42	92.33 185	89.03 258	97.27 216	87.39 197	96.83 284	93.20 201	86.48 256	94.36 244

USDC		90.00 275	88.96 274	93.10 286	94.81 278	88.16 307	98.71 269	95.54 335	93.66 137	83.75 315	97.20 217	65.58 333	98.31 206	83.96 300	87.49 251	92.85 320

MVSTER		95.53 151	95.22 150	96.45 186	98.56 141	97.72 82	99.91 70	97.67 215	92.38 183	91.39 216	97.14 218	97.24 14	97.30 253	94.80 163	87.85 245	94.34 248

LF4IMVS		89.25 286	88.85 275	90.45 312	92.81 315	81.19 342	98.12 298	94.79 345	91.44 211	86.29 300	97.11 219	65.30 336	98.11 219	88.53 260	85.25 264	92.07 328

mvs_anonymous		95.65 150	95.03 155	97.53 152	98.19 161	95.74 159	99.33 205	97.49 238	90.87 224	90.47 226	97.10 220	88.23 190	97.16 261	95.92 148	97.66 149	99.68 114

jajsoiax		91.92 232	91.18 234	94.15 254	91.35 330	90.95 266	99.00 241	97.42 245	92.61 171	87.38 284	97.08 221	72.46 308	97.36 248	94.53 173	88.77 233	94.13 270

XXY-MVS		91.82 233	90.46 243	95.88 201	93.91 292	95.40 169	98.87 257	97.69 214	88.63 262	87.87 276	97.08 221	74.38 302	97.89 232	91.66 219	84.07 275	94.35 247

LTVRE_ROB	88.28 18	90.29 268	89.05 273	94.02 260	95.08 274	90.15 282	97.19 315	97.43 243	84.91 312	83.99 313	97.06 223	74.00 304	98.28 209	84.08 297	87.71 247	93.62 304
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
mvs_tets		91.81 234	91.08 235	94.00 262	91.63 328	90.58 273	98.67 273	97.43 243	92.43 182	87.37 285	97.05 224	71.76 310	97.32 252	94.75 166	88.68 235	94.11 271

MVS_Test		96.46 127	95.74 138	98.61 101	98.18 162	97.23 105	99.31 208	97.15 270	91.07 220	98.84 87	97.05 224	88.17 191	98.97 161	94.39 175	97.50 151	99.61 127

ab-mvs		94.69 169	93.42 190	98.51 112	98.07 167	96.26 137	96.49 325	98.68 56	90.31 234	94.54 184	97.00 226	76.30 287	99.71 129	95.98 147	93.38 213	99.56 138

PS-MVSNAJss		93.64 198	93.31 195	94.61 235	92.11 321	92.19 239	99.12 224	97.38 250	92.51 179	88.45 265	96.99 227	91.20 150	97.29 256	94.36 176	87.71 247	94.36 244

IB-MVS	92.85 6	94.99 162	93.94 176	98.16 128	97.72 192	95.69 163	99.99 4	98.81 48	94.28 110	92.70 209	96.90 228	95.08 50	99.17 156	96.07 145	73.88 335	99.60 129
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
WR-MVS		92.31 225	91.25 233	95.48 209	94.45 283	95.29 171	99.60 166	98.68 56	90.10 236	88.07 274	96.89 229	80.68 254	96.80 286	93.14 204	79.67 308	94.36 244

SixPastTwentyTwo		88.73 288	88.01 289	90.88 306	91.85 325	82.24 335	98.22 295	95.18 343	88.97 252	82.26 320	96.89 229	71.75 311	96.67 292	84.00 298	82.98 280	93.72 302

UniMVSNet_NR-MVSNet		92.95 211	92.11 216	95.49 206	94.61 282	95.28 172	99.83 112	99.08 30	91.49 208	89.21 253	96.86 231	87.14 199	96.73 288	93.20 201	77.52 321	94.46 234

XVG-ACMP-BASELINE		91.22 247	90.75 238	92.63 292	93.73 295	85.61 319	98.52 281	97.44 242	92.77 162	89.90 234	96.85 232	66.64 330	98.39 197	92.29 212	88.61 236	93.89 289

TinyColmap		87.87 295	86.51 296	91.94 299	95.05 275	85.57 320	97.65 309	94.08 350	84.40 315	81.82 323	96.85 232	62.14 343	98.33 204	80.25 319	86.37 257	91.91 332

EU-MVSNet		90.14 273	90.34 247	89.54 318	92.55 317	81.06 343	98.69 271	98.04 188	91.41 214	86.59 293	96.84 234	80.83 252	93.31 345	86.20 285	81.91 287	94.26 252

TranMVSNet+NR-MVSNet		91.68 241	90.61 242	94.87 226	93.69 296	93.98 201	99.69 149	98.65 60	91.03 221	88.44 266	96.83 235	80.05 262	96.18 309	90.26 245	76.89 329	94.45 239

RRT_test8_iter05		94.58 174	94.11 171	95.98 199	97.88 176	96.11 148	99.89 82	97.45 240	91.66 204	88.28 271	96.71 236	96.53 24	97.40 246	94.73 168	83.85 278	94.45 239

bset_n11_16_dypcd		93.05 209	92.30 213	95.31 213	90.23 339	95.05 179	99.44 192	97.28 259	92.51 179	90.65 224	96.68 237	85.30 217	96.71 290	94.49 174	84.14 273	94.16 264

GA-MVS		93.83 189	92.84 200	96.80 174	95.73 260	93.57 208	99.88 85	97.24 262	92.57 176	92.92 205	96.66 238	78.73 271	97.67 238	87.75 269	94.06 207	99.17 179

CMPMVS	61.59 21	84.75 308	85.14 302	83.57 333	90.32 338	62.54 357	96.98 320	97.59 226	74.33 348	69.95 351	96.66 238	64.17 338	98.32 205	87.88 268	88.41 241	89.84 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DU-MVS		92.46 222	91.45 231	95.49 206	94.05 289	95.28 172	99.81 115	98.74 52	92.25 187	89.21 253	96.64 240	81.66 242	96.73 288	93.20 201	77.52 321	94.46 234

NR-MVSNet		91.56 242	90.22 250	95.60 205	94.05 289	95.76 158	98.25 292	98.70 54	91.16 218	80.78 329	96.64 240	83.23 233	96.57 295	91.41 221	77.73 320	94.46 234

CP-MVSNet		91.23 246	90.22 250	94.26 251	93.96 291	92.39 236	99.09 226	98.57 75	88.95 254	86.42 297	96.57 242	79.19 267	96.37 301	90.29 244	78.95 311	94.02 276

pmmvs4		92.10 230	91.07 236	95.18 217	92.82 314	94.96 181	99.48 186	96.83 301	87.45 276	88.66 264	96.56 243	83.78 228	96.83 284	89.29 252	84.77 268	93.75 298

PS-CasMVS		90.63 259	89.51 264	93.99 263	93.83 293	91.70 255	98.98 243	98.52 90	88.48 264	86.15 302	96.53 244	75.46 293	96.31 304	88.83 256	78.86 313	93.95 284

test-LLR		96.47 126	96.04 121	97.78 142	97.02 222	95.44 166	99.96 23	98.21 168	94.07 117	95.55 173	96.38 245	93.90 94	98.27 211	90.42 241	98.83 124	99.64 123

test-mter		96.39 130	95.93 132	97.78 142	97.02 222	95.44 166	99.96 23	98.21 168	91.81 200	95.55 173	96.38 245	95.17 47	98.27 211	90.42 241	98.83 124	99.64 123

MS-PatchMatch		90.65 257	90.30 248	91.71 302	94.22 287	85.50 321	98.24 293	97.70 213	88.67 260	86.42 297	96.37 247	67.82 326	98.03 224	83.62 302	99.62 98	91.60 333

test_part1		92.15 229	90.72 239	96.44 188	98.87 131	97.46 98	98.99 242	98.26 162	85.89 296	86.34 299	96.34 248	81.71 240	97.48 244	91.06 227	78.99 310	94.37 243

PEN-MVS		90.19 271	89.06 272	93.57 276	93.06 308	90.90 267	99.06 233	98.47 103	88.11 268	85.91 304	96.30 249	76.67 282	95.94 318	87.07 277	76.91 328	93.89 289

UGNet		95.33 155	94.57 163	97.62 151	98.55 142	94.85 183	98.67 273	99.32 24	95.75 65	96.80 148	96.27 250	72.18 309	99.96 53	94.58 172	99.05 121	98.04 208
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
DTE-MVSNet		89.40 282	88.24 286	92.88 289	92.66 316	89.95 286	99.10 225	98.22 167	87.29 278	85.12 309	96.22 251	76.27 288	95.30 326	83.56 303	75.74 332	93.41 307

TransMVSNet (Re)		87.25 296	85.28 301	93.16 283	93.56 297	91.03 264	98.54 279	94.05 351	83.69 320	81.09 327	96.16 252	75.32 294	96.40 300	76.69 334	68.41 344	92.06 329

pm-mvs1		89.36 283	87.81 290	94.01 261	93.40 302	91.93 245	98.62 276	96.48 317	86.25 293	83.86 314	96.14 253	73.68 305	97.04 272	86.16 286	75.73 333	93.04 317

Test_1112_low_res		95.72 146	94.83 158	98.42 119	97.79 184	96.41 131	99.65 157	96.65 312	92.70 165	92.86 208	96.13 254	92.15 138	99.30 152	91.88 217	93.64 210	99.55 139

TDRefinement		84.76 307	82.56 314	91.38 304	74.58 358	84.80 326	97.36 312	94.56 348	84.73 313	80.21 331	96.12 255	63.56 340	98.39 197	87.92 267	63.97 348	90.95 339

test_djsdf		92.83 213	92.29 214	94.47 244	91.90 324	92.46 234	99.55 174	97.27 260	91.17 216	89.96 231	96.07 256	81.10 248	96.89 280	94.67 170	88.91 229	94.05 275

miper_enhance_ethall		94.36 183	93.98 175	95.49 206	98.68 140	95.24 174	99.73 143	97.29 258	93.28 147	89.86 235	95.97 257	94.37 75	97.05 270	92.20 213	84.45 270	94.19 258

lessismore_v0				90.53 309	90.58 336	80.90 344		95.80 328		77.01 339	95.84 258	66.15 332	96.95 277	83.03 305	75.05 334	93.74 301

PVSNet_BlendedMVS		96.05 139	95.82 137	96.72 178	99.59 90	96.99 114	99.95 41	99.10 28	94.06 119	98.27 115	95.80 259	89.00 183	99.95 60	99.12 47	87.53 250	93.24 313

ppachtmachnet_test		89.58 281	88.35 283	93.25 282	92.40 318	90.44 277	99.33 205	96.73 308	85.49 305	85.90 305	95.77 260	81.09 249	96.00 317	76.00 336	82.49 282	93.30 311

pmmvs5		90.17 272	89.09 271	93.40 278	92.10 322	89.77 289	99.74 138	95.58 334	85.88 298	87.24 287	95.74 261	73.41 306	96.48 298	88.54 259	83.56 279	93.95 284

MDTV_nov1_ep13			95.69 139		97.90 175	94.15 196	95.98 332	98.44 108	93.12 151	97.98 123	95.74 261	95.10 49	98.58 181	90.02 247	96.92 166

eth_miper_zixun_eth		92.41 223	91.93 220	93.84 268	97.28 214	90.68 270	98.83 260	96.97 289	88.57 263	89.19 255	95.73 263	89.24 181	96.69 291	89.97 248	81.55 289	94.15 266

IterMVS-SCA-FT		90.85 254	90.16 253	92.93 288	96.72 239	89.96 285	98.89 252	96.99 285	88.95 254	86.63 292	95.67 264	76.48 285	95.00 328	87.04 278	84.04 277	93.84 293

Baseline_NR-MVSNet		90.33 266	89.51 264	92.81 290	92.84 312	89.95 286	99.77 127	93.94 352	84.69 314	89.04 257	95.66 265	81.66 242	96.52 296	90.99 230	76.98 327	91.97 331

cl-mvsnet2		93.77 193	93.25 197	95.33 212	99.49 99	94.43 192	99.61 165	98.09 183	90.38 231	89.16 256	95.61 266	90.56 163	97.34 250	91.93 215	84.45 270	94.21 257

K. test v3		88.05 292	87.24 294	90.47 311	91.82 326	82.23 336	98.96 246	97.42 245	89.05 248	76.93 340	95.60 267	68.49 323	95.42 322	85.87 289	81.01 298	93.75 298

SCA		94.69 169	93.81 180	97.33 163	97.10 217	94.44 191	98.86 258	98.32 151	93.30 146	96.17 164	95.59 268	76.48 285	97.95 229	91.06 227	97.43 152	99.59 130

Patchmatch-test		92.65 219	91.50 229	96.10 197	96.85 231	90.49 275	91.50 350	97.19 264	82.76 325	90.23 227	95.59 268	95.02 54	98.00 225	77.41 330	96.98 165	99.82 97

cl-mvsnet1		92.32 224	91.60 225	94.47 244	97.31 211	92.74 225	99.58 168	96.75 307	86.99 284	87.64 278	95.54 270	89.55 174	96.50 297	88.58 258	82.44 283	94.17 259

Anonymous20231211		89.86 276	88.44 282	94.13 256	98.93 123	90.68 270	98.54 279	98.26 162	76.28 341	86.73 290	95.54 270	70.60 316	97.56 241	90.82 235	80.27 305	94.15 266

miper_ehance_all_eth		93.16 205	92.60 205	94.82 229	97.57 198	93.56 209	99.50 182	97.07 278	88.75 258	88.85 260	95.52 272	90.97 156	96.74 287	90.77 236	84.45 270	94.17 259

cl-mvsnet____		92.31 225	91.58 226	94.52 240	97.33 210	92.77 223	99.57 170	96.78 306	86.97 285	87.56 280	95.51 273	89.43 175	96.62 293	88.60 257	82.44 283	94.16 264

tfpnnormal		89.29 284	87.61 291	94.34 250	94.35 285	94.13 197	98.95 247	98.94 36	83.94 316	84.47 311	95.51 273	74.84 298	97.39 247	77.05 333	80.41 302	91.48 335

DeepMVS_CX				82.92 335	95.98 252	58.66 359		96.01 325	92.72 163	78.34 337	95.51 273	58.29 349	98.08 220	82.57 307	85.29 263	92.03 330

cl_fuxian		92.53 220	91.87 222	94.52 240	97.40 205	92.99 221	99.40 194	96.93 294	87.86 271	88.69 263	95.44 276	89.95 169	96.44 299	90.45 240	80.69 301	94.14 269

IterMVS		90.91 251	90.17 252	93.12 284	96.78 237	90.42 278	98.89 252	97.05 281	89.03 249	86.49 295	95.42 277	76.59 284	95.02 327	87.22 276	84.09 274	93.93 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		93.07 208	92.13 215	95.88 201	94.84 277	96.24 141	99.88 85	98.98 34	92.49 181	89.25 251	95.40 278	87.09 200	97.14 263	93.13 205	78.16 316	94.26 252

tpm2		95.47 153	95.18 152	96.35 192	96.91 226	91.70 255	96.96 321	97.93 197	88.04 270	98.44 107	95.40 278	93.32 107	97.97 226	94.00 184	95.61 190	99.38 161

pmmvs6		85.69 300	83.84 306	91.26 305	90.00 341	84.41 327	97.82 306	96.15 323	75.86 343	81.29 326	95.39 280	61.21 345	96.87 282	83.52 304	73.29 336	92.50 324

IterMVS-LS		92.69 217	92.11 216	94.43 248	96.80 234	92.74 225	99.45 190	96.89 297	88.98 251	89.65 242	95.38 281	88.77 185	96.34 303	90.98 231	82.04 286	94.22 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		94.53 177	95.30 148	92.22 295	97.77 185	82.54 333	99.59 167	97.06 279	94.92 81	95.29 178	95.37 282	85.81 210	97.89 232	94.80 163	97.07 162	96.23 225

v2v482		91.30 243	90.07 255	95.01 221	93.13 304	93.79 204	99.77 127	97.02 282	88.05 269	89.25 251	95.37 282	80.73 253	97.15 262	87.28 275	80.04 307	94.09 272

FMVSNet3		92.69 217	91.58 226	95.99 198	98.29 152	97.42 101	99.26 216	97.62 219	89.80 242	89.68 239	95.32 284	81.62 244	96.27 306	87.01 280	85.65 260	94.29 250

MVP-Stereo		90.93 250	90.45 245	92.37 294	91.25 332	88.76 297	98.05 302	96.17 322	87.27 279	84.04 312	95.30 285	78.46 274	97.27 258	83.78 301	99.70 94	91.09 336
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
anonymousdsp		91.79 239	90.92 237	94.41 249	90.76 335	92.93 222	98.93 249	97.17 267	89.08 247	87.46 283	95.30 285	78.43 275	96.92 279	92.38 211	88.73 234	93.39 309

v1921920		90.46 262	89.12 270	94.50 242	92.96 311	92.46 234	99.49 184	96.98 287	86.10 294	89.61 244	95.30 285	78.55 273	97.03 274	82.17 310	80.89 300	94.01 278

VPA-MVSNet		92.70 216	91.55 228	96.16 195	95.09 273	96.20 142	98.88 254	99.00 33	91.02 222	91.82 213	95.29 288	76.05 291	97.96 228	95.62 152	81.19 292	94.30 249

PatchmatchNet		95.94 142	95.45 143	97.39 159	97.83 181	94.41 193	96.05 331	98.40 132	92.86 155	97.09 141	95.28 289	94.21 86	98.07 222	89.26 253	98.11 139	99.70 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_lstm_enhance		91.81 234	91.39 232	93.06 287	97.34 208	89.18 295	99.38 199	96.79 305	86.70 288	87.47 282	95.22 290	90.00 168	95.86 319	88.26 262	81.37 291	94.15 266

test_0402		85.58 301	83.94 305	90.50 310	93.81 294	85.04 324	98.55 277	95.20 342	76.01 342	79.72 333	95.13 291	64.15 339	96.26 307	66.04 352	86.88 254	90.21 344

tpmrst		96.27 137	95.98 125	97.13 166	97.96 172	93.15 217	96.34 327	98.17 174	92.07 191	98.71 96	95.12 292	93.91 93	98.73 173	94.91 160	96.62 169	99.50 149

V42		91.28 245	90.12 254	94.74 230	93.42 301	93.46 212	99.68 151	97.02 282	87.36 277	89.85 237	95.05 293	81.31 247	97.34 250	87.34 274	80.07 306	93.40 308

EPMVS		96.53 125	96.01 122	98.09 132	98.43 148	96.12 147	96.36 326	99.43 19	93.53 140	97.64 130	95.04 294	94.41 70	98.38 201	91.13 225	98.11 139	99.75 106

v1192		90.62 260	89.25 268	94.72 232	93.13 304	93.07 218	99.50 182	97.02 282	86.33 292	89.56 245	95.01 295	79.22 266	97.09 269	82.34 309	81.16 293	94.01 278

v148		90.70 256	89.63 259	93.92 265	92.97 310	90.97 265	99.75 135	96.89 297	87.51 274	88.27 272	95.01 295	81.67 241	97.04 272	87.40 273	77.17 326	93.75 298

FMVSNet2		91.02 249	89.56 261	95.41 210	97.53 199	95.74 159	98.98 243	97.41 247	87.05 281	88.43 268	95.00 297	71.34 312	96.24 308	85.12 292	85.21 265	94.25 254

our_test_3		90.39 263	89.48 266	93.12 284	92.40 318	89.57 291	99.33 205	96.35 319	87.84 272	85.30 307	94.99 298	84.14 226	96.09 313	80.38 318	84.56 269	93.71 303

v1144		91.09 248	89.83 256	94.87 226	93.25 303	93.69 207	99.62 164	96.98 287	86.83 287	89.64 243	94.99 298	80.94 250	97.05 270	85.08 293	81.16 293	93.87 291

v144192		90.79 255	89.52 263	94.59 236	93.11 307	92.77 223	99.56 172	96.99 285	86.38 291	89.82 238	94.95 300	80.50 258	97.10 267	83.98 299	80.41 302	93.90 288

CostFormer		96.10 138	95.88 135	96.78 175	97.03 221	92.55 233	97.08 318	97.83 208	90.04 239	98.72 95	94.89 301	95.01 56	98.29 207	96.54 141	95.77 186	99.50 149

v1240		90.20 270	88.79 277	94.44 246	93.05 309	92.27 238	99.38 199	96.92 295	85.89 296	89.36 248	94.87 302	77.89 276	97.03 274	80.66 317	81.08 296	94.01 278

v7n		89.65 280	88.29 285	93.72 270	92.22 320	90.56 274	99.07 232	97.10 274	85.42 307	86.73 290	94.72 303	80.06 261	97.13 264	81.14 315	78.12 317	93.49 306

GBi-Net		90.88 252	89.82 257	94.08 257	97.53 199	91.97 242	98.43 284	96.95 290	87.05 281	89.68 239	94.72 303	71.34 312	96.11 310	87.01 280	85.65 260	94.17 259

test1		90.88 252	89.82 257	94.08 257	97.53 199	91.97 242	98.43 284	96.95 290	87.05 281	89.68 239	94.72 303	71.34 312	96.11 310	87.01 280	85.65 260	94.17 259

FMVSNet1		88.50 289	86.64 295	94.08 257	95.62 268	91.97 242	98.43 284	96.95 290	83.00 322	86.08 303	94.72 303	59.09 348	96.11 310	81.82 313	84.07 275	94.17 259

dp		95.05 160	94.43 165	96.91 171	97.99 171	92.73 227	96.29 328	97.98 192	89.70 243	95.93 167	94.67 307	93.83 97	98.45 190	86.91 283	96.53 171	99.54 143

test20.03		84.72 309	83.99 303	86.91 329	88.19 347	80.62 346	98.88 254	95.94 326	88.36 266	78.87 334	94.62 308	68.75 321	89.11 354	66.52 350	75.82 331	91.00 337

D2MVS		92.76 214	92.59 208	93.27 281	95.13 272	89.54 292	99.69 149	99.38 21	92.26 186	87.59 279	94.61 309	85.05 220	97.79 234	91.59 220	88.01 244	92.47 325

v8		90.54 261	89.17 269	94.66 233	93.43 300	93.40 215	99.20 219	96.94 293	85.76 299	87.56 280	94.51 310	81.96 239	97.19 260	84.94 294	78.25 315	93.38 310

v10		90.25 269	88.82 276	94.57 238	93.53 298	93.43 213	99.08 228	96.87 299	85.00 309	87.34 286	94.51 310	80.93 251	97.02 276	82.85 306	79.23 309	93.26 312

ADS-MVSNet2		93.80 192	93.88 178	93.55 277	97.87 178	85.94 318	94.24 337	96.84 300	90.07 237	96.43 157	94.48 312	90.29 166	95.37 323	87.44 271	97.23 158	99.36 164

ADS-MVSNet		94.79 165	94.02 174	97.11 168	97.87 178	93.79 204	94.24 337	98.16 177	90.07 237	96.43 157	94.48 312	90.29 166	98.19 217	87.44 271	97.23 158	99.36 164

WR-MVS_H		91.30 243	90.35 246	94.15 254	94.17 288	92.62 232	99.17 222	98.94 36	88.87 256	86.48 296	94.46 314	84.36 224	96.61 294	88.19 263	78.51 314	93.21 314

LCM-MVSNet-Re		92.31 225	92.60 205	91.43 303	97.53 199	79.27 349	99.02 240	91.83 356	92.07 191	80.31 330	94.38 315	83.50 230	95.48 321	97.22 128	97.58 150	99.54 143

tpmvs		94.28 185	93.57 185	96.40 189	98.55 142	91.50 260	95.70 336	98.55 83	87.47 275	92.15 211	94.26 316	91.42 146	98.95 162	88.15 264	95.85 184	98.76 199

tpm		93.70 197	93.41 192	94.58 237	95.36 271	87.41 312	97.01 319	96.90 296	90.85 225	96.72 150	94.14 317	90.40 164	96.84 283	90.75 237	88.54 239	99.51 147

Anonymous20231206		86.32 298	85.42 300	89.02 321	89.11 344	80.53 347	99.05 237	95.28 339	85.43 306	82.82 318	93.92 318	74.40 301	93.44 344	66.99 349	81.83 288	93.08 316

UnsupCasMVSNet_eth		85.52 302	83.99 303	90.10 314	89.36 343	83.51 329	96.65 323	97.99 190	89.14 246	75.89 344	93.83 319	63.25 341	93.92 338	81.92 312	67.90 346	92.88 319

tpm cat1		93.51 199	92.52 210	96.47 184	97.77 185	91.47 261	96.13 329	98.06 186	80.98 331	92.91 206	93.78 320	89.66 172	98.87 163	87.03 279	96.39 174	99.09 186

EG-PatchMatch MVS		85.35 305	83.81 307	89.99 316	90.39 337	81.89 338	98.21 296	96.09 324	81.78 329	74.73 346	93.72 321	51.56 355	97.12 266	79.16 324	88.61 236	90.96 338

test_method		80.79 318	79.70 321	84.08 332	92.83 313	67.06 355	99.51 180	95.42 336	54.34 355	81.07 328	93.53 322	44.48 358	92.22 347	78.90 325	77.23 325	92.94 318

N_pmnet		80.06 321	80.78 319	77.89 336	91.94 323	45.28 365	98.80 263	56.82 368	78.10 339	80.08 332	93.33 323	77.03 278	95.76 320	68.14 348	82.81 281	92.64 321

MDA-MVSNet-bldmvs		84.09 312	81.52 318	91.81 301	91.32 331	88.00 310	98.67 273	95.92 327	80.22 333	55.60 358	93.32 324	68.29 325	93.60 343	73.76 338	76.61 330	93.82 295

CR-MVSNet		93.45 202	92.62 204	95.94 200	96.29 243	92.66 229	92.01 348	96.23 320	92.62 170	96.94 143	93.31 325	91.04 154	96.03 315	79.23 321	95.96 181	99.13 184

Patchmtry		89.70 279	88.49 281	93.33 279	96.24 245	89.94 288	91.37 351	96.23 320	78.22 338	87.69 277	93.31 325	91.04 154	96.03 315	80.18 320	82.10 285	94.02 276

MIMVSNet		90.30 267	88.67 279	95.17 218	96.45 242	91.64 257	92.39 346	97.15 270	85.99 295	90.50 225	93.19 327	66.95 329	94.86 331	82.01 311	93.43 211	99.01 189

YYNet1		85.50 304	83.33 309	92.00 298	90.89 334	88.38 306	99.22 218	96.55 314	79.60 336	57.26 356	92.72 328	79.09 269	93.78 341	77.25 331	77.37 324	93.84 293

MVS_0304		89.28 285	88.31 284	92.21 296	97.05 220	86.53 315	97.76 308	99.57 12	85.58 304	93.86 196	92.71 329	51.04 356	96.30 305	84.49 296	92.72 217	93.79 296

MDA-MVSNet_test_wron		85.51 303	83.32 310	92.10 297	90.96 333	88.58 302	99.20 219	96.52 315	79.70 335	57.12 357	92.69 330	79.11 268	93.86 340	77.10 332	77.46 323	93.86 292

MIMVSNet1		82.58 316	80.51 320	88.78 323	86.68 349	84.20 328	96.65 323	95.41 337	78.75 337	78.59 336	92.44 331	51.88 354	89.76 353	65.26 353	78.95 311	92.38 327

KD-MVS_2432*1600		88.00 293	86.10 297	93.70 273	96.91 226	94.04 198	97.17 316	97.12 272	84.93 310	81.96 321	92.41 332	92.48 130	94.51 334	79.23 321	52.68 355	92.56 322

miper_refine_blended		88.00 293	86.10 297	93.70 273	96.91 226	94.04 198	97.17 316	97.12 272	84.93 310	81.96 321	92.41 332	92.48 130	94.51 334	79.23 321	52.68 355	92.56 322

FMVSNet5		88.32 290	87.47 292	90.88 306	96.90 229	88.39 305	97.28 313	95.68 331	82.60 326	84.67 310	92.40 334	79.83 263	91.16 350	76.39 335	81.51 290	93.09 315

DSMNet-mixed		88.28 291	88.24 286	88.42 326	89.64 342	75.38 351	98.06 301	89.86 359	85.59 303	88.20 273	92.14 335	76.15 290	91.95 348	78.46 326	96.05 179	97.92 209

patchmatchnet-post											91.70 336	95.12 48	97.95 229

OpenMVS_ROB	79.82 20	83.77 314	81.68 317	90.03 315	88.30 346	82.82 330	98.46 282	95.22 341	73.92 349	76.00 343	91.29 337	55.00 352	96.94 278	68.40 347	88.51 240	90.34 342

Anonymous20240521		85.15 306	83.81 307	89.16 320	88.32 345	82.69 331	98.80 263	95.74 329	79.72 334	81.53 325	90.99 338	65.38 335	94.16 336	72.69 340	81.11 295	90.63 341

Patchmatch-RL test		86.90 297	85.98 299	89.67 317	84.45 352	75.59 350	89.71 353	92.43 354	86.89 286	77.83 338	90.94 339	94.22 83	93.63 342	87.75 269	69.61 339	99.79 100

CL-MVSNet_2432*1600		84.50 310	83.15 312	88.53 325	86.00 350	81.79 339	98.82 261	97.35 252	85.12 308	83.62 316	90.91 340	76.66 283	91.40 349	69.53 345	60.36 352	92.40 326

FPMVS		68.72 323	68.72 326	68.71 341	65.95 362	44.27 367	95.97 333	94.74 346	51.13 356	53.26 359	90.50 341	25.11 364	83.00 358	60.80 355	80.97 299	78.87 354

new_pmnet		84.49 311	82.92 313	89.21 319	90.03 340	82.60 332	96.89 322	95.62 333	80.59 332	75.77 345	89.17 342	65.04 337	94.79 332	72.12 341	81.02 297	90.23 343

DIV-MVS_2432*1600		83.59 315	82.06 315	88.20 327	86.93 348	80.70 345	97.21 314	96.38 318	82.87 323	82.49 319	88.97 343	67.63 327	92.32 346	73.75 339	62.30 351	91.58 334

PM-MVS		80.47 319	78.88 323	85.26 331	83.79 354	72.22 352	95.89 334	91.08 357	85.71 302	76.56 342	88.30 344	36.64 359	93.90 339	82.39 308	69.57 340	89.66 347

pmmvs3		80.27 320	77.77 324	87.76 328	80.32 356	82.43 334	98.23 294	91.97 355	72.74 350	78.75 335	87.97 345	57.30 351	90.99 351	70.31 343	62.37 350	89.87 345

pmmvs-eth3d		84.03 313	81.97 316	90.20 313	84.15 353	87.09 313	98.10 300	94.73 347	83.05 321	74.10 347	87.77 346	65.56 334	94.01 337	81.08 316	69.24 341	89.49 348

test123		37.68 334	39.14 337	33.31 347	19.94 369	24.83 370	98.36 288	9.75 370	15.53 365	51.31 360	87.14 347	19.62 367	17.74 366	47.10 359	3.47 365	57.36 359

new-patchmatchnet		81.19 317	79.34 322	86.76 330	82.86 355	80.36 348	97.92 304	95.27 340	82.09 328	72.02 348	86.87 348	62.81 342	90.74 352	71.10 342	63.08 349	89.19 350

ambc				83.23 334	77.17 357	62.61 356	87.38 355	94.55 349		76.72 341	86.65 349	30.16 360	96.36 302	84.85 295	69.86 338	90.73 340

PatchT		90.38 264	88.75 278	95.25 216	95.99 250	90.16 281	91.22 352	97.54 230	76.80 340	97.26 137	86.01 350	91.88 142	96.07 314	66.16 351	95.91 183	99.51 147

RPMNet		89.76 278	87.28 293	97.19 165	96.29 243	92.66 229	92.01 348	98.31 153	70.19 352	96.94 143	85.87 351	87.25 198	99.78 111	62.69 354	95.96 181	99.13 184

UnsupCasMVSNet_bld		79.97 322	77.03 325	88.78 323	85.62 351	81.98 337	93.66 342	97.35 252	75.51 346	70.79 350	83.05 352	48.70 357	94.91 330	78.31 327	60.29 353	89.46 349

LCM-MVSNet		67.77 324	64.73 328	76.87 337	62.95 364	56.25 361	89.37 354	93.74 353	44.53 358	61.99 353	80.74 353	20.42 366	86.53 356	69.37 346	59.50 354	87.84 351

PMMVS2		67.15 325	64.15 329	76.14 338	70.56 361	62.07 358	93.89 340	87.52 363	58.09 354	60.02 354	78.32 354	22.38 365	84.54 357	59.56 356	47.03 357	81.80 353

JIA-IIPM		91.76 240	90.70 240	94.94 224	96.11 246	87.51 311	93.16 344	98.13 182	75.79 344	97.58 131	77.68 355	92.84 121	97.97 226	88.47 261	96.54 170	99.33 168

PMVS	49.05 23	53.75 329	51.34 333	60.97 344	40.80 368	34.68 368	74.82 359	89.62 361	37.55 360	28.67 366	72.12 356	7.09 370	81.63 359	43.17 361	68.21 345	66.59 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS-HIRNet		86.22 299	83.19 311	95.31 213	96.71 240	90.29 279	92.12 347	97.33 255	62.85 353	86.82 289	70.37 357	69.37 319	97.49 243	75.12 337	97.99 145	98.15 206

gg-mvs-nofinetune		93.51 199	91.86 223	98.47 114	97.72 192	97.96 77	92.62 345	98.51 97	74.70 347	97.33 136	69.59 358	98.91 3	97.79 234	97.77 116	99.56 104	99.67 117

Gipuma		66.95 326	65.00 327	72.79 339	91.52 329	67.96 354	66.16 360	95.15 344	47.89 357	58.54 355	67.99 359	29.74 361	87.54 355	50.20 358	77.83 319	62.87 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		56.10 328	52.24 331	67.66 342	49.27 366	56.82 360	83.94 356	82.02 364	70.47 351	33.28 365	64.54 360	17.23 368	69.16 362	45.59 360	23.85 361	77.02 355

E-PMN		52.30 330	52.18 332	52.67 345	71.51 359	45.40 364	93.62 343	76.60 366	36.01 361	43.50 362	64.13 361	27.11 363	67.31 363	31.06 363	26.06 359	45.30 362

test_post											63.35 362	94.43 69	98.13 218

MVE	53.74 22	51.54 331	47.86 335	62.60 343	59.56 365	50.93 362	79.41 358	77.69 365	35.69 362	36.27 364	61.76 363	5.79 372	69.63 361	37.97 362	36.61 358	67.24 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		51.44 332	51.22 334	52.11 346	70.71 360	44.97 366	94.04 339	75.66 367	35.34 363	42.40 363	61.56 364	28.93 362	65.87 364	27.64 364	24.73 360	45.49 361

test_post1							95.78 335				59.23 365	93.20 114	97.74 236	91.06 227

X-MVStestdata		93.83 189	92.06 218	99.15 61	99.94 14	97.50 94	99.94 56	98.42 127	96.22 49	99.41 55	41.37 366	94.34 76	99.96 53	98.92 60	99.95 51	99.99 20

wuyk23d		20.37 336	20.84 339	18.99 349	65.34 363	27.73 369	50.43 361	7.67 371	9.50 366	8.01 367	6.34 367	6.13 371	26.24 365	23.40 365	10.69 364	2.99 363

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		7.60 338	10.13 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	91.20 150	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

IU-MVS					99.93 26	99.31 7		98.41 131	97.71 8	99.84 8				100.00 1	100.00 1	100.00 1

save fliter					99.82 65	98.79 33	99.96 23	98.40 132	97.66 10

test_0728_SECOND				99.82 5	99.94 14	99.47 5	99.95 41	98.43 116					100.00 1	99.99 5	100.00 1	100.00 1

GSMVS																99.59 130

test_part2					99.89 45	99.25 13				99.49 49

sam_mvs1												94.72 64				99.59 130

sam_mvs												94.25 82

MTGPA								98.28 158

MTMP							99.87 88	96.49 316

test9_res														99.71 29	99.99 20	100.00 1

agg_prior2														99.48 36	100.00 1	100.00 1

agg_prior					99.93 26	98.77 36		98.43 116		99.63 36			99.85 94

test_prior4						98.05 71	99.94 56

test_prior				99.43 35	99.94 14	98.49 57		98.65 60					99.80 106			99.99 20

旧先验2							99.46 189		94.21 112	99.85 6			99.95 60	96.96 135

新几何2							99.40 194

无先验							99.49 184	98.71 53	93.46 142				100.00 1	94.36 176		99.99 20

原ACMM2							99.90 74

testdata2													99.99 36	90.54 239

segment_acmp												96.68 22

testdata1							99.28 214		96.35 48

test12				99.43 35	99.74 77	98.56 53		98.40 132		99.65 33		94.76 63	99.75 121		99.98 33	99.99 20

plane_prior7					95.71 263	91.59 259

plane_prior6					95.76 258	91.72 254						80.47 259

plane_prior5								97.87 203					98.37 202	97.79 114	89.55 223	94.52 231

plane_prior3						91.64 257			96.63 38	93.01 203

plane_prior2							99.84 106		96.38 44

plane_prior1					95.73 260

plane_prior						91.74 251	99.86 99		96.76 34						89.59 222

n2								0.00 372

nn								0.00 372

door-mid								89.69 360

test11								98.44 108

door								90.31 358

HQP5-MVS						91.85 247

HQP-NCC					95.78 254		99.87 88		96.82 30	93.37 199

ACMP_Plane					95.78 254		99.87 88		96.82 30	93.37 199

BP-MVS														97.92 109

HQP4-MVS										93.37 199			98.39 197			94.53 229

HQP3-MVS								97.89 201							89.60 220

HQP2-MVS												80.65 255

MDTV_nov1_ep13_2view						96.26 137	96.11 330		91.89 196	98.06 121		94.40 71		94.30 179		99.67 117

ACMMP++_ref															87.04 253

ACMMP++															88.23 242

Test By Simon												92.82 123

High-resolution multi-view benchmark