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.

SED-MVS		99.28 5	99.11 6	99.77 6	99.93 26	99.30 8	99.96 23	98.43 114	97.27 20	99.80 16	99.94 4	96.71 20	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 26	99.31 7		98.41 129	97.71 8	99.84 8				100.00 1	100.00 1	100.00 1

test_241102_TWO								98.43 114	97.27 20	99.80 16	99.94 4	97.18 17	100.00 1	100.00 1	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 40	98.32 149	97.28 18	99.83 10	99.91 13	97.22 15	100.00 1	99.99 5	100.00 1	99.89 90

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

test_0728_SECOND				99.82 5	99.94 14	99.47 5	99.95 40	98.43 114					100.00 1	99.99 5	100.00 1	100.00 1

DPE-MVS		99.26 6	99.10 7	99.74 7	99.89 45	99.24 14	99.87 87	98.44 106	97.48 15	99.64 34	99.94 4	96.68 22	99.99 36	99.99 5	100.00 1	99.99 20

agg_prior2														99.48 36	100.00 1	100.00 1

region2R		98.54 39	98.37 40	99.05 73	99.96 8	97.18 106	99.96 23	98.55 81	94.87 83	99.45 50	99.85 33	94.07 89	100.00 1	98.67 77	100.00 1	99.98 51

test_prior3		98.99 13	98.84 14	99.43 35	99.94 14	98.49 57	99.95 40	98.65 59	95.78 58	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 40		95.78 58	99.73 26	99.76 72	96.00 29		99.78 20	100.00 1

MSLP-MVS++		99.13 7	99.01 9	99.49 31	99.94 14	98.46 59	99.98 8	98.86 44	97.10 25	99.80 16	99.94 4	95.92 33	100.00 1	99.51 34	100.00 1	100.00 1

APDe-MVS		99.06 10	98.91 12	99.51 28	99.94 14	98.76 40	99.91 69	98.39 133	97.20 24	99.46 49	99.85 33	95.53 42	99.79 109	99.86 12	100.00 1	99.99 20

MCST-MVS		99.32 3	99.14 4	99.86 3	99.97 3	99.59 3	99.97 16	98.64 62	98.47 2	99.13 76	99.92 11	96.38 26	100.00 1	99.74 24	100.00 1	100.00 1

CDPH-MVS		98.65 31	98.36 42	99.49 31	99.94 14	98.73 41	99.87 87	98.33 147	93.97 121	99.76 24	99.87 26	94.99 58	99.75 120	98.55 84	100.00 1	99.98 51

mPP-MVS		98.39 52	98.20 50	98.97 81	99.97 3	96.92 116	99.95 40	98.38 137	95.04 78	98.61 99	99.80 58	93.39 104	100.00 1	98.64 81	100.00 1	99.98 51

CNVR-MVS		99.40 1	99.26 1	99.84 4	99.98 2	99.51 4	99.98 8	98.69 54	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 66	98.02 6	99.90 2	99.95 3	97.33 13	100.00 1	99.54 33	100.00 1	100.00 1

MG-MVS		98.91 17	98.65 20	99.68 11	99.94 14	99.07 18	99.64 160	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

ZNCC-MVS		98.31 56	98.03 60	99.17 57	99.88 49	97.59 87	99.94 55	98.44 106	94.31 106	98.50 103	99.82 53	93.06 117	99.99 36	98.30 92	99.99 20	99.93 81

testtj		98.89 18	98.69 18	99.52 26	99.94 14	98.56 53	99.90 73	98.55 81	95.14 77	99.72 29	99.84 46	95.46 43	100.00 1	99.65 32	99.99 20	99.99 20

SMA-MVS		98.76 26	98.48 29	99.62 15	99.87 52	98.87 27	99.86 98	98.38 137	93.19 147	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
test9_res														99.71 29	99.99 20	100.00 1

agg_prior1		98.88 19	98.66 19	99.54 23	99.93 26	98.77 36	99.96 23	98.43 114	94.63 92	99.63 35	99.85 33	95.79 37	99.85 94	99.72 28	99.99 20	99.99 20

HPM-MVS++		99.07 9	98.88 13	99.63 12	99.90 42	99.02 19	99.95 40	98.56 76	97.56 13	99.44 51	99.85 33	95.38 45	100.00 1	99.31 43	99.99 20	99.87 93

HPM-MVS_fast		97.80 80	97.50 80	98.68 95	99.79 70	96.42 129	99.88 84	98.16 174	91.75 199	98.94 85	99.54 108	91.82 143	99.65 137	97.62 118	99.99 20	99.99 20

HPM-MVS		97.96 70	97.72 72	98.68 95	99.84 60	96.39 132	99.90 73	98.17 171	92.61 169	98.62 98	99.57 105	91.87 141	99.67 135	98.87 66	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
APD-MVS		98.62 32	98.35 43	99.41 39	99.90 42	98.51 56	99.87 87	98.36 142	94.08 114	99.74 25	99.73 83	94.08 88	99.74 124	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
CP-MVS		98.45 46	98.32 44	98.87 86	99.96 8	96.62 123	99.97 16	98.39 133	94.43 98	98.90 86	99.87 26	94.30 81	100.00 1	99.04 54	99.99 20	99.99 20

SteuartSystems-ACMMP		99.02 11	98.97 11	99.18 54	98.72 136	97.71 83	99.98 8	98.44 106	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.
CPTT-MVS		97.64 86	97.32 88	98.58 105	99.97 3	95.77 156	99.96 23	98.35 144	89.90 237	98.36 109	99.79 62	91.18 151	99.99 36	98.37 89	99.99 20	99.99 20

DeepC-MVS_fast	96.59 1	98.81 22	98.54 26	99.62 15	99.90 42	98.85 29	99.24 213	98.47 101	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
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

ETH3 D test6400		98.81 22	98.54 26	99.59 18	99.93 26	98.93 22	99.93 61	98.46 103	94.56 93	99.84 8	99.92 11	94.32 80	99.86 90	99.96 8	99.98 33	100.00 1

ETH3D-3000-0.1		98.68 29	98.42 31	99.47 34	99.83 63	98.57 51	99.90 73	98.37 140	93.81 129	99.81 12	99.90 17	94.34 76	99.86 90	99.84 13	99.98 33	99.97 63

MSP-MVS		99.09 8	99.12 5	98.98 80	99.93 26	97.24 103	99.95 40	98.42 125	97.50 14	99.52 47	99.88 22	97.43 12	99.71 128	99.50 35	99.98 33	100.00 1

TSAR-MVS + MP.		98.93 15	98.77 16	99.41 39	99.74 77	98.67 44	99.77 126	98.38 137	96.73 35	99.88 3	99.74 81	94.89 62	99.59 139	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
train_agg		98.88 19	98.65 20	99.59 18	99.92 35	98.92 23	99.96 23	98.43 114	94.35 103	99.71 30	99.86 29	95.94 31	99.85 94	99.69 31	99.98 33	99.99 20

HFP-MVS		98.56 37	98.37 40	99.14 63	99.96 8	97.43 98	99.95 40	98.61 68	94.77 85	99.31 63	99.85 33	94.22 83	100.00 1	98.70 75	99.98 33	99.98 51

#test#		98.59 35	98.41 33	99.14 63	99.96 8	97.43 98	99.95 40	98.61 68	95.00 79	99.31 63	99.85 33	94.22 83	100.00 1	98.78 72	99.98 33	99.98 51

ACMMPR		98.50 42	98.32 44	99.05 73	99.96 8	97.18 106	99.95 40	98.60 70	94.77 85	99.31 63	99.84 46	93.73 98	100.00 1	98.70 75	99.98 33	99.98 51

test12				99.43 35	99.74 77	98.56 53		98.40 130		99.65 33		94.76 63	99.75 120		99.98 33	99.99 20

PAPM_NR		98.12 66	97.93 68	98.70 94	99.94 14	96.13 145	99.82 112	98.43 114	94.56 93	97.52 130	99.70 88	94.40 71	99.98 42	97.00 131	99.98 33	99.99 20

ZD-MVS					99.92 35	98.57 51		98.52 88	92.34 181	99.31 63	99.83 49	95.06 52	99.80 106	99.70 30	99.97 44

9.14			98.38 38		99.87 52		99.91 69	98.33 147	93.22 146	99.78 22	99.89 19	94.57 68	99.85 94	99.84 13	99.97 44

MP-MVS		98.23 63	97.97 64	99.03 75	99.94 14	97.17 109	99.95 40	98.39 133	94.70 88	98.26 115	99.81 57	91.84 142	100.00 1	98.85 67	99.97 44	99.93 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
114514_t		97.41 93	96.83 101	99.14 63	99.51 98	97.83 80	99.89 81	98.27 159	88.48 261	99.06 78	99.66 97	90.30 162	99.64 138	96.32 141	99.97 44	99.96 70

SD-MVS		98.92 16	98.70 17	99.56 21	99.70 85	98.73 41	99.94 55	98.34 146	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	97.00 112	99.75 134	99.50 16	93.90 126	99.37 60	99.76 72	93.24 113	100.00 1	97.75 116	99.96 48	99.98 51

API-MVS		97.86 74	97.66 74	98.47 114	99.52 96	95.41 167	99.47 185	98.87 43	91.68 200	98.84 87	99.85 33	92.34 132	99.99 36	98.44 87	99.96 48	100.00 1

ETH3D cwj APD-0.16		98.40 51	98.07 59	99.40 41	99.59 90	98.41 60	99.86 98	98.24 161	92.18 185	99.73 26	99.87 26	93.47 103	99.85 94	99.74 24	99.95 51	99.93 81

SR-MVS		98.46 45	98.30 46	98.93 84	99.88 49	97.04 111	99.84 105	98.35 144	94.92 80	99.32 62	99.80 58	93.35 105	99.78 111	99.30 44	99.95 51	99.96 70

XVS		98.70 28	98.55 25	99.15 61	99.94 14	97.50 94	99.94 55	98.42 125	96.22 49	99.41 54	99.78 66	94.34 76	99.96 53	98.92 61	99.95 51	99.99 20

X-MVStestdata		93.83 186	92.06 214	99.15 61	99.94 14	97.50 94	99.94 55	98.42 125	96.22 49	99.41 54	41.37 357	94.34 76	99.96 53	98.92 61	99.95 51	99.99 20

原ACMM1				98.96 82	99.73 81	96.99 113		98.51 95	94.06 117	99.62 37	99.85 33	94.97 59	99.96 53	95.11 153	99.95 51	99.92 87

test222					99.55 94	97.41 101	99.34 201	98.55 81	91.86 194	99.27 68	99.83 49	93.84 96			99.95 51	99.99 20

DPM-MVS		98.83 21	98.46 30	99.97 1	99.33 106	99.92 1	99.96 23	98.44 106	97.96 7	99.55 42	99.94 4	97.18 17	100.00 1	93.81 186	99.94 57	99.98 51

新几何1				99.42 38	99.75 76	98.27 65		98.63 65	92.69 164	99.55 42	99.82 53	94.40 71	100.00 1	91.21 221	99.94 57	99.99 20

旧先验1					99.76 74	97.52 91		98.64 62			99.85 33	95.63 39			99.94 57	99.99 20

testdata				98.42 119	99.47 100	95.33 169		98.56 76	93.78 131	99.79 21	99.85 33	93.64 101	99.94 68	94.97 155	99.94 57	100.00 1

DELS-MVS		98.54 39	98.22 48	99.50 29	99.15 111	98.65 48	100.00 1	98.58 72	97.70 9	98.21 117	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
MVS_111021_HR		98.72 27	98.62 22	99.01 78	99.36 105	97.18 106	99.93 61	99.90 1	96.81 33	98.67 95	99.77 68	93.92 92	99.89 79	99.27 45	99.94 57	99.96 70

xxxxxxxxxxxxxcwj		98.98 14	98.79 15	99.54 23	99.82 65	98.79 33	99.96 23	97.52 232	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 73	98.21 165	93.53 138	99.81 12	99.89 19	94.70 65	99.86 90	99.84 13	99.93 63	99.96 70

PHI-MVS		98.41 49	98.21 49	99.03 75	99.86 54	97.10 110	99.98 8	98.80 49	90.78 224	99.62 37	99.78 66	95.30 46	100.00 1	99.80 18	99.93 63	99.99 20

DeepPCF-MVS	95.94 2	97.71 84	98.98 10	93.92 261	99.63 88	81.76 333	99.96 23	98.56 76	99.47 1	99.19 74	99.99 1	94.16 87	100.00 1	99.92 9	99.93 63	100.00 1

test1172		98.38 53	98.25 47	98.77 90	99.88 49	96.56 126	99.80 119	98.36 142	94.68 89	99.20 71	99.80 58	93.28 110	99.78 111	99.34 42	99.92 67	99.98 51

SR-MVS-dyc-post		98.31 56	98.17 52	98.71 93	99.79 70	96.37 133	99.76 131	98.31 151	94.43 98	99.40 58	99.75 77	93.28 110	99.78 111	98.90 64	99.92 67	99.97 63

RE-MVS-def			98.13 55		99.79 70	96.37 133	99.76 131	98.31 151	94.43 98	99.40 58	99.75 77	92.95 119		98.90 64	99.92 67	99.97 63

1121		98.03 69	97.57 79	99.40 41	99.74 77	98.21 66	98.31 281	98.62 66	92.78 159	99.53 44	99.83 49	95.08 50	100.00 1	94.36 173	99.92 67	99.99 20

APD-MVS_3200maxsize		98.25 62	98.08 58	98.78 89	99.81 68	96.60 124	99.82 112	98.30 154	93.95 123	99.37 60	99.77 68	92.84 121	99.76 117	98.95 58	99.92 67	99.97 63

Regformer-1		98.79 24	98.60 23	99.36 45	99.85 55	98.34 62	99.87 87	98.52 88	96.05 53	99.41 54	99.79 62	94.93 60	99.76 117	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 87	98.52 88	96.04 54	99.41 54	99.79 62	94.92 61	99.76 117	99.05 50	99.90 72	99.98 51

MP-MVS-pluss		98.07 68	97.64 75	99.38 44	99.74 77	98.41 60	99.74 137	98.18 170	93.35 142	96.45 154	99.85 33	92.64 127	99.97 51	98.91 63	99.89 74	99.77 104
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPM		98.60 33	98.42 31	99.14 63	96.05 242	98.96 20	99.90 73	99.35 23	96.68 37	98.35 110	99.66 97	96.45 25	98.51 185	99.45 37	99.89 74	99.96 70

zzz-MVS		98.33 55	98.00 62	99.30 47	99.85 55	97.93 78	99.80 119	98.28 156	95.76 60	97.18 137	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	99.39 195	98.28 156	95.76 60	97.18 137	99.88 22	92.74 124	100.00 1	98.67 77	99.88 76	99.99 20

MVS		96.60 123	95.56 142	99.72 9	96.85 225	99.22 15	98.31 281	98.94 36	91.57 203	90.90 218	99.61 102	86.66 200	99.96 53	97.36 122	99.88 76	99.99 20

MVS_111021_LR		98.42 48	98.38 38	98.53 111	99.39 103	95.79 155	99.87 87	99.86 2	96.70 36	98.78 90	99.79 62	92.03 138	99.90 75	99.17 46	99.86 79	99.88 92

ACMMP_NAP		98.49 43	98.14 54	99.54 23	99.66 87	98.62 50	99.85 101	98.37 140	94.68 89	99.53 44	99.83 49	92.87 120	100.00 1	98.66 80	99.84 80	99.99 20

Regformer-3		98.58 36	98.41 33	99.10 69	99.84 60	97.57 88	99.66 153	98.52 88	95.79 57	99.01 81	99.77 68	94.40 71	99.75 120	98.82 68	99.83 81	99.98 51

Regformer-4		98.56 37	98.39 37	99.08 71	99.84 60	97.52 91	99.66 153	98.52 88	95.76 60	99.01 81	99.77 68	94.33 79	99.75 120	98.80 71	99.83 81	99.98 51

QAPM		95.40 154	94.17 169	99.10 69	96.92 221	97.71 83	99.40 191	98.68 55	89.31 242	88.94 255	98.89 158	82.48 230	99.96 53	93.12 203	99.83 81	99.62 125

PAPR		98.52 41	98.16 53	99.58 20	99.97 3	98.77 36	99.95 40	98.43 114	95.35 71	98.03 120	99.75 77	94.03 90	99.98 42	98.11 98	99.83 81	99.99 20

3Dnovator+	91.53 11	96.31 133	95.24 149	99.52 26	96.88 224	98.64 49	99.72 145	98.24 161	95.27 75	88.42 266	98.98 149	82.76 229	99.94 68	97.10 129	99.83 81	99.96 70

3Dnovator	91.47 12	96.28 136	95.34 147	99.08 71	96.82 227	97.47 97	99.45 188	98.81 47	95.52 68	89.39 243	99.00 146	81.97 232	99.95 60	97.27 124	99.83 81	99.84 95

LS3D		95.84 144	95.11 154	98.02 136	99.85 55	95.10 177	98.74 258	98.50 99	87.22 277	93.66 195	99.86 29	87.45 192	99.95 60	90.94 229	99.81 87	99.02 187

CHOSEN 280x420		99.01 12	99.03 8	98.95 83	99.38 104	98.87 27	98.46 274	99.42 20	97.03 27	99.02 80	99.09 138	99.35 1	98.21 214	99.73 27	99.78 88	99.77 104

GST-MVS		98.27 59	97.97 64	99.17 57	99.92 35	97.57 88	99.93 61	98.39 133	94.04 119	98.80 89	99.74 81	92.98 118	100.00 1	98.16 95	99.76 89	99.93 81

OpenMVS	90.15 15	94.77 166	93.59 182	98.33 123	96.07 241	97.48 96	99.56 171	98.57 74	90.46 227	86.51 290	98.95 155	78.57 265	99.94 68	93.86 182	99.74 90	97.57 214

1314		96.84 111	95.96 130	99.48 33	96.74 232	98.52 55	98.31 281	98.86 44	95.82 56	89.91 229	98.98 149	87.49 191	99.96 53	97.80 111	99.73 91	99.96 70

abl_6		97.67 85	97.34 86	98.66 97	99.68 86	96.11 148	99.68 150	98.14 177	93.80 130	99.27 68	99.70 88	88.65 184	99.98 42	97.46 120	99.72 92	99.89 90

DP-MVS Recon		98.41 49	98.02 61	99.56 21	99.97 3	98.70 43	99.92 65	98.44 106	92.06 190	98.40 108	99.84 46	95.68 38	100.00 1	98.19 93	99.71 93	99.97 63

MVP-Stereo		90.93 245	90.45 240	92.37 289	91.25 326	88.76 291	98.05 295	96.17 315	87.27 276	84.04 307	95.30 281	78.46 267	97.27 255	83.78 298	99.70 94	91.09 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PS-MVSNAJ		98.44 47	98.20 50	99.16 59	98.80 133	98.92 23	99.54 175	98.17 171	97.34 16	99.85 6	99.85 33	91.20 148	99.89 79	99.41 40	99.67 95	98.69 199

BH-w/o		95.71 148	95.38 146	96.68 178	98.49 144	92.28 233	99.84 105	97.50 236	92.12 187	92.06 209	98.79 168	84.69 216	98.67 178	95.29 152	99.66 96	99.09 185

MAR-MVS		97.43 89	97.19 90	98.15 132	99.47 100	94.79 185	99.05 233	98.76 50	92.65 167	98.66 96	99.82 53	88.52 185	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
MS-PatchMatch		90.65 252	90.30 243	91.71 297	94.22 282	85.50 316	98.24 286	97.70 210	88.67 257	86.42 293	96.37 243	67.82 318	98.03 221	83.62 299	99.62 98	91.60 326

MVSFormer		96.94 107	96.60 108	97.95 137	97.28 210	97.70 85	99.55 173	97.27 257	91.17 213	99.43 52	99.54 108	90.92 156	96.89 278	94.67 168	99.62 98	99.25 174

lupinMVS		97.85 75	97.60 77	98.62 100	97.28 210	97.70 85	99.99 4	97.55 226	95.50 69	99.43 52	99.67 95	90.92 156	98.71 175	98.40 88	99.62 98	99.45 154

BH-untuned		95.18 157	94.83 158	96.22 191	98.36 148	91.22 258	99.80 119	97.32 254	90.91 220	91.08 216	98.67 172	83.51 224	98.54 184	94.23 178	99.61 101	98.92 189

DeepC-MVS	94.51 4	96.92 109	96.40 115	98.45 116	99.16 110	95.90 152	99.66 153	98.06 183	96.37 47	94.37 186	99.49 111	83.29 227	99.90 75	97.63 117	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
GG-mvs-BLEND				98.54 109	98.21 158	98.01 73	93.87 332	98.52 88		97.92 122	97.92 200	99.02 2	97.94 228	98.17 94	99.58 103	99.67 117

gg-mvs-nofinetune		93.51 196	91.86 219	98.47 114	97.72 190	97.96 77	92.62 336	98.51 95	74.70 338	97.33 134	69.59 349	98.91 3	97.79 231	97.77 114	99.56 104	99.67 117

BH-RMVSNet		95.18 157	94.31 167	97.80 141	98.17 161	95.23 174	99.76 131	97.53 230	92.52 176	94.27 188	99.25 131	76.84 274	98.80 166	90.89 231	99.54 105	99.35 165

EI-MVSNet-Vis-set		98.27 59	98.11 57	98.75 92	99.83 63	96.59 125	99.40 191	98.51 95	95.29 73	98.51 102	99.76 72	93.60 102	99.71 128	98.53 85	99.52 106	99.95 78

TAPA-MVS	92.12 8	94.42 178	93.60 181	96.90 171	99.33 106	91.78 246	99.78 123	98.00 186	89.89 238	94.52 183	99.47 112	91.97 139	99.18 154	69.90 336	99.52 106	99.73 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	95.54 3	97.93 72	97.89 69	98.05 135	99.82 65	94.77 186	99.92 65	98.46 103	93.93 124	97.20 136	99.27 127	95.44 44	99.97 51	97.41 121	99.51 108	99.41 159
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
jason		97.24 98	96.86 100	98.38 122	95.73 254	97.32 102	99.97 16	97.40 248	95.34 72	98.60 100	99.54 108	87.70 189	98.56 182	97.94 108	99.47 109	99.25 174
jason: jason.
CSCG		97.10 102	97.04 96	97.27 164	99.89 45	91.92 242	99.90 73	99.07 31	88.67 257	95.26 177	99.82 53	93.17 115	99.98 42	98.15 96	99.47 109	99.90 89

CNLPA		97.76 82	97.38 83	98.92 85	99.53 95	96.84 117	99.87 87	98.14 177	93.78 131	96.55 152	99.69 91	92.28 133	99.98 42	97.13 127	99.44 111	99.93 81

AdaColmap		97.23 99	96.80 103	98.51 112	99.99 1	95.60 163	99.09 222	98.84 46	93.32 143	96.74 147	99.72 84	86.04 205	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 95	97.00 28	98.52 101	99.71 86	87.80 188	99.95 60	99.75 22	99.38 113	99.83 96

F-COLMAP		96.93 108	96.95 99	96.87 172	99.71 84	91.74 247	99.85 101	97.95 192	93.11 150	95.72 170	99.16 136	92.35 131	99.94 68	95.32 151	99.35 114	98.92 189

EI-MVSNet-UG-set		98.14 65	97.99 63	98.60 102	99.80 69	96.27 135	99.36 200	98.50 99	95.21 76	98.30 112	99.75 77	93.29 109	99.73 127	98.37 89	99.30 115	99.81 98

PVSNet_Blended		97.94 71	97.64 75	98.83 88	99.59 90	96.99 113	100.00 1	99.10 28	95.38 70	98.27 113	99.08 139	89.00 179	99.95 60	99.12 47	99.25 116	99.57 137

PatchMatch-RL		96.04 140	95.40 144	97.95 137	99.59 90	95.22 175	99.52 177	99.07 31	93.96 122	96.49 153	98.35 189	82.28 231	99.82 105	90.15 243	99.22 117	98.81 196

CHOSEN 1792x2688		96.81 112	96.53 111	97.64 149	98.91 127	93.07 214	99.65 156	99.80 3	95.64 65	95.39 174	98.86 164	84.35 220	99.90 75	96.98 132	99.16 118	99.95 78

EIA-MVS		97.53 88	97.46 81	97.76 145	98.04 167	94.84 182	99.98 8	97.61 220	94.41 101	97.90 123	99.59 103	92.40 130	98.87 163	98.04 102	99.13 119	99.59 130

UGNet		95.33 155	94.57 163	97.62 151	98.55 140	94.85 181	98.67 265	99.32 24	95.75 63	96.80 146	96.27 245	72.18 301	99.96 53	94.58 170	99.05 120	98.04 206
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
CS-MVS		97.84 76	97.69 73	98.31 124	98.28 152	96.27 135	100.00 1	97.52 232	95.29 73	99.25 70	99.65 99	91.18 151	98.94 162	98.96 57	99.04 121	99.73 108

CANet_DTU		96.76 115	96.15 119	98.60 102	98.78 134	97.53 90	99.84 105	97.63 215	97.25 23	99.20 71	99.64 100	81.36 239	99.98 42	92.77 206	98.89 122	98.28 202

TESTMET0.1,1		96.74 117	96.26 117	98.16 129	97.36 204	96.48 127	99.96 23	98.29 155	91.93 192	95.77 169	98.07 195	95.54 40	98.29 207	90.55 235	98.89 122	99.70 112

test-LLR		96.47 126	96.04 121	97.78 142	97.02 218	95.44 165	99.96 23	98.21 165	94.07 115	95.55 171	96.38 241	93.90 94	98.27 211	90.42 238	98.83 124	99.64 123

test-mter		96.39 130	95.93 132	97.78 142	97.02 218	95.44 165	99.96 23	98.21 165	91.81 197	95.55 171	96.38 241	95.17 47	98.27 211	90.42 238	98.83 124	99.64 123

PVSNet	91.05 13	97.13 101	96.69 106	98.45 116	99.52 96	95.81 154	99.95 40	99.65 10	94.73 87	99.04 79	99.21 134	84.48 218	99.95 60	94.92 156	98.74 126	99.58 136

EPNet		98.49 43	98.40 35	98.77 90	99.62 89	96.80 119	99.90 73	99.51 15	97.60 12	99.20 71	99.36 123	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
xiu_mvs_v2_base		98.23 63	97.97 64	99.02 77	98.69 137	98.66 46	99.52 177	98.08 182	97.05 26	99.86 4	99.86 29	90.65 158	99.71 128	99.39 41	98.63 128	98.69 199

ETV-MVS		97.92 73	97.80 71	98.25 127	98.14 163	96.48 127	99.98 8	97.63 215	95.61 66	99.29 67	99.46 114	92.55 129	98.82 165	99.02 56	98.54 129	99.46 152

Vis-MVSNet		95.72 146	95.15 153	97.45 155	97.62 194	94.28 193	99.28 210	98.24 161	94.27 109	96.84 144	98.94 156	79.39 258	98.76 171	93.25 197	98.49 130	99.30 170
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PCF-MVS	94.20 5	95.18 157	94.10 171	98.43 118	98.55 140	95.99 150	97.91 298	97.31 255	90.35 230	89.48 242	99.22 133	85.19 213	99.89 79	90.40 240	98.47 131	99.41 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		94.37 179	93.36 191	97.40 158	98.88 130	93.95 199	99.37 198	97.38 249	85.75 297	90.80 219	99.17 135	84.11 222	99.88 85	86.35 281	98.43 132	98.36 201

PVSNet_Blended_VisFu		97.27 97	96.81 102	98.66 97	98.81 132	96.67 121	99.92 65	98.64 62	94.51 95	96.38 158	98.49 183	89.05 178	99.88 85	97.10 129	98.34 133	99.43 157

EPNet_dtu		95.71 148	95.39 145	96.66 179	98.92 125	93.41 210	99.57 169	98.90 40	96.19 51	97.52 130	98.56 181	92.65 126	97.36 244	77.89 322	98.33 134	99.20 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v1_base_debu		97.43 89	97.06 93	98.55 106	97.74 186	98.14 67	99.31 205	97.86 202	96.43 41	99.62 37	99.69 91	85.56 209	99.68 132	99.05 50	98.31 135	97.83 208

xiu_mvs_v1_base		97.43 89	97.06 93	98.55 106	97.74 186	98.14 67	99.31 205	97.86 202	96.43 41	99.62 37	99.69 91	85.56 209	99.68 132	99.05 50	98.31 135	97.83 208

xiu_mvs_v1_base_debi		97.43 89	97.06 93	98.55 106	97.74 186	98.14 67	99.31 205	97.86 202	96.43 41	99.62 37	99.69 91	85.56 209	99.68 132	99.05 50	98.31 135	97.83 208

OMC-MVS		97.28 96	97.23 89	97.41 157	99.76 74	93.36 212	99.65 156	97.95 192	96.03 55	97.41 133	99.70 88	89.61 169	99.51 142	96.73 138	98.25 138	99.38 161

mvs-test1		95.53 151	95.97 128	94.20 249	97.77 183	85.44 317	99.95 40	97.06 273	94.92 80	96.58 150	98.72 170	85.81 206	98.98 159	94.80 161	98.11 139	98.18 203

DP-MVS		94.54 174	93.42 187	97.91 140	99.46 102	94.04 197	98.93 244	97.48 238	81.15 322	90.04 226	99.55 106	87.02 197	99.95 60	88.97 253	98.11 139	99.73 108

EPMVS		96.53 125	96.01 122	98.09 133	98.43 146	96.12 147	96.36 317	99.43 19	93.53 138	97.64 128	95.04 290	94.41 70	98.38 201	91.13 223	98.11 139	99.75 106

PatchmatchNet		95.94 142	95.45 143	97.39 159	97.83 179	94.41 191	96.05 322	98.40 130	92.86 153	97.09 139	95.28 285	94.21 86	98.07 220	89.26 251	98.11 139	99.70 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
baseline2		96.71 119	96.49 112	97.37 160	95.63 261	95.96 151	99.74 137	98.88 42	92.94 152	91.61 211	98.97 151	97.72 5	98.62 180	94.83 160	98.08 143	97.53 215

ACMMP		97.74 83	97.44 82	98.66 97	99.92 35	96.13 145	99.18 217	99.45 17	94.84 84	96.41 157	99.71 86	91.40 145	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
MVS-HIRNet		86.22 293	83.19 304	95.31 210	96.71 234	90.29 274	92.12 338	97.33 253	62.85 345	86.82 285	70.37 348	69.37 311	97.49 240	75.12 331	97.99 145	98.15 204

PMMVS		96.76 115	96.76 104	96.76 175	98.28 152	92.10 237	99.91 69	97.98 189	94.12 112	99.53 44	99.39 120	86.93 198	98.73 173	96.95 134	97.73 146	99.45 154

UA-Net		96.54 124	95.96 130	98.27 126	98.23 157	95.71 160	98.00 296	98.45 105	93.72 134	98.41 106	99.27 127	88.71 183	99.66 136	91.19 222	97.69 147	99.44 156

TSAR-MVS + GP.		98.60 33	98.51 28	98.86 87	99.73 81	96.63 122	99.97 16	97.92 196	98.07 5	98.76 91	99.55 106	95.00 57	99.94 68	99.91 11	97.68 148	99.99 20

mvs_anonymous		95.65 150	95.03 155	97.53 152	98.19 159	95.74 158	99.33 202	97.49 237	90.87 221	90.47 222	97.10 217	88.23 186	97.16 259	95.92 146	97.66 149	99.68 115

LCM-MVSNet-Re		92.31 221	92.60 202	91.43 298	97.53 197	79.27 341	99.02 236	91.83 347	92.07 188	80.31 320	94.38 311	83.50 225	95.48 318	97.22 126	97.58 150	99.54 143

MVS_Test		96.46 127	95.74 138	98.61 101	98.18 160	97.23 104	99.31 205	97.15 267	91.07 217	98.84 87	97.05 221	88.17 187	98.97 160	94.39 172	97.50 151	99.61 127

SCA		94.69 168	93.81 178	97.33 163	97.10 213	94.44 189	98.86 252	98.32 149	93.30 144	96.17 162	95.59 264	76.48 277	97.95 226	91.06 225	97.43 152	99.59 130

Vis-MVSNet (Re-imp)		96.32 132	95.98 125	97.35 162	97.93 172	94.82 183	99.47 185	98.15 176	91.83 195	95.09 178	99.11 137	91.37 146	97.47 241	93.47 195	97.43 152	99.74 107

diffmvs		97.00 105	96.64 107	98.09 133	97.64 193	96.17 144	99.81 114	97.19 261	94.67 91	98.95 84	99.28 124	86.43 202	98.76 171	98.37 89	97.42 154	99.33 167

IS-MVSNet		96.29 135	95.90 134	97.45 155	98.13 164	94.80 184	99.08 224	97.61 220	92.02 191	95.54 173	98.96 153	90.64 159	98.08 218	93.73 191	97.41 155	99.47 151

Effi-MVS+		96.30 134	95.69 139	98.16 129	97.85 178	96.26 137	97.41 305	97.21 260	90.37 229	98.65 97	98.58 179	86.61 201	98.70 176	97.11 128	97.37 156	99.52 146

DWT-MVSNet_test		97.31 95	97.19 90	97.66 148	98.24 156	94.67 187	98.86 252	98.20 169	93.60 137	98.09 118	98.89 158	97.51 7	98.78 168	94.04 180	97.28 157	99.55 139

ADS-MVSNet2		93.80 189	93.88 176	93.55 271	97.87 176	85.94 313	94.24 328	96.84 295	90.07 234	96.43 155	94.48 308	90.29 163	95.37 320	87.44 268	97.23 158	99.36 163

ADS-MVSNet		94.79 164	94.02 172	97.11 168	97.87 176	93.79 201	94.24 328	98.16 174	90.07 234	96.43 155	94.48 308	90.29 163	98.19 215	87.44 268	97.23 158	99.36 163

EPP-MVSNet		96.69 120	96.60 108	96.96 169	97.74 186	93.05 216	99.37 198	98.56 76	88.75 255	95.83 168	99.01 144	96.01 28	98.56 182	96.92 135	97.20 160	99.25 174

Fast-Effi-MVS+		95.02 161	94.19 168	97.52 153	97.88 174	94.55 188	99.97 16	97.08 271	88.85 254	94.47 185	97.96 199	84.59 217	98.41 193	89.84 246	97.10 161	99.59 130

Effi-MVS+-dtu		94.53 176	95.30 148	92.22 290	97.77 183	82.54 327	99.59 166	97.06 273	94.92 80	95.29 176	95.37 278	85.81 206	97.89 229	94.80 161	97.07 162	96.23 222

casdiffmvs		96.42 129	95.97 128	97.77 144	97.30 209	94.98 178	99.84 105	97.09 270	93.75 133	96.58 150	99.26 130	85.07 214	98.78 168	97.77 114	97.04 163	99.54 143

sss		97.57 87	97.03 97	99.18 54	98.37 147	98.04 72	99.73 142	99.38 21	93.46 140	98.76 91	99.06 140	91.21 147	99.89 79	96.33 140	97.01 164	99.62 125

Patchmatch-test		92.65 215	91.50 225	96.10 194	96.85 225	90.49 270	91.50 341	97.19 261	82.76 317	90.23 223	95.59 264	95.02 54	98.00 222	77.41 324	96.98 165	99.82 97

MDTV_nov1_ep13			95.69 139		97.90 173	94.15 195	95.98 323	98.44 106	93.12 149	97.98 121	95.74 256	95.10 49	98.58 181	90.02 244	96.92 166

Fast-Effi-MVS+-dtu		93.72 193	93.86 177	93.29 274	97.06 215	86.16 311	99.80 119	96.83 296	92.66 166	92.58 207	97.83 201	81.39 238	97.67 235	89.75 248	96.87 167	96.05 224

baseline		96.43 128	95.98 125	97.76 145	97.34 205	95.17 176	99.51 179	97.17 264	93.92 125	96.90 143	99.28 124	85.37 212	98.64 179	97.50 119	96.86 168	99.46 152

tpmrst		96.27 137	95.98 125	97.13 166	97.96 170	93.15 213	96.34 318	98.17 171	92.07 188	98.71 94	95.12 288	93.91 93	98.73 173	94.91 158	96.62 169	99.50 149

JIA-IIPM		91.76 235	90.70 235	94.94 220	96.11 240	87.51 306	93.16 335	98.13 179	75.79 335	97.58 129	77.68 346	92.84 121	97.97 223	88.47 259	96.54 170	99.33 167

dp		95.05 160	94.43 165	96.91 170	97.99 169	92.73 223	96.29 319	97.98 189	89.70 240	95.93 165	94.67 303	93.83 97	98.45 190	86.91 280	96.53 171	99.54 143

COLMAP_ROB	90.47 14	92.18 224	91.49 226	94.25 248	99.00 117	88.04 304	98.42 279	96.70 304	82.30 319	88.43 264	99.01 144	76.97 272	99.85 94	86.11 284	96.50 172	94.86 225
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.51 196	92.52 207	96.47 182	97.77 183	91.47 257	96.13 320	98.06 183	80.98 323	92.91 203	93.78 316	89.66 168	98.87 163	87.03 276	96.39 173	99.09 185

thisisatest0515		97.41 93	97.02 98	98.59 104	97.71 192	97.52 91	99.97 16	98.54 85	91.83 195	97.45 132	99.04 141	97.50 8	99.10 156	94.75 164	96.37 174	99.16 179

AllTest		92.48 217	91.64 220	95.00 218	99.01 115	88.43 297	98.94 243	96.82 298	86.50 286	88.71 257	98.47 187	74.73 291	99.88 85	85.39 287	96.18 175	96.71 218

TestCases				95.00 218	99.01 115	88.43 297		96.82 298	86.50 286	88.71 257	98.47 187	74.73 291	99.88 85	85.39 287	96.18 175	96.71 218

thisisatest0530		97.10 102	96.72 105	98.22 128	97.60 195	96.70 120	99.92 65	98.54 85	91.11 216	97.07 140	98.97 151	97.47 9	99.03 157	93.73 191	96.09 177	98.92 189

DSMNet-mixed		88.28 286	88.24 281	88.42 319	89.64 335	75.38 343	98.06 294	89.86 350	85.59 299	88.20 269	92.14 328	76.15 282	91.95 340	78.46 320	96.05 178	97.92 207

TR-MVS		94.54 174	93.56 184	97.49 154	97.96 170	94.34 192	98.71 261	97.51 235	90.30 232	94.51 184	98.69 171	75.56 284	98.77 170	92.82 205	95.99 179	99.35 165

CR-MVSNet		93.45 199	92.62 201	95.94 197	96.29 237	92.66 225	92.01 339	96.23 313	92.62 168	96.94 141	93.31 320	91.04 153	96.03 312	79.23 318	95.96 180	99.13 183

RPMNet		89.76 273	87.28 288	97.19 165	96.29 237	92.66 225	92.01 339	98.31 151	70.19 344	96.94 141	85.87 342	87.25 194	99.78 111	62.69 345	95.96 180	99.13 183

PatchT		90.38 259	88.75 273	95.25 211	95.99 244	90.16 276	91.22 343	97.54 228	76.80 331	97.26 135	86.01 341	91.88 140	96.07 311	66.16 342	95.91 182	99.51 147

tpmvs		94.28 182	93.57 183	96.40 186	98.55 140	91.50 256	95.70 327	98.55 81	87.47 272	92.15 208	94.26 312	91.42 144	98.95 161	88.15 262	95.85 183	98.76 198

TAMVS		95.85 143	95.58 141	96.65 180	97.07 214	93.50 207	99.17 218	97.82 206	91.39 212	95.02 179	98.01 196	92.20 134	97.30 249	93.75 190	95.83 184	99.14 182

CostFormer		96.10 138	95.88 135	96.78 174	97.03 217	92.55 229	97.08 309	97.83 205	90.04 236	98.72 93	94.89 297	95.01 56	98.29 207	96.54 139	95.77 185	99.50 149

tttt0517		96.85 110	96.49 112	97.92 139	97.48 201	95.89 153	99.85 101	98.54 85	90.72 225	96.63 149	98.93 157	97.47 9	99.02 158	93.03 204	95.76 186	98.85 193

HY-MVS	92.50 7	97.79 81	97.17 92	99.63 12	98.98 118	99.32 6	97.49 304	99.52 13	95.69 64	98.32 111	97.41 208	93.32 107	99.77 115	98.08 101	95.75 187	99.81 98

CDS-MVSNet		96.34 131	96.07 120	97.13 166	97.37 203	94.96 179	99.53 176	97.91 197	91.55 204	95.37 175	98.32 190	95.05 53	97.13 262	93.80 187	95.75 187	99.30 170
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		95.47 153	95.18 152	96.35 189	96.91 222	91.70 251	96.96 312	97.93 194	88.04 267	98.44 105	95.40 274	93.32 107	97.97 223	94.00 181	95.61 189	99.38 161

WTY-MVS		98.10 67	97.60 77	99.60 17	98.92 125	99.28 12	99.89 81	99.52 13	95.58 67	98.24 116	99.39 120	93.33 106	99.74 124	97.98 107	95.58 190	99.78 103

HyFIR lowres test		96.66 122	96.43 114	97.36 161	99.05 113	93.91 200	99.70 147	99.80 3	90.54 226	96.26 160	98.08 194	92.15 136	98.23 213	96.84 137	95.46 191	99.93 81

cascas		94.64 171	93.61 179	97.74 147	97.82 180	96.26 137	99.96 23	97.78 208	85.76 295	94.00 191	97.54 205	76.95 273	99.21 153	97.23 125	95.43 192	97.76 212

CVMVSNet		94.68 170	94.94 156	93.89 263	96.80 228	86.92 309	99.06 229	98.98 34	94.45 96	94.23 189	99.02 142	85.60 208	95.31 322	90.91 230	95.39 193	99.43 157

test_yl		97.83 77	97.37 84	99.21 51	99.18 108	97.98 75	99.64 160	99.27 25	91.43 209	97.88 124	98.99 147	95.84 35	99.84 103	98.82 68	95.32 194	99.79 100

DCV-MVSNet		97.83 77	97.37 84	99.21 51	99.18 108	97.98 75	99.64 160	99.27 25	91.43 209	97.88 124	98.99 147	95.84 35	99.84 103	98.82 68	95.32 194	99.79 100

LFMVS		94.75 167	93.56 184	98.30 125	99.03 114	95.70 161	98.74 258	97.98 189	87.81 270	98.47 104	99.39 120	67.43 319	99.53 140	98.01 103	95.20 196	99.67 117

thres200		96.96 106	96.21 118	99.22 50	98.97 119	98.84 30	99.85 101	99.71 5	93.17 148	96.26 160	98.88 160	89.87 167	99.51 142	94.26 177	94.91 197	99.31 169

thres100view900		96.74 117	95.92 133	99.18 54	98.90 128	98.77 36	99.74 137	99.71 5	92.59 171	95.84 166	98.86 164	89.25 175	99.50 144	93.84 183	94.57 198	99.27 172

tfpn200view9		96.79 113	95.99 123	99.19 53	98.94 121	98.82 31	99.78 123	99.71 5	92.86 153	96.02 163	98.87 162	89.33 173	99.50 144	93.84 183	94.57 198	99.27 172

thres400		96.78 114	95.99 123	99.16 59	98.94 121	98.82 31	99.78 123	99.71 5	92.86 153	96.02 163	98.87 162	89.33 173	99.50 144	93.84 183	94.57 198	99.16 179

thres600view7		96.69 120	95.87 136	99.14 63	98.90 128	98.78 35	99.74 137	99.71 5	92.59 171	95.84 166	98.86 164	89.25 175	99.50 144	93.44 196	94.50 201	99.16 179

VNet		97.21 100	96.57 110	99.13 68	98.97 119	97.82 81	99.03 235	99.21 27	94.31 106	99.18 75	98.88 160	86.26 204	99.89 79	98.93 60	94.32 202	99.69 114

alignmvs		97.81 79	97.33 87	99.25 49	98.77 135	98.66 46	99.99 4	98.44 106	94.40 102	98.41 106	99.47 112	93.65 100	99.42 150	98.57 83	94.26 203	99.67 117

VDD-MVS		93.77 190	92.94 196	96.27 190	98.55 140	90.22 275	98.77 257	97.79 207	90.85 222	96.82 145	99.42 116	61.18 337	99.77 115	98.95 58	94.13 204	98.82 195

VDDNet		93.12 203	91.91 217	96.76 175	96.67 235	92.65 227	98.69 263	98.21 165	82.81 316	97.75 127	99.28 124	61.57 335	99.48 148	98.09 100	94.09 205	98.15 204

GA-MVS		93.83 186	92.84 197	96.80 173	95.73 254	93.57 205	99.88 84	97.24 259	92.57 174	92.92 202	96.66 234	78.73 264	97.67 235	87.75 266	94.06 206	99.17 178

canonicalmvs		97.09 104	96.32 116	99.39 43	98.93 123	98.95 21	99.72 145	97.35 251	94.45 96	97.88 124	99.42 116	86.71 199	99.52 141	98.48 86	93.97 207	99.72 111

1112_ss		96.01 141	95.20 151	98.42 119	97.80 181	96.41 130	99.65 156	96.66 305	92.71 162	92.88 204	99.40 118	92.16 135	99.30 151	91.92 214	93.66 208	99.55 139

Test_1112_low_res		95.72 146	94.83 158	98.42 119	97.79 182	96.41 130	99.65 156	96.65 306	92.70 163	92.86 205	96.13 249	92.15 136	99.30 151	91.88 215	93.64 209	99.55 139

MIMVSNet		90.30 262	88.67 274	95.17 214	96.45 236	91.64 253	92.39 337	97.15 267	85.99 292	90.50 221	93.19 322	66.95 320	94.86 328	82.01 308	93.43 210	99.01 188

XVG-OURS-SEG-HR		94.79 164	94.70 162	95.08 215	98.05 166	89.19 287	99.08 224	97.54 228	93.66 135	94.87 180	99.58 104	78.78 263	99.79 109	97.31 123	93.40 211	96.25 220

ab-mvs		94.69 168	93.42 187	98.51 112	98.07 165	96.26 137	96.49 316	98.68 55	90.31 231	94.54 182	97.00 223	76.30 279	99.71 128	95.98 145	93.38 212	99.56 138

test0.0.03 1		93.86 185	93.61 179	94.64 230	95.02 271	92.18 236	99.93 61	98.58 72	94.07 115	87.96 271	98.50 182	93.90 94	94.96 326	81.33 311	93.17 213	96.78 217

RPSCF		91.80 232	92.79 199	88.83 316	98.15 162	69.87 345	98.11 292	96.60 307	83.93 310	94.33 187	99.27 127	79.60 257	99.46 149	91.99 212	93.16 214	97.18 216

XVG-OURS		94.82 163	94.74 161	95.06 216	98.00 168	89.19 287	99.08 224	97.55 226	94.10 113	94.71 181	99.62 101	80.51 250	99.74 124	96.04 144	93.06 215	96.25 220

MVS_0304		89.28 280	88.31 279	92.21 291	97.05 216	86.53 310	97.76 301	99.57 12	85.58 300	93.86 194	92.71 324	51.04 347	96.30 302	84.49 293	92.72 216	93.79 293

Anonymous202405211		93.10 204	91.99 215	96.40 186	99.10 112	89.65 284	98.88 248	97.93 194	83.71 312	94.00 191	98.75 169	68.79 312	99.88 85	95.08 154	91.71 217	99.68 115

Anonymous20240529		92.10 225	90.65 236	96.47 182	98.82 131	90.61 267	98.72 260	98.67 58	75.54 336	93.90 193	98.58 179	66.23 322	99.90 75	94.70 167	90.67 218	98.90 192

HQP3-MVS								97.89 198							89.60 219

HQP-MVS		94.61 172	94.50 164	94.92 221	95.78 248	91.85 243	99.87 87	97.89 198	96.82 30	93.37 196	98.65 173	80.65 248	98.39 197	97.92 109	89.60 219	94.53 226

plane_prior						91.74 247	99.86 98		96.76 34						89.59 221

HQP_MVS		94.49 177	94.36 166	94.87 222	95.71 257	91.74 247	99.84 105	97.87 200	96.38 44	93.01 200	98.59 177	80.47 252	98.37 202	97.79 112	89.55 222	94.52 228

plane_prior5								97.87 200					98.37 202	97.79 112	89.55 222	94.52 228

CLD-MVS		94.06 184	93.90 175	94.55 235	96.02 243	90.69 264	99.98 8	97.72 209	96.62 39	91.05 217	98.85 167	77.21 270	98.47 186	98.11 98	89.51 224	94.48 230
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		93.21 201	92.80 198	94.44 242	93.12 301	90.85 263	99.77 126	97.61 220	96.19 51	91.56 212	98.65 173	75.16 289	98.47 186	93.78 189	89.39 225	93.99 277

LPG-MVS_test		92.96 206	92.71 200	93.71 267	95.43 263	88.67 293	99.75 134	97.62 217	92.81 156	90.05 224	98.49 183	75.24 287	98.40 195	95.84 148	89.12 226	94.07 269

LGP-MVS_train				93.71 267	95.43 263	88.67 293		97.62 217	92.81 156	90.05 224	98.49 183	75.24 287	98.40 195	95.84 148	89.12 226	94.07 269

test_djsdf		92.83 209	92.29 210	94.47 240	91.90 318	92.46 230	99.55 173	97.27 257	91.17 213	89.96 227	96.07 251	81.10 241	96.89 278	94.67 168	88.91 228	94.05 271

testgi		89.01 282	88.04 283	91.90 295	93.49 294	84.89 320	99.73 142	95.66 324	93.89 128	85.14 303	98.17 192	59.68 338	94.66 330	77.73 323	88.88 229	96.16 223

ACMM	91.95 10	92.88 208	92.52 207	93.98 260	95.75 253	89.08 290	99.77 126	97.52 232	93.00 151	89.95 228	97.99 198	76.17 281	98.46 189	93.63 194	88.87 230	94.39 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	92.05 9	92.74 211	92.42 209	93.73 265	95.91 247	88.72 292	99.81 114	97.53 230	94.13 111	87.00 284	98.23 191	74.07 295	98.47 186	96.22 142	88.86 231	93.99 277
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jajsoiax		91.92 227	91.18 230	94.15 250	91.35 324	90.95 261	99.00 237	97.42 244	92.61 169	87.38 280	97.08 218	72.46 300	97.36 244	94.53 171	88.77 232	94.13 266

anonymousdsp		91.79 234	90.92 233	94.41 245	90.76 329	92.93 218	98.93 244	97.17 264	89.08 244	87.46 279	95.30 281	78.43 268	96.92 277	92.38 208	88.73 233	93.39 306

mvs_tets		91.81 229	91.08 231	94.00 258	91.63 322	90.58 268	98.67 265	97.43 242	92.43 179	87.37 281	97.05 221	71.76 302	97.32 248	94.75 164	88.68 234	94.11 267

XVG-ACMP-BASELINE		91.22 242	90.75 234	92.63 287	93.73 290	85.61 314	98.52 273	97.44 241	92.77 160	89.90 230	96.85 229	66.64 321	98.39 197	92.29 209	88.61 235	93.89 286

EG-PatchMatch MVS		85.35 299	83.81 301	89.99 311	90.39 331	81.89 332	98.21 289	96.09 317	81.78 321	74.73 336	93.72 317	51.56 346	97.12 264	79.16 319	88.61 235	90.96 330

UniMVSNet_ETH3D		90.06 269	88.58 275	94.49 239	94.67 276	88.09 303	97.81 300	97.57 225	83.91 311	88.44 262	97.41 208	57.44 341	97.62 237	91.41 219	88.59 237	97.77 211

tpm		93.70 194	93.41 189	94.58 233	95.36 265	87.41 307	97.01 310	96.90 291	90.85 222	96.72 148	94.14 313	90.40 161	96.84 281	90.75 234	88.54 238	99.51 147

OpenMVS_ROB	79.82 20	83.77 307	81.68 309	90.03 310	88.30 338	82.82 325	98.46 274	95.22 332	73.92 340	76.00 333	91.29 330	55.00 343	96.94 276	68.40 338	88.51 239	90.34 333

CMPMVS	61.59 21	84.75 302	85.14 296	83.57 324	90.32 332	62.54 348	96.98 311	97.59 224	74.33 339	69.95 341	96.66 234	64.17 328	98.32 205	87.88 265	88.41 240	89.84 337
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ACMMP++															88.23 241

ITE_SJBPF				92.38 288	95.69 259	85.14 318		95.71 322	92.81 156	89.33 246	98.11 193	70.23 309	98.42 192	85.91 285	88.16 242	93.59 302

D2MVS		92.76 210	92.59 205	93.27 275	95.13 267	89.54 286	99.69 148	99.38 21	92.26 183	87.59 275	94.61 305	85.05 215	97.79 231	91.59 218	88.01 243	92.47 319

EI-MVSNet		93.73 192	93.40 190	94.74 226	96.80 228	92.69 224	99.06 229	97.67 213	88.96 250	91.39 213	99.02 142	88.75 182	97.30 249	91.07 224	87.85 244	94.22 252

MVSTER		95.53 151	95.22 150	96.45 184	98.56 139	97.72 82	99.91 69	97.67 213	92.38 180	91.39 213	97.14 215	97.24 14	97.30 249	94.80 161	87.85 244	94.34 244

PS-MVSNAJss		93.64 195	93.31 192	94.61 231	92.11 315	92.19 235	99.12 220	97.38 249	92.51 177	88.45 261	96.99 224	91.20 148	97.29 252	94.36 173	87.71 246	94.36 240

LTVRE_ROB	88.28 18	90.29 263	89.05 268	94.02 256	95.08 269	90.15 277	97.19 308	97.43 242	84.91 305	83.99 308	97.06 220	74.00 296	98.28 209	84.08 294	87.71 246	93.62 301
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
ACMH	89.72 17	90.64 253	89.63 254	93.66 269	95.64 260	88.64 295	98.55 269	97.45 239	89.03 246	81.62 316	97.61 204	69.75 310	98.41 193	89.37 249	87.62 248	93.92 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PVSNet_BlendedMVS		96.05 139	95.82 137	96.72 177	99.59 90	96.99 113	99.95 40	99.10 28	94.06 117	98.27 113	95.80 254	89.00 179	99.95 60	99.12 47	87.53 249	93.24 310

USDC		90.00 270	88.96 269	93.10 281	94.81 273	88.16 302	98.71 261	95.54 327	93.66 135	83.75 310	97.20 214	65.58 324	98.31 206	83.96 297	87.49 250	92.85 316

RRT_MVS		95.23 156	94.77 160	96.61 181	98.28 152	98.32 63	99.81 114	97.41 246	92.59 171	91.28 215	97.76 202	95.02 54	97.23 256	93.65 193	87.14 251	94.28 248

ACMMP++_ref															87.04 252

test_0402		85.58 295	83.94 299	90.50 305	93.81 289	85.04 319	98.55 269	95.20 333	76.01 333	79.72 323	95.13 287	64.15 329	96.26 304	66.04 343	86.88 253	90.21 335

FIs		94.10 183	93.43 186	96.11 193	94.70 275	96.82 118	99.58 167	98.93 39	92.54 175	89.34 245	97.31 211	87.62 190	97.10 265	94.22 179	86.58 254	94.40 238

FC-MVSNet-test		93.81 188	93.15 195	95.80 201	94.30 281	96.20 142	99.42 190	98.89 41	92.33 182	89.03 254	97.27 213	87.39 193	96.83 282	93.20 198	86.48 255	94.36 240

TinyColmap		87.87 288	86.51 291	91.94 294	95.05 270	85.57 315	97.65 302	94.08 341	84.40 308	81.82 315	96.85 229	62.14 334	98.33 204	80.25 316	86.37 256	91.91 325

ACMH+	89.98 16	90.35 260	89.54 257	92.78 286	95.99 244	86.12 312	98.81 255	97.18 263	89.38 241	83.14 312	97.76 202	68.42 316	98.43 191	89.11 252	86.05 257	93.78 294

baseline1		95.78 145	94.86 157	98.54 109	98.47 145	98.07 70	99.06 229	97.99 187	92.68 165	94.13 190	98.62 176	93.28 110	98.69 177	93.79 188	85.76 258	98.84 194

GBi-Net		90.88 247	89.82 252	94.08 253	97.53 197	91.97 238	98.43 276	96.95 285	87.05 278	89.68 235	94.72 299	71.34 304	96.11 307	87.01 277	85.65 259	94.17 256

test1		90.88 247	89.82 252	94.08 253	97.53 197	91.97 238	98.43 276	96.95 285	87.05 278	89.68 235	94.72 299	71.34 304	96.11 307	87.01 277	85.65 259	94.17 256

FMVSNet3		92.69 213	91.58 222	95.99 195	98.29 150	97.42 100	99.26 212	97.62 217	89.80 239	89.68 235	95.32 280	81.62 237	96.27 303	87.01 277	85.65 259	94.29 247

DeepMVS_CX				82.92 326	95.98 246	58.66 350		96.01 318	92.72 161	78.34 327	95.51 269	58.29 340	98.08 218	82.57 304	85.29 262	92.03 323

LF4IMVS		89.25 281	88.85 270	90.45 307	92.81 309	81.19 335	98.12 291	94.79 336	91.44 208	86.29 295	97.11 216	65.30 326	98.11 217	88.53 258	85.25 263	92.07 321

FMVSNet2		91.02 244	89.56 256	95.41 207	97.53 197	95.74 158	98.98 238	97.41 246	87.05 278	88.43 264	95.00 293	71.34 304	96.24 305	85.12 289	85.21 264	94.25 251

testing_2		85.10 300	81.72 308	95.22 212	82.25 346	94.16 194	97.54 303	97.01 279	88.15 264	62.23 343	86.43 340	44.43 349	97.18 258	92.28 210	85.20 265	94.31 245

ET-MVSNet_ETH3D		94.37 179	93.28 193	97.64 149	98.30 149	97.99 74	99.99 4	97.61 220	94.35 103	71.57 339	99.45 115	96.23 27	95.34 321	96.91 136	85.14 266	99.59 130

OurMVSNet-221017-0		89.81 272	89.48 261	90.83 303	91.64 321	81.21 334	98.17 290	95.38 329	91.48 206	85.65 301	97.31 211	72.66 299	97.29 252	88.15 262	84.83 267	93.97 279

pmmvs4		92.10 225	91.07 232	95.18 213	92.82 308	94.96 179	99.48 184	96.83 296	87.45 273	88.66 260	96.56 239	83.78 223	96.83 282	89.29 250	84.77 268	93.75 295

our_test_3		90.39 258	89.48 261	93.12 279	92.40 312	89.57 285	99.33 202	96.35 312	87.84 269	85.30 302	94.99 294	84.14 221	96.09 310	80.38 315	84.56 269	93.71 300

cl-mvsnet2		93.77 190	93.25 194	95.33 209	99.49 99	94.43 190	99.61 164	98.09 180	90.38 228	89.16 252	95.61 262	90.56 160	97.34 246	91.93 213	84.45 270	94.21 254

miper_ehance_all_eth		93.16 202	92.60 202	94.82 225	97.57 196	93.56 206	99.50 180	97.07 272	88.75 255	88.85 256	95.52 268	90.97 155	96.74 285	90.77 233	84.45 270	94.17 256

miper_enhance_ethall		94.36 181	93.98 173	95.49 203	98.68 138	95.24 173	99.73 142	97.29 256	93.28 145	89.86 231	95.97 252	94.37 75	97.05 268	92.20 211	84.45 270	94.19 255

IterMVS		90.91 246	90.17 247	93.12 279	96.78 231	90.42 273	98.89 246	97.05 275	89.03 246	86.49 291	95.42 273	76.59 276	95.02 324	87.22 273	84.09 273	93.93 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		88.50 284	86.64 290	94.08 253	95.62 262	91.97 238	98.43 276	96.95 285	83.00 315	86.08 298	94.72 299	59.09 339	96.11 307	81.82 310	84.07 274	94.17 256

XXY-MVS		91.82 228	90.46 238	95.88 198	93.91 287	95.40 168	98.87 251	97.69 212	88.63 259	87.87 272	97.08 218	74.38 294	97.89 229	91.66 217	84.07 274	94.35 243

IterMVS-SCA-FT		90.85 249	90.16 248	92.93 283	96.72 233	89.96 279	98.89 246	96.99 280	88.95 251	86.63 288	95.67 259	76.48 277	95.00 325	87.04 275	84.04 276	93.84 290

RRT_test8_iter05		94.58 173	94.11 170	95.98 196	97.88 174	96.11 148	99.89 81	97.45 239	91.66 201	88.28 267	96.71 233	96.53 24	97.40 242	94.73 166	83.85 277	94.45 236

pmmvs5		90.17 267	89.09 266	93.40 272	92.10 316	89.77 283	99.74 137	95.58 326	85.88 294	87.24 283	95.74 256	73.41 298	96.48 295	88.54 257	83.56 278	93.95 281

SixPastTwentyTwo		88.73 283	88.01 284	90.88 301	91.85 319	82.24 329	98.22 288	95.18 334	88.97 249	82.26 314	96.89 226	71.75 303	96.67 289	84.00 295	82.98 279	93.72 299

N_pmnet		80.06 312	80.78 311	77.89 327	91.94 317	45.28 356	98.80 256	56.82 359	78.10 330	80.08 322	93.33 318	77.03 271	95.76 317	68.14 339	82.81 280	92.64 317

ppachtmachnet_test		89.58 276	88.35 278	93.25 276	92.40 312	90.44 272	99.33 202	96.73 303	85.49 301	85.90 300	95.77 255	81.09 242	96.00 314	76.00 330	82.49 281	93.30 308

cl-mvsnet_		92.31 221	91.58 222	94.52 236	97.33 207	92.77 219	99.57 169	96.78 301	86.97 282	87.56 276	95.51 269	89.43 171	96.62 290	88.60 255	82.44 282	94.16 261

cl-mvsnet1		92.32 220	91.60 221	94.47 240	97.31 208	92.74 221	99.58 167	96.75 302	86.99 281	87.64 274	95.54 266	89.55 170	96.50 294	88.58 256	82.44 282	94.17 256

Patchmtry		89.70 274	88.49 276	93.33 273	96.24 239	89.94 282	91.37 342	96.23 313	78.22 329	87.69 273	93.31 320	91.04 153	96.03 312	80.18 317	82.10 284	94.02 272

IterMVS-LS		92.69 213	92.11 212	94.43 244	96.80 228	92.74 221	99.45 188	96.89 292	88.98 248	89.65 238	95.38 277	88.77 181	96.34 300	90.98 228	82.04 285	94.22 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EU-MVSNet		90.14 268	90.34 242	89.54 313	92.55 311	81.06 336	98.69 263	98.04 185	91.41 211	86.59 289	96.84 231	80.83 245	93.31 339	86.20 282	81.91 286	94.26 249

Anonymous20231206		86.32 292	85.42 294	89.02 315	89.11 337	80.53 339	99.05 233	95.28 330	85.43 302	82.82 313	93.92 314	74.40 293	93.44 338	66.99 340	81.83 287	93.08 313

eth_miper_zixun_eth		92.41 219	91.93 216	93.84 264	97.28 210	90.68 265	98.83 254	96.97 284	88.57 260	89.19 251	95.73 258	89.24 177	96.69 288	89.97 245	81.55 288	94.15 262

FMVSNet5		88.32 285	87.47 287	90.88 301	96.90 223	88.39 299	97.28 307	95.68 323	82.60 318	84.67 305	92.40 327	79.83 256	91.16 341	76.39 329	81.51 289	93.09 312

miper_lstm_enhance		91.81 229	91.39 228	93.06 282	97.34 205	89.18 289	99.38 196	96.79 300	86.70 285	87.47 278	95.22 286	90.00 165	95.86 316	88.26 260	81.37 290	94.15 262

VPA-MVSNet		92.70 212	91.55 224	96.16 192	95.09 268	96.20 142	98.88 248	99.00 33	91.02 219	91.82 210	95.29 284	76.05 283	97.96 225	95.62 150	81.19 291	94.30 246

v1192		90.62 255	89.25 263	94.72 228	93.13 299	93.07 214	99.50 180	97.02 276	86.33 289	89.56 241	95.01 291	79.22 259	97.09 267	82.34 306	81.16 292	94.01 274

v1144		91.09 243	89.83 251	94.87 222	93.25 298	93.69 204	99.62 163	96.98 282	86.83 284	89.64 239	94.99 294	80.94 243	97.05 268	85.08 290	81.16 292	93.87 288

v1240		90.20 265	88.79 272	94.44 242	93.05 304	92.27 234	99.38 196	96.92 290	85.89 293	89.36 244	94.87 298	77.89 269	97.03 272	80.66 314	81.08 294	94.01 274

new_pmnet		84.49 304	82.92 305	89.21 314	90.03 333	82.60 326	96.89 313	95.62 325	80.59 324	75.77 335	89.17 333	65.04 327	94.79 329	72.12 333	81.02 295	90.23 334

K. test v3		88.05 287	87.24 289	90.47 306	91.82 320	82.23 330	98.96 241	97.42 244	89.05 245	76.93 330	95.60 263	68.49 315	95.42 319	85.87 286	81.01 296	93.75 295

FPMVS		68.72 314	68.72 317	68.71 332	65.95 353	44.27 358	95.97 324	94.74 337	51.13 347	53.26 350	90.50 332	25.11 355	83.00 349	60.80 346	80.97 297	78.87 345

v1921920		90.46 257	89.12 265	94.50 238	92.96 306	92.46 230	99.49 182	96.98 282	86.10 291	89.61 240	95.30 281	78.55 266	97.03 272	82.17 307	80.89 298	94.01 274

cl_fuxian		92.53 216	91.87 218	94.52 236	97.40 202	92.99 217	99.40 191	96.93 289	87.86 268	88.69 259	95.44 272	89.95 166	96.44 296	90.45 237	80.69 299	94.14 265

tfpnnormal		89.29 279	87.61 286	94.34 246	94.35 280	94.13 196	98.95 242	98.94 36	83.94 309	84.47 306	95.51 269	74.84 290	97.39 243	77.05 327	80.41 300	91.48 327

v144192		90.79 250	89.52 258	94.59 232	93.11 302	92.77 219	99.56 171	96.99 280	86.38 288	89.82 234	94.95 296	80.50 251	97.10 265	83.98 296	80.41 300	93.90 285

nrg030		93.51 196	92.53 206	96.45 184	94.36 279	97.20 105	99.81 114	97.16 266	91.60 202	89.86 231	97.46 206	86.37 203	97.68 234	95.88 147	80.31 302	94.46 231

Anonymous20231211		89.86 271	88.44 277	94.13 252	98.93 123	90.68 265	98.54 271	98.26 160	76.28 332	86.73 286	95.54 266	70.60 308	97.56 238	90.82 232	80.27 303	94.15 262

V42		91.28 240	90.12 249	94.74 226	93.42 296	93.46 208	99.68 150	97.02 276	87.36 274	89.85 233	95.05 289	81.31 240	97.34 246	87.34 271	80.07 304	93.40 305

v2v482		91.30 238	90.07 250	95.01 217	93.13 299	93.79 201	99.77 126	97.02 276	88.05 266	89.25 247	95.37 278	80.73 246	97.15 260	87.28 272	80.04 305	94.09 268

WR-MVS		92.31 221	91.25 229	95.48 206	94.45 278	95.29 170	99.60 165	98.68 55	90.10 233	88.07 270	96.89 226	80.68 247	96.80 284	93.14 201	79.67 306	94.36 240

v10		90.25 264	88.82 271	94.57 234	93.53 293	93.43 209	99.08 224	96.87 294	85.00 304	87.34 282	94.51 306	80.93 244	97.02 274	82.85 303	79.23 307	93.26 309

CP-MVSNet		91.23 241	90.22 245	94.26 247	93.96 286	92.39 232	99.09 222	98.57 74	88.95 251	86.42 293	96.57 238	79.19 260	96.37 298	90.29 241	78.95 308	94.02 272

MIMVSNet1		82.58 308	80.51 312	88.78 317	86.68 340	84.20 323	96.65 314	95.41 328	78.75 328	78.59 326	92.44 326	51.88 345	89.76 344	65.26 344	78.95 308	92.38 320

PS-CasMVS		90.63 254	89.51 259	93.99 259	93.83 288	91.70 251	98.98 238	98.52 88	88.48 261	86.15 297	96.53 240	75.46 285	96.31 301	88.83 254	78.86 310	93.95 281

WR-MVS_H		91.30 238	90.35 241	94.15 250	94.17 283	92.62 228	99.17 218	98.94 36	88.87 253	86.48 292	94.46 310	84.36 219	96.61 291	88.19 261	78.51 311	93.21 311

v8		90.54 256	89.17 264	94.66 229	93.43 295	93.40 211	99.20 215	96.94 288	85.76 295	87.56 276	94.51 306	81.96 233	97.19 257	84.94 291	78.25 312	93.38 307

UniMVSNet (Re)		93.07 205	92.13 211	95.88 198	94.84 272	96.24 141	99.88 84	98.98 34	92.49 178	89.25 247	95.40 274	87.09 196	97.14 261	93.13 202	78.16 313	94.26 249

v7n		89.65 275	88.29 280	93.72 266	92.22 314	90.56 269	99.07 228	97.10 269	85.42 303	86.73 286	94.72 299	80.06 254	97.13 262	81.14 312	78.12 314	93.49 303

VPNet		91.81 229	90.46 238	95.85 200	94.74 274	95.54 164	98.98 238	98.59 71	92.14 186	90.77 220	97.44 207	68.73 314	97.54 239	94.89 159	77.89 315	94.46 231

Gipuma		66.95 317	65.00 318	72.79 330	91.52 323	67.96 346	66.16 351	95.15 335	47.89 348	58.54 346	67.99 350	29.74 352	87.54 346	50.20 349	77.83 316	62.87 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
NR-MVSNet		91.56 237	90.22 245	95.60 202	94.05 284	95.76 157	98.25 285	98.70 53	91.16 215	80.78 319	96.64 236	83.23 228	96.57 292	91.41 219	77.73 317	94.46 231

UniMVSNet_NR-MVSNet		92.95 207	92.11 212	95.49 203	94.61 277	95.28 171	99.83 111	99.08 30	91.49 205	89.21 249	96.86 228	87.14 195	96.73 286	93.20 198	77.52 318	94.46 231

DU-MVS		92.46 218	91.45 227	95.49 203	94.05 284	95.28 171	99.81 114	98.74 51	92.25 184	89.21 249	96.64 236	81.66 235	96.73 286	93.20 198	77.52 318	94.46 231

test_part1		87.55 289	85.69 293	93.15 278	95.35 266	88.21 301	98.30 284	97.70 210	71.40 342	83.19 311	95.62 261	62.37 333	97.28 254	89.77 247	77.47 320	93.96 280

MDA-MVSNet_test_wron		85.51 297	83.32 303	92.10 292	90.96 327	88.58 296	99.20 215	96.52 309	79.70 326	57.12 348	92.69 325	79.11 261	93.86 334	77.10 326	77.46 321	93.86 289

YYNet1		85.50 298	83.33 302	92.00 293	90.89 328	88.38 300	99.22 214	96.55 308	79.60 327	57.26 347	92.72 323	79.09 262	93.78 335	77.25 325	77.37 322	93.84 290

v148		90.70 251	89.63 254	93.92 261	92.97 305	90.97 260	99.75 134	96.89 292	87.51 271	88.27 268	95.01 291	81.67 234	97.04 270	87.40 270	77.17 323	93.75 295

Baseline_NR-MVSNet		90.33 261	89.51 259	92.81 285	92.84 307	89.95 280	99.77 126	93.94 343	84.69 307	89.04 253	95.66 260	81.66 235	96.52 293	90.99 227	76.98 324	91.97 324

PEN-MVS		90.19 266	89.06 267	93.57 270	93.06 303	90.90 262	99.06 229	98.47 101	88.11 265	85.91 299	96.30 244	76.67 275	95.94 315	87.07 274	76.91 325	93.89 286

TranMVSNet+NR-MVSNet		91.68 236	90.61 237	94.87 222	93.69 291	93.98 198	99.69 148	98.65 59	91.03 218	88.44 262	96.83 232	80.05 255	96.18 306	90.26 242	76.89 326	94.45 236

MDA-MVSNet-bldmvs		84.09 305	81.52 310	91.81 296	91.32 325	88.00 305	98.67 265	95.92 320	80.22 325	55.60 349	93.32 319	68.29 317	93.60 337	73.76 332	76.61 327	93.82 292

test20.03		84.72 303	83.99 297	86.91 321	88.19 339	80.62 338	98.88 248	95.94 319	88.36 263	78.87 324	94.62 304	68.75 313	89.11 345	66.52 341	75.82 328	91.00 329

DTE-MVSNet		89.40 277	88.24 281	92.88 284	92.66 310	89.95 280	99.10 221	98.22 164	87.29 275	85.12 304	96.22 246	76.27 280	95.30 323	83.56 300	75.74 329	93.41 304

pm-mvs1		89.36 278	87.81 285	94.01 257	93.40 297	91.93 241	98.62 268	96.48 311	86.25 290	83.86 309	96.14 248	73.68 297	97.04 270	86.16 283	75.73 330	93.04 314

lessismore_v0				90.53 304	90.58 330	80.90 337		95.80 321		77.01 329	95.84 253	66.15 323	96.95 275	83.03 302	75.05 331	93.74 298

IB-MVS	92.85 6	94.99 162	93.94 174	98.16 129	97.72 190	95.69 162	99.99 4	98.81 47	94.28 108	92.70 206	96.90 225	95.08 50	99.17 155	96.07 143	73.88 332	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
pmmvs6		85.69 294	83.84 300	91.26 300	90.00 334	84.41 322	97.82 299	96.15 316	75.86 334	81.29 317	95.39 276	61.21 336	96.87 280	83.52 301	73.29 333	92.50 318

ambc				83.23 325	77.17 348	62.61 347	87.38 346	94.55 340		76.72 331	86.65 339	30.16 351	96.36 299	84.85 292	69.86 334	90.73 332

Patchmatch-RL test		86.90 291	85.98 292	89.67 312	84.45 342	75.59 342	89.71 344	92.43 345	86.89 283	77.83 328	90.94 331	94.22 83	93.63 336	87.75 266	69.61 335	99.79 100

PM-MVS		80.47 310	78.88 314	85.26 323	83.79 344	72.22 344	95.89 325	91.08 348	85.71 298	76.56 332	88.30 334	36.64 350	93.90 333	82.39 305	69.57 336	89.66 338

pmmvs-eth3d		84.03 306	81.97 307	90.20 308	84.15 343	87.09 308	98.10 293	94.73 338	83.05 314	74.10 337	87.77 336	65.56 325	94.01 331	81.08 313	69.24 337	89.49 339

AUN-MVS		93.28 200	92.60 202	95.34 208	98.29 150	90.09 278	99.31 205	98.56 76	91.80 198	96.35 159	98.00 197	89.38 172	98.28 209	92.46 207	69.22 338	97.64 213

TransMVSNet (Re)		87.25 290	85.28 295	93.16 277	93.56 292	91.03 259	98.54 271	94.05 342	83.69 313	81.09 318	96.16 247	75.32 286	96.40 297	76.69 328	68.41 339	92.06 322

PMVS	49.05 23	53.75 320	51.34 324	60.97 335	40.80 359	34.68 359	74.82 350	89.62 352	37.55 351	28.67 357	72.12 347	7.09 361	81.63 350	43.17 352	68.21 340	66.59 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UnsupCasMVSNet_eth		85.52 296	83.99 297	90.10 309	89.36 336	83.51 324	96.65 314	97.99 187	89.14 243	75.89 334	93.83 315	63.25 331	93.92 332	81.92 309	67.90 341	92.88 315

PVSNet_0	88.03 19	91.80 232	90.27 244	96.38 188	98.27 155	90.46 271	99.94 55	99.61 11	93.99 120	86.26 296	97.39 210	71.13 307	99.89 79	98.77 73	67.05 342	98.79 197

TDRefinement		84.76 301	82.56 306	91.38 299	74.58 349	84.80 321	97.36 306	94.56 339	84.73 306	80.21 321	96.12 250	63.56 330	98.39 197	87.92 264	63.97 343	90.95 331

new-patchmatchnet		81.19 309	79.34 313	86.76 322	82.86 345	80.36 340	97.92 297	95.27 331	82.09 320	72.02 338	86.87 338	62.81 332	90.74 343	71.10 334	63.08 344	89.19 341

pmmvs3		80.27 311	77.77 315	87.76 320	80.32 347	82.43 328	98.23 287	91.97 346	72.74 341	78.75 325	87.97 335	57.30 342	90.99 342	70.31 335	62.37 345	89.87 336

UnsupCasMVSNet_bld		79.97 313	77.03 316	88.78 317	85.62 341	81.98 331	93.66 333	97.35 251	75.51 337	70.79 340	83.05 343	48.70 348	94.91 327	78.31 321	60.29 346	89.46 340

LCM-MVSNet		67.77 315	64.73 319	76.87 328	62.95 355	56.25 352	89.37 345	93.74 344	44.53 349	61.99 344	80.74 344	20.42 357	86.53 347	69.37 337	59.50 347	87.84 342

PMMVS2		67.15 316	64.15 320	76.14 329	70.56 352	62.07 349	93.89 331	87.52 354	58.09 346	60.02 345	78.32 345	22.38 356	84.54 348	59.56 347	47.03 348	81.80 344

MVE	53.74 22	51.54 322	47.86 326	62.60 334	59.56 356	50.93 353	79.41 349	77.69 356	35.69 353	36.27 355	61.76 354	5.79 363	69.63 352	37.97 353	36.61 349	67.24 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		52.30 321	52.18 323	52.67 336	71.51 350	45.40 355	93.62 334	76.60 357	36.01 352	43.50 353	64.13 352	27.11 354	67.31 354	31.06 354	26.06 350	45.30 353

EMVS		51.44 323	51.22 325	52.11 337	70.71 351	44.97 357	94.04 330	75.66 358	35.34 354	42.40 354	61.56 355	28.93 353	65.87 355	27.64 355	24.73 351	45.49 352

ANet_high		56.10 319	52.24 322	67.66 333	49.27 357	56.82 351	83.94 347	82.02 355	70.47 343	33.28 356	64.54 351	17.23 359	69.16 353	45.59 351	23.85 352	77.02 346

tmp_tt		65.23 318	62.94 321	72.13 331	44.90 358	50.03 354	81.05 348	89.42 353	38.45 350	48.51 352	99.90 17	54.09 344	78.70 351	91.84 216	18.26 353	87.64 343

testmvs		40.60 324	44.45 327	29.05 339	19.49 361	14.11 362	99.68 150	18.47 360	20.74 355	64.59 342	98.48 186	10.95 360	17.09 358	56.66 348	11.01 354	55.94 351

wuyk23d		20.37 327	20.84 330	18.99 340	65.34 354	27.73 360	50.43 352	7.67 362	9.50 357	8.01 358	6.34 358	6.13 362	26.24 356	23.40 356	10.69 355	2.99 354

test123		37.68 325	39.14 328	33.31 338	19.94 360	24.83 361	98.36 280	9.75 361	15.53 356	51.31 351	87.14 337	19.62 358	17.74 357	47.10 350	3.47 356	57.36 350

uanet_test		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

cdsmvs_eth3d_5k		23.43 326	31.24 329	0.00 341	0.00 362	0.00 363	0.00 353	98.09 180	0.00 358	0.00 359	99.67 95	83.37 226	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		7.60 329	10.13 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	91.20 148	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

ab-mvs-re		8.28 328	11.04 331	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	99.40 118	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

test_241102_ONE					99.93 26	99.30 8		98.43 114	97.26 22	99.80 16	99.88 22	96.71 20	100.00 1

save fliter					99.82 65	98.79 33	99.96 23	98.40 130	97.66 10

test0726					99.93 26	99.29 10	99.96 23	98.42 125	97.28 18	99.86 4	99.94 4	97.22 15

GSMVS																99.59 130

test_part2					99.89 45	99.25 13				99.49 48

sam_mvs1												94.72 64				99.59 130

sam_mvs												94.25 82

MTGPA								98.28 156

test_post1							95.78 326				59.23 356	93.20 114	97.74 233	91.06 225

test_post											63.35 353	94.43 69	98.13 216

patchmatchnet-post											91.70 329	95.12 48	97.95 226

MTMP							99.87 87	96.49 310

gm-plane-assit					96.97 220	93.76 203			91.47 207		98.96 153		98.79 167	94.92 156

TEST9					99.92 35	98.92 23	99.96 23	98.43 114	93.90 126	99.71 30	99.86 29	95.88 34	99.85 94

test_8					99.92 35	98.88 26	99.96 23	98.43 114	94.35 103	99.69 32	99.85 33	95.94 31	99.85 94

agg_prior					99.93 26	98.77 36		98.43 114		99.63 35			99.85 94

test_prior4						98.05 71	99.94 55

test_prior				99.43 35	99.94 14	98.49 57		98.65 59					99.80 106			99.99 20

旧先验2							99.46 187		94.21 110	99.85 6			99.95 60	96.96 133

新几何2							99.40 191

无先验							99.49 182	98.71 52	93.46 140				100.00 1	94.36 173		99.99 20

原ACMM2							99.90 73

testdata2													99.99 36	90.54 236

segment_acmp												96.68 22

testdata1							99.28 210		96.35 48

plane_prior7					95.71 257	91.59 255

plane_prior6					95.76 252	91.72 250						80.47 252

plane_prior4											98.59 177

plane_prior3						91.64 253			96.63 38	93.01 200

plane_prior2							99.84 105		96.38 44

plane_prior1					95.73 254

n2								0.00 363

nn								0.00 363

door-mid								89.69 351

test11								98.44 106

door								90.31 349

HQP5-MVS						91.85 243

HQP-NCC					95.78 248		99.87 87		96.82 30	93.37 196

ACMP_Plane					95.78 248		99.87 87		96.82 30	93.37 196

BP-MVS														97.92 109

HQP4-MVS										93.37 196			98.39 197			94.53 226

HQP2-MVS												80.65 248

NP-MVS					95.77 251	91.79 245					98.65 173

MDTV_nov1_ep13_2view						96.26 137	96.11 321		91.89 193	98.06 119		94.40 71		94.30 176		99.67 117

Test By Simon												92.82 123

High-resolution multi-view benchmark