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	98.43 116	97.27 20	99.80 16	99.94 4	96.71 20	100.00 1	100.00 1	100.00 1	100.00 1

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

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

DVP-MVS		99.30 4	99.16 3	99.73 8	99.93 26	99.29 10	99.95 40	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

test_0728_SECOND				99.82 5	99.94 14	99.47 5	99.95 40	98.43 116					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	98.44 108	97.48 15	99.64 34	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
agg_prior2														99.48 36	100.00 1	100.00 1

region2R		98.54 39	98.37 40	99.05 73	99.96 8	97.18 107	99.96 23	98.55 83	94.87 85	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 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 40		95.78 60	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 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

APDe-MVS		99.06 10	98.91 12	99.51 28	99.94 14	98.76 40	99.91 69	98.39 135	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 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

CDPH-MVS		98.65 31	98.36 42	99.49 31	99.94 14	98.73 41	99.87 87	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

mPP-MVS		98.39 52	98.20 50	98.97 81	99.97 3	96.92 117	99.95 40	98.38 139	95.04 80	98.61 101	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 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

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	98.44 108	94.31 108	98.50 105	99.82 53	93.06 117	99.99 36	98.30 92	99.99 20	99.93 81

testtj		98.89 18	98.69 18	99.52 26	99.94 14	98.56 53	99.90 73	98.55 83	95.14 79	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	98.38 139	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
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	98.43 116	94.63 94	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 77	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	96.42 130	99.88 84	98.16 177	91.75 202	98.94 85	99.54 108	91.82 145	99.65 138	97.62 120	99.99 20	99.99 20

HPM-MVS		97.96 70	97.72 72	98.68 95	99.84 60	96.39 133	99.90 73	98.17 174	92.61 171	98.62 100	99.57 105	91.87 143	99.67 136	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	98.36 144	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
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

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.
CPTT-MVS		97.64 86	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

DeepC-MVS_fast	96.59 1	98.81 22	98.54 26	99.62 15	99.90 42	98.85 29	99.24 215	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
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	98.46 105	94.56 95	99.84 8	99.92 11	94.32 80	99.86 90	99.96 8	99.98 33	100.00 1

ETH3D-3000-0.1		98.68 29	98.42 31	99.47 34	99.83 63	98.57 51	99.90 73	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

MSP-MVS		99.09 8	99.12 5	98.98 80	99.93 26	97.24 104	99.95 40	98.42 127	97.50 14	99.52 47	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
TSAR-MVS + MP.		98.93 15	98.77 16	99.41 39	99.74 77	98.67 44	99.77 126	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
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

HFP-MVS		98.56 37	98.37 40	99.14 63	99.96 8	97.43 99	99.95 40	98.61 69	94.77 87	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 99	99.95 40	98.61 69	95.00 81	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	97.18 107	99.95 40	98.60 71	94.77 87	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		98.40 132		99.65 33		94.76 63	99.75 121		99.98 33	99.99 20

PAPM_NR		98.12 66	97.93 68	98.70 94	99.94 14	96.13 146	99.82 112	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

ZD-MVS					99.92 35	98.57 51		98.52 90	92.34 184	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	98.33 149	93.22 148	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	97.17 110	99.95 40	98.39 135	94.70 90	98.26 117	99.81 57	91.84 144	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	98.27 161	88.48 264	99.06 78	99.66 97	90.30 166	99.64 139	96.32 143	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	98.34 148	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	99.75 134	99.50 16	93.90 128	99.37 60	99.76 72	93.24 113	100.00 1	97.75 118	99.96 48	99.98 51

API-MVS		97.86 74	97.66 74	98.47 114	99.52 96	95.41 168	99.47 185	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

ETH3D cwj APD-0.16		98.40 51	98.07 59	99.40 41	99.59 90	98.41 60	99.86 98	98.24 164	92.18 188	99.73 26	99.87 26	93.47 103	99.85 94	99.74 24	99.95 51	99.93 81

SR-MVS		98.46 45	98.30 46	98.93 84	99.88 49	97.04 112	99.84 105	98.35 146	94.92 82	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	98.42 127	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		93.83 188	92.06 217	99.15 61	99.94 14	97.50 94	99.94 55	98.42 127	96.22 49	99.41 54	41.37 364	94.34 76	99.96 53	98.92 61	99.95 51	99.99 20

原ACMM1				98.96 82	99.73 81	96.99 114		98.51 97	94.06 119	99.62 37	99.85 33	94.97 59	99.96 53	95.11 155	99.95 51	99.92 87

test222					99.55 94	97.41 102	99.34 202	98.55 83	91.86 197	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	98.44 108	97.96 7	99.55 42	99.94 4	97.18 17	100.00 1	93.81 189	99.94 57	99.98 51

新几何1				99.42 38	99.75 76	98.27 65		98.63 66	92.69 166	99.55 42	99.82 53	94.40 71	100.00 1	91.21 223	99.94 57	99.99 20

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

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

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
MVS_111021_HR		98.72 27	98.62 22	99.01 78	99.36 105	97.18 107	99.93 61	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

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 73	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

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 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	93.92 264	99.63 88	81.76 339	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

test1172		98.38 53	98.25 47	98.77 90	99.88 49	96.56 127	99.80 119	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

SR-MVS-dyc-post		98.31 56	98.17 52	98.71 93	99.79 70	96.37 134	99.76 131	98.31 153	94.43 100	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	96.37 134	99.76 131	98.31 153	94.43 100	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	98.31 287	98.62 67	92.78 161	99.53 44	99.83 49	95.08 50	100.00 1	94.36 176	99.92 67	99.99 20

APD-MVS_3200maxsize		98.25 62	98.08 58	98.78 89	99.81 68	96.60 125	99.82 112	98.30 156	93.95 125	99.37 60	99.77 68	92.84 121	99.76 118	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 90	96.05 53	99.41 54	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 87	98.52 90	96.04 54	99.41 54	99.79 62	94.92 61	99.76 118	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	98.18 173	93.35 144	96.45 156	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	96.05 247	98.96 20	99.90 73	99.35 23	96.68 37	98.35 112	99.66 97	96.45 25	98.51 186	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	98.28 158	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	99.39 196	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

MVS		96.60 123	95.56 142	99.72 9	96.85 230	99.22 15	98.31 287	98.94 36	91.57 206	90.90 220	99.61 102	86.66 204	99.96 53	97.36 124	99.88 76	99.99 20

MVS_111021_LR		98.42 48	98.38 38	98.53 111	99.39 103	95.79 156	99.87 87	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

ACMMP_NAP		98.49 43	98.14 54	99.54 23	99.66 87	98.62 50	99.85 101	98.37 142	94.68 91	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 90	95.79 59	99.01 81	99.77 68	94.40 71	99.75 121	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 90	95.76 62	99.01 81	99.77 68	94.33 79	99.75 121	98.80 71	99.83 81	99.98 51

QAPM		95.40 154	94.17 170	99.10 69	96.92 224	97.71 83	99.40 192	98.68 56	89.31 245	88.94 258	98.89 158	82.48 235	99.96 53	93.12 206	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	98.43 116	95.35 73	98.03 122	99.75 77	94.03 90	99.98 42	98.11 98	99.83 81	99.99 20

3Dnovator+	91.53 11	96.31 133	95.24 149	99.52 26	96.88 229	98.64 49	99.72 145	98.24 164	95.27 77	88.42 269	98.98 149	82.76 234	99.94 68	97.10 131	99.83 81	99.96 70

3Dnovator	91.47 12	96.28 136	95.34 147	99.08 71	96.82 232	97.47 97	99.45 188	98.81 48	95.52 70	89.39 246	99.00 146	81.97 237	99.95 60	97.27 126	99.83 81	99.84 95

LS3D		95.84 144	95.11 154	98.02 136	99.85 55	95.10 178	98.74 264	98.50 101	87.22 279	93.66 197	99.86 29	87.45 196	99.95 60	90.94 232	99.81 87	99.02 187

CHOSEN 280x420		99.01 12	99.03 8	98.95 83	99.38 104	98.87 27	98.46 280	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

GST-MVS		98.27 59	97.97 64	99.17 57	99.92 35	97.57 88	99.93 61	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

OpenMVS	90.15 15	94.77 167	93.59 184	98.33 123	96.07 246	97.48 96	99.56 171	98.57 75	90.46 230	86.51 293	98.95 155	78.57 271	99.94 68	93.86 185	99.74 90	97.57 216

1314		96.84 111	95.96 130	99.48 33	96.74 237	98.52 55	98.31 287	98.86 45	95.82 58	89.91 232	98.98 149	87.49 195	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	96.11 149	99.68 150	98.14 180	93.80 132	99.27 68	99.70 88	88.65 188	99.98 42	97.46 122	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	98.44 108	92.06 193	98.40 110	99.84 46	95.68 38	100.00 1	98.19 93	99.71 93	99.97 63

MVP-Stereo		90.93 249	90.45 244	92.37 293	91.25 330	88.76 296	98.05 300	96.17 321	87.27 278	84.04 311	95.30 284	78.46 273	97.27 257	83.78 300	99.70 94	91.09 334
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	98.80 135	98.92 23	99.54 175	98.17 174	97.34 16	99.85 6	99.85 33	91.20 150	99.89 79	99.41 40	99.67 95	98.69 200

BH-w/o		95.71 148	95.38 146	96.68 178	98.49 146	92.28 236	99.84 105	97.50 238	92.12 190	92.06 211	98.79 168	84.69 221	98.67 179	95.29 154	99.66 96	99.09 185

MAR-MVS		97.43 89	97.19 90	98.15 132	99.47 100	94.79 187	99.05 235	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
MS-PatchMatch		90.65 256	90.30 247	91.71 301	94.22 286	85.50 320	98.24 291	97.70 213	88.67 260	86.42 296	96.37 246	67.82 325	98.03 223	83.62 301	99.62 98	91.60 331

MVSFormer		96.94 107	96.60 108	97.95 137	97.28 213	97.70 85	99.55 173	97.27 261	91.17 216	99.43 52	99.54 108	90.92 158	96.89 279	94.67 170	99.62 98	99.25 174

lupinMVS		97.85 75	97.60 77	98.62 100	97.28 213	97.70 85	99.99 4	97.55 228	95.50 71	99.43 52	99.67 95	90.92 158	98.71 176	98.40 88	99.62 98	99.45 154

BH-untuned		95.18 157	94.83 158	96.22 193	98.36 150	91.22 262	99.80 119	97.32 257	90.91 223	91.08 218	98.67 172	83.51 229	98.54 185	94.23 181	99.61 101	98.92 189

DeepC-MVS	94.51 4	96.92 109	96.40 115	98.45 116	99.16 110	95.90 153	99.66 153	98.06 186	96.37 47	94.37 188	99.49 111	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
GG-mvs-BLEND				98.54 109	98.21 161	98.01 73	93.87 339	98.52 90		97.92 124	97.92 202	99.02 2	97.94 230	98.17 94	99.58 103	99.67 117

gg-mvs-nofinetune		93.51 198	91.86 222	98.47 114	97.72 193	97.96 77	92.62 343	98.51 97	74.70 346	97.33 136	69.59 356	98.91 3	97.79 233	97.77 116	99.56 104	99.67 117

BH-RMVSNet		95.18 157	94.31 168	97.80 141	98.17 164	95.23 175	99.76 131	97.53 232	92.52 178	94.27 190	99.25 131	76.84 280	98.80 167	90.89 234	99.54 105	99.35 165

EI-MVSNet-Vis-set		98.27 59	98.11 57	98.75 92	99.83 63	96.59 126	99.40 192	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

TAPA-MVS	92.12 8	94.42 179	93.60 183	96.90 171	99.33 106	91.78 249	99.78 123	98.00 189	89.89 241	94.52 185	99.47 112	91.97 141	99.18 155	69.90 342	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	94.77 188	99.92 65	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
jason		97.24 98	96.86 100	98.38 122	95.73 259	97.32 103	99.97 16	97.40 250	95.34 74	98.60 102	99.54 108	87.70 193	98.56 183	97.94 108	99.47 109	99.25 174
jason: jason.
CSCG		97.10 102	97.04 96	97.27 164	99.89 45	91.92 245	99.90 73	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

CNLPA		97.76 82	97.38 83	98.92 85	99.53 95	96.84 118	99.87 87	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

AdaColmap		97.23 99	96.80 103	98.51 112	99.99 1	95.60 164	99.09 224	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

F-COLMAP		96.93 108	96.95 99	96.87 172	99.71 84	91.74 250	99.85 101	97.95 195	93.11 152	95.72 172	99.16 136	92.35 133	99.94 68	95.32 153	99.35 114	98.92 189

EI-MVSNet-UG-set		98.14 65	97.99 63	98.60 102	99.80 69	96.27 136	99.36 201	98.50 101	95.21 78	98.30 114	99.75 77	93.29 109	99.73 128	98.37 89	99.30 115	99.81 98

PVSNet_Blended		97.94 71	97.64 75	98.83 88	99.59 90	96.99 114	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

PatchMatch-RL		96.04 140	95.40 144	97.95 137	99.59 90	95.22 176	99.52 177	99.07 31	93.96 124	96.49 155	98.35 190	82.28 236	99.82 105	90.15 246	99.22 117	98.81 196

CHOSEN 1792x2688		96.81 112	96.53 111	97.64 149	98.91 127	93.07 217	99.65 156	99.80 3	95.64 67	95.39 176	98.86 164	84.35 225	99.90 75	96.98 134	99.16 118	99.95 78

EIA-MVS		97.53 88	97.46 81	97.76 145	98.04 170	94.84 184	99.98 8	97.61 222	94.41 103	97.90 125	99.59 103	92.40 132	98.87 164	98.04 102	99.13 119	99.59 130

UGNet		95.33 155	94.57 163	97.62 151	98.55 142	94.85 183	98.67 271	99.32 24	95.75 65	96.80 148	96.27 249	72.18 308	99.96 53	94.58 172	99.05 120	98.04 207
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	98.28 154	96.27 136	100.00 1	97.52 234	95.29 75	99.25 70	99.65 99	91.18 153	98.94 163	98.96 57	99.04 121	99.73 108

CANet_DTU		96.76 115	96.15 119	98.60 102	98.78 136	97.53 90	99.84 105	97.63 217	97.25 23	99.20 71	99.64 100	81.36 245	99.98 42	92.77 209	98.89 122	98.28 203

TESTMET0.1,1		96.74 117	96.26 117	98.16 129	97.36 207	96.48 128	99.96 23	98.29 157	91.93 195	95.77 171	98.07 196	95.54 40	98.29 208	90.55 238	98.89 122	99.70 112

test-LLR		96.47 126	96.04 121	97.78 142	97.02 221	95.44 166	99.96 23	98.21 168	94.07 117	95.55 173	96.38 244	93.90 94	98.27 212	90.42 241	98.83 124	99.64 123

test-mter		96.39 130	95.93 132	97.78 142	97.02 221	95.44 166	99.96 23	98.21 168	91.81 200	95.55 173	96.38 244	95.17 47	98.27 212	90.42 241	98.83 124	99.64 123

PVSNet	91.05 13	97.13 101	96.69 106	98.45 116	99.52 96	95.81 155	99.95 40	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

EPNet		98.49 43	98.40 35	98.77 90	99.62 89	96.80 120	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	98.69 139	98.66 46	99.52 177	98.08 185	97.05 26	99.86 4	99.86 29	90.65 162	99.71 129	99.39 41	98.63 128	98.69 200

ETV-MVS		97.92 73	97.80 71	98.25 127	98.14 166	96.48 128	99.98 8	97.63 217	95.61 68	99.29 67	99.46 114	92.55 129	98.82 166	99.02 56	98.54 129	99.46 152

Vis-MVSNet		95.72 146	95.15 153	97.45 155	97.62 197	94.28 195	99.28 212	98.24 164	94.27 111	96.84 146	98.94 156	79.39 264	98.76 172	93.25 200	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	94.10 172	98.43 118	98.55 142	95.99 151	97.91 303	97.31 258	90.35 233	89.48 245	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
MSDG		94.37 181	93.36 193	97.40 158	98.88 130	93.95 202	99.37 199	97.38 251	85.75 300	90.80 221	99.17 135	84.11 227	99.88 85	86.35 283	98.43 132	98.36 202

PVSNet_Blended_VisFu		97.27 97	96.81 102	98.66 97	98.81 134	96.67 122	99.92 65	98.64 63	94.51 97	96.38 160	98.49 183	89.05 182	99.88 85	97.10 131	98.34 133	99.43 157

EPNet_dtu		95.71 148	95.39 145	96.66 179	98.92 125	93.41 213	99.57 169	98.90 41	96.19 51	97.52 132	98.56 181	92.65 126	97.36 247	77.89 326	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	97.74 189	98.14 67	99.31 206	97.86 205	96.43 41	99.62 37	99.69 91	85.56 213	99.68 133	99.05 50	98.31 135	97.83 209

xiu_mvs_v1_base		97.43 89	97.06 93	98.55 106	97.74 189	98.14 67	99.31 206	97.86 205	96.43 41	99.62 37	99.69 91	85.56 213	99.68 133	99.05 50	98.31 135	97.83 209

xiu_mvs_v1_base_debi		97.43 89	97.06 93	98.55 106	97.74 189	98.14 67	99.31 206	97.86 205	96.43 41	99.62 37	99.69 91	85.56 213	99.68 133	99.05 50	98.31 135	97.83 209

OMC-MVS		97.28 96	97.23 89	97.41 157	99.76 74	93.36 215	99.65 156	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

mvs-test1		95.53 151	95.97 128	94.20 252	97.77 186	85.44 321	99.95 40	97.06 279	94.92 82	96.58 152	98.72 170	85.81 210	98.98 160	94.80 163	98.11 139	98.18 204

DP-MVS		94.54 175	93.42 189	97.91 140	99.46 102	94.04 198	98.93 247	97.48 240	81.15 329	90.04 229	99.55 106	87.02 201	99.95 60	88.97 255	98.11 139	99.73 108

EPMVS		96.53 125	96.01 122	98.09 133	98.43 148	96.12 148	96.36 324	99.43 19	93.53 140	97.64 130	95.04 293	94.41 70	98.38 202	91.13 225	98.11 139	99.75 106

PatchmatchNet		95.94 142	95.45 143	97.39 159	97.83 182	94.41 193	96.05 329	98.40 132	92.86 155	97.09 141	95.28 288	94.21 86	98.07 222	89.26 253	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	95.63 266	95.96 152	99.74 137	98.88 43	92.94 154	91.61 213	98.97 151	97.72 5	98.62 181	94.83 162	98.08 143	97.53 217

ACMMP		97.74 83	97.44 82	98.66 97	99.92 35	96.13 146	99.18 219	99.45 17	94.84 86	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
MVS-HIRNet		86.22 298	83.19 310	95.31 212	96.71 239	90.29 278	92.12 345	97.33 256	62.85 352	86.82 288	70.37 355	69.37 318	97.49 242	75.12 335	97.99 145	98.15 205

PMMVS		96.76 115	96.76 104	96.76 175	98.28 154	92.10 240	99.91 69	97.98 192	94.12 114	99.53 44	99.39 120	86.93 202	98.73 174	96.95 136	97.73 146	99.45 154

UA-Net		96.54 124	95.96 130	98.27 126	98.23 160	95.71 161	98.00 301	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

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	99.55 106	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	98.19 162	95.74 159	99.33 203	97.49 239	90.87 224	90.47 225	97.10 219	88.23 190	97.16 260	95.92 148	97.66 149	99.68 115

LCM-MVSNet-Re		92.31 224	92.60 204	91.43 302	97.53 200	79.27 348	99.02 238	91.83 354	92.07 191	80.31 328	94.38 314	83.50 230	95.48 320	97.22 128	97.58 150	99.54 143

MVS_Test		96.46 127	95.74 138	98.61 101	98.18 163	97.23 105	99.31 206	97.15 271	91.07 220	98.84 87	97.05 223	88.17 191	98.97 161	94.39 175	97.50 151	99.61 127

SCA		94.69 169	93.81 180	97.33 163	97.10 216	94.44 191	98.86 256	98.32 151	93.30 146	96.17 164	95.59 267	76.48 284	97.95 228	91.06 227	97.43 152	99.59 130

Vis-MVSNet (Re-imp)		96.32 132	95.98 125	97.35 162	97.93 175	94.82 185	99.47 185	98.15 179	91.83 198	95.09 180	99.11 137	91.37 148	97.47 244	93.47 198	97.43 152	99.74 107

diffmvs		97.00 105	96.64 107	98.09 133	97.64 196	96.17 145	99.81 114	97.19 265	94.67 93	98.95 84	99.28 124	86.43 206	98.76 172	98.37 89	97.42 154	99.33 167

IS-MVSNet		96.29 135	95.90 134	97.45 155	98.13 167	94.80 186	99.08 226	97.61 222	92.02 194	95.54 175	98.96 153	90.64 163	98.08 220	93.73 194	97.41 155	99.47 151

Effi-MVS+		96.30 134	95.69 139	98.16 129	97.85 181	96.26 138	97.41 309	97.21 264	90.37 232	98.65 99	98.58 179	86.61 205	98.70 177	97.11 130	97.37 156	99.52 146

DWT-MVSNet_test		97.31 95	97.19 90	97.66 148	98.24 159	94.67 189	98.86 256	98.20 172	93.60 139	98.09 120	98.89 158	97.51 7	98.78 169	94.04 183	97.28 157	99.55 139

ADS-MVSNet2		93.80 191	93.88 178	93.55 276	97.87 179	85.94 317	94.24 335	96.84 300	90.07 237	96.43 157	94.48 311	90.29 167	95.37 322	87.44 270	97.23 158	99.36 163

ADS-MVSNet		94.79 165	94.02 174	97.11 168	97.87 179	93.79 204	94.24 335	98.16 177	90.07 237	96.43 157	94.48 311	90.29 167	98.19 217	87.44 270	97.23 158	99.36 163

EPP-MVSNet		96.69 120	96.60 108	96.96 169	97.74 189	93.05 219	99.37 199	98.56 77	88.75 258	95.83 170	99.01 144	96.01 28	98.56 183	96.92 137	97.20 160	99.25 174

Fast-Effi-MVS+		95.02 161	94.19 169	97.52 153	97.88 177	94.55 190	99.97 16	97.08 277	88.85 257	94.47 187	97.96 201	84.59 222	98.41 194	89.84 249	97.10 161	99.59 130

Effi-MVS+-dtu		94.53 177	95.30 148	92.22 294	97.77 186	82.54 332	99.59 166	97.06 279	94.92 82	95.29 178	95.37 281	85.81 210	97.89 231	94.80 163	97.07 162	96.23 224

casdiffmvs		96.42 129	95.97 128	97.77 144	97.30 212	94.98 180	99.84 105	97.09 276	93.75 135	96.58 152	99.26 130	85.07 219	98.78 169	97.77 116	97.04 163	99.54 143

sss		97.57 87	97.03 97	99.18 54	98.37 149	98.04 72	99.73 142	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

Patchmatch-test		92.65 218	91.50 228	96.10 196	96.85 230	90.49 274	91.50 348	97.19 265	82.76 324	90.23 226	95.59 267	95.02 54	98.00 224	77.41 328	96.98 165	99.82 97

MDTV_nov1_ep13			95.69 139		97.90 176	94.15 196	95.98 330	98.44 108	93.12 151	97.98 123	95.74 260	95.10 49	98.58 182	90.02 247	96.92 166

Fast-Effi-MVS+-dtu		93.72 195	93.86 179	93.29 279	97.06 218	86.16 315	99.80 119	96.83 301	92.66 168	92.58 209	97.83 203	81.39 244	97.67 237	89.75 250	96.87 167	96.05 226

baseline		96.43 128	95.98 125	97.76 145	97.34 208	95.17 177	99.51 179	97.17 268	93.92 127	96.90 145	99.28 124	85.37 216	98.64 180	97.50 121	96.86 168	99.46 152

tpmrst		96.27 137	95.98 125	97.13 166	97.96 173	93.15 216	96.34 325	98.17 174	92.07 191	98.71 96	95.12 291	93.91 93	98.73 174	94.91 160	96.62 169	99.50 149

JIA-IIPM		91.76 239	90.70 239	94.94 223	96.11 245	87.51 310	93.16 342	98.13 182	75.79 343	97.58 131	77.68 353	92.84 121	97.97 225	88.47 261	96.54 170	99.33 167

dp		95.05 160	94.43 165	96.91 170	97.99 172	92.73 226	96.29 326	97.98 192	89.70 243	95.93 167	94.67 306	93.83 97	98.45 191	86.91 282	96.53 171	99.54 143

COLMAP_ROB	90.47 14	92.18 227	91.49 229	94.25 251	99.00 117	88.04 308	98.42 285	96.70 309	82.30 326	88.43 267	99.01 144	76.97 278	99.85 94	86.11 286	96.50 172	94.86 227
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.51 198	92.52 209	96.47 183	97.77 186	91.47 260	96.13 327	98.06 186	80.98 330	92.91 205	93.78 319	89.66 172	98.87 164	87.03 278	96.39 173	99.09 185

thisisatest0515		97.41 93	97.02 98	98.59 104	97.71 195	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	96.37 174	99.16 179

AllTest		92.48 220	91.64 223	95.00 221	99.01 115	88.43 302	98.94 246	96.82 303	86.50 288	88.71 260	98.47 187	74.73 298	99.88 85	85.39 289	96.18 175	96.71 220

TestCases				95.00 221	99.01 115	88.43 302		96.82 303	86.50 288	88.71 260	98.47 187	74.73 298	99.88 85	85.39 289	96.18 175	96.71 220

thisisatest0530		97.10 102	96.72 105	98.22 128	97.60 198	96.70 121	99.92 65	98.54 87	91.11 219	97.07 142	98.97 151	97.47 9	99.03 158	93.73 194	96.09 177	98.92 189

DSMNet-mixed		88.28 290	88.24 285	88.42 325	89.64 340	75.38 350	98.06 299	89.86 357	85.59 302	88.20 272	92.14 333	76.15 289	91.95 346	78.46 324	96.05 178	97.92 208

TR-MVS		94.54 175	93.56 186	97.49 154	97.96 173	94.34 194	98.71 267	97.51 237	90.30 235	94.51 186	98.69 171	75.56 291	98.77 171	92.82 208	95.99 179	99.35 165

CR-MVSNet		93.45 201	92.62 203	95.94 199	96.29 242	92.66 228	92.01 346	96.23 319	92.62 170	96.94 143	93.31 323	91.04 155	96.03 314	79.23 320	95.96 180	99.13 183

RPMNet		89.76 277	87.28 292	97.19 165	96.29 242	92.66 228	92.01 346	98.31 153	70.19 351	96.94 143	85.87 349	87.25 198	99.78 111	62.69 352	95.96 180	99.13 183

PatchT		90.38 263	88.75 277	95.25 215	95.99 249	90.16 280	91.22 350	97.54 230	76.80 339	97.26 137	86.01 348	91.88 142	96.07 313	66.16 349	95.91 182	99.51 147

tpmvs		94.28 184	93.57 185	96.40 188	98.55 142	91.50 259	95.70 334	98.55 83	87.47 274	92.15 210	94.26 315	91.42 146	98.95 162	88.15 264	95.85 183	98.76 198

TAMVS		95.85 143	95.58 141	96.65 180	97.07 217	93.50 210	99.17 220	97.82 209	91.39 215	95.02 181	98.01 197	92.20 136	97.30 252	93.75 193	95.83 184	99.14 182

CostFormer		96.10 138	95.88 135	96.78 174	97.03 220	92.55 232	97.08 316	97.83 208	90.04 239	98.72 95	94.89 300	95.01 56	98.29 208	96.54 141	95.77 185	99.50 149

tttt0517		96.85 110	96.49 112	97.92 139	97.48 204	95.89 154	99.85 101	98.54 87	90.72 228	96.63 151	98.93 157	97.47 9	99.02 159	93.03 207	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	97.49 308	99.52 13	95.69 66	98.32 113	97.41 210	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	97.37 206	94.96 181	99.53 176	97.91 200	91.55 207	95.37 177	98.32 191	95.05 53	97.13 263	93.80 190	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	96.35 191	96.91 225	91.70 254	96.96 319	97.93 197	88.04 269	98.44 107	95.40 277	93.32 107	97.97 225	94.00 184	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 69	98.24 118	99.39 120	93.33 106	99.74 125	97.98 107	95.58 190	99.78 103

HyFIR lowres test		96.66 122	96.43 114	97.36 161	99.05 113	93.91 203	99.70 147	99.80 3	90.54 229	96.26 162	98.08 195	92.15 138	98.23 215	96.84 139	95.46 191	99.93 81

cascas		94.64 172	93.61 181	97.74 147	97.82 183	96.26 138	99.96 23	97.78 211	85.76 298	94.00 193	97.54 207	76.95 279	99.21 154	97.23 127	95.43 192	97.76 213

CVMVSNet		94.68 171	94.94 156	93.89 266	96.80 233	86.92 313	99.06 231	98.98 34	94.45 98	94.23 191	99.02 142	85.60 212	95.31 324	90.91 233	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	91.43 212	97.88 126	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	91.43 212	97.88 126	98.99 147	95.84 35	99.84 103	98.82 68	95.32 194	99.79 100

LFMVS		94.75 168	93.56 186	98.30 125	99.03 114	95.70 162	98.74 264	97.98 192	87.81 272	98.47 106	99.39 120	67.43 327	99.53 141	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	93.17 150	96.26 162	98.88 160	89.87 171	99.51 143	94.26 180	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	92.59 173	95.84 168	98.86 164	89.25 179	99.50 145	93.84 186	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	92.86 155	96.02 165	98.87 162	89.33 177	99.50 145	93.84 186	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	92.86 155	96.02 165	98.87 162	89.33 177	99.50 145	93.84 186	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	92.59 173	95.84 168	98.86 164	89.25 179	99.50 145	93.44 199	94.50 201	99.16 179

VNet		97.21 100	96.57 110	99.13 68	98.97 119	97.82 81	99.03 237	99.21 27	94.31 108	99.18 75	98.88 160	86.26 208	99.89 79	98.93 60	94.32 202	99.69 114

alignmvs		97.81 79	97.33 87	99.25 49	98.77 137	98.66 46	99.99 4	98.44 108	94.40 104	98.41 108	99.47 112	93.65 100	99.42 151	98.57 83	94.26 203	99.67 117

VDD-MVS		93.77 192	92.94 198	96.27 192	98.55 142	90.22 279	98.77 263	97.79 210	90.85 225	96.82 147	99.42 116	61.18 345	99.77 115	98.95 58	94.13 204	98.82 195

VDDNet		93.12 205	91.91 220	96.76 175	96.67 240	92.65 230	98.69 269	98.21 168	82.81 323	97.75 129	99.28 124	61.57 343	99.48 149	98.09 100	94.09 205	98.15 205

GA-MVS		93.83 188	92.84 199	96.80 173	95.73 259	93.57 208	99.88 84	97.24 263	92.57 176	92.92 204	96.66 237	78.73 270	97.67 237	87.75 268	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	97.35 253	94.45 98	97.88 126	99.42 116	86.71 203	99.52 142	98.48 86	93.97 207	99.72 111

1112_ss		96.01 141	95.20 151	98.42 119	97.80 184	96.41 131	99.65 156	96.66 310	92.71 164	92.88 206	99.40 118	92.16 137	99.30 152	91.92 216	93.66 208	99.55 139

Test_1112_low_res		95.72 146	94.83 158	98.42 119	97.79 185	96.41 131	99.65 156	96.65 311	92.70 165	92.86 207	96.13 253	92.15 138	99.30 152	91.88 217	93.64 209	99.55 139

MIMVSNet		90.30 266	88.67 278	95.17 217	96.45 241	91.64 256	92.39 344	97.15 271	85.99 294	90.50 224	93.19 325	66.95 328	94.86 330	82.01 310	93.43 210	99.01 188

XVG-OURS-SEG-HR		94.79 165	94.70 162	95.08 218	98.05 169	89.19 292	99.08 226	97.54 230	93.66 137	94.87 182	99.58 104	78.78 269	99.79 109	97.31 125	93.40 211	96.25 222

ab-mvs		94.69 169	93.42 189	98.51 112	98.07 168	96.26 138	96.49 323	98.68 56	90.31 234	94.54 184	97.00 225	76.30 286	99.71 129	95.98 147	93.38 212	99.56 138

test0.0.03 1		93.86 187	93.61 181	94.64 233	95.02 275	92.18 239	99.93 61	98.58 73	94.07 117	87.96 274	98.50 182	93.90 94	94.96 328	81.33 313	93.17 213	96.78 219

RPSCF		91.80 236	92.79 201	88.83 321	98.15 165	69.87 352	98.11 297	96.60 312	83.93 316	94.33 189	99.27 127	79.60 263	99.46 150	91.99 214	93.16 214	97.18 218

XVG-OURS		94.82 164	94.74 161	95.06 219	98.00 171	89.19 292	99.08 226	97.55 228	94.10 115	94.71 183	99.62 101	80.51 256	99.74 125	96.04 146	93.06 215	96.25 222

MVS_0304		89.28 284	88.31 283	92.21 295	97.05 219	86.53 314	97.76 306	99.57 12	85.58 303	93.86 196	92.71 327	51.04 355	96.30 304	84.49 295	92.72 216	93.79 295

Anonymous202405211		93.10 206	91.99 218	96.40 188	99.10 112	89.65 289	98.88 252	97.93 197	83.71 318	94.00 193	98.75 169	68.79 319	99.88 85	95.08 156	91.71 217	99.68 115

Anonymous20240529		92.10 229	90.65 240	96.47 183	98.82 133	90.61 271	98.72 266	98.67 59	75.54 344	93.90 195	98.58 179	66.23 330	99.90 75	94.70 169	90.67 218	98.90 192

HQP3-MVS								97.89 201							89.60 219

HQP-MVS		94.61 173	94.50 164	94.92 224	95.78 253	91.85 246	99.87 87	97.89 201	96.82 30	93.37 198	98.65 173	80.65 254	98.39 198	97.92 109	89.60 219	94.53 228

plane_prior						91.74 250	99.86 98		96.76 34						89.59 221

HQP_MVS		94.49 178	94.36 166	94.87 225	95.71 262	91.74 250	99.84 105	97.87 203	96.38 44	93.01 202	98.59 177	80.47 258	98.37 203	97.79 114	89.55 222	94.52 230

plane_prior5								97.87 203					98.37 203	97.79 114	89.55 222	94.52 230

CLD-MVS		94.06 186	93.90 177	94.55 238	96.02 248	90.69 268	99.98 8	97.72 212	96.62 39	91.05 219	98.85 167	77.21 276	98.47 187	98.11 98	89.51 224	94.48 232
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		93.21 203	92.80 200	94.44 245	93.12 305	90.85 267	99.77 126	97.61 222	96.19 51	91.56 214	98.65 173	75.16 296	98.47 187	93.78 192	89.39 225	93.99 280

LPG-MVS_test		92.96 209	92.71 202	93.71 270	95.43 268	88.67 298	99.75 134	97.62 219	92.81 158	90.05 227	98.49 183	75.24 294	98.40 196	95.84 150	89.12 226	94.07 272

LGP-MVS_train				93.71 270	95.43 268	88.67 298		97.62 219	92.81 158	90.05 227	98.49 183	75.24 294	98.40 196	95.84 150	89.12 226	94.07 272

test_djsdf		92.83 212	92.29 213	94.47 243	91.90 322	92.46 233	99.55 173	97.27 261	91.17 216	89.96 230	96.07 255	81.10 247	96.89 279	94.67 170	88.91 228	94.05 274

testgi		89.01 286	88.04 287	91.90 299	93.49 298	84.89 324	99.73 142	95.66 331	93.89 130	85.14 307	98.17 193	59.68 346	94.66 332	77.73 327	88.88 229	96.16 225

ACMM	91.95 10	92.88 211	92.52 209	93.98 263	95.75 258	89.08 295	99.77 126	97.52 234	93.00 153	89.95 231	97.99 200	76.17 288	98.46 190	93.63 197	88.87 230	94.39 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	92.05 9	92.74 214	92.42 211	93.73 268	95.91 252	88.72 297	99.81 114	97.53 232	94.13 113	87.00 287	98.23 192	74.07 302	98.47 187	96.22 144	88.86 231	93.99 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jajsoiax		91.92 231	91.18 233	94.15 253	91.35 328	90.95 265	99.00 239	97.42 246	92.61 171	87.38 283	97.08 220	72.46 307	97.36 247	94.53 173	88.77 232	94.13 269

anonymousdsp		91.79 238	90.92 236	94.41 248	90.76 333	92.93 221	98.93 247	97.17 268	89.08 247	87.46 282	95.30 284	78.43 274	96.92 278	92.38 211	88.73 233	93.39 308

mvs_tets		91.81 233	91.08 234	94.00 261	91.63 326	90.58 272	98.67 271	97.43 244	92.43 182	87.37 284	97.05 223	71.76 309	97.32 251	94.75 166	88.68 234	94.11 270

XVG-ACMP-BASELINE		91.22 246	90.75 237	92.63 291	93.73 294	85.61 318	98.52 279	97.44 243	92.77 162	89.90 233	96.85 231	66.64 329	98.39 198	92.29 212	88.61 235	93.89 288

EG-PatchMatch MVS		85.35 304	83.81 306	89.99 315	90.39 335	81.89 337	98.21 294	96.09 323	81.78 328	74.73 344	93.72 320	51.56 354	97.12 265	79.16 323	88.61 235	90.96 336

UniMVSNet_ETH3D		90.06 273	88.58 279	94.49 242	94.67 280	88.09 307	97.81 305	97.57 227	83.91 317	88.44 265	97.41 210	57.44 349	97.62 239	91.41 221	88.59 237	97.77 212

tpm		93.70 196	93.41 191	94.58 236	95.36 270	87.41 311	97.01 317	96.90 296	90.85 225	96.72 150	94.14 316	90.40 165	96.84 282	90.75 237	88.54 238	99.51 147

OpenMVS_ROB	79.82 20	83.77 313	81.68 316	90.03 314	88.30 344	82.82 329	98.46 280	95.22 339	73.92 348	76.00 341	91.29 335	55.00 351	96.94 277	68.40 345	88.51 239	90.34 340

CMPMVS	61.59 21	84.75 307	85.14 301	83.57 331	90.32 336	62.54 355	96.98 318	97.59 226	74.33 347	69.95 349	96.66 237	64.17 337	98.32 206	87.88 267	88.41 240	89.84 344
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ACMMP++															88.23 241

ITE_SJBPF				92.38 292	95.69 264	85.14 322		95.71 329	92.81 158	89.33 249	98.11 194	70.23 316	98.42 193	85.91 287	88.16 242	93.59 304

D2MVS		92.76 213	92.59 207	93.27 280	95.13 271	89.54 291	99.69 148	99.38 21	92.26 186	87.59 278	94.61 308	85.05 220	97.79 233	91.59 220	88.01 243	92.47 323

EI-MVSNet		93.73 194	93.40 192	94.74 229	96.80 233	92.69 227	99.06 231	97.67 215	88.96 253	91.39 215	99.02 142	88.75 186	97.30 252	91.07 226	87.85 244	94.22 254

MVSTER		95.53 151	95.22 150	96.45 185	98.56 141	97.72 82	99.91 69	97.67 215	92.38 183	91.39 215	97.14 217	97.24 14	97.30 252	94.80 163	87.85 244	94.34 247

PS-MVSNAJss		93.64 197	93.31 194	94.61 234	92.11 319	92.19 238	99.12 222	97.38 251	92.51 179	88.45 264	96.99 226	91.20 150	97.29 255	94.36 176	87.71 246	94.36 243

LTVRE_ROB	88.28 18	90.29 267	89.05 272	94.02 259	95.08 273	90.15 281	97.19 313	97.43 244	84.91 311	83.99 312	97.06 222	74.00 303	98.28 210	84.08 296	87.71 246	93.62 303
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	90.64 257	89.63 258	93.66 274	95.64 265	88.64 300	98.55 275	97.45 241	89.03 249	81.62 323	97.61 206	69.75 317	98.41 194	89.37 251	87.62 248	93.92 286
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	96.99 114	99.95 40	99.10 28	94.06 119	98.27 115	95.80 258	89.00 183	99.95 60	99.12 47	87.53 249	93.24 312

USDC		90.00 274	88.96 273	93.10 285	94.81 277	88.16 306	98.71 267	95.54 334	93.66 137	83.75 314	97.20 216	65.58 332	98.31 207	83.96 299	87.49 250	92.85 318

RRT_MVS		95.23 156	94.77 160	96.61 181	98.28 154	98.32 63	99.81 114	97.41 248	92.59 173	91.28 217	97.76 204	95.02 54	97.23 258	93.65 196	87.14 251	94.28 250

ACMMP++_ref															87.04 252

test_0402		85.58 300	83.94 304	90.50 309	93.81 293	85.04 323	98.55 275	95.20 340	76.01 341	79.72 331	95.13 290	64.15 338	96.26 306	66.04 350	86.88 253	90.21 342

FIs		94.10 185	93.43 188	96.11 195	94.70 279	96.82 119	99.58 167	98.93 40	92.54 177	89.34 248	97.31 213	87.62 194	97.10 266	94.22 182	86.58 254	94.40 240

FC-MVSNet-test		93.81 190	93.15 197	95.80 203	94.30 285	96.20 143	99.42 191	98.89 42	92.33 185	89.03 257	97.27 215	87.39 197	96.83 283	93.20 201	86.48 255	94.36 243

TinyColmap		87.87 294	86.51 295	91.94 298	95.05 274	85.57 319	97.65 307	94.08 348	84.40 314	81.82 322	96.85 231	62.14 342	98.33 205	80.25 318	86.37 256	91.91 330

ACMH+	89.98 16	90.35 264	89.54 261	92.78 290	95.99 249	86.12 316	98.81 260	97.18 267	89.38 244	83.14 316	97.76 204	68.42 323	98.43 192	89.11 254	86.05 257	93.78 296

baseline1		95.78 145	94.86 157	98.54 109	98.47 147	98.07 70	99.06 231	97.99 190	92.68 167	94.13 192	98.62 176	93.28 110	98.69 178	93.79 191	85.76 258	98.84 194

GBi-Net		90.88 251	89.82 256	94.08 256	97.53 200	91.97 241	98.43 282	96.95 290	87.05 280	89.68 238	94.72 302	71.34 311	96.11 309	87.01 279	85.65 259	94.17 258

test1		90.88 251	89.82 256	94.08 256	97.53 200	91.97 241	98.43 282	96.95 290	87.05 280	89.68 238	94.72 302	71.34 311	96.11 309	87.01 279	85.65 259	94.17 258

FMVSNet3		92.69 216	91.58 225	95.99 197	98.29 152	97.42 101	99.26 214	97.62 219	89.80 242	89.68 238	95.32 283	81.62 243	96.27 305	87.01 279	85.65 259	94.29 249

DeepMVS_CX				82.92 333	95.98 251	58.66 357		96.01 324	92.72 163	78.34 335	95.51 272	58.29 348	98.08 220	82.57 306	85.29 262	92.03 328

LF4IMVS		89.25 285	88.85 274	90.45 311	92.81 313	81.19 341	98.12 296	94.79 343	91.44 211	86.29 299	97.11 218	65.30 335	98.11 219	88.53 260	85.25 263	92.07 326

FMVSNet2		91.02 248	89.56 260	95.41 209	97.53 200	95.74 159	98.98 241	97.41 248	87.05 280	88.43 267	95.00 296	71.34 311	96.24 307	85.12 291	85.21 264	94.25 253

ET-MVSNet_ETH3D		94.37 181	93.28 195	97.64 149	98.30 151	97.99 74	99.99 4	97.61 222	94.35 105	71.57 347	99.45 115	96.23 27	95.34 323	96.91 138	85.14 265	99.59 130

OurMVSNet-221017-0		89.81 276	89.48 265	90.83 307	91.64 325	81.21 340	98.17 295	95.38 336	91.48 209	85.65 305	97.31 213	72.66 306	97.29 255	88.15 264	84.83 266	93.97 282

pmmvs4		92.10 229	91.07 235	95.18 216	92.82 312	94.96 181	99.48 184	96.83 301	87.45 275	88.66 263	96.56 242	83.78 228	96.83 283	89.29 252	84.77 267	93.75 297

our_test_3		90.39 262	89.48 265	93.12 283	92.40 316	89.57 290	99.33 203	96.35 318	87.84 271	85.30 306	94.99 297	84.14 226	96.09 312	80.38 317	84.56 268	93.71 302

cl-mvsnet2		93.77 192	93.25 196	95.33 211	99.49 99	94.43 192	99.61 164	98.09 183	90.38 231	89.16 255	95.61 265	90.56 164	97.34 249	91.93 215	84.45 269	94.21 256

miper_ehance_all_eth		93.16 204	92.60 204	94.82 228	97.57 199	93.56 209	99.50 180	97.07 278	88.75 258	88.85 259	95.52 271	90.97 157	96.74 286	90.77 236	84.45 269	94.17 258

miper_enhance_ethall		94.36 183	93.98 175	95.49 205	98.68 140	95.24 174	99.73 142	97.29 259	93.28 147	89.86 234	95.97 256	94.37 75	97.05 269	92.20 213	84.45 269	94.19 257

bset_n11_16_dypcd		93.05 208	92.30 212	95.31 212	90.23 337	95.05 179	99.44 190	97.28 260	92.51 179	90.65 223	96.68 236	85.30 217	96.71 289	94.49 174	84.14 272	94.16 263

IterMVS		90.91 250	90.17 251	93.12 283	96.78 236	90.42 277	98.89 250	97.05 281	89.03 249	86.49 294	95.42 276	76.59 283	95.02 326	87.22 275	84.09 273	93.93 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		88.50 288	86.64 294	94.08 256	95.62 267	91.97 241	98.43 282	96.95 290	83.00 321	86.08 302	94.72 302	59.09 347	96.11 309	81.82 312	84.07 274	94.17 258

XXY-MVS		91.82 232	90.46 242	95.88 200	93.91 291	95.40 169	98.87 255	97.69 214	88.63 262	87.87 275	97.08 220	74.38 301	97.89 231	91.66 219	84.07 274	94.35 246

IterMVS-SCA-FT		90.85 253	90.16 252	92.93 287	96.72 238	89.96 284	98.89 250	96.99 285	88.95 254	86.63 291	95.67 263	76.48 284	95.00 327	87.04 277	84.04 276	93.84 292

RRT_test8_iter05		94.58 174	94.11 171	95.98 198	97.88 177	96.11 149	99.89 81	97.45 241	91.66 204	88.28 270	96.71 235	96.53 24	97.40 245	94.73 168	83.85 277	94.45 238

pmmvs5		90.17 271	89.09 270	93.40 277	92.10 320	89.77 288	99.74 137	95.58 333	85.88 297	87.24 286	95.74 260	73.41 305	96.48 297	88.54 259	83.56 278	93.95 283

SixPastTwentyTwo		88.73 287	88.01 288	90.88 305	91.85 323	82.24 334	98.22 293	95.18 341	88.97 252	82.26 319	96.89 228	71.75 310	96.67 291	84.00 297	82.98 279	93.72 301

N_pmnet		80.06 319	80.78 318	77.89 334	91.94 321	45.28 363	98.80 261	56.82 366	78.10 338	80.08 330	93.33 321	77.03 277	95.76 319	68.14 346	82.81 280	92.64 319

ppachtmachnet_test		89.58 280	88.35 282	93.25 281	92.40 316	90.44 276	99.33 203	96.73 308	85.49 304	85.90 304	95.77 259	81.09 248	96.00 316	76.00 334	82.49 281	93.30 310

cl-mvsnet_		92.31 224	91.58 225	94.52 239	97.33 210	92.77 222	99.57 169	96.78 306	86.97 284	87.56 279	95.51 272	89.43 175	96.62 292	88.60 257	82.44 282	94.16 263

cl-mvsnet1		92.32 223	91.60 224	94.47 243	97.31 211	92.74 224	99.58 167	96.75 307	86.99 283	87.64 277	95.54 269	89.55 174	96.50 296	88.58 258	82.44 282	94.17 258

Patchmtry		89.70 278	88.49 280	93.33 278	96.24 244	89.94 287	91.37 349	96.23 319	78.22 337	87.69 276	93.31 323	91.04 155	96.03 314	80.18 319	82.10 284	94.02 275

IterMVS-LS		92.69 216	92.11 215	94.43 247	96.80 233	92.74 224	99.45 188	96.89 297	88.98 251	89.65 241	95.38 280	88.77 185	96.34 302	90.98 231	82.04 285	94.22 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EU-MVSNet		90.14 272	90.34 246	89.54 317	92.55 315	81.06 342	98.69 269	98.04 188	91.41 214	86.59 292	96.84 233	80.83 251	93.31 344	86.20 284	81.91 286	94.26 251

Anonymous20231206		86.32 297	85.42 299	89.02 320	89.11 342	80.53 346	99.05 235	95.28 337	85.43 305	82.82 317	93.92 317	74.40 300	93.44 343	66.99 347	81.83 287	93.08 315

eth_miper_zixun_eth		92.41 222	91.93 219	93.84 267	97.28 213	90.68 269	98.83 258	96.97 289	88.57 263	89.19 254	95.73 262	89.24 181	96.69 290	89.97 248	81.55 288	94.15 265

FMVSNet5		88.32 289	87.47 291	90.88 305	96.90 228	88.39 304	97.28 311	95.68 330	82.60 325	84.67 309	92.40 332	79.83 262	91.16 348	76.39 333	81.51 289	93.09 314

miper_lstm_enhance		91.81 233	91.39 231	93.06 286	97.34 208	89.18 294	99.38 197	96.79 305	86.70 287	87.47 281	95.22 289	90.00 169	95.86 318	88.26 262	81.37 290	94.15 265

VPA-MVSNet		92.70 215	91.55 227	96.16 194	95.09 272	96.20 143	98.88 252	99.00 33	91.02 222	91.82 212	95.29 287	76.05 290	97.96 227	95.62 152	81.19 291	94.30 248

v1192		90.62 259	89.25 267	94.72 231	93.13 303	93.07 217	99.50 180	97.02 282	86.33 291	89.56 244	95.01 294	79.22 265	97.09 268	82.34 308	81.16 292	94.01 277

v1144		91.09 247	89.83 255	94.87 225	93.25 302	93.69 207	99.62 163	96.98 287	86.83 286	89.64 242	94.99 297	80.94 249	97.05 269	85.08 292	81.16 292	93.87 290

Anonymous20240521		85.15 305	83.81 306	89.16 319	88.32 343	82.69 330	98.80 261	95.74 328	79.72 333	81.53 324	90.99 336	65.38 334	94.16 335	72.69 338	81.11 294	90.63 339

v1240		90.20 269	88.79 276	94.44 245	93.05 308	92.27 237	99.38 197	96.92 295	85.89 295	89.36 247	94.87 301	77.89 275	97.03 273	80.66 316	81.08 295	94.01 277

new_pmnet		84.49 310	82.92 312	89.21 318	90.03 338	82.60 331	96.89 320	95.62 332	80.59 331	75.77 343	89.17 340	65.04 336	94.79 331	72.12 339	81.02 296	90.23 341

K. test v3		88.05 291	87.24 293	90.47 310	91.82 324	82.23 335	98.96 244	97.42 246	89.05 248	76.93 338	95.60 266	68.49 322	95.42 321	85.87 288	81.01 297	93.75 297

FPMVS		68.72 321	68.72 324	68.71 339	65.95 360	44.27 365	95.97 331	94.74 344	51.13 354	53.26 357	90.50 339	25.11 362	83.00 356	60.80 353	80.97 298	78.87 352

v1921920		90.46 261	89.12 269	94.50 241	92.96 310	92.46 233	99.49 182	96.98 287	86.10 293	89.61 243	95.30 284	78.55 272	97.03 273	82.17 309	80.89 299	94.01 277

cl_fuxian		92.53 219	91.87 221	94.52 239	97.40 205	92.99 220	99.40 192	96.93 294	87.86 270	88.69 262	95.44 275	89.95 170	96.44 298	90.45 240	80.69 300	94.14 268

tfpnnormal		89.29 283	87.61 290	94.34 249	94.35 284	94.13 197	98.95 245	98.94 36	83.94 315	84.47 310	95.51 272	74.84 297	97.39 246	77.05 331	80.41 301	91.48 333

v144192		90.79 254	89.52 262	94.59 235	93.11 306	92.77 222	99.56 171	96.99 285	86.38 290	89.82 237	94.95 299	80.50 257	97.10 266	83.98 298	80.41 301	93.90 287

nrg030		93.51 198	92.53 208	96.45 185	94.36 283	97.20 106	99.81 114	97.16 270	91.60 205	89.86 234	97.46 208	86.37 207	97.68 236	95.88 149	80.31 303	94.46 233

Anonymous20231211		89.86 275	88.44 281	94.13 255	98.93 123	90.68 269	98.54 277	98.26 162	76.28 340	86.73 289	95.54 269	70.60 315	97.56 240	90.82 235	80.27 304	94.15 265

V42		91.28 244	90.12 253	94.74 229	93.42 300	93.46 211	99.68 150	97.02 282	87.36 276	89.85 236	95.05 292	81.31 246	97.34 249	87.34 273	80.07 305	93.40 307

v2v482		91.30 242	90.07 254	95.01 220	93.13 303	93.79 204	99.77 126	97.02 282	88.05 268	89.25 250	95.37 281	80.73 252	97.15 261	87.28 274	80.04 306	94.09 271

WR-MVS		92.31 224	91.25 232	95.48 208	94.45 282	95.29 171	99.60 165	98.68 56	90.10 236	88.07 273	96.89 228	80.68 253	96.80 285	93.14 204	79.67 307	94.36 243

v10		90.25 268	88.82 275	94.57 237	93.53 297	93.43 212	99.08 226	96.87 299	85.00 308	87.34 285	94.51 309	80.93 250	97.02 275	82.85 305	79.23 308	93.26 311

test_part1		92.15 228	90.72 238	96.44 187	98.87 131	97.46 98	98.99 240	98.26 162	85.89 295	86.34 298	96.34 247	81.71 239	97.48 243	91.06 227	78.99 309	94.37 242

CP-MVSNet		91.23 245	90.22 249	94.26 250	93.96 290	92.39 235	99.09 224	98.57 75	88.95 254	86.42 296	96.57 241	79.19 266	96.37 300	90.29 244	78.95 310	94.02 275

MIMVSNet1		82.58 315	80.51 319	88.78 322	86.68 347	84.20 327	96.65 321	95.41 335	78.75 336	78.59 334	92.44 329	51.88 353	89.76 351	65.26 351	78.95 310	92.38 325

PS-CasMVS		90.63 258	89.51 263	93.99 262	93.83 292	91.70 254	98.98 241	98.52 90	88.48 264	86.15 301	96.53 243	75.46 292	96.31 303	88.83 256	78.86 312	93.95 283

WR-MVS_H		91.30 242	90.35 245	94.15 253	94.17 287	92.62 231	99.17 220	98.94 36	88.87 256	86.48 295	94.46 313	84.36 224	96.61 293	88.19 263	78.51 313	93.21 313

v8		90.54 260	89.17 268	94.66 232	93.43 299	93.40 214	99.20 217	96.94 293	85.76 298	87.56 279	94.51 309	81.96 238	97.19 259	84.94 293	78.25 314	93.38 309

UniMVSNet (Re)		93.07 207	92.13 214	95.88 200	94.84 276	96.24 142	99.88 84	98.98 34	92.49 181	89.25 250	95.40 277	87.09 200	97.14 262	93.13 205	78.16 315	94.26 251

v7n		89.65 279	88.29 284	93.72 269	92.22 318	90.56 273	99.07 230	97.10 275	85.42 306	86.73 289	94.72 302	80.06 260	97.13 263	81.14 314	78.12 316	93.49 305

VPNet		91.81 233	90.46 242	95.85 202	94.74 278	95.54 165	98.98 241	98.59 72	92.14 189	90.77 222	97.44 209	68.73 321	97.54 241	94.89 161	77.89 317	94.46 233

Gipuma		66.95 324	65.00 325	72.79 337	91.52 327	67.96 353	66.16 358	95.15 342	47.89 355	58.54 353	67.99 357	29.74 359	87.54 353	50.20 356	77.83 318	62.87 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
NR-MVSNet		91.56 241	90.22 249	95.60 204	94.05 288	95.76 158	98.25 290	98.70 54	91.16 218	80.78 327	96.64 239	83.23 233	96.57 294	91.41 221	77.73 319	94.46 233

UniMVSNet_NR-MVSNet		92.95 210	92.11 215	95.49 205	94.61 281	95.28 172	99.83 111	99.08 30	91.49 208	89.21 252	96.86 230	87.14 199	96.73 287	93.20 201	77.52 320	94.46 233

DU-MVS		92.46 221	91.45 230	95.49 205	94.05 288	95.28 172	99.81 114	98.74 52	92.25 187	89.21 252	96.64 239	81.66 241	96.73 287	93.20 201	77.52 320	94.46 233

MDA-MVSNet_test_wron		85.51 302	83.32 309	92.10 296	90.96 331	88.58 301	99.20 217	96.52 314	79.70 334	57.12 355	92.69 328	79.11 267	93.86 339	77.10 330	77.46 322	93.86 291

YYNet1		85.50 303	83.33 308	92.00 297	90.89 332	88.38 305	99.22 216	96.55 313	79.60 335	57.26 354	92.72 326	79.09 268	93.78 340	77.25 329	77.37 323	93.84 292

v148		90.70 255	89.63 258	93.92 264	92.97 309	90.97 264	99.75 134	96.89 297	87.51 273	88.27 271	95.01 294	81.67 240	97.04 271	87.40 272	77.17 324	93.75 297

Baseline_NR-MVSNet		90.33 265	89.51 263	92.81 289	92.84 311	89.95 285	99.77 126	93.94 350	84.69 313	89.04 256	95.66 264	81.66 241	96.52 295	90.99 230	76.98 325	91.97 329

PEN-MVS		90.19 270	89.06 271	93.57 275	93.06 307	90.90 266	99.06 231	98.47 103	88.11 267	85.91 303	96.30 248	76.67 281	95.94 317	87.07 276	76.91 326	93.89 288

TranMVSNet+NR-MVSNet		91.68 240	90.61 241	94.87 225	93.69 295	93.98 201	99.69 148	98.65 60	91.03 221	88.44 265	96.83 234	80.05 261	96.18 308	90.26 245	76.89 327	94.45 238

MDA-MVSNet-bldmvs		84.09 311	81.52 317	91.81 300	91.32 329	88.00 309	98.67 271	95.92 326	80.22 332	55.60 356	93.32 322	68.29 324	93.60 342	73.76 336	76.61 328	93.82 294

test20.03		84.72 308	83.99 302	86.91 328	88.19 345	80.62 345	98.88 252	95.94 325	88.36 266	78.87 332	94.62 307	68.75 320	89.11 352	66.52 348	75.82 329	91.00 335

DTE-MVSNet		89.40 281	88.24 285	92.88 288	92.66 314	89.95 285	99.10 223	98.22 167	87.29 277	85.12 308	96.22 250	76.27 287	95.30 325	83.56 302	75.74 330	93.41 306

pm-mvs1		89.36 282	87.81 289	94.01 260	93.40 301	91.93 244	98.62 274	96.48 316	86.25 292	83.86 313	96.14 252	73.68 304	97.04 271	86.16 285	75.73 331	93.04 316

lessismore_v0				90.53 308	90.58 334	80.90 343		95.80 327		77.01 337	95.84 257	66.15 331	96.95 276	83.03 304	75.05 332	93.74 300

IB-MVS	92.85 6	94.99 162	93.94 176	98.16 129	97.72 193	95.69 163	99.99 4	98.81 48	94.28 110	92.70 208	96.90 227	95.08 50	99.17 156	96.07 145	73.88 333	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		85.69 299	83.84 305	91.26 304	90.00 339	84.41 326	97.82 304	96.15 322	75.86 342	81.29 325	95.39 279	61.21 344	96.87 281	83.52 303	73.29 334	92.50 322

hse-mvs3		94.92 163	94.36 166	96.59 182	98.85 132	91.29 261	98.93 247	98.94 36	95.90 56	98.77 91	98.42 189	90.89 160	99.77 115	97.80 111	70.76 335	98.72 199

ambc				83.23 332	77.17 355	62.61 354	87.38 353	94.55 347		76.72 339	86.65 347	30.16 358	96.36 301	84.85 294	69.86 336	90.73 338

Patchmatch-RL test		86.90 296	85.98 298	89.67 316	84.45 350	75.59 349	89.71 351	92.43 352	86.89 285	77.83 336	90.94 337	94.22 83	93.63 341	87.75 268	69.61 337	99.79 100

PM-MVS		80.47 317	78.88 321	85.26 330	83.79 352	72.22 351	95.89 332	91.08 355	85.71 301	76.56 340	88.30 342	36.64 357	93.90 338	82.39 307	69.57 338	89.66 345

pmmvs-eth3d		84.03 312	81.97 315	90.20 312	84.15 351	87.09 312	98.10 298	94.73 345	83.05 320	74.10 345	87.77 344	65.56 333	94.01 336	81.08 315	69.24 339	89.49 346

AUN-MVS		93.28 202	92.60 204	95.34 210	98.29 152	90.09 282	99.31 206	98.56 77	91.80 201	96.35 161	98.00 198	89.38 176	98.28 210	92.46 210	69.22 340	97.64 214

hse-mvs2		94.38 180	94.08 173	95.31 212	98.27 157	90.02 283	99.29 211	98.56 77	95.90 56	98.77 91	98.00 198	90.89 160	98.26 214	97.80 111	69.20 341	97.64 214

TransMVSNet (Re)		87.25 295	85.28 300	93.16 282	93.56 296	91.03 263	98.54 277	94.05 349	83.69 319	81.09 326	96.16 251	75.32 293	96.40 299	76.69 332	68.41 342	92.06 327

PMVS	49.05 23	53.75 327	51.34 331	60.97 342	40.80 366	34.68 366	74.82 357	89.62 359	37.55 358	28.67 364	72.12 354	7.09 368	81.63 357	43.17 359	68.21 343	66.59 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UnsupCasMVSNet_eth		85.52 301	83.99 302	90.10 313	89.36 341	83.51 328	96.65 321	97.99 190	89.14 246	75.89 342	93.83 318	63.25 340	93.92 337	81.92 311	67.90 344	92.88 317

PVSNet_0	88.03 19	91.80 236	90.27 248	96.38 190	98.27 157	90.46 275	99.94 55	99.61 11	93.99 122	86.26 300	97.39 212	71.13 314	99.89 79	98.77 73	67.05 345	98.79 197

TDRefinement		84.76 306	82.56 313	91.38 303	74.58 356	84.80 325	97.36 310	94.56 346	84.73 312	80.21 329	96.12 254	63.56 339	98.39 198	87.92 266	63.97 346	90.95 337

new-patchmatchnet		81.19 316	79.34 320	86.76 329	82.86 353	80.36 347	97.92 302	95.27 338	82.09 327	72.02 346	86.87 346	62.81 341	90.74 350	71.10 340	63.08 347	89.19 348

pmmvs3		80.27 318	77.77 322	87.76 327	80.32 354	82.43 333	98.23 292	91.97 353	72.74 349	78.75 333	87.97 343	57.30 350	90.99 349	70.31 341	62.37 348	89.87 343

DIV-MVS_2432*1600		83.59 314	82.06 314	88.20 326	86.93 346	80.70 344	97.21 312	96.38 317	82.87 322	82.49 318	88.97 341	67.63 326	92.32 345	73.75 337	62.30 349	91.58 332

CL-MVSNet_2432*1600		84.50 309	83.15 311	88.53 324	86.00 348	81.79 338	98.82 259	97.35 253	85.12 307	83.62 315	90.91 338	76.66 282	91.40 347	69.53 343	60.36 350	92.40 324

UnsupCasMVSNet_bld		79.97 320	77.03 323	88.78 322	85.62 349	81.98 336	93.66 340	97.35 253	75.51 345	70.79 348	83.05 350	48.70 356	94.91 329	78.31 325	60.29 351	89.46 347

LCM-MVSNet		67.77 322	64.73 326	76.87 335	62.95 362	56.25 359	89.37 352	93.74 351	44.53 356	61.99 351	80.74 351	20.42 364	86.53 354	69.37 344	59.50 352	87.84 349

KD-MVS_2432*1600		88.00 292	86.10 296	93.70 272	96.91 225	94.04 198	97.17 314	97.12 273	84.93 309	81.96 320	92.41 330	92.48 130	94.51 333	79.23 320	52.68 353	92.56 320

miper_refine_blended		88.00 292	86.10 296	93.70 272	96.91 225	94.04 198	97.17 314	97.12 273	84.93 309	81.96 320	92.41 330	92.48 130	94.51 333	79.23 320	52.68 353	92.56 320

PMMVS2		67.15 323	64.15 327	76.14 336	70.56 359	62.07 356	93.89 338	87.52 361	58.09 353	60.02 352	78.32 352	22.38 363	84.54 355	59.56 354	47.03 355	81.80 351

MVE	53.74 22	51.54 329	47.86 333	62.60 341	59.56 363	50.93 360	79.41 356	77.69 363	35.69 360	36.27 362	61.76 361	5.79 370	69.63 359	37.97 360	36.61 356	67.24 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		52.30 328	52.18 330	52.67 343	71.51 357	45.40 362	93.62 341	76.60 364	36.01 359	43.50 360	64.13 359	27.11 361	67.31 361	31.06 361	26.06 357	45.30 360

EMVS		51.44 330	51.22 332	52.11 344	70.71 358	44.97 364	94.04 337	75.66 365	35.34 361	42.40 361	61.56 362	28.93 360	65.87 362	27.64 362	24.73 358	45.49 359

ANet_high		56.10 326	52.24 329	67.66 340	49.27 364	56.82 358	83.94 354	82.02 362	70.47 350	33.28 363	64.54 358	17.23 366	69.16 360	45.59 358	23.85 359	77.02 353

tmp_tt		65.23 325	62.94 328	72.13 338	44.90 365	50.03 361	81.05 355	89.42 360	38.45 357	48.51 359	99.90 17	54.09 352	78.70 358	91.84 218	18.26 360	87.64 350

testmvs		40.60 331	44.45 334	29.05 346	19.49 368	14.11 369	99.68 150	18.47 367	20.74 362	64.59 350	98.48 186	10.95 367	17.09 365	56.66 355	11.01 361	55.94 358

wuyk23d		20.37 334	20.84 337	18.99 347	65.34 361	27.73 367	50.43 359	7.67 369	9.50 364	8.01 365	6.34 365	6.13 369	26.24 363	23.40 363	10.69 362	2.99 361

test123		37.68 332	39.14 335	33.31 345	19.94 367	24.83 368	98.36 286	9.75 368	15.53 363	51.31 358	87.14 345	19.62 365	17.74 364	47.10 357	3.47 363	57.36 357

uanet_test		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

cdsmvs_eth3d_5k		23.43 333	31.24 336	0.00 348	0.00 369	0.00 370	0.00 360	98.09 183	0.00 365	0.00 366	99.67 95	83.37 231	0.00 366	0.00 364	0.00 364	0.00 362

pcd_1.5k_mvsjas		7.60 336	10.13 339	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	91.20 150	0.00 366	0.00 364	0.00 364	0.00 362

sosnet-low-res		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

sosnet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

uncertanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

Regformer		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

ab-mvs-re		8.28 335	11.04 338	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	99.40 118	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

uanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

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

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

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

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								98.28 158

test_post1							95.78 333				59.23 363	93.20 114	97.74 235	91.06 227

test_post											63.35 360	94.43 69	98.13 218

patchmatchnet-post											91.70 334	95.12 48	97.95 228

MTMP							99.87 87	96.49 315

gm-plane-assit					96.97 223	93.76 206			91.47 210		98.96 153		98.79 168	94.92 158

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

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_prior					99.93 26	98.77 36		98.43 116		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 60					99.80 106			99.99 20

旧先验2							99.46 187		94.21 112	99.85 6			99.95 60	96.96 135

新几何2							99.40 192

无先验							99.49 182	98.71 53	93.46 142				100.00 1	94.36 176		99.99 20

原ACMM2							99.90 73

testdata2													99.99 36	90.54 239

segment_acmp												96.68 22

testdata1							99.28 212		96.35 48

plane_prior7					95.71 262	91.59 258

plane_prior6					95.76 257	91.72 253						80.47 258

plane_prior4											98.59 177

plane_prior3						91.64 256			96.63 38	93.01 202

plane_prior2							99.84 105		96.38 44

plane_prior1					95.73 259

n2								0.00 370

nn								0.00 370

door-mid								89.69 358

test11								98.44 108

door								90.31 356

HQP5-MVS						91.85 246

HQP-NCC					95.78 253		99.87 87		96.82 30	93.37 198

ACMP_Plane					95.78 253		99.87 87		96.82 30	93.37 198

BP-MVS														97.92 109

HQP4-MVS										93.37 198			98.39 198			94.53 228

HQP2-MVS												80.65 254

NP-MVS					95.77 256	91.79 248					98.65 173

MDTV_nov1_ep13_2view						96.26 138	96.11 328		91.89 196	98.06 121		94.40 71		94.30 179		99.67 117

Test By Simon												92.82 123

High-resolution multi-view benchmark