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.

APDe-MVS		99.02 2	98.84 1	99.55 5	99.57 32	98.96 11	99.39 5	98.93 37	97.38 23	99.41 10	99.54 1	96.66 12	99.84 51	98.86 1	99.85 3	99.87 1

test_0728_THIRD									97.32 26	99.45 8	99.46 8	97.88 1	99.94 3	98.47 15	99.86 1	99.85 2

DVP-MVS		98.74 7	98.55 9	99.29 30	99.75 3	98.23 47	99.26 18	98.88 49	97.52 14	99.41 10	98.78 107	96.00 33	99.79 86	97.79 46	99.59 67	99.85 2

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 63	98.88 49					99.94 3	98.47 15	99.81 10	99.84 4

IU-MVS					99.71 20	99.23 5		98.64 131	95.28 113	99.63 3				98.35 23	99.81 10	99.83 5

test_241102_TWO								98.87 55	97.65 9	99.53 7	99.48 6	97.34 7	99.94 3	98.43 18	99.80 16	99.83 5

DPE-MVS		98.92 3	98.67 5	99.65 2	99.58 31	99.20 6	98.42 164	98.91 43	97.58 13	99.54 6	99.46 8	97.10 8	99.94 3	97.64 56	99.84 8	99.83 5

CHOSEN 1792x2688		97.12 106	96.80 100	98.08 120	99.30 70	94.56 208	98.05 212	99.71 1	93.57 194	97.09 130	98.91 95	88.17 201	99.89 34	96.87 97	99.56 75	99.81 8

EI-MVSNet-Vis-set		98.47 35	98.39 18	98.69 75	99.46 47	96.49 119	98.30 180	98.69 112	97.21 35	98.84 42	99.36 25	95.41 52	99.78 90	98.62 5	99.65 57	99.80 9

ACMMP_NAP		98.61 16	98.30 30	99.55 5	99.62 29	98.95 12	98.82 92	98.81 75	95.80 86	99.16 23	99.47 7	95.37 55	99.92 20	97.89 40	99.75 37	99.79 10

Regformer-4		98.64 13	98.53 10	98.99 60	99.43 52	97.37 81	98.40 166	98.79 87	97.46 18	99.09 27	99.31 31	95.86 41	99.80 74	98.64 3	99.76 31	99.79 10

HPM-MVS		98.36 42	98.10 44	99.13 52	99.74 7	97.82 67	99.53 1	98.80 85	94.63 146	98.61 58	98.97 83	95.13 65	99.77 95	97.65 55	99.83 9	99.79 10
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
region2R		98.61 16	98.38 19	99.29 30	99.74 7	98.16 53	99.23 21	98.93 37	96.15 73	98.94 35	99.17 52	95.91 38	99.94 3	97.55 65	99.79 18	99.78 13

Regformer-3		98.59 19	98.50 14	98.86 70	99.43 52	97.05 95	98.40 166	98.68 115	97.43 19	99.06 28	99.31 31	95.80 42	99.77 95	98.62 5	99.76 31	99.78 13

XVS		98.70 9	98.49 15	99.34 22	99.70 22	98.35 41	99.29 14	98.88 49	97.40 20	98.46 63	99.20 48	95.90 39	99.89 34	97.85 42	99.74 40	99.78 13

X-MVStestdata		94.06 256	92.30 275	99.34 22	99.70 22	98.35 41	99.29 14	98.88 49	97.40 20	98.46 63	43.50 346	95.90 39	99.89 34	97.85 42	99.74 40	99.78 13

ACMMPR		98.59 19	98.36 21	99.29 30	99.74 7	98.15 54	99.23 21	98.95 34	96.10 78	98.93 39	99.19 51	95.70 43	99.94 3	97.62 57	99.79 18	99.78 13

PGM-MVS		98.49 33	98.23 38	99.27 37	99.72 12	98.08 57	98.99 59	99.49 5	95.43 103	99.03 29	99.32 29	95.56 45	99.94 3	96.80 101	99.77 25	99.78 13

SteuartSystems-ACMMP		98.90 4	98.75 3	99.36 20	99.22 89	98.43 31	99.10 43	98.87 55	97.38 23	99.35 13	99.40 12	97.78 3	99.87 43	97.77 47	99.85 3	99.78 13
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		98.55 28	98.25 34	99.46 11	99.76 1	98.64 20	98.55 146	98.74 97	97.27 32	98.02 85	99.39 13	94.81 72	99.96 1	97.91 37	99.79 18	99.77 20

MTAPA		98.58 22	98.29 31	99.46 11	99.76 1	98.64 20	98.90 73	98.74 97	97.27 32	98.02 85	99.39 13	94.81 72	99.96 1	97.91 37	99.79 18	99.77 20

mPP-MVS		98.51 32	98.26 33	99.25 38	99.75 3	98.04 58	99.28 16	98.81 75	96.24 69	98.35 72	99.23 41	95.46 49	99.94 3	97.42 70	99.81 10	99.77 20

HPM-MVS_fast		98.38 40	98.13 42	99.12 54	99.75 3	97.86 65	99.44 4	98.82 69	94.46 153	98.94 35	99.20 48	95.16 64	99.74 101	97.58 61	99.85 3	99.77 20

CP-MVS		98.57 25	98.36 21	99.19 42	99.66 26	97.86 65	99.34 11	98.87 55	95.96 81	98.60 59	99.13 60	96.05 31	99.94 3	97.77 47	99.86 1	99.77 20

HyFIR lowres test		96.90 114	96.49 118	98.14 115	99.33 60	95.56 161	97.38 261	99.65 2	92.34 238	97.61 117	98.20 167	89.29 171	99.10 180	96.97 84	97.60 165	99.77 20

testtj		98.33 47	97.95 51	99.47 10	99.49 43	98.70 18	98.83 89	98.86 60	95.48 100	98.91 41	99.17 52	95.48 48	99.93 14	95.80 134	99.53 80	99.76 26

SMA-MVS		98.58 22	98.25 34	99.56 4	99.51 37	99.04 10	98.95 67	98.80 85	93.67 190	99.37 12	99.52 3	96.52 16	99.89 34	98.06 32	99.81 10	99.76 26

HFP-MVS		98.63 15	98.40 17	99.32 27	99.72 12	98.29 44	99.23 21	98.96 32	96.10 78	98.94 35	99.17 52	96.06 29	99.92 20	97.62 57	99.78 22	99.75 28

#test#		98.54 30	98.27 32	99.32 27	99.72 12	98.29 44	98.98 62	98.96 32	95.65 94	98.94 35	99.17 52	96.06 29	99.92 20	97.21 77	99.78 22	99.75 28

Regformer-1		98.66 11	98.51 13	99.12 54	99.35 55	97.81 68	98.37 168	98.76 93	97.49 16	99.20 21	99.21 44	96.08 28	99.79 86	98.42 19	99.73 42	99.75 28

Regformer-2		98.69 10	98.52 11	99.19 42	99.35 55	98.01 60	98.37 168	98.81 75	97.48 17	99.21 20	99.21 44	96.13 26	99.80 74	98.40 21	99.73 42	99.75 28

CPTT-MVS		97.72 69	97.32 80	98.92 66	99.64 27	97.10 94	99.12 41	98.81 75	92.34 238	98.09 79	99.08 72	93.01 101	99.92 20	96.06 124	99.77 25	99.75 28

ZNCC-MVS		98.49 33	98.20 40	99.35 21	99.73 11	98.39 32	99.19 31	98.86 60	95.77 87	98.31 75	99.10 65	95.46 49	99.93 14	97.57 64	99.81 10	99.74 33

MCST-MVS		98.65 12	98.37 20	99.48 9	99.60 30	98.87 14	98.41 165	98.68 115	97.04 45	98.52 62	98.80 105	96.78 11	99.83 54	97.93 36	99.61 63	99.74 33

APD-MVS		98.35 43	98.00 49	99.42 14	99.51 37	98.72 16	98.80 99	98.82 69	94.52 150	99.23 19	99.25 39	95.54 47	99.80 74	96.52 110	99.77 25	99.74 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		98.78 5	98.62 6	99.24 39	99.69 24	98.28 46	99.14 36	98.66 126	96.84 50	99.56 4	99.31 31	96.34 18	99.70 109	98.32 24	99.73 42	99.73 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet-UG-set		98.41 38	98.34 25	98.61 80	99.45 50	96.32 127	98.28 183	98.68 115	97.17 38	98.74 49	99.37 21	95.25 61	99.79 86	98.57 7	99.54 79	99.73 36

MP-MVS		98.33 47	98.01 48	99.28 34	99.75 3	98.18 51	99.22 25	98.79 87	96.13 75	97.92 98	99.23 41	94.54 79	99.94 3	96.74 105	99.78 22	99.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SR-MVS		98.57 25	98.35 23	99.24 39	99.53 35	98.18 51	99.09 44	98.82 69	96.58 60	99.10 26	99.32 29	95.39 53	99.82 61	97.70 53	99.63 60	99.72 39

GST-MVS		98.43 37	98.12 43	99.34 22	99.72 12	98.38 33	99.09 44	98.82 69	95.71 90	98.73 51	99.06 74	95.27 59	99.93 14	97.07 81	99.63 60	99.72 39

APD-MVS_3200maxsize		98.53 31	98.33 28	99.15 51	99.50 39	97.92 64	99.15 35	98.81 75	96.24 69	99.20 21	99.37 21	95.30 58	99.80 74	97.73 49	99.67 53	99.72 39

DeepPCF-MVS	96.37 2	97.93 60	98.48 16	96.30 237	99.00 104	89.54 303	97.43 258	98.87 55	98.16 2	99.26 17	99.38 20	96.12 27	99.64 120	98.30 25	99.77 25	99.72 39

NCCC		98.61 16	98.35 23	99.38 16	99.28 77	98.61 22	98.45 157	98.76 93	97.82 5	98.45 66	98.93 92	96.65 13	99.83 54	97.38 72	99.41 92	99.71 43

3Dnovator+	94.38 6	97.43 89	96.78 103	99.38 16	97.83 190	98.52 25	99.37 7	98.71 107	97.09 44	92.99 265	99.13 60	89.36 169	99.89 34	96.97 84	99.57 70	99.71 43

OPU-MVS				99.37 19	99.24 87	99.05 9	99.02 54				99.16 57	97.81 2	99.37 152	97.24 75	99.73 42	99.70 45

ACMMP		98.23 51	97.95 51	99.09 56	99.74 7	97.62 73	99.03 52	99.41 6	95.98 80	97.60 119	99.36 25	94.45 84	99.93 14	97.14 78	98.85 117	99.70 45
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
MSP-MVS		99.03 1	98.83 2	99.63 3	99.72 12	99.25 2	98.97 63	98.58 141	97.62 10	99.45 8	99.46 8	97.42 5	99.94 3	98.47 15	99.81 10	99.69 47

test9_res														96.39 116	99.57 70	99.69 47

abl_6		98.30 50	98.03 47	99.13 52	99.56 33	97.76 69	99.13 39	98.82 69	96.14 74	99.26 17	99.37 21	93.33 97	99.93 14	96.96 86	99.67 53	99.69 47

CNVR-MVS		98.78 5	98.56 8	99.45 13	99.32 63	98.87 14	98.47 156	98.81 75	97.72 6	98.76 48	99.16 57	97.05 9	99.78 90	98.06 32	99.66 56	99.69 47

MVS_111021_HR		98.47 35	98.34 25	98.88 69	99.22 89	97.32 82	97.91 224	99.58 3	97.20 36	98.33 73	99.00 81	95.99 34	99.64 120	98.05 34	99.76 31	99.69 47

DeepC-MVS_fast	96.70 1	98.55 28	98.34 25	99.18 46	99.25 81	98.04 58	98.50 153	98.78 89	97.72 6	98.92 40	99.28 36	95.27 59	99.82 61	97.55 65	99.77 25	99.69 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
train_agg		97.97 54	97.52 68	99.33 26	99.31 65	98.50 27	97.92 222	98.73 101	92.98 215	97.74 106	98.68 116	96.20 22	99.80 74	96.59 107	99.57 70	99.68 53

agg_prior2														95.87 131	99.57 70	99.68 53

CDPH-MVS		97.94 59	97.49 71	99.28 34	99.47 44	98.44 29	97.91 224	98.67 123	92.57 230	98.77 47	98.85 99	95.93 37	99.72 103	95.56 144	99.69 51	99.68 53

DP-MVS		96.59 124	95.93 134	98.57 82	99.34 57	96.19 133	98.70 121	98.39 178	89.45 303	94.52 199	99.35 27	91.85 123	99.85 48	92.89 225	98.88 114	99.68 53

xxxxxxxxxxxxxcwj		98.72 8	98.52 11	99.30 29	99.46 47	98.38 33	98.21 189	98.71 107	97.95 3	99.32 14	99.39 13	96.22 19	99.84 51	97.72 50	99.73 42	99.67 57

SF-MVS		98.59 19	98.32 29	99.41 15	99.54 34	98.71 17	99.04 50	98.81 75	95.12 122	99.32 14	99.39 13	96.22 19	99.84 51	97.72 50	99.73 42	99.67 57

MP-MVS-pluss		98.31 49	97.92 53	99.49 8	99.72 12	98.88 13	98.43 162	98.78 89	94.10 161	97.69 110	99.42 11	95.25 61	99.92 20	98.09 31	99.80 16	99.67 57
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MG-MVS		97.81 64	97.60 62	98.44 95	99.12 98	95.97 142	97.75 240	98.78 89	96.89 49	98.46 63	99.22 43	93.90 94	99.68 115	94.81 165	99.52 82	99.67 57

agg_prior1		97.95 58	97.51 70	99.28 34	99.30 70	98.38 33	97.81 235	98.72 103	93.16 209	97.57 120	98.66 119	96.14 25	99.81 65	96.63 106	99.56 75	99.66 61

HPM-MVS++		98.58 22	98.25 34	99.55 5	99.50 39	99.08 8	98.72 116	98.66 126	97.51 15	98.15 76	98.83 102	95.70 43	99.92 20	97.53 67	99.67 53	99.66 61

UA-Net		97.96 55	97.62 60	98.98 62	98.86 115	97.47 78	98.89 77	99.08 21	96.67 57	98.72 52	99.54 1	93.15 100	99.81 65	94.87 161	98.83 118	99.65 63

test_prior3		98.22 52	97.90 54	99.19 42	99.31 65	98.22 48	97.80 236	98.84 64	96.12 76	97.89 100	98.69 114	95.96 35	99.70 109	96.89 91	99.60 64	99.65 63

test_prior				99.19 42	99.31 65	98.22 48		98.84 64					99.70 109			99.65 63

ETH3 D test6400		97.59 78	97.01 92	99.34 22	99.40 54	98.56 23	98.20 192	98.81 75	91.63 260	98.44 67	98.85 99	93.98 93	99.82 61	94.11 188	99.69 51	99.64 66

SD-MVS		98.64 13	98.68 4	98.53 88	99.33 60	98.36 40	98.90 73	98.85 63	97.28 28	99.72 2	99.39 13	96.63 14	97.60 312	98.17 27	99.85 3	99.64 66
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
3Dnovator	94.51 5	97.46 84	96.93 96	99.07 57	97.78 192	97.64 71	99.35 10	99.06 22	97.02 46	93.75 239	99.16 57	89.25 172	99.92 20	97.22 76	99.75 37	99.64 66

ETH3D-3000-0.1		98.35 43	98.00 49	99.38 16	99.47 44	98.68 19	98.67 127	98.84 64	94.66 145	99.11 25	99.25 39	95.46 49	99.81 65	96.80 101	99.73 42	99.63 69

test12				99.18 46	99.16 94	98.19 50		98.53 151		98.07 80		95.13 65	99.72 103		99.56 75	99.63 69

旧先验1					99.29 73	97.48 77		98.70 111			99.09 70	95.56 45			99.47 85	99.61 71

test222					99.23 88	97.17 92	97.40 259	98.66 126	88.68 309	98.05 81	98.96 88	94.14 89			99.53 80	99.61 71

1121		97.37 94	96.77 107	99.16 49	99.34 57	97.99 63	98.19 196	98.68 115	90.14 294	98.01 89	98.97 83	94.80 74	99.87 43	93.36 209	99.46 88	99.61 71

无先验							97.58 252	98.72 103	91.38 266				99.87 43	93.36 209		99.60 74

CVMVSNet		95.43 169	96.04 131	93.57 306	97.93 184	83.62 333	98.12 206	98.59 136	95.68 91	96.56 156	99.02 76	87.51 217	97.51 316	93.56 205	97.44 167	99.60 74

新几何1				99.16 49	99.34 57	98.01 60		98.69 112	90.06 295	98.13 77	98.95 90	94.60 77	99.89 34	91.97 249	99.47 85	99.59 76

PHI-MVS		98.34 45	98.06 45	99.18 46	99.15 96	98.12 56	99.04 50	99.09 20	93.32 203	98.83 44	99.10 65	96.54 15	99.83 54	97.70 53	99.76 31	99.59 76

testdata				98.26 108	99.20 92	95.36 169		98.68 115	91.89 252	98.60 59	99.10 65	94.44 85	99.82 61	94.27 182	99.44 90	99.58 78

Test_1112_low_res		96.34 133	95.66 146	98.36 102	98.56 139	95.94 145	97.71 242	98.07 235	92.10 247	94.79 194	97.29 236	91.75 125	99.56 131	94.17 185	96.50 188	99.58 78

1112_ss		96.63 121	96.00 133	98.50 90	98.56 139	96.37 124	98.18 200	98.10 228	92.92 218	94.84 190	98.43 140	92.14 116	99.58 128	94.35 179	96.51 187	99.56 80

ETH3D cwj APD-0.16		97.96 55	97.52 68	99.29 30	99.05 100	98.52 25	98.33 172	98.68 115	93.18 207	98.68 53	99.13 60	94.62 76	99.83 54	96.45 112	99.55 78	99.52 81

PAPM_NR		97.46 84	97.11 87	98.50 90	99.50 39	96.41 123	98.63 132	98.60 134	95.18 118	97.06 134	98.06 176	94.26 88	99.57 129	93.80 197	98.87 116	99.52 81

CSCG		97.85 63	97.74 58	98.20 112	99.67 25	95.16 176	99.22 25	99.32 7	93.04 212	97.02 136	98.92 94	95.36 56	99.91 29	97.43 69	99.64 59	99.52 81

DeepC-MVS	95.98 3	97.88 61	97.58 63	98.77 72	99.25 81	96.93 99	98.83 89	98.75 96	96.96 48	96.89 143	99.50 4	90.46 154	99.87 43	97.84 44	99.76 31	99.52 81
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		98.05 53	97.76 57	98.90 68	98.73 123	97.27 85	98.35 170	98.78 89	97.37 25	97.72 108	98.96 88	91.53 133	99.92 20	98.79 2	99.65 57	99.51 85

TSAR-MVS + GP.		98.38 40	98.24 37	98.81 71	99.22 89	97.25 89	98.11 208	98.29 198	97.19 37	98.99 34	99.02 76	96.22 19	99.67 116	98.52 13	98.56 130	99.51 85

原ACMM1				98.65 78	99.32 63	96.62 110		98.67 123	93.27 206	97.81 102	98.97 83	95.18 63	99.83 54	93.84 195	99.46 88	99.50 87

VNet		97.79 66	97.40 77	98.96 64	98.88 113	97.55 75	98.63 132	98.93 37	96.74 54	99.02 30	98.84 101	90.33 157	99.83 54	98.53 9	96.66 181	99.50 87

EPNet		97.28 97	96.87 99	98.51 89	94.98 319	96.14 134	98.90 73	97.02 299	98.28 1	95.99 176	99.11 63	91.36 135	99.89 34	96.98 83	99.19 103	99.50 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		97.70 70	97.46 73	98.44 95	99.27 78	95.91 150	98.63 132	99.16 17	94.48 152	97.67 111	98.88 97	92.80 103	99.91 29	97.11 79	99.12 105	99.50 87

MVS_111021_LR		98.34 45	98.23 38	98.67 77	99.27 78	96.90 101	97.95 221	99.58 3	97.14 40	98.44 67	99.01 80	95.03 68	99.62 125	97.91 37	99.75 37	99.50 87

casdiffmvs		97.63 74	97.41 76	98.28 105	98.33 155	96.14 134	98.82 92	98.32 188	96.38 66	97.95 93	99.21 44	91.23 140	99.23 162	98.12 29	98.37 139	99.48 92

WTY-MVS		97.37 94	96.92 97	98.72 74	98.86 115	96.89 103	98.31 178	98.71 107	95.26 114	97.67 111	98.56 130	92.21 114	99.78 90	95.89 129	96.85 176	99.48 92

MSLP-MVS++		98.56 27	98.57 7	98.55 84	99.26 80	96.80 104	98.71 117	99.05 24	97.28 28	98.84 42	99.28 36	96.47 17	99.40 149	98.52 13	99.70 50	99.47 94

114514_t		96.93 112	96.27 124	98.92 66	99.50 39	97.63 72	98.85 85	98.90 44	84.80 327	97.77 103	99.11 63	92.84 102	99.66 117	94.85 162	99.77 25	99.47 94

IS-MVSNet		97.22 99	96.88 98	98.25 109	98.85 117	96.36 125	99.19 31	97.97 245	95.39 105	97.23 126	98.99 82	91.11 142	98.93 200	94.60 170	98.59 128	99.47 94

PAPR		96.84 116	96.24 126	98.65 78	98.72 127	96.92 100	97.36 265	98.57 142	93.33 202	96.67 151	97.57 219	94.30 87	99.56 131	91.05 264	98.59 128	99.47 94

LFMVS		95.86 150	94.98 174	98.47 93	98.87 114	96.32 127	98.84 88	96.02 318	93.40 200	98.62 57	99.20 48	74.99 326	99.63 123	97.72 50	97.20 171	99.46 98

Vis-MVSNet (Re-imp)		96.87 115	96.55 115	97.83 133	98.73 123	95.46 166	99.20 29	98.30 196	94.96 131	96.60 155	98.87 98	90.05 160	98.59 234	93.67 201	98.60 127	99.46 98

Vis-MVSNet		97.42 90	97.11 87	98.34 103	98.66 132	96.23 130	99.22 25	99.00 27	96.63 59	98.04 83	99.21 44	88.05 206	99.35 153	96.01 127	99.21 101	99.45 100
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Anonymous202405211		95.28 181	94.49 194	97.67 147	99.00 104	93.75 232	98.70 121	97.04 296	90.66 284	96.49 163	98.80 105	78.13 311	99.83 54	96.21 120	95.36 210	99.44 101

DPM-MVS		97.55 82	96.99 94	99.23 41	99.04 102	98.55 24	97.17 280	98.35 184	94.85 136	97.93 97	98.58 127	95.07 67	99.71 108	92.60 229	99.34 97	99.43 102

DELS-MVS		98.40 39	98.20 40	98.99 60	99.00 104	97.66 70	97.75 240	98.89 46	97.71 8	98.33 73	98.97 83	94.97 69	99.88 42	98.42 19	99.76 31	99.42 103
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
baseline		97.64 73	97.44 75	98.25 109	98.35 150	96.20 131	99.00 57	98.32 188	96.33 68	98.03 84	99.17 52	91.35 136	99.16 168	98.10 30	98.29 144	99.39 104

sss		97.39 92	96.98 95	98.61 80	98.60 138	96.61 112	98.22 188	98.93 37	93.97 169	98.01 89	98.48 136	91.98 121	99.85 48	96.45 112	98.15 146	99.39 104

EPP-MVSNet		97.46 84	97.28 81	97.99 125	98.64 134	95.38 168	99.33 13	98.31 190	93.61 193	97.19 127	99.07 73	94.05 90	99.23 162	96.89 91	98.43 138	99.37 106

test_yl		97.22 99	96.78 103	98.54 86	98.73 123	96.60 113	98.45 157	98.31 190	94.70 139	98.02 85	98.42 142	90.80 148	99.70 109	96.81 99	96.79 178	99.34 107

DCV-MVSNet		97.22 99	96.78 103	98.54 86	98.73 123	96.60 113	98.45 157	98.31 190	94.70 139	98.02 85	98.42 142	90.80 148	99.70 109	96.81 99	96.79 178	99.34 107

diffmvs		97.58 79	97.40 77	98.13 117	98.32 157	95.81 154	98.06 211	98.37 181	96.20 71	98.74 49	98.89 96	91.31 138	99.25 159	98.16 28	98.52 131	99.34 107

MVSFormer		97.57 80	97.49 71	97.84 132	98.07 175	95.76 155	99.47 2	98.40 176	94.98 129	98.79 45	98.83 102	92.34 108	98.41 258	96.91 88	99.59 67	99.34 107

jason		97.32 96	97.08 89	98.06 122	97.45 221	95.59 158	97.87 230	97.91 250	94.79 137	98.55 61	98.83 102	91.12 141	99.23 162	97.58 61	99.60 64	99.34 107
jason: jason.
QAPM		96.29 134	95.40 150	98.96 64	97.85 189	97.60 74	99.23 21	98.93 37	89.76 298	93.11 262	99.02 76	89.11 177	99.93 14	91.99 248	99.62 62	99.34 107

mvs_anonymous		96.70 120	96.53 117	97.18 170	98.19 166	93.78 229	98.31 178	98.19 209	94.01 166	94.47 201	98.27 162	92.08 119	98.46 245	97.39 71	97.91 152	99.31 113

lupinMVS		97.44 88	97.22 84	98.12 119	98.07 175	95.76 155	97.68 245	97.76 255	94.50 151	98.79 45	98.61 122	92.34 108	99.30 156	97.58 61	99.59 67	99.31 113

CDS-MVSNet		96.99 110	96.69 109	97.90 130	98.05 178	95.98 137	98.20 192	98.33 187	93.67 190	96.95 137	98.49 135	93.54 95	98.42 251	95.24 156	97.74 160	99.31 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Patchmatch-RL test		91.49 287	90.85 287	93.41 307	91.37 335	84.40 331	92.81 337	95.93 322	91.87 253	87.25 319	94.87 321	88.99 180	96.53 330	92.54 235	82.00 329	99.30 116

BH-RMVSNet		95.92 148	95.32 159	97.69 145	98.32 157	94.64 200	98.19 196	97.45 277	94.56 147	96.03 174	98.61 122	85.02 257	99.12 174	90.68 269	99.06 107	99.30 116

Patchmatch-test		94.42 232	93.68 246	96.63 205	97.60 204	91.76 271	94.83 331	97.49 274	89.45 303	94.14 221	97.10 247	88.99 180	98.83 214	85.37 319	98.13 147	99.29 118

TAMVS		97.02 109	96.79 102	97.70 144	98.06 177	95.31 173	98.52 148	98.31 190	93.95 170	97.05 135	98.61 122	93.49 96	98.52 240	95.33 150	97.81 156	99.29 118

PVSNet_Blended		97.38 93	97.12 86	98.14 115	99.25 81	95.35 171	97.28 272	99.26 8	93.13 210	97.94 95	98.21 166	92.74 104	99.81 65	96.88 94	99.40 94	99.27 120

PatchmatchNet		95.71 157	95.52 148	96.29 238	97.58 206	90.72 290	96.84 303	97.52 270	94.06 162	97.08 131	96.96 269	89.24 173	98.90 205	92.03 247	98.37 139	99.26 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CHOSEN 280x420		97.18 103	97.18 85	97.20 168	98.81 119	93.27 249	95.78 323	99.15 18	95.25 115	96.79 149	98.11 173	92.29 110	99.07 183	98.56 8	99.85 3	99.25 122

PLC	95.07 4	97.20 102	96.78 103	98.44 95	99.29 73	96.31 129	98.14 203	98.76 93	92.41 236	96.39 167	98.31 157	94.92 71	99.78 90	94.06 190	98.77 121	99.23 123
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LCM-MVSNet-Re		95.22 184	95.32 159	94.91 283	98.18 168	87.85 324	98.75 105	95.66 324	95.11 123	88.96 313	96.85 278	90.26 159	97.65 310	95.65 142	98.44 136	99.22 124

GSMVS																99.20 125

sam_mvs1												89.45 167				99.20 125

SCA		95.46 166	95.13 166	96.46 227	97.67 199	91.29 281	97.33 268	97.60 262	94.68 142	96.92 141	97.10 247	83.97 277	98.89 206	92.59 231	98.32 143	99.20 125

Effi-MVS+		97.12 106	96.69 109	98.39 101	98.19 166	96.72 108	97.37 263	98.43 172	93.71 183	97.65 114	98.02 178	92.20 115	99.25 159	96.87 97	97.79 157	99.19 128

alignmvs		97.56 81	97.07 90	99.01 59	98.66 132	98.37 39	98.83 89	98.06 239	96.74 54	98.00 91	97.65 211	90.80 148	99.48 144	98.37 22	96.56 185	99.19 128

DP-MVS Recon		97.86 62	97.46 73	99.06 58	99.53 35	98.35 41	98.33 172	98.89 46	92.62 227	98.05 81	98.94 91	95.34 57	99.65 118	96.04 125	99.42 91	99.19 128

OMC-MVS		97.55 82	97.34 79	98.20 112	99.33 60	95.92 149	98.28 183	98.59 136	95.52 99	97.97 92	99.10 65	93.28 99	99.49 140	95.09 158	98.88 114	99.19 128

MDTV_nov1_ep13_2view						84.26 332	96.89 299		90.97 282	97.90 99		89.89 163		93.91 193		99.18 132

MVS_Test		97.28 97	97.00 93	98.13 117	98.33 155	95.97 142	98.74 108	98.07 235	94.27 157	98.44 67	98.07 175	92.48 106	99.26 158	96.43 114	98.19 145	99.16 133

ab-mvs		96.42 130	95.71 142	98.55 84	98.63 135	96.75 107	97.88 229	98.74 97	93.84 175	96.54 160	98.18 169	85.34 254	99.75 99	95.93 128	96.35 191	99.15 134

PVSNet	91.96 18	96.35 132	96.15 128	96.96 184	99.17 93	92.05 266	96.08 316	98.68 115	93.69 186	97.75 105	97.80 201	88.86 186	99.69 114	94.26 183	99.01 108	99.15 134

tpm		94.13 249	93.80 236	95.12 277	96.50 277	87.91 323	97.44 256	95.89 323	92.62 227	96.37 168	96.30 298	84.13 274	98.30 271	93.24 212	91.66 260	99.14 136

F-COLMAP		97.09 108	96.80 100	97.97 126	99.45 50	94.95 189	98.55 146	98.62 133	93.02 213	96.17 172	98.58 127	94.01 91	99.81 65	93.95 192	98.90 112	99.14 136

Anonymous20240529		95.10 191	94.22 208	97.75 139	99.01 103	94.26 218	98.87 82	98.83 68	85.79 324	96.64 152	98.97 83	78.73 308	99.85 48	96.27 117	94.89 211	99.12 138

PMMVS		96.60 122	96.33 122	97.41 161	97.90 186	93.93 225	97.35 266	98.41 174	92.84 222	97.76 104	97.45 227	91.10 143	99.20 165	96.26 118	97.91 152	99.11 139

GA-MVS		94.81 207	94.03 219	97.14 172	97.15 242	93.86 227	96.76 306	97.58 263	94.00 167	94.76 195	97.04 260	80.91 295	98.48 242	91.79 252	96.25 199	99.09 140

EPNet_dtu		95.21 185	94.95 176	95.99 247	96.17 290	90.45 294	98.16 202	97.27 287	96.77 52	93.14 261	98.33 155	90.34 156	98.42 251	85.57 316	98.81 120	99.09 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	93.98 7	95.35 177	94.56 191	97.74 140	99.13 97	94.83 194	98.33 172	98.64 131	86.62 316	96.29 169	98.61 122	94.00 92	99.29 157	80.00 330	99.41 92	99.09 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		97.67 71	97.23 83	98.98 62	98.70 128	98.38 33	99.34 11	98.39 178	96.76 53	97.67 111	97.40 231	92.26 111	99.49 140	98.28 26	96.28 197	99.08 143

VDD-MVS		95.82 153	95.23 162	97.61 152	98.84 118	93.98 224	98.68 124	97.40 281	95.02 128	97.95 93	99.34 28	74.37 330	99.78 90	98.64 3	96.80 177	99.08 143

EIA-MVS		97.75 67	97.58 63	98.27 106	98.38 148	96.44 121	99.01 55	98.60 134	95.88 83	97.26 125	97.53 222	94.97 69	99.33 155	97.38 72	99.20 102	99.05 145

tttt0517		96.07 140	95.51 149	97.78 136	98.41 147	94.84 192	99.28 16	94.33 336	94.26 158	97.64 115	98.64 121	84.05 275	99.47 145	95.34 149	97.60 165	99.03 146

ET-MVSNet_ETH3D		94.13 249	92.98 263	97.58 153	98.22 162	96.20 131	97.31 270	95.37 325	94.53 148	79.56 334	97.63 215	86.51 233	97.53 315	96.91 88	90.74 271	99.02 147

ADS-MVSNet2		94.58 222	94.40 203	95.11 278	98.00 179	88.74 314	96.04 317	97.30 284	90.15 292	96.47 164	96.64 288	87.89 209	97.56 314	90.08 276	97.06 172	99.02 147

ADS-MVSNet		95.00 196	94.45 199	96.63 205	98.00 179	91.91 268	96.04 317	97.74 257	90.15 292	96.47 164	96.64 288	87.89 209	98.96 195	90.08 276	97.06 172	99.02 147

CNLPA		97.45 87	97.03 91	98.73 73	99.05 100	97.44 80	98.07 210	98.53 151	95.32 111	96.80 148	98.53 131	93.32 98	99.72 103	94.31 181	99.31 99	99.02 147

AdaColmap		97.15 105	96.70 108	98.48 92	99.16 94	96.69 109	98.01 216	98.89 46	94.44 154	96.83 144	98.68 116	90.69 151	99.76 97	94.36 178	99.29 100	98.98 151

Fast-Effi-MVS+		96.28 136	95.70 143	98.03 123	98.29 159	95.97 142	98.58 138	98.25 204	91.74 255	95.29 183	97.23 240	91.03 145	99.15 171	92.90 223	97.96 151	98.97 152

EPMVS		94.99 197	94.48 195	96.52 220	97.22 234	91.75 272	97.23 274	91.66 344	94.11 160	97.28 124	96.81 280	85.70 248	98.84 212	93.04 219	97.28 170	98.97 152

LS3D		97.16 104	96.66 112	98.68 76	98.53 142	97.19 91	98.93 70	98.90 44	92.83 223	95.99 176	99.37 21	92.12 117	99.87 43	93.67 201	99.57 70	98.97 152

HY-MVS	93.96 8	96.82 117	96.23 127	98.57 82	98.46 145	97.00 96	98.14 203	98.21 206	93.95 170	96.72 150	97.99 182	91.58 128	99.76 97	94.51 175	96.54 186	98.95 155

thisisatest0530		96.01 143	95.36 155	97.97 126	98.38 148	95.52 164	98.88 80	94.19 338	94.04 163	97.64 115	98.31 157	83.82 282	99.46 146	95.29 153	97.70 162	98.93 156

MIMVSNet		93.26 270	92.21 276	96.41 230	97.73 197	93.13 254	95.65 324	97.03 297	91.27 275	94.04 226	96.06 306	75.33 324	97.19 320	86.56 309	96.23 200	98.92 157

baseline1		95.84 151	95.12 167	98.01 124	98.49 144	95.98 137	98.73 112	97.03 297	95.37 108	96.22 170	98.19 168	89.96 162	99.16 168	94.60 170	87.48 309	98.90 158

TESTMET0.1,1		94.18 247	93.69 245	95.63 263	96.92 254	89.12 309	96.91 294	94.78 331	93.17 208	94.88 189	96.45 294	78.52 309	98.92 201	93.09 216	98.50 133	98.85 159

dp		94.15 248	93.90 230	94.90 284	97.31 229	86.82 329	96.97 289	97.19 291	91.22 277	96.02 175	96.61 290	85.51 250	99.02 190	90.00 280	94.30 213	98.85 159

PAPM		94.95 201	94.00 223	97.78 136	97.04 248	95.65 157	96.03 319	98.25 204	91.23 276	94.19 219	97.80 201	91.27 139	98.86 211	82.61 325	97.61 164	98.84 161

VDDNet		95.36 176	94.53 192	97.86 131	98.10 174	95.13 179	98.85 85	97.75 256	90.46 287	98.36 71	99.39 13	73.27 332	99.64 120	97.98 35	96.58 184	98.81 162

CostFormer		94.95 201	94.73 184	95.60 264	97.28 230	89.06 310	97.53 254	96.89 307	89.66 300	96.82 146	96.72 283	86.05 243	98.95 199	95.53 145	96.13 203	98.79 163

UGNet		96.78 118	96.30 123	98.19 114	98.24 160	95.89 152	98.88 80	98.93 37	97.39 22	96.81 147	97.84 195	82.60 286	99.90 32	96.53 109	99.49 83	98.79 163
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
UniMVSNet_ETH3D		94.24 242	93.33 257	96.97 183	97.19 239	93.38 246	98.74 108	98.57 142	91.21 278	93.81 236	98.58 127	72.85 333	98.77 220	95.05 159	93.93 228	98.77 165

test-LLR		95.10 191	94.87 179	95.80 257	96.77 262	89.70 300	96.91 294	95.21 326	95.11 123	94.83 192	95.72 313	87.71 213	98.97 192	93.06 217	98.50 133	98.72 166

test-mter		94.08 254	93.51 252	95.80 257	96.77 262	89.70 300	96.91 294	95.21 326	92.89 220	94.83 192	95.72 313	77.69 314	98.97 192	93.06 217	98.50 133	98.72 166

CS-MVS		97.81 64	97.61 61	98.41 99	98.52 143	97.15 93	99.09 44	98.55 146	96.18 72	97.61 117	97.20 243	94.59 78	99.39 150	97.62 57	99.10 106	98.70 168

DWT-MVSNet_test		94.82 206	94.36 204	96.20 241	97.35 227	90.79 288	98.34 171	96.57 317	92.91 219	95.33 182	96.44 295	82.00 288	99.12 174	94.52 174	95.78 208	98.70 168

MAR-MVS		96.91 113	96.40 120	98.45 94	98.69 130	96.90 101	98.66 130	98.68 115	92.40 237	97.07 133	97.96 183	91.54 132	99.75 99	93.68 199	98.92 111	98.69 170
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
thisisatest0515		95.61 164	94.89 178	97.76 138	98.15 171	95.15 178	96.77 305	94.41 334	92.95 217	97.18 128	97.43 229	84.78 262	99.45 147	94.63 167	97.73 161	98.68 171

BH-untuned		95.95 146	95.72 139	96.65 202	98.55 141	92.26 262	98.23 187	97.79 254	93.73 181	94.62 196	98.01 180	88.97 184	99.00 191	93.04 219	98.51 132	98.68 171

PCF-MVS	93.45 11	94.68 213	93.43 255	98.42 98	98.62 136	96.77 106	95.48 325	98.20 208	84.63 328	93.34 253	98.32 156	88.55 193	99.81 65	84.80 321	98.96 110	98.68 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CANet_DTU		96.96 111	96.55 115	98.21 111	98.17 170	96.07 136	97.98 219	98.21 206	97.24 34	97.13 129	98.93 92	86.88 229	99.91 29	95.00 160	99.37 96	98.66 174

PatchMatch-RL		96.59 124	96.03 132	98.27 106	99.31 65	96.51 118	97.91 224	99.06 22	93.72 182	96.92 141	98.06 176	88.50 195	99.65 118	91.77 253	99.00 109	98.66 174

tpmrst		95.63 161	95.69 144	95.44 269	97.54 211	88.54 317	96.97 289	97.56 264	93.50 196	97.52 122	96.93 273	89.49 165	99.16 168	95.25 155	96.42 190	98.64 176

IB-MVS	91.98 17	93.27 269	91.97 279	97.19 169	97.47 216	93.41 244	97.09 284	95.99 319	93.32 203	92.47 281	95.73 311	78.06 312	99.53 137	94.59 172	82.98 327	98.62 177
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
DSMNet-mixed		92.52 280	92.58 271	92.33 314	94.15 327	82.65 336	98.30 180	94.26 337	89.08 307	92.65 274	95.73 311	85.01 258	95.76 333	86.24 311	97.76 159	98.59 178

tpm2		94.19 245	93.76 241	95.46 268	97.23 233	89.04 311	97.31 270	96.85 310	87.08 315	96.21 171	96.79 281	83.75 283	98.74 221	92.43 239	96.23 200	98.59 178

ETV-MVS		97.96 55	97.81 55	98.40 100	98.42 146	97.27 85	98.73 112	98.55 146	96.84 50	98.38 70	97.44 228	95.39 53	99.35 153	97.62 57	98.89 113	98.58 180

MSDG		95.93 147	95.30 161	97.83 133	98.90 111	95.36 169	96.83 304	98.37 181	91.32 271	94.43 206	98.73 113	90.27 158	99.60 126	90.05 278	98.82 119	98.52 181

PatchT		93.06 275	91.97 279	96.35 234	96.69 268	92.67 259	94.48 333	97.08 293	86.62 316	97.08 131	92.23 332	87.94 208	97.90 301	78.89 334	96.69 180	98.49 182

CR-MVSNet		94.76 210	94.15 214	96.59 210	97.00 249	93.43 242	94.96 327	97.56 264	92.46 231	96.93 139	96.24 299	88.15 202	97.88 305	87.38 305	96.65 182	98.46 183

RPMNet		92.52 280	91.17 284	96.59 210	97.00 249	93.43 242	94.96 327	97.26 288	82.27 331	96.93 139	92.12 333	86.98 227	97.88 305	76.32 338	96.65 182	98.46 183

thres600view7		95.49 165	94.77 181	97.67 147	98.98 107	95.02 182	98.85 85	96.90 305	95.38 106	96.63 153	96.90 274	84.29 268	99.59 127	88.65 298	96.33 192	98.40 185

thres400		95.38 173	94.62 188	97.65 150	98.94 109	94.98 186	98.68 124	96.93 303	95.33 109	96.55 158	96.53 291	84.23 271	99.56 131	88.11 299	96.29 194	98.40 185

TR-MVS		94.94 203	94.20 209	97.17 171	97.75 193	94.14 221	97.59 251	97.02 299	92.28 242	95.75 178	97.64 213	83.88 279	98.96 195	89.77 282	96.15 202	98.40 185

JIA-IIPM		93.35 266	92.49 272	95.92 251	96.48 279	90.65 291	95.01 326	96.96 301	85.93 322	96.08 173	87.33 337	87.70 215	98.78 219	91.35 259	95.58 209	98.34 188

PVSNet_0	88.72 19	91.28 289	90.03 293	95.00 281	97.99 181	87.29 327	94.84 330	98.50 160	92.06 248	89.86 307	95.19 318	79.81 302	99.39 150	92.27 240	69.79 340	98.33 189

1314		96.25 138	95.73 138	97.79 135	97.13 243	95.55 163	98.19 196	98.59 136	93.47 197	92.03 290	97.82 199	91.33 137	99.49 140	94.62 169	98.44 136	98.32 190

RPSCF		94.87 205	95.40 150	93.26 310	98.89 112	82.06 338	98.33 172	98.06 239	90.30 291	96.56 156	99.26 38	87.09 224	99.49 140	93.82 196	96.32 193	98.24 191

tpmvs		94.60 219	94.36 204	95.33 272	97.46 217	88.60 316	96.88 300	97.68 258	91.29 273	93.80 237	96.42 296	88.58 190	99.24 161	91.06 262	96.04 205	98.17 192

BH-w/o		95.38 173	95.08 169	96.26 239	98.34 154	91.79 270	97.70 243	97.43 279	92.87 221	94.24 216	97.22 241	88.66 189	98.84 212	91.55 257	97.70 162	98.16 193

tpm cat1		93.36 265	92.80 266	95.07 280	97.58 206	87.97 322	96.76 306	97.86 252	82.17 332	93.53 244	96.04 307	86.13 241	99.13 173	89.24 293	95.87 206	98.10 194

MVS		94.67 216	93.54 251	98.08 120	96.88 258	96.56 116	98.19 196	98.50 160	78.05 336	92.69 273	98.02 178	91.07 144	99.63 123	90.09 275	98.36 141	98.04 195

AllTest		95.24 183	94.65 187	96.99 180	99.25 81	93.21 252	98.59 136	98.18 212	91.36 267	93.52 245	98.77 109	84.67 263	99.72 103	89.70 285	97.87 154	98.02 196

TestCases				96.99 180	99.25 81	93.21 252		98.18 212	91.36 267	93.52 245	98.77 109	84.67 263	99.72 103	89.70 285	97.87 154	98.02 196

gg-mvs-nofinetune		92.21 283	90.58 289	97.13 173	96.75 265	95.09 180	95.85 321	89.40 347	85.43 326	94.50 200	81.98 340	80.80 298	98.40 264	92.16 241	98.33 142	97.88 198

baseline2		95.11 190	94.52 193	96.87 190	96.65 271	93.56 238	98.27 185	94.10 340	93.45 198	92.02 291	97.43 229	87.45 221	99.19 166	93.88 194	97.41 169	97.87 199

mvs-test1		96.60 122	96.68 111	96.37 232	97.89 187	91.81 269	98.56 144	98.10 228	96.57 61	96.52 162	97.94 185	90.81 146	99.45 147	95.72 137	98.01 149	97.86 200

thres100view900		95.38 173	94.70 185	97.41 161	98.98 107	94.92 190	98.87 82	96.90 305	95.38 106	96.61 154	96.88 275	84.29 268	99.56 131	88.11 299	96.29 194	97.76 201

tfpn200view9		95.32 180	94.62 188	97.43 160	98.94 109	94.98 186	98.68 124	96.93 303	95.33 109	96.55 158	96.53 291	84.23 271	99.56 131	88.11 299	96.29 194	97.76 201

XVG-OURS-SEG-HR		96.51 127	96.34 121	97.02 179	98.77 121	93.76 230	97.79 238	98.50 160	95.45 102	96.94 138	99.09 70	87.87 211	99.55 136	96.76 104	95.83 207	97.74 203

OpenMVS	93.04 13	95.83 152	95.00 172	98.32 104	97.18 240	97.32 82	99.21 28	98.97 30	89.96 296	91.14 298	99.05 75	86.64 232	99.92 20	93.38 207	99.47 85	97.73 204

testgi		93.06 275	92.45 273	94.88 285	96.43 281	89.90 297	98.75 105	97.54 269	95.60 95	91.63 295	97.91 187	74.46 329	97.02 322	86.10 312	93.67 231	97.72 205

XVG-OURS		96.55 126	96.41 119	96.99 180	98.75 122	93.76 230	97.50 255	98.52 153	95.67 92	96.83 144	99.30 34	88.95 185	99.53 137	95.88 130	96.26 198	97.69 206

cascas		94.63 218	93.86 233	96.93 187	96.91 256	94.27 217	96.00 320	98.51 155	85.55 325	94.54 198	96.23 301	84.20 273	98.87 209	95.80 134	96.98 175	97.66 207

test0.0.03 1		94.08 254	93.51 252	95.80 257	95.53 311	92.89 258	97.38 261	95.97 320	95.11 123	92.51 280	96.66 285	87.71 213	96.94 323	87.03 307	93.67 231	97.57 208

MVS-HIRNet		89.46 302	88.40 302	92.64 312	97.58 206	82.15 337	94.16 336	93.05 343	75.73 338	90.90 300	82.52 339	79.42 304	98.33 266	83.53 323	98.68 122	97.43 209

xiu_mvs_v2_base		97.66 72	97.70 59	97.56 155	98.61 137	95.46 166	97.44 256	98.46 165	97.15 39	98.65 56	98.15 170	94.33 86	99.80 74	97.84 44	98.66 126	97.41 210

Effi-MVS+-dtu		96.29 134	96.56 114	95.51 265	97.89 187	90.22 296	98.80 99	98.10 228	96.57 61	96.45 166	96.66 285	90.81 146	98.91 202	95.72 137	97.99 150	97.40 211

PS-MVSNAJ		97.73 68	97.77 56	97.62 151	98.68 131	95.58 159	97.34 267	98.51 155	97.29 27	98.66 55	97.88 190	94.51 80	99.90 32	97.87 41	99.17 104	97.39 212

thres200		95.25 182	94.57 190	97.28 166	98.81 119	94.92 190	98.20 192	97.11 292	95.24 117	96.54 160	96.22 303	84.58 265	99.53 137	87.93 303	96.50 188	97.39 212

xiu_mvs_v1_base_debu		97.60 75	97.56 65	97.72 141	98.35 150	95.98 137	97.86 231	98.51 155	97.13 41	99.01 31	98.40 144	91.56 129	99.80 74	98.53 9	98.68 122	97.37 214

xiu_mvs_v1_base		97.60 75	97.56 65	97.72 141	98.35 150	95.98 137	97.86 231	98.51 155	97.13 41	99.01 31	98.40 144	91.56 129	99.80 74	98.53 9	98.68 122	97.37 214

xiu_mvs_v1_base_debi		97.60 75	97.56 65	97.72 141	98.35 150	95.98 137	97.86 231	98.51 155	97.13 41	99.01 31	98.40 144	91.56 129	99.80 74	98.53 9	98.68 122	97.37 214

API-MVS		97.41 91	97.25 82	97.91 129	98.70 128	96.80 104	98.82 92	98.69 112	94.53 148	98.11 78	98.28 159	94.50 83	99.57 129	94.12 187	99.49 83	97.37 214

Fast-Effi-MVS+-dtu		95.87 149	95.85 136	95.91 252	97.74 196	91.74 273	98.69 123	98.15 220	95.56 97	94.92 188	97.68 210	88.98 183	98.79 218	93.19 214	97.78 158	97.20 218

COLMAP_ROB	93.27 12	95.33 179	94.87 179	96.71 197	99.29 73	93.24 251	98.58 138	98.11 226	89.92 297	93.57 243	99.10 65	86.37 238	99.79 86	90.78 267	98.10 148	97.09 219
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RRT_test8_iter05		94.56 223	94.19 210	95.67 262	97.60 204	91.34 277	98.93 70	98.42 173	94.75 138	93.39 251	97.87 191	79.00 307	98.61 230	96.78 103	90.99 269	97.07 220

PS-MVSNAJss		96.43 129	96.26 125	96.92 189	95.84 303	95.08 181	99.16 34	98.50 160	95.87 84	93.84 235	98.34 154	94.51 80	98.61 230	96.88 94	93.45 238	97.06 221

nrg030		96.28 136	95.72 139	97.96 128	96.90 257	98.15 54	99.39 5	98.31 190	95.47 101	94.42 207	98.35 150	92.09 118	98.69 223	97.50 68	89.05 293	97.04 222

FIs		96.51 127	96.12 129	97.67 147	97.13 243	97.54 76	99.36 8	99.22 14	95.89 82	94.03 227	98.35 150	91.98 121	98.44 248	96.40 115	92.76 248	97.01 223

FC-MVSNet-test		96.42 130	96.05 130	97.53 157	96.95 252	97.27 85	99.36 8	99.23 12	95.83 85	93.93 229	98.37 148	92.00 120	98.32 267	96.02 126	92.72 249	97.00 224

EU-MVSNet		93.66 261	94.14 215	92.25 315	95.96 299	83.38 334	98.52 148	98.12 224	94.69 141	92.61 275	98.13 172	87.36 222	96.39 332	91.82 251	90.00 279	96.98 225

VPNet		94.99 197	94.19 210	97.40 163	97.16 241	96.57 115	98.71 117	98.97 30	95.67 92	94.84 190	98.24 165	80.36 300	98.67 227	96.46 111	87.32 312	96.96 226

XXY-MVS		95.20 186	94.45 199	97.46 158	96.75 265	96.56 116	98.86 84	98.65 130	93.30 205	93.27 255	98.27 162	84.85 261	98.87 209	94.82 164	91.26 265	96.96 226

TranMVSNet+NR-MVSNet		95.14 189	94.48 195	97.11 175	96.45 280	96.36 125	99.03 52	99.03 25	95.04 127	93.58 242	97.93 186	88.27 198	98.03 292	94.13 186	86.90 318	96.95 228

HQP_MVS		96.14 139	95.90 135	96.85 191	97.42 222	94.60 206	98.80 99	98.56 144	97.28 28	95.34 180	98.28 159	87.09 224	99.03 188	96.07 122	94.27 214	96.92 229

plane_prior5								98.56 144					99.03 188	96.07 122	94.27 214	96.92 229

UniMVSNet_NR-MVSNet		95.71 157	95.15 165	97.40 163	96.84 260	96.97 97	98.74 108	99.24 10	95.16 119	93.88 232	97.72 206	91.68 126	98.31 269	95.81 132	87.25 313	96.92 229

DU-MVS		95.42 170	94.76 182	97.40 163	96.53 275	96.97 97	98.66 130	98.99 29	95.43 103	93.88 232	97.69 207	88.57 191	98.31 269	95.81 132	87.25 313	96.92 229

NR-MVSNet		94.98 199	94.16 213	97.44 159	96.53 275	97.22 90	98.74 108	98.95 34	94.96 131	89.25 312	97.69 207	89.32 170	98.18 279	94.59 172	87.40 311	96.92 229

jajsoiax		95.45 168	95.03 171	96.73 196	95.42 316	94.63 201	99.14 36	98.52 153	95.74 88	93.22 256	98.36 149	83.87 280	98.65 228	96.95 87	94.04 223	96.91 234

mvs_tets		95.41 172	95.00 172	96.65 202	95.58 309	94.42 211	99.00 57	98.55 146	95.73 89	93.21 257	98.38 147	83.45 284	98.63 229	97.09 80	94.00 225	96.91 234

WR-MVS		95.15 188	94.46 197	97.22 167	96.67 270	96.45 120	98.21 189	98.81 75	94.15 159	93.16 258	97.69 207	87.51 217	98.30 271	95.29 153	88.62 299	96.90 236

VPA-MVSNet		95.75 155	95.11 168	97.69 145	97.24 232	97.27 85	98.94 69	99.23 12	95.13 121	95.51 179	97.32 234	85.73 247	98.91 202	97.33 74	89.55 286	96.89 237

Anonymous20231211		94.10 252	93.26 260	96.61 207	99.11 99	94.28 216	99.01 55	98.88 49	86.43 318	92.81 268	97.57 219	81.66 291	98.68 226	94.83 163	89.02 295	96.88 238

test_djsdf		96.00 144	95.69 144	96.93 187	95.72 305	95.49 165	99.47 2	98.40 176	94.98 129	94.58 197	97.86 192	89.16 175	98.41 258	96.91 88	94.12 222	96.88 238

HQP4-MVS										94.45 202			98.96 195			96.87 240

ACMM	93.85 9	95.69 159	95.38 154	96.61 207	97.61 203	93.84 228	98.91 72	98.44 169	95.25 115	94.28 213	98.47 137	86.04 245	99.12 174	95.50 146	93.95 227	96.87 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP-MVS		95.72 156	95.40 150	96.69 200	97.20 236	94.25 219	98.05 212	98.46 165	96.43 63	94.45 202	97.73 204	86.75 230	98.96 195	95.30 151	94.18 218	96.86 242

testing_2		90.61 296	88.50 301	96.95 185	90.08 339	95.57 160	97.69 244	98.06 239	93.02 213	76.55 335	92.48 331	61.18 342	98.44 248	95.45 148	91.98 255	96.84 243

EI-MVSNet		95.96 145	95.83 137	96.36 233	97.93 184	93.70 236	98.12 206	98.27 199	93.70 185	95.07 184	99.02 76	92.23 113	98.54 238	94.68 166	93.46 236	96.84 243

IterMVS-LS		95.46 166	95.21 163	96.22 240	98.12 172	93.72 235	98.32 177	98.13 223	93.71 183	94.26 214	97.31 235	92.24 112	98.10 285	94.63 167	90.12 277	96.84 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		94.94 203	94.30 206	96.83 192	96.72 267	95.56 161	99.11 42	98.95 34	93.89 172	92.42 283	97.90 188	87.19 223	98.12 284	94.32 180	88.21 302	96.82 246

RRT_MVS		96.04 142	95.53 147	97.56 155	97.07 247	97.32 82	98.57 143	98.09 231	95.15 120	95.02 186	98.44 139	88.20 200	98.58 236	96.17 121	93.09 245	96.79 247

PS-CasMVS		94.67 216	93.99 225	96.71 197	96.68 269	95.26 174	99.13 39	99.03 25	93.68 188	92.33 284	97.95 184	85.35 253	98.10 285	93.59 203	88.16 304	96.79 247

UniMVSNet (Re)		95.78 154	95.19 164	97.58 153	96.99 251	97.47 78	98.79 103	99.18 16	95.60 95	93.92 230	97.04 260	91.68 126	98.48 242	95.80 134	87.66 308	96.79 247

MVSTER		96.06 141	95.72 139	97.08 177	98.23 161	95.93 148	98.73 112	98.27 199	94.86 135	95.07 184	98.09 174	88.21 199	98.54 238	96.59 107	93.46 236	96.79 247

LPG-MVS_test		95.62 162	95.34 156	96.47 224	97.46 217	93.54 239	98.99 59	98.54 149	94.67 143	94.36 209	98.77 109	85.39 251	99.11 177	95.71 139	94.15 220	96.76 251

LGP-MVS_train				96.47 224	97.46 217	93.54 239		98.54 149	94.67 143	94.36 209	98.77 109	85.39 251	99.11 177	95.71 139	94.15 220	96.76 251

GG-mvs-BLEND				96.59 210	96.34 284	94.98 186	96.51 314	88.58 348		93.10 263	94.34 324	80.34 301	98.05 291	89.53 288	96.99 174	96.74 253

PEN-MVS		94.42 232	93.73 243	96.49 222	96.28 286	94.84 192	99.17 33	99.00 27	93.51 195	92.23 286	97.83 198	86.10 242	97.90 301	92.55 234	86.92 317	96.74 253

OurMVSNet-221017-0		94.21 243	94.00 223	94.85 286	95.60 308	89.22 308	98.89 77	97.43 279	95.29 112	92.18 287	98.52 134	82.86 285	98.59 234	93.46 206	91.76 258	96.74 253

v2v482		94.69 211	94.03 219	96.65 202	96.17 290	94.79 197	98.67 127	98.08 233	92.72 224	94.00 228	97.16 245	87.69 216	98.45 246	92.91 222	88.87 297	96.72 256

GBi-Net		94.49 228	93.80 236	96.56 215	98.21 163	95.00 183	98.82 92	98.18 212	92.46 231	94.09 223	97.07 254	81.16 292	97.95 297	92.08 243	92.14 252	96.72 256

test1		94.49 228	93.80 236	96.56 215	98.21 163	95.00 183	98.82 92	98.18 212	92.46 231	94.09 223	97.07 254	81.16 292	97.95 297	92.08 243	92.14 252	96.72 256

FMVSNet1		93.19 273	92.07 277	96.56 215	97.54 211	95.00 183	98.82 92	98.18 212	90.38 290	92.27 285	97.07 254	73.68 331	97.95 297	89.36 292	91.30 263	96.72 256

v1192		94.32 237	93.58 249	96.53 219	96.10 293	94.45 210	98.50 153	98.17 217	91.54 262	94.19 219	97.06 257	86.95 228	98.43 250	90.14 274	89.57 284	96.70 260

v1240		94.06 256	93.29 259	96.34 235	96.03 297	93.90 226	98.44 160	98.17 217	91.18 279	94.13 222	97.01 264	86.05 243	98.42 251	89.13 295	89.50 287	96.70 260

FMVSNet3		94.97 200	94.26 207	97.11 175	98.18 168	96.62 110	98.56 144	98.26 203	93.67 190	94.09 223	97.10 247	84.25 270	98.01 293	92.08 243	92.14 252	96.70 260

FMVSNet2		94.47 230	93.61 248	97.04 178	98.21 163	96.43 122	98.79 103	98.27 199	92.46 231	93.50 248	97.09 251	81.16 292	98.00 295	91.09 260	91.93 256	96.70 260

ACMH	92.88 16	94.55 224	93.95 227	96.34 235	97.63 202	93.26 250	98.81 98	98.49 164	93.43 199	89.74 308	98.53 131	81.91 289	99.08 182	93.69 198	93.30 242	96.70 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		94.20 244	93.47 254	96.40 231	95.98 298	94.08 222	98.52 148	98.15 220	91.33 270	94.25 215	97.20 243	86.41 237	98.42 251	90.04 279	89.39 289	96.69 265

ACMP	93.49 10	95.34 178	94.98 174	96.43 229	97.67 199	93.48 241	98.73 112	98.44 169	94.94 134	92.53 278	98.53 131	84.50 267	99.14 172	95.48 147	94.00 225	96.66 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CLD-MVS		95.62 162	95.34 156	96.46 227	97.52 214	93.75 232	97.27 273	98.46 165	95.53 98	94.42 207	98.00 181	86.21 240	98.97 192	96.25 119	94.37 212	96.66 266
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v144192		94.39 234	93.70 244	96.48 223	96.06 295	94.35 215	98.58 138	98.16 219	91.45 264	94.33 211	97.02 262	87.50 219	98.45 246	91.08 261	89.11 292	96.63 268

IterMVS		94.09 253	93.85 234	94.80 289	97.99 181	90.35 295	97.18 278	98.12 224	93.68 188	92.46 282	97.34 232	84.05 275	97.41 317	92.51 236	91.33 262	96.62 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		94.59 221	93.92 228	96.60 209	96.21 287	94.78 198	98.59 136	98.14 222	91.86 254	94.21 218	97.02 262	87.97 207	98.41 258	91.72 254	89.57 284	96.61 270

OPM-MVS		95.69 159	95.33 158	96.76 195	96.16 292	94.63 201	98.43 162	98.39 178	96.64 58	95.02 186	98.78 107	85.15 256	99.05 184	95.21 157	94.20 217	96.60 271

LTVRE_ROB	92.95 15	94.60 219	93.90 230	96.68 201	97.41 225	94.42 211	98.52 148	98.59 136	91.69 258	91.21 297	98.35 150	84.87 260	99.04 187	91.06 262	93.44 239	96.60 271
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
IterMVS-SCA-FT		94.11 251	93.87 232	94.85 286	97.98 183	90.56 293	97.18 278	98.11 226	93.75 178	92.58 276	97.48 224	83.97 277	97.41 317	92.48 238	91.30 263	96.58 273

pmmvs5		93.65 263	92.97 264	95.68 261	95.49 312	92.37 261	98.20 192	97.28 286	89.66 300	92.58 276	97.26 237	82.14 287	98.09 287	93.18 215	90.95 270	96.58 273

K. test v3		92.55 279	91.91 281	94.48 296	95.64 307	89.24 307	99.07 47	94.88 330	94.04 163	86.78 321	97.59 217	77.64 317	97.64 311	92.08 243	89.43 288	96.57 275

SixPastTwentyTwo		93.34 267	92.86 265	94.75 290	95.67 306	89.41 306	98.75 105	96.67 315	93.89 172	90.15 306	98.25 164	80.87 296	98.27 276	90.90 265	90.64 272	96.57 275

miper_lstm_enhance		94.33 236	94.07 218	95.11 278	97.75 193	90.97 285	97.22 275	98.03 242	91.67 259	92.76 270	96.97 267	90.03 161	97.78 308	92.51 236	89.64 283	96.56 277

MDA-MVSNet_test_wron		90.71 294	89.38 298	94.68 292	94.83 322	90.78 289	97.19 277	97.46 275	87.60 312	72.41 340	95.72 313	86.51 233	96.71 327	85.92 314	86.80 319	96.56 277

ACMH+	92.99 14	94.30 238	93.77 239	95.88 255	97.81 191	92.04 267	98.71 117	98.37 181	93.99 168	90.60 304	98.47 137	80.86 297	99.05 184	92.75 227	92.40 251	96.55 279

eth_miper_zixun_eth		94.68 213	94.41 202	95.47 267	97.64 201	91.71 274	96.73 308	98.07 235	92.71 225	93.64 240	97.21 242	90.54 153	98.17 280	93.38 207	89.76 281	96.54 280

YYNet1		90.70 295	89.39 297	94.62 294	94.79 323	90.65 291	97.20 276	97.46 275	87.54 313	72.54 339	95.74 310	86.51 233	96.66 328	86.00 313	86.76 320	96.54 280

cl-mvsnet1		94.52 226	94.03 219	95.99 247	97.57 210	93.38 246	97.05 285	97.94 248	91.74 255	92.81 268	97.10 247	89.12 176	98.07 289	92.60 229	90.30 275	96.53 282

cl_fuxian		94.79 208	94.43 201	95.89 254	97.75 193	93.12 255	97.16 281	98.03 242	92.23 243	93.46 250	97.05 259	91.39 134	98.01 293	93.58 204	89.21 291	96.53 282

Patchmtry		93.22 271	92.35 274	95.84 256	96.77 262	93.09 256	94.66 332	97.56 264	87.37 314	92.90 266	96.24 299	88.15 202	97.90 301	87.37 306	90.10 278	96.53 282

cl-mvsnet_		94.51 227	94.01 222	96.02 246	97.58 206	93.40 245	97.05 285	97.96 247	91.73 257	92.76 270	97.08 253	89.06 179	98.13 283	92.61 228	90.29 276	96.52 285

v7n		94.19 245	93.43 255	96.47 224	95.90 300	94.38 214	99.26 18	98.34 186	91.99 249	92.76 270	97.13 246	88.31 197	98.52 240	89.48 290	87.70 307	96.52 285

MDA-MVSNet-bldmvs		89.97 299	88.35 303	94.83 288	95.21 317	91.34 277	97.64 248	97.51 271	88.36 310	71.17 341	96.13 305	79.22 305	96.63 329	83.65 322	86.27 321	96.52 285

cl-mvsnet2		94.68 213	94.19 210	96.13 244	98.11 173	93.60 237	96.94 291	98.31 190	92.43 235	93.32 254	96.87 277	86.51 233	98.28 275	94.10 189	91.16 266	96.51 288

lessismore_v0				94.45 299	94.93 321	88.44 318		91.03 345		86.77 322	97.64 213	76.23 321	98.42 251	90.31 273	85.64 325	96.51 288

anonymousdsp		95.42 170	94.91 177	96.94 186	95.10 318	95.90 151	99.14 36	98.41 174	93.75 178	93.16 258	97.46 225	87.50 219	98.41 258	95.63 143	94.03 224	96.50 290

v148		94.29 239	93.76 241	95.91 252	96.10 293	92.93 257	98.58 138	97.97 245	92.59 229	93.47 249	96.95 271	88.53 194	98.32 267	92.56 233	87.06 315	96.49 291

our_test_3		93.65 263	93.30 258	94.69 291	95.45 314	89.68 302	96.91 294	97.65 260	91.97 250	91.66 294	96.88 275	89.67 164	97.93 300	88.02 302	91.49 261	96.48 292

XVG-ACMP-BASELINE		94.54 225	94.14 215	95.75 260	96.55 274	91.65 275	98.11 208	98.44 169	94.96 131	94.22 217	97.90 188	79.18 306	99.11 177	94.05 191	93.85 229	96.48 292

DTE-MVSNet		93.98 258	93.26 260	96.14 243	96.06 295	94.39 213	99.20 29	98.86 60	93.06 211	91.78 292	97.81 200	85.87 246	97.58 313	90.53 270	86.17 322	96.46 294

miper_ehance_all_eth		95.01 195	94.69 186	95.97 249	97.70 198	93.31 248	97.02 287	98.07 235	92.23 243	93.51 247	96.96 269	91.85 123	98.15 281	93.68 199	91.16 266	96.44 295

v8		94.47 230	93.77 239	96.57 214	96.36 283	94.83 194	99.05 49	98.19 209	91.92 251	93.16 258	96.97 267	88.82 188	98.48 242	91.69 255	87.79 306	96.39 296

WR-MVS_H		95.05 194	94.46 197	96.81 193	96.86 259	95.82 153	99.24 20	99.24 10	93.87 174	92.53 278	96.84 279	90.37 155	98.24 277	93.24 212	87.93 305	96.38 297

miper_enhance_ethall		95.10 191	94.75 183	96.12 245	97.53 213	93.73 234	96.61 311	98.08 233	92.20 246	93.89 231	96.65 287	92.44 107	98.30 271	94.21 184	91.16 266	96.34 298

V42		94.78 209	94.14 215	96.70 199	96.33 285	95.22 175	98.97 63	98.09 231	92.32 240	94.31 212	97.06 257	88.39 196	98.55 237	92.90 223	88.87 297	96.34 298

v10		94.29 239	93.55 250	96.51 221	96.39 282	94.80 196	98.99 59	98.19 209	91.35 269	93.02 264	96.99 265	88.09 204	98.41 258	90.50 271	88.41 301	96.33 300

MVS_0304		92.81 277	92.01 278	95.23 273	97.46 217	91.33 279	98.17 201	98.81 75	91.13 280	93.80 237	95.68 316	66.08 339	98.06 290	90.79 266	96.13 203	96.32 301

pmmvs4		94.69 211	93.99 225	96.81 193	95.74 304	95.94 145	97.40 259	97.67 259	90.42 289	93.37 252	97.59 217	89.08 178	98.20 278	92.97 221	91.67 259	96.30 302

ppachtmachnet_test		93.22 271	92.63 270	94.97 282	95.45 314	90.84 286	96.88 300	97.88 251	90.60 285	92.08 289	97.26 237	88.08 205	97.86 307	85.12 320	90.33 274	96.22 303

PVSNet_BlendedMVS		96.73 119	96.60 113	97.12 174	99.25 81	95.35 171	98.26 186	99.26 8	94.28 156	97.94 95	97.46 225	92.74 104	99.81 65	96.88 94	93.32 241	96.20 304

pm-mvs1		93.94 259	93.06 262	96.59 210	96.49 278	95.16 176	98.95 67	98.03 242	92.32 240	91.08 299	97.84 195	84.54 266	98.41 258	92.16 241	86.13 324	96.19 305

Anonymous20231206		91.66 286	91.10 285	93.33 308	94.02 329	87.35 326	98.58 138	97.26 288	90.48 286	90.16 305	96.31 297	83.83 281	96.53 330	79.36 332	89.90 280	96.12 306

ITE_SJBPF				95.44 269	97.42 222	91.32 280		97.50 272	95.09 126	93.59 241	98.35 150	81.70 290	98.88 208	89.71 284	93.39 240	96.12 306

FMVSNet5		91.81 284	90.92 286	94.49 295	97.21 235	92.09 264	98.00 218	97.55 268	89.31 305	90.86 301	95.61 317	74.48 328	95.32 335	85.57 316	89.70 282	96.07 308

UnsupCasMVSNet_eth		90.99 292	89.92 294	94.19 302	94.08 328	89.83 298	97.13 283	98.67 123	93.69 186	85.83 326	96.19 304	75.15 325	96.74 324	89.14 294	79.41 334	96.00 309

USDC		93.33 268	92.71 268	95.21 274	96.83 261	90.83 287	96.91 294	97.50 272	93.84 175	90.72 302	98.14 171	77.69 314	98.82 215	89.51 289	93.21 244	95.97 310

pmmvs6		91.77 285	90.63 288	95.17 276	94.69 325	91.24 282	98.67 127	97.92 249	86.14 320	89.62 309	97.56 221	75.79 323	98.34 265	90.75 268	84.56 326	95.94 311

N_pmnet		87.12 307	87.77 305	85.17 324	95.46 313	61.92 347	97.37 263	70.66 353	85.83 323	88.73 315	96.04 307	85.33 255	97.76 309	80.02 329	90.48 273	95.84 312

MIMVSNet1		89.67 301	88.28 304	93.82 304	92.81 333	91.08 284	98.01 216	97.45 277	87.95 311	87.90 318	95.87 309	67.63 337	94.56 338	78.73 335	88.18 303	95.83 313

TransMVSNet (Re)		92.67 278	91.51 283	96.15 242	96.58 273	94.65 199	98.90 73	96.73 311	90.86 283	89.46 311	97.86 192	85.62 249	98.09 287	86.45 310	81.12 332	95.71 314

Baseline_NR-MVSNet		94.35 235	93.81 235	95.96 250	96.20 288	94.05 223	98.61 135	96.67 315	91.44 265	93.85 234	97.60 216	88.57 191	98.14 282	94.39 177	86.93 316	95.68 315

D2MVS		95.18 187	95.08 169	95.48 266	97.10 245	92.07 265	98.30 180	99.13 19	94.02 165	92.90 266	96.73 282	89.48 166	98.73 222	94.48 176	93.60 235	95.65 316

TinyColmap		92.31 282	91.53 282	94.65 293	96.92 254	89.75 299	96.92 292	96.68 314	90.45 288	89.62 309	97.85 194	76.06 322	98.81 216	86.74 308	92.51 250	95.41 317

MS-PatchMatch		93.84 260	93.63 247	94.46 298	96.18 289	89.45 304	97.76 239	98.27 199	92.23 243	92.13 288	97.49 223	79.50 303	98.69 223	89.75 283	99.38 95	95.25 318

LF4IMVS		93.14 274	92.79 267	94.20 301	95.88 301	88.67 315	97.66 247	97.07 294	93.81 177	91.71 293	97.65 211	77.96 313	98.81 216	91.47 258	91.92 257	95.12 319

tfpnnormal		93.66 261	92.70 269	96.55 218	96.94 253	95.94 145	98.97 63	99.19 15	91.04 281	91.38 296	97.34 232	84.94 259	98.61 230	85.45 318	89.02 295	95.11 320

EG-PatchMatch MVS		91.13 290	90.12 292	94.17 303	94.73 324	89.00 312	98.13 205	97.81 253	89.22 306	85.32 328	96.46 293	67.71 336	98.42 251	87.89 304	93.82 230	95.08 321

TDRefinement		91.06 291	89.68 295	95.21 274	85.35 342	91.49 276	98.51 152	97.07 294	91.47 263	88.83 314	97.84 195	77.31 318	99.09 181	92.79 226	77.98 335	95.04 322

MVP-Stereo		94.28 241	93.92 228	95.35 271	94.95 320	92.60 260	97.97 220	97.65 260	91.61 261	90.68 303	97.09 251	86.32 239	98.42 251	89.70 285	99.34 97	95.02 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test20.03		90.89 293	90.38 290	92.43 313	93.48 330	88.14 321	98.33 172	97.56 264	93.40 200	87.96 317	96.71 284	80.69 299	94.13 339	79.15 333	86.17 322	95.01 324

ambc				89.49 320	86.66 341	75.78 341	92.66 338	96.72 312		86.55 323	92.50 330	46.01 344	97.90 301	90.32 272	82.09 328	94.80 325

test_0402		91.32 288	90.27 291	94.48 296	96.60 272	91.12 283	98.50 153	97.22 290	86.10 321	88.30 316	96.98 266	77.65 316	97.99 296	78.13 336	92.94 247	94.34 326

new_pmnet		90.06 298	89.00 300	93.22 311	94.18 326	88.32 320	96.42 315	96.89 307	86.19 319	85.67 327	93.62 325	77.18 319	97.10 321	81.61 327	89.29 290	94.23 327

CMPMVS	66.06 21	89.70 300	89.67 296	89.78 319	93.19 331	76.56 340	97.00 288	98.35 184	80.97 333	81.57 333	97.75 203	74.75 327	98.61 230	89.85 281	93.63 233	94.17 328
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PM-MVS		87.77 305	86.55 308	91.40 318	91.03 337	83.36 335	96.92 292	95.18 328	91.28 274	86.48 324	93.42 326	53.27 343	96.74 324	89.43 291	81.97 330	94.11 329

pmmvs-eth3d		90.36 297	89.05 299	94.32 300	91.10 336	92.12 263	97.63 250	96.95 302	88.86 308	84.91 329	93.13 327	78.32 310	96.74 324	88.70 297	81.81 331	94.09 330

new-patchmatchnet		88.50 304	87.45 306	91.67 317	90.31 338	85.89 330	97.16 281	97.33 283	89.47 302	83.63 331	92.77 328	76.38 320	95.06 337	82.70 324	77.29 336	94.06 331

pmmvs3		86.67 308	84.86 310	92.11 316	88.16 340	87.19 328	96.63 310	94.75 332	79.88 334	87.22 320	92.75 329	66.56 338	95.20 336	81.24 328	76.56 337	93.96 332

UnsupCasMVSNet_bld		87.17 306	85.12 309	93.31 309	91.94 334	88.77 313	94.92 329	98.30 196	84.30 329	82.30 332	90.04 334	63.96 341	97.25 319	85.85 315	74.47 339	93.93 333

LCM-MVSNet		78.70 309	76.24 313	86.08 322	77.26 348	71.99 344	94.34 334	96.72 312	61.62 342	76.53 336	89.33 335	33.91 350	92.78 341	81.85 326	74.60 338	93.46 334

OpenMVS_ROB	86.42 20	89.00 303	87.43 307	93.69 305	93.08 332	89.42 305	97.91 224	96.89 307	78.58 335	85.86 325	94.69 322	69.48 335	98.29 274	77.13 337	93.29 243	93.36 335

DeepMVS_CX				86.78 321	97.09 246	72.30 343		95.17 329	75.92 337	84.34 330	95.19 318	70.58 334	95.35 334	79.98 331	89.04 294	92.68 336

PMMVS2		77.95 311	75.44 314	85.46 323	82.54 343	74.95 342	94.23 335	93.08 342	72.80 339	74.68 337	87.38 336	36.36 349	91.56 342	73.95 339	63.94 341	89.87 337

FPMVS		77.62 312	77.14 311	79.05 327	79.25 346	60.97 348	95.79 322	95.94 321	65.96 340	67.93 342	94.40 323	37.73 348	88.88 344	68.83 340	88.46 300	87.29 338

tmp_tt		68.90 314	66.97 315	74.68 329	50.78 352	59.95 349	87.13 342	83.47 351	38.80 347	62.21 343	96.23 301	64.70 340	76.91 349	88.91 296	30.49 346	87.19 339

ANet_high		69.08 313	65.37 316	80.22 326	65.99 350	71.96 345	90.91 341	90.09 346	82.62 330	49.93 347	78.39 342	29.36 351	81.75 345	62.49 342	38.52 345	86.95 340

MVE	62.14 22	63.28 318	59.38 320	74.99 328	74.33 349	65.47 346	85.55 343	80.50 352	52.02 345	51.10 346	75.00 345	10.91 355	80.50 346	51.60 344	53.40 342	78.99 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	61.03 23	65.95 315	63.57 318	73.09 330	57.90 351	51.22 352	85.05 344	93.93 341	54.45 343	44.32 348	83.57 338	13.22 352	89.15 343	58.68 343	81.00 333	78.91 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		78.40 310	76.75 312	83.38 325	95.54 310	80.43 339	79.42 345	97.40 281	64.67 341	73.46 338	80.82 341	45.65 345	93.14 340	66.32 341	87.43 310	76.56 343
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		64.07 317	63.26 319	66.53 332	81.73 345	58.81 351	91.85 339	84.75 350	51.93 346	59.09 345	75.13 344	43.32 346	79.09 348	42.03 346	39.47 344	61.69 344

E-PMN		64.94 316	64.25 317	67.02 331	82.28 344	59.36 350	91.83 340	85.63 349	52.69 344	60.22 344	77.28 343	41.06 347	80.12 347	46.15 345	41.14 343	61.57 345

test123		20.95 322	23.72 324	12.64 334	13.54 354	8.19 354	96.55 313	6.13 356	7.48 350	16.74 350	37.98 348	12.97 353	6.05 351	16.69 348	5.43 349	23.68 346

testmvs		21.48 321	24.95 323	11.09 335	14.89 353	6.47 355	96.56 312	9.87 355	7.55 349	17.93 349	39.02 347	9.43 356	5.90 352	16.56 349	12.72 348	20.91 347

wuyk23d		30.17 319	30.18 322	30.16 333	78.61 347	43.29 353	66.79 346	14.21 354	17.31 348	14.82 351	11.93 351	11.55 354	41.43 350	37.08 347	19.30 347	5.76 348

uanet_test		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

test_part1				0.00 336		0.00 356	0.00 347	98.84 64				0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

cdsmvs_eth3d_5k		23.98 320	31.98 321	0.00 336	0.00 355	0.00 356	0.00 347	98.59 136	0.00 351	0.00 352	98.61 122	90.60 152	0.00 353	0.00 350	0.00 350	0.00 349

pcd_1.5k_mvsjas		7.88 324	10.50 326	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	94.51 80	0.00 353	0.00 350	0.00 350	0.00 349

sosnet-low-res		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

sosnet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

uncertanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

Regformer		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

ab-mvs-re		8.20 323	10.94 325	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	98.43 140	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

uanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

test_241102_ONE					99.71 20	99.24 4		98.87 55	97.62 10	99.73 1	99.39 13	97.53 4	99.74 101

9.14			98.06 45		99.47 44		98.71 117	98.82 69	94.36 155	99.16 23	99.29 35	96.05 31	99.81 65	97.00 82	99.71 49

save fliter					99.46 47	98.38 33	98.21 189	98.71 107	97.95 3

test0726					99.72 12	99.25 2	99.06 48	98.88 49	97.62 10	99.56 4	99.50 4	97.42 5

test_part2					99.63 28	99.18 7				99.27 16

sam_mvs												88.99 180

MTGPA								98.74 97

test_post1							96.68 309				30.43 350	87.85 212	98.69 223	92.59 231

test_post											31.83 349	88.83 187	98.91 202

patchmatchnet-post											95.10 320	89.42 168	98.89 206

MTMP							98.89 77	94.14 339

gm-plane-assit					95.88 301	87.47 325			89.74 299		96.94 272		99.19 166	93.32 211

TEST9					99.31 65	98.50 27	97.92 222	98.73 101	92.63 226	97.74 106	98.68 116	96.20 22	99.80 74

test_8					99.29 73	98.44 29	97.89 228	98.72 103	92.98 215	97.70 109	98.66 119	96.20 22	99.80 74

agg_prior					99.30 70	98.38 33		98.72 103		97.57 120			99.81 65

test_prior4						98.01 60	97.86 231

test_prior2							97.80 236		96.12 76	97.89 100	98.69 114	95.96 35		96.89 91	99.60 64

旧先验2							97.57 253		91.30 272	98.67 54			99.80 74	95.70 141

新几何2							97.64 248

原ACMM2							97.67 246

testdata2													99.89 34	91.65 256

segment_acmp												96.85 10

testdata1							97.32 269		96.34 67

plane_prior7					97.42 222	94.63 201

plane_prior6					97.35 227	94.61 204						87.09 224

plane_prior4											98.28 159

plane_prior3						94.61 204			97.02 46	95.34 180

plane_prior2							98.80 99		97.28 28

plane_prior1					97.37 226

plane_prior						94.60 206	98.44 160		96.74 54						94.22 216

n2								0.00 357

nn								0.00 357

door-mid								94.37 335

test11								98.66 126

door								94.64 333

HQP5-MVS						94.25 219

HQP-NCC					97.20 236		98.05 212		96.43 63	94.45 202

ACMP_Plane					97.20 236		98.05 212		96.43 63	94.45 202

BP-MVS														95.30 151

HQP3-MVS								98.46 165							94.18 218

HQP2-MVS												86.75 230

NP-MVS					97.28 230	94.51 209					97.73 204

MDTV_nov1_ep13			95.40 150		97.48 215	88.34 319	96.85 302	97.29 285	93.74 180	97.48 123	97.26 237	89.18 174	99.05 184	91.92 250	97.43 168

ACMMP++_ref															92.97 246

ACMMP++															93.61 234

Test By Simon												94.64 75

High-resolution multi-view benchmark