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.

agg_prior2														99.48 24	100.00 1	100.00 1

region2R		98.54 32	98.37 32	99.05 66	99.96 8	97.18 98	99.96 19	98.55 88	94.87 71	99.45 36	99.85 21	94.07 75	100.00 1	98.67 57	100.00 1	99.98 44

test_prior3		98.99 11	98.84 12	99.43 27	99.94 14	98.49 50	99.95 31	98.65 67	95.78 50	99.73 14	99.76 56	96.00 26	99.80 88	99.78 9	100.00 1	99.99 12

test_prior2							99.95 31		95.78 50	99.73 14	99.76 56	96.00 26		99.78 9	100.00 1

MSLP-MVS++		99.13 5	99.01 6	99.49 23	99.94 14	98.46 52	99.98 6	98.86 53	97.10 15	99.80 9	99.94 4	95.92 30	100.00 1	99.51 22	100.00 1	100.00 1

APDe-MVS		99.06 8	98.91 10	99.51 21	99.94 14	98.76 32	99.91 56	98.39 126	97.20 14	99.46 35	99.85 21	95.53 37	99.79 90	99.86 5	100.00 1	99.99 12

MCST-MVS		99.32 3	99.14 3	99.86 1	99.97 3	99.59 1	99.97 12	98.64 70	98.47 2	99.13 55	99.92 6	96.38 22	100.00 1	99.74 13	100.00 1	100.00 1

CDPH-MVS		98.65 24	98.36 34	99.49 23	99.94 14	98.73 33	99.87 71	98.33 136	93.97 101	99.76 12	99.87 15	94.99 50	99.75 97	98.55 64	100.00 1	99.98 44

mPP-MVS		98.39 43	98.20 40	98.97 73	99.97 3	96.92 107	99.95 31	98.38 129	95.04 67	98.61 77	99.80 44	93.39 90	100.00 1	98.64 61	100.00 1	99.98 44

CNVR-MVS		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 63	98.20 3	99.93 1	99.98 2	96.82 13	100.00 1	99.75 11	100.00 1	99.99 12

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 74	98.02 6	99.90 2	99.95 3	97.33 9	100.00 1	99.54 21	100.00 1	100.00 1

MG-MVS		98.91 14	98.65 16	99.68 7	99.94 14	99.07 11	99.64 151	99.44 23	97.33 12	99.00 62	99.72 65	94.03 76	99.98 32	98.73 54	100.00 1	100.00 1

SMA-MVS		98.82 18	98.55 22	99.65 8	99.87 39	98.95 14	99.86 86	98.35 133	93.19 122	99.83 7	99.94 4	96.17 23	100.00 1	99.74 13	99.99 13	100.00 1

test_part1								98.41 122				97.20 11			99.99 13	99.99 12

ESAPD		99.18 4	98.99 7	99.75 3	99.89 36	99.25 6	99.88 66	98.41 122	96.14 43	99.49 33	99.91 7	97.20 11	100.00 1	99.99 1	99.99 13	99.99 12

test9_res														99.71 18	99.99 13	100.00 1

agg_prior1		98.88 15	98.66 15	99.54 18	99.93 24	98.77 26	99.96 19	98.43 112	94.63 78	99.63 21	99.85 21	95.79 32	99.85 79	99.72 17	99.99 13	99.99 12

HPM-MVS++		99.07 6	98.88 11	99.63 9	99.90 33	99.02 12	99.95 31	98.56 84	97.56 9	99.44 37	99.85 21	95.38 39	100.00 1	99.31 30	99.99 13	99.87 75

HPM-MVS_fast		97.80 63	97.50 63	98.68 87	99.79 53	96.42 118	99.88 66	98.16 156	91.75 177	98.94 63	99.54 87	91.82 120	99.65 114	97.62 93	99.99 13	99.99 12

HPM-MVS		97.96 57	97.72 56	98.68 87	99.84 46	96.39 121	99.90 59	98.17 153	92.61 146	98.62 76	99.57 84	91.87 118	99.67 112	98.87 48	99.99 13	99.99 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS		98.62 25	98.35 35	99.41 31	99.90 33	98.51 49	99.87 71	98.36 132	94.08 94	99.74 13	99.73 64	94.08 74	99.74 101	99.42 27	99.99 13	99.99 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		98.45 38	98.32 36	98.87 79	99.96 8	96.62 113	99.97 12	98.39 126	94.43 83	98.90 64	99.87 15	94.30 67	100.00 1	99.04 40	99.99 13	99.99 12

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 43	98.72 117	97.71 72	99.98 6	98.44 107	96.85 20	99.80 9	99.91 7	97.57 4	99.85 79	99.44 26	99.99 13	99.99 12
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		97.64 69	97.32 68	98.58 96	99.97 3	95.77 140	99.96 19	98.35 133	89.90 215	98.36 86	99.79 45	91.18 128	99.99 28	98.37 69	99.99 13	99.99 12

DeepC-MVS_fast	96.59 1	98.81 19	98.54 24	99.62 12	99.90 33	98.85 21	99.24 197	98.47 103	98.14 4	99.08 56	99.91 7	93.09 98	100.00 1	99.04 40	99.99 13	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
HSP-MVS		99.07 6	99.11 4	98.95 75	99.93 24	97.24 95	99.95 31	98.32 137	97.50 10	99.52 32	99.88 12	97.43 6	99.71 105	99.50 23	99.98 26	99.89 72

TSAR-MVS + MP.		98.93 12	98.77 13	99.41 31	99.74 57	98.67 36	99.77 110	98.38 129	96.73 26	99.88 3	99.74 63	94.89 54	99.59 116	99.80 7	99.98 26	99.97 54

train_agg		98.88 15	98.65 16	99.59 14	99.92 27	98.92 16	99.96 19	98.43 112	94.35 85	99.71 16	99.86 17	95.94 28	99.85 79	99.69 19	99.98 26	99.99 12

agg_prior3		98.84 17	98.62 18	99.47 26	99.92 27	98.56 46	99.96 19	98.43 112	94.07 95	99.67 19	99.85 21	96.05 24	99.85 79	99.69 19	99.98 26	99.99 12

HFP-MVS		98.56 30	98.37 32	99.14 52	99.96 8	97.43 84	99.95 31	98.61 76	94.77 73	99.31 46	99.85 21	94.22 69	100.00 1	98.70 55	99.98 26	99.98 44

#test#		98.59 28	98.41 27	99.14 52	99.96 8	97.43 84	99.95 31	98.61 76	95.00 68	99.31 46	99.85 21	94.22 69	100.00 1	98.78 52	99.98 26	99.98 44

ACMMPR		98.50 35	98.32 36	99.05 66	99.96 8	97.18 98	99.95 31	98.60 78	94.77 73	99.31 46	99.84 35	93.73 85	100.00 1	98.70 55	99.98 26	99.98 44

test12				99.43 27	99.74 57	98.56 46		98.40 124		99.65 20		94.76 55	99.75 97		99.98 26	99.99 12

PAPM_NR		98.12 53	97.93 53	98.70 86	99.94 14	96.13 131	99.82 96	98.43 112	94.56 79	97.52 106	99.70 69	94.40 60	99.98 32	97.00 105	99.98 26	99.99 12

MP-MVS		98.23 50	97.97 50	99.03 68	99.94 14	97.17 101	99.95 31	98.39 126	94.70 76	98.26 92	99.81 43	91.84 119	100.00 1	98.85 49	99.97 35	99.93 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
114514_t		97.41 76	96.83 80	99.14 52	99.51 78	97.83 69	99.89 64	98.27 144	88.48 237	99.06 57	99.66 78	90.30 136	99.64 115	96.32 114	99.97 35	99.96 58

SD-MVS		98.92 13	98.70 14	99.56 16	99.70 65	98.73 33	99.94 45	98.34 135	96.38 34	99.81 8	99.76 56	94.59 57	99.98 32	99.84 6	99.96 37	99.97 54

PGM-MVS		98.34 44	98.13 44	98.99 72	99.92 27	97.00 103	99.75 117	99.50 21	93.90 105	99.37 44	99.76 56	93.24 95	100.00 1	97.75 91	99.96 37	99.98 44

API-MVS		97.86 60	97.66 57	98.47 108	99.52 76	95.41 152	99.47 171	98.87 52	91.68 178	98.84 65	99.85 21	92.34 109	99.99 28	98.44 67	99.96 37	100.00 1

XVS		98.70 23	98.55 22	99.15 50	99.94 14	97.50 80	99.94 45	98.42 120	96.22 39	99.41 40	99.78 50	94.34 64	99.96 43	98.92 45	99.95 40	99.99 12

X-MVStestdata		93.83 174	92.06 196	99.15 50	99.94 14	97.50 80	99.94 45	98.42 120	96.22 39	99.41 40	41.37 358	94.34 64	99.96 43	98.92 45	99.95 40	99.99 12

原ACMM1				98.96 74	99.73 61	96.99 104		98.51 97	94.06 98	99.62 23	99.85 21	94.97 51	99.96 43	95.11 128	99.95 40	99.92 69

test222					99.55 74	97.41 87	99.34 185	98.55 88	91.86 174	99.27 49	99.83 37	93.84 83			99.95 40	99.99 12

新几何1				99.42 30	99.75 56	98.27 57		98.63 73	92.69 140	99.55 28	99.82 40	94.40 60	100.00 1	91.21 190	99.94 44	99.99 12

旧先验1					99.76 54	97.52 78		98.64 70			99.85 21	95.63 34			99.94 44	99.99 12

testdata				98.42 113	99.47 79	95.33 154		98.56 84	93.78 109	99.79 11	99.85 21	93.64 88	99.94 59	94.97 129	99.94 44	100.00 1

DELS-MVS		98.54 32	98.22 38	99.50 22	99.15 87	98.65 39	100.00 1	98.58 80	97.70 7	98.21 94	99.24 106	92.58 107	99.94 59	98.63 62	99.94 44	99.92 69
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 22	98.62 18	99.01 71	99.36 84	97.18 98	99.93 50	99.90 1	96.81 24	98.67 73	99.77 52	93.92 78	99.89 69	99.27 31	99.94 44	99.96 58

PHI-MVS		98.41 41	98.21 39	99.03 68	99.86 40	97.10 102	99.98 6	98.80 58	90.78 204	99.62 23	99.78 50	95.30 40	100.00 1	99.80 7	99.93 49	99.99 12

DeepPCF-MVS	95.94 2	97.71 67	98.98 8	93.92 247	99.63 68	81.76 322	99.96 19	98.56 84	99.47 1	99.19 53	99.99 1	94.16 73	100.00 1	99.92 3	99.93 49	100.00 1

1121		98.03 56	97.57 62	99.40 33	99.74 57	98.21 58	98.31 273	98.62 74	92.78 135	99.53 29	99.83 37	95.08 44	100.00 1	94.36 143	99.92 51	99.99 12

APD-MVS_3200maxsize		98.25 49	98.08 46	98.78 82	99.81 51	96.60 114	99.82 96	98.30 139	93.95 103	99.37 44	99.77 52	92.84 100	99.76 94	98.95 42	99.92 51	99.97 54

Regformer-1		98.79 20	98.60 20	99.36 36	99.85 41	98.34 54	99.87 71	98.52 91	96.05 45	99.41 40	99.79 45	94.93 52	99.76 94	99.07 35	99.90 53	99.99 12

Regformer-2		98.78 21	98.59 21	99.36 36	99.85 41	98.32 55	99.87 71	98.52 91	96.04 46	99.41 40	99.79 45	94.92 53	99.76 94	99.05 36	99.90 53	99.98 44

MP-MVS-pluss		98.07 55	97.64 58	99.38 35	99.74 57	98.41 53	99.74 120	98.18 152	93.35 119	96.45 127	99.85 21	92.64 106	99.97 41	98.91 47	99.89 55	99.77 85
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPM		98.60 26	98.42 26	99.14 52	96.05 211	98.96 13	99.90 59	99.35 27	96.68 28	98.35 87	99.66 78	96.45 21	98.51 169	99.45 25	99.89 55	99.96 58

zzz-MVS		98.33 45	98.00 48	99.30 38	99.85 41	97.93 67	99.80 101	98.28 141	95.76 52	97.18 113	99.88 12	92.74 103	100.00 1	98.67 57	99.88 57	99.99 12

MTAPA		98.29 46	97.96 52	99.30 38	99.85 41	97.93 67	99.39 180	98.28 141	95.76 52	97.18 113	99.88 12	92.74 103	100.00 1	98.67 57	99.88 57	99.99 12

MVS		96.60 106	95.56 131	99.72 4	96.85 194	99.22 8	98.31 273	98.94 38	91.57 180	90.90 197	99.61 82	86.66 174	99.96 43	97.36 96	99.88 57	99.99 12

MVS_111021_LR		98.42 40	98.38 31	98.53 101	99.39 82	95.79 139	99.87 71	99.86 2	96.70 27	98.78 68	99.79 45	92.03 115	99.90 66	99.17 32	99.86 60	99.88 74

ACMMP_Plus		98.49 36	98.14 43	99.54 18	99.66 67	98.62 41	99.85 88	98.37 131	94.68 77	99.53 29	99.83 37	92.87 99	100.00 1	98.66 60	99.84 61	99.99 12

Regformer-3		98.58 29	98.41 27	99.10 58	99.84 46	97.57 76	99.66 144	98.52 91	95.79 49	99.01 60	99.77 52	94.40 60	99.75 97	98.82 50	99.83 62	99.98 44

Regformer-4		98.56 30	98.39 30	99.08 60	99.84 46	97.52 78	99.66 144	98.52 91	95.76 52	99.01 60	99.77 52	94.33 66	99.75 97	98.80 51	99.83 62	99.98 44

QAPM		95.40 144	94.17 158	99.10 58	96.92 190	97.71 72	99.40 177	98.68 64	89.31 220	88.94 239	98.89 126	82.48 204	99.96 43	93.12 173	99.83 62	99.62 105

PAPR		98.52 34	98.16 42	99.58 15	99.97 3	98.77 26	99.95 31	98.43 112	95.35 62	98.03 97	99.75 61	94.03 76	99.98 32	98.11 75	99.83 62	99.99 12

3Dnovator+	91.53 11	96.31 124	95.24 138	99.52 20	96.88 193	98.64 40	99.72 131	98.24 145	95.27 65	88.42 247	98.98 120	82.76 203	99.94 59	97.10 103	99.83 62	99.96 58

3Dnovator	91.47 12	96.28 127	95.34 136	99.08 60	96.82 196	97.47 83	99.45 174	98.81 56	95.52 59	89.39 229	99.00 119	81.97 213	99.95 51	97.27 98	99.83 62	99.84 77

LS3D		95.84 135	95.11 143	98.02 131	99.85 41	95.10 160	98.74 242	98.50 101	87.22 259	93.66 176	99.86 17	87.45 166	99.95 51	90.94 197	99.81 68	99.02 179

CHOSEN 280x420		99.01 10	99.03 5	98.95 75	99.38 83	98.87 20	98.46 263	99.42 25	97.03 17	99.02 59	99.09 112	99.35 1	98.21 198	99.73 16	99.78 69	99.77 85

OpenMVS	90.15 15	94.77 157	93.59 168	98.33 119	96.07 210	97.48 82	99.56 159	98.57 82	90.46 206	86.51 267	98.95 124	78.57 261	99.94 59	93.86 153	99.74 70	97.57 200

1314		96.84 93	95.96 107	99.48 25	96.74 201	98.52 48	98.31 273	98.86 53	95.82 48	89.91 211	98.98 120	87.49 165	99.96 43	97.80 87	99.73 71	99.96 58

abl_6		97.67 68	97.34 66	98.66 89	99.68 66	96.11 135	99.68 139	98.14 159	93.80 108	99.27 49	99.70 69	88.65 158	99.98 32	97.46 94	99.72 72	99.89 72

DP-MVS Recon		98.41 41	98.02 47	99.56 16	99.97 3	98.70 35	99.92 52	98.44 107	92.06 170	98.40 85	99.84 35	95.68 33	100.00 1	98.19 71	99.71 73	99.97 54

MVP-Stereo		90.93 231	90.45 217	92.37 282	91.25 314	88.76 280	98.05 287	96.17 297	87.27 258	84.04 286	95.30 249	78.46 263	97.27 238	83.78 277	99.70 74	91.09 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PS-MVSNAJ		98.44 39	98.20 40	99.16 46	98.80 114	98.92 16	99.54 163	98.17 153	97.34 11	99.85 5	99.85 21	91.20 125	99.89 69	99.41 28	99.67 75	98.69 187

BH-w/o		95.71 138	95.38 135	96.68 168	98.49 130	92.28 226	99.84 91	97.50 215	92.12 166	92.06 190	98.79 149	84.69 190	98.67 157	95.29 127	99.66 76	99.09 177

MAR-MVS		97.43 72	97.19 70	98.15 126	99.47 79	94.79 167	99.05 218	98.76 59	92.65 144	98.66 74	99.82 40	88.52 159	99.98 32	98.12 74	99.63 77	99.67 97
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		90.65 237	90.30 220	91.71 291	94.22 248	85.50 304	98.24 278	97.70 194	88.67 234	86.42 270	96.37 220	67.82 315	98.03 205	83.62 278	99.62 78	91.60 312

MVSFormer		96.94 89	96.60 88	97.95 132	97.28 181	97.70 74	99.55 161	97.27 234	91.17 194	99.43 38	99.54 87	90.92 131	96.89 265	94.67 138	99.62 78	99.25 156

lupinMVS		97.85 61	97.60 60	98.62 92	97.28 181	97.70 74	99.99 3	97.55 206	95.50 60	99.43 38	99.67 76	90.92 131	98.71 155	98.40 68	99.62 78	99.45 129

BH-untuned		95.18 147	94.83 146	96.22 179	98.36 133	91.22 252	99.80 101	97.32 232	90.91 200	91.08 195	98.67 152	83.51 198	98.54 168	94.23 148	99.61 81	98.92 181

DeepC-MVS	94.51 4	96.92 91	96.40 93	98.45 110	99.16 86	95.90 137	99.66 144	98.06 165	96.37 37	94.37 171	99.49 90	83.29 201	99.90 66	97.63 92	99.61 81	99.55 117
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				98.54 100	98.21 140	98.01 65	93.87 328	98.52 91		97.92 99	97.92 179	99.02 2	97.94 211	98.17 72	99.58 83	99.67 97

gg-mvs-nofinetune		93.51 183	91.86 198	98.47 108	97.72 171	97.96 66	92.62 333	98.51 97	74.70 331	97.33 109	69.59 347	98.91 3	97.79 214	97.77 90	99.56 84	99.67 97

BH-RMVSNet		95.18 147	94.31 155	97.80 135	98.17 143	95.23 158	99.76 116	97.53 210	92.52 154	94.27 173	99.25 105	76.84 271	98.80 148	90.89 199	99.54 85	99.35 146

EI-MVSNet-Vis-set		98.27 47	98.11 45	98.75 84	99.83 49	96.59 115	99.40 177	98.51 97	95.29 64	98.51 80	99.76 56	93.60 89	99.71 105	98.53 65	99.52 86	99.95 63

TAPA-MVS	92.12 8	94.42 167	93.60 167	96.90 161	99.33 85	91.78 239	99.78 105	98.00 169	89.89 216	94.52 168	99.47 91	91.97 116	99.18 137	69.90 325	99.52 86	99.73 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	95.54 3	97.93 59	97.89 54	98.05 130	99.82 50	94.77 168	99.92 52	98.46 105	93.93 104	97.20 111	99.27 102	95.44 38	99.97 41	97.41 95	99.51 88	99.41 134
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
jason		97.24 80	96.86 79	98.38 118	95.73 223	97.32 94	99.97 12	97.40 225	95.34 63	98.60 78	99.54 87	87.70 163	98.56 166	97.94 84	99.47 89	99.25 156
jason: jason.
CSCG		97.10 85	97.04 76	97.27 154	99.89 36	91.92 235	99.90 59	99.07 33	88.67 234	95.26 152	99.82 40	93.17 97	99.98 32	98.15 73	99.47 89	99.90 71

CNLPA		97.76 65	97.38 65	98.92 77	99.53 75	96.84 108	99.87 71	98.14 159	93.78 109	96.55 124	99.69 72	92.28 110	99.98 32	97.13 101	99.44 91	99.93 66

AdaColmap		97.23 81	96.80 82	98.51 102	99.99 1	95.60 148	99.09 207	98.84 55	93.32 120	96.74 121	99.72 65	86.04 179	100.00 1	98.01 79	99.43 92	99.94 65

CANet		98.27 47	97.82 55	99.63 9	99.72 63	99.10 10	99.98 6	98.51 97	97.00 18	98.52 79	99.71 67	87.80 162	99.95 51	99.75 11	99.38 93	99.83 78

F-COLMAP		96.93 90	96.95 78	96.87 162	99.71 64	91.74 241	99.85 88	97.95 174	93.11 125	95.72 145	99.16 110	92.35 108	99.94 59	95.32 126	99.35 94	98.92 181

MVS_0304		97.52 71	96.79 83	99.69 6	99.59 70	99.30 4	99.97 12	98.01 168	96.99 19	98.84 65	99.79 45	78.90 258	99.96 43	99.74 13	99.32 95	99.81 80

EI-MVSNet-UG-set		98.14 52	97.99 49	98.60 94	99.80 52	96.27 122	99.36 184	98.50 101	95.21 66	98.30 89	99.75 61	93.29 94	99.73 104	98.37 69	99.30 96	99.81 80

PVSNet_Blended		97.94 58	97.64 58	98.83 81	99.59 70	96.99 104	100.00 1	99.10 30	95.38 61	98.27 90	99.08 113	89.00 153	99.95 51	99.12 33	99.25 97	99.57 115

PatchMatch-RL		96.04 131	95.40 133	97.95 132	99.59 70	95.22 159	99.52 165	99.07 33	93.96 102	96.49 125	98.35 169	82.28 205	99.82 87	90.15 210	99.22 98	98.81 184

CHOSEN 1792x2688		96.81 94	96.53 91	97.64 141	98.91 103	93.07 207	99.65 147	99.80 3	95.64 57	95.39 149	98.86 132	84.35 194	99.90 66	96.98 106	99.16 99	99.95 63

UGNet		95.33 145	94.57 151	97.62 142	98.55 126	94.85 163	98.67 249	99.32 28	95.75 55	96.80 120	96.27 222	72.18 298	99.96 43	94.58 140	99.05 100	98.04 194
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
CANet_DTU		96.76 97	96.15 98	98.60 94	98.78 115	97.53 77	99.84 91	97.63 198	97.25 13	99.20 51	99.64 80	81.36 227	99.98 32	92.77 175	98.89 101	98.28 190

TESTMET0.1,1		96.74 99	96.26 96	98.16 123	97.36 180	96.48 117	99.96 19	98.29 140	91.93 172	95.77 144	98.07 175	95.54 35	98.29 192	90.55 202	98.89 101	99.70 93

test-LLR		96.47 115	96.04 100	97.78 136	97.02 187	95.44 150	99.96 19	98.21 148	94.07 95	95.55 146	96.38 218	93.90 81	98.27 195	90.42 204	98.83 103	99.64 103

test-mter		96.39 121	95.93 109	97.78 136	97.02 187	95.44 150	99.96 19	98.21 148	91.81 176	95.55 146	96.38 218	95.17 41	98.27 195	90.42 204	98.83 103	99.64 103

PVSNet	91.05 13	97.13 84	96.69 86	98.45 110	99.52 76	95.81 138	99.95 31	99.65 16	94.73 75	99.04 58	99.21 108	84.48 192	99.95 51	94.92 130	98.74 105	99.58 114

EPNet		98.49 36	98.40 29	98.77 83	99.62 69	96.80 110	99.90 59	99.51 20	97.60 8	99.20 51	99.36 100	93.71 86	99.91 65	97.99 81	98.71 106	99.61 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v2_base		98.23 50	97.97 50	99.02 70	98.69 118	98.66 37	99.52 165	98.08 164	97.05 16	99.86 4	99.86 17	90.65 133	99.71 105	99.39 29	98.63 107	98.69 187

Vis-MVSNet		95.72 136	95.15 142	97.45 147	97.62 173	94.28 174	99.28 194	98.24 145	94.27 89	96.84 118	98.94 125	79.39 250	98.76 152	93.25 167	98.49 108	99.30 151
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PCF-MVS	94.20 5	95.18 147	94.10 159	98.43 112	98.55 126	95.99 136	97.91 290	97.31 233	90.35 208	89.48 228	99.22 107	85.19 189	99.89 69	90.40 206	98.47 109	99.41 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		94.37 169	93.36 178	97.40 150	98.88 108	93.95 180	99.37 182	97.38 227	85.75 280	90.80 198	99.17 109	84.11 196	99.88 75	86.35 257	98.43 110	98.36 189

PVSNet_Blended_VisFu		97.27 79	96.81 81	98.66 89	98.81 113	96.67 111	99.92 52	98.64 70	94.51 80	96.38 131	98.49 163	89.05 152	99.88 75	97.10 103	98.34 111	99.43 132

EPNet_dtu		95.71 138	95.39 134	96.66 169	98.92 101	93.41 197	99.57 157	98.90 50	96.19 41	97.52 106	98.56 161	92.65 105	97.36 225	77.89 310	98.33 112	99.20 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v1_base_debu		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 189	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

xiu_mvs_v1_base		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 189	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

xiu_mvs_v1_base_debi		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 189	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

OMC-MVS		97.28 78	97.23 69	97.41 149	99.76 54	93.36 201	99.65 147	97.95 174	96.03 47	97.41 108	99.70 69	89.61 141	99.51 119	96.73 111	98.25 116	99.38 141

mvs-test1		95.53 141	95.97 106	94.20 236	97.77 164	85.44 305	99.95 31	97.06 247	94.92 69	96.58 123	98.72 150	85.81 181	98.98 142	94.80 134	98.11 117	98.18 191

DP-MVS		94.54 163	93.42 174	97.91 134	99.46 81	94.04 178	98.93 229	97.48 217	81.15 314	90.04 208	99.55 85	87.02 171	99.95 51	88.97 225	98.11 117	99.73 90

EPMVS		96.53 108	96.01 101	98.09 129	98.43 131	96.12 134	96.36 311	99.43 24	93.53 116	97.64 103	95.04 262	94.41 59	98.38 186	91.13 192	98.11 117	99.75 87

PatchmatchNet		95.94 133	95.45 132	97.39 151	97.83 160	94.41 172	96.05 317	98.40 124	92.86 128	97.09 115	95.28 253	94.21 72	98.07 204	89.26 223	98.11 117	99.70 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMMP		97.74 66	97.44 64	98.66 89	99.92 27	96.13 131	99.18 201	99.45 22	94.84 72	96.41 130	99.71 67	91.40 122	99.99 28	97.99 81	98.03 121	99.87 75
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		86.22 285	83.19 300	95.31 195	96.71 203	90.29 265	92.12 335	97.33 231	62.85 342	86.82 263	70.37 346	69.37 309	97.49 220	75.12 320	97.99 122	98.15 192

PMMVS		96.76 97	96.76 85	96.76 165	98.28 135	92.10 230	99.91 56	97.98 171	94.12 92	99.53 29	99.39 97	86.93 172	98.73 153	96.95 108	97.73 123	99.45 129

UA-Net		96.54 107	95.96 107	98.27 121	98.23 139	95.71 145	98.00 288	98.45 106	93.72 111	98.41 83	99.27 102	88.71 157	99.66 113	91.19 191	97.69 124	99.44 131

TSAR-MVS + GP.		98.60 26	98.51 25	98.86 80	99.73 61	96.63 112	99.97 12	97.92 177	98.07 5	98.76 69	99.55 85	95.00 49	99.94 59	99.91 4	97.68 125	99.99 12

mvs_anonymous		95.65 140	95.03 144	97.53 143	98.19 141	95.74 142	99.33 186	97.49 216	90.87 201	90.47 201	97.10 194	88.23 160	97.16 243	95.92 119	97.66 126	99.68 96

LCM-MVSNet-Re		92.31 203	92.60 187	91.43 292	97.53 175	79.27 330	99.02 221	91.83 344	92.07 168	80.31 300	94.38 287	83.50 199	95.48 300	97.22 100	97.58 127	99.54 121

MVS_Test		96.46 116	95.74 123	98.61 93	98.18 142	97.23 96	99.31 189	97.15 243	91.07 197	98.84 65	97.05 198	88.17 161	98.97 143	94.39 142	97.50 128	99.61 107

diffmvs		95.25 146	94.26 156	98.23 122	98.13 145	96.59 115	99.12 204	97.18 239	85.78 276	97.64 103	96.70 210	85.92 180	98.87 145	90.40 206	97.45 129	99.24 159

Patchmatch-test1		94.39 168	93.46 172	97.17 155	97.10 183	94.44 171	98.86 237	98.32 137	93.30 121	96.17 134	95.38 244	76.48 275	97.34 227	88.12 233	97.43 130	99.74 88

Vis-MVSNet (Re-imp)		96.32 123	95.98 104	97.35 153	97.93 153	94.82 164	99.47 171	98.15 158	91.83 175	95.09 163	99.11 111	91.37 123	97.47 221	93.47 163	97.43 130	99.74 88

IS-MVSNet		96.29 126	95.90 112	97.45 147	98.13 145	94.80 165	99.08 209	97.61 203	92.02 171	95.54 148	98.96 122	90.64 134	98.08 202	93.73 161	97.41 132	99.47 128

Effi-MVS+		96.30 125	95.69 124	98.16 123	97.85 158	96.26 123	97.41 295	97.21 237	90.37 207	98.65 75	98.58 160	86.61 175	98.70 156	97.11 102	97.37 133	99.52 123

DWT-MVSNet_test		97.31 77	97.19 70	97.66 140	98.24 138	94.67 169	98.86 237	98.20 151	93.60 115	98.09 95	98.89 126	97.51 5	98.78 150	94.04 151	97.28 134	99.55 117

test_normal		92.44 202	90.54 214	98.12 127	91.85 306	96.18 130	99.68 139	97.73 191	92.66 142	75.76 317	93.74 297	70.49 305	99.04 141	95.71 124	97.27 135	99.13 174

DI_MVS_plusplus_test		92.48 199	90.60 213	98.11 128	91.88 305	96.13 131	99.64 151	97.73 191	92.69 140	76.02 313	93.79 295	70.49 305	99.07 139	95.88 120	97.26 136	99.14 172

ADS-MVSNet2		93.80 177	93.88 163	93.55 256	97.87 156	85.94 300	94.24 324	96.84 279	90.07 212	96.43 128	94.48 284	90.29 137	95.37 302	87.44 239	97.23 137	99.36 144

ADS-MVSNet		94.79 155	94.02 160	97.11 158	97.87 156	93.79 183	94.24 324	98.16 156	90.07 212	96.43 128	94.48 284	90.29 137	98.19 199	87.44 239	97.23 137	99.36 144

EPP-MVSNet		96.69 102	96.60 88	96.96 159	97.74 167	93.05 209	99.37 182	98.56 84	88.75 233	95.83 143	99.01 117	96.01 25	98.56 166	96.92 109	97.20 139	99.25 156

Test4		88.80 269	85.91 278	97.48 146	87.33 328	95.72 144	99.29 193	97.04 256	92.82 131	70.35 331	91.46 311	44.37 346	97.43 222	93.37 166	97.17 140	99.29 153

Fast-Effi-MVS+		95.02 152	94.19 157	97.52 144	97.88 155	94.55 170	99.97 12	97.08 246	88.85 232	94.47 170	97.96 178	84.59 191	98.41 178	89.84 212	97.10 141	99.59 111

Effi-MVS+-dtu		94.53 165	95.30 137	92.22 285	97.77 164	82.54 316	99.59 155	97.06 247	94.92 69	95.29 151	95.37 246	85.81 181	97.89 212	94.80 134	97.07 142	96.23 208

sss		97.57 70	97.03 77	99.18 43	98.37 132	98.04 64	99.73 126	99.38 26	93.46 117	98.76 69	99.06 114	91.21 124	99.89 69	96.33 113	97.01 143	99.62 105

Patchmatch-test		92.65 198	91.50 202	96.10 182	96.85 194	90.49 261	91.50 338	97.19 238	82.76 300	90.23 202	95.59 237	95.02 47	98.00 206	77.41 313	96.98 144	99.82 79

MDTV_nov1_ep13			95.69 124		97.90 154	94.15 176	95.98 318	98.44 107	93.12 124	97.98 98	95.74 232	95.10 43	98.58 165	90.02 211	96.92 145

Fast-Effi-MVS+-dtu		93.72 180	93.86 164	93.29 259	97.06 185	86.16 298	99.80 101	96.83 280	92.66 142	92.58 188	97.83 180	81.39 226	97.67 217	89.75 213	96.87 146	96.05 210

tpmrst		96.27 128	95.98 104	97.13 156	97.96 151	93.15 206	96.34 312	98.17 153	92.07 168	98.71 72	95.12 256	93.91 80	98.73 153	94.91 132	96.62 147	99.50 126

JIA-IIPM		91.76 214	90.70 211	94.94 208	96.11 209	87.51 293	93.16 331	98.13 161	75.79 328	97.58 105	77.68 343	92.84 100	97.97 207	88.47 229	96.54 148	99.33 148

dp		95.05 151	94.43 153	96.91 160	97.99 150	92.73 216	96.29 313	97.98 171	89.70 218	95.93 137	94.67 280	93.83 84	98.45 175	86.91 253	96.53 149	99.54 121

COLMAP_ROB	90.47 14	92.18 205	91.49 203	94.25 235	99.00 93	88.04 291	98.42 268	96.70 285	82.30 304	88.43 245	99.01 117	76.97 269	99.85 79	86.11 260	96.50 150	94.86 211
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.51 183	92.52 189	96.47 172	97.77 164	91.47 251	96.13 315	98.06 165	80.98 315	92.91 184	93.78 296	89.66 140	98.87 145	87.03 249	96.39 151	99.09 177

AllTest		92.48 199	91.64 199	95.00 204	99.01 91	88.43 286	98.94 228	96.82 282	86.50 267	88.71 240	98.47 167	74.73 288	99.88 75	85.39 265	96.18 152	96.71 203

TestCases				95.00 204	99.01 91	88.43 286		96.82 282	86.50 267	88.71 240	98.47 167	74.73 288	99.88 75	85.39 265	96.18 152	96.71 203

DSMNet-mixed		88.28 273	88.24 264	88.42 314	89.64 323	75.38 332	98.06 286	89.86 349	85.59 282	88.20 249	92.14 309	76.15 279	91.95 332	78.46 308	96.05 154	97.92 195

PatchFormer-LS_test		97.01 87	96.79 83	97.69 139	98.26 137	94.80 165	98.66 252	98.13 161	93.70 112	97.86 101	98.80 144	95.54 35	98.67 157	94.12 150	96.00 155	99.60 109

TR-MVS		94.54 163	93.56 170	97.49 145	97.96 151	94.34 173	98.71 244	97.51 214	90.30 210	94.51 169	98.69 151	75.56 281	98.77 151	92.82 174	95.99 156	99.35 146

CR-MVSNet		93.45 186	92.62 186	95.94 184	96.29 206	92.66 218	92.01 336	96.23 295	92.62 145	96.94 116	93.31 302	91.04 129	96.03 293	79.23 303	95.96 157	99.13 174

RPMNet		89.39 262	87.20 274	95.94 184	96.29 206	92.66 218	92.01 336	97.63 198	70.19 339	96.94 116	85.87 339	87.25 168	96.03 293	62.69 336	95.96 157	99.13 174

PatchT		90.38 244	88.75 256	95.25 196	95.99 213	90.16 267	91.22 340	97.54 208	76.80 325	97.26 110	86.01 338	91.88 117	96.07 292	66.16 333	95.91 159	99.51 124

tpmvs		94.28 170	93.57 169	96.40 175	98.55 126	91.50 250	95.70 322	98.55 88	87.47 254	92.15 189	94.26 288	91.42 121	98.95 144	88.15 231	95.85 160	98.76 186

TAMVS		95.85 134	95.58 130	96.65 170	97.07 184	93.50 190	99.17 202	97.82 188	91.39 187	95.02 164	98.01 176	92.20 111	97.30 233	93.75 160	95.83 161	99.14 172

CostFormer		96.10 129	95.88 113	96.78 164	97.03 186	92.55 222	97.08 302	97.83 187	90.04 214	98.72 71	94.89 272	95.01 48	98.29 192	96.54 112	95.77 162	99.50 126

HY-MVS	92.50 7	97.79 64	97.17 72	99.63 9	98.98 95	99.32 3	97.49 294	99.52 18	95.69 56	98.32 88	97.41 186	93.32 92	99.77 92	98.08 78	95.75 163	99.81 80

CDS-MVSNet		96.34 122	96.07 99	97.13 156	97.37 179	94.96 161	99.53 164	97.91 178	91.55 181	95.37 150	98.32 170	95.05 46	97.13 249	93.80 158	95.75 163	99.30 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		95.47 143	95.18 141	96.35 177	96.91 191	91.70 245	96.96 305	97.93 176	88.04 244	98.44 82	95.40 241	93.32 92	97.97 207	94.00 152	95.61 165	99.38 141

WTY-MVS		98.10 54	97.60 60	99.60 13	98.92 101	99.28 5	99.89 64	99.52 18	95.58 58	98.24 93	99.39 97	93.33 91	99.74 101	97.98 83	95.58 166	99.78 84

HyFIR lowres test		96.66 105	96.43 92	97.36 152	99.05 89	93.91 181	99.70 133	99.80 3	90.54 205	96.26 132	98.08 174	92.15 113	98.23 197	96.84 110	95.46 167	99.93 66

cascas		94.64 161	93.61 165	97.74 138	97.82 161	96.26 123	99.96 19	97.78 190	85.76 277	94.00 175	97.54 183	76.95 270	99.21 136	97.23 99	95.43 168	97.76 199

CVMVSNet		94.68 160	94.94 145	93.89 249	96.80 197	86.92 297	99.06 215	98.98 36	94.45 81	94.23 174	99.02 115	85.60 183	95.31 303	90.91 198	95.39 169	99.43 132

LFMVS		94.75 158	93.56 170	98.30 120	99.03 90	95.70 146	98.74 242	97.98 171	87.81 246	98.47 81	99.39 97	67.43 316	99.53 117	98.01 79	95.20 170	99.67 97

tpmp4_e23		95.15 150	94.69 150	96.55 171	97.84 159	91.77 240	97.10 301	97.91 178	88.33 240	97.19 112	95.06 260	93.92 78	98.51 169	89.64 214	95.19 171	99.37 143

thres200		96.96 88	96.21 97	99.22 41	98.97 96	98.84 22	99.85 88	99.71 5	93.17 123	96.26 132	98.88 128	89.87 139	99.51 119	94.26 147	94.91 172	99.31 149

conf200view11		96.73 101	95.92 110	99.16 46	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.84 154	94.57 173	99.20 160

thres100view900		96.74 99	95.92 110	99.18 43	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.84 154	94.57 173	99.27 154

tfpn200view9		96.79 95	95.99 102	99.19 42	98.94 98	98.82 23	99.78 105	99.71 5	92.86 128	96.02 135	98.87 130	89.33 142	99.50 121	93.84 154	94.57 173	99.27 154

thres400		96.78 96	95.99 102	99.16 46	98.94 98	98.82 23	99.78 105	99.71 5	92.86 128	96.02 135	98.87 130	89.33 142	99.50 121	93.84 154	94.57 173	99.16 166

tfpn111		96.69 102	95.87 120	99.16 46	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.44 164	94.50 177	99.20 160

thres600view7		96.69 102	95.87 120	99.14 52	98.90 104	98.78 25	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.44 164	94.50 177	99.16 166

tfpn_ndepth		97.21 82	96.63 87	98.92 77	99.06 88	98.28 56	99.95 31	98.91 42	92.96 127	96.49 125	98.67 152	97.40 7	99.07 139	91.87 186	94.38 179	99.41 134

view600		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

view800		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

conf0.05thres1000		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

tfpn		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

VNet		97.21 82	96.57 90	99.13 57	98.97 96	97.82 70	99.03 220	99.21 29	94.31 87	99.18 54	98.88 128	86.26 178	99.89 69	98.93 44	94.32 184	99.69 95

testpf		89.10 266	88.73 257	90.24 302	97.59 174	83.48 313	74.22 351	97.39 226	79.66 319	89.64 224	93.92 292	86.38 176	95.76 298	85.42 264	94.31 185	91.49 313

alignmvs		97.81 62	97.33 67	99.25 40	98.77 116	98.66 37	99.99 3	98.44 107	94.40 84	98.41 83	99.47 91	93.65 87	99.42 133	98.57 63	94.26 186	99.67 97

tfpn1000		96.90 92	96.29 95	98.74 85	99.00 93	98.09 62	99.92 52	98.91 42	92.08 167	95.85 138	98.65 154	97.39 8	98.83 147	90.56 201	94.23 187	99.31 149

VDD-MVS		93.77 178	92.94 181	96.27 178	98.55 126	90.22 266	98.77 241	97.79 189	90.85 202	96.82 119	99.42 93	61.18 332	99.77 92	98.95 42	94.13 188	98.82 183

conf0.01		96.52 113	95.88 113	98.41 116	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.20 160

conf0.002		96.52 113	95.88 113	98.41 116	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.20 160

thresconf0.02		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpn_n400		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpnconf		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpnview11		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

VDDNet		93.12 188	91.91 197	96.76 165	96.67 204	92.65 220	98.69 246	98.21 148	82.81 299	97.75 102	99.28 101	61.57 330	99.48 127	98.09 77	94.09 189	98.15 192

GA-MVS		93.83 174	92.84 182	96.80 163	95.73 223	93.57 189	99.88 66	97.24 236	92.57 152	92.92 183	96.66 211	78.73 260	97.67 217	87.75 236	94.06 196	99.17 165

canonicalmvs		97.09 86	96.32 94	99.39 34	98.93 100	98.95 14	99.72 131	97.35 229	94.45 81	97.88 100	99.42 93	86.71 173	99.52 118	98.48 66	93.97 197	99.72 92

1112_ss		96.01 132	95.20 140	98.42 113	97.80 162	96.41 119	99.65 147	96.66 286	92.71 138	92.88 185	99.40 95	92.16 112	99.30 134	91.92 184	93.66 198	99.55 117

Test_1112_low_res		95.72 136	94.83 146	98.42 113	97.79 163	96.41 119	99.65 147	96.65 287	92.70 139	92.86 186	96.13 226	92.15 113	99.30 134	91.88 185	93.64 199	99.55 117

MIMVSNet		90.30 247	88.67 258	95.17 199	96.45 205	91.64 247	92.39 334	97.15 243	85.99 273	90.50 200	93.19 304	66.95 317	94.86 310	82.01 288	93.43 200	99.01 180

XVG-OURS-SEG-HR		94.79 155	94.70 149	95.08 200	98.05 148	89.19 277	99.08 209	97.54 208	93.66 113	94.87 165	99.58 83	78.78 259	99.79 90	97.31 97	93.40 201	96.25 206

ab-mvs		94.69 159	93.42 174	98.51 102	98.07 147	96.26 123	96.49 309	98.68 64	90.31 209	94.54 167	97.00 200	76.30 276	99.71 105	95.98 118	93.38 202	99.56 116

test2356		86.43 282	87.59 271	82.95 322	85.90 330	69.43 335	99.79 104	96.63 288	85.76 277	83.44 290	94.99 266	80.45 245	86.52 344	68.12 330	93.21 203	92.90 297

test0.0.03 1		93.86 173	93.61 165	94.64 221	95.02 238	92.18 229	99.93 50	98.58 80	94.07 95	87.96 251	98.50 162	93.90 81	94.96 308	81.33 291	93.17 204	96.78 202

RPSCF		91.80 211	92.79 184	88.83 311	98.15 144	69.87 334	98.11 284	96.60 289	83.93 295	94.33 172	99.27 102	79.60 249	99.46 128	91.99 183	93.16 205	97.18 201

XVG-OURS		94.82 154	94.74 148	95.06 201	98.00 149	89.19 277	99.08 209	97.55 206	94.10 93	94.71 166	99.62 81	80.51 241	99.74 101	96.04 117	93.06 206	96.25 206

testus		83.91 304	84.49 284	82.17 324	85.68 331	66.11 340	99.68 139	93.53 339	86.55 266	82.60 293	94.91 270	56.70 338	88.19 340	68.46 327	92.31 207	92.21 304

HQP3-MVS								97.89 180							89.60 208

HQP-MVS		94.61 162	94.50 152	94.92 210	95.78 217	91.85 236	99.87 71	97.89 180	96.82 21	93.37 177	98.65 154	80.65 239	98.39 182	97.92 85	89.60 208	94.53 212

plane_prior						91.74 241	99.86 86		96.76 25						89.59 210

HQP_MVS		94.49 166	94.36 154	94.87 213	95.71 226	91.74 241	99.84 91	97.87 182	96.38 34	93.01 181	98.59 158	80.47 243	98.37 187	97.79 88	89.55 211	94.52 214

plane_prior5								97.87 182					98.37 187	97.79 88	89.55 211	94.52 214

CLD-MVS		94.06 172	93.90 162	94.55 226	96.02 212	90.69 258	99.98 6	97.72 193	96.62 30	91.05 196	98.85 137	77.21 267	98.47 171	98.11 75	89.51 213	94.48 216
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		93.21 187	92.80 183	94.44 229	93.12 283	90.85 257	99.77 110	97.61 203	96.19 41	91.56 192	98.65 154	75.16 286	98.47 171	93.78 159	89.39 214	93.99 256

LPG-MVS_test		92.96 190	92.71 185	93.71 252	95.43 231	88.67 282	99.75 117	97.62 200	92.81 132	90.05 205	98.49 163	75.24 284	98.40 180	95.84 122	89.12 215	94.07 245

LGP-MVS_train				93.71 252	95.43 231	88.67 282		97.62 200	92.81 132	90.05 205	98.49 163	75.24 284	98.40 180	95.84 122	89.12 215	94.07 245

test_djsdf		92.83 193	92.29 192	94.47 228	91.90 304	92.46 223	99.55 161	97.27 234	91.17 194	89.96 209	96.07 228	81.10 230	96.89 265	94.67 138	88.91 217	94.05 247

testgi		89.01 267	88.04 266	91.90 289	93.49 263	84.89 308	99.73 126	95.66 306	93.89 107	85.14 281	98.17 172	59.68 334	94.66 312	77.73 311	88.88 218	96.16 209

ACMM	91.95 10	92.88 192	92.52 189	93.98 246	95.75 222	89.08 279	99.77 110	97.52 212	93.00 126	89.95 210	97.99 177	76.17 278	98.46 174	93.63 162	88.87 219	94.39 224
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	92.05 9	92.74 194	92.42 191	93.73 250	95.91 216	88.72 281	99.81 98	97.53 210	94.13 91	87.00 260	98.23 171	74.07 292	98.47 171	96.22 115	88.86 220	93.99 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jajsoiax		91.92 207	91.18 206	94.15 237	91.35 312	90.95 255	99.00 222	97.42 222	92.61 146	87.38 256	97.08 195	72.46 297	97.36 225	94.53 141	88.77 221	94.13 242

anonymousdsp		91.79 213	90.92 209	94.41 232	90.76 317	92.93 212	98.93 229	97.17 241	89.08 222	87.46 255	95.30 249	78.43 264	96.92 264	92.38 176	88.73 222	93.39 287

mvs_tets		91.81 209	91.08 207	94.00 244	91.63 310	90.58 259	98.67 249	97.43 220	92.43 157	87.37 257	97.05 198	71.76 299	97.32 230	94.75 137	88.68 223	94.11 243

XVG-ACMP-BASELINE		91.22 227	90.75 210	92.63 272	93.73 256	85.61 302	98.52 260	97.44 219	92.77 136	89.90 212	96.85 206	66.64 318	98.39 182	92.29 177	88.61 224	93.89 268

EG-PatchMatch MVS		85.35 296	83.81 297	89.99 306	90.39 319	81.89 321	98.21 281	96.09 299	81.78 311	74.73 318	93.72 298	51.56 343	97.12 251	79.16 304	88.61 224	90.96 317

tpm		93.70 181	93.41 176	94.58 224	95.36 233	87.41 295	97.01 303	96.90 274	90.85 202	96.72 122	94.14 291	90.40 135	96.84 268	90.75 200	88.54 226	99.51 124

OpenMVS_ROB	79.82 20	83.77 305	81.68 305	90.03 305	88.30 326	82.82 314	98.46 263	95.22 323	73.92 334	76.00 314	91.29 312	55.00 339	96.94 263	68.40 328	88.51 227	90.34 320

CMPMVS	61.59 21	84.75 299	85.14 281	83.57 319	90.32 320	62.54 343	96.98 304	97.59 205	74.33 332	69.95 332	96.66 211	64.17 324	98.32 190	87.88 235	88.41 228	89.84 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LP		86.76 278	84.85 282	92.50 276	95.08 235	85.89 301	89.97 341	96.97 266	75.28 330	84.97 283	90.68 314	80.78 236	95.13 305	61.64 338	88.31 229	96.46 205

ACMMP++															88.23 230

ITE_SJBPF				92.38 281	95.69 228	85.14 306		95.71 304	92.81 132	89.33 232	98.11 173	70.23 307	98.42 177	85.91 261	88.16 231	93.59 283

pcd1.5k->3k		37.58 333	39.62 333	31.46 345	92.73 292	0.00 363	0.00 354	97.52 212	0.00 358	0.00 359	0.00 360	78.40 265	0.00 361	0.00 358	87.90 232	94.37 225

EI-MVSNet		93.73 179	93.40 177	94.74 217	96.80 197	92.69 217	99.06 215	97.67 196	88.96 228	91.39 193	99.02 115	88.75 156	97.30 233	91.07 193	87.85 233	94.22 237

MVSTER		95.53 141	95.22 139	96.45 173	98.56 125	97.72 71	99.91 56	97.67 196	92.38 158	91.39 193	97.14 192	97.24 10	97.30 233	94.80 134	87.85 233	94.34 230

PS-MVSNAJss		93.64 182	93.31 179	94.61 222	92.11 300	92.19 228	99.12 204	97.38 227	92.51 155	88.45 243	96.99 201	91.20 125	97.29 236	94.36 143	87.71 235	94.36 226

LTVRE_ROB	88.28 18	90.29 248	89.05 251	94.02 242	95.08 235	90.15 268	97.19 300	97.43 220	84.91 287	83.99 287	97.06 197	74.00 293	98.28 194	84.08 273	87.71 235	93.62 282
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	90.64 238	89.63 237	93.66 254	95.64 229	88.64 284	98.55 256	97.45 218	89.03 224	81.62 296	97.61 182	69.75 308	98.41 178	89.37 221	87.62 237	93.92 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PVSNet_BlendedMVS		96.05 130	95.82 122	96.72 167	99.59 70	96.99 104	99.95 31	99.10 30	94.06 98	98.27 90	95.80 230	89.00 153	99.95 51	99.12 33	87.53 238	93.24 292

USDC		90.00 254	88.96 252	93.10 264	94.81 240	88.16 290	98.71 244	95.54 310	93.66 113	83.75 289	97.20 191	65.58 320	98.31 191	83.96 276	87.49 239	92.85 300

ACMMP++_ref															87.04 240

test_0402		85.58 292	83.94 295	90.50 299	93.81 255	85.04 307	98.55 256	95.20 324	76.01 326	79.72 303	95.13 255	64.15 325	96.26 285	66.04 334	86.88 241	90.21 325

FIs		94.10 171	93.43 173	96.11 181	94.70 242	96.82 109	99.58 156	98.93 41	92.54 153	89.34 231	97.31 188	87.62 164	97.10 252	94.22 149	86.58 242	94.40 223

FC-MVSNet-test		93.81 176	93.15 180	95.80 189	94.30 247	96.20 128	99.42 176	98.89 51	92.33 159	89.03 238	97.27 190	87.39 167	96.83 269	93.20 168	86.48 243	94.36 226

TinyColmap		87.87 275	86.51 276	91.94 288	95.05 237	85.57 303	97.65 292	94.08 333	84.40 293	81.82 295	96.85 206	62.14 329	98.33 189	80.25 296	86.37 244	91.91 309

ACMH+	89.98 16	90.35 245	89.54 240	92.78 270	95.99 213	86.12 299	98.81 239	97.18 239	89.38 219	83.14 291	97.76 181	68.42 313	98.43 176	89.11 224	86.05 245	93.78 275

GBi-Net		90.88 233	89.82 235	94.08 239	97.53 175	91.97 231	98.43 265	96.95 268	87.05 260	89.68 220	94.72 276	71.34 301	96.11 288	87.01 250	85.65 246	94.17 239

test1		90.88 233	89.82 235	94.08 239	97.53 175	91.97 231	98.43 265	96.95 268	87.05 260	89.68 220	94.72 276	71.34 301	96.11 288	87.01 250	85.65 246	94.17 239

FMVSNet3		92.69 196	91.58 200	95.99 183	98.29 134	97.42 86	99.26 196	97.62 200	89.80 217	89.68 220	95.32 248	81.62 222	96.27 284	87.01 250	85.65 246	94.29 233

DeepMVS_CX				82.92 323	95.98 215	58.66 347		96.01 300	92.72 137	78.34 307	95.51 238	58.29 336	98.08 202	82.57 284	85.29 249	92.03 307

LF4IMVS		89.25 265	88.85 253	90.45 301	92.81 291	81.19 324	98.12 283	94.79 327	91.44 185	86.29 272	97.11 193	65.30 322	98.11 201	88.53 228	85.25 250	92.07 305

FMVSNet2		91.02 230	89.56 239	95.41 194	97.53 175	95.74 142	98.98 223	97.41 224	87.05 260	88.43 245	95.00 265	71.34 301	96.24 286	85.12 267	85.21 251	94.25 236

testing_2		85.10 297	81.72 304	95.22 197	82.25 337	94.16 175	97.54 293	97.01 260	88.15 241	62.23 339	86.43 336	44.43 345	97.18 242	92.28 182	85.20 252	94.31 231

OurMVSNet-221017-0		89.81 255	89.48 244	90.83 297	91.64 309	81.21 323	98.17 282	95.38 319	91.48 183	85.65 279	97.31 188	72.66 296	97.29 236	88.15 231	84.83 253	93.97 261

pmmvs4		92.10 206	91.07 208	95.18 198	92.82 290	94.96 161	99.48 170	96.83 280	87.45 255	88.66 242	96.56 216	83.78 197	96.83 269	89.29 222	84.77 254	93.75 276

our_test_3		90.39 243	89.48 244	93.12 262	92.40 295	89.57 276	99.33 186	96.35 294	87.84 245	85.30 280	94.99 266	84.14 195	96.09 291	80.38 295	84.56 255	93.71 281

IterMVS		90.91 232	90.17 227	93.12 262	96.78 200	90.42 264	98.89 231	97.05 251	89.03 224	86.49 268	95.42 240	76.59 273	95.02 306	87.22 246	84.09 256	93.93 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		88.50 271	86.64 275	94.08 239	95.62 230	91.97 231	98.43 265	96.95 268	83.00 298	86.08 275	94.72 276	59.09 335	96.11 288	81.82 290	84.07 257	94.17 239

XXY-MVS		91.82 208	90.46 215	95.88 186	93.91 253	95.40 153	98.87 235	97.69 195	88.63 236	87.87 252	97.08 195	74.38 291	97.89 212	91.66 188	84.07 257	94.35 229

semantic-postprocess				92.93 267	96.72 202	89.96 271		96.99 261	88.95 229	86.63 265	95.67 234	76.50 274	95.00 307	87.04 248	84.04 259	93.84 272

pmmvs5		90.17 252	89.09 249	93.40 257	92.10 301	89.77 275	99.74 120	95.58 308	85.88 275	87.24 259	95.74 232	73.41 295	96.48 278	88.54 227	83.56 260	93.95 262

SixPastTwentyTwo		88.73 270	88.01 267	90.88 295	91.85 306	82.24 318	98.22 280	95.18 325	88.97 227	82.26 294	96.89 203	71.75 300	96.67 274	84.00 274	82.98 261	93.72 280

N_pmnet		80.06 310	80.78 307	77.89 327	91.94 302	45.28 356	98.80 240	56.82 360	78.10 323	80.08 302	93.33 300	77.03 268	95.76 298	68.14 329	82.81 262	92.64 301

ppachtmachnet_test		89.58 258	88.35 262	93.25 260	92.40 295	90.44 263	99.33 186	96.73 284	85.49 283	85.90 277	95.77 231	81.09 231	96.00 296	76.00 319	82.49 263	93.30 289

Patchmtry		89.70 256	88.49 259	93.33 258	96.24 208	89.94 274	91.37 339	96.23 295	78.22 322	87.69 253	93.31 302	91.04 129	96.03 293	80.18 297	82.10 264	94.02 248

IterMVS-LS		92.69 196	92.11 194	94.43 231	96.80 197	92.74 215	99.45 174	96.89 275	88.98 226	89.65 223	95.38 244	88.77 155	96.34 282	90.98 196	82.04 265	94.22 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EU-MVSNet		90.14 253	90.34 219	89.54 308	92.55 294	81.06 325	98.69 246	98.04 167	91.41 186	86.59 266	96.84 208	80.83 235	93.31 330	86.20 258	81.91 266	94.26 234

Anonymous20231206		86.32 283	85.42 279	89.02 310	89.11 325	80.53 328	99.05 218	95.28 321	85.43 284	82.82 292	93.92 292	74.40 290	93.44 329	66.99 331	81.83 267	93.08 295

FMVSNet5		88.32 272	87.47 272	90.88 295	96.90 192	88.39 288	97.28 299	95.68 305	82.60 301	84.67 284	92.40 308	79.83 248	91.16 333	76.39 318	81.51 268	93.09 294

v7		91.20 228	89.99 233	94.82 216	93.57 258	93.41 197	99.57 157	96.98 263	86.83 264	89.88 213	95.22 254	81.01 232	97.14 247	85.53 263	81.31 269	93.90 266

VPA-MVSNet		92.70 195	91.55 201	96.16 180	95.09 234	96.20 128	98.88 232	99.00 35	91.02 199	91.82 191	95.29 252	76.05 280	97.96 209	95.62 125	81.19 270	94.30 232

v1192		90.62 240	89.25 246	94.72 219	93.13 281	93.07 207	99.50 167	97.02 257	86.33 270	89.56 227	95.01 263	79.22 253	97.09 254	82.34 286	81.16 271	94.01 250

v1144		91.09 229	89.83 234	94.87 213	93.25 278	93.69 188	99.62 153	96.98 263	86.83 264	89.64 224	94.99 266	80.94 233	97.05 255	85.08 268	81.16 271	93.87 270

v1240		90.20 250	88.79 255	94.44 229	93.05 286	92.27 227	99.38 181	96.92 272	85.89 274	89.36 230	94.87 275	77.89 266	97.03 259	80.66 294	81.08 273	94.01 250

new_pmnet		84.49 301	82.92 301	89.21 309	90.03 321	82.60 315	96.89 306	95.62 307	80.59 316	75.77 316	89.17 316	65.04 323	94.79 311	72.12 322	81.02 274	90.23 324

K. test v3		88.05 274	87.24 273	90.47 300	91.82 308	82.23 319	98.96 226	97.42 222	89.05 223	76.93 310	95.60 236	68.49 312	95.42 301	85.87 262	81.01 275	93.75 276

FPMVS		68.72 317	68.72 318	68.71 336	65.95 350	44.27 358	95.97 319	94.74 328	51.13 345	53.26 347	90.50 315	25.11 354	83.00 348	60.80 339	80.97 276	78.87 345

v1921920		90.46 242	89.12 248	94.50 227	92.96 288	92.46 223	99.49 168	96.98 263	86.10 272	89.61 226	95.30 249	78.55 262	97.03 259	82.17 287	80.89 277	94.01 250

tfpnnormal		89.29 264	87.61 270	94.34 233	94.35 246	94.13 177	98.95 227	98.94 38	83.94 294	84.47 285	95.51 238	74.84 287	97.39 223	77.05 316	80.41 278	91.48 314

v144192		90.79 235	89.52 241	94.59 223	93.11 284	92.77 214	99.56 159	96.99 261	86.38 269	89.82 216	94.95 269	80.50 242	97.10 252	83.98 275	80.41 278	93.90 266

nrg030		93.51 183	92.53 188	96.45 173	94.36 245	97.20 97	99.81 98	97.16 242	91.60 179	89.86 214	97.46 184	86.37 177	97.68 216	95.88 120	80.31 280	94.46 217

v6		91.44 217	90.27 223	94.93 209	93.44 265	93.44 192	99.73 126	97.05 251	87.57 247	90.05 205	95.10 259	81.87 216	97.39 223	87.45 238	80.17 281	93.98 260

v1neww		91.44 217	90.28 221	94.91 211	93.50 261	93.43 193	99.73 126	97.06 247	87.55 248	90.08 203	95.11 257	81.98 211	97.32 230	87.41 241	80.15 282	93.99 256

v7new		91.44 217	90.28 221	94.91 211	93.50 261	93.43 193	99.73 126	97.06 247	87.55 248	90.08 203	95.11 257	81.98 211	97.32 230	87.41 241	80.15 282	93.99 256

V42		91.28 225	90.12 231	94.74 217	93.42 267	93.46 191	99.68 139	97.02 257	87.36 256	89.85 215	95.05 261	81.31 228	97.34 227	87.34 244	80.07 284	93.40 286

v2v482		91.30 223	90.07 232	95.01 203	93.13 281	93.79 183	99.77 110	97.02 257	88.05 243	89.25 233	95.37 246	80.73 237	97.15 245	87.28 245	80.04 285	94.09 244

v1141		91.36 221	90.14 229	95.00 204	93.33 273	93.79 183	99.78 105	97.05 251	87.52 252	89.75 218	94.89 272	82.13 207	97.21 239	86.84 256	80.00 286	94.00 253

v1		91.36 221	90.14 229	95.04 202	93.35 271	93.80 182	99.77 110	97.05 251	87.53 251	89.77 217	94.91 270	81.99 210	97.33 229	86.90 255	79.98 287	94.00 253

divwei89l23v2f112		91.37 220	90.15 228	95.00 204	93.35 271	93.78 186	99.78 105	97.05 251	87.54 250	89.73 219	94.89 272	82.24 206	97.21 239	86.91 253	79.90 288	94.00 253

WR-MVS		92.31 203	91.25 205	95.48 193	94.45 244	95.29 155	99.60 154	98.68 64	90.10 211	88.07 250	96.89 203	80.68 238	96.80 271	93.14 171	79.67 289	94.36 226

v10		90.25 249	88.82 254	94.57 225	93.53 260	93.43 193	99.08 209	96.87 278	85.00 286	87.34 258	94.51 282	80.93 234	97.02 261	82.85 283	79.23 290	93.26 291

CP-MVSNet		91.23 226	90.22 225	94.26 234	93.96 252	92.39 225	99.09 207	98.57 82	88.95 229	86.42 270	96.57 215	79.19 254	96.37 280	90.29 208	78.95 291	94.02 248

MIMVSNet1		82.58 306	80.51 308	88.78 312	86.68 329	84.20 311	96.65 307	95.41 318	78.75 321	78.59 306	92.44 307	51.88 342	89.76 336	65.26 335	78.95 291	92.38 303

PS-CasMVS		90.63 239	89.51 242	93.99 245	93.83 254	91.70 245	98.98 223	98.52 91	88.48 237	86.15 274	96.53 217	75.46 282	96.31 283	88.83 226	78.86 293	93.95 262

WR-MVS_H		91.30 223	90.35 218	94.15 237	94.17 249	92.62 221	99.17 202	98.94 38	88.87 231	86.48 269	94.46 286	84.36 193	96.61 275	88.19 230	78.51 294	93.21 293

V4		89.55 259	88.41 260	92.98 265	92.21 299	90.03 269	98.87 235	96.91 273	84.51 291	86.84 262	94.21 290	79.37 251	97.15 245	84.45 272	78.28 295	91.76 310

v52		89.55 259	88.41 260	92.98 265	92.32 297	90.01 270	98.88 232	96.89 275	84.51 291	86.89 261	94.22 289	79.23 252	97.16 243	84.46 271	78.27 296	91.76 310

v8		90.54 241	89.17 247	94.66 220	93.43 266	93.40 200	99.20 199	96.94 271	85.76 277	87.56 254	94.51 282	81.96 214	97.19 241	84.94 269	78.25 297	93.38 288

UniMVSNet (Re)		93.07 189	92.13 193	95.88 186	94.84 239	96.24 127	99.88 66	98.98 36	92.49 156	89.25 233	95.40 241	87.09 170	97.14 247	93.13 172	78.16 298	94.26 234

v7n		89.65 257	88.29 263	93.72 251	92.22 298	90.56 260	99.07 213	97.10 245	85.42 285	86.73 264	94.72 276	80.06 246	97.13 249	81.14 292	78.12 299	93.49 284

VPNet		91.81 209	90.46 215	95.85 188	94.74 241	95.54 149	98.98 223	98.59 79	92.14 165	90.77 199	97.44 185	68.73 311	97.54 219	94.89 133	77.89 300	94.46 217

Gipuma		66.95 321	65.00 320	72.79 332	91.52 311	67.96 336	66.16 352	95.15 326	47.89 346	58.54 342	67.99 349	29.74 351	87.54 342	50.20 347	77.83 301	62.87 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
NR-MVSNet		91.56 216	90.22 225	95.60 190	94.05 250	95.76 141	98.25 277	98.70 62	91.16 196	80.78 299	96.64 213	83.23 202	96.57 276	91.41 189	77.73 302	94.46 217

test1235678		78.45 313	77.88 312	80.16 326	77.83 342	62.18 344	98.36 270	93.45 340	77.46 324	69.08 334	88.23 318	60.33 333	85.41 345	58.46 341	77.68 303	92.90 297

UniMVSNet_NR-MVSNet		92.95 191	92.11 194	95.49 191	94.61 243	95.28 156	99.83 95	99.08 32	91.49 182	89.21 235	96.86 205	87.14 169	96.73 272	93.20 168	77.52 304	94.46 217

DU-MVS		92.46 201	91.45 204	95.49 191	94.05 250	95.28 156	99.81 98	98.74 60	92.25 160	89.21 235	96.64 213	81.66 220	96.73 272	93.20 168	77.52 304	94.46 217

MDA-MVSNet_test_wron		85.51 294	83.32 299	92.10 286	90.96 315	88.58 285	99.20 199	96.52 291	79.70 318	57.12 344	92.69 306	79.11 256	93.86 324	77.10 315	77.46 306	93.86 271

YYNet1		85.50 295	83.33 298	92.00 287	90.89 316	88.38 289	99.22 198	96.55 290	79.60 320	57.26 343	92.72 305	79.09 257	93.78 326	77.25 314	77.37 307	93.84 272

v148		90.70 236	89.63 237	93.92 247	92.97 287	90.97 254	99.75 117	96.89 275	87.51 253	88.27 248	95.01 263	81.67 219	97.04 256	87.40 243	77.17 308	93.75 276

Baseline_NR-MVSNet		90.33 246	89.51 242	92.81 269	92.84 289	89.95 272	99.77 110	93.94 335	84.69 290	89.04 237	95.66 235	81.66 220	96.52 277	90.99 195	76.98 309	91.97 308

PEN-MVS		90.19 251	89.06 250	93.57 255	93.06 285	90.90 256	99.06 215	98.47 103	88.11 242	85.91 276	96.30 221	76.67 272	95.94 297	87.07 247	76.91 310	93.89 268

TranMVSNet+NR-MVSNet		91.68 215	90.61 212	94.87 213	93.69 257	93.98 179	99.69 134	98.65 67	91.03 198	88.44 244	96.83 209	80.05 247	96.18 287	90.26 209	76.89 311	94.45 222

MDA-MVSNet-bldmvs		84.09 302	81.52 306	91.81 290	91.32 313	88.00 292	98.67 249	95.92 302	80.22 317	55.60 345	93.32 301	68.29 314	93.60 328	73.76 321	76.61 312	93.82 274

test20.03		84.72 300	83.99 291	86.91 316	88.19 327	80.62 327	98.88 232	95.94 301	88.36 239	78.87 304	94.62 281	68.75 310	89.11 337	66.52 332	75.82 313	91.00 316

DTE-MVSNet		89.40 261	88.24 264	92.88 268	92.66 293	89.95 272	99.10 206	98.22 147	87.29 257	85.12 282	96.22 223	76.27 277	95.30 304	83.56 279	75.74 314	93.41 285

pm-mvs1		89.36 263	87.81 268	94.01 243	93.40 269	91.93 234	98.62 253	96.48 293	86.25 271	83.86 288	96.14 225	73.68 294	97.04 256	86.16 259	75.73 315	93.04 296

test12356		75.26 314	75.12 315	75.67 331	74.02 345	60.60 346	96.43 310	92.15 342	74.17 333	66.35 337	88.11 319	52.29 341	84.36 347	57.41 342	75.12 316	82.05 342

lessismore_v0				90.53 298	90.58 318	80.90 326		95.80 303		77.01 309	95.84 229	66.15 319	96.95 262	83.03 282	75.05 317	93.74 279

v748		88.94 268	87.72 269	92.61 273	91.91 303	87.50 294	99.07 213	96.97 266	84.76 288	85.79 278	93.63 299	79.19 254	97.04 256	83.16 281	75.03 318	93.28 290

IB-MVS	92.85 6	94.99 153	93.94 161	98.16 123	97.72 171	95.69 147	99.99 3	98.81 56	94.28 88	92.70 187	96.90 202	95.08 44	99.17 138	96.07 116	73.88 319	99.60 109
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
1111		79.11 312	78.74 311	80.23 325	78.33 340	67.13 337	97.31 297	93.65 337	71.34 336	68.35 335	87.87 321	85.42 187	88.46 338	52.93 345	73.46 320	85.11 341

pmmvs6		85.69 291	83.84 296	91.26 294	90.00 322	84.41 310	97.82 291	96.15 298	75.86 327	81.29 297	95.39 243	61.21 331	96.87 267	83.52 280	73.29 321	92.50 302

ambc				83.23 320	77.17 343	62.61 342	87.38 345	94.55 331		76.72 311	86.65 335	30.16 350	96.36 281	84.85 270	69.86 322	90.73 319

Patchmatch-RL test		86.90 277	85.98 277	89.67 307	84.45 333	75.59 331	89.71 342	92.43 341	86.89 263	77.83 308	90.94 313	94.22 69	93.63 327	87.75 236	69.61 323	99.79 83

PM-MVS		80.47 308	78.88 310	85.26 318	83.79 335	72.22 333	95.89 320	91.08 345	85.71 281	76.56 312	88.30 317	36.64 347	93.90 323	82.39 285	69.57 324	89.66 334

pmmvs-eth3d		84.03 303	81.97 303	90.20 303	84.15 334	87.09 296	98.10 285	94.73 329	83.05 297	74.10 326	87.77 323	65.56 321	94.01 319	81.08 293	69.24 325	89.49 336

TransMVSNet (Re)		87.25 276	85.28 280	93.16 261	93.56 259	91.03 253	98.54 258	94.05 334	83.69 296	81.09 298	96.16 224	75.32 283	96.40 279	76.69 317	68.41 326	92.06 306

PMVS	49.05 23	53.75 326	51.34 328	60.97 341	40.80 359	34.68 359	74.82 350	89.62 351	37.55 351	28.67 356	72.12 345	7.09 362	81.63 350	43.17 353	68.21 327	66.59 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UnsupCasMVSNet_eth		85.52 293	83.99 291	90.10 304	89.36 324	83.51 312	96.65 307	97.99 170	89.14 221	75.89 315	93.83 294	63.25 327	93.92 322	81.92 289	67.90 328	92.88 299

PVSNet_0	88.03 19	91.80 211	90.27 223	96.38 176	98.27 136	90.46 262	99.94 45	99.61 17	93.99 100	86.26 273	97.39 187	71.13 304	99.89 69	98.77 53	67.05 329	98.79 185

v11		86.09 289	83.98 293	92.42 279	93.29 275	93.41 197	98.52 260	95.30 320	81.73 312	74.27 323	87.20 330	81.24 229	93.85 325	77.68 312	66.61 330	90.00 331

v16		86.52 280	84.49 284	92.60 274	93.45 264	93.31 202	98.60 255	95.52 312	82.30 304	74.59 321	87.70 324	81.95 215	94.18 314	79.93 300	66.38 331	90.30 322

v18		86.59 279	84.57 283	92.65 271	93.41 268	93.43 193	98.69 246	95.55 309	82.44 302	74.71 319	87.68 325	82.11 208	94.21 313	80.14 298	66.37 332	90.32 321

v17		86.51 281	84.49 284	92.57 275	93.38 270	93.29 203	98.61 254	95.54 310	82.32 303	74.69 320	87.63 326	82.03 209	94.17 315	80.02 299	66.17 333	90.26 323

v15		86.26 284	84.19 287	92.47 277	93.29 275	93.28 204	98.53 259	95.47 313	82.24 306	74.34 322	87.34 328	81.71 218	94.07 316	79.39 301	65.42 334	90.06 329

v13		86.06 290	83.97 294	92.34 284	93.25 278	92.85 213	98.26 276	95.44 317	81.70 313	74.02 328	87.11 333	81.58 224	94.00 320	78.94 307	65.41 335	90.18 327

V14		86.22 285	84.15 288	92.41 280	93.30 274	93.16 205	98.47 262	95.47 313	82.10 307	74.27 323	87.41 327	81.73 217	94.02 318	79.26 302	65.37 336	90.04 330

V9		86.16 287	84.07 289	92.43 278	93.27 277	93.04 210	98.40 269	95.45 315	81.98 309	74.18 325	87.31 329	81.58 224	94.06 317	79.12 305	65.33 337	90.20 326

v12		86.10 288	84.01 290	92.37 282	93.23 280	92.96 211	98.33 272	95.45 315	81.87 310	74.05 327	87.15 331	81.60 223	93.98 321	79.09 306	65.28 338	90.18 327

TDRefinement		84.76 298	82.56 302	91.38 293	74.58 344	84.80 309	97.36 296	94.56 330	84.73 289	80.21 301	96.12 227	63.56 326	98.39 182	87.92 234	63.97 339	90.95 318

new-patchmatchnet		81.19 307	79.34 309	86.76 317	82.86 336	80.36 329	97.92 289	95.27 322	82.09 308	72.02 329	86.87 334	62.81 328	90.74 335	71.10 323	63.08 340	89.19 338

pmmvs3		80.27 309	77.77 313	87.76 315	80.32 339	82.43 317	98.23 279	91.97 343	72.74 335	78.75 305	87.97 320	57.30 337	90.99 334	70.31 324	62.37 341	89.87 332

testmv		67.54 319	65.93 319	72.37 333	64.46 353	54.05 350	95.09 323	90.07 347	68.90 341	55.16 346	77.63 344	30.39 349	82.61 349	49.42 348	62.26 342	80.45 344

Anonymous20231211		74.17 315	71.17 317	83.17 321	80.58 338	67.02 339	96.27 314	94.45 332	57.31 344	69.60 333	86.25 337	33.67 348	92.96 331	61.86 337	60.50 343	89.54 335

UnsupCasMVSNet_bld		79.97 311	77.03 314	88.78 312	85.62 332	81.98 320	93.66 329	97.35 229	75.51 329	70.79 330	83.05 340	48.70 344	94.91 309	78.31 309	60.29 344	89.46 337

LCM-MVSNet		67.77 318	64.73 321	76.87 328	62.95 354	56.25 349	89.37 343	93.74 336	44.53 348	61.99 340	80.74 341	20.42 357	86.53 343	69.37 326	59.50 345	87.84 339

PMMVS2		67.15 320	64.15 322	76.14 329	70.56 348	62.07 345	93.89 327	87.52 353	58.09 343	60.02 341	78.32 342	22.38 355	84.54 346	59.56 340	47.03 346	81.80 343

PNet_i23d		56.44 324	53.54 325	65.14 339	65.34 351	50.33 353	89.06 344	79.57 355	45.77 347	35.75 354	68.95 348	10.75 361	74.40 352	48.48 349	38.20 347	70.70 348

MVE	53.74 22	51.54 328	47.86 330	62.60 340	59.56 355	50.93 352	79.41 348	77.69 357	35.69 353	36.27 353	61.76 354	5.79 365	69.63 354	37.97 354	36.61 348	67.24 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		50.36 330	45.43 331	65.16 338	51.13 356	51.75 351	77.46 349	78.42 356	41.45 349	26.98 357	54.30 357	6.13 363	74.03 353	46.82 351	26.19 349	69.71 349

E-PMN		52.30 327	52.18 327	52.67 342	71.51 346	45.40 355	93.62 330	76.60 358	36.01 352	43.50 351	64.13 352	27.11 353	67.31 356	31.06 355	26.06 350	45.30 357

EMVS		51.44 329	51.22 329	52.11 343	70.71 347	44.97 357	94.04 326	75.66 359	35.34 354	42.40 352	61.56 355	28.93 352	65.87 357	27.64 356	24.73 351	45.49 356

ANet_high		56.10 325	52.24 326	67.66 337	49.27 357	56.82 348	83.94 346	82.02 354	70.47 338	33.28 355	64.54 351	17.23 359	69.16 355	45.59 352	23.85 352	77.02 346

no-one		63.48 323	59.26 324	76.14 329	66.71 349	65.06 341	92.75 332	89.92 348	68.96 340	46.96 350	66.55 350	21.74 356	87.68 341	57.07 343	22.69 353	75.68 347

tmp_tt		65.23 322	62.94 323	72.13 334	44.90 358	50.03 354	81.05 347	89.42 352	38.45 350	48.51 349	99.90 11	54.09 340	78.70 351	91.84 187	18.26 354	87.64 340

.test1245		71.48 316	71.80 316	70.51 335	78.33 340	67.13 337	97.31 297	93.65 337	71.34 336	68.35 335	87.87 321	85.42 187	88.46 338	52.93 345	11.01 355	55.94 354

testmvs		40.60 331	44.45 332	29.05 346	19.49 361	14.11 362	99.68 139	18.47 361	20.74 355	64.59 338	98.48 166	10.95 360	17.09 360	56.66 344	11.01 355	55.94 354

wuyk23d		20.37 335	20.84 336	18.99 347	65.34 351	27.73 360	50.43 353	7.67 363	9.50 357	8.01 358	6.34 359	6.13 363	26.24 358	23.40 357	10.69 357	2.99 358

test123		37.68 332	39.14 334	33.31 344	19.94 360	24.83 361	98.36 270	9.75 362	15.53 356	51.31 348	87.14 332	19.62 358	17.74 359	47.10 350	3.47 358	57.36 353

cdsmvs_eth3d_5k		23.43 334	31.24 335	0.00 348	0.00 362	0.00 363	0.00 354	98.09 163	0.00 358	0.00 359	99.67 76	83.37 200	0.00 361	0.00 358	0.00 359	0.00 359

pcd_1.5k_mvsjas		7.60 337	10.13 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	91.20 125	0.00 361	0.00 358	0.00 359	0.00 359

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

ab-mvs-re		8.28 336	11.04 337	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	99.40 95	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

GSMVS																99.59 111

test_part3							99.88 66		96.14 43		99.91 7		100.00 1	99.99 1

test_part2					99.89 36	99.25 6				99.49 33

sam_mvs1												94.72 56				99.59 111

sam_mvs												94.25 68

MTGPA								98.28 141

test_post1							95.78 321				59.23 356	93.20 96	97.74 215	91.06 194

test_post											63.35 353	94.43 58	98.13 200

patchmatchnet-post											91.70 310	95.12 42	97.95 210

MTMP								96.49 292

gm-plane-assit					96.97 189	93.76 187			91.47 184		98.96 122		98.79 149	94.92 130

TEST9					99.92 27	98.92 16	99.96 19	98.43 112	93.90 105	99.71 16	99.86 17	95.88 31	99.85 79

test_8					99.92 27	98.88 19	99.96 19	98.43 112	94.35 85	99.69 18	99.85 21	95.94 28	99.85 79

agg_prior					99.93 24	98.77 26		98.43 112		99.63 21			99.85 79

test_prior4						98.05 63	99.94 45

test_prior				99.43 27	99.94 14	98.49 50		98.65 67					99.80 88			99.99 12

旧先验2							99.46 173		94.21 90	99.85 5			99.95 51	96.96 107

新几何2							99.40 177

无先验							99.49 168	98.71 61	93.46 117				100.00 1	94.36 143		99.99 12

原ACMM2							99.90 59

testdata2													99.99 28	90.54 203

segment_acmp												96.68 14

testdata1							99.28 194		96.35 38

plane_prior7					95.71 226	91.59 249

plane_prior6					95.76 221	91.72 244						80.47 243

plane_prior4											98.59 158

plane_prior3						91.64 247			96.63 29	93.01 181

plane_prior2							99.84 91		96.38 34

plane_prior1					95.73 223

n2								0.00 364

nn								0.00 364

door-mid								89.69 350

test11								98.44 107

door								90.31 346

HQP5-MVS						91.85 236

HQP-NCC					95.78 217		99.87 71		96.82 21	93.37 177

ACMP_Plane					95.78 217		99.87 71		96.82 21	93.37 177

BP-MVS														97.92 85

HQP4-MVS										93.37 177			98.39 182			94.53 212

HQP2-MVS												80.65 239

NP-MVS					95.77 220	91.79 238					98.65 154

MDTV_nov1_ep13_2view						96.26 123	96.11 316		91.89 173	98.06 96		94.40 60		94.30 146		99.67 97

Test By Simon												92.82 102

High-resolution multi-view benchmark