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.

AdaColmap		97.23 82	96.80 83	98.51 103	99.99 1	95.60 150	99.09 210	98.84 56	93.32 120	96.74 125	99.72 65	86.04 182	100.00 1	98.01 79	99.43 91	99.94 64

CNVR-MVS		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 64	98.20 3	99.93 1	99.98 2	96.82 11	100.00 1	99.75 10	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 72	98.47 2	99.13 56	99.92 7	96.38 21	100.00 1	99.74 12	100.00 1	100.00 1

mPP-MVS		98.39 43	98.20 40	98.97 74	99.97 3	96.92 108	99.95 31	98.38 132	95.04 67	98.61 80	99.80 44	93.39 90	100.00 1	98.64 61	100.00 1	99.98 43

CPTT-MVS		97.64 70	97.32 69	98.58 97	99.97 3	95.77 142	99.96 19	98.35 137	89.90 219	98.36 89	99.79 45	91.18 128	99.99 26	98.37 69	99.99 14	99.99 12

DP-MVS Recon		98.41 41	98.02 47	99.56 16	99.97 3	98.70 35	99.92 52	98.44 109	92.06 172	98.40 88	99.84 35	95.68 32	100.00 1	98.19 71	99.71 72	99.97 53

PAPR		98.52 34	98.16 42	99.58 15	99.97 3	98.77 26	99.95 31	98.43 115	95.35 62	98.03 101	99.75 61	94.03 76	99.98 31	98.11 75	99.83 61	99.99 12

HFP-MVS		98.56 30	98.37 32	99.14 53	99.96 8	97.43 85	99.95 31	98.61 78	94.77 73	99.31 47	99.85 21	94.22 69	100.00 1	98.70 55	99.98 25	99.98 43

region2R		98.54 32	98.37 32	99.05 67	99.96 8	97.18 99	99.96 19	98.55 90	94.87 71	99.45 37	99.85 21	94.07 75	100.00 1	98.67 57	100.00 1	99.98 43

#test#		98.59 28	98.41 27	99.14 53	99.96 8	97.43 85	99.95 31	98.61 78	95.00 68	99.31 47	99.85 21	94.22 69	100.00 1	98.78 52	99.98 25	99.98 43

ACMMPR		98.50 35	98.32 36	99.05 67	99.96 8	97.18 99	99.95 31	98.60 80	94.77 73	99.31 47	99.84 35	93.73 85	100.00 1	98.70 55	99.98 25	99.98 43

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 76	98.02 6	99.90 2	99.95 3	97.33 9	100.00 1	99.54 20	100.00 1	100.00 1

CP-MVS		98.45 38	98.32 36	98.87 80	99.96 8	96.62 114	99.97 12	98.39 129	94.43 83	98.90 66	99.87 15	94.30 67	100.00 1	99.04 39	99.99 14	99.99 12

XVS		98.70 23	98.55 22	99.15 51	99.94 14	97.50 81	99.94 45	98.42 123	96.22 40	99.41 41	99.78 50	94.34 64	99.96 42	98.92 44	99.95 39	99.99 12

test_prior3		98.99 11	98.84 12	99.43 27	99.94 14	98.49 50	99.95 31	98.65 69	95.78 50	99.73 14	99.76 56	96.00 24	99.80 90	99.78 8	100.00 1	99.99 12

X-MVStestdata		93.83 177	92.06 199	99.15 51	99.94 14	97.50 81	99.94 45	98.42 123	96.22 40	99.41 41	41.37 364	94.34 64	99.96 42	98.92 44	99.95 39	99.99 12

test_prior				99.43 27	99.94 14	98.49 50		98.65 69					99.80 90			99.99 12

MSLP-MVS++		99.13 5	99.01 7	99.49 23	99.94 14	98.46 52	99.98 6	98.86 54	97.10 16	99.80 8	99.94 4	95.92 28	100.00 1	99.51 21	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	98.39 129	97.20 15	99.46 36	99.85 21	95.53 37	99.79 92	99.86 4	100.00 1	99.99 12

MP-MVS		98.23 50	97.97 50	99.03 69	99.94 14	97.17 102	99.95 31	98.39 129	94.70 76	98.26 95	99.81 43	91.84 119	100.00 1	98.85 48	99.97 34	99.93 65
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 24	98.36 34	99.49 23	99.94 14	98.73 33	99.87 69	98.33 139	93.97 101	99.76 12	99.87 15	94.99 50	99.75 99	98.55 64	100.00 1	99.98 43

PAPM_NR		98.12 53	97.93 53	98.70 87	99.94 14	96.13 133	99.82 96	98.43 115	94.56 79	97.52 110	99.70 69	94.40 60	99.98 31	97.00 105	99.98 25	99.99 12

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 13	99.00 64	99.72 65	94.03 76	99.98 31	98.73 54	100.00 1	100.00 1

HSP-MVS		99.07 6	99.11 4	98.95 76	99.93 24	97.24 96	99.95 31	98.32 140	97.50 10	99.52 33	99.88 12	97.43 6	99.71 107	99.50 22	99.98 25	99.89 71

agg_prior1		98.88 15	98.66 15	99.54 18	99.93 24	98.77 26	99.96 19	98.43 115	94.63 78	99.63 22	99.85 21	95.79 31	99.85 80	99.72 16	99.99 14	99.99 12

agg_prior					99.93 24	98.77 26		98.43 115		99.63 22			99.85 80

TEST9					99.92 27	98.92 15	99.96 19	98.43 115	93.90 105	99.71 16	99.86 17	95.88 29	99.85 80

train_agg		98.88 15	98.65 16	99.59 14	99.92 27	98.92 15	99.96 19	98.43 115	94.35 85	99.71 16	99.86 17	95.94 26	99.85 80	99.69 18	99.98 25	99.99 12

test_8					99.92 27	98.88 18	99.96 19	98.43 115	94.35 85	99.69 18	99.85 21	95.94 26	99.85 80

agg_prior3		98.84 17	98.62 18	99.47 26	99.92 27	98.56 46	99.96 19	98.43 115	94.07 95	99.67 19	99.85 21	96.05 22	99.85 80	99.69 18	99.98 25	99.99 12

PGM-MVS		98.34 44	98.13 44	98.99 73	99.92 27	97.00 104	99.75 117	99.50 21	93.90 105	99.37 45	99.76 56	93.24 95	100.00 1	97.75 91	99.96 36	99.98 43

ACMMP		97.74 67	97.44 64	98.66 90	99.92 27	96.13 133	99.18 204	99.45 22	94.84 72	96.41 134	99.71 67	91.40 122	99.99 26	97.99 81	98.03 120	99.87 74
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
HPM-MVS++		99.07 6	98.88 11	99.63 8	99.90 33	99.02 12	99.95 31	98.56 86	97.56 9	99.44 38	99.85 21	95.38 39	100.00 1	99.31 29	99.99 14	99.87 74

APD-MVS		98.62 25	98.35 35	99.41 31	99.90 33	98.51 49	99.87 69	98.36 136	94.08 94	99.74 13	99.73 64	94.08 74	99.74 103	99.42 26	99.99 14	99.99 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	96.59 1	98.81 18	98.54 23	99.62 11	99.90 33	98.85 21	99.24 200	98.47 105	98.14 4	99.08 57	99.91 8	93.09 98	100.00 1	99.04 39	99.99 14	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
ESAPD		99.26 4	99.10 5	99.74 3	99.89 36	99.24 7	99.87 69	98.44 109	97.48 11	99.64 21	99.94 4	96.68 12	99.99 26	99.99 1	100.00 1	99.99 12

test_part2					99.89 36	99.25 6				99.49 34

v1.0		41.15 337	54.87 331	0.00 354	99.89 36	0.00 369	0.00 360	98.41 125	96.14 44	99.49 34	99.91 8	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

CSCG		97.10 86	97.04 77	97.27 157	99.89 36	91.92 239	99.90 59	99.07 34	88.67 238	95.26 156	99.82 40	93.17 97	99.98 31	98.15 73	99.47 88	99.90 70

SMA-MVS		98.76 21	98.48 25	99.62 11	99.87 40	98.87 19	99.86 86	98.38 132	93.19 122	99.77 11	99.94 4	95.54 34	100.00 1	99.74 12	99.99 14	100.00 1

PHI-MVS		98.41 41	98.21 39	99.03 69	99.86 41	97.10 103	99.98 6	98.80 59	90.78 208	99.62 24	99.78 50	95.30 40	100.00 1	99.80 6	99.93 48	99.99 12

zzz-MVS		98.33 45	98.00 48	99.30 38	99.85 42	97.93 68	99.80 101	98.28 144	95.76 52	97.18 117	99.88 12	92.74 103	100.00 1	98.67 57	99.88 56	99.99 12

MTAPA		98.29 46	97.96 52	99.30 38	99.85 42	97.93 68	99.39 183	98.28 144	95.76 52	97.18 117	99.88 12	92.74 103	100.00 1	98.67 57	99.88 56	99.99 12

Regformer-1		98.79 19	98.60 20	99.36 36	99.85 42	98.34 54	99.87 69	98.52 93	96.05 45	99.41 41	99.79 45	94.93 52	99.76 96	99.07 34	99.90 52	99.99 12

Regformer-2		98.78 20	98.59 21	99.36 36	99.85 42	98.32 55	99.87 69	98.52 93	96.04 46	99.41 41	99.79 45	94.92 53	99.76 96	99.05 35	99.90 52	99.98 43

LS3D		95.84 139	95.11 147	98.02 133	99.85 42	95.10 163	98.74 247	98.50 103	87.22 263	93.66 182	99.86 17	87.45 169	99.95 50	90.94 202	99.81 67	99.02 182

Regformer-3		98.58 29	98.41 27	99.10 59	99.84 47	97.57 77	99.66 144	98.52 93	95.79 49	99.01 62	99.77 52	94.40 60	99.75 99	98.82 49	99.83 61	99.98 43

Regformer-4		98.56 30	98.39 30	99.08 61	99.84 47	97.52 79	99.66 144	98.52 93	95.76 52	99.01 62	99.77 52	94.33 66	99.75 99	98.80 51	99.83 61	99.98 43

HPM-MVS		97.96 57	97.72 56	98.68 88	99.84 47	96.39 122	99.90 59	98.17 157	92.61 147	98.62 79	99.57 84	91.87 118	99.67 114	98.87 47	99.99 14	99.99 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set		98.27 47	98.11 45	98.75 85	99.83 50	96.59 116	99.40 180	98.51 99	95.29 64	98.51 83	99.76 56	93.60 89	99.71 107	98.53 65	99.52 85	99.95 62

PLC	95.54 3	97.93 59	97.89 54	98.05 132	99.82 51	94.77 171	99.92 52	98.46 107	93.93 104	97.20 115	99.27 102	95.44 38	99.97 40	97.41 95	99.51 87	99.41 138
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 49	98.08 46	98.78 83	99.81 52	96.60 115	99.82 96	98.30 142	93.95 103	99.37 45	99.77 52	92.84 100	99.76 96	98.95 41	99.92 50	99.97 53

EI-MVSNet-UG-set		98.14 52	97.99 49	98.60 95	99.80 53	96.27 124	99.36 187	98.50 103	95.21 66	98.30 92	99.75 61	93.29 94	99.73 106	98.37 69	99.30 95	99.81 79

HPM-MVS_fast		97.80 64	97.50 63	98.68 88	99.79 54	96.42 119	99.88 66	98.16 160	91.75 179	98.94 65	99.54 87	91.82 120	99.65 116	97.62 93	99.99 14	99.99 12

旧先验1					99.76 55	97.52 79		98.64 72			99.85 21	95.63 33			99.94 43	99.99 12

OMC-MVS		97.28 79	97.23 70	97.41 152	99.76 55	93.36 205	99.65 147	97.95 178	96.03 47	97.41 112	99.70 69	89.61 142	99.51 121	96.73 112	98.25 115	99.38 145

新几何1				99.42 30	99.75 57	98.27 57		98.63 75	92.69 141	99.55 29	99.82 40	94.40 60	100.00 1	91.21 195	99.94 43	99.99 12

MP-MVS-pluss		98.07 55	97.64 58	99.38 35	99.74 58	98.41 53	99.74 120	98.18 156	93.35 119	96.45 131	99.85 21	92.64 106	99.97 40	98.91 46	99.89 54	99.77 85
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 12	98.77 13	99.41 31	99.74 58	98.67 36	99.77 110	98.38 132	96.73 27	99.88 3	99.74 63	94.89 54	99.59 118	99.80 6	99.98 25	99.97 53

1121		98.03 56	97.57 62	99.40 33	99.74 58	98.21 58	98.31 280	98.62 76	92.78 136	99.53 30	99.83 37	95.08 44	100.00 1	94.36 147	99.92 50	99.99 12

test12				99.43 27	99.74 58	98.56 46		98.40 127		99.65 20		94.76 55	99.75 99		99.98 25	99.99 12

原ACMM1				98.96 75	99.73 62	96.99 105		98.51 99	94.06 98	99.62 24	99.85 21	94.97 51	99.96 42	95.11 130	99.95 39	99.92 68

TSAR-MVS + GP.		98.60 26	98.51 24	98.86 81	99.73 62	96.63 113	99.97 12	97.92 182	98.07 5	98.76 71	99.55 85	95.00 49	99.94 58	99.91 3	97.68 124	99.99 12

CANet		98.27 47	97.82 55	99.63 8	99.72 64	99.10 10	99.98 6	98.51 99	97.00 19	98.52 82	99.71 67	87.80 164	99.95 50	99.75 10	99.38 92	99.83 77

F-COLMAP		96.93 92	96.95 79	96.87 165	99.71 65	91.74 245	99.85 88	97.95 178	93.11 125	95.72 149	99.16 110	92.35 108	99.94 58	95.32 128	99.35 93	98.92 184

SD-MVS		98.92 13	98.70 14	99.56 16	99.70 66	98.73 33	99.94 45	98.34 138	96.38 35	99.81 7	99.76 56	94.59 57	99.98 31	99.84 5	99.96 36	99.97 53

abl_6		97.67 69	97.34 67	98.66 90	99.68 67	96.11 137	99.68 139	98.14 163	93.80 108	99.27 50	99.70 69	88.65 160	99.98 31	97.46 94	99.72 71	99.89 71

ACMMP_Plus		98.49 36	98.14 43	99.54 18	99.66 68	98.62 41	99.85 88	98.37 135	94.68 77	99.53 30	99.83 37	92.87 99	100.00 1	98.66 60	99.84 60	99.99 12

DeepPCF-MVS	95.94 2	97.71 68	98.98 8	93.92 254	99.63 69	81.76 329	99.96 19	98.56 86	99.47 1	99.19 54	99.99 1	94.16 73	100.00 1	99.92 2	99.93 48	100.00 1

EPNet		98.49 36	98.40 29	98.77 84	99.62 70	96.80 111	99.90 59	99.51 20	97.60 8	99.20 52	99.36 100	93.71 86	99.91 64	97.99 81	98.71 105	99.61 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		97.52 72	96.79 84	99.69 6	99.59 71	99.30 4	99.97 12	98.01 172	96.99 20	98.84 67	99.79 45	78.90 261	99.96 42	99.74 12	99.32 94	99.81 79

PVSNet_BlendedMVS		96.05 134	95.82 125	96.72 170	99.59 71	96.99 105	99.95 31	99.10 31	94.06 98	98.27 93	95.80 235	89.00 155	99.95 50	99.12 32	87.53 242	93.24 297

PVSNet_Blended		97.94 58	97.64 58	98.83 82	99.59 71	96.99 105	100.00 1	99.10 31	95.38 61	98.27 93	99.08 113	89.00 155	99.95 50	99.12 32	99.25 96	99.57 118

PatchMatch-RL		96.04 135	95.40 137	97.95 134	99.59 71	95.22 162	99.52 167	99.07 34	93.96 102	96.49 129	98.35 172	82.28 208	99.82 89	90.15 215	99.22 97	98.81 188

test222					99.55 75	97.41 88	99.34 188	98.55 90	91.86 176	99.27 50	99.83 37	93.84 83			99.95 39	99.99 12

CNLPA		97.76 66	97.38 65	98.92 78	99.53 76	96.84 109	99.87 69	98.14 163	93.78 109	96.55 128	99.69 72	92.28 110	99.98 31	97.13 101	99.44 90	99.93 65

API-MVS		97.86 60	97.66 57	98.47 109	99.52 77	95.41 154	99.47 174	98.87 53	91.68 181	98.84 67	99.85 21	92.34 109	99.99 26	98.44 67	99.96 36	100.00 1

PVSNet	91.05 13	97.13 85	96.69 87	98.45 111	99.52 77	95.81 140	99.95 31	99.65 16	94.73 75	99.04 60	99.21 108	84.48 194	99.95 50	94.92 133	98.74 104	99.58 116

114514_t		97.41 77	96.83 81	99.14 53	99.51 79	97.83 70	99.89 64	98.27 147	88.48 241	99.06 58	99.66 78	90.30 136	99.64 117	96.32 115	99.97 34	99.96 57

testdata				98.42 114	99.47 80	95.33 156		98.56 86	93.78 109	99.79 10	99.85 21	93.64 88	99.94 58	94.97 132	99.94 43	100.00 1

MAR-MVS		97.43 73	97.19 71	98.15 128	99.47 80	94.79 170	99.05 222	98.76 60	92.65 145	98.66 77	99.82 40	88.52 161	99.98 31	98.12 74	99.63 76	99.67 98
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
DP-MVS		94.54 166	93.42 177	97.91 136	99.46 82	94.04 182	98.93 233	97.48 222	81.15 319	90.04 214	99.55 85	87.02 174	99.95 50	88.97 230	98.11 116	99.73 90

MVS_111021_LR		98.42 40	98.38 31	98.53 102	99.39 83	95.79 141	99.87 69	99.86 2	96.70 28	98.78 70	99.79 45	92.03 115	99.90 65	99.17 31	99.86 59	99.88 73

CHOSEN 280x420		99.01 10	99.03 6	98.95 76	99.38 84	98.87 19	98.46 270	99.42 25	97.03 18	99.02 61	99.09 112	99.35 1	98.21 202	99.73 15	99.78 68	99.77 85

MVS_111021_HR		98.72 22	98.62 18	99.01 72	99.36 85	97.18 99	99.93 50	99.90 1	96.81 25	98.67 75	99.77 52	93.92 78	99.89 69	99.27 30	99.94 43	99.96 57

TAPA-MVS	92.12 8	94.42 170	93.60 170	96.90 164	99.33 86	91.78 243	99.78 105	98.00 173	89.89 220	94.52 172	99.47 91	91.97 116	99.18 139	69.90 331	99.52 85	99.73 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
0601test		97.83 62	97.37 66	99.21 42	99.18 87	97.98 66	99.64 151	99.27 29	91.43 189	97.88 104	98.99 120	95.84 30	99.84 88	98.82 49	95.32 171	99.79 82

DeepC-MVS	94.51 4	96.92 93	96.40 95	98.45 111	99.16 88	95.90 139	99.66 144	98.06 169	96.37 38	94.37 175	99.49 90	83.29 203	99.90 65	97.63 92	99.61 80	99.55 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		98.54 32	98.22 38	99.50 22	99.15 89	98.65 39	100.00 1	98.58 82	97.70 7	98.21 97	99.24 106	92.58 107	99.94 58	98.63 62	99.94 43	99.92 68
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
Anonymous202405211		93.10 192	91.99 200	96.40 179	99.10 90	89.65 282	98.88 236	97.93 180	83.71 300	94.00 179	98.75 151	68.79 314	99.88 75	95.08 131	91.71 210	99.68 96

tfpn_ndepth		97.21 83	96.63 88	98.92 78	99.06 91	98.28 56	99.95 31	98.91 43	92.96 128	96.49 129	98.67 154	97.40 7	99.07 141	91.87 191	94.38 181	99.41 138

HyFIR lowres test		96.66 108	96.43 94	97.36 155	99.05 92	93.91 185	99.70 133	99.80 3	90.54 209	96.26 136	98.08 177	92.15 113	98.23 201	96.84 111	95.46 168	99.93 65

LFMVS		94.75 161	93.56 173	98.30 121	99.03 93	95.70 148	98.74 247	97.98 175	87.81 250	98.47 84	99.39 97	67.43 321	99.53 119	98.01 79	95.20 172	99.67 98

AllTest		92.48 203	91.64 203	95.00 209	99.01 94	88.43 293	98.94 232	96.82 290	86.50 272	88.71 246	98.47 170	74.73 291	99.88 75	85.39 271	96.18 153	96.71 207

TestCases				95.00 209	99.01 94	88.43 293		96.82 290	86.50 272	88.71 246	98.47 170	74.73 291	99.88 75	85.39 271	96.18 153	96.71 207

tfpn1000		96.90 94	96.29 97	98.74 86	99.00 96	98.09 62	99.92 52	98.91 43	92.08 169	95.85 142	98.65 156	97.39 8	98.83 148	90.56 207	94.23 189	99.31 153

COLMAP_ROB	90.47 14	92.18 209	91.49 207	94.25 241	99.00 96	88.04 298	98.42 275	96.70 293	82.30 309	88.43 251	99.01 117	76.97 272	99.85 80	86.11 265	96.50 151	94.86 215
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HY-MVS	92.50 7	97.79 65	97.17 73	99.63 8	98.98 98	99.32 3	97.49 301	99.52 18	95.69 56	98.32 91	97.41 190	93.32 92	99.77 94	98.08 78	95.75 164	99.81 79

VNet		97.21 83	96.57 91	99.13 58	98.97 99	97.82 71	99.03 224	99.21 30	94.31 87	99.18 55	98.88 129	86.26 181	99.89 69	98.93 43	94.32 186	99.69 95

thres200		96.96 90	96.21 99	99.22 41	98.97 99	98.84 22	99.85 88	99.71 5	93.17 123	96.26 136	98.88 129	89.87 140	99.51 121	94.26 151	94.91 174	99.31 153

tfpn200view9		96.79 97	95.99 105	99.19 43	98.94 101	98.82 23	99.78 105	99.71 5	92.86 129	96.02 139	98.87 131	89.33 143	99.50 123	93.84 158	94.57 175	99.27 158

thres400		96.78 98	95.99 105	99.16 47	98.94 101	98.82 23	99.78 105	99.71 5	92.86 129	96.02 139	98.87 131	89.33 143	99.50 123	93.84 158	94.57 175	99.16 169

Anonymous20231211		89.86 261	88.44 266	94.13 245	98.93 103	90.68 263	98.54 264	98.26 148	76.28 331	86.73 270	95.54 243	70.60 308	97.56 223	90.82 205	80.27 286	94.15 246

canonicalmvs		97.09 87	96.32 96	99.39 34	98.93 103	98.95 14	99.72 131	97.35 236	94.45 81	97.88 104	99.42 93	86.71 176	99.52 120	98.48 66	93.97 199	99.72 92

EPNet_dtu		95.71 142	95.39 138	96.66 172	98.92 105	93.41 201	99.57 159	98.90 51	96.19 42	97.52 110	98.56 164	92.65 105	97.36 230	77.89 316	98.33 111	99.20 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 54	97.60 60	99.60 13	98.92 105	99.28 5	99.89 64	99.52 18	95.58 58	98.24 96	99.39 97	93.33 91	99.74 103	97.98 83	95.58 167	99.78 84

CHOSEN 1792x2688		96.81 96	96.53 93	97.64 144	98.91 107	93.07 211	99.65 147	99.80 3	95.64 57	95.39 153	98.86 133	84.35 196	99.90 65	96.98 106	99.16 98	99.95 62

tfpn111		96.69 104	95.87 123	99.16 47	98.90 108	98.77 26	99.74 120	99.71 5	92.59 149	95.84 143	98.86 133	89.25 145	99.50 123	93.44 169	94.50 179	99.20 163

conf200view11		96.73 103	95.92 113	99.16 47	98.90 108	98.77 26	99.74 120	99.71 5	92.59 149	95.84 143	98.86 133	89.25 145	99.50 123	93.84 158	94.57 175	99.20 163

thres100view900		96.74 101	95.92 113	99.18 44	98.90 108	98.77 26	99.74 120	99.71 5	92.59 149	95.84 143	98.86 133	89.25 145	99.50 123	93.84 158	94.57 175	99.27 158

thres600view7		96.69 104	95.87 123	99.14 53	98.90 108	98.78 25	99.74 120	99.71 5	92.59 149	95.84 143	98.86 133	89.25 145	99.50 123	93.44 169	94.50 179	99.16 169

MSDG		94.37 172	93.36 181	97.40 153	98.88 112	93.95 184	99.37 185	97.38 234	85.75 284	90.80 204	99.17 109	84.11 198	99.88 75	86.35 262	98.43 109	98.36 193

view600		96.46 119	95.59 129	99.06 63	98.87 113	98.60 42	99.69 134	99.71 5	92.20 162	95.23 157	98.80 145	89.17 149	99.43 131	92.29 182	94.37 182	99.16 169

view800		96.46 119	95.59 129	99.06 63	98.87 113	98.60 42	99.69 134	99.71 5	92.20 162	95.23 157	98.80 145	89.17 149	99.43 131	92.29 182	94.37 182	99.16 169

conf0.05thres1000		96.46 119	95.59 129	99.06 63	98.87 113	98.60 42	99.69 134	99.71 5	92.20 162	95.23 157	98.80 145	89.17 149	99.43 131	92.29 182	94.37 182	99.16 169

tfpn		96.46 119	95.59 129	99.06 63	98.87 113	98.60 42	99.69 134	99.71 5	92.20 162	95.23 157	98.80 145	89.17 149	99.43 131	92.29 182	94.37 182	99.16 169

Anonymous20240529		92.10 210	90.65 216	96.47 175	98.82 117	90.61 264	98.72 249	98.67 68	75.54 335	93.90 181	98.58 162	66.23 324	99.90 65	94.70 141	90.67 211	98.90 186

PVSNet_Blended_VisFu		97.27 80	96.81 82	98.66 90	98.81 118	96.67 112	99.92 52	98.64 72	94.51 80	96.38 135	98.49 166	89.05 154	99.88 75	97.10 103	98.34 110	99.43 136

PS-MVSNAJ		98.44 39	98.20 40	99.16 47	98.80 119	98.92 15	99.54 165	98.17 157	97.34 12	99.85 5	99.85 21	91.20 125	99.89 69	99.41 27	99.67 74	98.69 191

CANet_DTU		96.76 99	96.15 100	98.60 95	98.78 120	97.53 78	99.84 91	97.63 203	97.25 14	99.20 52	99.64 80	81.36 230	99.98 31	92.77 180	98.89 100	98.28 194

alignmvs		97.81 63	97.33 68	99.25 40	98.77 121	98.66 37	99.99 3	98.44 109	94.40 84	98.41 86	99.47 91	93.65 87	99.42 135	98.57 63	94.26 188	99.67 98

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 44	98.72 122	97.71 73	99.98 6	98.44 109	96.85 21	99.80 8	99.91 8	97.57 4	99.85 80	99.44 25	99.99 14	99.99 12
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 50	97.97 50	99.02 71	98.69 123	98.66 37	99.52 167	98.08 168	97.05 17	99.86 4	99.86 17	90.65 133	99.71 107	99.39 28	98.63 106	98.69 191

conf0.01		96.52 116	95.88 116	98.41 117	98.59 124	97.38 89	99.87 69	98.91 43	91.32 192	95.22 161	98.83 139	96.57 14	98.66 163	89.55 220	94.09 191	99.20 163

conf0.002		96.52 116	95.88 116	98.41 117	98.59 124	97.38 89	99.87 69	98.91 43	91.32 192	95.22 161	98.83 139	96.57 14	98.66 163	89.55 220	94.09 191	99.20 163

thresconf0.02		96.53 111	95.88 116	98.48 105	98.59 124	97.38 89	99.87 69	98.91 43	91.32 192	95.22 161	98.83 139	96.57 14	98.66 163	89.55 220	94.09 191	99.40 141

tfpn_n400		96.53 111	95.88 116	98.48 105	98.59 124	97.38 89	99.87 69	98.91 43	91.32 192	95.22 161	98.83 139	96.57 14	98.66 163	89.55 220	94.09 191	99.40 141

tfpnconf		96.53 111	95.88 116	98.48 105	98.59 124	97.38 89	99.87 69	98.91 43	91.32 192	95.22 161	98.83 139	96.57 14	98.66 163	89.55 220	94.09 191	99.40 141

tfpnview11		96.53 111	95.88 116	98.48 105	98.59 124	97.38 89	99.87 69	98.91 43	91.32 192	95.22 161	98.83 139	96.57 14	98.66 163	89.55 220	94.09 191	99.40 141

MVSTER		95.53 145	95.22 143	96.45 177	98.56 130	97.72 72	99.91 56	97.67 201	92.38 159	91.39 199	97.14 196	97.24 10	97.30 239	94.80 137	87.85 237	94.34 234

VDD-MVS		93.77 181	92.94 184	96.27 183	98.55 131	90.22 272	98.77 246	97.79 194	90.85 206	96.82 123	99.42 93	61.18 338	99.77 94	98.95 41	94.13 190	98.82 187

tpmvs		94.28 173	93.57 172	96.40 179	98.55 131	91.50 254	95.70 328	98.55 90	87.47 258	92.15 195	94.26 295	91.42 121	98.95 146	88.15 236	95.85 161	98.76 190

UGNet		95.33 149	94.57 155	97.62 145	98.55 131	94.85 166	98.67 255	99.32 28	95.75 55	96.80 124	96.27 227	72.18 301	99.96 42	94.58 144	99.05 99	98.04 198
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
PCF-MVS	94.20 5	95.18 150	94.10 162	98.43 113	98.55 131	95.99 138	97.91 297	97.31 240	90.35 212	89.48 234	99.22 107	85.19 191	99.89 69	90.40 212	98.47 108	99.41 138
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-w/o		95.71 142	95.38 139	96.68 171	98.49 135	92.28 230	99.84 91	97.50 220	92.12 167	92.06 196	98.79 150	84.69 192	98.67 161	95.29 129	99.66 75	99.09 180

EPMVS		96.53 111	96.01 104	98.09 131	98.43 136	96.12 136	96.36 318	99.43 24	93.53 116	97.64 108	95.04 268	94.41 59	98.38 190	91.13 197	98.11 116	99.75 87

sss		97.57 71	97.03 78	99.18 44	98.37 137	98.04 64	99.73 126	99.38 26	93.46 117	98.76 71	99.06 114	91.21 124	99.89 69	96.33 114	97.01 143	99.62 106

BH-untuned		95.18 150	94.83 150	96.22 184	98.36 138	91.22 256	99.80 101	97.32 239	90.91 204	91.08 201	98.67 154	83.51 200	98.54 172	94.23 152	99.61 80	98.92 184

FMVSNet3		92.69 200	91.58 204	95.99 188	98.29 139	97.42 87	99.26 199	97.62 205	89.80 221	89.68 226	95.32 254	81.62 225	96.27 290	87.01 255	85.65 250	94.29 237

PMMVS		96.76 99	96.76 86	96.76 168	98.28 140	92.10 234	99.91 56	97.98 175	94.12 92	99.53 30	99.39 97	86.93 175	98.73 157	96.95 108	97.73 122	99.45 133

PVSNet_0	88.03 19	91.80 216	90.27 228	96.38 181	98.27 141	90.46 268	99.94 45	99.61 17	93.99 100	86.26 280	97.39 191	71.13 307	99.89 69	98.77 53	67.05 335	98.79 189

PatchFormer-LS_test		97.01 88	96.79 84	97.69 142	98.26 142	94.80 168	98.66 258	98.13 165	93.70 112	97.86 106	98.80 145	95.54 34	98.67 161	94.12 154	96.00 156	99.60 110

DWT-MVSNet_test		97.31 78	97.19 71	97.66 143	98.24 143	94.67 172	98.86 242	98.20 155	93.60 115	98.09 99	98.89 127	97.51 5	98.78 152	94.04 155	97.28 133	99.55 121

UA-Net		96.54 110	95.96 110	98.27 122	98.23 144	95.71 147	98.00 295	98.45 108	93.72 111	98.41 86	99.27 102	88.71 159	99.66 115	91.19 196	97.69 123	99.44 135

GG-mvs-BLEND				98.54 101	98.21 145	98.01 65	93.87 334	98.52 93		97.92 103	97.92 182	99.02 2	97.94 215	98.17 72	99.58 82	99.67 98

mvs_anonymous		95.65 144	95.03 148	97.53 146	98.19 146	95.74 144	99.33 189	97.49 221	90.87 205	90.47 207	97.10 199	88.23 162	97.16 249	95.92 121	97.66 125	99.68 96

MVS_Test		96.46 119	95.74 126	98.61 94	98.18 147	97.23 97	99.31 192	97.15 251	91.07 201	98.84 67	97.05 203	88.17 163	98.97 145	94.39 146	97.50 128	99.61 108

BH-RMVSNet		95.18 150	94.31 159	97.80 138	98.17 148	95.23 161	99.76 116	97.53 215	92.52 155	94.27 177	99.25 105	76.84 274	98.80 150	90.89 204	99.54 84	99.35 150

RPSCF		91.80 216	92.79 187	88.83 318	98.15 149	69.87 341	98.11 291	96.60 297	83.93 299	94.33 176	99.27 102	79.60 252	99.46 130	91.99 188	93.16 207	97.18 205

IS-MVSNet		96.29 129	95.90 115	97.45 150	98.13 150	94.80 168	99.08 212	97.61 208	92.02 173	95.54 152	98.96 123	90.64 134	98.08 206	93.73 165	97.41 131	99.47 132

ab-mvs		94.69 162	93.42 177	98.51 103	98.07 151	96.26 125	96.49 316	98.68 65	90.31 213	94.54 171	97.00 205	76.30 279	99.71 107	95.98 120	93.38 204	99.56 120

casdiffmvs1		96.98 89	96.57 91	98.22 124	98.06 152	96.28 123	99.58 157	97.41 230	92.98 127	99.06 58	97.44 188	90.11 139	98.77 153	96.88 110	97.64 126	99.59 112

XVG-OURS-SEG-HR		94.79 158	94.70 153	95.08 205	98.05 153	89.19 284	99.08 212	97.54 213	93.66 113	94.87 169	99.58 83	78.78 262	99.79 92	97.31 97	93.40 203	96.25 210

XVG-OURS		94.82 157	94.74 152	95.06 206	98.00 154	89.19 284	99.08 212	97.55 211	94.10 93	94.71 170	99.62 81	80.51 244	99.74 103	96.04 119	93.06 208	96.25 210

dp		95.05 154	94.43 157	96.91 163	97.99 155	92.73 220	96.29 320	97.98 175	89.70 222	95.93 141	94.67 286	93.83 84	98.45 179	86.91 258	96.53 150	99.54 125

tpmrst		96.27 131	95.98 107	97.13 159	97.96 156	93.15 210	96.34 319	98.17 157	92.07 170	98.71 74	95.12 262	93.91 80	98.73 157	94.91 135	96.62 148	99.50 130

TR-MVS		94.54 166	93.56 173	97.49 148	97.96 156	94.34 176	98.71 250	97.51 219	90.30 214	94.51 173	98.69 153	75.56 284	98.77 153	92.82 179	95.99 157	99.35 150

casdiffmvs		96.67 107	96.14 101	98.27 122	97.95 158	96.49 117	99.48 172	97.29 241	92.09 168	98.67 75	97.12 197	89.10 153	98.74 156	96.27 116	97.25 136	99.57 118

Vis-MVSNet (Re-imp)		96.32 126	95.98 107	97.35 156	97.93 159	94.82 167	99.47 174	98.15 162	91.83 177	95.09 167	99.11 111	91.37 123	97.47 226	93.47 168	97.43 129	99.74 88

diffmvs		96.06 133	95.46 135	97.89 137	97.92 160	95.28 158	99.16 207	97.28 242	91.73 180	98.16 98	96.74 215	87.48 168	98.81 149	93.69 166	96.95 145	99.58 116

MDTV_nov1_ep13			95.69 127		97.90 161	94.15 179	95.98 324	98.44 109	93.12 124	97.98 102	95.74 237	95.10 43	98.58 169	90.02 216	96.92 146

Fast-Effi-MVS+		95.02 155	94.19 160	97.52 147	97.88 162	94.55 173	99.97 12	97.08 254	88.85 236	94.47 174	97.96 181	84.59 193	98.41 182	89.84 217	97.10 141	99.59 112

ADS-MVSNet2		93.80 180	93.88 166	93.55 263	97.87 163	85.94 307	94.24 330	96.84 287	90.07 216	96.43 132	94.48 291	90.29 137	95.37 308	87.44 244	97.23 137	99.36 148

ADS-MVSNet		94.79 158	94.02 163	97.11 161	97.87 163	93.79 187	94.24 330	98.16 160	90.07 216	96.43 132	94.48 291	90.29 137	98.19 203	87.44 244	97.23 137	99.36 148

Effi-MVS+		96.30 128	95.69 127	98.16 125	97.85 165	96.26 125	97.41 302	97.21 246	90.37 211	98.65 78	98.58 162	86.61 178	98.70 160	97.11 102	97.37 132	99.52 127

tpmp4_e23		95.15 153	94.69 154	96.55 174	97.84 166	91.77 244	97.10 308	97.91 183	88.33 244	97.19 116	95.06 266	93.92 78	98.51 173	89.64 219	95.19 173	99.37 147

PatchmatchNet		95.94 137	95.45 136	97.39 154	97.83 167	94.41 175	96.05 323	98.40 127	92.86 129	97.09 119	95.28 259	94.21 72	98.07 208	89.26 228	98.11 116	99.70 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 164	93.61 168	97.74 141	97.82 168	96.26 125	99.96 19	97.78 195	85.76 281	94.00 179	97.54 186	76.95 273	99.21 138	97.23 99	95.43 169	97.76 203

1112_ss		96.01 136	95.20 144	98.42 114	97.80 169	96.41 120	99.65 147	96.66 294	92.71 139	92.88 191	99.40 95	92.16 112	99.30 136	91.92 189	93.66 200	99.55 121

Test_1112_low_res		95.72 140	94.83 150	98.42 114	97.79 170	96.41 120	99.65 147	96.65 295	92.70 140	92.86 192	96.13 231	92.15 113	99.30 136	91.88 190	93.64 201	99.55 121

Effi-MVS+-dtu		94.53 168	95.30 141	92.22 292	97.77 171	82.54 323	99.59 156	97.06 255	94.92 69	95.29 155	95.37 252	85.81 183	97.89 216	94.80 137	97.07 142	96.23 212

mvs-test1		95.53 145	95.97 109	94.20 242	97.77 171	85.44 312	99.95 31	97.06 255	94.92 69	96.58 127	98.72 152	85.81 183	98.98 144	94.80 137	98.11 116	98.18 195

tpm cat1		93.51 186	92.52 192	96.47 175	97.77 171	91.47 255	96.13 321	98.06 169	80.98 320	92.91 190	93.78 303	89.66 141	98.87 147	87.03 254	96.39 152	99.09 180

xiu_mvs_v1_base_debu		97.43 73	97.06 74	98.55 98	97.74 174	98.14 59	99.31 192	97.86 189	96.43 32	99.62 24	99.69 72	85.56 186	99.68 111	99.05 35	98.31 112	97.83 200

xiu_mvs_v1_base		97.43 73	97.06 74	98.55 98	97.74 174	98.14 59	99.31 192	97.86 189	96.43 32	99.62 24	99.69 72	85.56 186	99.68 111	99.05 35	98.31 112	97.83 200

xiu_mvs_v1_base_debi		97.43 73	97.06 74	98.55 98	97.74 174	98.14 59	99.31 192	97.86 189	96.43 32	99.62 24	99.69 72	85.56 186	99.68 111	99.05 35	98.31 112	97.83 200

EPP-MVSNet		96.69 104	96.60 89	96.96 162	97.74 174	93.05 213	99.37 185	98.56 86	88.75 237	95.83 147	99.01 117	96.01 23	98.56 170	96.92 109	97.20 139	99.25 160

gg-mvs-nofinetune		93.51 186	91.86 202	98.47 109	97.72 178	97.96 67	92.62 339	98.51 99	74.70 338	97.33 113	69.59 353	98.91 3	97.79 218	97.77 90	99.56 83	99.67 98

IB-MVS	92.85 6	94.99 156	93.94 164	98.16 125	97.72 178	95.69 149	99.99 3	98.81 57	94.28 88	92.70 193	96.90 207	95.08 44	99.17 140	96.07 118	73.88 325	99.60 110
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
Vis-MVSNet		95.72 140	95.15 146	97.45 150	97.62 180	94.28 177	99.28 197	98.24 149	94.27 89	96.84 122	98.94 126	79.39 253	98.76 155	93.25 172	98.49 107	99.30 155
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testpf		89.10 273	88.73 263	90.24 309	97.59 181	83.48 320	74.22 357	97.39 233	79.66 324	89.64 230	93.92 299	86.38 179	95.76 304	85.42 270	94.31 187	91.49 319

LCM-MVSNet-Re		92.31 207	92.60 190	91.43 299	97.53 182	79.27 337	99.02 225	91.83 351	92.07 170	80.31 308	94.38 294	83.50 201	95.48 306	97.22 100	97.58 127	99.54 125

GBi-Net		90.88 238	89.82 240	94.08 246	97.53 182	91.97 235	98.43 272	96.95 276	87.05 265	89.68 226	94.72 282	71.34 304	96.11 294	87.01 255	85.65 250	94.17 243

test1		90.88 238	89.82 240	94.08 246	97.53 182	91.97 235	98.43 272	96.95 276	87.05 265	89.68 226	94.72 282	71.34 304	96.11 294	87.01 255	85.65 250	94.17 243

FMVSNet2		91.02 235	89.56 244	95.41 199	97.53 182	95.74 144	98.98 227	97.41 230	87.05 265	88.43 251	95.00 271	71.34 304	96.24 292	85.12 273	85.21 255	94.25 240

CDS-MVSNet		96.34 125	96.07 102	97.13 159	97.37 186	94.96 164	99.53 166	97.91 183	91.55 184	95.37 154	98.32 173	95.05 46	97.13 255	93.80 162	95.75 164	99.30 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 101	96.26 98	98.16 125	97.36 187	96.48 118	99.96 19	98.29 143	91.93 174	95.77 148	98.07 178	95.54 34	98.29 196	90.55 208	98.89 100	99.70 93

MVSFormer		96.94 91	96.60 89	97.95 134	97.28 188	97.70 75	99.55 163	97.27 243	91.17 198	99.43 39	99.54 87	90.92 131	96.89 271	94.67 142	99.62 77	99.25 160

lupinMVS		97.85 61	97.60 60	98.62 93	97.28 188	97.70 75	99.99 3	97.55 211	95.50 60	99.43 39	99.67 76	90.92 131	98.71 159	98.40 68	99.62 77	99.45 133

Patchmatch-test1		94.39 171	93.46 175	97.17 158	97.10 190	94.44 174	98.86 242	98.32 140	93.30 121	96.17 138	95.38 250	76.48 278	97.34 232	88.12 238	97.43 129	99.74 88

TAMVS		95.85 138	95.58 133	96.65 173	97.07 191	93.50 194	99.17 205	97.82 193	91.39 191	95.02 168	98.01 179	92.20 111	97.30 239	93.75 164	95.83 162	99.14 175

Fast-Effi-MVS+-dtu		93.72 183	93.86 167	93.29 266	97.06 192	86.16 305	99.80 101	96.83 288	92.66 143	92.58 194	97.83 183	81.39 229	97.67 221	89.75 218	96.87 147	96.05 214

CostFormer		96.10 132	95.88 116	96.78 167	97.03 193	92.55 226	97.08 309	97.83 192	90.04 218	98.72 73	94.89 278	95.01 48	98.29 196	96.54 113	95.77 163	99.50 130

test-LLR		96.47 118	96.04 103	97.78 139	97.02 194	95.44 152	99.96 19	98.21 152	94.07 95	95.55 150	96.38 223	93.90 81	98.27 199	90.42 210	98.83 102	99.64 104

test-mter		96.39 124	95.93 112	97.78 139	97.02 194	95.44 152	99.96 19	98.21 152	91.81 178	95.55 150	96.38 223	95.17 41	98.27 199	90.42 210	98.83 102	99.64 104

gm-plane-assit					96.97 196	93.76 191			91.47 187		98.96 123		98.79 151	94.92 133

QAPM		95.40 148	94.17 161	99.10 59	96.92 197	97.71 73	99.40 180	98.68 65	89.31 224	88.94 245	98.89 127	82.48 207	99.96 42	93.12 178	99.83 61	99.62 106

tpm2		95.47 147	95.18 145	96.35 182	96.91 198	91.70 249	96.96 312	97.93 180	88.04 248	98.44 85	95.40 247	93.32 92	97.97 211	94.00 156	95.61 166	99.38 145

FMVSNet5		88.32 279	87.47 279	90.88 302	96.90 199	88.39 295	97.28 306	95.68 313	82.60 306	84.67 291	92.40 315	79.83 251	91.16 338	76.39 324	81.51 272	93.09 299

3Dnovator+	91.53 11	96.31 127	95.24 142	99.52 20	96.88 200	98.64 40	99.72 131	98.24 149	95.27 65	88.42 253	98.98 121	82.76 206	99.94 58	97.10 103	99.83 61	99.96 57

Patchmatch-test		92.65 202	91.50 206	96.10 187	96.85 201	90.49 267	91.50 344	97.19 247	82.76 305	90.23 208	95.59 242	95.02 47	98.00 210	77.41 319	96.98 144	99.82 78

MVS		96.60 109	95.56 134	99.72 4	96.85 201	99.22 8	98.31 280	98.94 39	91.57 183	90.90 203	99.61 82	86.66 177	99.96 42	97.36 96	99.88 56	99.99 12

3Dnovator	91.47 12	96.28 130	95.34 140	99.08 61	96.82 203	97.47 84	99.45 177	98.81 57	95.52 59	89.39 235	99.00 119	81.97 216	99.95 50	97.27 98	99.83 61	99.84 76

EI-MVSNet		93.73 182	93.40 180	94.74 223	96.80 204	92.69 221	99.06 219	97.67 201	88.96 232	91.39 199	99.02 115	88.75 158	97.30 239	91.07 198	87.85 237	94.22 241

CVMVSNet		94.68 163	94.94 149	93.89 256	96.80 204	86.92 304	99.06 219	98.98 37	94.45 81	94.23 178	99.02 115	85.60 185	95.31 309	90.91 203	95.39 170	99.43 136

IterMVS-LS		92.69 200	92.11 197	94.43 237	96.80 204	92.74 219	99.45 177	96.89 283	88.98 230	89.65 229	95.38 250	88.77 157	96.34 288	90.98 201	82.04 269	94.22 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.91 237	90.17 232	93.12 269	96.78 207	90.42 270	98.89 235	97.05 259	89.03 228	86.49 275	95.42 246	76.59 276	95.02 312	87.22 251	84.09 260	93.93 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 95	95.96 110	99.48 25	96.74 208	98.52 48	98.31 280	98.86 54	95.82 48	89.91 217	98.98 121	87.49 167	99.96 42	97.80 87	99.73 70	99.96 57

semantic-postprocess				92.93 274	96.72 209	89.96 277		96.99 269	88.95 233	86.63 272	95.67 239	76.50 277	95.00 313	87.04 253	84.04 263	93.84 277

MVS-HIRNet		86.22 292	83.19 307	95.31 200	96.71 210	90.29 271	92.12 341	97.33 238	62.85 349	86.82 269	70.37 352	69.37 313	97.49 225	75.12 326	97.99 121	98.15 196

VDDNet		93.12 191	91.91 201	96.76 168	96.67 211	92.65 224	98.69 252	98.21 152	82.81 304	97.75 107	99.28 101	61.57 336	99.48 129	98.09 77	94.09 191	98.15 196

MIMVSNet		90.30 253	88.67 264	95.17 204	96.45 212	91.64 251	92.39 340	97.15 251	85.99 278	90.50 206	93.19 311	66.95 322	94.86 316	82.01 294	93.43 202	99.01 183

CR-MVSNet		93.45 189	92.62 189	95.94 189	96.29 213	92.66 222	92.01 342	96.23 303	92.62 146	96.94 120	93.31 309	91.04 129	96.03 299	79.23 309	95.96 158	99.13 177

RPMNet		89.39 269	87.20 281	95.94 189	96.29 213	92.66 222	92.01 342	97.63 203	70.19 346	96.94 120	85.87 345	87.25 171	96.03 299	62.69 342	95.96 158	99.13 177

Patchmtry		89.70 263	88.49 265	93.33 265	96.24 215	89.94 280	91.37 345	96.23 303	78.22 327	87.69 259	93.31 309	91.04 129	96.03 299	80.18 303	82.10 268	94.02 253

JIA-IIPM		91.76 219	90.70 215	94.94 213	96.11 216	87.51 300	93.16 337	98.13 165	75.79 334	97.58 109	77.68 349	92.84 100	97.97 211	88.47 234	96.54 149	99.33 152

OpenMVS	90.15 15	94.77 160	93.59 171	98.33 120	96.07 217	97.48 83	99.56 161	98.57 84	90.46 210	86.51 274	98.95 125	78.57 264	99.94 58	93.86 157	99.74 69	97.57 204

PAPM		98.60 26	98.42 26	99.14 53	96.05 218	98.96 13	99.90 59	99.35 27	96.68 29	98.35 90	99.66 78	96.45 20	98.51 173	99.45 24	99.89 54	99.96 57

CLD-MVS		94.06 175	93.90 165	94.55 232	96.02 219	90.69 262	99.98 6	97.72 198	96.62 31	91.05 202	98.85 138	77.21 270	98.47 175	98.11 75	89.51 217	94.48 220
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 250	88.75 262	95.25 201	95.99 220	90.16 273	91.22 346	97.54 213	76.80 330	97.26 114	86.01 344	91.88 117	96.07 298	66.16 339	95.91 160	99.51 128

ACMH+	89.98 16	90.35 251	89.54 245	92.78 277	95.99 220	86.12 306	98.81 244	97.18 248	89.38 223	83.14 299	97.76 184	68.42 318	98.43 180	89.11 229	86.05 249	93.78 280

DeepMVS_CX				82.92 329	95.98 222	58.66 353		96.01 308	92.72 138	78.34 315	95.51 244	58.29 342	98.08 206	82.57 290	85.29 253	92.03 313

ACMP	92.05 9	92.74 198	92.42 194	93.73 257	95.91 223	88.72 288	99.81 98	97.53 215	94.13 91	87.00 266	98.23 174	74.07 295	98.47 175	96.22 117	88.86 224	93.99 261
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP-NCC					95.78 224		99.87 69		96.82 22	93.37 183

ACMP_Plane					95.78 224		99.87 69		96.82 22	93.37 183

HQP-MVS		94.61 165	94.50 156	94.92 215	95.78 224	91.85 240	99.87 69	97.89 185	96.82 22	93.37 183	98.65 156	80.65 242	98.39 186	97.92 85	89.60 212	94.53 216

NP-MVS					95.77 227	91.79 242					98.65 156

plane_prior6					95.76 228	91.72 248						80.47 246

ACMM	91.95 10	92.88 196	92.52 192	93.98 253	95.75 229	89.08 286	99.77 110	97.52 217	93.00 126	89.95 216	97.99 180	76.17 281	98.46 178	93.63 167	88.87 223	94.39 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 177	92.84 185	96.80 166	95.73 230	93.57 193	99.88 66	97.24 245	92.57 153	92.92 189	96.66 216	78.73 263	97.67 221	87.75 241	94.06 198	99.17 168

plane_prior1					95.73 230

jason		97.24 81	96.86 80	98.38 119	95.73 230	97.32 95	99.97 12	97.40 232	95.34 63	98.60 81	99.54 87	87.70 165	98.56 170	97.94 84	99.47 88	99.25 160
jason: jason.
HQP_MVS		94.49 169	94.36 158	94.87 218	95.71 233	91.74 245	99.84 91	97.87 187	96.38 35	93.01 187	98.59 160	80.47 246	98.37 191	97.79 88	89.55 215	94.52 218

plane_prior7					95.71 233	91.59 253

ITE_SJBPF				92.38 288	95.69 235	85.14 313		95.71 312	92.81 133	89.33 238	98.11 176	70.23 311	98.42 181	85.91 266	88.16 235	93.59 288

ACMH	89.72 17	90.64 244	89.63 242	93.66 261	95.64 236	88.64 291	98.55 262	97.45 223	89.03 228	81.62 304	97.61 185	69.75 312	98.41 182	89.37 226	87.62 241	93.92 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		88.50 278	86.64 282	94.08 246	95.62 237	91.97 235	98.43 272	96.95 276	83.00 303	86.08 282	94.72 282	59.09 341	96.11 294	81.82 296	84.07 261	94.17 243

LPG-MVS_test		92.96 194	92.71 188	93.71 259	95.43 238	88.67 289	99.75 117	97.62 205	92.81 133	90.05 211	98.49 166	75.24 287	98.40 184	95.84 124	89.12 219	94.07 250

LGP-MVS_train				93.71 259	95.43 238	88.67 289		97.62 205	92.81 133	90.05 211	98.49 166	75.24 287	98.40 184	95.84 124	89.12 219	94.07 250

tpm		93.70 184	93.41 179	94.58 230	95.36 240	87.41 302	97.01 310	96.90 282	90.85 206	96.72 126	94.14 298	90.40 135	96.84 274	90.75 206	88.54 230	99.51 128

VPA-MVSNet		92.70 199	91.55 205	96.16 185	95.09 241	96.20 130	98.88 236	99.00 36	91.02 203	91.82 197	95.29 258	76.05 283	97.96 213	95.62 127	81.19 274	94.30 236

LP		86.76 285	84.85 289	92.50 283	95.08 242	85.89 308	89.97 347	96.97 274	75.28 337	84.97 290	90.68 321	80.78 239	95.13 311	61.64 343	88.31 233	96.46 209

LTVRE_ROB	88.28 18	90.29 254	89.05 257	94.02 249	95.08 242	90.15 274	97.19 307	97.43 225	84.91 291	83.99 295	97.06 202	74.00 296	98.28 198	84.08 279	87.71 239	93.62 287
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
TinyColmap		87.87 282	86.51 283	91.94 295	95.05 244	85.57 310	97.65 299	94.08 340	84.40 297	81.82 303	96.85 211	62.14 335	98.33 193	80.25 302	86.37 248	91.91 315

test0.0.03 1		93.86 176	93.61 168	94.64 227	95.02 245	92.18 233	99.93 50	98.58 82	94.07 95	87.96 257	98.50 165	93.90 81	94.96 314	81.33 297	93.17 206	96.78 206

UniMVSNet (Re)		93.07 193	92.13 196	95.88 191	94.84 246	96.24 129	99.88 66	98.98 37	92.49 157	89.25 239	95.40 247	87.09 173	97.14 253	93.13 177	78.16 304	94.26 238

USDC		90.00 260	88.96 258	93.10 271	94.81 247	88.16 297	98.71 250	95.54 318	93.66 113	83.75 297	97.20 195	65.58 326	98.31 195	83.96 282	87.49 243	92.85 306

VPNet		91.81 214	90.46 220	95.85 193	94.74 248	95.54 151	98.98 227	98.59 81	92.14 166	90.77 205	97.44 188	68.73 316	97.54 224	94.89 136	77.89 306	94.46 221

FIs		94.10 174	93.43 176	96.11 186	94.70 249	96.82 110	99.58 157	98.93 42	92.54 154	89.34 237	97.31 192	87.62 166	97.10 258	94.22 153	86.58 246	94.40 227

UniMVSNet_NR-MVSNet		92.95 195	92.11 197	95.49 196	94.61 250	95.28 158	99.83 95	99.08 33	91.49 185	89.21 241	96.86 210	87.14 172	96.73 278	93.20 173	77.52 310	94.46 221

WR-MVS		92.31 207	91.25 209	95.48 198	94.45 251	95.29 157	99.60 155	98.68 65	90.10 215	88.07 256	96.89 208	80.68 241	96.80 277	93.14 176	79.67 295	94.36 230

nrg030		93.51 186	92.53 191	96.45 177	94.36 252	97.20 98	99.81 98	97.16 250	91.60 182	89.86 220	97.46 187	86.37 180	97.68 220	95.88 122	80.31 285	94.46 221

tfpnnormal		89.29 271	87.61 277	94.34 239	94.35 253	94.13 180	98.95 231	98.94 39	83.94 298	84.47 292	95.51 244	74.84 290	97.39 228	77.05 322	80.41 283	91.48 320

FC-MVSNet-test		93.81 179	93.15 183	95.80 194	94.30 254	96.20 130	99.42 179	98.89 52	92.33 160	89.03 244	97.27 194	87.39 170	96.83 275	93.20 173	86.48 247	94.36 230

MS-PatchMatch		90.65 243	90.30 225	91.71 298	94.22 255	85.50 311	98.24 285	97.70 199	88.67 238	86.42 277	96.37 225	67.82 320	98.03 209	83.62 284	99.62 77	91.60 318

WR-MVS_H		91.30 228	90.35 223	94.15 243	94.17 256	92.62 225	99.17 205	98.94 39	88.87 235	86.48 276	94.46 293	84.36 195	96.61 281	88.19 235	78.51 300	93.21 298

DU-MVS		92.46 205	91.45 208	95.49 196	94.05 257	95.28 158	99.81 98	98.74 61	92.25 161	89.21 241	96.64 218	81.66 223	96.73 278	93.20 173	77.52 310	94.46 221

NR-MVSNet		91.56 221	90.22 230	95.60 195	94.05 257	95.76 143	98.25 284	98.70 63	91.16 200	80.78 307	96.64 218	83.23 204	96.57 282	91.41 194	77.73 308	94.46 221

CP-MVSNet		91.23 231	90.22 230	94.26 240	93.96 259	92.39 229	99.09 210	98.57 84	88.95 233	86.42 277	96.57 220	79.19 257	96.37 286	90.29 213	78.95 297	94.02 253

XXY-MVS		91.82 213	90.46 220	95.88 191	93.91 260	95.40 155	98.87 240	97.69 200	88.63 240	87.87 258	97.08 200	74.38 294	97.89 216	91.66 193	84.07 261	94.35 233

PS-CasMVS		90.63 245	89.51 247	93.99 252	93.83 261	91.70 249	98.98 227	98.52 93	88.48 241	86.15 281	96.53 222	75.46 285	96.31 289	88.83 231	78.86 299	93.95 267

test_0402		85.58 299	83.94 302	90.50 306	93.81 262	85.04 314	98.55 262	95.20 332	76.01 332	79.72 311	95.13 261	64.15 331	96.26 291	66.04 340	86.88 245	90.21 331

XVG-ACMP-BASELINE		91.22 232	90.75 214	92.63 279	93.73 263	85.61 309	98.52 267	97.44 224	92.77 137	89.90 218	96.85 211	66.64 323	98.39 186	92.29 182	88.61 228	93.89 273

TranMVSNet+NR-MVSNet		91.68 220	90.61 217	94.87 218	93.69 264	93.98 183	99.69 134	98.65 69	91.03 202	88.44 250	96.83 214	80.05 250	96.18 293	90.26 214	76.89 317	94.45 226

v7		91.20 233	89.99 238	94.82 221	93.57 265	93.41 201	99.57 159	96.98 271	86.83 269	89.88 219	95.22 260	81.01 235	97.14 253	85.53 269	81.31 273	93.90 271

TransMVSNet (Re)		87.25 283	85.28 287	93.16 268	93.56 266	91.03 257	98.54 264	94.05 341	83.69 301	81.09 306	96.16 229	75.32 286	96.40 285	76.69 323	68.41 332	92.06 312

Anonymous20240521		90.82 240	89.48 249	94.82 221	93.53 267	94.07 181	99.07 216	97.43 225	87.10 264	84.14 293	94.60 288	83.11 205	97.32 235	85.73 268	80.61 282	93.08 300

v10		90.25 255	88.82 260	94.57 231	93.53 267	93.43 197	99.08 212	96.87 286	85.00 290	87.34 264	94.51 289	80.93 237	97.02 267	82.85 289	79.23 296	93.26 296

v1neww		91.44 222	90.28 226	94.91 216	93.50 269	93.43 197	99.73 126	97.06 255	87.55 252	90.08 209	95.11 263	81.98 214	97.32 235	87.41 246	80.15 288	93.99 261

v7new		91.44 222	90.28 226	94.91 216	93.50 269	93.43 197	99.73 126	97.06 255	87.55 252	90.08 209	95.11 263	81.98 214	97.32 235	87.41 246	80.15 288	93.99 261

testgi		89.01 274	88.04 273	91.90 296	93.49 271	84.89 315	99.73 126	95.66 314	93.89 107	85.14 288	98.17 175	59.68 340	94.66 318	77.73 317	88.88 222	96.16 213

v16		86.52 287	84.49 291	92.60 281	93.45 272	93.31 206	98.60 261	95.52 320	82.30 309	74.59 329	87.70 331	81.95 218	94.18 320	79.93 306	66.38 337	90.30 328

v6		91.44 222	90.27 228	94.93 214	93.44 273	93.44 196	99.73 126	97.05 259	87.57 251	90.05 211	95.10 265	81.87 219	97.39 228	87.45 243	80.17 287	93.98 265

v8		90.54 247	89.17 253	94.66 226	93.43 274	93.40 204	99.20 202	96.94 279	85.76 281	87.56 260	94.51 289	81.96 217	97.19 247	84.94 275	78.25 303	93.38 293

V42		91.28 230	90.12 236	94.74 223	93.42 275	93.46 195	99.68 139	97.02 265	87.36 260	89.85 221	95.05 267	81.31 231	97.34 232	87.34 249	80.07 290	93.40 291

v18		86.59 286	84.57 290	92.65 278	93.41 276	93.43 197	98.69 252	95.55 317	82.44 307	74.71 327	87.68 332	82.11 211	94.21 319	80.14 304	66.37 338	90.32 327

pm-mvs1		89.36 270	87.81 275	94.01 250	93.40 277	91.93 238	98.62 259	96.48 301	86.25 276	83.86 296	96.14 230	73.68 297	97.04 262	86.16 264	75.73 321	93.04 302

v17		86.51 288	84.49 291	92.57 282	93.38 278	93.29 207	98.61 260	95.54 318	82.32 308	74.69 328	87.63 333	82.03 212	94.17 321	80.02 305	66.17 339	90.26 329

divwei89l23v2f112		91.37 225	90.15 233	95.00 209	93.35 279	93.78 190	99.78 105	97.05 259	87.54 254	89.73 225	94.89 278	82.24 209	97.21 245	86.91 258	79.90 294	94.00 258

v1		91.36 226	90.14 234	95.04 207	93.35 279	93.80 186	99.77 110	97.05 259	87.53 255	89.77 223	94.91 276	81.99 213	97.33 234	86.90 260	79.98 293	94.00 258

v1141		91.36 226	90.14 234	95.00 209	93.33 281	93.79 187	99.78 105	97.05 259	87.52 256	89.75 224	94.89 278	82.13 210	97.21 245	86.84 261	80.00 292	94.00 258

V14		86.22 292	84.15 295	92.41 287	93.30 282	93.16 209	98.47 269	95.47 321	82.10 312	74.27 331	87.41 334	81.73 220	94.02 324	79.26 308	65.37 342	90.04 336

v15		86.26 291	84.19 294	92.47 284	93.29 283	93.28 208	98.53 266	95.47 321	82.24 311	74.34 330	87.34 335	81.71 221	94.07 322	79.39 307	65.42 340	90.06 335

v11		86.09 296	83.98 300	92.42 286	93.29 283	93.41 201	98.52 267	95.30 328	81.73 317	74.27 331	87.20 337	81.24 232	93.85 331	77.68 318	66.61 336	90.00 337

V9		86.16 294	84.07 296	92.43 285	93.27 285	93.04 214	98.40 276	95.45 323	81.98 314	74.18 333	87.31 336	81.58 227	94.06 323	79.12 311	65.33 343	90.20 332

v1144		91.09 234	89.83 239	94.87 218	93.25 286	93.69 192	99.62 154	96.98 271	86.83 269	89.64 230	94.99 272	80.94 236	97.05 261	85.08 274	81.16 275	93.87 275

v13		86.06 297	83.97 301	92.34 291	93.25 286	92.85 217	98.26 283	95.44 325	81.70 318	74.02 336	87.11 340	81.58 227	94.00 326	78.94 313	65.41 341	90.18 333

v12		86.10 295	84.01 297	92.37 289	93.23 288	92.96 215	98.33 279	95.45 323	81.87 315	74.05 335	87.15 338	81.60 226	93.98 327	79.09 312	65.28 344	90.18 333

v1192		90.62 246	89.25 252	94.72 225	93.13 289	93.07 211	99.50 169	97.02 265	86.33 275	89.56 233	95.01 269	79.22 256	97.09 260	82.34 292	81.16 275	94.01 255

v2v482		91.30 228	90.07 237	95.01 208	93.13 289	93.79 187	99.77 110	97.02 265	88.05 247	89.25 239	95.37 252	80.73 240	97.15 251	87.28 250	80.04 291	94.09 249

OPM-MVS		93.21 190	92.80 186	94.44 235	93.12 291	90.85 261	99.77 110	97.61 208	96.19 42	91.56 198	98.65 156	75.16 289	98.47 175	93.78 163	89.39 218	93.99 261

v144192		90.79 241	89.52 246	94.59 229	93.11 292	92.77 218	99.56 161	96.99 269	86.38 274	89.82 222	94.95 275	80.50 245	97.10 258	83.98 281	80.41 283	93.90 271

PEN-MVS		90.19 257	89.06 256	93.57 262	93.06 293	90.90 260	99.06 219	98.47 105	88.11 246	85.91 283	96.30 226	76.67 275	95.94 303	87.07 252	76.91 316	93.89 273

v1240		90.20 256	88.79 261	94.44 235	93.05 294	92.27 231	99.38 184	96.92 280	85.89 279	89.36 236	94.87 281	77.89 269	97.03 265	80.66 300	81.08 277	94.01 255

v148		90.70 242	89.63 242	93.92 254	92.97 295	90.97 258	99.75 117	96.89 283	87.51 257	88.27 254	95.01 269	81.67 222	97.04 262	87.40 248	77.17 314	93.75 281

v1921920		90.46 248	89.12 254	94.50 233	92.96 296	92.46 227	99.49 170	96.98 271	86.10 277	89.61 232	95.30 255	78.55 265	97.03 265	82.17 293	80.89 281	94.01 255

Baseline_NR-MVSNet		90.33 252	89.51 247	92.81 276	92.84 297	89.95 278	99.77 110	93.94 342	84.69 294	89.04 243	95.66 240	81.66 223	96.52 283	90.99 200	76.98 315	91.97 314

pmmvs4		92.10 210	91.07 212	95.18 203	92.82 298	94.96 164	99.48 172	96.83 288	87.45 259	88.66 248	96.56 221	83.78 199	96.83 275	89.29 227	84.77 258	93.75 281

LF4IMVS		89.25 272	88.85 259	90.45 308	92.81 299	81.19 331	98.12 290	94.79 335	91.44 188	86.29 279	97.11 198	65.30 328	98.11 205	88.53 233	85.25 254	92.07 311

pcd1.5k->3k		37.58 340	39.62 340	31.46 351	92.73 300	0.00 369	0.00 360	97.52 217	0.00 364	0.00 366	0.00 366	78.40 268	0.00 366	0.00 363	87.90 236	94.37 229

DTE-MVSNet		89.40 268	88.24 271	92.88 275	92.66 301	89.95 278	99.10 209	98.22 151	87.29 261	85.12 289	96.22 228	76.27 280	95.30 310	83.56 285	75.74 320	93.41 290

EU-MVSNet		90.14 259	90.34 224	89.54 315	92.55 302	81.06 332	98.69 252	98.04 171	91.41 190	86.59 273	96.84 213	80.83 238	93.31 336	86.20 263	81.91 270	94.26 238

our_test_3		90.39 249	89.48 249	93.12 269	92.40 303	89.57 283	99.33 189	96.35 302	87.84 249	85.30 287	94.99 272	84.14 197	96.09 297	80.38 301	84.56 259	93.71 286

ppachtmachnet_test		89.58 265	88.35 269	93.25 267	92.40 303	90.44 269	99.33 189	96.73 292	85.49 287	85.90 284	95.77 236	81.09 234	96.00 302	76.00 325	82.49 267	93.30 294

v52		89.55 266	88.41 267	92.98 272	92.32 305	90.01 276	98.88 236	96.89 283	84.51 295	86.89 267	94.22 296	79.23 255	97.16 249	84.46 277	78.27 302	91.76 316

v7n		89.65 264	88.29 270	93.72 258	92.22 306	90.56 266	99.07 216	97.10 253	85.42 289	86.73 270	94.72 282	80.06 249	97.13 255	81.14 298	78.12 305	93.49 289

V4		89.55 266	88.41 267	92.98 272	92.21 307	90.03 275	98.87 240	96.91 281	84.51 295	86.84 268	94.21 297	79.37 254	97.15 251	84.45 278	78.28 301	91.76 316

PS-MVSNAJss		93.64 185	93.31 182	94.61 228	92.11 308	92.19 232	99.12 208	97.38 234	92.51 156	88.45 249	96.99 206	91.20 125	97.29 242	94.36 147	87.71 239	94.36 230

pmmvs5		90.17 258	89.09 255	93.40 264	92.10 309	89.77 281	99.74 120	95.58 316	85.88 280	87.24 265	95.74 237	73.41 298	96.48 284	88.54 232	83.56 264	93.95 267

N_pmnet		80.06 317	80.78 314	77.89 333	91.94 310	45.28 362	98.80 245	56.82 367	78.10 328	80.08 310	93.33 307	77.03 271	95.76 304	68.14 335	82.81 266	92.64 307

v748		88.94 275	87.72 276	92.61 280	91.91 311	87.50 301	99.07 216	96.97 274	84.76 292	85.79 285	93.63 306	79.19 257	97.04 262	83.16 287	75.03 324	93.28 295

test_djsdf		92.83 197	92.29 195	94.47 234	91.90 312	92.46 227	99.55 163	97.27 243	91.17 198	89.96 215	96.07 233	81.10 233	96.89 271	94.67 142	88.91 221	94.05 252

DI_MVS_plusplus_test		92.48 203	90.60 218	98.11 130	91.88 313	96.13 133	99.64 151	97.73 196	92.69 141	76.02 321	93.79 302	70.49 309	99.07 141	95.88 122	97.26 135	99.14 175

test_normal		92.44 206	90.54 219	98.12 129	91.85 314	96.18 132	99.68 139	97.73 196	92.66 143	75.76 325	93.74 304	70.49 309	99.04 143	95.71 126	97.27 134	99.13 177

SixPastTwentyTwo		88.73 277	88.01 274	90.88 302	91.85 314	82.24 325	98.22 287	95.18 333	88.97 231	82.26 302	96.89 208	71.75 303	96.67 280	84.00 280	82.98 265	93.72 285

K. test v3		88.05 281	87.24 280	90.47 307	91.82 316	82.23 326	98.96 230	97.42 228	89.05 227	76.93 318	95.60 241	68.49 317	95.42 307	85.87 267	81.01 279	93.75 281

OurMVSNet-221017-0		89.81 262	89.48 249	90.83 304	91.64 317	81.21 330	98.17 289	95.38 327	91.48 186	85.65 286	97.31 192	72.66 299	97.29 242	88.15 236	84.83 257	93.97 266

mvs_tets		91.81 214	91.08 211	94.00 251	91.63 318	90.58 265	98.67 255	97.43 225	92.43 158	87.37 263	97.05 203	71.76 302	97.32 235	94.75 140	88.68 227	94.11 248

Gipuma		66.95 327	65.00 326	72.79 338	91.52 319	67.96 343	66.16 358	95.15 334	47.89 352	58.54 349	67.99 355	29.74 356	87.54 347	50.20 352	77.83 307	62.87 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
jajsoiax		91.92 212	91.18 210	94.15 243	91.35 320	90.95 259	99.00 226	97.42 228	92.61 147	87.38 262	97.08 200	72.46 300	97.36 230	94.53 145	88.77 225	94.13 247

MDA-MVSNet-bldmvs		84.09 309	81.52 313	91.81 297	91.32 321	88.00 299	98.67 255	95.92 310	80.22 322	55.60 352	93.32 308	68.29 319	93.60 334	73.76 327	76.61 318	93.82 279

MVP-Stereo		90.93 236	90.45 222	92.37 289	91.25 322	88.76 287	98.05 294	96.17 305	87.27 262	84.04 294	95.30 255	78.46 266	97.27 244	83.78 283	99.70 73	91.09 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 301	83.32 306	92.10 293	90.96 323	88.58 292	99.20 202	96.52 299	79.70 323	57.12 351	92.69 313	79.11 259	93.86 330	77.10 321	77.46 312	93.86 276

YYNet1		85.50 302	83.33 305	92.00 294	90.89 324	88.38 296	99.22 201	96.55 298	79.60 325	57.26 350	92.72 312	79.09 260	93.78 332	77.25 320	77.37 313	93.84 277

anonymousdsp		91.79 218	90.92 213	94.41 238	90.76 325	92.93 216	98.93 233	97.17 249	89.08 226	87.46 261	95.30 255	78.43 267	96.92 270	92.38 181	88.73 226	93.39 292

lessismore_v0				90.53 305	90.58 326	80.90 333		95.80 311		77.01 317	95.84 234	66.15 325	96.95 268	83.03 288	75.05 323	93.74 284

EG-PatchMatch MVS		85.35 303	83.81 304	89.99 313	90.39 327	81.89 328	98.21 288	96.09 307	81.78 316	74.73 326	93.72 305	51.56 349	97.12 257	79.16 310	88.61 228	90.96 323

CMPMVS	61.59 21	84.75 306	85.14 288	83.57 326	90.32 328	62.54 349	96.98 311	97.59 210	74.33 339	69.95 340	96.66 216	64.17 330	98.32 194	87.88 240	88.41 232	89.84 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 308	82.92 308	89.21 316	90.03 329	82.60 322	96.89 313	95.62 315	80.59 321	75.77 324	89.17 323	65.04 329	94.79 317	72.12 328	81.02 278	90.23 330

pmmvs6		85.69 298	83.84 303	91.26 301	90.00 330	84.41 317	97.82 298	96.15 306	75.86 333	81.29 305	95.39 249	61.21 337	96.87 273	83.52 286	73.29 327	92.50 308

DSMNet-mixed		88.28 280	88.24 271	88.42 321	89.64 331	75.38 339	98.06 293	89.86 356	85.59 286	88.20 255	92.14 316	76.15 282	91.95 337	78.46 314	96.05 155	97.92 199

UnsupCasMVSNet_eth		85.52 300	83.99 298	90.10 311	89.36 332	83.51 319	96.65 314	97.99 174	89.14 225	75.89 323	93.83 301	63.25 333	93.92 328	81.92 295	67.90 334	92.88 305

Anonymous20231206		86.32 290	85.42 286	89.02 317	89.11 333	80.53 335	99.05 222	95.28 329	85.43 288	82.82 300	93.92 299	74.40 293	93.44 335	66.99 337	81.83 271	93.08 300

OpenMVS_ROB	79.82 20	83.77 312	81.68 312	90.03 312	88.30 334	82.82 321	98.46 270	95.22 331	73.92 341	76.00 322	91.29 319	55.00 345	96.94 269	68.40 334	88.51 231	90.34 326

test20.03		84.72 307	83.99 298	86.91 323	88.19 335	80.62 334	98.88 236	95.94 309	88.36 243	78.87 312	94.62 287	68.75 315	89.11 342	66.52 338	75.82 319	91.00 322

Test4		88.80 276	85.91 285	97.48 149	87.33 336	95.72 146	99.29 196	97.04 264	92.82 132	70.35 339	91.46 318	44.37 352	97.43 227	93.37 171	97.17 140	99.29 157

MIMVSNet1		82.58 313	80.51 315	88.78 319	86.68 337	84.20 318	96.65 314	95.41 326	78.75 326	78.59 314	92.44 314	51.88 348	89.76 341	65.26 341	78.95 297	92.38 309

test2356		86.43 289	87.59 278	82.95 328	85.90 338	69.43 342	99.79 104	96.63 296	85.76 281	83.44 298	94.99 272	80.45 248	86.52 349	68.12 336	93.21 205	92.90 303

testus		83.91 311	84.49 291	82.17 330	85.68 339	66.11 346	99.68 139	93.53 346	86.55 271	82.60 301	94.91 276	56.70 344	88.19 345	68.46 333	92.31 209	92.21 310

UnsupCasMVSNet_bld		79.97 318	77.03 321	88.78 319	85.62 340	81.98 327	93.66 335	97.35 236	75.51 336	70.79 338	83.05 346	48.70 350	94.91 315	78.31 315	60.29 349	89.46 342

Patchmatch-RL test		86.90 284	85.98 284	89.67 314	84.45 341	75.59 338	89.71 348	92.43 348	86.89 268	77.83 316	90.94 320	94.22 69	93.63 333	87.75 241	69.61 329	99.79 82

pmmvs-eth3d		84.03 310	81.97 310	90.20 310	84.15 342	87.09 303	98.10 292	94.73 337	83.05 302	74.10 334	87.77 330	65.56 327	94.01 325	81.08 299	69.24 331	89.49 341

PM-MVS		80.47 315	78.88 317	85.26 325	83.79 343	72.22 340	95.89 326	91.08 352	85.71 285	76.56 320	88.30 324	36.64 353	93.90 329	82.39 291	69.57 330	89.66 340

new-patchmatchnet		81.19 314	79.34 316	86.76 324	82.86 344	80.36 336	97.92 296	95.27 330	82.09 313	72.02 337	86.87 341	62.81 334	90.74 340	71.10 329	63.08 346	89.19 343

testing_2		85.10 304	81.72 311	95.22 202	82.25 345	94.16 178	97.54 300	97.01 268	88.15 245	62.23 346	86.43 343	44.43 351	97.18 248	92.28 187	85.20 256	94.31 235

pmmvs3		80.27 316	77.77 320	87.76 322	80.32 346	82.43 324	98.23 286	91.97 350	72.74 342	78.75 313	87.97 327	57.30 343	90.99 339	70.31 330	62.37 347	89.87 338

1111		79.11 319	78.74 318	80.23 331	78.33 347	67.13 344	97.31 304	93.65 344	71.34 343	68.35 342	87.87 328	85.42 189	88.46 343	52.93 350	73.46 326	85.11 346

.test1245		71.48 322	71.80 323	70.51 341	78.33 347	67.13 344	97.31 304	93.65 344	71.34 343	68.35 342	87.87 328	85.42 189	88.46 343	52.93 350	11.01 360	55.94 359

test1235678		78.45 320	77.88 319	80.16 332	77.83 349	62.18 350	98.36 277	93.45 347	77.46 329	69.08 341	88.23 325	60.33 339	85.41 350	58.46 346	77.68 309	92.90 303

ambc				83.23 327	77.17 350	62.61 348	87.38 351	94.55 339		76.72 319	86.65 342	30.16 355	96.36 287	84.85 276	69.86 328	90.73 325

TDRefinement		84.76 305	82.56 309	91.38 300	74.58 351	84.80 316	97.36 303	94.56 338	84.73 293	80.21 309	96.12 232	63.56 332	98.39 186	87.92 239	63.97 345	90.95 324

test12356		75.26 321	75.12 322	75.67 337	74.02 352	60.60 352	96.43 317	92.15 349	74.17 340	66.35 344	88.11 326	52.29 347	84.36 352	57.41 347	75.12 322	82.05 347

E-PMN		52.30 333	52.18 334	52.67 348	71.51 353	45.40 361	93.62 336	76.60 365	36.01 358	43.50 358	64.13 358	27.11 358	67.31 361	31.06 360	26.06 355	45.30 362

EMVS		51.44 335	51.22 336	52.11 349	70.71 354	44.97 363	94.04 332	75.66 366	35.34 360	42.40 359	61.56 361	28.93 357	65.87 362	27.64 361	24.73 356	45.49 361

PMMVS2		67.15 326	64.15 328	76.14 335	70.56 355	62.07 351	93.89 333	87.52 360	58.09 350	60.02 348	78.32 348	22.38 360	84.54 351	59.56 345	47.03 351	81.80 348

no-one		63.48 329	59.26 330	76.14 335	66.71 356	65.06 347	92.75 338	89.92 355	68.96 347	46.96 357	66.55 356	21.74 361	87.68 346	57.07 348	22.69 358	75.68 352

FPMVS		68.72 323	68.72 324	68.71 342	65.95 357	44.27 364	95.97 325	94.74 336	51.13 351	53.26 354	90.50 322	25.11 359	83.00 353	60.80 344	80.97 280	78.87 350

PNet_i23d		56.44 330	53.54 332	65.14 345	65.34 358	50.33 359	89.06 350	79.57 362	45.77 353	35.75 361	68.95 354	10.75 366	74.40 357	48.48 354	38.20 352	70.70 353

wuyk23d		20.37 342	20.84 343	18.99 353	65.34 358	27.73 366	50.43 359	7.67 370	9.50 363	8.01 365	6.34 365	6.13 368	26.24 363	23.40 362	10.69 362	2.99 363

testmv		67.54 325	65.93 325	72.37 339	64.46 360	54.05 356	95.09 329	90.07 354	68.90 348	55.16 353	77.63 350	30.39 354	82.61 354	49.42 353	62.26 348	80.45 349

LCM-MVSNet		67.77 324	64.73 327	76.87 334	62.95 361	56.25 355	89.37 349	93.74 343	44.53 354	61.99 347	80.74 347	20.42 362	86.53 348	69.37 332	59.50 350	87.84 344

MVE	53.74 22	51.54 334	47.86 337	62.60 346	59.56 362	50.93 358	79.41 354	77.69 364	35.69 359	36.27 360	61.76 360	5.79 370	69.63 359	37.97 359	36.61 353	67.24 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		50.36 336	45.43 338	65.16 344	51.13 363	51.75 357	77.46 355	78.42 363	41.45 355	26.98 364	54.30 363	6.13 368	74.03 358	46.82 356	26.19 354	69.71 354

ANet_high		56.10 331	52.24 333	67.66 343	49.27 364	56.82 354	83.94 352	82.02 361	70.47 345	33.28 362	64.54 357	17.23 364	69.16 360	45.59 357	23.85 357	77.02 351

tmp_tt		65.23 328	62.94 329	72.13 340	44.90 365	50.03 360	81.05 353	89.42 359	38.45 356	48.51 356	99.90 11	54.09 346	78.70 356	91.84 192	18.26 359	87.64 345

PMVS	49.05 23	53.75 332	51.34 335	60.97 347	40.80 366	34.68 365	74.82 356	89.62 358	37.55 357	28.67 363	72.12 351	7.09 367	81.63 355	43.17 358	68.21 333	66.59 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 339	39.14 341	33.31 350	19.94 367	24.83 367	98.36 277	9.75 369	15.53 362	51.31 355	87.14 339	19.62 363	17.74 364	47.10 355	3.47 363	57.36 358

testmvs		40.60 338	44.45 339	29.05 352	19.49 368	14.11 368	99.68 139	18.47 368	20.74 361	64.59 345	98.48 169	10.95 365	17.09 365	56.66 349	11.01 360	55.94 359

cdsmvs_eth3d_5k		23.43 341	31.24 342	0.00 354	0.00 369	0.00 369	0.00 360	98.09 167	0.00 364	0.00 366	99.67 76	83.37 202	0.00 366	0.00 363	0.00 364	0.00 364

pcd_1.5k_mvsjas		7.60 344	10.13 345	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	91.20 125	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		8.28 343	11.04 344	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	99.40 95	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 345	0.00 346	0.00 354	0.00 369	0.00 369	0.00 360	0.00 371	0.00 364	0.00 366	0.00 366	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																99.59 112

test_part1				0.00 354		0.00 369	0.00 360	98.41 125				0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sam_mvs1												94.72 56				99.59 112

sam_mvs												94.25 68

MTGPA								98.28 144

test_post1							95.78 327				59.23 362	93.20 96	97.74 219	91.06 199

test_post											63.35 359	94.43 58	98.13 204

patchmatchnet-post											91.70 317	95.12 42	97.95 214

MTMP							99.87 69	96.49 300

test9_res														99.71 17	99.99 14	100.00 1

agg_prior2														99.48 23	100.00 1	100.00 1

test_prior4						98.05 63	99.94 45

test_prior2							99.95 31		95.78 50	99.73 14	99.76 56	96.00 24		99.78 8	100.00 1

旧先验2							99.46 176		94.21 90	99.85 5			99.95 50	96.96 107

新几何2							99.40 180

无先验							99.49 170	98.71 62	93.46 117				100.00 1	94.36 147		99.99 12

原ACMM2							99.90 59

testdata2													99.99 26	90.54 209

segment_acmp												96.68 12

testdata1							99.28 197		96.35 39

plane_prior5								97.87 187					98.37 191	97.79 88	89.55 215	94.52 218

plane_prior4											98.59 160

plane_prior3						91.64 251			96.63 30	93.01 187

plane_prior2							99.84 91		96.38 35

plane_prior						91.74 245	99.86 86		96.76 26						89.59 214

n2								0.00 371

nn								0.00 371

door-mid								89.69 357

test11								98.44 109

door								90.31 353

HQP5-MVS						91.85 240

BP-MVS														97.92 85

HQP4-MVS										93.37 183			98.39 186			94.53 216

HQP3-MVS								97.89 185							89.60 212

HQP2-MVS												80.65 242

MDTV_nov1_ep13_2view						96.26 125	96.11 322		91.89 175	98.06 100		94.40 60		94.30 150		99.67 98

ACMMP++_ref															87.04 244

ACMMP++															88.23 234

Test By Simon												92.82 102

High-resolution multi-view benchmark