High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 17	98.96 2	99.39 5	98.93 33	97.38 15	99.41 2	99.54 1	96.66 5	99.84 33	98.86 1	99.85 3	99.87 1

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 8	99.22 63	98.43 12	99.10 40	98.87 46	97.38 15	99.35 3	99.40 4	97.78 1	99.87 26	97.77 26	99.85 3	99.78 5
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		98.78 3	98.56 5	99.45 4	99.32 38	98.87 3	98.47 131	98.81 56	97.72 4	98.76 27	99.16 37	97.05 2	99.78 60	98.06 17	99.66 36	99.69 29

XVS		98.70 4	98.49 10	99.34 9	99.70 11	98.35 18	99.29 14	98.88 44	97.40 12	98.46 38	99.20 30	95.90 27	99.89 19	97.85 23	99.74 28	99.78 5

Regformer-2		98.69 5	98.52 7	99.19 22	99.35 31	98.01 35	98.37 138	98.81 56	97.48 9	99.21 7	99.21 27	96.13 15	99.80 48	98.40 12	99.73 30	99.75 15

Regformer-1		98.66 6	98.51 8	99.12 33	99.35 31	97.81 43	98.37 138	98.76 67	97.49 8	99.20 8	99.21 27	96.08 18	99.79 56	98.42 10	99.73 30	99.75 15

MCST-MVS		98.65 7	98.37 15	99.48 3	99.60 16	98.87 3	98.41 135	98.68 85	97.04 28	98.52 37	98.80 75	96.78 4	99.83 34	97.93 20	99.61 40	99.74 20

Regformer-4		98.64 8	98.53 6	98.99 39	99.43 29	97.37 56	98.40 136	98.79 63	97.46 10	99.09 10	99.31 17	95.86 29	99.80 48	98.64 2	99.76 20	99.79 3

SD-MVS		98.64 8	98.68 3	98.53 64	99.33 36	98.36 17	98.90 55	98.85 50	97.28 18	99.72 1	99.39 5	96.63 7	97.60 241	98.17 16	99.85 3	99.64 45

HFP-MVS		98.63 10	98.40 12	99.32 12	99.72 8	98.29 21	99.23 20	98.96 28	96.10 50	98.94 15	99.17 34	96.06 19	99.92 9	97.62 32	99.78 11	99.75 15

region2R		98.61 11	98.38 14	99.29 14	99.74 4	98.16 28	99.23 20	98.93 33	96.15 46	98.94 15	99.17 34	95.91 26	99.94 1	97.55 37	99.79 9	99.78 5

NCCC		98.61 11	98.35 18	99.38 6	99.28 52	98.61 6	98.45 132	98.76 67	97.82 3	98.45 41	98.93 66	96.65 6	99.83 34	97.38 44	99.41 66	99.71 26

Regformer-3		98.59 13	98.50 9	98.86 48	99.43 29	97.05 66	98.40 136	98.68 85	97.43 11	99.06 11	99.31 17	95.80 30	99.77 65	98.62 4	99.76 20	99.78 5

ACMMPR		98.59 13	98.36 16	99.29 14	99.74 4	98.15 29	99.23 20	98.95 30	96.10 50	98.93 19	99.19 33	95.70 31	99.94 1	97.62 32	99.79 9	99.78 5

HPM-MVS++		98.58 15	98.25 25	99.55 1	99.50 21	99.08 1	98.72 98	98.66 95	97.51 7	98.15 48	98.83 72	95.70 31	99.92 9	97.53 39	99.67 34	99.66 40

CP-MVS		98.57 16	98.36 16	99.19 22	99.66 14	97.86 40	99.34 11	98.87 46	95.96 53	98.60 34	99.13 39	96.05 21	99.94 1	97.77 26	99.86 2	99.77 12

MSLP-MVS		98.56 17	98.57 4	98.55 62	99.26 55	96.80 75	98.71 99	99.05 20	97.28 18	98.84 21	99.28 20	96.47 9	99.40 106	98.52 8	99.70 32	99.47 69

DeepC-MVS_fast	96.70 1	98.55 18	98.34 19	99.18 26	99.25 56	98.04 33	98.50 128	98.78 65	97.72 4	98.92 20	99.28 20	95.27 41	99.82 39	97.55 37	99.77 14	99.69 29
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
#test#		98.54 19	98.27 23	99.32 12	99.72 8	98.29 21	98.98 50	98.96 28	95.65 62	98.94 15	99.17 34	96.06 19	99.92 9	97.21 47	99.78 11	99.75 15

APD-MVS_3200maxsize		98.53 20	98.33 22	99.15 31	99.50 21	97.92 39	99.15 35	98.81 56	96.24 44	99.20 8	99.37 8	95.30 40	99.80 48	97.73 28	99.67 34	99.72 24

mPP-MVS		98.51 21	98.26 24	99.25 20	99.75 1	98.04 33	99.28 16	98.81 56	96.24 44	98.35 45	99.23 24	95.46 36	99.94 1	97.42 42	99.81 8	99.77 12

PGM-MVS		98.49 22	98.23 27	99.27 19	99.72 8	98.08 32	98.99 47	99.49 3	95.43 69	99.03 12	99.32 16	95.56 33	99.94 1	96.80 62	99.77 14	99.78 5

EI-MVSNet-Vis-set		98.47 23	98.39 13	98.69 53	99.46 26	96.49 87	98.30 148	98.69 82	97.21 22	98.84 21	99.36 12	95.41 37	99.78 60	98.62 4	99.65 37	99.80 2

MVS_111021_HR		98.47 23	98.34 19	98.88 47	99.22 63	97.32 57	97.91 186	99.58 1	97.20 23	98.33 46	99.00 57	95.99 22	99.64 85	98.05 18	99.76 20	99.69 29

EI-MVSNet-UG-set		98.41 25	98.34 19	98.61 58	99.45 27	96.32 94	98.28 150	98.68 85	97.17 25	98.74 28	99.37 8	95.25 42	99.79 56	98.57 6	99.54 54	99.73 22

DELS-MVS		98.40 26	98.20 29	98.99 39	99.00 74	97.66 45	97.75 200	98.89 41	97.71 6	98.33 46	98.97 59	94.97 48	99.88 25	98.42 10	99.76 20	99.42 76
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
TSAR-MVS		98.38 27	98.24 26	98.81 49	99.22 63	97.25 61	98.11 168	98.29 146	97.19 24	98.99 14	99.02 52	96.22 10	99.67 81	98.52 8	98.56 95	99.51 62

HPM-MVS_fast		98.38 27	98.13 30	99.12 33	99.75 1	97.86 40	99.44 4	98.82 53	94.46 105	98.94 15	99.20 30	95.16 44	99.74 71	97.58 34	99.85 3	99.77 12

HPM-MVS		98.36 29	98.10 31	99.13 32	99.74 4	97.82 42	99.53 1	98.80 62	94.63 98	98.61 33	98.97 59	95.13 45	99.77 65	97.65 31	99.83 7	99.79 3

APD-MVS		98.35 30	98.00 34	99.42 5	99.51 20	98.72 5	98.80 78	98.82 53	94.52 101	99.23 6	99.25 23	95.54 35	99.80 48	96.52 72	99.77 14	99.74 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 31	98.23 27	98.67 55	99.27 53	96.90 72	97.95 180	99.58 1	97.14 26	98.44 42	99.01 56	95.03 47	99.62 90	97.91 21	99.75 26	99.50 63

PHI-MVS		98.34 31	98.06 32	99.18 26	99.15 70	98.12 31	99.04 43	99.09 16	93.32 146	98.83 23	99.10 43	96.54 8	99.83 34	97.70 30	99.76 20	99.59 53

MP-MVS		98.33 33	98.01 33	99.28 16	99.75 1	98.18 27	99.22 26	98.79 63	96.13 47	97.92 63	99.23 24	94.54 53	99.94 1	96.74 64	99.78 11	99.73 22

ACMMP		98.23 34	97.95 35	99.09 35	99.74 4	97.62 48	99.03 44	99.41 4	95.98 52	97.60 77	99.36 12	94.45 58	99.93 7	97.14 48	98.85 86	99.70 28
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
test_prior3		98.22 35	97.90 36	99.19 22	99.31 40	98.22 24	97.80 196	98.84 51	96.12 48	97.89 65	98.69 82	95.96 23	99.70 77	96.89 56	99.60 41	99.65 42

train_agg		97.97 36	97.52 42	99.33 11	99.31 40	98.50 8	97.92 182	98.73 72	92.98 157	97.74 71	98.68 84	96.20 11	99.80 48	96.59 68	99.57 46	99.68 34

UA-Net		97.96 37	97.62 39	98.98 41	98.86 80	97.47 53	98.89 58	99.08 17	96.67 38	98.72 29	99.54 1	93.15 72	99.81 41	94.87 113	98.83 87	99.65 42

agg_prior1		97.95 38	97.51 43	99.28 16	99.30 45	98.38 13	97.81 195	98.72 74	93.16 151	97.57 79	98.66 87	96.14 14	99.81 41	96.63 67	99.56 52	99.66 40

CDPH-MVS		97.94 39	97.49 44	99.28 16	99.47 25	98.44 10	97.91 186	98.67 92	92.57 170	98.77 26	98.85 70	95.93 25	99.72 72	95.56 99	99.69 33	99.68 34

DeepPCF-MVS	96.37 2	97.93 40	98.48 11	96.30 184	99.00 74	89.54 242	97.43 218	98.87 46	98.16 2	99.26 4	99.38 7	96.12 16	99.64 85	98.30 14	99.77 14	99.72 24

DeepC-MVS	95.98 3	97.88 41	97.58 41	98.77 50	99.25 56	96.93 70	98.83 68	98.75 70	96.96 31	96.89 97	99.50 3	90.46 109	99.87 26	97.84 25	99.76 20	99.52 58
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior3		97.87 42	97.42 48	99.23 21	99.29 47	98.23 23	97.92 182	98.72 74	92.38 183	97.59 78	98.64 89	96.09 17	99.79 56	96.59 68	99.57 46	99.68 34

DP-MVS Recon		97.86 43	97.46 46	99.06 37	99.53 19	98.35 18	98.33 141	98.89 41	92.62 167	98.05 53	98.94 65	95.34 39	99.65 83	96.04 83	99.42 65	99.19 91

CSCG		97.85 44	97.74 38	98.20 81	99.67 13	95.16 131	99.22 26	99.32 5	93.04 154	97.02 91	98.92 67	95.36 38	99.91 15	97.43 41	99.64 38	99.52 58

MG-MVS		97.81 45	97.60 40	98.44 71	99.12 72	95.97 102	97.75 200	98.78 65	96.89 32	98.46 38	99.22 26	93.90 66	99.68 80	94.81 116	99.52 56	99.67 39

VNet		97.79 46	97.40 49	98.96 43	98.88 78	97.55 50	98.63 110	98.93 33	96.74 35	99.02 13	98.84 71	90.33 112	99.83 34	98.53 7	96.66 132	99.50 63

PS-MVSNAJ		97.73 47	97.77 37	97.62 110	98.68 92	95.58 117	97.34 224	98.51 118	97.29 17	98.66 31	97.88 143	94.51 54	99.90 17	97.87 22	99.17 75	97.39 161

CPTT-MVS		97.72 48	97.32 51	98.92 45	99.64 15	97.10 65	99.12 38	98.81 56	92.34 184	98.09 51	99.08 49	93.01 73	99.92 9	96.06 82	99.77 14	99.75 15

PVSNet_Blended_VisFu		97.70 49	97.46 46	98.44 71	99.27 53	95.91 109	98.63 110	99.16 15	94.48 104	97.67 74	98.88 68	92.80 75	99.91 15	97.11 49	99.12 76	99.50 63

canonicalmvs		97.67 50	97.23 54	98.98 41	98.70 89	98.38 13	99.34 11	98.39 134	96.76 34	97.67 74	97.40 172	92.26 81	99.49 101	98.28 15	96.28 144	99.08 104

MVSFormer		97.57 51	97.49 44	97.84 98	98.07 118	95.76 113	99.47 2	98.40 132	94.98 85	98.79 24	98.83 72	92.34 79	98.41 203	96.91 54	99.59 44	99.34 79

alignmvs		97.56 52	97.07 60	99.01 38	98.66 93	98.37 16	98.83 68	98.06 188	96.74 35	98.00 58	97.65 162	90.80 106	99.48 105	98.37 13	96.56 136	99.19 91

OMC-MVS		97.55 53	97.34 50	98.20 81	99.33 36	95.92 107	98.28 150	98.59 102	95.52 66	97.97 59	99.10 43	93.28 71	99.49 101	95.09 112	98.88 83	99.19 91

PAPM_NR		97.46 54	97.11 57	98.50 66	99.50 21	96.41 90	98.63 110	98.60 101	95.18 76	97.06 89	98.06 131	94.26 61	99.57 93	93.80 142	98.87 85	99.52 58

EPP-MVSNet		97.46 54	97.28 52	97.99 92	98.64 95	95.38 123	99.33 13	98.31 141	93.61 138	97.19 85	99.07 50	94.05 63	99.23 115	96.89 56	98.43 102	99.37 78

3Dnovator	94.51 5	97.46 54	96.93 64	99.07 36	97.78 132	97.64 46	99.35 10	99.06 18	97.02 29	93.75 185	99.16 37	89.25 121	99.92 9	97.22 46	99.75 26	99.64 45

CNLPA		97.45 57	97.03 61	98.73 51	99.05 73	97.44 55	98.07 172	98.53 114	95.32 72	96.80 103	98.53 97	93.32 70	99.72 72	94.31 129	99.31 71	99.02 107

lupinMVS		97.44 58	97.22 55	98.12 86	98.07 118	95.76 113	97.68 205	97.76 198	94.50 102	98.79 24	98.61 90	92.34 79	99.30 111	97.58 34	99.59 44	99.31 82

3Dnovator+	94.38 6	97.43 59	96.78 71	99.38 6	97.83 130	98.52 7	99.37 7	98.71 79	97.09 27	92.99 203	99.13 39	89.36 119	99.89 19	96.97 52	99.57 46	99.71 26

Vis-MVSNet		97.42 60	97.11 57	98.34 76	98.66 93	96.23 97	99.22 26	99.00 23	96.63 39	98.04 55	99.21 27	88.05 160	99.35 110	96.01 85	99.21 73	99.45 75
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 61	97.25 53	97.91 95	98.70 89	96.80 75	98.82 70	98.69 82	94.53 100	98.11 50	98.28 118	94.50 57	99.57 93	94.12 134	99.49 57	97.37 162

sss		97.39 62	96.98 63	98.61 58	98.60 98	96.61 82	98.22 154	98.93 33	93.97 117	98.01 56	98.48 102	91.98 90	99.85 31	96.45 74	98.15 110	99.39 77

PVSNet_Blended		97.38 63	97.12 56	98.14 84	99.25 56	95.35 126	97.28 227	99.26 6	93.13 152	97.94 61	98.21 125	92.74 76	99.81 41	96.88 59	99.40 68	99.27 87

liao		97.37 64	96.77 73	99.16 29	99.34 33	97.99 38	98.19 157	98.68 85	90.14 227	98.01 56	98.97 59	94.80 50	99.87 26	93.36 151	99.46 62	99.61 48

WTY-MVS		97.37 64	96.92 65	98.72 52	98.86 80	96.89 74	98.31 146	98.71 79	95.26 74	97.67 74	98.56 96	92.21 84	99.78 60	95.89 88	96.85 129	99.48 68

jason		97.32 66	97.08 59	98.06 91	97.45 151	95.59 116	97.87 193	97.91 194	94.79 92	98.55 36	98.83 72	91.12 102	99.23 115	97.58 34	99.60 41	99.34 79
jason: jason.
MVS_Test		97.28 67	97.00 62	98.13 85	98.33 105	95.97 102	98.74 93	98.07 186	94.27 108	98.44 42	98.07 130	92.48 78	99.26 113	96.43 75	98.19 109	99.16 95

EPNet		97.28 67	96.87 68	98.51 65	94.98 262	96.14 99	98.90 55	97.02 248	98.28 1	95.99 122	99.11 41	91.36 99	99.89 19	96.98 51	99.19 74	99.50 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		97.22 69	96.88 67	98.25 80	98.85 83	96.36 92	99.19 32	97.97 192	95.39 71	97.23 84	98.99 58	91.11 103	98.93 147	94.60 120	98.59 93	99.47 69

PLC	95.07 4	97.20 70	96.78 71	98.44 71	99.29 47	96.31 96	98.14 163	98.76 67	92.41 181	96.39 113	98.31 117	94.92 49	99.78 60	94.06 135	98.77 90	99.23 89
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		97.16 71	96.66 76	98.68 54	98.53 102	97.19 63	98.93 53	98.90 39	92.83 164	95.99 122	99.37 8	92.12 86	99.87 26	93.67 145	99.57 46	98.97 112

AdaColmap		97.15 72	96.70 74	98.48 68	99.16 68	96.69 79	98.01 176	98.89 41	94.44 106	96.83 99	98.68 84	90.69 107	99.76 67	94.36 126	99.29 72	98.98 111

HyFIR		97.09 73	96.89 66	97.69 106	98.86 80	94.03 184	97.52 214	99.21 13	93.65 137	96.71 105	98.80 75	91.46 98	99.52 100	95.90 87	99.94 1	99.52 58

F-COLMAP		97.09 73	96.80 69	97.97 93	99.45 27	94.95 138	98.55 121	98.62 100	93.02 155	96.17 117	98.58 95	94.01 64	99.81 41	93.95 137	98.90 82	99.14 98

TAMVS		97.02 75	96.79 70	97.70 105	98.06 120	95.31 128	98.52 123	98.31 141	93.95 118	97.05 90	98.61 90	93.49 68	98.52 180	95.33 105	97.81 117	99.29 86

CDS-MVSNet		96.99 76	96.69 75	97.90 96	98.05 121	95.98 101	98.20 156	98.33 140	93.67 135	96.95 92	98.49 101	93.54 67	98.42 196	95.24 111	97.74 119	99.31 82
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
114514_t		96.93 77	96.27 85	98.92 45	99.50 21	97.63 47	98.85 65	98.90 39	84.80 269	97.77 68	99.11 41	92.84 74	99.66 82	94.85 114	99.77 14	99.47 69

MAR-MVS		96.91 78	96.40 81	98.45 70	98.69 91	96.90 72	98.66 108	98.68 85	92.40 182	97.07 88	97.96 137	91.54 97	99.75 69	93.68 144	98.92 81	98.69 127
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
Vis-MVSNet (Re-imp)		96.87 79	96.55 78	97.83 99	98.73 87	95.46 122	99.20 30	98.30 144	94.96 87	96.60 107	98.87 69	90.05 116	98.59 169	93.67 145	98.60 92	99.46 73

PAPR		96.84 80	96.24 87	98.65 56	98.72 88	96.92 71	97.36 222	98.57 107	93.33 145	96.67 106	97.57 168	94.30 60	99.56 95	91.05 202	98.59 93	99.47 69

HY-MVS	93.96 8	96.82 81	96.23 88	98.57 60	98.46 103	97.00 67	98.14 163	98.21 156	93.95 118	96.72 104	97.99 136	91.58 96	99.76 67	94.51 124	96.54 137	98.95 115

UGNet		96.78 82	96.30 84	98.19 83	98.24 108	95.89 110	98.88 60	98.93 33	97.39 14	96.81 102	97.84 147	82.60 229	99.90 17	96.53 71	99.49 57	98.79 122
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
PVSNet_BlendedMVS		96.73 83	96.60 77	97.12 126	99.25 56	95.35 126	98.26 152	99.26 6	94.28 107	97.94 61	97.46 170	92.74 76	99.81 41	96.88 59	93.32 186	96.20 238

mvs_anonymous		96.70 84	96.53 79	97.18 122	98.19 113	93.78 190	98.31 146	98.19 159	94.01 113	94.47 143	98.27 121	92.08 88	98.46 188	97.39 43	97.91 112	99.31 82

1112_ss		96.63 85	96.00 94	98.50 66	98.56 99	96.37 91	98.18 161	98.10 183	92.92 159	94.84 132	98.43 105	92.14 85	99.58 92	94.35 127	96.51 138	99.56 57

PMMVS		96.60 86	96.33 83	97.41 116	97.90 128	93.93 186	97.35 223	98.41 131	92.84 163	97.76 69	97.45 171	91.10 104	99.20 117	96.26 78	97.91 112	99.11 100

DP-MVS		96.59 87	95.93 95	98.57 60	99.34 33	96.19 98	98.70 102	98.39 134	89.45 244	94.52 141	99.35 14	91.85 92	99.85 31	92.89 165	98.88 83	99.68 34

PatchMatch-RL		96.59 87	96.03 93	98.27 78	99.31 40	96.51 86	97.91 186	99.06 18	93.72 128	96.92 96	98.06 131	88.50 151	99.65 83	91.77 189	99.00 79	98.66 130

XVG-OURS		96.55 89	96.41 80	96.99 132	98.75 86	93.76 191	97.50 216	98.52 116	95.67 60	96.83 99	99.30 19	88.95 127	99.53 98	95.88 89	96.26 145	97.69 156

FIs		96.51 90	96.12 90	97.67 109	97.13 173	97.54 51	99.36 8	99.22 12	95.89 54	94.03 176	98.35 110	91.98 90	98.44 193	96.40 76	92.76 193	97.01 167

XVG-OURS-SEG-HR		96.51 90	96.34 82	97.02 131	98.77 85	93.76 191	97.79 198	98.50 120	95.45 68	96.94 93	99.09 47	87.87 166	99.55 97	96.76 63	95.83 154	97.74 153

PS-MVSNAJss		96.43 92	96.26 86	96.92 139	95.84 246	95.08 134	99.16 34	98.50 120	95.87 55	93.84 183	98.34 114	94.51 54	98.61 167	96.88 59	93.45 183	97.06 164

FC-MVSNet-test		96.42 93	96.05 91	97.53 113	96.95 180	97.27 59	99.36 8	99.23 10	95.83 56	93.93 178	98.37 108	92.00 89	98.32 209	96.02 84	92.72 194	97.00 168

ab-mvs		96.42 93	95.71 103	98.55 62	98.63 96	96.75 78	97.88 192	98.74 71	93.84 123	96.54 110	98.18 126	85.34 208	99.75 69	95.93 86	96.35 142	99.15 96

PVSNet	91.96 18	96.35 95	96.15 89	96.96 135	99.17 67	92.05 216	96.08 259	98.68 85	93.69 131	97.75 70	97.80 152	88.86 130	99.69 79	94.26 131	99.01 78	99.15 96

Test_1112_low_res		96.34 96	95.66 106	98.36 75	98.56 99	95.94 105	97.71 202	98.07 186	92.10 188	94.79 136	97.29 176	91.75 93	99.56 95	94.17 132	96.50 139	99.58 55

diffmvs		96.32 97	95.74 98	98.07 90	98.26 107	96.14 99	98.53 122	98.23 154	90.10 228	96.88 98	97.73 155	90.16 115	99.15 119	93.90 139	97.85 116	98.91 117

QAPM		96.29 98	95.40 108	98.96 43	97.85 129	97.60 49	99.23 20	98.93 33	89.76 239	93.11 201	99.02 52	89.11 125	99.93 7	91.99 183	99.62 39	99.34 79

nrg030		96.28 99	95.72 100	97.96 94	96.90 184	98.15 29	99.39 5	98.31 141	95.47 67	94.42 152	98.35 110	92.09 87	98.69 162	97.50 40	89.05 226	97.04 166

liao1		96.25 100	95.73 99	97.79 102	97.13 173	95.55 120	98.19 157	98.59 102	93.47 141	92.03 224	97.82 150	91.33 100	99.49 101	94.62 119	98.44 100	98.32 144

HQP_MVS		96.14 101	95.90 96	96.85 140	97.42 152	94.60 167	98.80 78	98.56 108	97.28 18	95.34 126	98.28 118	87.09 180	99.03 135	96.07 80	94.27 162	96.92 173

MVSTER		96.06 102	95.72 100	97.08 129	98.23 109	95.93 106	98.73 96	98.27 147	94.86 91	95.07 129	98.09 129	88.21 155	98.54 173	96.59 68	93.46 181	96.79 190

test_djsdf		96.00 103	95.69 104	96.93 137	95.72 249	95.49 121	99.47 2	98.40 132	94.98 85	94.58 139	97.86 144	89.16 124	98.41 203	96.91 54	94.12 169	96.88 182

EI-MVSNet		95.96 104	95.83 97	96.36 180	97.93 126	93.70 195	98.12 166	98.27 147	93.70 130	95.07 129	99.02 52	92.23 83	98.54 173	94.68 117	93.46 181	96.84 186

BH-untuned		95.95 105	95.72 100	96.65 155	98.55 101	92.26 214	98.23 153	97.79 197	93.73 127	94.62 138	98.01 134	88.97 126	99.00 138	93.04 158	98.51 96	98.68 128

MSDG		95.93 106	95.30 117	97.83 99	98.90 76	95.36 124	96.83 248	98.37 136	91.32 210	94.43 151	98.73 81	90.27 113	99.60 91	90.05 216	98.82 88	98.52 135

BH-RMVSNet		95.92 107	95.32 115	97.69 106	98.32 106	94.64 162	98.19 157	97.45 225	94.56 99	96.03 120	98.61 90	85.02 210	99.12 122	90.68 205	99.06 77	99.30 85

LFMVS		95.86 108	94.98 128	98.47 69	98.87 79	96.32 94	98.84 67	96.02 268	93.40 143	98.62 32	99.20 30	74.99 262	99.63 88	97.72 29	97.20 125	99.46 73

OpenMVS	93.04 13	95.83 109	95.00 126	98.32 77	97.18 170	97.32 57	99.21 29	98.97 26	89.96 232	91.14 229	99.05 51	86.64 187	99.92 9	93.38 150	99.47 59	97.73 154

VDD-MVS		95.82 110	95.23 119	97.61 111	98.84 84	93.98 185	98.68 106	97.40 230	95.02 84	97.95 60	99.34 15	74.37 267	99.78 60	98.64 2	96.80 130	99.08 104

UniMVSNet (Re)		95.78 111	95.19 121	97.58 112	96.99 179	97.47 53	98.79 82	99.18 14	95.60 63	93.92 179	97.04 194	91.68 94	98.48 183	95.80 93	87.66 247	96.79 190

VPA-MVSNet		95.75 112	95.11 123	97.69 106	97.24 163	97.27 59	98.94 52	99.23 10	95.13 78	95.51 125	97.32 174	85.73 201	98.91 149	97.33 45	89.55 220	96.89 181

HQP-MVS		95.72 113	95.40 108	96.69 147	97.20 167	94.25 179	98.05 173	98.46 125	96.43 40	94.45 144	97.73 155	86.75 185	98.96 142	95.30 106	94.18 165	96.86 185

UniMVSNet_NR-MVSNet		95.71 114	95.15 122	97.40 117	96.84 187	96.97 68	98.74 93	99.24 8	95.16 77	93.88 180	97.72 158	91.68 94	98.31 211	95.81 91	87.25 252	96.92 173

PatchmatchNet		95.71 114	95.52 107	96.29 185	97.58 140	90.72 230	96.84 247	97.52 211	94.06 111	97.08 86	96.96 203	89.24 122	98.90 150	92.03 182	98.37 103	99.26 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMM	93.85 9	95.69 116	95.38 112	96.61 160	97.61 138	93.84 189	98.91 54	98.44 128	95.25 75	94.28 162	98.47 103	86.04 199	99.12 122	95.50 101	93.95 173	96.87 183
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 117	95.69 104	95.44 213	97.54 143	88.54 258	96.97 236	97.56 205	93.50 140	97.52 81	96.93 208	89.49 118	99.16 118	95.25 110	96.42 141	98.64 131

LPG-MVS_test		95.62 118	95.34 113	96.47 173	97.46 148	93.54 196	98.99 47	98.54 111	94.67 94	94.36 154	98.77 77	85.39 205	99.11 126	95.71 95	94.15 167	96.76 193

CLD-MVS		95.62 118	95.34 113	96.46 176	97.52 145	93.75 193	97.27 228	98.46 125	95.53 65	94.42 152	98.00 135	86.21 194	98.97 139	96.25 79	94.37 160	96.66 210
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchFormer-LS_test		95.47 120	95.27 118	96.08 193	97.59 139	90.66 231	98.10 170	97.34 234	93.98 116	96.08 118	96.15 234	87.65 173	99.12 122	95.27 109	95.24 158	98.44 140

IterMVS-LS		95.46 121	95.21 120	96.22 187	98.12 116	93.72 194	98.32 145	98.13 172	93.71 129	94.26 163	97.31 175	92.24 82	98.10 221	94.63 118	90.12 212	96.84 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
jajsoiax		95.45 122	95.03 125	96.73 143	95.42 258	94.63 163	99.14 36	98.52 116	95.74 57	93.22 196	98.36 109	83.87 223	98.65 165	96.95 53	94.04 170	96.91 178

CVMVSNet		95.43 123	96.04 92	93.57 250	97.93 126	83.62 274	98.12 166	98.59 102	95.68 59	96.56 108	99.02 52	87.51 175	97.51 244	93.56 148	97.44 122	99.60 51

DU-MVS		95.42 124	94.76 133	97.40 117	96.53 202	96.97 68	98.66 108	98.99 25	95.43 69	93.88 180	97.69 159	88.57 146	98.31 211	95.81 91	87.25 252	96.92 173

mvs_tets		95.41 125	95.00 126	96.65 155	95.58 253	94.42 171	99.00 46	98.55 110	95.73 58	93.21 197	98.38 107	83.45 226	98.63 166	97.09 50	94.00 171	96.91 178

BH-w/o		95.38 126	95.08 124	96.26 186	98.34 104	91.79 219	97.70 203	97.43 227	92.87 162	94.24 165	97.22 178	88.66 144	98.84 155	91.55 193	97.70 120	98.16 147

VDDNet		95.36 127	94.53 137	97.86 97	98.10 117	95.13 132	98.85 65	97.75 199	90.46 221	98.36 44	99.39 5	73.27 269	99.64 85	97.98 19	96.58 135	98.81 121

TAPA-MVS	93.98 7	95.35 128	94.56 136	97.74 104	99.13 71	94.83 148	98.33 141	98.64 99	86.62 258	96.29 115	98.61 90	94.00 65	99.29 112	80.00 267	99.41 66	99.09 101
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 129	94.98 128	96.43 177	97.67 135	93.48 198	98.73 96	98.44 128	94.94 90	92.53 211	98.53 97	84.50 216	99.14 120	95.48 102	94.00 171	96.66 210
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 130	94.87 131	96.71 144	99.29 47	93.24 204	98.58 116	98.11 179	89.92 235	93.57 188	99.10 43	86.37 192	99.79 56	90.78 204	98.10 111	97.09 163
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		95.24 131	94.65 135	96.99 132	99.25 56	93.21 205	98.59 114	98.18 162	91.36 206	93.52 190	98.77 77	84.67 214	99.72 72	89.70 224	97.87 114	98.02 150

LCM-MVSNet-Re		95.22 132	95.32 115	94.91 229	98.18 114	87.85 265	98.75 89	95.66 274	95.11 79	88.96 246	96.85 211	90.26 114	97.65 239	95.65 98	98.44 100	99.22 90

EPNet_dtu		95.21 133	94.95 130	95.99 194	96.17 232	90.45 234	98.16 162	97.27 239	96.77 33	93.14 200	98.33 115	90.34 111	98.42 196	85.57 256	98.81 89	99.09 101
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 134	94.45 142	97.46 114	96.75 192	96.56 84	98.86 64	98.65 98	93.30 148	93.27 195	98.27 121	84.85 213	98.87 152	94.82 115	91.26 208	96.96 170

WR-MVS		95.15 135	94.46 140	97.22 120	96.67 197	96.45 88	98.21 155	98.81 56	94.15 109	93.16 198	97.69 159	87.51 175	98.30 213	95.29 108	88.62 236	96.90 180

TranMVSNet+NR-MVSNet		95.14 136	94.48 138	97.11 127	96.45 206	96.36 92	99.03 44	99.03 21	95.04 83	93.58 187	97.93 139	88.27 154	98.03 225	94.13 133	86.90 257	96.95 172

test-LLR		95.10 137	94.87 131	95.80 202	96.77 189	89.70 240	96.91 240	95.21 276	95.11 79	94.83 134	95.72 245	87.71 169	98.97 139	93.06 156	98.50 97	98.72 124

WR-MVS_H		95.05 138	94.46 140	96.81 142	96.86 186	95.82 112	99.24 19	99.24 8	93.87 122	92.53 211	96.84 212	90.37 110	98.24 217	93.24 153	87.93 242	96.38 234

ADS-MVSNet		95.00 139	94.45 142	96.63 158	98.00 122	91.91 218	96.04 260	97.74 200	90.15 225	96.47 111	96.64 219	87.89 164	98.96 142	90.08 214	97.06 126	99.02 107

VPNet		94.99 140	94.19 153	97.40 117	97.16 171	96.57 83	98.71 99	98.97 26	95.67 60	94.84 132	98.24 124	80.36 243	98.67 164	96.46 73	87.32 250	96.96 170

EPMVS		94.99 140	94.48 138	96.52 169	97.22 165	91.75 220	97.23 229	91.66 292	94.11 110	97.28 83	96.81 213	85.70 202	98.84 155	93.04 158	97.28 124	98.97 112

NR-MVSNet		94.98 142	94.16 154	97.44 115	96.53 202	97.22 62	98.74 93	98.95 30	94.96 87	89.25 244	97.69 159	89.32 120	98.18 218	94.59 121	87.40 249	96.92 173

FMVSNet3		94.97 143	94.26 148	97.11 127	98.18 114	96.62 80	98.56 120	98.26 151	93.67 135	94.09 172	97.10 183	84.25 218	98.01 226	92.08 178	92.14 197	96.70 201

CostFormer		94.95 144	94.73 134	95.60 207	97.28 161	89.06 249	97.53 213	96.89 251	89.66 242	96.82 101	96.72 216	86.05 197	98.95 146	95.53 100	96.13 150	98.79 122

PAPM		94.95 144	94.00 165	97.78 103	97.04 176	95.65 115	96.03 262	98.25 152	91.23 215	94.19 168	97.80 152	91.27 101	98.86 154	82.61 261	97.61 121	98.84 120

CP-MVSNet		94.94 146	94.30 147	96.83 141	96.72 194	95.56 119	99.11 39	98.95 30	93.89 120	92.42 216	97.90 141	87.19 179	98.12 220	94.32 128	88.21 239	96.82 189

TR-MVS		94.94 146	94.20 152	97.17 123	97.75 133	94.14 181	97.59 210	97.02 248	92.28 186	95.75 124	97.64 164	83.88 222	98.96 142	89.77 220	96.15 149	98.40 141

RPSCF		94.87 148	95.40 108	93.26 253	98.89 77	82.06 280	98.33 141	98.06 188	90.30 224	96.56 108	99.26 22	87.09 180	99.49 101	93.82 141	96.32 143	98.24 145

v1neww		94.83 149	94.22 149	96.68 150	96.39 209	94.85 141	98.87 61	98.11 179	92.45 176	94.45 144	97.06 189	88.82 134	98.54 173	92.93 161	88.91 229	96.65 212

v7new		94.83 149	94.22 149	96.68 150	96.39 209	94.85 141	98.87 61	98.11 179	92.45 176	94.45 144	97.06 189	88.82 134	98.54 173	92.93 161	88.91 229	96.65 212

v6		94.83 149	94.21 151	96.69 147	96.36 213	94.85 141	98.87 61	98.11 179	92.46 171	94.44 150	97.05 193	88.76 140	98.57 171	92.95 160	88.92 228	96.65 212

DWT-MVSNet_test		94.82 152	94.36 145	96.20 188	97.35 158	90.79 228	98.34 140	96.57 261	92.91 160	95.33 128	96.44 225	82.00 230	99.12 122	94.52 123	95.78 155	98.70 126

GA-MVS		94.81 153	94.03 163	97.14 124	97.15 172	93.86 188	96.76 249	97.58 204	94.00 114	94.76 137	97.04 194	80.91 236	98.48 183	91.79 188	96.25 146	99.09 101

V42		94.78 154	94.14 156	96.70 146	96.33 219	95.22 130	98.97 51	98.09 184	92.32 185	94.31 158	97.06 189	88.39 152	98.55 172	92.90 164	88.87 231	96.34 236

divwei89l23v2f112		94.76 155	94.12 159	96.67 153	96.28 225	94.85 141	98.69 103	98.12 174	92.44 178	94.29 161	96.94 205	88.85 132	98.48 183	92.67 168	88.79 235	96.67 207

CR-MVSNet		94.76 155	94.15 155	96.59 162	97.00 177	93.43 199	94.96 272	97.56 205	92.46 171	96.93 94	96.24 229	88.15 156	97.88 236	87.38 244	96.65 133	98.46 138

v1141		94.75 157	94.11 160	96.67 153	96.27 227	94.86 140	98.69 103	98.12 174	92.43 179	94.31 158	96.94 205	88.78 139	98.48 183	92.63 170	88.85 233	96.67 207

v1		94.75 157	94.11 160	96.69 147	96.27 227	94.87 139	98.69 103	98.12 174	92.43 179	94.32 157	96.94 205	88.71 143	98.54 173	92.66 169	88.84 234	96.67 207

DI_MVS_test_dynamic		94.74 159	93.62 187	98.09 88	95.34 259	95.92 107	98.09 171	97.34 234	94.66 96	85.89 259	95.91 239	80.49 242	99.38 108	96.66 66	98.22 107	98.97 112

DI_MVS_test_normal		94.72 160	93.59 189	98.11 87	95.30 260	95.95 104	97.91 186	97.39 232	94.64 97	85.70 262	95.88 240	80.52 241	99.36 109	96.69 65	98.30 106	99.01 110

v7		94.69 161	94.04 162	96.62 159	96.41 208	94.79 156	98.78 84	98.13 172	91.89 192	94.30 160	97.16 180	88.13 158	98.45 190	91.96 185	89.65 217	96.61 217

v2v482		94.69 161	94.03 163	96.65 155	96.17 232	94.79 156	98.67 107	98.08 185	92.72 165	94.00 177	97.16 180	87.69 172	98.45 190	92.91 163	88.87 231	96.72 197

PCF-MVS	93.45 11	94.68 163	93.43 198	98.42 74	98.62 97	96.77 77	95.48 268	98.20 158	84.63 270	93.34 194	98.32 116	88.55 148	99.81 41	84.80 258	98.96 80	98.68 128
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HC-MVS		94.67 164	93.54 192	98.08 89	96.88 185	96.56 84	98.19 157	98.50 120	78.05 284	92.69 208	98.02 133	91.07 105	99.63 88	90.09 213	98.36 104	98.04 149

PS-CasMVS		94.67 164	93.99 167	96.71 144	96.68 196	95.26 129	99.13 37	99.03 21	93.68 133	92.33 218	97.95 138	85.35 207	98.10 221	93.59 147	88.16 241	96.79 190

cascas		94.63 166	93.86 173	96.93 137	96.91 183	94.27 178	96.00 263	98.51 118	85.55 265	94.54 140	96.23 231	84.20 219	98.87 152	95.80 93	96.98 128	97.66 157

tpmvs		94.60 167	94.36 145	95.33 222	97.46 148	88.60 256	96.88 245	97.68 201	91.29 212	93.80 184	96.42 226	88.58 145	99.24 114	91.06 200	96.04 151	98.17 146

LTVRE_ROB	92.95 15	94.60 167	93.90 171	96.68 150	97.41 155	94.42 171	98.52 123	98.59 102	91.69 196	91.21 228	98.35 110	84.87 212	99.04 134	91.06 200	93.44 184	96.60 219
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
v1144		94.59 169	93.92 169	96.60 161	96.21 229	94.78 158	98.59 114	98.14 171	91.86 195	94.21 167	97.02 197	87.97 161	98.41 203	91.72 190	89.57 218	96.61 217

ADS-MVSNet2		94.58 170	94.40 144	95.11 226	98.00 122	88.74 253	96.04 260	97.30 237	90.15 225	96.47 111	96.64 219	87.89 164	97.56 243	90.08 214	97.06 126	99.02 107

ACMH	92.88 16	94.55 171	93.95 168	96.34 182	97.63 136	93.26 203	98.81 75	98.49 124	93.43 142	89.74 240	98.53 97	81.91 231	99.08 130	93.69 143	93.30 187	96.70 201
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-ACMP-BASELINE		94.54 172	94.14 156	95.75 205	96.55 201	91.65 221	98.11 168	98.44 128	94.96 87	94.22 166	97.90 141	79.18 248	99.11 126	94.05 136	93.85 174	96.48 231

GBi-Net		94.49 173	93.80 176	96.56 165	98.21 110	95.00 135	98.82 70	98.18 162	92.46 171	94.09 172	97.07 186	81.16 233	97.95 229	92.08 178	92.14 197	96.72 197

test1		94.49 173	93.80 176	96.56 165	98.21 110	95.00 135	98.82 70	98.18 162	92.46 171	94.09 172	97.07 186	81.16 233	97.95 229	92.08 178	92.14 197	96.72 197

v8		94.47 175	93.77 179	96.57 164	96.36 213	94.83 148	99.05 42	98.19 159	91.92 191	93.16 198	96.97 202	88.82 134	98.48 183	91.69 191	87.79 245	96.39 233

FMVSNet2		94.47 175	93.61 188	97.04 130	98.21 110	96.43 89	98.79 82	98.27 147	92.46 171	93.50 192	97.09 184	81.16 233	98.00 227	91.09 198	91.93 201	96.70 201

PEN-MVS		94.42 177	93.73 183	96.49 171	96.28 225	94.84 146	99.17 33	99.00 23	93.51 139	92.23 220	97.83 149	86.10 196	97.90 233	92.55 172	86.92 256	96.74 195

v144192		94.39 178	93.70 184	96.48 172	96.06 237	94.35 175	98.58 116	98.16 169	91.45 201	94.33 156	97.02 197	87.50 177	98.45 190	91.08 199	89.11 225	96.63 215

Baseline_NR-MVSNet		94.35 179	93.81 175	95.96 195	96.20 230	94.05 183	98.61 113	96.67 258	91.44 202	93.85 182	97.60 166	88.57 146	98.14 219	94.39 125	86.93 255	95.68 250

v1192		94.32 180	93.58 190	96.53 168	96.10 235	94.45 170	98.50 128	98.17 167	91.54 199	94.19 168	97.06 189	86.95 184	98.43 195	90.14 212	89.57 218	96.70 201

ACMH+	92.99 14	94.30 181	93.77 179	95.88 199	97.81 131	92.04 217	98.71 99	98.37 136	93.99 115	90.60 235	98.47 103	80.86 238	99.05 131	92.75 167	92.40 196	96.55 223

v148		94.29 182	93.76 181	95.91 198	96.10 235	92.93 209	98.58 116	97.97 192	92.59 169	93.47 193	96.95 204	88.53 149	98.32 209	92.56 171	87.06 254	96.49 230

v10		94.29 182	93.55 191	96.51 170	96.39 209	94.80 153	98.99 47	98.19 159	91.35 208	93.02 202	96.99 200	88.09 159	98.41 203	90.50 209	88.41 238	96.33 237

MVP-PatchMatch		94.28 184	93.92 169	95.35 221	94.95 263	92.60 213	97.97 179	97.65 202	91.61 197	90.68 234	97.09 184	86.32 193	98.42 196	89.70 224	99.34 70	95.02 259

OurMVSNet-221017-0		94.21 185	94.00 165	94.85 232	95.60 252	89.22 247	98.89 58	97.43 227	95.29 73	92.18 222	98.52 100	82.86 228	98.59 169	93.46 149	91.76 204	96.74 195

v1921920		94.20 186	93.47 197	96.40 179	95.98 240	94.08 182	98.52 123	98.15 170	91.33 209	94.25 164	97.20 179	86.41 190	98.42 196	90.04 217	89.39 223	96.69 206

v7n		94.19 187	93.43 198	96.47 173	95.90 243	94.38 174	99.26 17	98.34 139	91.99 190	92.76 207	97.13 182	88.31 153	98.52 180	89.48 228	87.70 246	96.52 226

tpm2		94.19 187	93.76 181	95.46 211	97.23 164	89.04 250	97.31 226	96.85 254	87.08 257	96.21 116	96.79 214	83.75 225	98.74 161	92.43 175	96.23 147	98.59 133

v52		94.18 189	93.52 193	96.13 191	95.95 242	94.29 177	99.23 20	98.21 156	91.42 203	92.84 205	96.89 209	87.85 167	98.53 179	91.51 194	87.81 243	95.57 253

V4		94.18 189	93.52 193	96.13 191	95.89 244	94.31 176	99.23 20	98.22 155	91.42 203	92.82 206	96.89 209	87.93 163	98.52 180	91.51 194	87.81 243	95.58 252

TESTMET0.1,1		94.18 189	93.69 185	95.63 206	96.92 181	89.12 248	96.91 240	94.78 281	93.17 150	94.88 131	96.45 224	78.52 249	98.92 148	93.09 155	98.50 97	98.85 118

dp		94.15 192	93.90 171	94.90 230	97.31 160	86.82 269	96.97 236	97.19 243	91.22 216	96.02 121	96.61 221	85.51 204	99.02 137	90.00 218	94.30 161	98.85 118

tpm		94.13 193	93.80 176	95.12 225	96.50 204	87.91 264	97.44 217	95.89 273	92.62 167	96.37 114	96.30 228	84.13 220	98.30 213	93.24 153	91.66 205	99.14 98

IterMVS		94.09 194	93.85 174	94.80 234	97.99 124	90.35 235	97.18 232	98.12 174	93.68 133	92.46 215	97.34 173	84.05 221	97.41 246	92.51 174	91.33 206	96.62 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 195	93.51 195	95.80 202	96.77 189	89.70 240	96.91 240	95.21 276	92.89 161	94.83 134	95.72 245	77.69 252	98.97 139	93.06 156	98.50 97	98.72 124

test0.0.03 1		94.08 195	93.51 195	95.80 202	95.53 255	92.89 210	97.38 220	95.97 270	95.11 79	92.51 213	96.66 218	87.71 169	96.94 251	87.03 247	93.67 176	97.57 158

v1240		94.06 197	93.29 201	96.34 182	96.03 239	93.90 187	98.44 133	98.17 167	91.18 217	94.13 171	97.01 199	86.05 197	98.42 196	89.13 233	89.50 221	96.70 201

X-MVStestdata		94.06 197	92.30 213	99.34 9	99.70 11	98.35 18	99.29 14	98.88 44	97.40 12	98.46 38	43.50 297	95.90 27	99.89 19	97.85 23	99.74 28	99.78 5

DTE-MVSNet		93.98 199	93.26 202	96.14 190	96.06 237	94.39 173	99.20 30	98.86 49	93.06 153	91.78 225	97.81 151	85.87 200	97.58 242	90.53 208	86.17 261	96.46 232

tpmp4_e23		93.91 200	93.42 200	95.38 219	97.62 137	88.59 257	97.52 214	97.34 234	87.94 253	94.17 170	96.79 214	82.91 227	99.05 131	90.62 207	95.91 152	98.50 136

MS-PatchMatch		93.84 201	93.63 186	94.46 243	96.18 231	89.45 243	97.76 199	98.27 147	92.23 187	92.13 223	97.49 169	79.50 245	98.69 162	89.75 222	99.38 69	95.25 255

pm-mvs1		93.78 202	93.06 203	95.96 195	95.53 255	93.28 202	97.92 182	97.59 203	89.99 230	92.35 217	97.03 196	86.39 191	98.08 224	90.28 211	90.43 211	96.52 226

v748		93.75 203	93.06 203	95.82 201	95.73 248	92.64 212	99.25 18	98.24 153	91.60 198	92.22 221	96.52 222	87.60 174	98.46 188	90.64 206	85.72 263	96.36 235

EU-MVSNet		93.66 204	94.14 156	92.25 259	95.96 241	83.38 275	98.52 123	98.12 174	94.69 93	92.61 210	98.13 128	87.36 178	96.39 268	91.82 187	90.00 214	96.98 169

tpm cat1		93.36 205	92.80 206	95.07 227	97.58 140	87.97 263	96.76 249	97.86 195	82.17 277	93.53 189	96.04 237	86.13 195	99.13 121	89.24 231	95.87 153	98.10 148

JIA-IIPM		93.35 206	92.49 210	95.92 197	96.48 205	90.65 232	95.01 271	96.96 250	85.93 263	96.08 118	87.33 284	87.70 171	98.78 160	91.35 197	95.58 156	98.34 142

SixPastTwentyTwo		93.34 207	92.86 205	94.75 235	95.67 250	89.41 245	98.75 89	96.67 258	93.89 120	90.15 238	98.25 123	80.87 237	98.27 216	90.90 203	90.64 209	96.57 220

USDC		93.33 208	92.71 208	95.21 223	96.83 188	90.83 227	96.91 240	97.50 217	93.84 123	90.72 233	98.14 127	77.69 252	98.82 157	89.51 227	93.21 190	95.97 243

IB-MVS	91.98 17	93.27 209	91.97 216	97.19 121	97.47 147	93.41 201	97.09 234	95.99 269	93.32 146	92.47 214	95.73 243	78.06 250	99.53 98	94.59 121	82.98 267	98.62 132
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
MIMVSNet		93.26 210	92.21 214	96.41 178	97.73 134	93.13 207	95.65 267	97.03 247	91.27 214	94.04 175	96.06 236	75.33 260	97.19 248	86.56 249	96.23 147	98.92 116

Patchmtry		93.22 211	92.35 212	95.84 200	96.77 189	93.09 208	94.66 277	97.56 205	87.37 256	92.90 204	96.24 229	88.15 156	97.90 233	87.37 245	90.10 213	96.53 225

FMVSNet1		93.19 212	92.07 215	96.56 165	97.54 143	95.00 135	98.82 70	98.18 162	90.38 223	92.27 219	97.07 186	73.68 268	97.95 229	89.36 230	91.30 207	96.72 197

LF4IMVS		93.14 213	92.79 207	94.20 245	95.88 245	88.67 255	97.66 207	97.07 245	93.81 125	91.71 226	97.65 162	77.96 251	98.81 158	91.47 196	91.92 202	95.12 256

testgi		93.06 214	92.45 211	94.88 231	96.43 207	89.90 237	98.75 89	97.54 210	95.60 63	91.63 227	97.91 140	74.46 266	97.02 250	86.10 252	93.67 176	97.72 155

PatchT		93.06 214	91.97 216	96.35 181	96.69 195	92.67 211	94.48 278	97.08 244	86.62 258	97.08 86	92.23 278	87.94 162	97.90 233	78.89 271	96.69 131	98.49 137

TransMVSNet (Re)		92.67 216	91.51 220	96.15 189	96.58 199	94.65 161	98.90 55	96.73 255	90.86 219	89.46 242	97.86 144	85.62 203	98.09 223	86.45 250	81.12 271	95.71 249

K. test v3		92.55 217	91.91 218	94.48 241	95.64 251	89.24 246	99.07 41	94.88 280	94.04 112	86.78 256	97.59 167	77.64 255	97.64 240	92.08 178	89.43 222	96.57 220

DSMNet-mixed		92.52 218	92.58 209	92.33 258	94.15 269	82.65 278	98.30 148	94.26 286	89.08 248	92.65 209	95.73 243	85.01 211	95.76 270	86.24 251	97.76 118	98.59 133

RPMNet		92.52 218	91.17 221	96.59 162	97.00 177	93.43 199	94.96 272	97.26 240	82.27 276	96.93 94	92.12 279	86.98 183	97.88 236	76.32 276	96.65 133	98.46 138

TinyColmap		92.31 220	91.53 219	94.65 237	96.92 181	89.75 239	96.92 238	96.68 257	90.45 222	89.62 241	97.85 146	76.06 259	98.81 158	86.74 248	92.51 195	95.41 254

gg-mvs-nofinetune		92.21 221	90.58 233	97.13 125	96.75 192	95.09 133	95.85 265	89.40 295	85.43 266	94.50 142	81.98 288	80.80 239	98.40 207	92.16 177	98.33 105	97.88 152

Test4		92.21 221	90.34 235	97.82 101	92.83 275	95.87 111	97.94 181	98.05 191	94.50 102	82.12 276	94.48 255	59.54 284	98.54 173	95.39 104	98.22 107	99.06 106

v18		92.10 223	90.97 223	95.50 208	96.34 216	94.85 141	98.82 70	97.52 211	89.99 230	85.31 266	93.26 263	88.90 129	96.92 252	88.82 235	79.77 275	94.73 261

v17		92.08 224	90.94 224	95.48 210	96.34 216	94.83 148	98.81 75	97.52 211	89.95 233	85.32 264	93.24 264	88.91 128	96.91 253	88.76 236	79.63 276	94.71 263

v16		92.08 224	90.94 224	95.49 209	96.38 212	94.84 146	98.81 75	97.51 214	89.94 234	85.25 267	93.28 262	88.86 130	96.91 253	88.70 237	79.78 274	94.72 262

v15		91.94 226	90.77 227	95.43 215	96.31 223	94.83 148	98.77 85	97.50 217	89.92 235	85.13 268	93.08 266	88.76 140	96.86 255	88.40 238	79.10 278	94.61 267

V14		91.93 227	90.76 228	95.42 218	96.33 219	94.81 152	98.77 85	97.51 214	89.86 237	85.09 269	93.13 265	88.80 138	96.83 257	88.32 239	79.06 280	94.60 268

V9		91.91 228	90.73 229	95.45 212	96.32 222	94.80 153	98.77 85	97.50 217	89.81 238	85.03 271	93.08 266	88.76 140	96.86 255	88.24 240	79.03 281	94.69 264

v12		91.89 229	90.70 230	95.43 215	96.31 223	94.80 153	98.76 88	97.50 217	89.76 239	84.95 272	93.00 269	88.82 134	96.82 259	88.23 241	79.00 282	94.68 266

v13		91.88 230	90.69 231	95.43 215	96.33 219	94.78 158	98.75 89	97.50 217	89.68 241	84.93 273	92.98 270	88.84 133	96.83 257	88.14 242	79.09 279	94.69 264

v11		91.85 231	90.68 232	95.36 220	96.34 216	94.74 160	98.80 78	97.43 227	89.60 243	85.09 269	93.03 268	88.53 149	96.75 260	87.37 245	79.96 273	94.58 269

FMVSNet5		91.81 232	90.92 226	94.49 240	97.21 166	92.09 215	98.00 178	97.55 209	89.31 246	90.86 232	95.61 248	74.48 265	95.32 272	85.57 256	89.70 216	96.07 241

Anonymous20231206		91.66 233	91.10 222	93.33 251	94.02 271	87.35 266	98.58 116	97.26 240	90.48 220	90.16 237	96.31 227	83.83 224	96.53 266	79.36 269	89.90 215	96.12 239

test_0402		91.32 234	90.27 236	94.48 241	96.60 198	91.12 225	98.50 128	97.22 242	86.10 261	88.30 249	96.98 201	77.65 254	97.99 228	78.13 273	92.94 192	94.34 271

PVSNet_0	88.72 19	91.28 235	90.03 238	95.00 228	97.99 124	87.29 267	94.84 275	98.50 120	92.06 189	89.86 239	95.19 249	79.81 244	99.39 107	92.27 176	69.79 288	98.33 143

EG-PatchMatch MVS		91.13 236	90.12 237	94.17 247	94.73 267	89.00 251	98.13 165	97.81 196	89.22 247	85.32 264	96.46 223	67.71 277	98.42 196	87.89 243	93.82 175	95.08 257

LP		91.12 237	89.99 239	94.53 239	96.35 215	88.70 254	93.86 282	97.35 233	84.88 268	90.98 230	94.77 253	84.40 217	97.43 245	75.41 278	91.89 203	97.47 159

TDRefinement		91.06 238	89.68 241	95.21 223	85.35 288	91.49 222	98.51 127	97.07 245	91.47 200	88.83 247	97.84 147	77.31 256	99.09 129	92.79 166	77.98 283	95.04 258

UnsupCasMVSNet_eth		90.99 239	89.92 240	94.19 246	94.08 270	89.83 238	97.13 233	98.67 92	93.69 131	85.83 261	96.19 233	75.15 261	96.74 261	89.14 232	79.41 277	96.00 242

test20.03		90.89 240	90.38 234	92.43 257	93.48 272	88.14 262	98.33 141	97.56 205	93.40 143	87.96 251	96.71 217	80.69 240	94.13 275	79.15 270	86.17 261	95.01 260

MDA-MVSNet_test_wron		90.71 241	89.38 244	94.68 236	94.83 265	90.78 229	97.19 231	97.46 223	87.60 254	72.41 287	95.72 245	86.51 188	96.71 263	85.92 254	86.80 258	96.56 222

YYNet1		90.70 242	89.39 243	94.62 238	94.79 266	90.65 232	97.20 230	97.46 223	87.54 255	72.54 286	95.74 242	86.51 188	96.66 264	86.00 253	86.76 259	96.54 224

testing_2		90.61 243	88.50 249	96.95 136	90.08 282	95.57 118	97.69 204	98.06 188	93.02 155	76.55 282	92.48 276	61.18 283	98.44 193	95.45 103	91.98 200	96.84 186

new_pmnet		90.06 244	89.00 247	93.22 254	94.18 268	88.32 261	96.42 258	96.89 251	86.19 260	85.67 263	93.62 259	77.18 257	97.10 249	81.61 264	89.29 224	94.23 272

MDA-MVSNet-bldmvs		89.97 245	88.35 251	94.83 233	95.21 261	91.34 223	97.64 208	97.51 214	88.36 251	71.17 288	96.13 235	79.22 247	96.63 265	83.65 259	86.27 260	96.52 226

CMPMVS	66.06 21	89.70 246	89.67 242	89.78 263	93.19 273	76.56 284	97.00 235	98.35 138	80.97 279	81.57 277	97.75 154	74.75 264	98.61 167	89.85 219	93.63 178	94.17 273
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 247	88.28 252	93.82 248	92.81 276	91.08 226	98.01 176	97.45 225	87.95 252	87.90 252	95.87 241	67.63 278	94.56 274	78.73 272	88.18 240	95.83 246

MVS-HIRNet		89.46 248	88.40 250	92.64 255	97.58 140	82.15 279	94.16 281	93.05 291	75.73 286	90.90 231	82.52 287	79.42 246	98.33 208	83.53 260	98.68 91	97.43 160

OpenMVS_ROB	86.42 20	89.00 249	87.43 254	93.69 249	93.08 274	89.42 244	97.91 186	96.89 251	78.58 283	85.86 260	94.69 254	69.48 274	98.29 215	77.13 274	93.29 188	93.36 278

testus		88.91 250	89.08 246	88.40 265	91.39 279	76.05 285	96.56 254	96.48 262	89.38 245	89.39 243	95.17 251	70.94 272	93.56 278	77.04 275	95.41 157	95.61 251

testpf		88.74 251	89.09 245	87.69 266	95.78 247	83.16 277	84.05 293	94.13 288	85.22 267	90.30 236	94.39 257	74.92 263	95.80 269	89.77 220	93.28 189	84.10 288

test2356		88.68 252	88.61 248	88.87 264	89.90 283	78.23 282	95.11 270	96.66 260	88.66 250	89.06 245	94.33 258	73.14 270	92.56 282	75.56 277	95.11 159	95.81 247

Patchmatch-RL test		88.36 253	87.20 255	91.83 261	91.37 280	84.40 271	92.81 283	95.93 272	91.87 194	87.25 254	93.38 261	57.63 286	96.53 266	92.54 173	82.00 269	94.57 270

HyFIR lowres test		87.96 254	86.43 257	92.55 256	96.56 200	83.82 273	92.80 284	96.03 267	79.62 282	88.30 249	92.88 274	52.77 288	97.95 229	82.45 262	96.43 140	95.89 244

PM-MVS		87.77 255	86.55 256	91.40 262	91.03 281	83.36 276	96.92 238	95.18 278	91.28 213	86.48 258	93.42 260	53.27 287	96.74 261	89.43 229	81.97 270	94.11 274

UnsupCasMVSNet_bld		87.17 256	85.12 259	93.31 252	91.94 277	88.77 252	94.92 274	98.30 144	84.30 271	82.30 275	90.04 281	63.96 282	97.25 247	85.85 255	74.47 287	93.93 276

N_pmnet		87.12 257	87.77 253	85.17 273	95.46 257	61.92 297	97.37 221	70.66 302	85.83 264	88.73 248	96.04 237	85.33 209	97.76 238	80.02 266	90.48 210	95.84 245

pmmvs3		86.67 258	84.86 260	92.11 260	88.16 284	87.19 268	96.63 251	94.75 282	79.88 281	87.22 255	92.75 275	66.56 279	95.20 273	81.24 265	76.56 285	93.96 275

test1235678		86.26 259	85.81 258	87.62 267	86.97 287	75.00 288	96.55 256	96.32 265	86.08 262	81.32 278	92.98 270	73.10 271	92.05 283	71.64 281	87.32 250	95.81 247

1111		84.94 260	84.30 261	86.86 268	87.59 285	75.10 286	96.63 251	96.43 263	82.53 274	80.75 279	92.91 272	68.94 275	93.79 276	68.24 284	84.66 266	91.70 280

test12356		83.47 261	83.37 262	83.78 274	84.43 289	70.09 293	95.12 269	95.60 275	82.98 272	78.89 281	92.43 277	64.99 280	91.41 285	70.36 282	85.55 265	89.82 282

testmv		78.74 262	77.35 263	82.89 276	78.16 297	69.30 294	95.87 264	94.65 283	81.11 278	70.98 289	87.11 285	46.31 289	90.42 286	65.28 287	76.72 284	88.95 283

LCM-MVSNet		78.70 263	76.24 267	86.08 270	77.26 298	71.99 291	94.34 279	96.72 256	61.62 291	76.53 283	89.33 282	33.91 296	92.78 281	81.85 263	74.60 286	93.46 277

Gipuma		78.40 264	76.75 265	83.38 275	95.54 254	80.43 281	79.42 295	97.40 230	64.67 289	73.46 285	80.82 290	45.65 290	93.14 280	66.32 286	87.43 248	76.56 293
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 265	75.44 268	85.46 271	82.54 290	74.95 289	94.23 280	93.08 290	72.80 287	74.68 284	87.38 283	36.36 294	91.56 284	73.95 279	63.94 289	89.87 281

FPMVS		77.62 266	77.14 264	79.05 278	79.25 294	60.97 298	95.79 266	95.94 271	65.96 288	67.93 290	94.40 256	37.73 293	88.88 288	68.83 283	88.46 237	87.29 284

no-one		74.41 267	70.76 269	85.35 272	79.88 293	76.83 283	94.68 276	94.22 287	80.33 280	63.81 291	79.73 291	35.45 295	93.36 279	71.78 280	36.99 296	85.86 287

.test1245		73.05 268	76.31 266	63.27 286	87.59 285	75.10 286	96.63 251	96.43 263	82.53 274	80.75 279	92.91 272	68.94 275	93.79 276	68.24 284	12.72 299	20.91 297

ANet_high		69.08 269	65.37 271	80.22 277	65.99 301	71.96 292	90.91 287	90.09 294	82.62 273	49.93 297	78.39 292	29.36 297	81.75 291	62.49 290	38.52 295	86.95 286

DUST3R		68.90 270	66.97 270	74.68 282	50.78 303	59.95 299	87.13 289	83.47 300	38.80 297	62.21 292	96.23 231	64.70 281	76.91 295	88.91 234	30.49 297	87.19 285

PNet_i23d		67.70 271	65.07 272	75.60 280	78.61 295	59.61 300	89.14 288	88.24 296	61.83 290	52.37 295	80.89 289	18.91 298	84.91 290	62.70 289	52.93 291	82.28 289

PMVS	61.03 23	65.95 272	63.57 274	73.09 283	57.90 302	51.22 303	85.05 292	93.93 289	54.45 293	44.32 298	83.57 286	13.22 299	89.15 287	58.68 291	81.00 272	78.91 292
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 273	64.25 273	67.02 284	82.28 291	59.36 301	91.83 286	85.63 298	52.69 294	60.22 293	77.28 293	41.06 292	80.12 293	46.15 293	41.14 293	61.57 295

EMVS		64.07 274	63.26 275	66.53 285	81.73 292	58.81 302	91.85 285	84.75 299	51.93 296	59.09 294	75.13 294	43.32 291	79.09 294	42.03 294	39.47 294	61.69 294

wuykxyi23d		63.73 275	58.86 277	78.35 279	67.62 300	67.90 295	86.56 290	87.81 297	58.26 292	42.49 299	70.28 296	11.55 301	85.05 289	63.66 288	41.50 292	82.11 290

MVE	62.14 22	63.28 276	59.38 276	74.99 281	74.33 299	65.47 296	85.55 291	80.50 301	52.02 295	51.10 296	75.00 295	10.91 303	80.50 292	51.60 292	53.40 290	78.99 291
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pcd1.5k->3k		39.42 277	41.78 278	32.35 287	96.17 232	0.00 307	0.00 297	98.54 111	0.00 301	0.00 303	0.00 301	87.78 168	0.00 299	0.00 298	93.56 180	97.06 164

wuyk23d		30.17 278	30.18 280	30.16 288	78.61 295	43.29 304	66.79 296	14.21 303	17.31 298	14.82 302	11.93 300	11.55 301	41.43 296	37.08 295	19.30 298	5.76 299

cdsmvs_eth3d_5k		23.98 279	31.98 279	0.00 291	0.00 306	0.00 307	0.00 297	98.59 102	0.00 301	0.00 303	98.61 90	90.60 108	0.00 299	0.00 298	0.00 302	0.00 300

testmvs		21.48 280	24.95 281	11.09 290	14.89 304	6.47 306	96.56 254	9.87 304	7.55 299	17.93 300	39.02 298	9.43 304	5.90 298	16.56 297	12.72 299	20.91 297

test123		20.95 281	23.72 282	12.64 289	13.54 305	8.19 305	96.55 256	6.13 305	7.48 300	16.74 301	37.98 299	12.97 300	6.05 297	16.69 296	5.43 301	23.68 296

ab-mvs-re		8.20 282	10.94 283	0.00 291	0.00 306	0.00 307	0.00 297	0.00 306	0.00 301	0.00 303	98.43 105	0.00 305	0.00 299	0.00 298	0.00 302	0.00 300

pcd_1.5k_mvsjas		7.88 283	10.50 284	0.00 291	0.00 306	0.00 307	0.00 297	0.00 306	0.00 301	0.00 303	0.00 301	94.51 54	0.00 299	0.00 298	0.00 302	0.00 300

sosnet-low-res		0.00 284	0.00 285	0.00 291	0.00 306	0.00 307	0.00 297	0.00 306	0.00 301	0.00 303	0.00 301	0.00 305	0.00 299	0.00 298	0.00 302	0.00 300

sosnet		0.00 284	0.00 285	0.00 291	0.00 306	0.00 307	0.00 297	0.00 306	0.00 301	0.00 303	0.00 301	0.00 305	0.00 299	0.00 298	0.00 302	0.00 300

uncertanet		0.00 284	0.00 285	0.00 291	0.00 306	0.00 307	0.00 297	0.00 306	0.00 301	0.00 303	0.00 301	0.00 305	0.00 299	0.00 298	0.00 302	0.00 300

Regformer		0.00 284	0.00 285	0.00 291	0.00 306	0.00 307	0.00 297	0.00 306	0.00 301	0.00 303	0.00 301	0.00 305	0.00 299	0.00 298	0.00 302	0.00 300

uanet		0.00 284	0.00 285	0.00 291	0.00 306	0.00 307	0.00 297	0.00 306	0.00 301	0.00 303	0.00 301	0.00 305	0.00 299	0.00 298	0.00 302	0.00 300

test9_res														96.39 77	99.57 46	99.69 29

TEST9					99.31 40	98.50 8	97.92 182	98.73 72	92.63 166	97.74 71	98.68 84	96.20 11	99.80 48

Patchmatch-test1					91.45 278	84.69 270	83.85 294	96.06 266		87.35 253	91.74 280	58.00 285			82.27 268

Patchmatch-test					97.41 155	90.19 236					94.89 252

test_8					99.29 47	98.44 10	97.89 191	98.72 74	92.98 157	97.70 73	98.66 87	96.20 11	99.80 48

agg_prior2														95.87 90	99.57 46	99.68 34

agg_prior					99.30 45	98.38 13		98.72 74		97.57 79			99.81 41

TestCases				96.99 132	99.25 56	93.21 205		98.18 162	91.36 206	93.52 190	98.77 77	84.67 214	99.72 72	89.70 224	97.87 114	98.02 150

test_prior4						98.01 35	97.86 194

test_prior2							97.80 196		96.12 48	97.89 65	98.69 82	95.96 23		96.89 56	99.60 41

test_prior				99.19 22	99.31 40	98.22 24		98.84 51					99.70 77			99.65 42

旧先验2							97.57 212		91.30 211	98.67 30			99.80 48	95.70 97

新几何2							97.64 208

新几何1				99.16 29	99.34 33	98.01 35		98.69 82	90.06 229	98.13 49	98.95 64	94.60 52	99.89 19	91.97 184	99.47 59	99.59 53

旧先验1					99.29 47	97.48 52		98.70 81			99.09 47	95.56 33			99.47 59	99.61 48

无先验							97.58 211	98.72 74	91.38 205				99.87 26	93.36 151		99.60 51

原ACMM2							97.67 206

原ACMM1				98.65 56	99.32 38	96.62 80		98.67 92	93.27 149	97.81 67	98.97 59	95.18 43	99.83 34	93.84 140	99.46 62	99.50 63

test222					99.23 62	97.17 64	97.40 219	98.66 95	88.68 249	98.05 53	98.96 63	94.14 62			99.53 55	99.61 48

testdata2													99.89 19	91.65 192

segment_acmp												96.85 3

testdata				98.26 79	99.20 66	95.36 124		98.68 85	91.89 192	98.60 34	99.10 43	94.44 59	99.82 39	94.27 130	99.44 64	99.58 55

testdata1							97.32 225		96.34 43

test12				99.18 26	99.16 68	98.19 26		98.53 114		98.07 52		95.13 45	99.72 72		99.56 52	99.63 47

plane_prior7					97.42 152	94.63 163

plane_prior6					97.35 158	94.61 165						87.09 180

plane_prior5								98.56 108					99.03 135	96.07 80	94.27 162	96.92 173

plane_prior4											98.28 118

plane_prior3						94.61 165			97.02 29	95.34 126

plane_prior2							98.80 78		97.28 18

plane_prior1					97.37 157

plane_prior						94.60 167	98.44 133		96.74 35						94.22 164

abl_6					99.56 18	97.76 44		98.82 53		99.26 4	99.37 8	93.33 69				99.69 29

n2								0.00 306

nn								0.00 306

door-mid								94.37 285

lessismore_v0				94.45 244	94.93 264	88.44 259		91.03 293		86.77 257	97.64 164	76.23 258	98.42 196	90.31 210	85.64 264	96.51 229

LGP-MVS_train				96.47 173	97.46 148	93.54 196		98.54 111	94.67 94	94.36 154	98.77 77	85.39 205	99.11 126	95.71 95	94.15 167	96.76 193

test11								98.66 95

door								94.64 284

HQP5-MVS						94.25 179

HQP-NCC					97.20 167		98.05 173		96.43 40	94.45 144

ACMP_Plane					97.20 167		98.05 173		96.43 40	94.45 144

BP-MVS														95.30 106

HQP4-MVS										94.45 144			98.96 142			96.87 183

HQP3-MVS								98.46 125							94.18 165

HQP2-MVS												86.75 185

High-resolution multi-view benchmark