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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

CHOSEN 280x420		97.99 49	99.24 27	96.53 82	98.34 55	99.61 39	98.36 75	89.80 145	99.27 37	95.08 51	99.81 1	98.58 58	98.64 72	99.02 35	98.92 32	98.93 199	99.48 155

CANet_DTU		96.64 97	99.08 34	93.81 124	97.10 77	99.42 68	98.85 51	90.01 139	99.31 31	79.98 182	99.78 2	99.10 54	97.42 110	98.35 80	98.05 87	99.47 175	99.53 144

TSAR-MVS + GP.		98.66 36	99.36 20	97.85 42	97.16 76	99.46 62	99.03 46	94.59 57	99.09 60	97.19 26	99.73 3	99.95 16	99.39 25	98.95 38	98.69 43	99.75 48	99.65 122

ESAPD		99.39 2	99.55 4	99.20 2	99.63 17	99.71 8	99.66 7	98.33 4	99.29 34	98.40 8	99.64 4	99.98 1	99.31 30	99.56 7	98.96 29	99.85 4	99.70 92

APDe-MVS		99.49 1	99.64 1	99.32 1	99.74 4	99.74 3	99.75 1	98.34 2	99.56 9	98.72 3	99.57 5	99.97 5	99.53 15	99.65 2	99.25 14	99.84 6	99.77 51

MVS_111021_LR		98.67 34	99.41 17	97.81 43	99.37 33	99.53 54	98.51 61	95.52 43	99.27 37	94.85 56	99.56 6	99.69 45	99.04 58	99.36 19	98.88 35	99.60 146	99.58 135

MVS_111021_HR		98.59 38	99.36 20	97.68 44	99.42 31	99.61 39	98.14 85	94.81 50	99.31 31	95.00 53	99.51 7	99.79 41	99.00 61	98.94 39	98.83 39	99.69 85	99.57 140

tmp_tt				82.25 225	97.73 64	88.71 237	80.18 232	68.65 240	99.15 52	86.98 134	99.47 8	85.31 165	68.35 237	87.51 232	83.81 233	91.64 236

SMA-MVS		99.38 3	99.60 2	99.12 7	99.76 2	99.62 34	99.39 28	98.23 15	99.52 14	98.03 13	99.45 9	99.98 1	99.64 5	99.58 6	99.30 11	99.68 94	99.76 55

PHI-MVS		99.08 19	99.43 15	98.67 26	99.15 42	99.59 45	99.11 40	97.35 35	99.14 54	97.30 24	99.44 10	99.96 11	99.32 29	98.89 44	99.39 7	99.79 36	99.58 135

v1.0		91.56 207	85.17 227	99.01 16	99.70 7	99.69 12	99.40 27	98.31 6	98.94 79	97.70 19	99.40 11	99.97 5	99.17 43	99.54 9	98.67 44	99.78 39	0.00 242

RPSCF		97.61 60	98.16 74	96.96 73	98.10 57	99.00 103	98.84 52	93.76 83	99.45 18	94.78 59	99.39 12	99.31 52	98.53 77	96.61 156	95.43 166	97.74 215	97.93 204

ACMMP_Plus		99.05 22	99.45 10	98.58 28	99.73 5	99.60 43	99.64 9	98.28 11	99.23 44	94.57 61	99.35 13	99.97 5	99.55 13	99.63 3	98.66 45	99.70 83	99.74 70

HSP-MVS		99.31 5	99.43 15	99.17 3	99.68 11	99.75 2	99.72 2	98.31 6	99.45 18	98.16 10	99.28 14	99.98 1	99.30 32	99.34 21	98.41 61	99.81 27	99.81 32

zzz-MVS		99.31 5	99.44 13	99.16 5	99.73 5	99.65 21	99.63 11	98.26 12	99.27 37	98.01 14	99.27 15	99.97 5	99.60 7	99.59 5	98.58 51	99.71 74	99.73 74

HFP-MVS		99.32 4	99.53 6	99.07 11	99.69 8	99.59 45	99.63 11	98.31 6	99.56 9	97.37 23	99.27 15	99.97 5	99.70 3	99.35 20	99.24 16	99.71 74	99.76 55

MVSTER		97.16 75	97.71 88	96.52 83	95.97 112	98.48 137	98.63 58	92.10 97	98.68 106	95.96 39	99.23 17	91.79 127	96.87 123	98.76 53	97.37 123	99.57 160	99.68 106

ACMMPR		99.30 7	99.54 5	99.03 14	99.66 14	99.64 26	99.68 5	98.25 13	99.56 9	97.12 27	99.19 18	99.95 16	99.72 1	99.43 15	99.25 14	99.72 65	99.77 51

EPNet_dtu		96.30 106	98.53 56	93.70 128	98.97 46	98.24 153	97.36 112	94.23 63	98.85 86	79.18 196	99.19 18	98.47 60	94.09 200	97.89 117	98.21 78	98.39 207	98.85 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SteuartSystems-ACMMP		99.20 13	99.51 7	98.83 24	99.66 14	99.66 20	99.71 4	98.12 24	99.14 54	96.62 31	99.16 20	99.98 1	99.12 51	99.63 3	99.19 20	99.78 39	99.83 26
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		99.27 8	99.57 3	98.92 20	98.78 50	99.53 54	99.72 2	98.11 25	99.73 2	97.43 22	99.15 21	99.96 11	99.59 9	99.73 1	99.07 22	99.88 1	99.82 27

SD-MVS		99.25 10	99.50 8	98.96 18	98.79 49	99.55 52	99.33 31	98.29 10	99.75 1	97.96 15	99.15 21	99.95 16	99.61 6	99.17 27	99.06 23	99.81 27	99.84 22

CP-MVS		99.27 8	99.44 13	99.08 10	99.62 19	99.58 47	99.53 16	98.16 18	99.21 47	97.79 17	99.15 21	99.96 11	99.59 9	99.54 9	98.86 37	99.78 39	99.74 70

PGM-MVS		98.86 28	99.35 23	98.29 32	99.77 1	99.63 30	99.67 6	95.63 41	98.66 107	95.27 47	99.11 24	99.82 38	99.67 4	99.33 22	99.19 20	99.73 60	99.74 70

HPM-MVS++		99.10 18	99.30 24	98.86 21	99.69 8	99.48 60	99.59 14	98.34 2	99.26 40	96.55 34	99.10 25	99.96 11	99.36 26	99.25 25	98.37 66	99.64 124	99.66 119

MCST-MVS		99.11 17	99.27 26	98.93 19	99.67 12	99.33 82	99.51 18	98.31 6	99.28 35	96.57 33	99.10 25	99.90 29	99.71 2	99.19 26	98.35 68	99.82 14	99.71 90

LGP-MVS_train		96.23 107	96.89 113	95.46 107	97.32 70	98.77 118	98.81 53	93.60 86	98.58 110	85.52 142	99.08 27	86.67 151	97.83 103	97.87 118	97.51 114	99.69 85	99.73 74

PMMVS		97.52 63	98.39 59	96.51 84	95.82 116	98.73 124	97.80 100	93.05 94	98.76 102	94.39 73	99.07 28	97.03 75	98.55 76	98.31 82	97.61 110	99.43 181	99.21 171

DeepPCF-MVS	97.74 3	98.34 42	99.46 9	97.04 61	98.82 48	99.33 82	96.28 141	97.47 34	99.58 7	94.70 60	98.99 29	99.85 37	97.24 113	99.55 8	99.34 9	97.73 217	99.56 141

MP-MVS		99.07 20	99.36 20	98.74 25	99.63 17	99.57 49	99.66 7	98.25 13	99.00 74	95.62 40	98.97 30	99.94 24	99.54 14	99.51 11	98.79 42	99.71 74	99.73 74
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CSCG		98.90 27	98.93 46	98.85 22	99.75 3	99.72 4	99.49 19	96.58 38	99.38 21	98.05 12	98.97 30	97.87 65	99.49 18	97.78 122	98.92 32	99.78 39	99.90 4

tfpn_n400		97.32 69	98.38 60	96.09 94	96.07 104	99.30 86	98.00 92	93.84 80	99.35 26	90.50 114	98.93 32	94.24 108	98.30 86	98.65 60	98.60 49	99.83 10	99.60 131

tfpnconf		97.32 69	98.38 60	96.09 94	96.07 104	99.30 86	98.00 92	93.84 80	99.35 26	90.50 114	98.93 32	94.24 108	98.30 86	98.65 60	98.60 49	99.83 10	99.60 131

tfpnview11		97.32 69	98.33 64	96.14 92	96.07 104	99.31 85	98.08 89	93.96 75	99.25 42	90.50 114	98.93 32	94.24 108	98.38 81	98.61 65	98.36 67	99.84 6	99.59 133

EPNet		98.05 48	98.86 48	97.10 57	99.02 45	99.43 67	98.47 62	94.73 52	99.05 70	95.62 40	98.93 32	97.62 69	95.48 163	98.59 70	98.55 53	99.29 191	99.84 22
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS_fast	98.34 1	99.17 14	99.45 10	98.85 22	99.55 25	99.37 74	99.64 9	98.05 27	99.53 12	96.58 32	98.93 32	99.92 26	99.49 18	99.46 13	99.32 10	99.80 34	99.64 126
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + ACMM		98.77 30	99.45 10	97.98 40	99.37 33	99.46 62	99.44 25	98.13 23	99.65 4	92.30 101	98.91 37	99.95 16	99.05 57	99.42 16	98.95 30	99.58 156	99.82 27

APD-MVS		99.25 10	99.38 18	99.09 9	99.69 8	99.58 47	99.56 15	98.32 5	98.85 86	97.87 16	98.91 37	99.92 26	99.30 32	99.45 14	99.38 8	99.79 36	99.58 135
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		99.23 12	99.28 25	99.17 3	99.65 16	99.34 80	99.46 22	98.21 16	99.28 35	98.47 5	98.89 39	99.94 24	99.50 16	99.42 16	98.61 48	99.73 60	99.52 146

ACMMP		98.74 31	99.03 41	98.40 30	99.36 35	99.64 26	99.20 35	97.75 33	98.82 92	95.24 48	98.85 40	99.87 33	99.17 43	98.74 56	97.50 115	99.71 74	99.76 55
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
TSAR-MVS + COLMAP		96.79 85	96.55 119	97.06 60	97.70 65	98.46 138	99.07 43	96.23 39	99.38 21	91.32 109	98.80 41	85.61 161	98.69 70	97.64 132	96.92 130	99.37 186	99.06 180

UGNet		97.66 59	99.07 36	96.01 97	97.19 75	99.65 21	97.09 125	93.39 89	99.35 26	94.40 72	98.79 42	99.59 48	94.24 198	98.04 110	98.29 75	99.73 60	99.80 35
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
ACMP	96.25 10	96.62 99	96.72 115	96.50 85	96.96 79	98.75 121	97.80 100	94.30 61	98.85 86	93.12 93	98.78 43	86.61 152	97.23 114	97.73 126	96.61 137	99.62 136	99.71 90
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	96.26 9	96.67 96	96.69 116	96.66 78	97.29 73	98.46 138	96.48 138	95.09 46	99.21 47	93.19 92	98.78 43	86.73 150	98.17 89	97.84 120	96.32 145	99.74 54	99.49 154
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AdaColmap		99.06 21	98.98 44	99.15 6	99.60 21	99.30 86	99.38 29	98.16 18	99.02 73	98.55 4	98.71 45	99.57 50	99.58 12	99.09 31	97.84 97	99.64 124	99.36 163

train_agg		98.73 32	99.11 32	98.28 33	99.36 35	99.35 78	99.48 21	97.96 29	98.83 90	93.86 83	98.70 46	99.86 34	99.44 22	99.08 33	98.38 64	99.61 139	99.58 135

casdiffmvs		97.36 68	98.33 64	96.23 88	95.78 117	99.37 74	97.62 105	91.41 113	99.07 68	94.45 69	98.68 47	94.90 95	98.37 83	98.27 85	98.12 82	99.75 48	99.87 14

tfpn111		96.96 82	96.91 112	97.03 62	96.31 88	99.67 14	98.41 65	93.99 69	97.35 160	94.50 65	98.65 48	86.93 144	99.14 46	98.26 86	97.80 100	99.82 14	99.70 92

thresconf0.02		97.18 74	97.81 86	96.45 87	96.11 103	99.20 99	98.21 81	94.26 62	99.14 54	91.72 106	98.65 48	91.51 129	98.57 75	98.22 92	98.47 56	99.82 14	99.50 152

casdiffmvs1		97.69 58	98.72 52	96.49 86	96.00 109	99.40 70	98.26 80	91.54 110	99.52 14	94.56 62	98.61 50	96.41 79	98.79 66	98.60 68	98.58 51	99.80 34	99.91 3

CPTT-MVS		99.14 16	99.20 29	99.06 12	99.58 22	99.53 54	99.45 23	97.80 32	99.19 50	98.32 9	98.58 51	99.95 16	99.60 7	99.28 24	98.20 79	99.64 124	99.69 99

Effi-MVS+		95.81 115	97.31 104	94.06 120	95.09 135	99.35 78	97.24 118	88.22 162	98.54 113	85.38 144	98.52 52	88.68 136	98.70 69	98.32 81	97.93 91	99.74 54	99.84 22

PatchmatchNet		94.70 135	97.08 108	91.92 162	95.53 124	98.85 113	95.77 147	79.54 218	98.95 76	85.98 139	98.52 52	96.45 76	97.39 111	95.32 184	94.09 208	97.32 224	97.38 211
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpn1000		97.60 61	98.21 71	96.89 75	96.32 86	99.60 43	97.99 94	93.85 79	99.21 47	95.03 52	98.49 54	93.69 115	98.31 85	98.50 75	98.31 74	99.86 2	99.70 92

canonicalmvs		97.31 72	97.81 86	96.72 76	96.20 101	99.45 64	98.21 81	91.60 107	99.22 45	95.39 45	98.48 55	90.95 130	99.16 45	97.66 129	99.05 24	99.76 46	99.90 4

PatchMatch-RL		97.77 54	98.25 68	97.21 54	99.11 43	99.25 92	97.06 127	94.09 66	98.72 105	95.14 50	98.47 56	96.29 81	98.43 79	98.65 60	97.44 120	99.45 177	98.94 182

X-MVS		98.93 26	99.37 19	98.42 29	99.67 12	99.62 34	99.60 13	98.15 20	99.08 62	93.81 84	98.46 57	99.95 16	99.59 9	99.49 12	99.21 19	99.68 94	99.75 66

CDPH-MVS		98.41 40	99.10 33	97.61 46	99.32 39	99.36 76	99.49 19	96.15 40	98.82 92	91.82 104	98.41 58	99.66 46	99.10 54	98.93 40	98.97 28	99.75 48	99.58 135

tfpn_ndepth		97.71 56	98.30 66	97.02 66	96.31 88	99.56 50	98.05 91	93.94 77	98.95 76	95.59 42	98.40 59	94.79 99	98.39 80	98.40 79	98.42 60	99.86 2	99.56 141

UA-Net		97.13 76	99.14 31	94.78 112	97.21 74	99.38 72	97.56 106	92.04 98	98.48 117	88.03 127	98.39 60	99.91 28	94.03 201	99.33 22	99.23 17	99.81 27	99.25 168

Anonymous20240521		97.56 62	98.36 62	96.62 81	96.44 84	98.36 148	98.37 73	91.73 104	99.11 59	94.80 58	98.36 61	96.28 82	98.60 74	98.12 97	98.44 58	99.76 46	99.87 14

HQP-MVS		96.37 102	96.58 117	96.13 93	97.31 72	98.44 141	98.45 63	95.22 45	98.86 84	88.58 124	98.33 62	87.00 143	97.67 104	97.23 144	96.56 139	99.56 163	99.62 129

CostFormer		94.25 145	94.88 155	93.51 134	95.43 128	98.34 149	96.21 142	80.64 210	97.94 141	94.01 77	98.30 63	86.20 157	97.52 106	92.71 218	92.69 218	97.23 228	98.02 203

CNLPA		99.03 24	99.05 37	99.01 16	99.27 40	99.22 96	99.03 46	97.98 28	99.34 29	99.00 2	98.25 64	99.71 44	99.31 30	98.80 49	98.82 40	99.48 173	99.17 172

MVS_Test		97.30 73	98.54 55	95.87 98	95.74 118	99.28 89	98.19 83	91.40 114	99.18 51	91.59 107	98.17 65	96.18 83	98.63 73	98.61 65	98.55 53	99.66 107	99.78 44

Fast-Effi-MVS+-dtu		95.38 124	98.20 72	92.09 155	93.91 147	98.87 112	97.35 113	85.01 192	99.08 62	81.09 167	98.10 66	96.36 80	95.62 156	98.43 78	97.03 127	99.55 164	99.50 152

diffmvs		96.92 83	97.86 85	95.82 99	95.70 119	99.28 89	97.98 95	91.13 120	99.08 62	92.48 100	98.09 67	92.81 121	98.18 88	98.11 98	97.83 98	99.44 179	99.81 32

PVSNet_BlendedMVS		97.51 64	97.71 88	97.28 51	98.06 58	99.61 39	97.31 114	95.02 47	99.08 62	95.51 43	98.05 68	90.11 132	98.07 93	98.91 42	98.40 62	99.72 65	99.78 44

PVSNet_Blended		97.51 64	97.71 88	97.28 51	98.06 58	99.61 39	97.31 114	95.02 47	99.08 62	95.51 43	98.05 68	90.11 132	98.07 93	98.91 42	98.40 62	99.72 65	99.78 44

FC-MVSNet-train		97.04 78	97.91 84	96.03 96	96.00 109	98.41 144	96.53 137	93.42 88	99.04 72	93.02 95	98.03 70	94.32 106	97.47 109	97.93 115	97.77 105	99.75 48	99.88 12

Effi-MVS+-dtu		95.74 117	98.04 78	93.06 142	93.92 146	99.16 100	97.90 96	88.16 165	99.07 68	82.02 163	98.02 71	94.32 106	96.74 127	98.53 73	97.56 112	99.61 139	99.62 129

tpm		92.38 190	94.79 157	89.56 206	94.30 144	97.50 192	94.24 198	78.97 224	97.72 153	74.93 215	97.97 72	82.91 189	96.60 132	93.65 216	94.81 199	98.33 208	98.98 181

PLC	97.93 2	99.02 25	98.94 45	99.11 8	99.46 30	99.24 94	99.06 44	97.96 29	99.31 31	99.16 1	97.90 73	99.79 41	99.36 26	98.71 57	98.12 82	99.65 113	99.52 146
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OMC-MVS		98.84 29	99.01 43	98.65 27	99.39 32	99.23 95	99.22 34	96.70 37	99.40 20	97.77 18	97.89 74	99.80 39	99.21 36	99.02 35	98.65 46	99.57 160	99.07 179

MSLP-MVS++		99.15 15	99.24 27	99.04 13	99.52 28	99.49 59	99.09 42	98.07 26	99.37 23	98.47 5	97.79 75	99.89 31	99.50 16	98.93 40	99.45 4	99.61 139	99.76 55

NCCC		99.05 22	99.08 34	99.02 15	99.62 19	99.38 72	99.43 26	98.21 16	99.36 25	97.66 20	97.79 75	99.90 29	99.45 21	99.17 27	98.43 59	99.77 44	99.51 150

MDTV_nov1_ep13		95.57 119	97.48 95	93.35 139	95.43 128	98.97 107	97.19 120	83.72 205	98.92 82	87.91 129	97.75 77	96.12 85	97.88 101	96.84 155	95.64 164	97.96 213	98.10 200

TAPA-MVS	97.53 5	98.41 40	98.84 50	97.91 41	99.08 44	99.33 82	99.15 38	97.13 36	99.34 29	93.20 91	97.75 77	99.19 53	99.20 37	98.66 59	98.13 81	99.66 107	99.48 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		97.71 56	98.04 78	97.32 49	99.35 37	98.91 110	97.65 104	91.68 105	98.00 135	97.01 28	97.72 79	94.83 97	98.85 65	98.44 77	98.86 37	99.41 183	99.52 146
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
MIMVSNet		94.49 142	97.59 92	90.87 191	91.74 193	98.70 126	94.68 186	78.73 225	97.98 136	83.71 151	97.71 80	94.81 98	96.96 120	97.97 113	97.92 92	99.40 185	98.04 202

CANet		98.46 39	99.16 30	97.64 45	98.48 53	99.64 26	99.35 30	94.71 53	99.53 12	95.17 49	97.63 81	99.59 48	98.38 81	98.88 45	98.99 27	99.74 54	99.86 18

DWT-MVSNet_training		95.38 124	95.05 152	95.78 100	95.86 114	98.88 111	97.55 107	90.09 138	98.23 126	96.49 35	97.62 82	86.92 148	97.16 115	92.03 225	94.12 207	97.52 220	97.50 207

CLD-MVS		96.74 89	96.51 122	97.01 68	96.71 81	98.62 130	98.73 55	94.38 59	98.94 79	94.46 68	97.33 83	87.03 142	98.07 93	97.20 146	96.87 131	99.72 65	99.54 143
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_0304		98.14 47	99.03 41	97.10 57	98.05 60	99.63 30	99.27 33	94.33 60	99.63 6	93.06 94	97.32 84	99.05 55	98.09 92	98.82 48	98.87 36	99.81 27	99.89 8

testgi		95.67 118	97.48 95	93.56 131	95.07 136	99.00 103	95.33 157	88.47 159	98.80 95	86.90 135	97.30 85	92.33 124	95.97 147	97.66 129	97.91 93	99.60 146	99.38 162

abl_6				98.09 37	99.33 38	99.22 96	98.79 54	94.96 49	98.52 116	97.00 29	97.30 85	99.86 34	98.76 67			99.69 85	99.41 160

GBi-Net		96.98 80	98.00 81	95.78 100	93.81 150	97.98 158	98.09 86	91.32 115	98.80 95	93.92 80	97.21 87	95.94 87	97.89 98	98.07 104	98.34 70	99.68 94	99.67 111

test1		96.98 80	98.00 81	95.78 100	93.81 150	97.98 158	98.09 86	91.32 115	98.80 95	93.92 80	97.21 87	95.94 87	97.89 98	98.07 104	98.34 70	99.68 94	99.67 111

FMVSNet3		97.02 79	98.12 76	95.73 104	93.59 156	97.98 158	98.34 77	91.32 115	98.80 95	93.92 80	97.21 87	95.94 87	97.63 105	98.61 65	98.62 47	99.61 139	99.65 122

EPP-MVSNet		97.75 55	98.71 53	96.63 80	95.68 121	99.56 50	97.51 108	93.10 93	99.22 45	94.99 54	97.18 90	97.30 72	98.65 71	98.83 47	98.93 31	99.84 6	99.92 1

EPMVS		95.05 130	96.86 114	92.94 145	95.84 115	98.96 108	96.68 131	79.87 214	99.05 70	90.15 117	97.12 91	95.99 86	97.49 108	95.17 192	94.75 201	97.59 219	96.96 216

test2356		88.81 217	92.86 210	84.09 223	87.85 224	93.46 230	87.07 228	83.60 206	96.50 202	62.08 234	97.06 92	75.04 227	85.17 225	95.08 205	95.42 167	98.75 202	97.46 208

tpmp4_e23		93.84 155	94.58 162	92.98 144	95.41 131	98.29 150	96.81 129	80.57 211	98.15 129	90.53 113	97.00 93	84.39 174	96.91 121	93.69 214	92.45 220	97.67 218	98.06 201

dps		94.63 138	95.31 151	93.84 123	95.53 124	98.71 125	96.54 135	80.12 213	97.81 152	97.21 25	96.98 94	92.37 123	96.34 138	92.46 222	91.77 226	97.26 226	97.08 214

ADS-MVSNet		94.65 137	97.04 110	91.88 165	95.68 121	98.99 105	95.89 145	79.03 223	99.15 52	85.81 141	96.96 95	98.21 64	97.10 116	94.48 211	94.24 206	97.74 215	97.21 212

Vis-MVSNet (Re-imp)		97.40 67	98.89 47	95.66 105	95.99 111	99.62 34	97.82 98	93.22 92	98.82 92	91.40 108	96.94 96	98.56 59	95.70 152	99.14 29	99.41 6	99.79 36	99.75 66

LS3D		97.79 52	98.25 68	97.26 53	98.40 54	99.63 30	99.53 16	98.63 1	99.25 42	88.13 126	96.93 97	94.14 111	99.19 39	99.14 29	99.23 17	99.69 85	99.42 159

IS_MVSNet		97.86 51	98.86 48	96.68 77	96.02 107	99.72 4	98.35 76	93.37 91	98.75 104	94.01 77	96.88 98	98.40 61	98.48 78	99.09 31	99.42 5	99.83 10	99.80 35

conf0.05thres1000		96.34 104	96.47 126	96.17 90	96.16 102	99.71 8	97.82 98	93.46 87	98.10 131	90.69 111	96.75 99	85.26 166	99.11 53	98.05 108	97.65 107	99.82 14	99.80 35

3Dnovator+	96.92 7	98.71 33	99.05 37	98.32 31	99.53 26	99.34 80	99.06 44	94.61 55	99.65 4	97.49 21	96.75 99	99.86 34	99.44 22	98.78 51	99.30 11	99.81 27	99.67 111

QAPM		98.62 37	99.04 40	98.13 36	99.57 23	99.48 60	99.17 37	94.78 51	99.57 8	96.16 36	96.73 101	99.80 39	99.33 28	98.79 50	99.29 13	99.75 48	99.64 126

FMVSNet5		95.42 122	96.47 126	94.20 118	92.26 168	95.99 212	95.66 149	87.15 172	97.87 145	93.46 89	96.68 102	93.79 114	97.52 106	97.10 150	97.21 125	99.11 197	96.62 222

test0.0.03 1		96.69 94	98.12 76	95.01 110	95.49 126	98.99 105	95.86 146	90.82 123	98.38 120	92.54 99	96.66 103	97.33 70	95.75 150	97.75 125	98.34 70	99.60 146	99.40 161

MDA-MVSNet-bldmvs		87.84 221	89.22 222	86.23 217	81.74 235	96.77 208	83.74 230	89.57 147	94.50 224	72.83 221	96.64 104	64.47 235	92.71 212	81.43 235	92.28 223	96.81 230	98.47 194

PCF-MVS	97.50 6	98.18 46	98.35 63	97.99 39	98.65 51	99.36 76	98.94 49	98.14 22	98.59 109	93.62 87	96.61 105	99.76 43	99.03 59	97.77 123	97.45 119	99.57 160	98.89 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test-LLR		95.50 121	97.32 101	93.37 137	95.49 126	98.74 122	96.44 139	90.82 123	98.18 127	82.75 158	96.60 106	94.67 101	95.54 159	98.09 101	96.00 153	99.20 194	98.93 183

TESTMET0.1,1		94.95 132	97.32 101	92.20 152	92.62 161	98.74 122	96.44 139	86.67 178	98.18 127	82.75 158	96.60 106	94.67 101	95.54 159	98.09 101	96.00 153	99.20 194	98.93 183

CR-MVSNet		94.57 141	97.34 100	91.33 176	94.90 139	98.59 132	97.15 121	79.14 221	97.98 136	80.42 175	96.59 108	93.50 117	96.85 124	98.10 99	97.49 116	99.50 172	99.15 173

3Dnovator	96.92 7	98.67 34	99.05 37	98.23 35	99.57 23	99.45 64	99.11 40	94.66 54	99.69 3	96.80 30	96.55 109	99.61 47	99.40 24	98.87 46	99.49 3	99.85 4	99.66 119

test-mter		94.86 133	97.32 101	92.00 159	92.41 165	98.82 114	96.18 143	86.35 182	98.05 133	82.28 161	96.48 110	94.39 105	95.46 169	98.17 94	96.20 149	99.32 189	99.13 177

tfpn		96.22 108	95.62 146	96.93 74	96.29 95	99.72 4	98.34 77	93.94 77	97.96 139	93.94 79	96.45 111	79.09 220	99.22 34	98.28 83	98.06 86	99.83 10	99.78 44

ACMH	95.42 14	95.27 128	95.96 138	94.45 116	96.83 80	98.78 117	94.72 184	91.67 106	98.95 76	86.82 136	96.42 112	83.67 178	97.00 118	97.48 138	96.68 135	99.69 85	99.76 55
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GG-mvs-BLEND		69.11 232	98.13 75	35.26 238	3.49 246	98.20 155	94.89 165	2.38 243	98.42 119	5.82 247	96.37 113	98.60 57	5.97 243	98.75 55	97.98 90	99.01 198	98.61 190

RPMNet		94.66 136	97.16 106	91.75 168	94.98 137	98.59 132	97.00 128	78.37 227	97.98 136	83.78 148	96.27 114	94.09 113	96.91 121	97.36 140	96.73 133	99.48 173	99.09 178

DELS-MVS		98.19 45	98.77 51	97.52 47	98.29 56	99.71 8	99.12 39	94.58 58	98.80 95	95.38 46	96.24 115	98.24 63	97.92 97	99.06 34	99.52 1	99.82 14	99.79 42
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
view800		96.70 92	96.45 129	96.99 72	96.29 95	99.69 12	98.39 72	93.95 76	97.92 142	94.25 76	96.23 116	85.57 162	99.22 34	98.28 83	97.71 106	99.82 14	99.76 55

GA-MVS		93.93 151	96.31 135	91.16 182	93.61 154	98.79 115	95.39 156	90.69 127	98.25 124	73.28 219	96.15 117	88.42 137	94.39 196	97.76 124	95.35 170	99.58 156	99.45 157

PatchT		93.96 150	97.36 99	90.00 203	94.76 142	98.65 128	90.11 220	78.57 226	97.96 139	80.42 175	96.07 118	94.10 112	96.85 124	98.10 99	97.49 116	99.26 192	99.15 173

CDS-MVSNet		96.59 100	98.02 80	94.92 111	94.45 143	98.96 108	97.46 110	91.75 103	97.86 147	90.07 118	96.02 119	97.25 73	96.21 139	98.04 110	98.38 64	99.60 146	99.65 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
view600		96.70 92	96.44 131	97.01 68	96.28 98	99.67 14	98.42 64	93.99 69	97.87 145	94.34 74	95.99 120	85.94 158	99.20 37	98.26 86	97.64 108	99.82 14	99.73 74

thres600view7		96.69 94	96.43 133	97.00 70	96.28 98	99.67 14	98.41 65	93.99 69	97.85 148	94.29 75	95.96 121	85.91 159	99.19 39	98.26 86	97.63 109	99.82 14	99.73 74

OPM-MVS		96.22 108	95.85 142	96.65 79	97.75 63	98.54 135	99.00 48	95.53 42	96.88 184	89.88 120	95.95 122	86.46 154	98.07 93	97.65 131	96.63 136	99.67 102	98.83 189

conf200view11		96.75 87	96.51 122	97.03 62	96.31 88	99.67 14	98.41 65	93.99 69	97.35 160	94.50 65	95.90 123	86.93 144	99.14 46	98.26 86	97.80 100	99.82 14	99.70 92

thres100view900		96.72 90	96.47 126	97.00 70	96.31 88	99.52 57	98.28 79	94.01 67	97.35 160	94.52 63	95.90 123	86.93 144	99.09 56	98.07 104	97.87 95	99.81 27	99.63 128

tfpn200view9		96.75 87	96.51 122	97.03 62	96.31 88	99.67 14	98.41 65	93.99 69	97.35 160	94.52 63	95.90 123	86.93 144	99.14 46	98.26 86	97.80 100	99.82 14	99.70 92

MSDG		98.27 44	98.29 67	98.24 34	99.20 41	99.22 96	99.20 35	97.82 31	99.37 23	94.43 70	95.90 123	97.31 71	99.12 51	98.76 53	98.35 68	99.67 102	99.14 176

thres200		96.76 86	96.53 120	97.03 62	96.31 88	99.67 14	98.37 73	93.99 69	97.68 155	94.49 67	95.83 127	86.77 149	99.18 41	98.26 86	97.82 99	99.82 14	99.66 119

thres400		96.71 91	96.45 129	97.02 66	96.28 98	99.63 30	98.41 65	94.00 68	97.82 150	94.42 71	95.74 128	86.26 155	99.18 41	98.20 93	97.79 104	99.81 27	99.70 92

DeepC-MVS	97.63 4	98.33 43	98.57 54	98.04 38	98.62 52	99.65 21	99.45 23	98.15 20	99.51 16	92.80 97	95.74 128	96.44 78	99.46 20	99.37 18	99.50 2	99.78 39	99.81 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Vis-MVSNet		96.16 110	98.22 70	93.75 125	95.33 132	99.70 11	97.27 116	90.85 122	98.30 122	85.51 143	95.72 130	96.45 76	93.69 207	98.70 58	99.00 26	99.84 6	99.69 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testus		88.77 218	92.77 214	84.10 222	88.24 223	93.95 228	87.16 227	84.24 201	97.37 159	61.54 235	95.70 131	73.10 229	84.90 226	95.56 182	95.82 159	98.51 203	97.88 205

tpmrst		93.86 153	95.88 140	91.50 172	95.69 120	98.62 130	95.64 150	79.41 219	98.80 95	83.76 150	95.63 132	96.13 84	97.25 112	92.92 217	92.31 222	97.27 225	96.74 219

ACMH+	95.51 13	95.40 123	96.00 136	94.70 113	96.33 85	98.79 115	96.79 130	91.32 115	98.77 101	87.18 133	95.60 133	85.46 163	96.97 119	97.15 147	96.59 138	99.59 152	99.65 122

FC-MVSNet-test		96.07 112	97.94 83	93.89 122	93.60 155	98.67 127	96.62 134	90.30 134	98.76 102	88.62 123	95.57 134	97.63 68	94.48 194	97.97 113	97.48 118	99.71 74	99.52 146

conf0.002		96.31 105	95.63 145	97.11 56	96.29 95	99.64 26	98.41 65	94.11 64	97.35 160	94.86 55	95.49 135	81.06 210	99.14 46	98.14 96	98.02 89	99.82 14	99.69 99

conf0.01		96.35 103	95.71 143	97.10 57	96.30 94	99.65 21	98.41 65	94.10 65	97.35 160	94.82 57	95.44 136	81.88 205	99.14 46	98.16 95	97.80 100	99.82 14	99.69 99

DeepMVS_CX						96.85 206	87.43 226	89.27 149	98.30 122	75.55 212	95.05 137	79.47 218	92.62 213	89.48 231		95.18 235	95.96 223

IterMVS-LS		96.12 111	97.48 95	94.53 114	95.19 134	97.56 187	97.15 121	89.19 151	99.08 62	88.23 125	94.97 138	94.73 100	97.84 102	97.86 119	98.26 76	99.60 146	99.88 12
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IB-MVS	93.96 15	95.02 131	96.44 131	93.36 138	97.05 78	99.28 89	90.43 217	93.39 89	98.02 134	96.02 37	94.92 139	92.07 126	83.52 228	95.38 183	95.82 159	99.72 65	99.59 133
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
COLMAP_ROB	96.15 12	97.78 53	98.17 73	97.32 49	98.84 47	99.45 64	99.28 32	95.43 44	99.48 17	91.80 105	94.83 140	98.36 62	98.90 62	98.09 101	97.85 96	99.68 94	99.15 173
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testpf		91.80 205	94.43 166	88.74 209	93.89 148	95.30 225	92.05 211	71.77 236	97.52 157	87.24 132	94.77 141	92.68 122	91.48 216	91.75 228	92.11 225	96.02 233	96.89 217

OpenMVS	96.23 11	97.95 50	98.45 58	97.35 48	99.52 28	99.42 68	98.91 50	94.61 55	98.87 83	92.24 102	94.61 142	99.05 55	99.10 54	98.64 63	99.05 24	99.74 54	99.51 150

Fast-Effi-MVS+		95.38 124	96.52 121	94.05 121	94.15 145	99.14 101	97.24 118	86.79 176	98.53 114	87.62 131	94.51 143	87.06 141	98.76 67	98.60 68	98.04 88	99.72 65	99.77 51

CVMVSNet		95.33 127	97.09 107	93.27 140	95.23 133	98.39 146	95.49 153	92.58 95	97.71 154	83.00 157	94.44 144	93.28 118	93.92 204	97.79 121	98.54 55	99.41 183	99.45 157

IterMVS		94.81 134	97.71 88	91.42 174	94.83 141	97.63 180	97.38 111	85.08 190	98.93 81	75.67 211	94.02 145	97.64 67	96.66 130	98.45 76	97.60 111	98.90 200	99.72 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
USDC		94.26 144	94.83 156	93.59 130	96.02 107	98.44 141	97.84 97	88.65 157	98.86 84	82.73 160	94.02 145	80.56 211	96.76 126	97.28 143	96.15 152	99.55 164	98.50 193

UniMVSNet (Re)		94.58 140	95.34 149	93.71 127	92.25 169	98.08 157	94.97 162	91.29 119	97.03 177	87.94 128	93.97 147	86.25 156	96.07 144	96.27 171	95.97 156	99.72 65	99.79 42

PVSNet_Blended_VisFu		97.41 66	98.49 57	96.15 91	97.49 66	99.76 1	96.02 144	93.75 85	99.26 40	93.38 90	93.73 148	99.35 51	96.47 136	98.96 37	98.46 57	99.77 44	99.90 4

Anonymous202405211			97.40 98		96.45 83	99.54 53	98.08 89	93.79 82	98.24 125		93.55 149	94.41 104	98.88 64	98.04 110	98.24 77	99.75 48	99.76 55

LTVRE_ROB	93.20 16	92.84 170	94.92 153	90.43 199	92.83 159	98.63 129	97.08 126	87.87 168	97.91 143	68.42 225	93.54 150	79.46 219	96.62 131	97.55 135	97.40 122	99.74 54	99.92 1
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
MS-PatchMatch		95.99 113	97.26 105	94.51 115	97.46 67	98.76 120	97.27 116	86.97 175	99.09 60	89.83 121	93.51 151	97.78 66	96.18 141	97.53 136	95.71 163	99.35 187	98.41 195

SixPastTwentyTwo		93.44 159	95.32 150	91.24 180	92.11 172	98.40 145	92.77 207	88.64 158	98.09 132	77.83 201	93.51 151	85.74 160	96.52 135	96.91 153	94.89 198	99.59 152	99.73 74

tpm cat1		94.06 146	94.90 154	93.06 142	95.42 130	98.52 136	96.64 133	80.67 209	97.82 150	92.63 98	93.39 153	95.00 94	96.06 145	91.36 229	91.58 228	96.98 229	96.66 221

pmmvs4		95.09 129	95.90 139	94.14 119	92.29 167	97.70 172	95.45 154	90.31 132	98.60 108	90.70 110	93.25 154	89.90 134	96.67 129	97.13 148	95.42 167	99.44 179	99.28 166

UniMVSNet_NR-MVSNet		94.59 139	95.47 148	93.55 132	91.85 182	97.89 164	95.03 160	92.00 99	97.33 166	86.12 137	93.19 155	87.29 140	96.60 132	96.12 174	96.70 134	99.72 65	99.80 35

FMVSNet2		96.64 97	97.50 93	95.63 106	93.81 150	97.98 158	98.09 86	90.87 121	98.99 75	93.48 88	93.17 156	95.25 92	97.89 98	98.63 64	98.80 41	99.68 94	99.67 111

DU-MVS		93.98 149	94.44 165	93.44 135	91.66 197	97.77 166	95.03 160	91.57 108	97.17 171	86.12 137	93.13 157	81.13 209	96.60 132	95.10 203	97.01 129	99.67 102	99.80 35

NR-MVSNet		94.01 147	94.51 163	93.44 135	92.56 163	97.77 166	95.67 148	91.57 108	97.17 171	85.84 140	93.13 157	80.53 212	95.29 182	97.01 151	96.17 150	99.69 85	99.75 66

DI_MVS_plusplus_trai		96.90 84	97.49 94	96.21 89	95.61 123	99.40 70	98.72 56	92.11 96	99.14 54	92.98 96	93.08 159	95.14 93	98.13 91	98.05 108	97.91 93	99.74 54	99.73 74

TAMVS		95.53 120	96.50 125	94.39 117	93.86 149	99.03 102	96.67 132	89.55 148	97.33 166	90.64 112	93.02 160	91.58 128	96.21 139	97.72 127	97.43 121	99.43 181	99.36 163

EU-MVSNet		92.80 173	94.76 158	90.51 197	91.88 180	96.74 209	92.48 209	88.69 156	96.21 205	79.00 197	91.51 161	87.82 138	91.83 215	95.87 179	96.27 146	99.21 193	98.92 186

pmmvs5		92.71 180	94.27 168	90.90 189	91.42 206	97.74 168	93.23 203	86.66 179	95.99 212	78.96 198	91.45 162	83.44 180	95.55 158	97.30 142	95.05 179	99.58 156	98.93 183

MDTV_nov1_ep13_2view		92.44 186	95.66 144	88.68 210	91.05 213	97.92 162	92.17 210	79.64 216	98.83 90	76.20 209	91.45 162	93.51 116	95.04 187	95.68 181	93.70 211	97.96 213	98.53 192

TinyColmap		94.00 148	94.35 167	93.60 129	95.89 113	98.26 151	97.49 109	88.82 154	98.56 112	83.21 154	91.28 164	80.48 213	96.68 128	97.34 141	96.26 148	99.53 169	98.24 198

Anonymous20231211		97.10 77	97.06 109	97.14 55	96.32 86	99.52 57	98.16 84	93.76 83	98.84 89	95.98 38	90.92 165	94.58 103	98.90 62	97.72 127	98.10 84	99.71 74	99.75 66

anonymousdsp		93.12 162	95.86 141	89.93 205	91.09 212	98.25 152	95.12 158	85.08 190	97.44 158	73.30 218	90.89 166	90.78 131	95.25 184	97.91 116	95.96 157	99.71 74	99.82 27

tfpnnormal		93.85 154	94.12 171	93.54 133	93.22 158	98.24 153	95.45 154	91.96 101	94.61 222	83.91 146	90.74 167	81.75 207	97.04 117	97.49 137	96.16 151	99.68 94	99.84 22

test20.03		90.65 212	93.71 187	87.09 214	90.44 219	96.24 210	89.74 222	85.46 186	95.59 219	72.99 220	90.68 168	85.33 164	84.41 227	95.94 178	95.10 178	99.52 170	97.06 215

WR-MVS_H		93.54 157	94.67 159	92.22 150	91.95 178	97.91 163	94.58 192	88.75 155	96.64 194	83.88 147	90.66 169	85.13 167	94.40 195	96.54 161	95.91 158	99.73 60	99.89 8

TranMVSNet+NR-MVSNet		93.67 156	94.14 169	93.13 141	91.28 211	97.58 185	95.60 151	91.97 100	97.06 175	84.05 145	90.64 170	82.22 200	96.17 142	94.94 207	96.78 132	99.69 85	99.78 44

WR-MVS		93.43 160	94.48 164	92.21 151	91.52 204	97.69 176	94.66 188	89.98 140	96.86 185	83.43 152	90.12 171	85.03 168	93.94 203	96.02 177	95.82 159	99.71 74	99.82 27

v18		92.63 182	93.67 188	91.43 173	92.13 170	95.65 213	95.09 159	85.44 187	97.06 175	80.78 169	90.06 172	83.06 184	95.47 168	95.16 196	95.01 185	99.64 124	99.67 111

v17		92.55 183	93.65 189	91.27 179	92.11 172	95.63 214	94.89 165	85.15 188	97.12 174	80.39 178	90.02 173	83.02 185	95.45 170	95.17 192	94.92 195	99.66 107	99.68 106

v8		92.87 169	93.87 184	91.72 170	92.05 174	97.50 192	94.79 176	88.20 163	96.85 186	80.11 181	90.01 174	82.86 191	95.48 163	95.15 200	94.90 196	99.66 107	99.80 35

v6		93.11 163	93.98 177	92.10 154	92.01 175	97.71 169	94.86 172	90.15 135	96.96 180	80.47 174	90.01 174	83.26 182	95.48 163	95.17 192	95.01 185	99.64 124	99.76 55

v1neww		93.06 164	93.94 179	92.03 157	91.99 176	97.70 172	94.79 176	90.14 136	96.93 182	80.13 179	89.97 176	83.01 186	95.48 163	95.16 196	95.01 185	99.63 130	99.76 55

v7new		93.06 164	93.94 179	92.03 157	91.99 176	97.70 172	94.79 176	90.14 136	96.93 182	80.13 179	89.97 176	83.01 186	95.48 163	95.16 196	95.01 185	99.63 130	99.76 55

v16		92.66 181	93.80 185	91.32 177	92.13 170	95.62 215	94.89 165	85.12 189	97.20 169	80.66 170	89.96 178	83.93 176	95.49 162	95.17 192	95.04 180	99.63 130	99.68 106

pm-mvs1		94.27 143	95.57 147	92.75 146	92.58 162	98.13 156	94.87 169	90.71 126	96.70 190	83.78 148	89.94 179	89.85 135	94.96 189	97.58 134	97.07 126	99.61 139	99.72 86

LP		92.12 200	94.60 160	89.22 208	94.96 138	98.45 140	93.01 205	77.58 228	97.85 148	77.26 205	89.80 180	93.00 120	94.54 191	93.69 214	92.58 219	98.00 212	96.83 218

V42		93.05 166	93.90 183	92.04 156	91.91 179	97.66 178	94.91 164	89.91 141	96.85 186	80.58 172	89.66 181	83.43 181	95.37 175	95.03 206	94.90 196	99.59 152	99.78 44

pmmvs3		88.19 220	91.27 217	84.60 221	85.60 228	93.66 229	85.68 229	81.13 208	92.36 229	63.66 233	89.51 182	77.10 225	93.22 209	96.37 165	92.40 221	98.30 209	97.46 208

CP-MVSNet		93.25 161	94.00 176	92.38 149	91.65 199	97.56 187	94.38 195	89.20 150	96.05 210	83.16 155	89.51 182	81.97 204	96.16 143	96.43 163	96.56 139	99.71 74	99.89 8

FMVSNet1		95.77 116	96.41 134	95.03 109	93.42 157	97.86 165	97.11 124	89.89 142	98.53 114	92.00 103	89.17 184	93.23 119	98.15 90	98.07 104	98.34 70	99.61 139	99.69 99

MVS-HIRNet		92.51 184	95.97 137	88.48 212	93.73 153	98.37 147	90.33 218	75.36 235	98.32 121	77.78 202	89.15 185	94.87 96	95.14 186	97.62 133	96.39 143	98.51 203	97.11 213

TDRefinement		93.04 167	93.57 193	92.41 148	96.58 82	98.77 118	97.78 102	91.96 101	98.12 130	80.84 168	89.13 186	79.87 217	87.78 220	96.44 162	94.50 205	99.54 168	98.15 199

TransMVSNet (Re)		93.45 158	94.08 173	92.72 147	92.83 159	97.62 183	94.94 163	91.54 110	95.65 218	83.06 156	88.93 187	83.53 179	94.25 197	97.41 139	97.03 127	99.67 102	98.40 197

v12		92.18 198	93.29 203	90.88 190	91.70 195	95.59 218	94.61 190	84.36 200	96.65 193	79.59 189	88.85 188	82.03 203	95.35 177	95.22 186	95.04 180	99.65 113	99.68 106

DTE-MVSNet		92.42 189	92.85 211	91.91 163	90.87 214	96.97 205	94.53 194	89.81 143	95.86 215	81.59 165	88.83 189	77.88 224	95.01 188	94.34 212	96.35 144	99.64 124	99.73 74

V14		92.31 194	93.41 198	91.03 185	91.80 186	95.59 218	94.79 176	84.70 194	96.58 197	79.83 183	88.79 190	82.98 188	95.41 172	95.22 186	95.02 184	99.65 113	99.67 111

v2v482		92.77 177	93.52 197	91.90 164	91.59 202	97.63 180	94.57 193	90.31 132	96.80 188	79.22 194	88.74 191	81.55 208	96.04 146	95.26 185	94.97 191	99.66 107	99.69 99

v15		92.27 195	93.33 200	91.04 184	91.83 183	95.60 216	94.79 176	84.88 193	96.66 192	79.66 188	88.72 192	82.45 198	95.40 173	95.19 191	95.00 189	99.65 113	99.67 111

V9		92.24 196	93.32 202	90.98 187	91.76 189	95.58 220	94.83 174	84.50 198	96.68 191	79.73 186	88.66 193	82.39 199	95.39 174	95.22 186	95.03 182	99.65 113	99.67 111

v1141		92.79 175	93.61 190	91.84 167	91.75 190	97.71 169	94.74 182	90.33 129	96.58 197	79.21 195	88.59 194	82.53 196	95.36 176	95.16 196	94.96 192	99.63 130	99.72 86

divwei89l23v2f112		92.80 173	93.60 192	91.86 166	91.75 190	97.71 169	94.75 181	90.32 130	96.54 199	79.35 192	88.59 194	82.55 195	95.35 177	95.15 200	94.96 192	99.63 130	99.72 86

PEN-MVS		92.72 178	93.20 205	92.15 153	91.29 209	97.31 201	94.67 187	89.81 143	96.19 206	81.83 164	88.58 196	79.06 221	95.61 157	95.21 189	96.27 146	99.72 65	99.82 27

v1		92.81 171	93.57 193	91.94 161	91.79 187	97.70 172	94.80 175	90.32 130	96.52 200	79.75 185	88.47 197	82.46 197	95.32 179	95.14 202	94.96 192	99.63 130	99.73 74

Anonymous20231206		90.70 211	93.93 181	86.92 216	90.21 221	96.79 207	90.30 219	86.61 180	96.05 210	69.25 224	88.46 198	84.86 170	85.86 224	97.11 149	96.47 142	99.30 190	97.80 206

v13		92.16 199	93.28 204	90.85 192	91.75 190	95.58 220	94.65 189	84.23 202	96.49 203	79.51 191	88.40 199	82.58 194	95.31 181	95.21 189	95.03 182	99.66 107	99.68 106

v148		92.36 192	92.88 209	91.75 168	91.63 200	97.66 178	92.64 208	90.55 128	96.09 208	83.34 153	88.19 200	80.00 215	92.74 211	93.98 213	94.58 204	99.58 156	99.69 99

v7		92.97 168	94.11 172	91.65 171	91.83 183	97.55 189	94.86 172	88.19 164	96.96 180	79.72 187	88.16 201	84.68 171	95.63 154	96.33 168	95.30 172	99.65 113	99.77 51

v10		92.79 175	94.06 174	91.31 178	91.78 188	97.29 203	94.87 169	86.10 183	96.97 179	79.82 184	88.16 201	84.56 172	95.63 154	96.33 168	95.31 171	99.65 113	99.80 35

v1144		92.81 171	94.03 175	91.40 175	91.68 196	97.60 184	94.73 183	88.40 160	96.71 189	78.48 199	88.14 203	84.46 173	95.45 170	96.31 170	95.22 174	99.65 113	99.76 55

v11		92.43 187	93.77 186	90.85 192	91.72 194	95.58 220	94.87 169	84.07 204	96.98 178	79.28 193	88.03 204	84.22 175	95.53 161	96.55 160	95.36 169	99.65 113	99.70 92

gm-plane-assit		89.44 216	92.82 213	85.49 219	91.37 208	95.34 224	79.55 234	82.12 207	91.68 230	64.79 231	87.98 205	80.26 214	95.66 153	98.51 74	97.56 112	99.45 177	98.41 195

N_pmnet		92.21 197	94.60 160	89.42 207	91.88 180	97.38 199	89.15 223	89.74 146	97.89 144	73.75 217	87.94 206	92.23 125	93.85 205	96.10 175	93.20 214	98.15 211	97.43 210

Baseline_NR-MVSNet		93.87 152	93.98 177	93.75 125	91.66 197	97.02 204	95.53 152	91.52 112	97.16 173	87.77 130	87.93 207	83.69 177	96.35 137	95.10 203	97.23 124	99.68 94	99.73 74

v144192		92.38 190	93.55 196	91.00 186	91.44 205	97.47 195	94.27 196	87.41 171	96.52 200	78.03 200	87.50 208	82.65 193	95.32 179	95.82 180	95.15 176	99.55 164	99.78 44

PS-CasMVS		92.72 178	93.36 199	91.98 160	91.62 201	97.52 190	94.13 199	88.98 152	95.94 213	81.51 166	87.35 209	79.95 216	95.91 148	96.37 165	96.49 141	99.70 83	99.89 8

CHOSEN 1792x2688		96.41 101	96.99 111	95.74 103	98.01 61	99.72 4	97.70 103	90.78 125	99.13 58	90.03 119	87.35 209	95.36 91	98.33 84	98.59 70	98.91 34	99.59 152	99.87 14

v1192		92.43 187	93.61 190	91.05 183	91.53 203	97.43 196	94.61 190	87.99 166	96.60 195	76.72 207	87.11 211	82.74 192	95.85 149	96.35 167	95.30 172	99.60 146	99.74 70

v1921920		92.36 192	93.57 193	90.94 188	91.39 207	97.39 198	94.70 185	87.63 170	96.60 195	76.63 208	86.98 212	82.89 190	95.75 150	96.26 172	95.14 177	99.55 164	99.73 74

v52		91.94 202	93.10 206	90.57 195	90.62 216	97.50 192	93.98 200	87.02 173	95.86 215	77.67 203	86.93 213	82.16 202	94.53 192	94.71 209	94.70 202	99.61 139	99.85 20

V4		91.92 203	93.10 206	90.55 196	90.64 215	97.51 191	93.93 201	87.02 173	95.81 217	77.61 204	86.93 213	82.19 201	94.50 193	94.72 208	94.68 203	99.62 136	99.85 20

PM-MVS		89.55 215	90.30 220	88.67 211	87.06 225	95.60 216	90.88 215	84.51 197	96.14 207	75.75 210	86.89 215	63.47 236	94.64 190	96.85 154	93.89 209	99.17 196	99.29 165

v7n		91.61 206	92.95 208	90.04 202	90.56 218	97.69 176	93.74 202	85.59 185	95.89 214	76.95 206	86.60 216	78.60 223	93.76 206	97.01 151	94.99 190	99.65 113	99.87 14

v1240		91.99 201	93.33 200	90.44 198	91.29 209	97.30 202	94.25 197	86.79 176	96.43 204	75.49 213	86.34 217	81.85 206	95.29 182	96.42 164	95.22 174	99.52 170	99.73 74

v748		91.12 208	91.95 216	90.16 201	90.60 217	97.35 200	91.11 212	87.92 167	94.75 221	80.54 173	86.26 218	75.97 226	91.13 217	94.63 210	94.81 199	99.65 113	99.90 4

EG-PatchMatch MVS		92.45 185	93.92 182	90.72 194	92.56 163	98.43 143	94.88 168	84.54 196	97.18 170	79.55 190	86.12 219	83.23 183	93.15 210	97.22 145	96.00 153	99.67 102	99.27 167

new_pmnet		90.45 213	92.84 212	87.66 213	88.96 222	96.16 211	88.71 224	84.66 195	97.56 156	71.91 223	85.60 220	86.58 153	93.28 208	96.07 176	93.54 212	98.46 205	94.39 226

HyFIR lowres test		95.99 113	96.56 118	95.32 108	97.99 62	99.65 21	96.54 135	88.86 153	98.44 118	89.77 122	84.14 221	97.05 74	99.03 59	98.55 72	98.19 80	99.73 60	99.86 18

FPMVS		83.82 223	84.61 229	82.90 224	90.39 220	90.71 232	90.85 216	84.10 203	95.47 220	65.15 229	83.44 222	74.46 228	75.48 230	81.63 234	79.42 236	91.42 237	87.14 234

test12356		80.53 228	84.80 228	75.54 230	82.31 232	88.05 239	75.99 235	79.31 220	88.53 233	53.24 239	83.30 223	56.38 239	65.16 239	90.87 230	93.10 215	97.25 227	93.34 231

CMPMVS	70.31 18	90.74 210	91.06 218	90.36 200	97.32 70	97.43 196	92.97 206	87.82 169	93.50 226	75.34 214	83.27 224	84.90 169	92.19 214	92.64 221	91.21 229	96.50 231	94.46 225
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d		89.81 214	89.65 221	90.00 203	86.94 226	95.38 223	91.08 213	86.39 181	94.57 223	82.27 162	83.03 225	64.94 233	93.96 202	96.57 159	93.82 210	99.35 187	99.24 169

testmv		81.83 225	86.26 224	76.66 228	84.10 229	89.42 235	74.29 238	79.65 215	90.61 231	51.85 240	82.11 226	63.06 238	72.61 233	91.94 226	92.75 216	97.49 221	93.94 228

test1235678		81.83 225	86.26 224	76.66 228	84.10 229	89.41 236	74.29 238	79.64 216	90.60 232	51.84 241	82.11 226	63.07 237	72.61 233	91.94 226	92.75 216	97.49 221	93.94 228

new-patchmatchnet		86.12 222	87.30 223	84.74 220	86.92 227	95.19 227	83.57 231	84.42 199	92.67 228	65.66 228	80.32 228	64.72 234	89.41 219	92.33 224	89.21 230	98.43 206	96.69 220

gg-mvs-nofinetune		90.85 209	94.14 169	87.02 215	94.89 140	99.25 92	98.64 57	76.29 232	88.24 234	57.50 236	79.93 229	95.45 90	95.18 185	98.77 52	98.07 85	99.62 136	99.24 169

MIMVSNet1		88.61 219	90.68 219	86.19 218	81.56 236	95.30 225	87.78 225	85.98 184	94.19 225	72.30 222	78.84 230	78.90 222	90.06 218	96.59 157	95.47 165	99.46 176	95.49 224

pmmvs6		91.90 204	92.53 215	91.17 181	91.81 185	97.63 180	93.23 203	88.37 161	93.43 227	80.61 171	77.32 231	87.47 139	94.12 199	96.58 158	95.72 162	98.88 201	99.53 144

1111		82.87 224	85.67 226	79.62 227	81.86 233	89.62 233	74.44 236	68.81 238	87.44 235	66.59 226	76.83 232	70.33 231	87.71 221	92.65 219	93.37 213	98.28 210	89.42 232

.test1245		69.67 231	72.22 234	66.70 235	81.86 233	89.62 233	74.44 236	68.81 238	87.44 235	66.59 226	76.83 232	70.33 231	87.71 221	92.65 219	37.65 239	20.79 243	51.04 239

PMVS	72.60 17	76.39 230	77.66 233	74.92 231	81.04 237	69.37 245	68.47 241	80.54 212	85.39 237	65.07 230	73.52 234	72.91 230	65.67 238	80.35 236	76.81 237	88.71 239	85.25 238
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		77.26 229	79.47 232	74.70 232	76.00 239	88.37 238	74.22 240	76.34 231	78.31 238	54.13 237	69.96 235	52.50 241	70.14 236	84.83 233	88.71 231	97.35 223	93.58 230

ambc			80.99 231		80.04 238	90.84 231	90.91 214		96.09 208	74.18 216	62.81 236	30.59 246	82.44 229	96.25 173	91.77 226	95.91 234	98.56 191

Gipuma		81.40 227	81.78 230	80.96 226	83.21 231	85.61 240	79.73 233	76.25 233	97.33 166	64.21 232	55.32 237	55.55 240	86.04 223	92.43 223	92.20 224	96.32 232	93.99 227
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		68.12 234	68.11 236	68.14 234	75.51 240	71.76 243	55.38 244	77.20 230	77.78 239	37.79 244	53.59 238	43.61 242	74.72 231	67.05 240	76.70 238	88.27 241	86.24 236

testmvs		31.24 237	40.15 239	20.86 239	12.61 244	17.99 246	25.16 247	13.30 241	48.42 243	24.82 245	53.07 239	30.13 247	28.47 241	42.73 241	37.65 239	20.79 243	51.04 239

E-PMN		68.30 233	68.43 235	68.15 233	74.70 241	71.56 244	55.64 243	77.24 229	77.48 240	39.46 243	51.95 240	41.68 244	73.28 232	70.65 238	79.51 235	88.61 240	86.20 237

no-one		66.79 235	67.62 237	65.81 236	73.06 242	81.79 241	51.90 246	76.20 234	61.07 242	54.05 238	51.62 241	41.72 243	49.18 240	67.26 239	82.83 234	90.47 238	87.07 235

MVE	67.97 19	65.53 236	67.43 238	63.31 237	59.33 243	74.20 242	53.09 245	70.43 237	66.27 241	43.13 242	45.98 242	30.62 245	70.65 235	79.34 237	86.30 232	83.25 242	89.33 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		26.75 238	34.25 240	18.01 240	7.93 245	17.18 247	24.85 248	12.36 242	44.83 244	16.52 246	41.80 243	18.10 248	28.29 242	33.08 242	34.79 241	18.10 245	49.95 241

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

our_test_3					92.30 166	97.58 185	90.09 221

MTAPA										98.09 11		99.97 5

MTMP										98.46 7		99.96 11

Patchmatch-RL test							66.86 242

XVS					97.42 68	99.62 34	98.59 59			93.81 84		99.95 16				99.69 85

X-MVStestdata					97.42 68	99.62 34	98.59 59			93.81 84		99.95 16				99.69 85

mPP-MVS					99.53 26							99.89 31

NP-MVS									98.57 111

Patchmtry						98.59 132	97.15 121	79.14 221		80.42 175

High-resolution multi-view benchmark