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.

ESAPD		98.70 5	98.39 15	99.62 1	99.63 21	99.18 1	98.55 151	98.84 54	96.40 57	99.27 6	99.31 21	97.38 2	99.93 9	96.37 95	99.78 14	99.76 20

ACMMP_Plus		98.61 14	98.30 26	99.55 2	99.62 23	98.95 5	98.82 92	98.81 61	95.80 74	99.16 14	99.47 4	95.37 42	99.92 15	97.89 32	99.75 31	99.79 4

HPM-MVS++		98.58 19	98.25 30	99.55 2	99.50 29	99.08 3	98.72 122	98.66 107	97.51 8	98.15 57	98.83 83	95.70 35	99.92 15	97.53 52	99.67 41	99.66 50

APDe-MVS		99.02 1	98.84 1	99.55 2	99.57 25	98.96 4	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 7	99.84 44	98.86 2	99.85 2	99.87 1

MP-MVS-pluss		98.31 40	97.92 43	99.49 5	99.72 11	98.88 6	98.43 168	98.78 71	94.10 142	97.69 87	99.42 5	95.25 47	99.92 15	98.09 24	99.80 9	99.67 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MCST-MVS		98.65 10	98.37 18	99.48 6	99.60 24	98.87 7	98.41 170	98.68 97	97.04 38	98.52 46	98.80 86	96.78 6	99.83 45	97.93 28	99.61 50	99.74 27

MPTG		98.55 23	98.25 30	99.46 7	99.76 1	98.64 10	98.55 151	98.74 79	97.27 25	98.02 66	99.39 7	94.81 56	99.96 1	97.91 29	99.79 10	99.77 14

MTAPA		98.58 19	98.29 27	99.46 7	99.76 1	98.64 10	98.90 73	98.74 79	97.27 25	98.02 66	99.39 7	94.81 56	99.96 1	97.91 29	99.79 10	99.77 14

CNVR-MVS		98.78 3	98.56 6	99.45 9	99.32 47	98.87 7	98.47 164	98.81 61	97.72 4	98.76 35	99.16 44	97.05 4	99.78 76	98.06 25	99.66 44	99.69 37

APD-MVS		98.35 36	98.00 41	99.42 10	99.51 28	98.72 9	98.80 101	98.82 58	94.52 132	99.23 10	99.25 30	95.54 39	99.80 59	96.52 89	99.77 19	99.74 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		98.61 14	98.35 21	99.38 11	99.28 62	98.61 12	98.45 165	98.76 75	97.82 3	98.45 50	98.93 75	96.65 8	99.83 45	97.38 57	99.41 78	99.71 34

3Dnovator+	94.38 6	97.43 73	96.78 86	99.38 11	97.83 170	98.52 13	99.37 7	98.71 91	97.09 37	92.99 249	99.13 46	89.36 138	99.89 29	96.97 65	99.57 57	99.71 34

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 13	99.22 73	98.43 18	99.10 51	98.87 49	97.38 17	99.35 5	99.40 6	97.78 1	99.87 37	97.77 39	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
XVS		98.70 5	98.49 12	99.34 14	99.70 15	98.35 24	99.29 14	98.88 47	97.40 14	98.46 47	99.20 37	95.90 31	99.89 29	97.85 34	99.74 34	99.78 7

X-MVStestdata		94.06 242	92.30 260	99.34 14	99.70 15	98.35 24	99.29 14	98.88 47	97.40 14	98.46 47	43.50 350	95.90 31	99.89 29	97.85 34	99.74 34	99.78 7

train_agg		97.97 45	97.52 55	99.33 16	99.31 49	98.50 14	97.92 224	98.73 84	92.98 200	97.74 83	98.68 96	96.20 14	99.80 59	96.59 85	99.57 57	99.68 43

HFP-MVS		98.63 13	98.40 14	99.32 17	99.72 11	98.29 27	99.23 22	98.96 31	96.10 67	98.94 23	99.17 41	96.06 22	99.92 15	97.62 45	99.78 14	99.75 22

#test#		98.54 25	98.27 28	99.32 17	99.72 11	98.29 27	98.98 66	98.96 31	95.65 80	98.94 23	99.17 41	96.06 22	99.92 15	97.21 60	99.78 14	99.75 22

region2R		98.61 14	98.38 17	99.29 19	99.74 7	98.16 36	99.23 22	98.93 36	96.15 62	98.94 23	99.17 41	95.91 30	99.94 3	97.55 50	99.79 10	99.78 7

ACMMPR		98.59 17	98.36 19	99.29 19	99.74 7	98.15 37	99.23 22	98.95 33	96.10 67	98.93 27	99.19 40	95.70 35	99.94 3	97.62 45	99.79 10	99.78 7

agg_prior1		97.95 47	97.51 56	99.28 21	99.30 54	98.38 19	97.81 239	98.72 86	93.16 194	97.57 95	98.66 99	96.14 17	99.81 52	96.63 84	99.56 63	99.66 50

MP-MVS		98.33 39	98.01 40	99.28 21	99.75 3	98.18 35	99.22 28	98.79 69	96.13 64	97.92 75	99.23 31	94.54 61	99.94 3	96.74 81	99.78 14	99.73 29

CDPH-MVS		97.94 48	97.49 57	99.28 21	99.47 33	98.44 16	97.91 227	98.67 104	92.57 213	98.77 34	98.85 81	95.93 29	99.72 88	95.56 120	99.69 40	99.68 43

PGM-MVS		98.49 28	98.23 33	99.27 24	99.72 11	98.08 40	98.99 63	99.49 5	95.43 88	99.03 17	99.32 20	95.56 37	99.94 3	96.80 79	99.77 19	99.78 7

mPP-MVS		98.51 27	98.26 29	99.25 25	99.75 3	98.04 41	99.28 16	98.81 61	96.24 60	98.35 54	99.23 31	95.46 40	99.94 3	97.42 55	99.81 8	99.77 14

HSP-MVS		98.70 5	98.52 8	99.24 26	99.75 3	98.23 30	99.26 17	98.58 120	97.52 7	99.41 3	98.78 87	96.00 25	99.79 71	97.79 38	99.59 54	99.69 37

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 26	99.69 17	98.28 29	99.14 44	98.66 107	96.84 43	99.56 2	99.31 21	96.34 12	99.70 93	98.32 20	99.73 36	99.73 29

agg_prior3		97.87 51	97.42 61	99.23 28	99.29 57	98.23 30	97.92 224	98.72 86	92.38 226	97.59 94	98.64 101	96.09 20	99.79 71	96.59 85	99.57 57	99.68 43

Regformer-2		98.69 8	98.52 8	99.19 29	99.35 39	98.01 43	98.37 173	98.81 61	97.48 11	99.21 11	99.21 34	96.13 18	99.80 59	98.40 18	99.73 36	99.75 22

test_prior3		98.22 43	97.90 44	99.19 29	99.31 49	98.22 32	97.80 240	98.84 54	96.12 65	97.89 77	98.69 94	95.96 27	99.70 93	96.89 71	99.60 51	99.65 52

test_prior				99.19 29	99.31 49	98.22 32		98.84 54					99.70 93			99.65 52

CP-MVS		98.57 21	98.36 19	99.19 29	99.66 19	97.86 48	99.34 11	98.87 49	95.96 70	98.60 43	99.13 46	96.05 24	99.94 3	97.77 39	99.86 1	99.77 14

test12				99.18 33	99.16 78	98.19 34		98.53 128		98.07 61		95.13 51	99.72 88		99.56 63	99.63 57

PHI-MVS		98.34 37	98.06 38	99.18 33	99.15 80	98.12 39	99.04 59	99.09 19	93.32 189	98.83 31	99.10 50	96.54 10	99.83 45	97.70 43	99.76 25	99.59 63

DeepC-MVS_fast	96.70 1	98.55 23	98.34 22	99.18 33	99.25 66	98.04 41	98.50 161	98.78 71	97.72 4	98.92 28	99.28 27	95.27 46	99.82 50	97.55 50	99.77 19	99.69 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
新几何1				99.16 36	99.34 41	98.01 43		98.69 94	90.06 277	98.13 58	98.95 73	94.60 60	99.89 29	91.97 215	99.47 71	99.59 63

1121		97.37 78	96.77 88	99.16 36	99.34 41	97.99 46	98.19 196	98.68 97	90.14 275	98.01 68	98.97 67	94.80 58	99.87 37	93.36 174	99.46 74	99.61 58

APD-MVS_3200maxsize		98.53 26	98.33 25	99.15 38	99.50 29	97.92 47	99.15 43	98.81 61	96.24 60	99.20 12	99.37 12	95.30 45	99.80 59	97.73 41	99.67 41	99.72 32

abl_6		98.30 41	98.03 39	99.13 39	99.56 26	97.76 53	99.13 47	98.82 58	96.14 63	99.26 8	99.37 12	93.33 78	99.93 9	96.96 67	99.67 41	99.69 37

HPM-MVS		98.36 35	98.10 37	99.13 39	99.74 7	97.82 51	99.53 1	98.80 68	94.63 129	98.61 42	98.97 67	95.13 51	99.77 81	97.65 44	99.83 7	99.79 4

Regformer-1		98.66 9	98.51 10	99.12 41	99.35 39	97.81 52	98.37 173	98.76 75	97.49 10	99.20 12	99.21 34	96.08 21	99.79 71	98.42 16	99.73 36	99.75 22

HPM-MVS_fast		98.38 33	98.13 36	99.12 41	99.75 3	97.86 48	99.44 4	98.82 58	94.46 136	98.94 23	99.20 37	95.16 50	99.74 87	97.58 47	99.85 2	99.77 14

ACMMP		98.23 42	97.95 42	99.09 43	99.74 7	97.62 57	99.03 60	99.41 6	95.98 69	97.60 93	99.36 16	94.45 66	99.93 9	97.14 61	98.85 99	99.70 36
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
MVS_0304		97.70 58	97.25 66	99.07 44	98.90 97	97.83 50	98.20 192	98.74 79	97.51 8	98.03 65	99.06 58	86.12 225	99.93 9	99.02 1	99.64 47	99.44 86

3Dnovator	94.51 5	97.46 68	96.93 79	99.07 44	97.78 172	97.64 55	99.35 10	99.06 21	97.02 39	93.75 228	99.16 44	89.25 141	99.92 15	97.22 59	99.75 31	99.64 55

DP-MVS Recon		97.86 52	97.46 59	99.06 46	99.53 27	98.35 24	98.33 177	98.89 44	92.62 210	98.05 62	98.94 74	95.34 44	99.65 100	96.04 102	99.42 77	99.19 108

alignmvs		97.56 66	97.07 75	99.01 47	98.66 123	98.37 22	98.83 90	98.06 215	96.74 46	98.00 70	97.65 184	90.80 123	99.48 131	98.37 19	96.56 162	99.19 108

Regformer-4		98.64 11	98.53 7	98.99 48	99.43 37	97.37 65	98.40 171	98.79 69	97.46 12	99.09 15	99.31 21	95.86 33	99.80 59	98.64 4	99.76 25	99.79 4

DELS-MVS		98.40 32	98.20 35	98.99 48	99.00 88	97.66 54	97.75 244	98.89 44	97.71 6	98.33 55	98.97 67	94.97 54	99.88 36	98.42 16	99.76 25	99.42 87
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
canonicalmvs		97.67 60	97.23 68	98.98 50	98.70 119	98.38 19	99.34 11	98.39 154	96.76 45	97.67 88	97.40 199	92.26 91	99.49 127	98.28 22	96.28 179	99.08 122

UA-Net		97.96 46	97.62 49	98.98 50	98.86 108	97.47 62	98.89 77	99.08 20	96.67 49	98.72 37	99.54 1	93.15 81	99.81 52	94.87 136	98.83 100	99.65 52

VNet		97.79 55	97.40 62	98.96 52	98.88 106	97.55 59	98.63 138	98.93 36	96.74 46	99.02 18	98.84 82	90.33 129	99.83 45	98.53 10	96.66 158	99.50 73

QAPM		96.29 117	95.40 130	98.96 52	97.85 169	97.60 58	99.23 22	98.93 36	89.76 286	93.11 246	99.02 60	89.11 145	99.93 9	91.99 214	99.62 49	99.34 90

114514_t		96.93 94	96.27 105	98.92 54	99.50 29	97.63 56	98.85 86	98.90 42	84.80 322	97.77 80	99.11 48	92.84 83	99.66 99	94.85 137	99.77 19	99.47 79

CPTT-MVS		97.72 57	97.32 64	98.92 54	99.64 20	97.10 75	99.12 49	98.81 61	92.34 227	98.09 60	99.08 56	93.01 82	99.92 15	96.06 101	99.77 19	99.75 22

CANet		98.05 44	97.76 46	98.90 56	98.73 116	97.27 68	98.35 175	98.78 71	97.37 19	97.72 85	98.96 71	91.53 112	99.92 15	98.79 3	99.65 45	99.51 71

MVS_111021_HR		98.47 29	98.34 22	98.88 57	99.22 73	97.32 66	97.91 227	99.58 3	97.20 29	98.33 55	99.00 65	95.99 26	99.64 102	98.05 26	99.76 25	99.69 37

Regformer-3		98.59 17	98.50 11	98.86 58	99.43 37	97.05 76	98.40 171	98.68 97	97.43 13	99.06 16	99.31 21	95.80 34	99.77 81	98.62 6	99.76 25	99.78 7

TSAR-MVS + GP.		98.38 33	98.24 32	98.81 59	99.22 73	97.25 71	98.11 207	98.29 167	97.19 30	98.99 22	99.02 60	96.22 13	99.67 98	98.52 14	98.56 112	99.51 71

DeepC-MVS	95.98 3	97.88 50	97.58 51	98.77 60	99.25 66	96.93 80	98.83 90	98.75 78	96.96 41	96.89 117	99.50 3	90.46 126	99.87 37	97.84 36	99.76 25	99.52 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNLPA		97.45 71	97.03 76	98.73 61	99.05 83	97.44 64	98.07 211	98.53 128	95.32 100	96.80 124	98.53 109	93.32 79	99.72 88	94.31 152	99.31 84	99.02 125

WTY-MVS		97.37 78	96.92 80	98.72 62	98.86 108	96.89 84	98.31 182	98.71 91	95.26 102	97.67 88	98.56 108	92.21 94	99.78 76	95.89 106	96.85 155	99.48 78

EI-MVSNet-Vis-set		98.47 29	98.39 15	98.69 63	99.46 34	96.49 98	98.30 184	98.69 94	97.21 28	98.84 29	99.36 16	95.41 41	99.78 76	98.62 6	99.65 45	99.80 3

LS3D		97.16 86	96.66 93	98.68 64	98.53 133	97.19 73	98.93 71	98.90 42	92.83 207	95.99 161	99.37 12	92.12 97	99.87 37	93.67 168	99.57 57	98.97 130

MVS_111021_LR		98.34 37	98.23 33	98.67 65	99.27 63	96.90 82	97.95 222	99.58 3	97.14 33	98.44 51	99.01 64	95.03 53	99.62 107	97.91 29	99.75 31	99.50 73

原ACMM1				98.65 66	99.32 47	96.62 91		98.67 104	93.27 192	97.81 79	98.97 67	95.18 49	99.83 45	93.84 163	99.46 74	99.50 73

PAPR		96.84 98	96.24 107	98.65 66	98.72 118	96.92 81	97.36 270	98.57 121	93.33 188	96.67 127	97.57 191	94.30 69	99.56 117	91.05 236	98.59 110	99.47 79

EI-MVSNet-UG-set		98.41 31	98.34 22	98.61 68	99.45 35	96.32 105	98.28 186	98.68 97	97.17 31	98.74 36	99.37 12	95.25 47	99.79 71	98.57 8	99.54 66	99.73 29

sss		97.39 76	96.98 78	98.61 68	98.60 129	96.61 93	98.22 190	98.93 36	93.97 150	98.01 68	98.48 114	91.98 101	99.85 42	96.45 91	98.15 128	99.39 88

HY-MVS	93.96 8	96.82 99	96.23 108	98.57 70	98.46 134	97.00 77	98.14 202	98.21 178	93.95 151	96.72 126	97.99 155	91.58 107	99.76 83	94.51 147	96.54 163	98.95 134

DP-MVS		96.59 106	95.93 115	98.57 70	99.34 41	96.19 109	98.70 126	98.39 154	89.45 294	94.52 183	99.35 18	91.85 103	99.85 42	92.89 193	98.88 96	99.68 43

MSLP-MVS++		98.56 22	98.57 5	98.55 72	99.26 65	96.80 85	98.71 123	99.05 23	97.28 21	98.84 29	99.28 27	96.47 11	99.40 133	98.52 14	99.70 39	99.47 79

ab-mvs		96.42 112	95.71 124	98.55 72	98.63 126	96.75 88	97.88 233	98.74 79	93.84 156	96.54 136	98.18 142	85.34 246	99.75 85	95.93 105	96.35 172	99.15 114

SD-MVS		98.64 11	98.68 3	98.53 74	99.33 44	98.36 23	98.90 73	98.85 53	97.28 21	99.72 1	99.39 7	96.63 9	97.60 295	98.17 23	99.85 2	99.64 55

EPNet		97.28 81	96.87 82	98.51 75	94.98 310	96.14 110	98.90 73	97.02 283	98.28 1	95.99 161	99.11 48	91.36 113	99.89 29	96.98 64	99.19 87	99.50 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
1112_ss		96.63 103	96.00 114	98.50 76	98.56 130	96.37 102	98.18 200	98.10 208	92.92 202	94.84 174	98.43 117	92.14 96	99.58 114	94.35 150	96.51 164	99.56 67

PAPM_NR		97.46 68	97.11 72	98.50 76	99.50 29	96.41 101	98.63 138	98.60 114	95.18 106	97.06 107	98.06 149	94.26 70	99.57 115	93.80 165	98.87 98	99.52 68

AdaColmap		97.15 87	96.70 89	98.48 78	99.16 78	96.69 90	98.01 216	98.89 44	94.44 137	96.83 120	98.68 96	90.69 124	99.76 83	94.36 149	99.29 85	98.98 129

LFMVS		95.86 130	94.98 152	98.47 79	98.87 107	96.32 105	98.84 89	96.02 312	93.40 186	98.62 41	99.20 37	74.99 320	99.63 105	97.72 42	97.20 150	99.46 83

MAR-MVS		96.91 95	96.40 101	98.45 80	98.69 121	96.90 82	98.66 136	98.68 97	92.40 225	97.07 106	97.96 156	91.54 111	99.75 85	93.68 167	98.92 94	98.69 146
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
PVSNet_Blended_VisFu		97.70 58	97.46 59	98.44 81	99.27 63	95.91 133	98.63 138	99.16 17	94.48 135	97.67 88	98.88 79	92.80 84	99.91 24	97.11 62	99.12 89	99.50 73

MG-MVS		97.81 54	97.60 50	98.44 81	99.12 82	95.97 117	97.75 244	98.78 71	96.89 42	98.46 47	99.22 33	93.90 75	99.68 97	94.81 139	99.52 68	99.67 48

PLC	95.07 4	97.20 84	96.78 86	98.44 81	99.29 57	96.31 107	98.14 202	98.76 75	92.41 224	96.39 152	98.31 132	94.92 55	99.78 76	94.06 158	98.77 103	99.23 104
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	93.45 11	94.68 207	93.43 243	98.42 84	98.62 127	96.77 87	95.48 321	98.20 181	84.63 323	93.34 238	98.32 131	88.55 174	99.81 52	84.80 313	98.96 93	98.68 147
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+		97.12 88	96.69 90	98.39 85	98.19 148	96.72 89	97.37 268	98.43 149	93.71 165	97.65 91	98.02 151	92.20 95	99.25 143	96.87 77	97.79 140	99.19 108

Test_1112_low_res		96.34 115	95.66 128	98.36 86	98.56 130	95.94 121	97.71 246	98.07 213	92.10 233	94.79 178	97.29 208	91.75 104	99.56 117	94.17 155	96.50 165	99.58 65

Vis-MVSNet		97.42 74	97.11 72	98.34 87	98.66 123	96.23 108	99.22 28	99.00 26	96.63 51	98.04 64	99.21 34	88.05 187	99.35 138	96.01 104	99.21 86	99.45 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	93.04 13	95.83 131	95.00 150	98.32 88	97.18 212	97.32 66	99.21 31	98.97 29	89.96 279	91.14 277	99.05 59	86.64 217	99.92 15	93.38 173	99.47 71	97.73 186

PatchMatch-RL		96.59 106	96.03 113	98.27 89	99.31 49	96.51 97	97.91 227	99.06 21	93.72 164	96.92 115	98.06 149	88.50 177	99.65 100	91.77 221	99.00 92	98.66 149

testdata				98.26 90	99.20 76	95.36 153		98.68 97	91.89 237	98.60 43	99.10 50	94.44 67	99.82 50	94.27 153	99.44 76	99.58 65

IS-MVSNet		97.22 83	96.88 81	98.25 91	98.85 110	96.36 103	99.19 34	97.97 220	95.39 90	97.23 100	98.99 66	91.11 117	98.93 188	94.60 143	98.59 110	99.47 79

CANet_DTU		96.96 93	96.55 96	98.21 92	98.17 152	96.07 112	97.98 219	98.21 178	97.24 27	97.13 102	98.93 75	86.88 214	99.91 24	95.00 135	99.37 82	98.66 149

CSCG		97.85 53	97.74 47	98.20 93	99.67 18	95.16 160	99.22 28	99.32 7	93.04 197	97.02 109	98.92 77	95.36 43	99.91 24	97.43 54	99.64 47	99.52 68

OMC-MVS		97.55 67	97.34 63	98.20 93	99.33 44	95.92 131	98.28 186	98.59 115	95.52 85	97.97 71	99.10 50	93.28 80	99.49 127	95.09 134	98.88 96	99.19 108

UGNet		96.78 100	96.30 104	98.19 95	98.24 143	95.89 135	98.88 79	98.93 36	97.39 16	96.81 123	97.84 167	82.60 282	99.90 27	96.53 88	99.49 69	98.79 141
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_Blended		97.38 77	97.12 71	98.14 96	99.25 66	95.35 155	97.28 276	99.26 8	93.13 195	97.94 73	98.21 140	92.74 85	99.81 52	96.88 74	99.40 80	99.27 100

HyFIR lowres test		96.90 96	96.49 99	98.14 96	99.33 44	95.56 146	97.38 266	99.65 2	92.34 227	97.61 92	98.20 141	89.29 140	99.10 167	96.97 65	97.60 146	99.77 14

MVS_Test		97.28 81	97.00 77	98.13 98	98.33 139	95.97 117	98.74 117	98.07 213	94.27 139	98.44 51	98.07 148	92.48 87	99.26 142	96.43 92	98.19 127	99.16 113

lupinMVS		97.44 72	97.22 69	98.12 99	98.07 155	95.76 139	97.68 249	97.76 227	94.50 133	98.79 32	98.61 102	92.34 88	99.30 139	97.58 47	99.59 54	99.31 93

test_normal		94.72 203	93.59 234	98.11 100	95.30 307	95.95 120	97.91 227	97.39 263	94.64 128	85.70 311	95.88 290	80.52 295	99.36 137	96.69 82	98.30 124	99.01 128

DI_MVS_plusplus_test		94.74 202	93.62 232	98.09 101	95.34 306	95.92 131	98.09 210	97.34 265	94.66 127	85.89 308	95.91 289	80.49 296	99.38 136	96.66 83	98.22 125	98.97 130

MVS		94.67 208	93.54 237	98.08 102	96.88 228	96.56 95	98.19 196	98.50 137	78.05 336	92.69 254	98.02 151	91.07 119	99.63 105	90.09 254	98.36 121	98.04 175

CHOSEN 1792x2688		97.12 88	96.80 83	98.08 102	99.30 54	94.56 211	98.05 212	99.71 1	93.57 175	97.09 103	98.91 78	88.17 182	99.89 29	96.87 77	99.56 63	99.81 2

diffmvs		96.32 116	95.74 119	98.07 104	98.26 142	96.14 110	98.53 155	98.23 176	90.10 276	96.88 118	97.73 176	90.16 132	99.15 156	93.90 162	97.85 138	98.91 136

jason		97.32 80	97.08 74	98.06 105	97.45 194	95.59 143	97.87 234	97.91 223	94.79 122	98.55 45	98.83 83	91.12 116	99.23 145	97.58 47	99.60 51	99.34 90
jason: jason.
Fast-Effi-MVS+		96.28 119	95.70 125	98.03 106	98.29 141	95.97 117	98.58 144	98.25 173	91.74 241	95.29 168	97.23 211	91.03 120	99.15 156	92.90 191	97.96 133	98.97 130

EPP-MVSNet		97.46 68	97.28 65	97.99 107	98.64 125	95.38 152	99.33 13	98.31 162	93.61 174	97.19 101	99.07 57	94.05 72	99.23 145	96.89 71	98.43 119	99.37 89

F-COLMAP		97.09 90	96.80 83	97.97 108	99.45 35	94.95 172	98.55 151	98.62 113	93.02 198	96.17 156	98.58 107	94.01 73	99.81 52	93.95 160	98.90 95	99.14 116

nrg030		96.28 119	95.72 121	97.96 109	96.90 227	98.15 37	99.39 5	98.31 162	95.47 86	94.42 194	98.35 125	92.09 98	98.69 208	97.50 53	89.05 273	97.04 210

API-MVS		97.41 75	97.25 66	97.91 110	98.70 119	96.80 85	98.82 92	98.69 94	94.53 131	98.11 59	98.28 133	94.50 65	99.57 115	94.12 157	99.49 69	97.37 198

CDS-MVSNet		96.99 92	96.69 90	97.90 111	98.05 158	95.98 113	98.20 192	98.33 161	93.67 172	96.95 110	98.49 113	93.54 76	98.42 245	95.24 132	97.74 143	99.31 93
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet		95.36 167	94.53 180	97.86 112	98.10 154	95.13 162	98.85 86	97.75 228	90.46 268	98.36 53	99.39 7	73.27 327	99.64 102	97.98 27	96.58 161	98.81 140

MVSFormer		97.57 65	97.49 57	97.84 113	98.07 155	95.76 139	99.47 2	98.40 152	94.98 115	98.79 32	98.83 83	92.34 88	98.41 252	96.91 69	99.59 54	99.34 90

Vis-MVSNet (Re-imp)		96.87 97	96.55 96	97.83 114	98.73 116	95.46 150	99.20 32	98.30 165	94.96 117	96.60 131	98.87 80	90.05 133	98.59 217	93.67 168	98.60 109	99.46 83

MSDG		95.93 127	95.30 140	97.83 114	98.90 97	95.36 153	96.83 298	98.37 157	91.32 256	94.43 193	98.73 93	90.27 130	99.60 108	90.05 257	98.82 101	98.52 155

Test4		92.21 268	90.34 284	97.82 116	92.83 324	95.87 137	97.94 223	98.05 218	94.50 133	82.12 327	94.48 306	59.54 342	98.54 221	95.39 125	98.22 125	99.06 124

1314		96.25 121	95.73 120	97.79 117	97.13 215	95.55 148	98.19 196	98.59 115	93.47 178	92.03 271	97.82 171	91.33 114	99.49 127	94.62 142	98.44 117	98.32 170

PAPM		94.95 187	94.00 208	97.78 118	97.04 218	95.65 142	96.03 314	98.25 173	91.23 261	94.19 210	97.80 173	91.27 115	98.86 198	82.61 317	97.61 145	98.84 139

TAPA-MVS	93.98 7	95.35 168	94.56 179	97.74 119	99.13 81	94.83 189	98.33 177	98.64 112	86.62 310	96.29 154	98.61 102	94.00 74	99.29 141	80.00 322	99.41 78	99.09 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
conf0.01		95.56 148	94.84 164	97.72 120	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	98.02 176

conf0.002		95.56 148	94.84 164	97.72 120	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	98.02 176

xiu_mvs_v1_base_debu		97.60 62	97.56 52	97.72 120	98.35 135	95.98 113	97.86 235	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 198

xiu_mvs_v1_base		97.60 62	97.56 52	97.72 120	98.35 135	95.98 113	97.86 235	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 198

xiu_mvs_v1_base_debi		97.60 62	97.56 52	97.72 120	98.35 135	95.98 113	97.86 235	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 198

TAMVS		97.02 91	96.79 85	97.70 125	98.06 157	95.31 157	98.52 156	98.31 162	93.95 151	97.05 108	98.61 102	93.49 77	98.52 228	95.33 126	97.81 139	99.29 98

VPA-MVSNet		95.75 134	95.11 146	97.69 126	97.24 205	97.27 68	98.94 70	99.23 12	95.13 108	95.51 164	97.32 206	85.73 238	98.91 190	97.33 58	89.55 267	96.89 225

BH-RMVSNet		95.92 128	95.32 138	97.69 126	98.32 140	94.64 203	98.19 196	97.45 256	94.56 130	96.03 159	98.61 102	85.02 249	99.12 160	90.68 240	99.06 90	99.30 96

FIs		96.51 109	96.12 110	97.67 128	97.13 215	97.54 60	99.36 8	99.22 14	95.89 71	94.03 219	98.35 125	91.98 101	98.44 242	96.40 93	92.76 238	97.01 211

thres600view7		95.49 155	94.77 170	97.67 128	98.98 91	95.02 165	98.85 86	96.90 293	95.38 91	96.63 128	96.90 252	84.29 264	99.59 109	88.65 285	96.33 173	98.40 161

thres400		95.38 164	94.62 176	97.65 130	98.94 95	94.98 169	98.68 131	96.93 291	95.33 98	96.55 134	96.53 269	84.23 268	99.56 117	88.11 291	96.29 175	98.40 161

view600		95.60 144	94.93 156	97.62 131	99.05 83	94.85 178	99.09 52	97.01 285	95.36 94	96.52 138	97.37 200	84.55 257	99.59 109	89.07 276	96.39 168	98.40 161

view800		95.60 144	94.93 156	97.62 131	99.05 83	94.85 178	99.09 52	97.01 285	95.36 94	96.52 138	97.37 200	84.55 257	99.59 109	89.07 276	96.39 168	98.40 161

conf0.05thres1000		95.60 144	94.93 156	97.62 131	99.05 83	94.85 178	99.09 52	97.01 285	95.36 94	96.52 138	97.37 200	84.55 257	99.59 109	89.07 276	96.39 168	98.40 161

tfpn		95.60 144	94.93 156	97.62 131	99.05 83	94.85 178	99.09 52	97.01 285	95.36 94	96.52 138	97.37 200	84.55 257	99.59 109	89.07 276	96.39 168	98.40 161

PS-MVSNAJ		97.73 56	97.77 45	97.62 131	98.68 122	95.58 144	97.34 272	98.51 132	97.29 20	98.66 39	97.88 163	94.51 62	99.90 27	97.87 33	99.17 88	97.39 196

VDD-MVS		95.82 132	95.23 142	97.61 136	98.84 111	93.98 227	98.68 131	97.40 261	95.02 114	97.95 72	99.34 19	74.37 325	99.78 76	98.64 4	96.80 156	99.08 122

tfpn1000		95.72 135	95.11 146	97.58 137	99.00 88	95.73 141	99.24 20	95.49 326	94.08 143	96.87 119	97.45 197	85.81 237	99.30 139	91.78 220	96.22 184	97.71 188

UniMVSNet (Re)		95.78 133	95.19 144	97.58 137	96.99 221	97.47 62	98.79 106	99.18 16	95.60 81	93.92 222	97.04 235	91.68 105	98.48 232	95.80 111	87.66 295	96.79 235

xiu_mvs_v2_base		97.66 61	97.70 48	97.56 139	98.61 128	95.46 150	97.44 261	98.46 142	97.15 32	98.65 40	98.15 143	94.33 68	99.80 59	97.84 36	98.66 108	97.41 194

FC-MVSNet-test		96.42 112	96.05 111	97.53 140	96.95 222	97.27 68	99.36 8	99.23 12	95.83 73	93.93 221	98.37 123	92.00 100	98.32 261	96.02 103	92.72 239	97.00 212

thresconf0.02		95.50 151	94.84 164	97.51 141	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	97.37 198

tfpn_n400		95.50 151	94.84 164	97.51 141	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	97.37 198

tfpnconf		95.50 151	94.84 164	97.51 141	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	97.37 198

tfpnview11		95.50 151	94.84 164	97.51 141	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	97.37 198

conf200view11		95.40 163	94.70 173	97.50 145	98.98 91	94.92 173	98.87 80	96.90 293	95.38 91	96.61 129	96.88 255	84.29 264	99.56 117	88.11 291	96.29 175	98.02 176

XXY-MVS		95.20 177	94.45 185	97.46 146	96.75 235	96.56 95	98.86 85	98.65 111	93.30 191	93.27 239	98.27 136	84.85 253	98.87 196	94.82 138	91.26 255	96.96 214

NR-MVSNet		94.98 185	94.16 197	97.44 147	96.53 244	97.22 72	98.74 117	98.95 33	94.96 117	89.25 294	97.69 180	89.32 139	98.18 271	94.59 144	87.40 297	96.92 217

tfpn200view9		95.32 171	94.62 176	97.43 148	98.94 95	94.98 169	98.68 131	96.93 291	95.33 98	96.55 134	96.53 269	84.23 268	99.56 117	88.11 291	96.29 175	97.76 183

thres100view900		95.38 164	94.70 173	97.41 149	98.98 91	94.92 173	98.87 80	96.90 293	95.38 91	96.61 129	96.88 255	84.29 264	99.56 117	88.11 291	96.29 175	97.76 183

PMMVS		96.60 104	96.33 103	97.41 149	97.90 166	93.93 228	97.35 271	98.41 150	92.84 206	97.76 81	97.45 197	91.10 118	99.20 153	96.26 97	97.91 134	99.11 118

VPNet		94.99 183	94.19 196	97.40 151	97.16 213	96.57 94	98.71 123	98.97 29	95.67 78	94.84 174	98.24 139	80.36 297	98.67 211	96.46 90	87.32 298	96.96 214

UniMVSNet_NR-MVSNet		95.71 137	95.15 145	97.40 151	96.84 230	96.97 78	98.74 117	99.24 10	95.16 107	93.88 223	97.72 179	91.68 105	98.31 263	95.81 109	87.25 300	96.92 217

DU-MVS		95.42 160	94.76 171	97.40 151	96.53 244	96.97 78	98.66 136	98.99 28	95.43 88	93.88 223	97.69 180	88.57 172	98.31 263	95.81 109	87.25 300	96.92 217

tfpn_ndepth		95.53 150	94.90 161	97.39 154	98.96 94	95.88 136	99.05 57	95.27 327	93.80 159	96.95 110	96.93 250	85.53 241	99.40 133	91.54 226	96.10 187	96.89 225

thres200		95.25 173	94.57 178	97.28 155	98.81 112	94.92 173	98.20 192	97.11 277	95.24 105	96.54 136	96.22 282	84.58 256	99.53 124	87.93 295	96.50 165	97.39 196

WR-MVS		95.15 178	94.46 183	97.22 156	96.67 240	96.45 99	98.21 191	98.81 61	94.15 140	93.16 242	97.69 180	87.51 203	98.30 265	95.29 129	88.62 284	96.90 224

CHOSEN 280x420		97.18 85	97.18 70	97.20 157	98.81 112	93.27 244	95.78 319	99.15 18	95.25 103	96.79 125	98.11 146	92.29 90	99.07 170	98.56 9	99.85 2	99.25 102

IB-MVS	91.98 17	93.27 256	91.97 263	97.19 158	97.47 190	93.41 243	97.09 284	95.99 313	93.32 189	92.47 262	95.73 293	78.06 306	99.53 124	94.59 144	82.98 317	98.62 152
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
mvs_anonymous		96.70 102	96.53 98	97.18 159	98.19 148	93.78 232	98.31 182	98.19 182	94.01 146	94.47 185	98.27 136	92.08 99	98.46 237	97.39 56	97.91 134	99.31 93

TR-MVS		94.94 189	94.20 195	97.17 160	97.75 173	94.14 224	97.59 255	97.02 283	92.28 231	95.75 163	97.64 186	83.88 275	98.96 183	89.77 261	96.15 185	98.40 161

GA-MVS		94.81 196	94.03 206	97.14 161	97.15 214	93.86 230	96.76 299	97.58 234	94.00 147	94.76 179	97.04 235	80.91 290	98.48 232	91.79 219	96.25 181	99.09 119

gg-mvs-nofinetune		92.21 268	90.58 282	97.13 162	96.75 235	95.09 163	95.85 317	89.40 348	85.43 319	94.50 184	81.98 341	80.80 293	98.40 258	92.16 207	98.33 122	97.88 181

PVSNet_BlendedMVS		96.73 101	96.60 94	97.12 163	99.25 66	95.35 155	98.26 188	99.26 8	94.28 138	97.94 73	97.46 195	92.74 85	99.81 52	96.88 74	93.32 231	96.20 288

TranMVSNet+NR-MVSNet		95.14 179	94.48 181	97.11 164	96.45 249	96.36 103	99.03 60	99.03 24	95.04 113	93.58 230	97.93 159	88.27 180	98.03 279	94.13 156	86.90 305	96.95 216

FMVSNet3		94.97 186	94.26 191	97.11 164	98.18 150	96.62 91	98.56 149	98.26 172	93.67 172	94.09 215	97.10 223	84.25 267	98.01 280	92.08 209	92.14 242	96.70 247

MVSTER		96.06 123	95.72 121	97.08 166	98.23 144	95.93 124	98.73 120	98.27 168	94.86 121	95.07 169	98.09 147	88.21 181	98.54 221	96.59 85	93.46 226	96.79 235

FMVSNet2		94.47 219	93.61 233	97.04 167	98.21 145	96.43 100	98.79 106	98.27 168	92.46 214	93.50 235	97.09 225	81.16 287	98.00 281	91.09 232	91.93 246	96.70 247

XVG-OURS-SEG-HR		96.51 109	96.34 102	97.02 168	98.77 114	93.76 233	97.79 242	98.50 137	95.45 87	96.94 112	99.09 54	87.87 193	99.55 123	96.76 80	95.83 197	97.74 185

AllTest		95.24 174	94.65 175	96.99 169	99.25 66	93.21 247	98.59 142	98.18 185	91.36 252	93.52 233	98.77 89	84.67 254	99.72 88	89.70 265	97.87 136	98.02 176

TestCases				96.99 169	99.25 66	93.21 247		98.18 185	91.36 252	93.52 233	98.77 89	84.67 254	99.72 88	89.70 265	97.87 136	98.02 176

XVG-OURS		96.55 108	96.41 100	96.99 169	98.75 115	93.76 233	97.50 260	98.52 130	95.67 78	96.83 120	99.30 26	88.95 152	99.53 124	95.88 107	96.26 180	97.69 189

PVSNet	91.96 18	96.35 114	96.15 109	96.96 172	99.17 77	92.05 260	96.08 311	98.68 97	93.69 168	97.75 82	97.80 173	88.86 155	99.69 96	94.26 154	99.01 91	99.15 114

testing_2		90.61 292	88.50 299	96.95 173	90.08 332	95.57 145	97.69 248	98.06 215	93.02 198	76.55 334	92.48 330	61.18 341	98.44 242	95.45 124	91.98 245	96.84 231

anonymousdsp		95.42 160	94.91 160	96.94 174	95.10 309	95.90 134	99.14 44	98.41 150	93.75 160	93.16 242	97.46 195	87.50 205	98.41 252	95.63 119	94.03 215	96.50 278

test_djsdf		96.00 124	95.69 126	96.93 175	95.72 296	95.49 149	99.47 2	98.40 152	94.98 115	94.58 181	97.86 164	89.16 144	98.41 252	96.91 69	94.12 213	96.88 227

cascas		94.63 210	93.86 217	96.93 175	96.91 226	94.27 221	96.00 315	98.51 132	85.55 318	94.54 182	96.23 280	84.20 270	98.87 196	95.80 111	96.98 154	97.66 190

PS-MVSNAJss		96.43 111	96.26 106	96.92 177	95.84 292	95.08 164	99.16 42	98.50 137	95.87 72	93.84 226	98.34 129	94.51 62	98.61 214	96.88 74	93.45 228	97.06 208

HQP_MVS		96.14 122	95.90 116	96.85 178	97.42 195	94.60 209	98.80 101	98.56 122	97.28 21	95.34 165	98.28 133	87.09 209	99.03 176	96.07 99	94.27 205	96.92 217

CP-MVSNet		94.94 189	94.30 190	96.83 179	96.72 237	95.56 146	99.11 50	98.95 33	93.89 153	92.42 264	97.90 161	87.19 208	98.12 273	94.32 151	88.21 287	96.82 234

pmmvs4		94.69 204	93.99 210	96.81 180	95.74 294	95.94 121	97.40 264	97.67 231	90.42 270	93.37 237	97.59 189	89.08 146	98.20 270	92.97 186	91.67 250	96.30 287

WR-MVS_H		95.05 181	94.46 183	96.81 180	96.86 229	95.82 138	99.24 20	99.24 10	93.87 155	92.53 259	96.84 258	90.37 127	98.24 269	93.24 177	87.93 290	96.38 283

OPM-MVS		95.69 139	95.33 137	96.76 182	96.16 279	94.63 204	98.43 168	98.39 154	96.64 50	95.02 171	98.78 87	85.15 248	99.05 171	95.21 133	94.20 208	96.60 265

jajsoiax		95.45 158	95.03 149	96.73 183	95.42 305	94.63 204	99.14 44	98.52 130	95.74 75	93.22 240	98.36 124	83.87 276	98.65 212	96.95 68	94.04 214	96.91 222

PS-CasMVS		94.67 208	93.99 210	96.71 184	96.68 239	95.26 158	99.13 47	99.03 24	93.68 170	92.33 265	97.95 157	85.35 245	98.10 274	93.59 170	88.16 289	96.79 235

COLMAP_ROB	93.27 12	95.33 170	94.87 162	96.71 184	99.29 57	93.24 246	98.58 144	98.11 203	89.92 282	93.57 231	99.10 50	86.37 221	99.79 71	90.78 238	98.10 130	97.09 207
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
V42		94.78 197	94.14 199	96.70 186	96.33 263	95.22 159	98.97 67	98.09 211	92.32 229	94.31 200	97.06 230	88.39 178	98.55 220	92.90 191	88.87 279	96.34 285

v6		94.83 192	94.21 194	96.69 187	96.36 256	94.85 178	98.87 80	98.11 203	92.46 214	94.44 192	97.05 234	88.76 166	98.57 219	92.95 187	88.92 276	96.65 258

v1		94.75 200	94.11 203	96.69 187	96.27 271	94.87 176	98.69 127	98.12 198	92.43 222	94.32 199	96.94 246	88.71 169	98.54 221	92.66 197	88.84 282	96.67 253

HQP-MVS		95.72 135	95.40 130	96.69 187	97.20 209	94.25 222	98.05 212	98.46 142	96.43 54	94.45 186	97.73 176	86.75 215	98.96 183	95.30 127	94.18 209	96.86 230

v1neww		94.83 192	94.22 192	96.68 190	96.39 252	94.85 178	98.87 80	98.11 203	92.45 219	94.45 186	97.06 230	88.82 160	98.54 221	92.93 188	88.91 277	96.65 258

v7new		94.83 192	94.22 192	96.68 190	96.39 252	94.85 178	98.87 80	98.11 203	92.45 219	94.45 186	97.06 230	88.82 160	98.54 221	92.93 188	88.91 277	96.65 258

LTVRE_ROB	92.95 15	94.60 211	93.90 215	96.68 190	97.41 198	94.42 214	98.52 156	98.59 115	91.69 242	91.21 276	98.35 125	84.87 252	99.04 175	91.06 234	93.44 229	96.60 265
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
v1141		94.75 200	94.11 203	96.67 193	96.27 271	94.86 177	98.69 127	98.12 198	92.43 222	94.31 200	96.94 246	88.78 165	98.48 232	92.63 198	88.85 281	96.67 253

divwei89l23v2f112		94.76 198	94.12 202	96.67 193	96.28 269	94.85 178	98.69 127	98.12 198	92.44 221	94.29 203	96.94 246	88.85 157	98.48 232	92.67 196	88.79 283	96.67 253

mvs_tets		95.41 162	95.00 150	96.65 195	95.58 300	94.42 214	99.00 62	98.55 124	95.73 76	93.21 241	98.38 122	83.45 279	98.63 213	97.09 63	94.00 216	96.91 222

v2v482		94.69 204	94.03 206	96.65 195	96.17 276	94.79 197	98.67 134	98.08 212	92.72 208	94.00 220	97.16 214	87.69 200	98.45 239	92.91 190	88.87 279	96.72 243

BH-untuned		95.95 126	95.72 121	96.65 195	98.55 132	92.26 257	98.23 189	97.79 226	93.73 163	94.62 180	98.01 153	88.97 151	99.00 179	93.04 184	98.51 113	98.68 147

Patchmatch-test		94.42 221	93.68 230	96.63 198	97.60 181	91.76 265	94.83 329	97.49 253	89.45 294	94.14 213	97.10 223	88.99 147	98.83 201	85.37 312	98.13 129	99.29 98

ADS-MVSNet		95.00 182	94.45 185	96.63 198	98.00 159	91.91 262	96.04 312	97.74 229	90.15 273	96.47 149	96.64 266	87.89 191	98.96 183	90.08 255	97.06 151	99.02 125

v7		94.69 204	94.04 205	96.62 200	96.41 251	94.79 197	98.78 108	98.13 196	91.89 237	94.30 202	97.16 214	88.13 185	98.45 239	91.96 216	89.65 264	96.61 263

ACMM	93.85 9	95.69 139	95.38 134	96.61 201	97.61 180	93.84 231	98.91 72	98.44 146	95.25 103	94.28 204	98.47 115	86.04 235	99.12 160	95.50 122	93.95 218	96.87 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1144		94.59 213	93.92 213	96.60 202	96.21 273	94.78 199	98.59 142	98.14 195	91.86 240	94.21 209	97.02 237	87.97 188	98.41 252	91.72 222	89.57 265	96.61 263

GG-mvs-BLEND				96.59 203	96.34 259	94.98 169	96.51 309	88.58 349		93.10 247	94.34 309	80.34 298	98.05 278	89.53 268	96.99 153	96.74 240

pm-mvs1		93.94 245	93.06 248	96.59 203	96.49 247	95.16 160	98.95 69	98.03 219	92.32 229	91.08 278	97.84 167	84.54 261	98.41 252	92.16 207	86.13 311	96.19 289

CR-MVSNet		94.76 198	94.15 198	96.59 203	97.00 219	93.43 241	94.96 325	97.56 235	92.46 214	96.93 113	96.24 278	88.15 183	97.88 290	87.38 297	96.65 159	98.46 158

RPMNet		92.52 265	91.17 268	96.59 203	97.00 219	93.43 241	94.96 325	97.26 273	82.27 329	96.93 113	92.12 333	86.98 212	97.88 290	76.32 331	96.65 159	98.46 158

v8		94.47 219	93.77 223	96.57 207	96.36 256	94.83 189	99.05 57	98.19 182	91.92 236	93.16 242	96.97 242	88.82 160	98.48 232	91.69 223	87.79 293	96.39 282

GBi-Net		94.49 217	93.80 220	96.56 208	98.21 145	95.00 166	98.82 92	98.18 185	92.46 214	94.09 215	97.07 227	81.16 287	97.95 283	92.08 209	92.14 242	96.72 243

test1		94.49 217	93.80 220	96.56 208	98.21 145	95.00 166	98.82 92	98.18 185	92.46 214	94.09 215	97.07 227	81.16 287	97.95 283	92.08 209	92.14 242	96.72 243

FMVSNet1		93.19 259	92.07 262	96.56 208	97.54 186	95.00 166	98.82 92	98.18 185	90.38 271	92.27 266	97.07 227	73.68 326	97.95 283	89.36 272	91.30 253	96.72 243

tfpnnormal		93.66 249	92.70 255	96.55 211	96.94 223	95.94 121	98.97 67	99.19 15	91.04 264	91.38 275	97.34 204	84.94 251	98.61 214	85.45 311	89.02 275	95.11 308

v1192		94.32 225	93.58 235	96.53 212	96.10 280	94.45 213	98.50 161	98.17 190	91.54 245	94.19 210	97.06 230	86.95 213	98.43 244	90.14 253	89.57 265	96.70 247

EPMVS		94.99 183	94.48 181	96.52 213	97.22 207	91.75 266	97.23 278	91.66 345	94.11 141	97.28 99	96.81 259	85.70 239	98.84 199	93.04 184	97.28 149	98.97 130

v10		94.29 227	93.55 236	96.51 214	96.39 252	94.80 194	98.99 63	98.19 182	91.35 254	93.02 248	96.99 240	88.09 186	98.41 252	90.50 250	88.41 286	96.33 286

PEN-MVS		94.42 221	93.73 227	96.49 215	96.28 269	94.84 187	99.17 35	99.00 26	93.51 176	92.23 267	97.83 170	86.10 232	97.90 286	92.55 201	86.92 304	96.74 240

v144192		94.39 223	93.70 228	96.48 216	96.06 282	94.35 218	98.58 144	98.16 192	91.45 247	94.33 198	97.02 237	87.50 205	98.45 239	91.08 233	89.11 272	96.63 261

v7n		94.19 232	93.43 243	96.47 217	95.90 288	94.38 217	99.26 17	98.34 160	91.99 235	92.76 253	97.13 222	88.31 179	98.52 228	89.48 270	87.70 294	96.52 275

LPG-MVS_test		95.62 142	95.34 135	96.47 217	97.46 191	93.54 238	98.99 63	98.54 125	94.67 125	94.36 196	98.77 89	85.39 243	99.11 164	95.71 115	94.15 211	96.76 238

LGP-MVS_train				96.47 217	97.46 191	93.54 238		98.54 125	94.67 125	94.36 196	98.77 89	85.39 243	99.11 164	95.71 115	94.15 211	96.76 238

CLD-MVS		95.62 142	95.34 135	96.46 220	97.52 188	93.75 235	97.27 277	98.46 142	95.53 84	94.42 194	98.00 154	86.21 223	98.97 180	96.25 98	94.37 203	96.66 256
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMP	93.49 10	95.34 169	94.98 152	96.43 221	97.67 176	93.48 240	98.73 120	98.44 146	94.94 120	92.53 259	98.53 109	84.50 262	99.14 158	95.48 123	94.00 216	96.66 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MIMVSNet		93.26 257	92.21 261	96.41 222	97.73 175	93.13 249	95.65 320	97.03 282	91.27 260	94.04 218	96.06 286	75.33 318	97.19 303	86.56 302	96.23 182	98.92 135

v1921920		94.20 231	93.47 242	96.40 223	95.98 285	94.08 225	98.52 156	98.15 193	91.33 255	94.25 206	97.20 213	86.41 220	98.42 245	90.04 258	89.39 270	96.69 252

mvs-test1		96.60 104	96.68 92	96.37 224	97.89 167	91.81 263	98.56 149	98.10 208	96.57 52	96.52 138	97.94 158	90.81 121	99.45 132	95.72 113	98.01 131	97.86 182

EI-MVSNet		95.96 125	95.83 118	96.36 225	97.93 164	93.70 237	98.12 205	98.27 168	93.70 167	95.07 169	99.02 60	92.23 93	98.54 221	94.68 140	93.46 226	96.84 231

Patchmatch-test1		95.32 171	94.97 154	96.35 226	97.67 176	91.29 272	97.33 273	97.60 233	94.68 124	96.92 115	96.95 244	83.97 273	98.50 231	91.33 231	98.32 123	99.25 102

PatchT		93.06 261	91.97 263	96.35 226	96.69 238	92.67 253	94.48 332	97.08 278	86.62 310	97.08 104	92.23 332	87.94 189	97.90 286	78.89 326	96.69 157	98.49 157

v1240		94.06 242	93.29 246	96.34 228	96.03 284	93.90 229	98.44 166	98.17 190	91.18 263	94.13 214	97.01 239	86.05 233	98.42 245	89.13 275	89.50 268	96.70 247

ACMH	92.88 16	94.55 215	93.95 212	96.34 228	97.63 178	93.26 245	98.81 98	98.49 141	93.43 179	89.74 289	98.53 109	81.91 285	99.08 169	93.69 166	93.30 232	96.70 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DeepPCF-MVS	96.37 2	97.93 49	98.48 13	96.30 230	99.00 88	89.54 292	97.43 263	98.87 49	98.16 2	99.26 8	99.38 11	96.12 19	99.64 102	98.30 21	99.77 19	99.72 32

PatchmatchNet		95.71 137	95.52 129	96.29 231	97.58 183	90.72 279	96.84 297	97.52 241	94.06 144	97.08 104	96.96 243	89.24 142	98.90 193	92.03 213	98.37 120	99.26 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
BH-w/o		95.38 164	95.08 148	96.26 232	98.34 138	91.79 264	97.70 247	97.43 258	92.87 205	94.24 207	97.22 212	88.66 170	98.84 199	91.55 225	97.70 144	98.16 173

IterMVS-LS		95.46 157	95.21 143	96.22 233	98.12 153	93.72 236	98.32 181	98.13 196	93.71 165	94.26 205	97.31 207	92.24 92	98.10 274	94.63 141	90.12 259	96.84 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DWT-MVSNet_test		94.82 195	94.36 188	96.20 234	97.35 200	90.79 277	98.34 176	96.57 307	92.91 203	95.33 167	96.44 274	82.00 284	99.12 160	94.52 146	95.78 198	98.70 145

TransMVSNet (Re)		92.67 263	91.51 267	96.15 235	96.58 242	94.65 202	98.90 73	96.73 300	90.86 266	89.46 292	97.86 164	85.62 240	98.09 276	86.45 303	81.12 322	95.71 300

DTE-MVSNet		93.98 244	93.26 247	96.14 236	96.06 282	94.39 216	99.20 32	98.86 52	93.06 196	91.78 272	97.81 172	85.87 236	97.58 296	90.53 243	86.17 309	96.46 281

v52		94.18 234	93.52 238	96.13 237	95.95 287	94.29 220	99.23 22	98.21 178	91.42 249	92.84 251	96.89 253	87.85 194	98.53 227	91.51 227	87.81 291	95.57 304

V4		94.18 234	93.52 238	96.13 237	95.89 289	94.31 219	99.23 22	98.22 177	91.42 249	92.82 252	96.89 253	87.93 190	98.52 228	91.51 227	87.81 291	95.58 303

PatchFormer-LS_test		95.47 156	95.27 141	96.08 239	97.59 182	90.66 280	98.10 209	97.34 265	93.98 149	96.08 157	96.15 284	87.65 201	99.12 160	95.27 130	95.24 201	98.44 160

EPNet_dtu		95.21 176	94.95 155	95.99 240	96.17 276	90.45 284	98.16 201	97.27 272	96.77 44	93.14 245	98.33 130	90.34 128	98.42 245	85.57 309	98.81 102	99.09 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Baseline_NR-MVSNet		94.35 224	93.81 219	95.96 241	96.20 274	94.05 226	98.61 141	96.67 304	91.44 248	93.85 225	97.60 188	88.57 172	98.14 272	94.39 148	86.93 303	95.68 301

JIA-IIPM		93.35 253	92.49 257	95.92 242	96.48 248	90.65 281	95.01 324	96.96 289	85.93 316	96.08 157	87.33 337	87.70 199	98.78 206	91.35 230	95.58 199	98.34 168

Fast-Effi-MVS+-dtu		95.87 129	95.85 117	95.91 243	97.74 174	91.74 267	98.69 127	98.15 193	95.56 83	94.92 172	97.68 183	88.98 150	98.79 205	93.19 179	97.78 141	97.20 206

v148		94.29 227	93.76 225	95.91 243	96.10 280	92.93 251	98.58 144	97.97 220	92.59 212	93.47 236	96.95 244	88.53 175	98.32 261	92.56 200	87.06 302	96.49 279

ACMH+	92.99 14	94.30 226	93.77 223	95.88 245	97.81 171	92.04 261	98.71 123	98.37 157	93.99 148	90.60 284	98.47 115	80.86 292	99.05 171	92.75 195	92.40 241	96.55 272

Patchmtry		93.22 258	92.35 259	95.84 246	96.77 232	93.09 250	94.66 331	97.56 235	87.37 308	92.90 250	96.24 278	88.15 183	97.90 286	87.37 298	90.10 260	96.53 274

v748		93.75 248	93.06 248	95.82 247	95.73 295	92.64 254	99.25 19	98.24 175	91.60 244	92.22 268	96.52 271	87.60 202	98.46 237	90.64 241	85.72 312	96.36 284

test-LLR		95.10 180	94.87 162	95.80 248	96.77 232	89.70 290	96.91 290	95.21 328	95.11 109	94.83 176	95.72 295	87.71 197	98.97 180	93.06 182	98.50 114	98.72 143

test-mter		94.08 240	93.51 240	95.80 248	96.77 232	89.70 290	96.91 290	95.21 328	92.89 204	94.83 176	95.72 295	77.69 308	98.97 180	93.06 182	98.50 114	98.72 143

test0.0.03 1		94.08 240	93.51 240	95.80 248	95.53 302	92.89 252	97.38 266	95.97 314	95.11 109	92.51 261	96.66 264	87.71 197	96.94 306	87.03 300	93.67 221	97.57 191

XVG-ACMP-BASELINE		94.54 216	94.14 199	95.75 251	96.55 243	91.65 268	98.11 207	98.44 146	94.96 117	94.22 208	97.90 161	79.18 303	99.11 164	94.05 159	93.85 219	96.48 280

pmmvs5		93.65 251	92.97 250	95.68 252	95.49 303	92.37 256	98.20 192	97.28 271	89.66 290	92.58 257	97.26 209	82.14 283	98.09 276	93.18 180	90.95 256	96.58 267

TESTMET0.1,1		94.18 234	93.69 229	95.63 253	96.92 224	89.12 298	96.91 290	94.78 333	93.17 193	94.88 173	96.45 273	78.52 304	98.92 189	93.09 181	98.50 114	98.85 137

CostFormer		94.95 187	94.73 172	95.60 254	97.28 203	89.06 299	97.53 258	96.89 296	89.66 290	96.82 122	96.72 262	86.05 233	98.95 187	95.53 121	96.13 186	98.79 141

Effi-MVS+-dtu		96.29 117	96.56 95	95.51 255	97.89 167	90.22 286	98.80 101	98.10 208	96.57 52	96.45 151	96.66 264	90.81 121	98.91 190	95.72 113	97.99 132	97.40 195

v18		92.10 270	90.97 270	95.50 256	96.34 259	94.85 178	98.82 92	97.52 241	89.99 278	85.31 315	93.26 314	88.90 154	96.92 307	88.82 281	79.77 326	94.73 314

v16		92.08 271	90.94 271	95.49 257	96.38 255	94.84 187	98.81 98	97.51 244	89.94 281	85.25 316	93.28 313	88.86 155	96.91 308	88.70 283	79.78 325	94.72 315

v17		92.08 271	90.94 271	95.48 258	96.34 259	94.83 189	98.81 98	97.52 241	89.95 280	85.32 313	93.24 315	88.91 153	96.91 308	88.76 282	79.63 327	94.71 316

tpm2		94.19 232	93.76 225	95.46 259	97.23 206	89.04 300	97.31 275	96.85 299	87.08 309	96.21 155	96.79 260	83.75 278	98.74 207	92.43 205	96.23 182	98.59 153

V9		91.91 275	90.73 277	95.45 260	96.32 266	94.80 194	98.77 109	97.50 247	89.81 285	85.03 320	93.08 318	88.76 166	96.86 310	88.24 288	79.03 332	94.69 317

tpmrst		95.63 141	95.69 126	95.44 261	97.54 186	88.54 308	96.97 286	97.56 235	93.50 177	97.52 97	96.93 250	89.49 135	99.16 155	95.25 131	96.42 167	98.64 151

ITE_SJBPF				95.44 261	97.42 195	91.32 271		97.50 247	95.09 112	93.59 229	98.35 125	81.70 286	98.88 195	89.71 264	93.39 230	96.12 290

v15		91.94 273	90.77 275	95.43 263	96.31 267	94.83 189	98.77 109	97.50 247	89.92 282	85.13 317	93.08 318	88.76 166	96.86 310	88.40 286	79.10 329	94.61 320

v13		91.88 277	90.69 279	95.43 263	96.33 263	94.78 199	98.75 113	97.50 247	89.68 289	84.93 322	92.98 322	88.84 158	96.83 312	88.14 290	79.09 330	94.69 317

v12		91.89 276	90.70 278	95.43 263	96.31 267	94.80 194	98.76 112	97.50 247	89.76 286	84.95 321	93.00 321	88.82 160	96.82 314	88.23 289	79.00 333	94.68 319

V14		91.93 274	90.76 276	95.42 266	96.33 263	94.81 193	98.77 109	97.51 244	89.86 284	85.09 318	93.13 316	88.80 164	96.83 312	88.32 287	79.06 331	94.60 321

tpmp4_e23		93.91 246	93.42 245	95.38 267	97.62 179	88.59 307	97.52 259	97.34 265	87.94 305	94.17 212	96.79 260	82.91 280	99.05 171	90.62 242	95.91 195	98.50 156

v11		91.85 278	90.68 280	95.36 268	96.34 259	94.74 201	98.80 101	97.43 258	89.60 292	85.09 318	93.03 320	88.53 175	96.75 315	87.37 298	79.96 324	94.58 322

MVP-Stereo		94.28 229	93.92 213	95.35 269	94.95 311	92.60 255	97.97 220	97.65 232	91.61 243	90.68 283	97.09 225	86.32 222	98.42 245	89.70 265	99.34 83	95.02 311
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpmvs		94.60 211	94.36 188	95.33 270	97.46 191	88.60 306	96.88 295	97.68 230	91.29 258	93.80 227	96.42 275	88.58 171	99.24 144	91.06 234	96.04 194	98.17 172

TDRefinement		91.06 287	89.68 290	95.21 271	85.35 340	91.49 269	98.51 160	97.07 279	91.47 246	88.83 297	97.84 167	77.31 312	99.09 168	92.79 194	77.98 334	95.04 310

USDC		93.33 255	92.71 254	95.21 271	96.83 231	90.83 276	96.91 290	97.50 247	93.84 156	90.72 282	98.14 144	77.69 308	98.82 202	89.51 269	93.21 235	95.97 294

pmmvs6		91.77 280	90.63 281	95.17 273	94.69 316	91.24 273	98.67 134	97.92 222	86.14 313	89.62 290	97.56 192	75.79 317	98.34 259	90.75 239	84.56 316	95.94 295

tpm		94.13 238	93.80 220	95.12 274	96.50 246	87.91 314	97.44 261	95.89 317	92.62 210	96.37 153	96.30 277	84.13 271	98.30 265	93.24 177	91.66 251	99.14 116

ADS-MVSNet2		94.58 214	94.40 187	95.11 275	98.00 159	88.74 303	96.04 312	97.30 269	90.15 273	96.47 149	96.64 266	87.89 191	97.56 297	90.08 255	97.06 151	99.02 125

tpm cat1		93.36 252	92.80 252	95.07 276	97.58 183	87.97 313	96.76 299	97.86 224	82.17 330	93.53 232	96.04 287	86.13 224	99.13 159	89.24 273	95.87 196	98.10 174

PVSNet_0	88.72 19	91.28 284	90.03 287	95.00 277	97.99 161	87.29 318	94.84 328	98.50 137	92.06 234	89.86 288	95.19 299	79.81 299	99.39 135	92.27 206	69.79 341	98.33 169

LCM-MVSNet-Re		95.22 175	95.32 138	94.91 278	98.18 150	87.85 315	98.75 113	95.66 324	95.11 109	88.96 296	96.85 257	90.26 131	97.65 293	95.65 118	98.44 117	99.22 105

dp		94.15 237	93.90 215	94.90 279	97.31 202	86.82 320	96.97 286	97.19 276	91.22 262	96.02 160	96.61 268	85.51 242	99.02 178	90.00 259	94.30 204	98.85 137

testgi		93.06 261	92.45 258	94.88 280	96.43 250	89.90 287	98.75 113	97.54 240	95.60 81	91.63 274	97.91 160	74.46 324	97.02 305	86.10 305	93.67 221	97.72 187

semantic-postprocess				94.85 281	97.98 163	90.56 283		98.11 203	93.75 160	92.58 257	97.48 194	83.91 274	97.41 300	92.48 204	91.30 253	96.58 267

OurMVSNet-221017-0		94.21 230	94.00 208	94.85 281	95.60 299	89.22 297	98.89 77	97.43 258	95.29 101	92.18 269	98.52 112	82.86 281	98.59 217	93.46 172	91.76 249	96.74 240

MDA-MVSNet-bldmvs		89.97 295	88.35 301	94.83 283	95.21 308	91.34 270	97.64 252	97.51 244	88.36 303	71.17 340	96.13 285	79.22 302	96.63 321	83.65 314	86.27 308	96.52 275

IterMVS		94.09 239	93.85 218	94.80 284	97.99 161	90.35 285	97.18 281	98.12 198	93.68 170	92.46 263	97.34 204	84.05 272	97.41 300	92.51 203	91.33 252	96.62 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		93.34 254	92.86 251	94.75 285	95.67 297	89.41 295	98.75 113	96.67 304	93.89 153	90.15 287	98.25 138	80.87 291	98.27 268	90.90 237	90.64 257	96.57 269

MDA-MVSNet_test_wron		90.71 290	89.38 293	94.68 286	94.83 313	90.78 278	97.19 280	97.46 254	87.60 306	72.41 339	95.72 295	86.51 218	96.71 319	85.92 307	86.80 306	96.56 271

TinyColmap		92.31 267	91.53 266	94.65 287	96.92 224	89.75 289	96.92 288	96.68 303	90.45 269	89.62 290	97.85 166	76.06 316	98.81 203	86.74 301	92.51 240	95.41 305

YYNet1		90.70 291	89.39 292	94.62 288	94.79 314	90.65 281	97.20 279	97.46 254	87.54 307	72.54 338	95.74 292	86.51 218	96.66 320	86.00 306	86.76 307	96.54 273

LP		91.12 286	89.99 288	94.53 289	96.35 258	88.70 304	93.86 336	97.35 264	84.88 321	90.98 279	94.77 304	84.40 263	97.43 299	75.41 334	91.89 248	97.47 192

FMVSNet5		91.81 279	90.92 273	94.49 290	97.21 208	92.09 259	98.00 218	97.55 239	89.31 297	90.86 281	95.61 298	74.48 323	95.32 329	85.57 309	89.70 263	96.07 292

K. test v3		92.55 264	91.91 265	94.48 291	95.64 298	89.24 296	99.07 56	94.88 332	94.04 145	86.78 304	97.59 189	77.64 311	97.64 294	92.08 209	89.43 269	96.57 269

test_0402		91.32 283	90.27 285	94.48 291	96.60 241	91.12 274	98.50 161	97.22 275	86.10 314	88.30 299	96.98 241	77.65 310	97.99 282	78.13 328	92.94 237	94.34 324

MS-PatchMatch		93.84 247	93.63 231	94.46 293	96.18 275	89.45 293	97.76 243	98.27 168	92.23 232	92.13 270	97.49 193	79.50 300	98.69 208	89.75 263	99.38 81	95.25 306

lessismore_v0				94.45 294	94.93 312	88.44 309		91.03 346		86.77 305	97.64 186	76.23 315	98.42 245	90.31 252	85.64 313	96.51 277

pmmvs-eth3d		90.36 293	89.05 296	94.32 295	91.10 329	92.12 258	97.63 254	96.95 290	88.86 300	84.91 323	93.13 316	78.32 305	96.74 316	88.70 283	81.81 321	94.09 328

LF4IMVS		93.14 260	92.79 253	94.20 296	95.88 290	88.67 305	97.66 251	97.07 279	93.81 158	91.71 273	97.65 184	77.96 307	98.81 203	91.47 229	91.92 247	95.12 307

UnsupCasMVSNet_eth		90.99 288	89.92 289	94.19 297	94.08 319	89.83 288	97.13 283	98.67 104	93.69 168	85.83 310	96.19 283	75.15 319	96.74 316	89.14 274	79.41 328	96.00 293

EG-PatchMatch MVS		91.13 285	90.12 286	94.17 298	94.73 315	89.00 301	98.13 204	97.81 225	89.22 298	85.32 313	96.46 272	67.71 335	98.42 245	87.89 296	93.82 220	95.08 309

MIMVSNet1		89.67 297	88.28 302	93.82 299	92.81 325	91.08 275	98.01 216	97.45 256	87.95 304	87.90 301	95.87 291	67.63 336	94.56 332	78.73 327	88.18 288	95.83 297

OpenMVS_ROB	86.42 20	89.00 299	87.43 305	93.69 300	93.08 323	89.42 294	97.91 227	96.89 296	78.58 335	85.86 309	94.69 305	69.48 332	98.29 267	77.13 329	93.29 233	93.36 333

CVMVSNet		95.43 159	96.04 112	93.57 301	97.93 164	83.62 324	98.12 205	98.59 115	95.68 77	96.56 132	99.02 60	87.51 203	97.51 298	93.56 171	97.44 147	99.60 61

Patchmatch-RL test		91.49 282	90.85 274	93.41 302	91.37 328	84.40 322	92.81 337	95.93 316	91.87 239	87.25 302	94.87 303	88.99 147	96.53 322	92.54 202	82.00 319	99.30 96

Anonymous20231206		91.66 281	91.10 269	93.33 303	94.02 320	87.35 317	98.58 144	97.26 273	90.48 267	90.16 286	96.31 276	83.83 277	96.53 322	79.36 324	89.90 262	96.12 290

UnsupCasMVSNet_bld		87.17 305	85.12 308	93.31 304	91.94 326	88.77 302	94.92 327	98.30 165	84.30 324	82.30 326	90.04 334	63.96 340	97.25 302	85.85 308	74.47 340	93.93 331

RPSCF		94.87 191	95.40 130	93.26 305	98.89 105	82.06 330	98.33 177	98.06 215	90.30 272	96.56 132	99.26 29	87.09 209	99.49 127	93.82 164	96.32 174	98.24 171

new_pmnet		90.06 294	89.00 297	93.22 306	94.18 317	88.32 311	96.42 310	96.89 296	86.19 312	85.67 312	93.62 311	77.18 313	97.10 304	81.61 319	89.29 271	94.23 325

MVS-HIRNet		89.46 298	88.40 300	92.64 307	97.58 183	82.15 329	94.16 335	93.05 344	75.73 338	90.90 280	82.52 340	79.42 301	98.33 260	83.53 315	98.68 104	97.43 193

test20.03		90.89 289	90.38 283	92.43 308	93.48 321	88.14 312	98.33 177	97.56 235	93.40 186	87.96 300	96.71 263	80.69 294	94.13 333	79.15 325	86.17 309	95.01 312

DSMNet-mixed		92.52 265	92.58 256	92.33 309	94.15 318	82.65 328	98.30 184	94.26 338	89.08 299	92.65 255	95.73 293	85.01 250	95.76 327	86.24 304	97.76 142	98.59 153

EU-MVSNet		93.66 249	94.14 199	92.25 310	95.96 286	83.38 325	98.52 156	98.12 198	94.69 123	92.61 256	98.13 145	87.36 207	96.39 324	91.82 218	90.00 261	96.98 213

pmmvs3		86.67 307	84.86 309	92.11 311	88.16 335	87.19 319	96.63 302	94.75 334	79.88 334	87.22 303	92.75 327	66.56 337	95.20 330	81.24 320	76.56 337	93.96 330

new-patchmatchnet		88.50 303	87.45 304	91.67 312	90.31 331	85.89 321	97.16 282	97.33 268	89.47 293	83.63 325	92.77 326	76.38 314	95.06 331	82.70 316	77.29 335	94.06 329

PM-MVS		87.77 304	86.55 306	91.40 313	91.03 330	83.36 326	96.92 288	95.18 330	91.28 259	86.48 307	93.42 312	53.27 343	96.74 316	89.43 271	81.97 320	94.11 327

Anonymous20231211		83.69 310	81.50 312	90.26 314	89.23 334	80.10 332	97.97 220	97.06 281	72.79 340	82.05 328	92.57 328	50.28 344	96.32 325	76.15 332	75.38 338	94.37 323

CMPMVS	66.06 21	89.70 296	89.67 291	89.78 315	93.19 322	76.56 335	97.00 285	98.35 159	80.97 332	81.57 329	97.75 175	74.75 322	98.61 214	89.85 260	93.63 223	94.17 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ambc				89.49 316	86.66 339	75.78 337	92.66 338	96.72 301		86.55 306	92.50 329	46.01 346	97.90 286	90.32 251	82.09 318	94.80 313

test2356		88.68 302	88.61 298	88.87 317	89.90 333	78.23 333	95.11 323	96.66 306	88.66 302	89.06 295	94.33 310	73.14 328	92.56 340	75.56 333	95.11 202	95.81 298

testus		88.91 300	89.08 295	88.40 318	91.39 327	76.05 336	96.56 305	96.48 308	89.38 296	89.39 293	95.17 301	70.94 330	93.56 336	77.04 330	95.41 200	95.61 302

testpf		88.74 301	89.09 294	87.69 319	95.78 293	83.16 327	84.05 347	94.13 341	85.22 320	90.30 285	94.39 308	74.92 321	95.80 326	89.77 261	93.28 234	84.10 343

test1235678		86.26 308	85.81 307	87.62 320	86.97 338	75.00 340	96.55 307	96.32 311	86.08 315	81.32 330	92.98 322	73.10 329	92.05 341	71.64 337	87.32 298	95.81 298

1111		84.94 309	84.30 310	86.86 321	87.59 336	75.10 338	96.63 302	96.43 309	82.53 327	80.75 331	92.91 324	68.94 333	93.79 334	68.24 340	84.66 315	91.70 335

DeepMVS_CX				86.78 322	97.09 217	72.30 342		95.17 331	75.92 337	84.34 324	95.19 299	70.58 331	95.35 328	79.98 323	89.04 274	92.68 334

LCM-MVSNet		78.70 313	76.24 317	86.08 323	77.26 350	71.99 343	94.34 333	96.72 301	61.62 344	76.53 335	89.33 335	33.91 353	92.78 339	81.85 318	74.60 339	93.46 332

PMMVS2		77.95 315	75.44 318	85.46 324	82.54 342	74.95 341	94.23 334	93.08 343	72.80 339	74.68 336	87.38 336	36.36 351	91.56 342	73.95 335	63.94 342	89.87 336

no-one		74.41 317	70.76 319	85.35 325	79.88 345	76.83 334	94.68 330	94.22 339	80.33 333	63.81 343	79.73 344	35.45 352	93.36 337	71.78 336	36.99 349	85.86 342

N_pmnet		87.12 306	87.77 303	85.17 326	95.46 304	61.92 349	97.37 268	70.66 356	85.83 317	88.73 298	96.04 287	85.33 247	97.76 292	80.02 321	90.48 258	95.84 296

test12356		83.47 311	83.37 311	83.78 327	84.43 341	70.09 345	95.12 322	95.60 325	82.98 325	78.89 333	92.43 331	64.99 338	91.41 343	70.36 338	85.55 314	89.82 337

Gipuma		78.40 314	76.75 315	83.38 328	95.54 301	80.43 331	79.42 348	97.40 261	64.67 342	73.46 337	80.82 343	45.65 347	93.14 338	66.32 342	87.43 296	76.56 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		78.74 312	77.35 313	82.89 329	78.16 349	69.30 346	95.87 316	94.65 335	81.11 331	70.98 341	87.11 338	46.31 345	90.42 344	65.28 343	76.72 336	88.95 338

ANet_high		69.08 319	65.37 321	80.22 330	65.99 353	71.96 344	90.91 341	90.09 347	82.62 326	49.93 349	78.39 345	29.36 354	81.75 349	62.49 346	38.52 348	86.95 341

FPMVS		77.62 316	77.14 314	79.05 331	79.25 346	60.97 350	95.79 318	95.94 315	65.96 341	67.93 342	94.40 307	37.73 350	88.88 346	68.83 339	88.46 285	87.29 339

wuykxyi23d		63.73 325	58.86 327	78.35 332	67.62 352	67.90 347	86.56 344	87.81 351	58.26 345	42.49 351	70.28 349	11.55 358	85.05 347	63.66 344	41.50 345	82.11 345

PNet_i23d		67.70 321	65.07 322	75.60 333	78.61 347	59.61 352	89.14 342	88.24 350	61.83 343	52.37 347	80.89 342	18.91 355	84.91 348	62.70 345	52.93 344	82.28 344

MVE	62.14 22	63.28 326	59.38 326	74.99 334	74.33 351	65.47 348	85.55 345	80.50 355	52.02 348	51.10 348	75.00 348	10.91 360	80.50 350	51.60 348	53.40 343	78.99 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		68.90 320	66.97 320	74.68 335	50.78 355	59.95 351	87.13 343	83.47 354	38.80 350	62.21 344	96.23 280	64.70 339	76.91 353	88.91 280	30.49 350	87.19 340

PMVS	61.03 23	65.95 322	63.57 324	73.09 336	57.90 354	51.22 355	85.05 346	93.93 342	54.45 346	44.32 350	83.57 339	13.22 356	89.15 345	58.68 347	81.00 323	78.91 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 323	64.25 323	67.02 337	82.28 343	59.36 353	91.83 340	85.63 352	52.69 347	60.22 345	77.28 346	41.06 349	80.12 351	46.15 349	41.14 346	61.57 350

EMVS		64.07 324	63.26 325	66.53 338	81.73 344	58.81 354	91.85 339	84.75 353	51.93 349	59.09 346	75.13 347	43.32 348	79.09 352	42.03 350	39.47 347	61.69 349

.test1245		73.05 318	76.31 316	63.27 339	87.59 336	75.10 338	96.63 302	96.43 309	82.53 327	80.75 331	92.91 324	68.94 333	93.79 334	68.24 340	12.72 352	20.91 352

pcd1.5k->3k		39.42 327	41.78 328	32.35 340	96.17 276	0.00 359	0.00 350	98.54 125	0.00 354	0.00 355	0.00 356	87.78 196	0.00 357	0.00 354	93.56 225	97.06 208

wuyk23d		30.17 328	30.18 330	30.16 341	78.61 347	43.29 356	66.79 349	14.21 357	17.31 351	14.82 354	11.93 355	11.55 358	41.43 354	37.08 351	19.30 351	5.76 354

test123		20.95 331	23.72 332	12.64 342	13.54 357	8.19 357	96.55 307	6.13 359	7.48 353	16.74 353	37.98 352	12.97 357	6.05 355	16.69 352	5.43 354	23.68 351

testmvs		21.48 330	24.95 331	11.09 343	14.89 356	6.47 358	96.56 305	9.87 358	7.55 352	17.93 352	39.02 351	9.43 361	5.90 356	16.56 353	12.72 352	20.91 352

cdsmvs_eth3d_5k		23.98 329	31.98 329	0.00 344	0.00 358	0.00 359	0.00 350	98.59 115	0.00 354	0.00 355	98.61 102	90.60 125	0.00 357	0.00 354	0.00 355	0.00 355

pcd_1.5k_mvsjas		7.88 333	10.50 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	94.51 62	0.00 357	0.00 354	0.00 355	0.00 355

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		8.20 332	10.94 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	98.43 117	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																99.20 106

test_part3							98.55 151		96.40 57		99.31 21		99.93 9	96.37 95

test_part2					99.63 21	99.18 1				99.27 6

test_part1								98.84 54				97.38 2			99.78 14	99.76 20

sam_mvs1												89.45 136				99.20 106

sam_mvs												88.99 147

MTGPA								98.74 79

test_post1							96.68 301				30.43 354	87.85 194	98.69 208	92.59 199

test_post											31.83 353	88.83 159	98.91 190

patchmatchnet-post											95.10 302	89.42 137	98.89 194

MTMP								94.14 340

gm-plane-assit					95.88 290	87.47 316			89.74 288		96.94 246		99.19 154	93.32 176

test9_res														96.39 94	99.57 57	99.69 37

TEST9					99.31 49	98.50 14	97.92 224	98.73 84	92.63 209	97.74 83	98.68 96	96.20 14	99.80 59

test_8					99.29 57	98.44 16	97.89 232	98.72 86	92.98 200	97.70 86	98.66 99	96.20 14	99.80 59

agg_prior2														95.87 108	99.57 57	99.68 43

agg_prior					99.30 54	98.38 19		98.72 86		97.57 95			99.81 52

test_prior4						98.01 43	97.86 235

test_prior2							97.80 240		96.12 65	97.89 77	98.69 94	95.96 27		96.89 71	99.60 51

旧先验2							97.57 257		91.30 257	98.67 38			99.80 59	95.70 117

新几何2							97.64 252

旧先验1					99.29 57	97.48 61		98.70 93			99.09 54	95.56 37			99.47 71	99.61 58

无先验							97.58 256	98.72 86	91.38 251				99.87 37	93.36 174		99.60 61

原ACMM2							97.67 250

test222					99.23 72	97.17 74	97.40 264	98.66 107	88.68 301	98.05 62	98.96 71	94.14 71			99.53 67	99.61 58

testdata2													99.89 29	91.65 224

segment_acmp												96.85 5

testdata1							97.32 274		96.34 59

plane_prior7					97.42 195	94.63 204

plane_prior6					97.35 200	94.61 207						87.09 209

plane_prior5								98.56 122					99.03 176	96.07 99	94.27 205	96.92 217

plane_prior4											98.28 133

plane_prior3						94.61 207			97.02 39	95.34 165

plane_prior2							98.80 101		97.28 21

plane_prior1					97.37 199

plane_prior						94.60 209	98.44 166		96.74 46						94.22 207

n2								0.00 360

nn								0.00 360

door-mid								94.37 337

test11								98.66 107

door								94.64 336

HQP5-MVS						94.25 222

HQP-NCC					97.20 209		98.05 212		96.43 54	94.45 186

ACMP_Plane					97.20 209		98.05 212		96.43 54	94.45 186

BP-MVS														95.30 127

HQP4-MVS										94.45 186			98.96 183			96.87 228

HQP3-MVS								98.46 142							94.18 209

HQP2-MVS												86.75 215

NP-MVS					97.28 203	94.51 212					97.73 176

MDTV_nov1_ep13_2view						84.26 323	96.89 294		90.97 265	97.90 76		89.89 134		93.91 161		99.18 112

MDTV_nov1_ep13			95.40 130		97.48 189	88.34 310	96.85 296	97.29 270	93.74 162	97.48 98	97.26 209	89.18 143	99.05 171	91.92 217	97.43 148

ACMMP++_ref															92.97 236

ACMMP++															93.61 224

Test By Simon												94.64 59

High-resolution multi-view benchmark