High-res multi-view results

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

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

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

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

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

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

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

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 23	98.96 2	99.39 5	98.93 35	97.38 17	99.41 3	99.54 1	96.66 5	99.84 42	98.86 2	99.85 2	99.87 1

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 71	98.43 16	99.10 44	98.87 48	97.38 17	99.35 5	99.40 6	97.78 1	99.87 35	97.77 39	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 27	99.14 37	98.66 104	96.84 43	99.56 2	99.31 21	96.34 10	99.70 91	98.32 20	99.73 34	99.73 27

CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 45	98.87 5	98.47 147	98.81 58	97.72 4	98.76 33	99.16 42	97.05 2	99.78 74	98.06 25	99.66 42	99.69 35

HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 28	99.26 17	98.58 117	97.52 7	99.41 3	98.78 85	96.00 23	99.79 69	97.79 38	99.59 52	99.69 35

XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 22	99.29 14	98.88 46	97.40 14	98.46 45	99.20 35	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

Regformer-2		98.69 7	98.52 8	99.19 28	99.35 37	98.01 41	98.37 156	98.81 58	97.48 11	99.21 9	99.21 32	96.13 16	99.80 57	98.40 18	99.73 34	99.75 20

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 37	97.81 50	98.37 156	98.76 72	97.49 10	99.20 10	99.21 32	96.08 19	99.79 69	98.42 16	99.73 34	99.75 20

MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 22	98.87 5	98.41 153	98.68 94	97.04 38	98.52 44	98.80 84	96.78 4	99.83 43	97.93 28	99.61 48	99.74 25

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 35	97.37 63	98.40 154	98.79 66	97.46 12	99.09 13	99.31 21	95.86 31	99.80 57	98.64 4	99.76 23	99.79 4

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 42	98.36 21	98.90 63	98.85 52	97.28 21	99.72 1	99.39 7	96.63 7	97.60 277	98.17 23	99.85 2	99.64 53

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 25	99.23 21	98.96 30	96.10 65	98.94 21	99.17 39	96.06 20	99.92 13	97.62 45	99.78 14	99.75 20

ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 21	98.95 3	98.82 79	98.81 58	95.80 72	99.16 12	99.47 4	95.37 40	99.92 13	97.89 32	99.75 29	99.79 4

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 34	99.23 21	98.93 35	96.15 60	98.94 21	99.17 39	95.91 28	99.94 3	97.55 50	99.79 10	99.78 7

NCCC		98.61 13	98.35 20	99.38 10	99.28 60	98.61 10	98.45 148	98.76 72	97.82 3	98.45 48	98.93 73	96.65 6	99.83 43	97.38 57	99.41 76	99.71 32

Regformer-3		98.59 16	98.50 11	98.86 57	99.43 35	97.05 74	98.40 154	98.68 94	97.43 13	99.06 14	99.31 21	95.80 32	99.77 79	98.62 6	99.76 23	99.78 7

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 35	99.23 21	98.95 32	96.10 65	98.93 25	99.19 38	95.70 33	99.94 3	97.62 45	99.79 10	99.78 7

MTAPA		98.58 18	98.29 26	99.46 6	99.76 1	98.64 8	98.90 63	98.74 76	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 27	99.08 1	98.72 109	98.66 104	97.51 8	98.15 55	98.83 81	95.70 33	99.92 13	97.53 52	99.67 39	99.66 48

CP-MVS		98.57 20	98.36 18	99.19 28	99.66 19	97.86 46	99.34 11	98.87 48	95.96 68	98.60 41	99.13 44	96.05 22	99.94 3	97.77 39	99.86 1	99.77 14

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 63	96.80 83	98.71 110	99.05 22	97.28 21	98.84 27	99.28 25	96.47 9	99.40 124	98.52 14	99.70 37	99.47 77

MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 8	98.55 136	98.74 76	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 64	98.04 39	98.50 144	98.78 68	97.72 4	98.92 26	99.28 25	95.27 44	99.82 48	97.55 50	99.77 17	99.69 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 25	98.98 57	98.96 30	95.65 78	98.94 21	99.17 39	96.06 20	99.92 13	97.21 60	99.78 14	99.75 20

APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 27	97.92 45	99.15 36	98.81 58	96.24 58	99.20 10	99.37 12	95.30 43	99.80 57	97.73 41	99.67 39	99.72 30

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 39	99.28 16	98.81 58	96.24 58	98.35 52	99.23 29	95.46 38	99.94 3	97.42 55	99.81 8	99.77 14

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 38	98.99 54	99.49 5	95.43 86	99.03 15	99.32 20	95.56 35	99.94 3	96.80 79	99.77 17	99.78 7

EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 32	96.49 96	98.30 167	98.69 91	97.21 28	98.84 27	99.36 16	95.41 39	99.78 74	98.62 6	99.65 43	99.80 3

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 71	97.32 64	97.91 209	99.58 3	97.20 29	98.33 53	99.00 63	95.99 24	99.64 100	98.05 26	99.76 23	99.69 35

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 33	96.32 103	98.28 169	98.68 94	97.17 31	98.74 34	99.37 12	95.25 45	99.79 69	98.57 8	99.54 64	99.73 27

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 85	97.66 52	97.75 226	98.89 43	97.71 6	98.33 53	98.97 65	94.97 52	99.88 34	98.42 16	99.76 23	99.42 85
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 71	97.25 69	98.11 189	98.29 164	97.19 30	98.99 20	99.02 58	96.22 11	99.67 96	98.52 14	98.56 110	99.51 69

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 46	99.44 4	98.82 55	94.46 127	98.94 21	99.20 35	95.16 48	99.74 85	97.58 47	99.85 2	99.77 14

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 49	99.53 1	98.80 65	94.63 120	98.61 40	98.97 65	95.13 49	99.77 79	97.65 44	99.83 7	99.79 4

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 26	98.72 7	98.80 88	98.82 55	94.52 123	99.23 8	99.25 28	95.54 37	99.80 57	96.52 89	99.77 17	99.74 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 61	96.90 80	97.95 204	99.58 3	97.14 33	98.44 49	99.01 62	95.03 51	99.62 105	97.91 29	99.75 29	99.50 71

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 78	98.12 37	99.04 50	99.09 18	93.32 172	98.83 29	99.10 48	96.54 8	99.83 43	97.70 43	99.76 23	99.59 61

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 33	99.22 27	98.79 66	96.13 62	97.92 73	99.23 29	94.54 59	99.94 3	96.74 81	99.78 14	99.73 27

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 4	98.43 151	98.78 68	94.10 133	97.69 85	99.42 5	95.25 45	99.92 13	98.09 24	99.80 9	99.67 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
abl_6		98.30 40	98.03 38	99.13 38	99.56 24	97.76 51	99.13 40	98.82 55	96.14 61	99.26 6	99.37 12	93.33 76	99.93 9	96.96 67	99.67 39	99.69 35

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 55	99.03 51	99.41 6	95.98 67	97.60 91	99.36 16	94.45 64	99.93 9	97.14 61	98.85 97	99.70 34
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
test_prior3		98.22 42	97.90 43	99.19 28	99.31 47	98.22 30	97.80 222	98.84 53	96.12 63	97.89 75	98.69 92	95.96 25	99.70 91	96.89 71	99.60 49	99.65 50

CANet		98.05 43	97.76 45	98.90 55	98.73 99	97.27 66	98.35 158	98.78 68	97.37 19	97.72 83	98.96 69	91.53 110	99.92 13	98.79 3	99.65 43	99.51 69

train_agg		97.97 44	97.52 54	99.33 15	99.31 47	98.50 12	97.92 206	98.73 81	92.98 183	97.74 81	98.68 94	96.20 12	99.80 57	96.59 85	99.57 55	99.68 41

UA-Net		97.96 45	97.62 48	98.98 49	98.86 92	97.47 60	98.89 67	99.08 19	96.67 49	98.72 35	99.54 1	93.15 79	99.81 50	94.87 134	98.83 98	99.65 50

agg_prior1		97.95 46	97.51 55	99.28 20	99.30 52	98.38 17	97.81 221	98.72 83	93.16 177	97.57 93	98.66 97	96.14 15	99.81 50	96.63 84	99.56 61	99.66 48

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 31	98.44 14	97.91 209	98.67 101	92.57 196	98.77 32	98.85 79	95.93 27	99.72 86	95.56 118	99.69 38	99.68 41

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 213	99.00 85	89.54 274	97.43 245	98.87 48	98.16 2	99.26 6	99.38 11	96.12 17	99.64 100	98.30 21	99.77 17	99.72 30

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 64	96.93 78	98.83 77	98.75 75	96.96 41	96.89 114	99.50 3	90.46 124	99.87 35	97.84 36	99.76 23	99.52 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior3		97.87 50	97.42 60	99.23 27	99.29 55	98.23 28	97.92 206	98.72 83	92.38 209	97.59 92	98.64 99	96.09 18	99.79 69	96.59 85	99.57 55	99.68 41

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 25	98.35 22	98.33 160	98.89 43	92.62 193	98.05 60	98.94 72	95.34 42	99.65 98	96.04 100	99.42 75	99.19 104

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 149	99.22 27	99.32 7	93.04 180	97.02 107	98.92 75	95.36 41	99.91 22	97.43 54	99.64 45	99.52 66

MG-MVS		97.81 53	97.60 49	98.44 80	99.12 80	95.97 115	97.75 226	98.78 68	96.89 42	98.46 45	99.22 31	93.90 73	99.68 95	94.81 137	99.52 66	99.67 46

VNet		97.79 54	97.40 61	98.96 51	98.88 90	97.55 57	98.63 123	98.93 35	96.74 46	99.02 16	98.84 80	90.33 127	99.83 43	98.53 10	96.66 156	99.50 71

PS-MVSNAJ		97.73 55	97.77 44	97.62 126	98.68 105	95.58 133	97.34 254	98.51 129	97.29 20	98.66 37	97.88 161	94.51 60	99.90 25	97.87 33	99.17 86	97.39 183

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 73	99.12 42	98.81 58	92.34 210	98.09 58	99.08 54	93.01 80	99.92 13	96.06 99	99.77 17	99.75 20

MVS_0304		97.70 57	97.25 65	99.07 43	98.90 87	97.83 48	98.20 175	98.74 76	97.51 8	98.03 63	99.06 56	86.12 223	99.93 9	99.02 1	99.64 45	99.44 84

PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 61	95.91 124	98.63 123	99.16 16	94.48 126	97.67 86	98.88 77	92.80 82	99.91 22	97.11 62	99.12 87	99.50 71

canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 102	98.38 17	99.34 11	98.39 151	96.76 45	97.67 86	97.40 196	92.26 89	99.49 118	98.28 22	96.28 170	99.08 118

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 132	98.61 111	95.46 139	97.44 243	98.46 139	97.15 32	98.65 38	98.15 141	94.33 66	99.80 57	97.84 36	98.66 106	97.41 181

xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 118	95.98 111	97.86 217	98.51 129	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 184

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 118	95.98 111	97.86 217	98.51 129	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 184

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 118	95.98 111	97.86 217	98.51 129	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 184

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 138	95.76 129	99.47 2	98.40 149	94.98 106	98.79 30	98.83 81	92.34 86	98.41 234	96.91 69	99.59 52	99.34 88

alignmvs		97.56 65	97.07 74	99.01 46	98.66 106	98.37 20	98.83 77	98.06 212	96.74 46	98.00 68	97.65 182	90.80 121	99.48 122	98.37 19	96.56 160	99.19 104

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 42	95.92 122	98.28 169	98.59 112	95.52 83	97.97 69	99.10 48	93.28 78	99.49 118	95.09 132	98.88 94	99.19 104

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 27	96.41 99	98.63 123	98.60 111	95.18 97	97.06 105	98.06 147	94.26 68	99.57 111	93.80 163	98.87 96	99.52 66

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 108	95.38 141	99.33 13	98.31 159	93.61 163	97.19 99	99.07 55	94.05 70	99.23 134	96.89 71	98.43 117	99.37 87

3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 155	97.64 53	99.35 10	99.06 20	97.02 39	93.75 211	99.16 42	89.25 139	99.92 13	97.22 59	99.75 29	99.64 53

CNLPA		97.45 70	97.03 75	98.73 60	99.05 81	97.44 62	98.07 193	98.53 125	95.32 92	96.80 120	98.53 107	93.32 77	99.72 86	94.31 150	99.31 82	99.02 121

lupinMVS		97.44 71	97.22 68	98.12 98	98.07 138	95.76 129	97.68 231	97.76 224	94.50 124	98.79 30	98.61 100	92.34 86	99.30 129	97.58 47	99.59 52	99.31 91

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 153	98.52 11	99.37 7	98.71 88	97.09 37	92.99 232	99.13 44	89.36 136	99.89 27	96.97 65	99.57 55	99.71 32

Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 106	96.23 106	99.22 27	99.00 25	96.63 51	98.04 62	99.21 32	88.05 185	99.35 128	96.01 102	99.21 84	99.45 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 74	97.25 65	97.91 109	98.70 102	96.80 83	98.82 79	98.69 91	94.53 122	98.11 57	98.28 131	94.50 63	99.57 111	94.12 155	99.49 67	97.37 184

sss		97.39 75	96.98 77	98.61 67	98.60 112	96.61 91	98.22 173	98.93 35	93.97 140	98.01 66	98.48 112	91.98 99	99.85 40	96.45 91	98.15 126	99.39 86

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 64	95.35 144	97.28 258	99.26 8	93.13 178	97.94 71	98.21 138	92.74 83	99.81 50	96.88 74	99.40 78	99.27 98

1121		97.37 77	96.77 87	99.16 35	99.34 39	97.99 44	98.19 178	98.68 94	90.14 257	98.01 66	98.97 65	94.80 56	99.87 35	93.36 172	99.46 72	99.61 56

WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 92	96.89 82	98.31 165	98.71 88	95.26 94	97.67 86	98.56 106	92.21 92	99.78 74	95.89 104	96.85 153	99.48 76

jason		97.32 79	97.08 73	98.06 104	97.45 177	95.59 132	97.87 216	97.91 220	94.79 113	98.55 43	98.83 81	91.12 114	99.23 134	97.58 47	99.60 49	99.34 88
jason: jason.
MVS_Test		97.28 80	97.00 76	98.13 97	98.33 122	95.97 115	98.74 104	98.07 210	94.27 130	98.44 49	98.07 146	92.48 85	99.26 131	96.43 92	98.19 125	99.16 109

EPNet		97.28 80	96.87 81	98.51 74	94.98 292	96.14 108	98.90 63	97.02 279	98.28 1	95.99 144	99.11 46	91.36 111	99.89 27	96.98 64	99.19 85	99.50 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 94	96.36 101	99.19 33	97.97 217	95.39 88	97.23 98	98.99 64	91.11 115	98.93 171	94.60 141	98.59 108	99.47 77

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 55	96.31 105	98.14 184	98.76 72	92.41 207	96.39 135	98.31 130	94.92 53	99.78 74	94.06 156	98.77 101	99.23 102
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 84	97.18 69	97.20 141	98.81 96	93.27 226	95.78 301	99.15 17	95.25 95	96.79 121	98.11 144	92.29 88	99.07 153	98.56 9	99.85 2	99.25 100

LS3D		97.16 85	96.66 92	98.68 63	98.53 116	97.19 71	98.93 61	98.90 41	92.83 190	95.99 144	99.37 12	92.12 95	99.87 35	93.67 166	99.57 55	98.97 126

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 76	96.69 88	98.01 198	98.89 43	94.44 128	96.83 116	98.68 94	90.69 122	99.76 81	94.36 147	99.29 83	98.98 125

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 131	96.72 87	97.37 250	98.43 146	93.71 154	97.65 89	98.02 149	92.20 93	99.25 132	96.87 77	97.79 138	99.19 104

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 52	94.56 193	98.05 194	99.71 1	93.57 164	97.09 101	98.91 76	88.17 180	99.89 27	96.87 77	99.56 61	99.81 2

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 33	94.95 158	98.55 136	98.62 110	93.02 181	96.17 139	98.58 105	94.01 71	99.81 50	93.95 158	98.90 93	99.14 112

TAMVS		97.02 90	96.79 84	97.70 122	98.06 140	95.31 146	98.52 139	98.31 159	93.95 141	97.05 106	98.61 100	93.49 75	98.52 210	95.33 124	97.81 137	99.29 96

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 141	95.98 111	98.20 175	98.33 158	93.67 161	96.95 108	98.49 111	93.54 74	98.42 227	95.24 130	97.74 141	99.31 91
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 135	96.07 110	97.98 201	98.21 175	97.24 27	97.13 100	98.93 73	86.88 212	99.91 22	95.00 133	99.37 80	98.66 145

114514_t		96.93 93	96.27 104	98.92 53	99.50 27	97.63 54	98.85 74	98.90 41	84.80 304	97.77 78	99.11 46	92.84 81	99.66 97	94.85 135	99.77 17	99.47 77

MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 104	96.90 80	98.66 121	98.68 94	92.40 208	97.07 104	97.96 154	91.54 109	99.75 83	93.68 165	98.92 92	98.69 142
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
HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 42	95.56 135	97.38 248	99.65 2	92.34 210	97.61 90	98.20 139	89.29 138	99.10 150	96.97 65	97.60 144	99.77 14

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 99	95.46 139	99.20 31	98.30 162	94.96 108	96.60 124	98.87 78	90.05 131	98.59 199	93.67 166	98.60 107	99.46 81

PAPR		96.84 97	96.24 106	98.65 65	98.72 101	96.92 79	97.36 252	98.57 118	93.33 171	96.67 123	97.57 189	94.30 67	99.56 113	91.05 232	98.59 108	99.47 77

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 117	97.00 75	98.14 184	98.21 175	93.95 141	96.72 122	97.99 153	91.58 105	99.76 81	94.51 145	96.54 161	98.95 130

UGNet		96.78 99	96.30 103	98.19 94	98.24 126	95.89 126	98.88 69	98.93 35	97.39 16	96.81 119	97.84 165	82.60 264	99.90 25	96.53 88	99.49 67	98.79 137
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 147	99.25 64	95.35 144	98.26 171	99.26 8	94.28 129	97.94 71	97.46 193	92.74 83	99.81 50	96.88 74	93.32 214	96.20 269

mvs_anonymous		96.70 101	96.53 97	97.18 143	98.19 131	93.78 214	98.31 165	98.19 179	94.01 136	94.47 168	98.27 134	92.08 97	98.46 219	97.39 56	97.91 132	99.31 91

1112_ss		96.63 102	96.00 113	98.50 75	98.56 113	96.37 100	98.18 182	98.10 205	92.92 185	94.84 157	98.43 115	92.14 94	99.58 110	94.35 148	96.51 162	99.56 65

mvs-test1		96.60 103	96.68 91	96.37 207	97.89 150	91.81 245	98.56 134	98.10 205	96.57 52	96.52 128	97.94 156	90.81 119	99.45 123	95.72 111	98.01 129	97.86 172

PMMVS		96.60 103	96.33 102	97.41 136	97.90 149	93.93 210	97.35 253	98.41 147	92.84 189	97.76 79	97.45 195	91.10 116	99.20 136	96.26 95	97.91 132	99.11 114

DP-MVS		96.59 105	95.93 114	98.57 69	99.34 39	96.19 107	98.70 113	98.39 151	89.45 276	94.52 166	99.35 18	91.85 101	99.85 40	92.89 191	98.88 94	99.68 41

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 47	96.51 95	97.91 209	99.06 20	93.72 153	96.92 112	98.06 147	88.50 175	99.65 98	91.77 218	99.00 90	98.66 145

XVG-OURS		96.55 107	96.41 99	96.99 153	98.75 98	93.76 215	97.50 242	98.52 127	95.67 76	96.83 116	99.30 24	88.95 150	99.53 116	95.88 105	96.26 171	97.69 176

FIs		96.51 108	96.12 109	97.67 125	97.13 198	97.54 58	99.36 8	99.22 14	95.89 69	94.03 202	98.35 123	91.98 99	98.44 224	96.40 93	92.76 221	97.01 193

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 152	98.77 97	93.76 215	97.79 224	98.50 134	95.45 85	96.94 109	99.09 52	87.87 191	99.55 115	96.76 80	95.83 180	97.74 173

PS-MVSNAJss		96.43 110	96.26 105	96.92 161	95.84 274	95.08 153	99.16 35	98.50 134	95.87 70	93.84 209	98.34 127	94.51 60	98.61 197	96.88 74	93.45 211	97.06 190

FC-MVSNet-test		96.42 111	96.05 110	97.53 133	96.95 205	97.27 66	99.36 8	99.23 12	95.83 71	93.93 204	98.37 121	92.00 98	98.32 243	96.02 101	92.72 222	97.00 194

ab-mvs		96.42 111	95.71 123	98.55 71	98.63 109	96.75 86	97.88 215	98.74 76	93.84 146	96.54 127	98.18 140	85.34 236	99.75 83	95.93 103	96.35 168	99.15 110

PVSNet	91.96 18	96.35 113	96.15 108	96.96 156	99.17 75	92.05 242	96.08 293	98.68 94	93.69 157	97.75 80	97.80 171	88.86 153	99.69 94	94.26 152	99.01 89	99.15 110

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 113	95.94 119	97.71 228	98.07 210	92.10 216	94.79 161	97.29 203	91.75 102	99.56 113	94.17 153	96.50 163	99.58 63

diffmvs		96.32 115	95.74 118	98.07 103	98.26 125	96.14 108	98.53 138	98.23 173	90.10 258	96.88 115	97.73 174	90.16 130	99.15 139	93.90 160	97.85 136	98.91 132

Effi-MVS+-dtu		96.29 116	96.56 94	95.51 238	97.89 150	90.22 268	98.80 88	98.10 205	96.57 52	96.45 134	96.66 249	90.81 119	98.91 173	95.72 111	97.99 130	97.40 182

QAPM		96.29 116	95.40 129	98.96 51	97.85 152	97.60 56	99.23 21	98.93 35	89.76 268	93.11 229	99.02 58	89.11 143	99.93 9	91.99 212	99.62 47	99.34 88

Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 124	95.97 115	98.58 129	98.25 170	91.74 224	95.29 151	97.23 206	91.03 118	99.15 139	92.90 189	97.96 131	98.97 126

nrg030		96.28 118	95.72 120	97.96 108	96.90 209	98.15 35	99.39 5	98.31 159	95.47 84	94.42 177	98.35 123	92.09 96	98.69 191	97.50 53	89.05 256	97.04 192

1314		96.25 120	95.73 119	97.79 116	97.13 198	95.55 137	98.19 178	98.59 112	93.47 167	92.03 254	97.82 169	91.33 112	99.49 118	94.62 140	98.44 115	98.32 163

HQP_MVS		96.14 121	95.90 115	96.85 162	97.42 178	94.60 191	98.80 88	98.56 119	97.28 21	95.34 148	98.28 131	87.09 207	99.03 159	96.07 97	94.27 188	96.92 199

MVSTER		96.06 122	95.72 120	97.08 150	98.23 127	95.93 121	98.73 107	98.27 165	94.86 112	95.07 152	98.09 145	88.21 179	98.54 203	96.59 85	93.46 209	96.79 216

test_djsdf		96.00 123	95.69 125	96.93 159	95.72 278	95.49 138	99.47 2	98.40 149	94.98 106	94.58 164	97.86 162	89.16 142	98.41 234	96.91 69	94.12 196	96.88 208

EI-MVSNet		95.96 124	95.83 117	96.36 208	97.93 147	93.70 219	98.12 187	98.27 165	93.70 156	95.07 152	99.02 58	92.23 91	98.54 203	94.68 138	93.46 209	96.84 212

BH-untuned		95.95 125	95.72 120	96.65 179	98.55 115	92.26 239	98.23 172	97.79 223	93.73 152	94.62 163	98.01 151	88.97 149	99.00 162	93.04 182	98.51 111	98.68 143

MSDG		95.93 126	95.30 139	97.83 113	98.90 87	95.36 142	96.83 280	98.37 154	91.32 239	94.43 176	98.73 91	90.27 128	99.60 106	90.05 247	98.82 99	98.52 151

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 123	94.64 185	98.19 178	97.45 253	94.56 121	96.03 142	98.61 100	85.02 239	99.12 143	90.68 236	99.06 88	99.30 94

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 226	97.74 157	91.74 249	98.69 114	98.15 190	95.56 81	94.92 155	97.68 181	88.98 148	98.79 188	93.19 177	97.78 139	97.20 188

LFMVS		95.86 129	94.98 150	98.47 78	98.87 91	96.32 103	98.84 76	96.02 302	93.40 169	98.62 39	99.20 35	74.99 302	99.63 103	97.72 42	97.20 148	99.46 81

OpenMVS	93.04 13	95.83 130	95.00 148	98.32 87	97.18 195	97.32 64	99.21 30	98.97 28	89.96 261	91.14 259	99.05 57	86.64 215	99.92 13	93.38 171	99.47 69	97.73 174

VDD-MVS		95.82 131	95.23 141	97.61 130	98.84 95	93.98 209	98.68 118	97.40 258	95.02 105	97.95 70	99.34 19	74.37 307	99.78 74	98.64 4	96.80 154	99.08 118

UniMVSNet (Re)		95.78 132	95.19 143	97.58 131	96.99 204	97.47 60	98.79 93	99.18 15	95.60 79	93.92 205	97.04 224	91.68 103	98.48 214	95.80 109	87.66 277	96.79 216

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 188	97.27 66	98.94 60	99.23 12	95.13 99	95.51 147	97.32 201	85.73 229	98.91 173	97.33 58	89.55 250	96.89 207

HQP-MVS		95.72 134	95.40 129	96.69 171	97.20 192	94.25 204	98.05 194	98.46 139	96.43 54	94.45 169	97.73 174	86.75 213	98.96 166	95.30 125	94.18 192	96.86 211

UniMVSNet_NR-MVSNet		95.71 135	95.15 144	97.40 137	96.84 212	96.97 76	98.74 104	99.24 10	95.16 98	93.88 206	97.72 177	91.68 103	98.31 245	95.81 107	87.25 282	96.92 199

PatchmatchNet		95.71 135	95.52 128	96.29 214	97.58 166	90.72 261	96.84 279	97.52 238	94.06 134	97.08 102	96.96 232	89.24 140	98.90 176	92.03 211	98.37 118	99.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 137	95.33 136	96.76 166	96.16 261	94.63 186	98.43 151	98.39 151	96.64 50	95.02 154	98.78 85	85.15 238	99.05 154	95.21 131	94.20 191	96.60 246

ACMM	93.85 9	95.69 137	95.38 133	96.61 185	97.61 163	93.84 213	98.91 62	98.44 143	95.25 95	94.28 187	98.47 113	86.04 227	99.12 143	95.50 120	93.95 201	96.87 209
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 139	95.69 125	95.44 244	97.54 169	88.54 290	96.97 268	97.56 232	93.50 166	97.52 95	96.93 239	89.49 133	99.16 138	95.25 129	96.42 164	98.64 147

LPG-MVS_test		95.62 140	95.34 134	96.47 200	97.46 174	93.54 220	98.99 54	98.54 122	94.67 116	94.36 179	98.77 87	85.39 233	99.11 147	95.71 113	94.15 194	96.76 219

CLD-MVS		95.62 140	95.34 134	96.46 203	97.52 171	93.75 217	97.27 259	98.46 139	95.53 82	94.42 177	98.00 152	86.21 221	98.97 163	96.25 96	94.37 186	96.66 237
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
view800		95.60 142	94.93 154	97.62 126	99.05 81	94.85 161	99.09 45	97.01 281	95.36 89	96.52 128	97.37 197	84.55 245	99.59 107	89.07 266	96.39 165	98.40 157

conf0.05thres1000		95.60 142	94.93 154	97.62 126	99.05 81	94.85 161	99.09 45	97.01 281	95.36 89	96.52 128	97.37 197	84.55 245	99.59 107	89.07 266	96.39 165	98.40 157

tfpn		95.60 142	94.93 154	97.62 126	99.05 81	94.85 161	99.09 45	97.01 281	95.36 89	96.52 128	97.37 197	84.55 245	99.59 107	89.07 266	96.39 165	98.40 157

PatchFormer-LS_test		95.47 145	95.27 140	96.08 222	97.59 165	90.66 262	98.10 191	97.34 262	93.98 139	96.08 140	96.15 266	87.65 199	99.12 143	95.27 128	95.24 184	98.44 156

IterMVS-LS		95.46 146	95.21 142	96.22 216	98.12 136	93.72 218	98.32 164	98.13 193	93.71 154	94.26 188	97.31 202	92.24 90	98.10 256	94.63 139	90.12 242	96.84 212
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
jajsoiax		95.45 147	95.03 147	96.73 167	95.42 287	94.63 186	99.14 37	98.52 127	95.74 73	93.22 223	98.36 122	83.87 258	98.65 195	96.95 68	94.04 197	96.91 204

CVMVSNet		95.43 148	96.04 111	93.57 284	97.93 147	83.62 306	98.12 187	98.59 112	95.68 75	96.56 125	99.02 58	87.51 201	97.51 280	93.56 169	97.44 145	99.60 59

anonymousdsp		95.42 149	94.91 157	96.94 158	95.10 291	95.90 125	99.14 37	98.41 147	93.75 149	93.16 225	97.46 193	87.50 203	98.41 234	95.63 117	94.03 198	96.50 259

DU-MVS		95.42 149	94.76 160	97.40 137	96.53 226	96.97 76	98.66 121	98.99 27	95.43 86	93.88 206	97.69 178	88.57 170	98.31 245	95.81 107	87.25 282	96.92 199

mvs_tets		95.41 151	95.00 148	96.65 179	95.58 282	94.42 196	99.00 53	98.55 121	95.73 74	93.21 224	98.38 120	83.45 261	98.63 196	97.09 63	94.00 199	96.91 204

BH-w/o		95.38 152	95.08 146	96.26 215	98.34 121	91.79 246	97.70 229	97.43 255	92.87 188	94.24 190	97.22 207	88.66 168	98.84 182	91.55 222	97.70 142	98.16 166

VDDNet		95.36 153	94.53 164	97.86 111	98.10 137	95.13 151	98.85 74	97.75 225	90.46 250	98.36 51	99.39 7	73.27 309	99.64 100	97.98 27	96.58 159	98.81 136

TAPA-MVS	93.98 7	95.35 154	94.56 163	97.74 118	99.13 79	94.83 171	98.33 160	98.64 109	86.62 292	96.29 137	98.61 100	94.00 72	99.29 130	80.00 304	99.41 76	99.09 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 155	94.98 150	96.43 204	97.67 159	93.48 222	98.73 107	98.44 143	94.94 111	92.53 242	98.53 107	84.50 249	99.14 141	95.48 121	94.00 199	96.66 237
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 156	94.87 158	96.71 168	99.29 55	93.24 228	98.58 129	98.11 200	89.92 264	93.57 214	99.10 48	86.37 219	99.79 69	90.78 234	98.10 128	97.09 189
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-test1		95.32 157	94.97 152	96.35 209	97.67 159	91.29 254	97.33 255	97.60 230	94.68 115	96.92 112	96.95 233	83.97 255	98.50 213	91.33 227	98.32 121	99.25 100

AllTest		95.24 158	94.65 162	96.99 153	99.25 64	93.21 229	98.59 127	98.18 182	91.36 235	93.52 216	98.77 87	84.67 243	99.72 86	89.70 255	97.87 134	98.02 169

LCM-MVSNet-Re		95.22 159	95.32 137	94.91 261	98.18 133	87.85 297	98.75 100	95.66 308	95.11 100	88.96 278	96.85 242	90.26 129	97.65 275	95.65 116	98.44 115	99.22 103

EPNet_dtu		95.21 160	94.95 153	95.99 223	96.17 258	90.45 266	98.16 183	97.27 269	96.77 44	93.14 228	98.33 128	90.34 126	98.42 227	85.57 292	98.81 100	99.09 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 161	94.45 169	97.46 134	96.75 217	96.56 93	98.86 73	98.65 108	93.30 174	93.27 222	98.27 134	84.85 242	98.87 179	94.82 136	91.26 238	96.96 196

WR-MVS		95.15 162	94.46 167	97.22 140	96.67 222	96.45 97	98.21 174	98.81 58	94.15 131	93.16 225	97.69 178	87.51 201	98.30 247	95.29 127	88.62 266	96.90 206

TranMVSNet+NR-MVSNet		95.14 163	94.48 165	97.11 148	96.45 231	96.36 101	99.03 51	99.03 23	95.04 104	93.58 213	97.93 157	88.27 178	98.03 261	94.13 154	86.90 287	96.95 198

test-LLR		95.10 164	94.87 158	95.80 231	96.77 214	89.70 272	96.91 272	95.21 310	95.11 100	94.83 159	95.72 277	87.71 195	98.97 163	93.06 180	98.50 112	98.72 139

WR-MVS_H		95.05 165	94.46 167	96.81 164	96.86 211	95.82 128	99.24 20	99.24 10	93.87 145	92.53 242	96.84 243	90.37 125	98.24 251	93.24 175	87.93 272	96.38 264

ADS-MVSNet		95.00 166	94.45 169	96.63 182	98.00 142	91.91 244	96.04 294	97.74 226	90.15 255	96.47 132	96.64 251	87.89 189	98.96 166	90.08 245	97.06 149	99.02 121

VPNet		94.99 167	94.19 180	97.40 137	97.16 196	96.57 92	98.71 110	98.97 28	95.67 76	94.84 157	98.24 137	80.36 279	98.67 194	96.46 90	87.32 280	96.96 196

EPMVS		94.99 167	94.48 165	96.52 196	97.22 190	91.75 248	97.23 260	91.66 327	94.11 132	97.28 97	96.81 244	85.70 230	98.84 182	93.04 182	97.28 147	98.97 126

NR-MVSNet		94.98 169	94.16 181	97.44 135	96.53 226	97.22 70	98.74 104	98.95 32	94.96 108	89.25 276	97.69 178	89.32 137	98.18 253	94.59 142	87.40 279	96.92 199

FMVSNet3		94.97 170	94.26 175	97.11 148	98.18 133	96.62 89	98.56 134	98.26 169	93.67 161	94.09 198	97.10 212	84.25 251	98.01 262	92.08 207	92.14 225	96.70 228

CostFormer		94.95 171	94.73 161	95.60 237	97.28 186	89.06 281	97.53 240	96.89 286	89.66 272	96.82 118	96.72 247	86.05 225	98.95 170	95.53 119	96.13 176	98.79 137

PAPM		94.95 171	94.00 192	97.78 117	97.04 201	95.65 131	96.03 296	98.25 170	91.23 244	94.19 193	97.80 171	91.27 113	98.86 181	82.61 299	97.61 143	98.84 135

CP-MVSNet		94.94 173	94.30 174	96.83 163	96.72 219	95.56 135	99.11 43	98.95 32	93.89 143	92.42 247	97.90 159	87.19 206	98.12 255	94.32 149	88.21 269	96.82 215

TR-MVS		94.94 173	94.20 179	97.17 144	97.75 156	94.14 206	97.59 237	97.02 279	92.28 214	95.75 146	97.64 184	83.88 257	98.96 166	89.77 251	96.15 175	98.40 157

RPSCF		94.87 175	95.40 129	93.26 288	98.89 89	82.06 312	98.33 160	98.06 212	90.30 254	96.56 125	99.26 27	87.09 207	99.49 118	93.82 162	96.32 169	98.24 164

v1neww		94.83 176	94.22 176	96.68 174	96.39 234	94.85 161	98.87 70	98.11 200	92.45 202	94.45 169	97.06 219	88.82 158	98.54 203	92.93 186	88.91 259	96.65 239

v7new		94.83 176	94.22 176	96.68 174	96.39 234	94.85 161	98.87 70	98.11 200	92.45 202	94.45 169	97.06 219	88.82 158	98.54 203	92.93 186	88.91 259	96.65 239

v6		94.83 176	94.21 178	96.69 171	96.36 238	94.85 161	98.87 70	98.11 200	92.46 197	94.44 175	97.05 223	88.76 164	98.57 201	92.95 185	88.92 258	96.65 239

DWT-MVSNet_test		94.82 179	94.36 172	96.20 217	97.35 183	90.79 259	98.34 159	96.57 297	92.91 186	95.33 150	96.44 257	82.00 266	99.12 143	94.52 144	95.78 181	98.70 141

GA-MVS		94.81 180	94.03 190	97.14 145	97.15 197	93.86 212	96.76 281	97.58 231	94.00 137	94.76 162	97.04 224	80.91 272	98.48 214	91.79 217	96.25 172	99.09 115

V42		94.78 181	94.14 183	96.70 170	96.33 245	95.22 148	98.97 58	98.09 208	92.32 212	94.31 183	97.06 219	88.39 176	98.55 202	92.90 189	88.87 261	96.34 266

divwei89l23v2f112		94.76 182	94.12 186	96.67 177	96.28 251	94.85 161	98.69 114	98.12 195	92.44 204	94.29 186	96.94 235	88.85 155	98.48 214	92.67 194	88.79 265	96.67 234

CR-MVSNet		94.76 182	94.15 182	96.59 187	97.00 202	93.43 223	94.96 307	97.56 232	92.46 197	96.93 110	96.24 261	88.15 181	97.88 272	87.38 280	96.65 157	98.46 154

v1141		94.75 184	94.11 187	96.67 177	96.27 253	94.86 160	98.69 114	98.12 195	92.43 205	94.31 183	96.94 235	88.78 163	98.48 214	92.63 196	88.85 263	96.67 234

v1		94.75 184	94.11 187	96.69 171	96.27 253	94.87 159	98.69 114	98.12 195	92.43 205	94.32 182	96.94 235	88.71 167	98.54 203	92.66 195	88.84 264	96.67 234

DI_MVS_plusplus_test		94.74 186	93.62 216	98.09 100	95.34 288	95.92 122	98.09 192	97.34 262	94.66 118	85.89 290	95.91 271	80.49 278	99.38 126	96.66 83	98.22 123	98.97 126

test_normal		94.72 187	93.59 218	98.11 99	95.30 289	95.95 118	97.91 209	97.39 260	94.64 119	85.70 293	95.88 272	80.52 277	99.36 127	96.69 82	98.30 122	99.01 124

v7		94.69 188	94.04 189	96.62 184	96.41 233	94.79 179	98.78 95	98.13 193	91.89 220	94.30 185	97.16 209	88.13 183	98.45 221	91.96 214	89.65 247	96.61 244

v2v482		94.69 188	94.03 190	96.65 179	96.17 258	94.79 179	98.67 119	98.08 209	92.72 191	94.00 203	97.16 209	87.69 198	98.45 221	92.91 188	88.87 261	96.72 224

pmmvs4		94.69 188	93.99 194	96.81 164	95.74 276	95.94 119	97.40 246	97.67 228	90.42 252	93.37 220	97.59 187	89.08 144	98.20 252	92.97 184	91.67 233	96.30 268

PCF-MVS	93.45 11	94.68 191	93.43 227	98.42 83	98.62 110	96.77 85	95.48 303	98.20 178	84.63 305	93.34 221	98.32 129	88.55 172	99.81 50	84.80 295	98.96 91	98.68 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 192	93.54 221	98.08 101	96.88 210	96.56 93	98.19 178	98.50 134	78.05 318	92.69 237	98.02 149	91.07 117	99.63 103	90.09 244	98.36 119	98.04 168

PS-CasMVS		94.67 192	93.99 194	96.71 168	96.68 221	95.26 147	99.13 40	99.03 23	93.68 159	92.33 248	97.95 155	85.35 235	98.10 256	93.59 168	88.16 271	96.79 216

cascas		94.63 194	93.86 201	96.93 159	96.91 208	94.27 203	96.00 297	98.51 129	85.55 300	94.54 165	96.23 263	84.20 252	98.87 179	95.80 109	96.98 152	97.66 177

tpmvs		94.60 195	94.36 172	95.33 253	97.46 174	88.60 288	96.88 277	97.68 227	91.29 241	93.80 210	96.42 258	88.58 169	99.24 133	91.06 230	96.04 177	98.17 165

LTVRE_ROB	92.95 15	94.60 195	93.90 199	96.68 174	97.41 181	94.42 196	98.52 139	98.59 112	91.69 225	91.21 258	98.35 123	84.87 241	99.04 158	91.06 230	93.44 212	96.60 246
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
v1144		94.59 197	93.92 197	96.60 186	96.21 255	94.78 181	98.59 127	98.14 192	91.86 223	94.21 192	97.02 226	87.97 186	98.41 234	91.72 219	89.57 248	96.61 244

ADS-MVSNet2		94.58 198	94.40 171	95.11 258	98.00 142	88.74 285	96.04 294	97.30 266	90.15 255	96.47 132	96.64 251	87.89 189	97.56 279	90.08 245	97.06 149	99.02 121

ACMH	92.88 16	94.55 199	93.95 196	96.34 211	97.63 161	93.26 227	98.81 85	98.49 138	93.43 168	89.74 271	98.53 107	81.91 267	99.08 152	93.69 164	93.30 215	96.70 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-ACMP-BASELINE		94.54 200	94.14 183	95.75 234	96.55 225	91.65 250	98.11 189	98.44 143	94.96 108	94.22 191	97.90 159	79.18 285	99.11 147	94.05 157	93.85 202	96.48 261

GBi-Net		94.49 201	93.80 204	96.56 192	98.21 128	95.00 154	98.82 79	98.18 182	92.46 197	94.09 198	97.07 216	81.16 269	97.95 265	92.08 207	92.14 225	96.72 224

test1		94.49 201	93.80 204	96.56 192	98.21 128	95.00 154	98.82 79	98.18 182	92.46 197	94.09 198	97.07 216	81.16 269	97.95 265	92.08 207	92.14 225	96.72 224

v8		94.47 203	93.77 207	96.57 191	96.36 238	94.83 171	99.05 49	98.19 179	91.92 219	93.16 225	96.97 231	88.82 158	98.48 214	91.69 220	87.79 275	96.39 263

FMVSNet2		94.47 203	93.61 217	97.04 151	98.21 128	96.43 98	98.79 93	98.27 165	92.46 197	93.50 218	97.09 214	81.16 269	98.00 263	91.09 228	91.93 229	96.70 228

Patchmatch-test		94.42 205	93.68 214	96.63 182	97.60 164	91.76 247	94.83 311	97.49 250	89.45 276	94.14 196	97.10 212	88.99 145	98.83 184	85.37 294	98.13 127	99.29 96

PEN-MVS		94.42 205	93.73 211	96.49 198	96.28 251	94.84 169	99.17 34	99.00 25	93.51 165	92.23 250	97.83 168	86.10 224	97.90 268	92.55 199	86.92 286	96.74 221

v144192		94.39 207	93.70 212	96.48 199	96.06 264	94.35 200	98.58 129	98.16 189	91.45 230	94.33 181	97.02 226	87.50 203	98.45 221	91.08 229	89.11 255	96.63 242

Baseline_NR-MVSNet		94.35 208	93.81 203	95.96 224	96.20 256	94.05 208	98.61 126	96.67 294	91.44 231	93.85 208	97.60 186	88.57 170	98.14 254	94.39 146	86.93 285	95.68 282

v1192		94.32 209	93.58 219	96.53 195	96.10 262	94.45 195	98.50 144	98.17 187	91.54 228	94.19 193	97.06 219	86.95 211	98.43 226	90.14 243	89.57 248	96.70 228

ACMH+	92.99 14	94.30 210	93.77 207	95.88 228	97.81 154	92.04 243	98.71 110	98.37 154	93.99 138	90.60 266	98.47 113	80.86 274	99.05 154	92.75 193	92.40 224	96.55 253

v148		94.29 211	93.76 209	95.91 226	96.10 262	92.93 233	98.58 129	97.97 217	92.59 195	93.47 219	96.95 233	88.53 173	98.32 243	92.56 198	87.06 284	96.49 260

v10		94.29 211	93.55 220	96.51 197	96.39 234	94.80 176	98.99 54	98.19 179	91.35 237	93.02 231	96.99 229	88.09 184	98.41 234	90.50 240	88.41 268	96.33 267

MVP-Stereo		94.28 213	93.92 197	95.35 252	94.95 293	92.60 237	97.97 202	97.65 229	91.61 226	90.68 265	97.09 214	86.32 220	98.42 227	89.70 255	99.34 81	95.02 291
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		94.21 214	94.00 192	94.85 264	95.60 281	89.22 279	98.89 67	97.43 255	95.29 93	92.18 252	98.52 110	82.86 263	98.59 199	93.46 170	91.76 232	96.74 221

v1921920		94.20 215	93.47 226	96.40 206	95.98 267	94.08 207	98.52 139	98.15 190	91.33 238	94.25 189	97.20 208	86.41 218	98.42 227	90.04 248	89.39 253	96.69 233

v7n		94.19 216	93.43 227	96.47 200	95.90 270	94.38 199	99.26 17	98.34 157	91.99 218	92.76 236	97.13 211	88.31 177	98.52 210	89.48 260	87.70 276	96.52 256

tpm2		94.19 216	93.76 209	95.46 242	97.23 189	89.04 282	97.31 257	96.85 289	87.08 291	96.21 138	96.79 245	83.75 260	98.74 190	92.43 203	96.23 173	98.59 149

v52		94.18 218	93.52 222	96.13 220	95.95 269	94.29 202	99.23 21	98.21 175	91.42 232	92.84 234	96.89 240	87.85 192	98.53 209	91.51 223	87.81 273	95.57 285

V4		94.18 218	93.52 222	96.13 220	95.89 271	94.31 201	99.23 21	98.22 174	91.42 232	92.82 235	96.89 240	87.93 188	98.52 210	91.51 223	87.81 273	95.58 284

TESTMET0.1,1		94.18 218	93.69 213	95.63 236	96.92 206	89.12 280	96.91 272	94.78 315	93.17 176	94.88 156	96.45 256	78.52 286	98.92 172	93.09 179	98.50 112	98.85 133

dp		94.15 221	93.90 199	94.90 262	97.31 185	86.82 302	96.97 268	97.19 273	91.22 245	96.02 143	96.61 253	85.51 232	99.02 161	90.00 249	94.30 187	98.85 133

tpm		94.13 222	93.80 204	95.12 257	96.50 228	87.91 296	97.44 243	95.89 307	92.62 193	96.37 136	96.30 260	84.13 253	98.30 247	93.24 175	91.66 234	99.14 112

IterMVS		94.09 223	93.85 202	94.80 267	97.99 144	90.35 267	97.18 263	98.12 195	93.68 159	92.46 246	97.34 200	84.05 254	97.41 282	92.51 201	91.33 235	96.62 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 224	93.51 224	95.80 231	96.77 214	89.70 272	96.91 272	95.21 310	92.89 187	94.83 159	95.72 277	77.69 290	98.97 163	93.06 180	98.50 112	98.72 139

test0.0.03 1		94.08 224	93.51 224	95.80 231	95.53 284	92.89 234	97.38 248	95.97 304	95.11 100	92.51 244	96.66 249	87.71 195	96.94 288	87.03 283	93.67 204	97.57 178

v1240		94.06 226	93.29 230	96.34 211	96.03 266	93.90 211	98.44 149	98.17 187	91.18 246	94.13 197	97.01 228	86.05 225	98.42 227	89.13 265	89.50 251	96.70 228

X-MVStestdata		94.06 226	92.30 243	99.34 13	99.70 15	98.35 22	99.29 14	98.88 46	97.40 14	98.46 45	43.50 332	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

DTE-MVSNet		93.98 228	93.26 231	96.14 219	96.06 264	94.39 198	99.20 31	98.86 51	93.06 179	91.78 255	97.81 170	85.87 228	97.58 278	90.53 239	86.17 291	96.46 262

pm-mvs1		93.94 229	93.06 232	96.59 187	96.49 229	95.16 149	98.95 59	98.03 216	92.32 212	91.08 260	97.84 165	84.54 248	98.41 234	92.16 205	86.13 293	96.19 270

tpmp4_e23		93.91 230	93.42 229	95.38 250	97.62 162	88.59 289	97.52 241	97.34 262	87.94 287	94.17 195	96.79 245	82.91 262	99.05 154	90.62 238	95.91 178	98.50 152

MS-PatchMatch		93.84 231	93.63 215	94.46 276	96.18 257	89.45 275	97.76 225	98.27 165	92.23 215	92.13 253	97.49 191	79.50 282	98.69 191	89.75 253	99.38 79	95.25 287

v748		93.75 232	93.06 232	95.82 230	95.73 277	92.64 236	99.25 19	98.24 172	91.60 227	92.22 251	96.52 254	87.60 200	98.46 219	90.64 237	85.72 294	96.36 265

EU-MVSNet		93.66 233	94.14 183	92.25 293	95.96 268	83.38 307	98.52 139	98.12 195	94.69 114	92.61 239	98.13 143	87.36 205	96.39 306	91.82 216	90.00 244	96.98 195

pmmvs5		93.65 234	92.97 234	95.68 235	95.49 285	92.37 238	98.20 175	97.28 268	89.66 272	92.58 240	97.26 204	82.14 265	98.09 258	93.18 178	90.95 239	96.58 248

tpm cat1		93.36 235	92.80 236	95.07 259	97.58 166	87.97 295	96.76 281	97.86 221	82.17 312	93.53 215	96.04 269	86.13 222	99.13 142	89.24 263	95.87 179	98.10 167

JIA-IIPM		93.35 236	92.49 240	95.92 225	96.48 230	90.65 263	95.01 306	96.96 284	85.93 298	96.08 140	87.33 319	87.70 197	98.78 189	91.35 226	95.58 182	98.34 161

SixPastTwentyTwo		93.34 237	92.86 235	94.75 268	95.67 279	89.41 277	98.75 100	96.67 294	93.89 143	90.15 269	98.25 136	80.87 273	98.27 250	90.90 233	90.64 240	96.57 250

USDC		93.33 238	92.71 238	95.21 254	96.83 213	90.83 258	96.91 272	97.50 244	93.84 146	90.72 264	98.14 142	77.69 290	98.82 185	89.51 259	93.21 218	95.97 275

IB-MVS	91.98 17	93.27 239	91.97 246	97.19 142	97.47 173	93.41 225	97.09 266	95.99 303	93.32 172	92.47 245	95.73 275	78.06 288	99.53 116	94.59 142	82.98 299	98.62 148
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MIMVSNet		93.26 240	92.21 244	96.41 205	97.73 158	93.13 231	95.65 302	97.03 278	91.27 243	94.04 201	96.06 268	75.33 300	97.19 285	86.56 285	96.23 173	98.92 131

Patchmtry		93.22 241	92.35 242	95.84 229	96.77 214	93.09 232	94.66 313	97.56 232	87.37 290	92.90 233	96.24 261	88.15 181	97.90 268	87.37 281	90.10 243	96.53 255

FMVSNet1		93.19 242	92.07 245	96.56 192	97.54 169	95.00 154	98.82 79	98.18 182	90.38 253	92.27 249	97.07 216	73.68 308	97.95 265	89.36 262	91.30 236	96.72 224

LF4IMVS		93.14 243	92.79 237	94.20 279	95.88 272	88.67 287	97.66 233	97.07 275	93.81 148	91.71 256	97.65 182	77.96 289	98.81 186	91.47 225	91.92 230	95.12 288

testgi		93.06 244	92.45 241	94.88 263	96.43 232	89.90 269	98.75 100	97.54 237	95.60 79	91.63 257	97.91 158	74.46 306	97.02 287	86.10 288	93.67 204	97.72 175

PatchT		93.06 244	91.97 246	96.35 209	96.69 220	92.67 235	94.48 314	97.08 274	86.62 292	97.08 102	92.23 314	87.94 187	97.90 268	78.89 308	96.69 155	98.49 153

TransMVSNet (Re)		92.67 246	91.51 250	96.15 218	96.58 224	94.65 184	98.90 63	96.73 290	90.86 248	89.46 274	97.86 162	85.62 231	98.09 258	86.45 286	81.12 304	95.71 281

K. test v3		92.55 247	91.91 248	94.48 274	95.64 280	89.24 278	99.07 48	94.88 314	94.04 135	86.78 286	97.59 187	77.64 293	97.64 276	92.08 207	89.43 252	96.57 250

DSMNet-mixed		92.52 248	92.58 239	92.33 292	94.15 300	82.65 310	98.30 167	94.26 320	89.08 281	92.65 238	95.73 275	85.01 240	95.76 309	86.24 287	97.76 140	98.59 149

RPMNet		92.52 248	91.17 251	96.59 187	97.00 202	93.43 223	94.96 307	97.26 270	82.27 311	96.93 110	92.12 315	86.98 210	97.88 272	76.32 313	96.65 157	98.46 154

TinyColmap		92.31 250	91.53 249	94.65 270	96.92 206	89.75 271	96.92 270	96.68 293	90.45 251	89.62 272	97.85 164	76.06 298	98.81 186	86.74 284	92.51 223	95.41 286

gg-mvs-nofinetune		92.21 251	90.58 265	97.13 146	96.75 217	95.09 152	95.85 299	89.40 330	85.43 301	94.50 167	81.98 323	80.80 275	98.40 240	92.16 205	98.33 120	97.88 171

Test4		92.21 251	90.34 267	97.82 115	92.83 306	95.87 127	97.94 205	98.05 215	94.50 124	82.12 309	94.48 288	59.54 324	98.54 203	95.39 123	98.22 123	99.06 120

v18		92.10 253	90.97 253	95.50 239	96.34 241	94.85 161	98.82 79	97.52 238	89.99 260	85.31 297	93.26 296	88.90 152	96.92 289	88.82 270	79.77 308	94.73 294

v17		92.08 254	90.94 254	95.48 241	96.34 241	94.83 171	98.81 85	97.52 238	89.95 262	85.32 295	93.24 297	88.91 151	96.91 290	88.76 271	79.63 309	94.71 296

v16		92.08 254	90.94 254	95.49 240	96.38 237	94.84 169	98.81 85	97.51 241	89.94 263	85.25 298	93.28 295	88.86 153	96.91 290	88.70 272	79.78 307	94.72 295

v15		91.94 256	90.77 258	95.43 246	96.31 249	94.83 171	98.77 96	97.50 244	89.92 264	85.13 299	93.08 300	88.76 164	96.86 292	88.40 274	79.10 311	94.61 300

V14		91.93 257	90.76 259	95.42 249	96.33 245	94.81 175	98.77 96	97.51 241	89.86 266	85.09 300	93.13 298	88.80 162	96.83 294	88.32 275	79.06 313	94.60 301

V9		91.91 258	90.73 260	95.45 243	96.32 248	94.80 176	98.77 96	97.50 244	89.81 267	85.03 302	93.08 300	88.76 164	96.86 292	88.24 276	79.03 314	94.69 297

v12		91.89 259	90.70 261	95.43 246	96.31 249	94.80 176	98.76 99	97.50 244	89.76 268	84.95 303	93.00 303	88.82 158	96.82 296	88.23 277	79.00 315	94.68 299

v13		91.88 260	90.69 262	95.43 246	96.33 245	94.78 181	98.75 100	97.50 244	89.68 271	84.93 304	92.98 304	88.84 156	96.83 294	88.14 278	79.09 312	94.69 297

v11		91.85 261	90.68 263	95.36 251	96.34 241	94.74 183	98.80 88	97.43 255	89.60 274	85.09 300	93.03 302	88.53 173	96.75 297	87.37 281	79.96 306	94.58 302

FMVSNet5		91.81 262	90.92 256	94.49 273	97.21 191	92.09 241	98.00 200	97.55 236	89.31 279	90.86 263	95.61 280	74.48 305	95.32 311	85.57 292	89.70 246	96.07 273

pmmvs6		91.77 263	90.63 264	95.17 256	94.69 298	91.24 255	98.67 119	97.92 219	86.14 295	89.62 272	97.56 190	75.79 299	98.34 241	90.75 235	84.56 298	95.94 276

Anonymous20231206		91.66 264	91.10 252	93.33 286	94.02 302	87.35 299	98.58 129	97.26 270	90.48 249	90.16 268	96.31 259	83.83 259	96.53 304	79.36 306	89.90 245	96.12 271

Patchmatch-RL test		91.49 265	90.85 257	93.41 285	91.37 310	84.40 304	92.81 319	95.93 306	91.87 222	87.25 284	94.87 285	88.99 145	96.53 304	92.54 200	82.00 301	99.30 94

test_0402		91.32 266	90.27 268	94.48 274	96.60 223	91.12 256	98.50 144	97.22 272	86.10 296	88.30 281	96.98 230	77.65 292	97.99 264	78.13 310	92.94 220	94.34 304

PVSNet_0	88.72 19	91.28 267	90.03 270	95.00 260	97.99 144	87.29 300	94.84 310	98.50 134	92.06 217	89.86 270	95.19 281	79.81 281	99.39 125	92.27 204	69.79 323	98.33 162

EG-PatchMatch MVS		91.13 268	90.12 269	94.17 281	94.73 297	89.00 283	98.13 186	97.81 222	89.22 280	85.32 295	96.46 255	67.71 317	98.42 227	87.89 279	93.82 203	95.08 289

LP		91.12 269	89.99 271	94.53 272	96.35 240	88.70 286	93.86 318	97.35 261	84.88 303	90.98 261	94.77 286	84.40 250	97.43 281	75.41 316	91.89 231	97.47 179

TDRefinement		91.06 270	89.68 273	95.21 254	85.35 322	91.49 251	98.51 143	97.07 275	91.47 229	88.83 279	97.84 165	77.31 294	99.09 151	92.79 192	77.98 316	95.04 290

UnsupCasMVSNet_eth		90.99 271	89.92 272	94.19 280	94.08 301	89.83 270	97.13 265	98.67 101	93.69 157	85.83 292	96.19 265	75.15 301	96.74 298	89.14 264	79.41 310	96.00 274

test20.03		90.89 272	90.38 266	92.43 291	93.48 303	88.14 294	98.33 160	97.56 232	93.40 169	87.96 282	96.71 248	80.69 276	94.13 315	79.15 307	86.17 291	95.01 292

MDA-MVSNet_test_wron		90.71 273	89.38 276	94.68 269	94.83 295	90.78 260	97.19 262	97.46 251	87.60 288	72.41 321	95.72 277	86.51 216	96.71 301	85.92 290	86.80 288	96.56 252

YYNet1		90.70 274	89.39 275	94.62 271	94.79 296	90.65 263	97.20 261	97.46 251	87.54 289	72.54 320	95.74 274	86.51 216	96.66 302	86.00 289	86.76 289	96.54 254

testing_2		90.61 275	88.50 282	96.95 157	90.08 314	95.57 134	97.69 230	98.06 212	93.02 181	76.55 316	92.48 312	61.18 323	98.44 224	95.45 122	91.98 228	96.84 212

pmmvs-eth3d		90.36 276	89.05 279	94.32 278	91.10 311	92.12 240	97.63 236	96.95 285	88.86 282	84.91 305	93.13 298	78.32 287	96.74 298	88.70 272	81.81 303	94.09 308

new_pmnet		90.06 277	89.00 280	93.22 289	94.18 299	88.32 293	96.42 292	96.89 286	86.19 294	85.67 294	93.62 293	77.18 295	97.10 286	81.61 301	89.29 254	94.23 305

MDA-MVSNet-bldmvs		89.97 278	88.35 284	94.83 266	95.21 290	91.34 252	97.64 234	97.51 241	88.36 285	71.17 322	96.13 267	79.22 284	96.63 303	83.65 296	86.27 290	96.52 256

CMPMVS	66.06 21	89.70 279	89.67 274	89.78 298	93.19 304	76.56 317	97.00 267	98.35 156	80.97 314	81.57 311	97.75 173	74.75 304	98.61 197	89.85 250	93.63 206	94.17 306
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 280	88.28 285	93.82 282	92.81 307	91.08 257	98.01 198	97.45 253	87.95 286	87.90 283	95.87 273	67.63 318	94.56 314	78.73 309	88.18 270	95.83 278

MVS-HIRNet		89.46 281	88.40 283	92.64 290	97.58 166	82.15 311	94.16 317	93.05 326	75.73 320	90.90 262	82.52 322	79.42 283	98.33 242	83.53 297	98.68 102	97.43 180

OpenMVS_ROB	86.42 20	89.00 282	87.43 288	93.69 283	93.08 305	89.42 276	97.91 209	96.89 286	78.58 317	85.86 291	94.69 287	69.48 314	98.29 249	77.13 311	93.29 216	93.36 313

testus		88.91 283	89.08 278	88.40 301	91.39 309	76.05 318	96.56 287	96.48 298	89.38 278	89.39 275	95.17 283	70.94 312	93.56 318	77.04 312	95.41 183	95.61 283

testpf		88.74 284	89.09 277	87.69 302	95.78 275	83.16 309	84.05 329	94.13 323	85.22 302	90.30 267	94.39 290	74.92 303	95.80 308	89.77 251	93.28 217	84.10 323

test2356		88.68 285	88.61 281	88.87 300	89.90 315	78.23 315	95.11 305	96.66 296	88.66 284	89.06 277	94.33 292	73.14 310	92.56 322	75.56 315	95.11 185	95.81 279

new-patchmatchnet		88.50 286	87.45 287	91.67 295	90.31 313	85.89 303	97.16 264	97.33 265	89.47 275	83.63 307	92.77 308	76.38 296	95.06 313	82.70 298	77.29 317	94.06 309

PM-MVS		87.77 287	86.55 289	91.40 296	91.03 312	83.36 308	96.92 270	95.18 312	91.28 242	86.48 289	93.42 294	53.27 325	96.74 298	89.43 261	81.97 302	94.11 307

UnsupCasMVSNet_bld		87.17 288	85.12 291	93.31 287	91.94 308	88.77 284	94.92 309	98.30 162	84.30 306	82.30 308	90.04 316	63.96 322	97.25 284	85.85 291	74.47 322	93.93 311

N_pmnet		87.12 289	87.77 286	85.17 309	95.46 286	61.92 331	97.37 250	70.66 338	85.83 299	88.73 280	96.04 269	85.33 237	97.76 274	80.02 303	90.48 241	95.84 277

pmmvs3		86.67 290	84.86 292	92.11 294	88.16 317	87.19 301	96.63 284	94.75 316	79.88 316	87.22 285	92.75 309	66.56 319	95.20 312	81.24 302	76.56 319	93.96 310

test1235678		86.26 291	85.81 290	87.62 303	86.97 320	75.00 322	96.55 289	96.32 301	86.08 297	81.32 312	92.98 304	73.10 311	92.05 323	71.64 319	87.32 280	95.81 279

1111		84.94 292	84.30 293	86.86 304	87.59 318	75.10 320	96.63 284	96.43 299	82.53 309	80.75 313	92.91 306	68.94 315	93.79 316	68.24 322	84.66 297	91.70 315

Anonymous20231211		83.69 293	81.50 295	90.26 297	89.23 316	80.10 314	97.97 202	97.06 277	72.79 322	82.05 310	92.57 310	50.28 326	96.32 307	76.15 314	75.38 320	94.37 303

test12356		83.47 294	83.37 294	83.78 310	84.43 323	70.09 327	95.12 304	95.60 309	82.98 307	78.89 315	92.43 313	64.99 320	91.41 325	70.36 320	85.55 296	89.82 317

testmv		78.74 295	77.35 296	82.89 312	78.16 331	69.30 328	95.87 298	94.65 317	81.11 313	70.98 323	87.11 320	46.31 327	90.42 326	65.28 325	76.72 318	88.95 318

LCM-MVSNet		78.70 296	76.24 300	86.08 306	77.26 332	71.99 325	94.34 315	96.72 291	61.62 326	76.53 317	89.33 317	33.91 335	92.78 321	81.85 300	74.60 321	93.46 312

Gipuma		78.40 297	76.75 298	83.38 311	95.54 283	80.43 313	79.42 330	97.40 258	64.67 324	73.46 319	80.82 325	45.65 329	93.14 320	66.32 324	87.43 278	76.56 328
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 298	75.44 301	85.46 307	82.54 324	74.95 323	94.23 316	93.08 325	72.80 321	74.68 318	87.38 318	36.36 333	91.56 324	73.95 317	63.94 324	89.87 316

FPMVS		77.62 299	77.14 297	79.05 314	79.25 328	60.97 332	95.79 300	95.94 305	65.96 323	67.93 324	94.40 289	37.73 332	88.88 328	68.83 321	88.46 267	87.29 319

no-one		74.41 300	70.76 302	85.35 308	79.88 327	76.83 316	94.68 312	94.22 321	80.33 315	63.81 325	79.73 326	35.45 334	93.36 319	71.78 318	36.99 331	85.86 322

.test1245		73.05 301	76.31 299	63.27 322	87.59 318	75.10 320	96.63 284	96.43 299	82.53 309	80.75 313	92.91 306	68.94 315	93.79 316	68.24 322	12.72 334	20.91 332

ANet_high		69.08 302	65.37 304	80.22 313	65.99 335	71.96 326	90.91 323	90.09 329	82.62 308	49.93 331	78.39 327	29.36 336	81.75 331	62.49 328	38.52 330	86.95 321

tmp_tt		68.90 303	66.97 303	74.68 318	50.78 337	59.95 333	87.13 325	83.47 336	38.80 332	62.21 326	96.23 263	64.70 321	76.91 335	88.91 269	30.49 332	87.19 320

PNet_i23d		67.70 304	65.07 305	75.60 316	78.61 329	59.61 334	89.14 324	88.24 332	61.83 325	52.37 329	80.89 324	18.91 337	84.91 330	62.70 327	52.93 326	82.28 324

PMVS	61.03 23	65.95 305	63.57 307	73.09 319	57.90 336	51.22 337	85.05 328	93.93 324	54.45 328	44.32 332	83.57 321	13.22 338	89.15 327	58.68 329	81.00 305	78.91 327
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 306	64.25 306	67.02 320	82.28 325	59.36 335	91.83 322	85.63 334	52.69 329	60.22 327	77.28 328	41.06 331	80.12 333	46.15 331	41.14 328	61.57 330

EMVS		64.07 307	63.26 308	66.53 321	81.73 326	58.81 336	91.85 321	84.75 335	51.93 331	59.09 328	75.13 329	43.32 330	79.09 334	42.03 332	39.47 329	61.69 329

wuykxyi23d		63.73 308	58.86 310	78.35 315	67.62 334	67.90 329	86.56 326	87.81 333	58.26 327	42.49 333	70.28 331	11.55 340	85.05 329	63.66 326	41.50 327	82.11 325

MVE	62.14 22	63.28 309	59.38 309	74.99 317	74.33 333	65.47 330	85.55 327	80.50 337	52.02 330	51.10 330	75.00 330	10.91 342	80.50 332	51.60 330	53.40 325	78.99 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
pcd1.5k->3k		39.42 310	41.78 311	32.35 323	96.17 258	0.00 341	0.00 332	98.54 122	0.00 336	0.00 337	0.00 338	87.78 194	0.00 339	0.00 336	93.56 208	97.06 190

wuyk23d		30.17 311	30.18 313	30.16 324	78.61 329	43.29 338	66.79 331	14.21 339	17.31 333	14.82 336	11.93 337	11.55 340	41.43 336	37.08 333	19.30 333	5.76 334

cdsmvs_eth3d_5k		23.98 312	31.98 312	0.00 327	0.00 340	0.00 341	0.00 332	98.59 112	0.00 336	0.00 337	98.61 100	90.60 123	0.00 339	0.00 336	0.00 337	0.00 335

testmvs		21.48 313	24.95 314	11.09 326	14.89 338	6.47 340	96.56 287	9.87 340	7.55 334	17.93 334	39.02 333	9.43 343	5.90 338	16.56 335	12.72 334	20.91 332

test123		20.95 314	23.72 315	12.64 325	13.54 339	8.19 339	96.55 289	6.13 341	7.48 335	16.74 335	37.98 334	12.97 339	6.05 337	16.69 334	5.43 336	23.68 331

ab-mvs-re		8.20 315	10.94 316	0.00 327	0.00 340	0.00 341	0.00 332	0.00 342	0.00 336	0.00 337	98.43 115	0.00 344	0.00 339	0.00 336	0.00 337	0.00 335

pcd_1.5k_mvsjas		7.88 316	10.50 317	0.00 327	0.00 340	0.00 341	0.00 332	0.00 342	0.00 336	0.00 337	0.00 338	94.51 60	0.00 339	0.00 336	0.00 337	0.00 335

sosnet-low-res		0.00 317	0.00 318	0.00 327	0.00 340	0.00 341	0.00 332	0.00 342	0.00 336	0.00 337	0.00 338	0.00 344	0.00 339	0.00 336	0.00 337	0.00 335

sosnet		0.00 317	0.00 318	0.00 327	0.00 340	0.00 341	0.00 332	0.00 342	0.00 336	0.00 337	0.00 338	0.00 344	0.00 339	0.00 336	0.00 337	0.00 335

uncertanet		0.00 317	0.00 318	0.00 327	0.00 340	0.00 341	0.00 332	0.00 342	0.00 336	0.00 337	0.00 338	0.00 344	0.00 339	0.00 336	0.00 337	0.00 335

Regformer		0.00 317	0.00 318	0.00 327	0.00 340	0.00 341	0.00 332	0.00 342	0.00 336	0.00 337	0.00 338	0.00 344	0.00 339	0.00 336	0.00 337	0.00 335

uanet		0.00 317	0.00 318	0.00 327	0.00 340	0.00 341	0.00 332	0.00 342	0.00 336	0.00 337	0.00 338	0.00 344	0.00 339	0.00 336	0.00 337	0.00 335

sam_mvs1												89.45 134

sam_mvs												88.99 145

semantic-postprocess				94.85 264	97.98 146	90.56 265		98.11 200	93.75 149	92.58 240	97.48 192	83.91 256	97.41 282	92.48 202	91.30 236	96.58 248

ambc				89.49 299	86.66 321	75.78 319	92.66 320	96.72 291		86.55 288	92.50 311	46.01 328	97.90 268	90.32 241	82.09 300	94.80 293

MTGPA								98.74 76

test_post1							96.68 283				30.43 336	87.85 192	98.69 191	92.59 197

test_post											31.83 335	88.83 157	98.91 173

patchmatchnet-post											95.10 284	89.42 135	98.89 177

GG-mvs-BLEND				96.59 187	96.34 241	94.98 157	96.51 291	88.58 331		93.10 230	94.34 291	80.34 280	98.05 260	89.53 258	96.99 151	96.74 221

MTMP								94.14 322

gm-plane-assit					95.88 272	87.47 298			89.74 270		96.94 235		99.19 137	93.32 174

test9_res														96.39 94	99.57 55	99.69 35

TEST9					99.31 47	98.50 12	97.92 206	98.73 81	92.63 192	97.74 81	98.68 94	96.20 12	99.80 57

test_8					99.29 55	98.44 14	97.89 214	98.72 83	92.98 183	97.70 84	98.66 97	96.20 12	99.80 57

agg_prior2														95.87 106	99.57 55	99.68 41

agg_prior					99.30 52	98.38 17		98.72 83		97.57 93			99.81 50

TestCases				96.99 153	99.25 64	93.21 229		98.18 182	91.36 235	93.52 216	98.77 87	84.67 243	99.72 86	89.70 255	97.87 134	98.02 169

test_prior4						98.01 41	97.86 217

test_prior2							97.80 222		96.12 63	97.89 75	98.69 92	95.96 25		96.89 71	99.60 49

test_prior				99.19 28	99.31 47	98.22 30		98.84 53					99.70 91			99.65 50

旧先验2							97.57 239		91.30 240	98.67 36			99.80 57	95.70 115

新几何2							97.64 234

新几何1				99.16 35	99.34 39	98.01 41		98.69 91	90.06 259	98.13 56	98.95 71	94.60 58	99.89 27	91.97 213	99.47 69	99.59 61

旧先验1					99.29 55	97.48 59		98.70 90			99.09 52	95.56 35			99.47 69	99.61 56

无先验							97.58 238	98.72 83	91.38 234				99.87 35	93.36 172		99.60 59

原ACMM2							97.67 232

原ACMM1				98.65 65	99.32 45	96.62 89		98.67 101	93.27 175	97.81 77	98.97 65	95.18 47	99.83 43	93.84 161	99.46 72	99.50 71

test222					99.23 70	97.17 72	97.40 246	98.66 104	88.68 283	98.05 60	98.96 69	94.14 69			99.53 65	99.61 56

testdata2													99.89 27	91.65 221

segment_acmp												96.85 3

testdata				98.26 89	99.20 74	95.36 142		98.68 94	91.89 220	98.60 41	99.10 48	94.44 65	99.82 48	94.27 151	99.44 74	99.58 63

testdata1							97.32 256		96.34 57

test12				99.18 32	99.16 76	98.19 32		98.53 125		98.07 59		95.13 49	99.72 86		99.56 61	99.63 55

plane_prior7					97.42 178	94.63 186

plane_prior6					97.35 183	94.61 189						87.09 207

plane_prior5								98.56 119					99.03 159	96.07 97	94.27 188	96.92 199

plane_prior4											98.28 131

plane_prior3						94.61 189			97.02 39	95.34 148

plane_prior2							98.80 88		97.28 21

plane_prior1					97.37 182

plane_prior						94.60 191	98.44 149		96.74 46						94.22 190

n2								0.00 342

nn								0.00 342

door-mid								94.37 319

lessismore_v0				94.45 277	94.93 294	88.44 291		91.03 328		86.77 287	97.64 184	76.23 297	98.42 227	90.31 242	85.64 295	96.51 258

LGP-MVS_train				96.47 200	97.46 174	93.54 220		98.54 122	94.67 116	94.36 179	98.77 87	85.39 233	99.11 147	95.71 113	94.15 194	96.76 219

test11								98.66 104

door								94.64 318

HQP5-MVS						94.25 204

HQP-NCC					97.20 192		98.05 194		96.43 54	94.45 169

ACMP_Plane					97.20 192		98.05 194		96.43 54	94.45 169

BP-MVS														95.30 125

HQP4-MVS										94.45 169			98.96 166			96.87 209

HQP3-MVS								98.46 139							94.18 192

HQP2-MVS												86.75 213

NP-MVS					97.28 186	94.51 194					97.73 174

MDTV_nov1_ep13_2view						84.26 305	96.89 276		90.97 247	97.90 74		89.89 132		93.91 159		99.18 108

MDTV_nov1_ep13			95.40 129		97.48 172	88.34 292	96.85 278	97.29 267	93.74 151	97.48 96	97.26 204	89.18 141	99.05 154	91.92 215	97.43 146

ACMMP++_ref															92.97 219

ACMMP++															93.61 207

Test By Simon												94.64 57

ITE_SJBPF				95.44 244	97.42 178	91.32 253		97.50 244	95.09 103	93.59 212	98.35 123	81.70 268	98.88 178	89.71 254	93.39 213	96.12 271

DeepMVS_CX				86.78 305	97.09 200	72.30 324		95.17 313	75.92 319	84.34 306	95.19 281	70.58 313	95.35 310	79.98 305	89.04 257	92.68 314

High-resolution multi-view benchmark