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.

region2R		97.07 16	96.84 17	97.77 21	99.46 1	93.79 35	98.52 10	98.24 27	93.19 61	97.14 21	98.34 25	91.59 37	99.87 5	95.46 44	99.59 7	99.64 4

ACMMPR		97.07 16	96.84 17	97.79 18	99.44 2	93.88 31	98.52 10	98.31 21	93.21 58	97.15 20	98.33 28	91.35 39	99.86 6	95.63 39	99.59 7	99.62 5

HFP-MVS		97.14 13	96.92 14	97.83 14	99.42 3	94.12 25	98.52 10	98.32 19	93.21 58	97.18 18	98.29 34	92.08 26	99.83 13	95.63 39	99.59 7	99.54 17

#test#		97.02 19	96.75 24	97.83 14	99.42 3	94.12 25	98.15 29	98.32 19	92.57 81	97.18 18	98.29 34	92.08 26	99.83 13	95.12 49	99.59 7	99.54 17

HSP-MVS		97.53 4	97.49 4	97.63 33	99.40 5	93.77 38	98.53 9	97.85 87	95.55 5	98.56 3	97.81 59	93.90 4	99.65 39	96.62 13	99.21 48	99.48 26

mPP-MVS		96.86 25	96.60 27	97.64 31	99.40 5	93.44 45	98.50 13	98.09 48	93.27 57	95.95 58	98.33 28	91.04 43	99.88 3	95.20 46	99.57 11	99.60 8

MP-MVS		96.77 29	96.45 34	97.72 24	99.39 7	93.80 34	98.41 17	98.06 56	93.37 53	95.54 73	98.34 25	90.59 50	99.88 3	94.83 59	99.54 13	99.49 24

XVS		97.18 10	96.96 12	97.81 16	99.38 8	94.03 29	98.59 7	98.20 30	94.85 17	96.59 35	98.29 34	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

X-MVStestdata		91.71 169	89.67 222	97.81 16	99.38 8	94.03 29	98.59 7	98.20 30	94.85 17	96.59 35	32.69 339	91.70 34	99.80 18	95.66 37	99.40 30	99.62 5

MPTG		97.07 16	96.77 23	97.97 10	99.37 10	94.42 16	97.15 114	98.08 49	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

MTAPA		97.08 15	96.78 22	97.97 10	99.37 10	94.42 16	97.24 102	98.08 49	95.07 14	96.11 49	98.59 5	90.88 47	99.90 1	96.18 27	99.50 19	99.58 9

HPM-MVS		96.69 32	96.45 34	97.40 39	99.36 12	93.11 53	98.87 1	98.06 56	91.17 120	96.40 43	97.99 48	90.99 44	99.58 53	95.61 41	99.61 6	99.49 24

PGM-MVS		96.81 27	96.53 30	97.65 29	99.35 13	93.53 43	97.65 65	98.98 1	92.22 86	97.14 21	98.44 14	91.17 41	99.85 9	94.35 66	99.46 23	99.57 11

CP-MVS		97.02 19	96.81 20	97.64 31	99.33 14	93.54 42	98.80 3	98.28 23	92.99 67	96.45 42	98.30 33	91.90 31	99.85 9	95.61 41	99.68 2	99.54 17

HPM-MVS_fast		96.51 37	96.27 38	97.22 50	99.32 15	92.74 61	98.74 4	98.06 56	90.57 140	96.77 28	98.35 22	90.21 54	99.53 68	94.80 61	99.63 4	99.38 37

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 3	97.12 116	98.07 54	93.54 51	96.08 51	97.69 66	93.86 5	99.71 27	96.50 17	99.39 32	99.55 15

CPTT-MVS		95.57 58	95.19 59	96.70 61	99.27 17	91.48 95	98.33 20	98.11 44	87.79 212	95.17 77	98.03 44	87.09 90	99.61 45	93.51 81	99.42 28	99.02 62

TSAR-MVS + MP.		97.42 5	97.33 6	97.69 27	99.25 18	94.24 21	98.07 34	97.85 87	93.72 45	98.57 2	98.35 22	93.69 7	99.40 85	97.06 3	99.46 23	99.44 30

CSCG		96.05 49	95.91 45	96.46 77	99.24 19	90.47 128	98.30 21	98.57 11	89.01 174	93.97 95	97.57 79	92.62 16	99.76 21	94.66 64	99.27 43	99.15 53

ACMMP		96.27 44	95.93 44	97.28 45	99.24 19	92.62 65	98.25 25	98.81 3	92.99 67	94.56 84	98.39 20	88.96 63	99.85 9	94.57 65	97.63 94	99.36 39
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
MP-MVS-pluss		96.70 31	96.27 38	97.98 9	99.23 21	94.71 11	96.96 126	98.06 56	90.67 131	95.55 72	98.78 2	91.07 42	99.86 6	96.58 15	99.55 12	99.38 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 22	94.19 22	97.03 119	98.08 49	88.35 198	95.09 78	97.65 70	89.97 57	99.48 75	92.08 104	98.59 73	98.44 107

APDe-MVS		97.82 1	97.73 1	98.08 7	99.15 23	94.82 10	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 6	99.68 35	97.93 1	99.69 1	99.75 1

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 24	95.16 6	97.60 72	98.19 32	92.82 76	97.93 8	98.74 3	91.60 36	99.86 6	96.26 20	99.52 15	99.67 2

HPM-MVS++		97.34 7	96.97 11	98.47 1	99.08 25	96.16 1	97.55 77	97.97 77	95.59 4	96.61 33	97.89 50	92.57 17	99.84 12	95.95 32	99.51 17	99.40 33

114514_t		93.95 97	93.06 106	96.63 64	99.07 26	91.61 91	97.46 85	97.96 78	77.99 311	93.00 116	97.57 79	86.14 101	99.33 90	89.22 148	99.15 52	98.94 72

APD-MVS		96.95 22	96.60 27	98.01 8	99.03 27	94.93 9	97.72 58	98.10 46	91.50 109	98.01 6	98.32 30	92.33 21	99.58 53	94.85 58	99.51 17	99.53 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		96.81 27	96.71 25	97.12 54	99.01 28	92.31 71	97.98 40	98.06 56	93.11 64	97.44 13	98.55 9	90.93 45	99.55 63	96.06 29	99.25 44	99.51 21

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 29	94.44 15	96.35 189	97.88 82	86.98 233	96.65 32	97.89 50	91.99 30	99.47 76	92.26 95	99.46 23	99.39 34

CNVR-MVS		97.68 2	97.44 5	98.37 2	98.90 30	95.86 2	97.27 100	98.08 49	95.81 3	97.87 9	98.31 31	94.26 2	99.68 35	97.02 4	99.49 21	99.57 11

abl_6		96.40 40	96.21 40	96.98 58	98.89 31	92.20 76	97.89 43	98.03 65	93.34 56	97.22 17	98.42 16	87.93 77	99.72 26	95.10 50	99.07 59	99.02 62

PAPM_NR		95.01 69	94.59 70	96.26 89	98.89 31	90.68 123	97.24 102	97.73 93	91.80 103	92.93 121	96.62 122	89.13 62	99.14 104	89.21 149	97.78 91	98.97 68

NCCC		97.30 8	97.03 9	98.11 6	98.77 33	95.06 8	97.34 94	98.04 63	95.96 2	97.09 25	97.88 52	93.18 9	99.71 27	95.84 35	99.17 51	99.56 13

DP-MVS		92.76 137	91.51 158	96.52 69	98.77 33	90.99 112	97.38 92	96.08 223	82.38 286	89.29 209	97.87 53	83.77 124	99.69 33	81.37 275	96.69 120	98.89 78

MSLP-MVS++		96.94 23	97.06 8	96.59 67	98.72 35	91.86 86	97.67 62	98.49 12	94.66 27	97.24 16	98.41 19	92.31 24	98.94 123	96.61 14	99.46 23	98.96 69

TEST9					98.70 36	94.19 22	96.41 181	98.02 66	88.17 205	96.03 52	97.56 81	92.74 12	99.59 50

train_agg		96.30 43	95.83 46	97.72 24	98.70 36	94.19 22	96.41 181	98.02 66	88.58 190	96.03 52	97.56 81	92.73 13	99.59 50	95.04 51	99.37 37	99.39 34

test_8					98.67 38	94.06 28	96.37 188	98.01 68	88.58 190	95.98 57	97.55 83	92.73 13	99.58 53

agg_prior3		96.16 47	95.67 48	97.62 34	98.67 38	93.88 31	96.41 181	98.00 70	87.93 209	95.81 62	97.47 85	92.33 21	99.59 50	95.04 51	99.37 37	99.39 34

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 38	93.79 35	96.28 197	98.00 70	88.76 187	95.68 66	97.55 83	92.70 15	99.57 61	95.01 53	99.32 39	99.32 41

agg_prior					98.67 38	93.79 35		98.00 70		95.68 66			99.57 61

test_prior3		96.46 39	96.20 41	97.23 48	98.67 38	92.99 55	96.35 189	98.00 70	92.80 77	96.03 52	97.59 77	92.01 28	99.41 83	95.01 53	99.38 33	99.29 43

test_prior				97.23 48	98.67 38	92.99 55		98.00 70					99.41 83			99.29 43

DeepC-MVS_fast	93.89 2	96.93 24	96.64 26	97.78 19	98.64 44	94.30 18	97.41 86	98.04 63	94.81 22	96.59 35	98.37 21	91.24 40	99.64 44	95.16 47	99.52 15	99.42 32
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				97.32 42	98.60 45	93.59 41		97.75 91	81.58 293	95.75 65	97.85 56	90.04 56	99.67 37	86.50 202	99.13 54	98.69 89

原ACMM1				96.38 80	98.59 46	91.09 111		97.89 81	87.41 221	95.22 76	97.68 67	90.25 52	99.54 65	87.95 172	99.12 57	98.49 102

AdaColmap		94.34 84	93.68 88	96.31 84	98.59 46	91.68 90	96.59 172	97.81 89	89.87 148	92.15 133	97.06 99	83.62 126	99.54 65	89.34 144	98.07 84	97.70 138

PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 48	91.15 110	96.69 161	97.39 135	87.29 224	91.37 145	96.71 108	88.39 72	99.52 71	87.33 190	97.13 109	97.73 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1121		94.71 80	93.83 83	97.34 41	98.57 49	93.64 40	96.04 211	97.73 93	81.56 295	95.68 66	97.85 56	90.23 53	99.65 39	87.68 179	99.12 57	98.73 85

SD-MVS		97.41 6	97.53 2	97.06 55	98.57 49	94.46 14	97.92 42	98.14 39	94.82 21	99.01 1	98.55 9	94.18 3	97.41 265	96.94 5	99.64 3	99.32 41

test12				97.65 29	98.46 51	94.26 19		97.66 102		95.52 74		90.89 46	99.46 77		99.25 44	99.22 48

MVS_111021_HR		96.68 34	96.58 29	96.99 57	98.46 51	92.31 71	96.20 204	98.90 2	94.30 35	95.86 60	97.74 64	92.33 21	99.38 88	96.04 30	99.42 28	99.28 46

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 51	91.28 101	96.43 179	97.57 110	92.04 98	94.77 82	97.96 49	87.01 91	99.09 114	91.31 123	96.77 116	98.36 114

MG-MVS		95.61 57	95.38 54	96.31 84	98.42 54	90.53 126	96.04 211	97.48 118	93.47 52	95.67 69	98.10 40	89.17 61	99.25 94	91.27 124	98.77 68	99.13 55

PHI-MVS		96.77 29	96.46 33	97.71 26	98.40 55	94.07 27	98.21 28	98.45 15	89.86 149	97.11 24	98.01 46	92.52 19	99.69 33	96.03 31	99.53 14	99.36 39

F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 55	88.98 176	97.18 111	97.29 145	87.75 214	90.49 162	97.10 98	85.21 109	99.50 74	86.70 199	96.72 119	97.63 139

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 12	98.39 57	94.25 20	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 1	99.71 27	96.57 16	99.62 5	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
旧先验1					98.38 58	93.38 47		97.75 91			98.09 41	92.30 25			99.01 62	99.16 51

CNLPA		94.28 85	93.53 93	96.52 69	98.38 58	92.55 67	96.59 172	96.88 189	90.13 145	91.91 137	97.24 91	85.21 109	99.09 114	87.64 182	97.83 89	97.92 127

Regformer-3		96.85 26	96.80 21	97.01 56	98.34 60	92.02 82	96.96 126	97.76 90	95.01 16	97.08 26	98.42 16	91.71 33	99.54 65	96.80 9	99.13 54	99.48 26

Regformer-4		96.97 21	96.87 15	97.25 47	98.34 60	92.66 64	96.96 126	98.01 68	95.12 13	97.14 21	98.42 16	91.82 32	99.61 45	96.90 6	99.13 54	99.50 22

TAPA-MVS	90.10 7	92.30 154	91.22 168	95.56 114	98.33 62	89.60 148	96.79 146	97.65 104	81.83 290	91.52 142	97.23 92	87.94 76	98.91 125	71.31 311	98.37 77	98.17 118
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Regformer-1		97.10 14	96.96 12	97.54 36	98.32 63	93.48 44	96.83 139	97.99 75	95.20 12	97.46 12	98.25 37	92.48 20	99.58 53	96.79 11	99.29 41	99.55 15

Regformer-2		97.16 12	96.99 10	97.67 28	98.32 63	93.84 33	96.83 139	98.10 46	95.24 10	97.49 11	98.25 37	92.57 17	99.61 45	96.80 9	99.29 41	99.56 13

TSAR-MVS + GP.		96.69 32	96.49 31	97.27 46	98.31 65	93.39 46	96.79 146	96.72 195	94.17 36	97.44 13	97.66 69	92.76 11	99.33 90	96.86 8	97.76 93	99.08 60

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 66	89.38 163	95.18 251	98.48 14	85.60 253	93.76 97	97.11 97	83.15 132	99.61 45	91.33 122	98.72 70	99.19 49

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 161	98.27 66	89.46 157	96.73 151	98.36 16	90.17 144	94.36 87	95.24 187	88.02 74	99.58 53	93.44 84	90.72 207	94.36 271

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 66	89.46 157	95.47 239	98.36 16	88.84 181	94.36 87	96.09 144	88.02 74	99.58 53	93.44 84	98.18 81	98.40 110

EI-MVSNet-Vis-set		96.51 37	96.47 32	96.63 64	98.24 69	91.20 105	96.89 135	97.73 93	94.74 25	96.49 39	98.49 11	90.88 47	99.58 53	96.44 18	98.32 78	99.13 55

test222					98.24 69	92.21 74	95.33 243	97.60 107	79.22 306	95.25 75	97.84 58	88.80 66			99.15 52	98.72 86

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 69	89.88 141	94.58 258	98.49 12	85.06 260	93.78 96	95.78 159	82.86 153	98.67 144	91.77 111	95.71 137	99.07 61

MVS_111021_LR		96.24 45	96.19 42	96.39 79	98.23 72	91.35 100	96.24 202	98.79 4	93.99 39	95.80 63	97.65 70	89.92 58	99.24 95	95.87 33	99.20 49	98.58 92

EI-MVSNet-UG-set		96.34 42	96.30 37	96.47 75	98.20 73	90.93 116	96.86 137	97.72 96	94.67 26	96.16 48	98.46 12	90.43 51	99.58 53	96.23 21	97.96 87	98.90 76

PVSNet_Blended_VisFu		95.27 63	94.91 63	96.38 80	98.20 73	90.86 118	97.27 100	98.25 25	90.21 143	94.18 91	97.27 89	87.48 85	99.73 23	93.53 80	97.77 92	98.55 93

PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 75	90.80 120	95.27 248	97.18 149	87.96 208	91.86 139	95.68 166	80.44 202	98.99 121	84.01 243	97.54 96	96.89 164

testdata				95.46 123	98.18 76	88.90 178		97.66 102	82.73 284	97.03 27	98.07 42	90.06 55	98.85 131	89.67 138	98.98 63	98.64 91

LFMVS		93.60 108	92.63 119	96.52 69	98.13 77	91.27 102	97.94 41	93.39 312	90.57 140	96.29 44	98.31 31	69.00 293	99.16 101	94.18 67	95.87 133	99.12 57

DeepPCF-MVS	93.97 1	96.61 35	97.09 7	95.15 134	98.09 78	86.63 242	96.00 215	98.15 37	95.43 7	97.95 7	98.56 7	93.40 8	99.36 89	96.77 12	99.48 22	99.45 28

VNet		95.89 54	95.45 51	97.21 51	98.07 79	92.94 58	97.50 80	98.15 37	93.87 41	97.52 10	97.61 76	85.29 108	99.53 68	95.81 36	95.27 141	99.16 51

MAR-MVS		94.22 86	93.46 96	96.51 72	98.00 80	92.19 77	97.67 62	97.47 121	88.13 207	93.00 116	95.84 152	84.86 115	99.51 72	87.99 171	98.17 82	97.83 133
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
view600		92.55 140	91.68 146	95.18 129	97.98 81	89.44 159	98.00 36	94.57 286	92.09 92	93.17 110	95.52 174	78.14 239	99.11 106	81.61 265	94.04 159	96.98 155

view800		92.55 140	91.68 146	95.18 129	97.98 81	89.44 159	98.00 36	94.57 286	92.09 92	93.17 110	95.52 174	78.14 239	99.11 106	81.61 265	94.04 159	96.98 155

conf0.05thres1000		92.55 140	91.68 146	95.18 129	97.98 81	89.44 159	98.00 36	94.57 286	92.09 92	93.17 110	95.52 174	78.14 239	99.11 106	81.61 265	94.04 159	96.98 155

tfpn		92.55 140	91.68 146	95.18 129	97.98 81	89.44 159	98.00 36	94.57 286	92.09 92	93.17 110	95.52 174	78.14 239	99.11 106	81.61 265	94.04 159	96.98 155

DeepC-MVS	93.07 3	96.06 48	95.66 49	97.29 44	97.96 85	93.17 52	97.30 99	98.06 56	93.92 40	93.38 103	98.66 4	86.83 92	99.73 23	95.60 43	99.22 47	98.96 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
COLMAP_ROB	87.81 15	90.40 222	89.28 229	93.79 191	97.95 86	87.13 231	96.92 133	95.89 235	82.83 283	86.88 255	97.18 93	73.77 273	99.29 92	78.44 290	93.62 167	94.95 242
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		90.23 226	88.98 233	93.98 179	97.94 87	86.64 239	96.51 176	95.54 247	85.38 254	85.49 264	96.77 106	70.28 289	99.15 102	80.02 281	92.87 172	96.15 182

TestCases				93.98 179	97.94 87	86.64 239		95.54 247	85.38 254	85.49 264	96.77 106	70.28 289	99.15 102	80.02 281	92.87 172	96.15 182

thres100view900		92.43 147	91.58 153	94.98 142	97.92 89	89.37 164	97.71 60	94.66 283	92.20 88	93.31 105	94.90 196	78.06 243	99.08 116	81.40 272	94.08 155	96.48 174

thres600view7		92.49 146	91.60 152	95.18 129	97.91 90	89.47 155	97.65 65	94.66 283	92.18 91	93.33 104	94.91 195	78.06 243	99.10 111	81.61 265	94.06 158	96.98 155

API-MVS		94.84 78	94.49 75	95.90 100	97.90 91	92.00 83	97.80 50	97.48 118	89.19 164	94.81 81	96.71 108	88.84 65	99.17 100	88.91 157	98.76 69	96.53 171

VDD-MVS		93.82 101	93.08 105	96.02 96	97.88 92	89.96 140	97.72 58	95.85 236	92.43 83	95.86 60	98.44 14	68.42 297	99.39 86	96.31 19	94.85 145	98.71 88

tfpn200view9		92.38 150	91.52 156	94.95 145	97.85 93	89.29 168	97.41 86	94.88 278	92.19 89	93.27 107	94.46 216	78.17 236	99.08 116	81.40 272	94.08 155	96.48 174

thres400		92.42 148	91.52 156	95.12 137	97.85 93	89.29 168	97.41 86	94.88 278	92.19 89	93.27 107	94.46 216	78.17 236	99.08 116	81.40 272	94.08 155	96.98 155

DELS-MVS		96.61 35	96.38 36	97.30 43	97.79 95	93.19 51	95.96 216	98.18 34	95.23 11	95.87 59	97.65 70	91.45 38	99.70 32	95.87 33	99.44 27	99.00 67
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
PVSNet	86.66 18	92.24 157	91.74 145	93.73 198	97.77 96	83.69 273	92.88 292	96.72 195	87.91 210	93.00 116	94.86 198	78.51 231	99.05 119	86.53 200	97.45 101	98.47 105

MVS_0304		96.05 49	95.45 51	97.85 13	97.75 97	94.50 13	96.87 136	97.95 80	95.46 6	95.60 70	98.01 46	80.96 189	99.83 13	97.23 2	99.25 44	99.23 47

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 98	92.05 80	96.59 172	97.35 141	90.61 137	94.64 83	96.93 101	86.41 96	99.39 86	91.20 126	94.71 151	98.94 72

UA-Net		95.95 53	95.53 50	97.20 52	97.67 99	92.98 57	97.65 65	98.13 40	94.81 22	96.61 33	98.35 22	88.87 64	99.51 72	90.36 131	97.35 104	99.11 58

IS-MVSNet		94.90 75	94.52 74	96.05 95	97.67 99	90.56 125	98.44 15	96.22 218	93.21 58	93.99 93	97.74 64	85.55 106	98.45 161	89.98 132	97.86 88	99.14 54

PAPR		94.18 87	93.42 100	96.48 74	97.64 101	91.42 99	95.55 234	97.71 99	88.99 175	92.34 129	95.82 154	89.19 60	99.11 106	86.14 207	97.38 102	98.90 76

CANet		96.39 41	96.02 43	97.50 37	97.62 102	93.38 47	97.02 121	97.96 78	95.42 8	94.86 80	97.81 59	87.38 87	99.82 16	96.88 7	99.20 49	99.29 43

thres200		92.23 158	91.39 159	94.75 154	97.61 103	89.03 175	96.60 171	95.09 268	92.08 97	93.28 106	94.00 239	78.39 234	99.04 120	81.26 276	94.18 154	96.19 179

Vis-MVSNet (Re-imp)		94.15 88	93.88 82	94.95 145	97.61 103	87.92 214	98.10 31	95.80 239	92.22 86	93.02 115	97.45 86	84.53 119	97.91 229	88.24 166	97.97 86	99.02 62

canonicalmvs		96.02 51	95.45 51	97.75 23	97.59 105	95.15 7	98.28 22	97.60 107	94.52 29	96.27 45	96.12 141	87.65 81	99.18 99	96.20 26	94.82 147	98.91 75

LS3D		93.57 110	92.61 121	96.47 75	97.59 105	91.61 91	97.67 62	97.72 96	85.17 258	90.29 167	98.34 25	84.60 117	99.73 23	83.85 247	98.27 79	98.06 124

alignmvs		95.87 55	95.23 58	97.78 19	97.56 107	95.19 5	97.86 45	97.17 151	94.39 32	96.47 40	96.40 131	85.89 102	99.20 96	96.21 25	95.11 143	98.95 71

EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 108	89.56 150	98.67 5	97.00 174	90.69 130	94.24 90	97.62 75	89.79 59	98.81 134	93.39 87	96.49 124	98.92 74

PS-MVSNAJ		95.37 60	95.33 56	95.49 119	97.35 109	90.66 124	95.31 245	97.48 118	93.85 42	96.51 38	95.70 165	88.65 68	99.65 39	94.80 61	98.27 79	96.17 180

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 110	91.96 85	95.40 241	97.45 127	89.81 153	93.22 109	96.28 135	79.62 215	99.46 77	90.74 128	93.11 171	98.50 100

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 111	90.50 127	95.44 240	97.44 130	93.70 47	96.46 41	96.18 138	88.59 71	99.53 68	94.79 63	97.81 90	96.17 180

BH-untuned		92.94 129	92.62 120	93.92 187	97.22 111	86.16 246	96.40 185	96.25 216	90.06 146	89.79 189	96.17 140	83.19 130	98.35 167	87.19 193	97.27 106	97.24 152

Vis-MVSNet		95.23 64	94.81 64	96.51 72	97.18 113	91.58 94	98.26 24	98.12 41	94.38 33	94.90 79	98.15 39	82.28 168	98.92 124	91.45 121	98.58 74	99.01 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BH-RMVSNet		92.72 138	91.97 138	94.97 143	97.16 114	87.99 209	96.15 205	95.60 244	90.62 135	91.87 138	97.15 96	78.41 233	98.57 152	83.16 252	97.60 95	98.36 114

MSDG		91.42 188	90.24 200	94.96 144	97.15 115	88.91 177	93.69 277	96.32 212	85.72 252	86.93 253	96.47 128	80.24 206	98.98 122	80.57 278	95.05 144	96.98 155

HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 116	92.49 69	95.64 231	96.64 203	89.05 173	93.00 116	95.79 158	85.77 105	99.45 79	89.16 151	94.35 152	97.96 125

XVG-OURS		93.72 105	93.35 101	94.80 151	97.07 117	88.61 181	94.79 255	97.46 123	91.97 101	93.99 93	97.86 55	81.74 180	98.88 130	92.64 94	92.67 176	96.92 163

sss		94.51 82	93.80 84	96.64 62	97.07 117	91.97 84	96.32 193	98.06 56	88.94 178	94.50 85	96.78 105	84.60 117	99.27 93	91.90 107	96.02 129	98.68 90

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 150	97.06 119	88.53 183	95.28 246	97.45 127	91.68 106	94.08 92	97.68 67	82.41 166	98.90 126	93.84 76	92.47 177	96.98 155

1112_ss		93.37 115	92.42 129	96.21 91	97.05 120	90.99 112	96.31 194	96.72 195	86.87 239	89.83 187	96.69 112	86.51 95	99.14 104	88.12 168	93.67 165	98.50 100

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 121	89.97 138	95.53 236	96.64 203	85.38 254	89.65 197	95.18 188	85.86 103	99.10 111	87.70 177	93.58 170	98.49 102

BH-w/o		92.14 162	91.75 143	93.31 221	96.99 122	85.73 249	95.67 228	95.69 241	88.73 188	89.26 211	94.82 201	82.97 148	98.07 192	85.26 223	96.32 127	96.13 184

3Dnovator+	91.43 4	95.40 59	94.48 76	98.16 5	96.90 123	95.34 4	98.48 14	97.87 84	94.65 28	88.53 222	98.02 45	83.69 125	99.71 27	93.18 89	98.96 64	99.44 30

UGNet		94.04 95	93.28 103	96.31 84	96.85 124	91.19 106	97.88 44	97.68 101	94.40 31	93.00 116	96.18 138	73.39 276	99.61 45	91.72 112	98.46 75	98.13 119
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
VDDNet		93.05 125	92.07 133	96.02 96	96.84 125	90.39 130	98.08 33	95.85 236	86.22 247	95.79 64	98.46 12	67.59 300	99.19 97	94.92 57	94.85 145	98.47 105

RPSCF		90.75 212	90.86 178	90.42 289	96.84 125	76.29 312	95.61 233	96.34 211	83.89 273	91.38 144	97.87 53	76.45 252	98.78 136	87.16 195	92.23 180	96.20 178

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 127	89.55 151	96.70 159	97.17 151	91.17 120	95.60 70	96.11 143	87.87 78	98.76 139	93.01 92	97.17 108	98.72 86

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 244	96.82 128	81.89 284	96.92 133	93.71 307	92.41 84	84.30 271	94.60 210	85.08 111	97.03 278	91.51 118	97.36 103	98.40 110

Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 129	90.03 132	96.81 143	97.13 157	88.19 203	91.30 150	94.27 232	86.21 98	98.63 146	87.66 181	96.46 126	98.12 120

QAPM		93.45 113	92.27 131	96.98 58	96.77 129	92.62 65	98.39 18	98.12 41	84.50 268	88.27 228	97.77 62	82.39 167	99.81 17	85.40 221	98.81 67	98.51 98

CHOSEN 280x420		93.12 122	92.72 117	94.34 167	96.71 131	87.27 225	90.29 313	97.72 96	86.61 243	91.34 147	95.29 184	84.29 121	98.41 162	93.25 88	98.94 65	97.35 151

Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 132	91.47 96	96.41 181	97.41 134	91.02 125	94.50 85	95.92 148	87.53 84	98.78 136	93.89 74	96.81 115	98.84 82

PCF-MVS	89.48 11	91.56 181	89.95 211	96.36 82	96.60 133	92.52 68	92.51 297	97.26 146	79.41 304	88.90 214	96.56 124	84.04 122	99.55 63	77.01 296	97.30 105	97.01 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 134	91.71 87	96.25 199	97.35 141	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 185

xiu_mvs_v1_base		95.01 69	94.76 65	95.75 106	96.58 134	91.71 87	96.25 199	97.35 141	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 185

xiu_mvs_v1_base_debi		95.01 69	94.76 65	95.75 106	96.58 134	91.71 87	96.25 199	97.35 141	92.99 67	96.70 29	96.63 119	82.67 157	99.44 80	96.22 22	97.46 97	96.11 185

MVSTER		93.20 120	92.81 111	94.37 165	96.56 137	89.59 149	97.06 118	97.12 158	91.24 119	91.30 150	95.96 146	82.02 174	98.05 200	93.48 83	90.55 209	95.47 210

3Dnovator	91.36 5	95.19 67	94.44 78	97.44 38	96.56 137	93.36 49	98.65 6	98.36 16	94.12 37	89.25 212	98.06 43	82.20 171	99.77 20	93.41 86	99.32 39	99.18 50

FMVSNet3		91.78 168	90.69 187	95.03 139	96.53 139	92.27 73	97.02 121	96.93 184	89.79 154	89.35 206	94.65 208	77.01 250	97.47 260	86.12 208	88.82 224	95.35 221

GBi-Net		91.35 192	90.27 198	94.59 155	96.51 140	91.18 107	97.50 80	96.93 184	88.82 183	89.35 206	94.51 212	73.87 270	97.29 272	86.12 208	88.82 224	95.31 223

test1		91.35 192	90.27 198	94.59 155	96.51 140	91.18 107	97.50 80	96.93 184	88.82 183	89.35 206	94.51 212	73.87 270	97.29 272	86.12 208	88.82 224	95.31 223

FMVSNet2		91.31 194	90.08 205	94.99 140	96.51 140	92.21 74	97.41 86	96.95 182	88.82 183	88.62 219	94.75 204	73.87 270	97.42 264	85.20 224	88.55 230	95.35 221

ACMH+	87.92 14	90.20 227	89.18 231	93.25 223	96.48 143	86.45 243	96.99 124	96.68 200	88.83 182	84.79 268	96.22 137	70.16 291	98.53 155	84.42 236	88.04 232	94.77 260

diffmvs		93.43 114	92.75 114	95.48 121	96.47 144	89.61 147	96.09 208	97.14 155	85.97 250	93.09 114	95.35 182	84.87 114	98.55 154	89.51 142	96.26 128	98.28 116

CANet_DTU		94.37 83	93.65 89	96.55 68	96.46 145	92.13 78	96.21 203	96.67 202	94.38 33	93.53 100	97.03 100	79.34 218	99.71 27	90.76 127	98.45 76	97.82 134

mvs_anonymous		93.82 101	93.74 85	94.06 175	96.44 146	85.41 254	95.81 223	97.05 167	89.85 151	90.09 178	96.36 133	87.44 86	97.75 242	93.97 70	96.69 120	99.02 62

TR-MVS		91.48 185	90.59 190	94.16 172	96.40 147	87.33 223	95.67 228	95.34 257	87.68 216	91.46 143	95.52 174	76.77 251	98.35 167	82.85 256	93.61 168	96.79 167

ACMP	89.59 10	92.62 139	92.14 132	94.05 176	96.40 147	88.20 195	97.36 93	97.25 148	91.52 108	88.30 226	96.64 115	78.46 232	98.72 143	91.86 110	91.48 195	95.23 230
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		95.37 60	95.16 60	95.99 98	96.34 149	91.21 103	98.22 26	97.57 110	91.42 113	96.22 46	97.32 87	86.20 99	97.92 226	94.07 68	99.05 60	98.85 80

lupinMVS		94.99 73	94.56 71	96.29 87	96.34 149	91.21 103	95.83 222	96.27 214	88.93 179	96.22 46	96.88 103	86.20 99	98.85 131	95.27 45	99.05 60	98.82 83

ACMM	89.79 8	92.96 128	92.50 127	94.35 166	96.30 151	88.71 179	97.58 75	97.36 140	91.40 115	90.53 161	96.65 114	79.77 212	98.75 140	91.24 125	91.64 191	95.59 207
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		92.29 155	91.94 139	93.34 220	96.25 152	86.97 235	96.57 175	97.05 167	90.67 131	89.50 203	94.80 202	86.59 93	97.64 250	89.91 133	86.11 246	95.40 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP_MVS		93.78 103	93.43 98	94.82 148	96.21 153	89.99 135	97.74 54	97.51 116	94.85 17	91.34 147	96.64 115	81.32 185	98.60 149	93.02 90	92.23 180	95.86 191

plane_prior7					96.21 153	89.98 137

ACMH	87.59 16	90.53 220	89.42 227	93.87 188	96.21 153	87.92 214	97.24 102	96.94 183	88.45 194	83.91 277	96.27 136	71.92 278	98.62 148	84.43 235	89.43 220	95.05 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 156	91.46 97	96.33 192	97.04 170	88.97 177	93.56 98	96.51 126	87.55 83	97.89 230	89.80 135	95.95 131	98.44 107
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LTVRE_ROB	88.41 13	90.99 204	89.92 212	94.19 170	96.18 156	89.55 151	96.31 194	97.09 161	87.88 211	85.67 262	95.91 149	78.79 229	98.57 152	81.50 270	89.98 215	94.44 269
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
LPG-MVS_test		92.94 129	92.56 122	94.10 173	96.16 158	88.26 189	97.65 65	97.46 123	91.29 116	90.12 175	97.16 94	79.05 222	98.73 141	92.25 97	91.89 188	95.31 223

LGP-MVS_train				94.10 173	96.16 158	88.26 189		97.46 123	91.29 116	90.12 175	97.16 94	79.05 222	98.73 141	92.25 97	91.89 188	95.31 223

TAMVS		94.01 96	93.46 96	95.64 111	96.16 158	90.45 129	96.71 156	96.89 188	89.27 162	93.46 102	96.92 102	87.29 88	97.94 222	88.70 163	95.74 135	98.53 95

plane_prior1					96.14 161

CLD-MVS		92.98 127	92.53 125	94.32 168	96.12 162	89.20 172	95.28 246	97.47 121	92.66 79	89.90 182	95.62 168	80.58 199	98.40 163	92.73 93	92.40 178	95.38 219
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
plane_prior6					96.10 163	90.00 133						81.32 185

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 241	96.02 164	83.25 276	97.14 115	96.72 195	93.85 42	91.20 156	93.44 259	83.08 138	98.30 171	91.69 115	95.73 136	96.50 173

mvs-test1		93.63 107	93.69 87	93.46 215	96.02 164	84.61 264	97.24 102	96.72 195	93.85 42	92.30 130	95.76 160	83.08 138	98.89 128	91.69 115	96.54 123	96.87 165

NP-MVS					95.99 166	89.81 143					95.87 150

ADS-MVSNet2		89.45 240	88.59 238	92.03 259	95.86 167	82.26 282	90.93 309	94.32 296	83.23 281	91.28 153	91.81 284	79.01 226	95.99 298	79.52 283	91.39 197	97.84 131

ADS-MVSNet		89.89 233	88.68 237	93.53 211	95.86 167	84.89 261	90.93 309	95.07 270	83.23 281	91.28 153	91.81 284	79.01 226	97.85 232	79.52 283	91.39 197	97.84 131

HQP-NCC					95.86 167		96.65 164		93.55 48	90.14 169

ACMP_Plane					95.86 167		96.65 164		93.55 48	90.14 169

HQP-MVS		93.19 121	92.74 116	94.54 160	95.86 167	89.33 165	96.65 164	97.39 135	93.55 48	90.14 169	95.87 150	80.95 190	98.50 158	92.13 101	92.10 185	95.78 198

EI-MVSNet		93.03 126	92.88 110	93.48 213	95.77 172	86.98 234	96.44 177	97.12 158	90.66 133	91.30 150	97.64 73	86.56 94	98.05 200	89.91 133	90.55 209	95.41 213

CVMVSNet		91.23 196	91.75 143	89.67 295	95.77 172	74.69 314	96.44 177	94.88 278	85.81 251	92.18 132	97.64 73	79.07 221	95.58 306	88.06 169	95.86 134	98.74 84

FIs		94.09 92	93.70 86	95.27 127	95.70 174	92.03 81	98.10 31	98.68 7	93.36 55	90.39 165	96.70 110	87.63 82	97.94 222	92.25 97	90.50 211	95.84 194

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 174	92.39 70	97.86 45	98.66 9	92.30 85	92.09 135	95.37 181	80.49 201	98.40 163	93.95 71	85.86 247	95.75 202

Patchmatch-test1		91.54 183	90.85 179	93.59 207	95.59 176	84.95 260	94.72 256	95.58 246	90.82 126	92.25 131	93.58 252	75.80 256	97.41 265	83.35 249	95.98 130	98.40 110

VPNet		92.23 158	91.31 163	94.99 140	95.56 177	90.96 114	97.22 107	97.86 86	92.96 73	90.96 157	96.62 122	75.06 262	98.20 175	91.90 107	83.65 281	95.80 197

semantic-postprocess				91.82 264	95.52 178	84.20 267		96.15 221	90.61 137	87.39 243	94.27 232	75.63 258	96.44 285	87.34 189	86.88 243	94.82 254

jason		94.84 78	94.39 79	96.18 92	95.52 178	90.93 116	96.09 208	96.52 207	89.28 161	96.01 56	97.32 87	84.70 116	98.77 138	95.15 48	98.91 66	98.85 80
jason: jason.
FC-MVSNet-test		93.94 98	93.57 90	95.04 138	95.48 180	91.45 98	98.12 30	98.71 5	93.37 53	90.23 168	96.70 110	87.66 80	97.85 232	91.49 119	90.39 212	95.83 195

IterMVS		90.15 229	89.67 222	91.61 271	95.48 180	83.72 270	94.33 264	96.12 222	89.99 147	87.31 246	94.15 236	75.78 257	96.27 288	86.97 197	86.89 242	94.83 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		89.88 234	88.31 242	94.59 155	95.41 182	91.18 107	97.50 80	96.93 184	86.62 242	87.41 242	94.51 212	65.94 307	97.29 272	83.04 254	87.43 238	95.31 223

UniMVSNet (Re)		93.31 117	92.55 123	95.61 112	95.39 183	93.34 50	97.39 90	98.71 5	93.14 63	90.10 177	94.83 200	87.71 79	98.03 205	91.67 117	83.99 274	95.46 211

MVS-HIRNet		82.47 293	81.21 294	86.26 306	95.38 184	69.21 325	88.96 321	89.49 330	66.28 327	80.79 292	74.08 330	68.48 296	97.39 267	71.93 309	95.47 138	92.18 309

PatchmatchNet		91.91 166	91.35 160	93.59 207	95.38 184	84.11 268	93.15 288	95.39 251	89.54 155	92.10 134	93.68 248	82.82 155	98.13 181	84.81 227	95.32 140	98.52 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UniMVSNet_NR-MVSNet		93.37 115	92.67 118	95.47 122	95.34 186	92.83 59	97.17 112	98.58 10	92.98 72	90.13 173	95.80 155	88.37 73	97.85 232	91.71 113	83.93 275	95.73 204

ITE_SJBPF				92.43 245	95.34 186	85.37 255		95.92 228	91.47 110	87.75 235	96.39 132	71.00 285	97.96 220	82.36 262	89.86 218	93.97 278

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 186	92.73 62	98.27 23	98.12 41	84.86 263	85.78 261	97.75 63	78.89 228	99.74 22	87.50 186	98.65 71	96.73 168

1314		92.81 136	92.03 135	95.14 135	95.33 189	89.52 154	96.04 211	97.44 130	87.72 215	86.25 258	95.33 183	83.84 123	98.79 135	89.26 146	97.05 110	97.11 153

PAPM		91.52 184	90.30 196	95.20 128	95.30 190	89.83 142	93.38 283	96.85 191	86.26 246	88.59 221	95.80 155	84.88 113	98.15 180	75.67 299	95.93 132	97.63 139

Fast-Effi-MVS+-dtu		92.29 155	91.99 137	93.21 226	95.27 191	85.52 253	97.03 119	96.63 205	92.09 92	89.11 213	95.14 190	80.33 205	98.08 188	87.54 185	94.74 150	96.03 188

Patchmatch-test		89.42 241	87.99 245	93.70 201	95.27 191	85.11 256	88.98 320	94.37 294	81.11 296	87.10 250	93.69 247	82.28 168	97.50 258	74.37 302	94.76 148	98.48 104

PVSNet_0	82.17 19	85.46 283	83.64 284	90.92 280	95.27 191	79.49 303	90.55 312	95.60 244	83.76 276	83.00 280	89.95 291	71.09 284	97.97 216	82.75 258	60.79 330	95.31 223

IB-MVS	87.33 17	89.91 232	88.28 243	94.79 152	95.26 194	87.70 220	95.12 252	93.95 305	89.35 160	87.03 251	92.49 272	70.74 287	99.19 97	89.18 150	81.37 294	97.49 148
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
PatchFormer-LS_test		91.68 175	91.18 170	93.19 227	95.24 195	83.63 274	95.53 236	95.44 250	89.82 152	91.37 145	92.58 271	80.85 197	98.52 156	89.65 140	90.16 214	97.42 150

nrg030		94.05 94	93.31 102	96.27 88	95.22 196	94.59 12	98.34 19	97.46 123	92.93 74	91.21 155	96.64 115	87.23 89	98.22 174	94.99 56	85.80 248	95.98 189

MDTV_nov1_ep13			90.76 183		95.22 196	80.33 296	93.03 291	95.28 258	88.14 206	92.84 122	93.83 244	81.34 184	98.08 188	82.86 255	94.34 153

MVS		91.71 169	90.44 192	95.51 117	95.20 198	91.59 93	96.04 211	97.45 127	73.44 323	87.36 244	95.60 169	85.42 107	99.10 111	85.97 212	97.46 97	95.83 195

tpmp4_e23		89.58 238	88.59 238	92.54 243	95.16 199	81.53 286	94.11 270	95.09 268	81.66 291	88.60 220	93.44 259	75.11 261	98.33 170	82.45 261	91.72 190	97.75 135

tfpnnormal		89.70 237	88.40 241	93.60 206	95.15 200	90.10 131	97.56 76	98.16 36	87.28 225	86.16 259	94.63 209	77.57 248	98.05 200	74.48 300	84.59 269	92.65 293

tpmrst		91.44 187	91.32 162	91.79 266	95.15 200	79.20 306	93.42 282	95.37 253	88.55 192	93.49 101	93.67 249	82.49 163	98.27 172	90.41 130	89.34 221	97.90 128

WR-MVS		92.34 151	91.53 155	94.77 153	95.13 202	90.83 119	96.40 185	97.98 76	91.88 102	89.29 209	95.54 173	82.50 162	97.80 237	89.79 136	85.27 254	95.69 205

tpm cat1		88.36 261	87.21 260	91.81 265	95.13 202	80.55 294	92.58 296	95.70 240	74.97 319	87.45 240	91.96 282	78.01 245	98.17 179	80.39 280	88.74 227	96.72 169

WR-MVS_H		92.00 165	91.35 160	93.95 183	95.09 204	89.47 155	98.04 35	98.68 7	91.46 111	88.34 224	94.68 206	85.86 103	97.56 254	85.77 215	84.24 272	94.82 254

CP-MVSNet		91.89 167	91.24 166	93.82 189	95.05 205	88.57 182	97.82 49	98.19 32	91.70 105	88.21 229	95.76 160	81.96 175	97.52 257	87.86 173	84.65 268	95.37 220

DWT-MVSNet_test		90.76 210	89.89 213	93.38 218	95.04 206	83.70 272	95.85 221	94.30 297	88.19 203	90.46 163	92.80 266	73.61 274	98.50 158	88.16 167	90.58 208	97.95 126

test_0402		86.46 275	84.79 278	91.45 273	95.02 207	85.55 252	96.29 196	94.89 277	80.90 297	82.21 281	93.97 240	68.21 298	97.29 272	62.98 321	88.68 229	91.51 314

cascas		91.20 197	90.08 205	94.58 159	94.97 208	89.16 174	93.65 279	97.59 109	79.90 303	89.40 204	92.92 265	75.36 260	98.36 166	92.14 100	94.75 149	96.23 177

PS-CasMVS		91.55 182	90.84 181	93.69 202	94.96 209	88.28 188	97.84 48	98.24 27	91.46 111	88.04 231	95.80 155	79.67 214	97.48 259	87.02 196	84.54 270	95.31 223

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 210	92.83 59	97.16 113	98.24 27	93.02 66	90.13 173	95.71 163	83.47 127	97.85 232	91.71 113	83.93 275	95.78 198

NR-MVSNet		92.34 151	91.27 165	95.53 116	94.95 210	93.05 54	97.39 90	98.07 54	92.65 80	84.46 269	95.71 163	85.00 112	97.77 241	89.71 137	83.52 282	95.78 198

tpmvs		89.83 236	89.15 232	91.89 262	94.92 212	80.30 297	93.11 289	95.46 249	86.28 245	88.08 230	92.65 268	80.44 202	98.52 156	81.47 271	89.92 217	96.84 166

PMMVS		92.86 133	92.34 130	94.42 164	94.92 212	86.73 238	94.53 260	96.38 210	84.78 265	94.27 89	95.12 192	83.13 134	98.40 163	91.47 120	96.49 124	98.12 120

tpm2		89.96 231	89.21 230	92.23 250	94.91 214	81.25 288	93.78 275	94.42 292	80.62 301	91.56 141	93.44 259	76.44 253	97.94 222	85.60 218	92.08 187	97.49 148

TinyColmap		86.82 273	85.35 275	91.21 276	94.91 214	82.99 277	93.94 273	94.02 304	83.58 277	81.56 288	94.68 206	62.34 314	98.13 181	75.78 298	87.35 241	92.52 296

CostFormer		91.18 200	90.70 186	92.62 242	94.84 216	81.76 285	94.09 271	94.43 291	84.15 270	92.72 123	93.77 246	79.43 217	98.20 175	90.70 129	92.18 183	97.90 128

MIMVSNet		88.50 256	86.76 264	93.72 200	94.84 216	87.77 218	91.39 304	94.05 302	86.41 244	87.99 232	92.59 270	63.27 311	95.82 302	77.44 292	92.84 174	97.57 146

FMVSNet5		87.29 270	85.79 271	91.78 267	94.80 218	87.28 224	95.49 238	95.28 258	84.09 271	83.85 278	91.82 283	62.95 312	94.17 314	78.48 289	85.34 253	93.91 279

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 219	92.07 79	97.53 78	98.11 44	92.90 75	89.56 200	96.12 141	83.16 131	97.60 253	89.30 145	83.20 285	95.75 202

XXY-MVS		92.16 160	91.23 167	94.95 145	94.75 220	90.94 115	97.47 84	97.43 132	89.14 171	88.90 214	96.43 130	79.71 213	98.24 173	89.56 141	87.68 235	95.67 206

EPMVS		90.70 216	89.81 217	93.37 219	94.73 221	84.21 266	93.67 278	88.02 331	89.50 157	92.38 127	93.49 256	77.82 247	97.78 239	86.03 211	92.68 175	98.11 123

USDC		88.94 244	87.83 246	92.27 246	94.66 222	84.96 259	93.86 274	95.90 230	87.34 223	83.40 279	95.56 171	67.43 301	98.19 177	82.64 260	89.67 219	93.66 281

GA-MVS		91.38 190	90.31 195	94.59 155	94.65 223	87.62 221	94.34 263	96.19 219	90.73 129	90.35 166	93.83 244	71.84 279	97.96 220	87.22 192	93.61 168	98.21 117

OPM-MVS		93.28 118	92.76 112	94.82 148	94.63 224	90.77 122	96.65 164	97.18 149	93.72 45	91.68 140	97.26 90	79.33 219	98.63 146	92.13 101	92.28 179	95.07 236

test-LLR		91.42 188	91.19 169	92.12 256	94.59 225	80.66 291	94.29 265	92.98 314	91.11 122	90.76 159	92.37 274	79.02 224	98.07 192	88.81 161	96.74 117	97.63 139

test-mter		90.19 228	89.54 225	92.12 256	94.59 225	80.66 291	94.29 265	92.98 314	87.68 216	90.76 159	92.37 274	67.67 299	98.07 192	88.81 161	96.74 117	97.63 139

dp		88.90 246	88.26 244	90.81 282	94.58 227	76.62 311	92.85 293	94.93 276	85.12 259	90.07 180	93.07 263	75.81 255	98.12 183	80.53 279	87.42 239	97.71 137

PEN-MVS		91.20 197	90.44 192	93.48 213	94.49 228	87.91 216	97.76 52	98.18 34	91.29 116	87.78 234	95.74 162	80.35 204	97.33 270	85.46 220	82.96 286	95.19 232

gg-mvs-nofinetune		87.82 265	85.61 272	94.44 162	94.46 229	89.27 171	91.21 308	84.61 337	80.88 298	89.89 184	74.98 328	71.50 281	97.53 256	85.75 216	97.21 107	96.51 172

CR-MVSNet		90.82 209	89.77 218	93.95 183	94.45 230	87.19 229	90.23 314	95.68 242	86.89 238	92.40 125	92.36 277	80.91 193	97.05 276	81.09 277	93.95 163	97.60 144

RPMNet		88.52 254	86.72 266	93.95 183	94.45 230	87.19 229	90.23 314	94.99 273	77.87 313	92.40 125	87.55 318	80.17 208	97.05 276	68.84 315	93.95 163	97.60 144

TESTMET0.1,1		90.06 230	89.42 227	91.97 260	94.41 232	80.62 293	94.29 265	91.97 321	87.28 225	90.44 164	92.47 273	68.79 294	97.67 247	88.50 165	96.60 122	97.61 143

TransMVSNet (Re)		88.94 244	87.56 247	93.08 229	94.35 233	88.45 186	97.73 56	95.23 262	87.47 219	84.26 272	95.29 184	79.86 211	97.33 270	79.44 286	74.44 320	93.45 284

MS-PatchMatch		90.27 224	89.77 218	91.78 267	94.33 234	84.72 263	95.55 234	96.73 194	86.17 248	86.36 257	95.28 186	71.28 283	97.80 237	84.09 240	98.14 83	92.81 292

XVG-ACMP-BASELINE		90.93 206	90.21 203	93.09 228	94.31 235	85.89 247	95.33 243	97.26 146	91.06 124	89.38 205	95.44 180	68.61 295	98.60 149	89.46 143	91.05 202	94.79 258

pcd1.5k->3k		38.37 317	40.51 318	31.96 330	94.29 236	0.00 348	0.00 339	97.69 100	0.00 343	0.00 344	0.00 345	81.45 183	0.00 346	0.00 343	91.11 201	95.89 190

pm-mvs1		90.72 214	89.65 224	93.96 182	94.29 236	89.63 146	97.79 51	96.82 192	89.07 172	86.12 260	95.48 179	78.61 230	97.78 239	86.97 197	81.67 292	94.46 268

v1neww		91.70 172	91.01 171	93.75 195	94.19 238	88.14 200	97.20 108	96.98 175	89.18 166	89.87 185	94.44 218	83.10 136	98.06 197	89.06 153	85.09 258	95.06 239

v7new		91.70 172	91.01 171	93.75 195	94.19 238	88.14 200	97.20 108	96.98 175	89.18 166	89.87 185	94.44 218	83.10 136	98.06 197	89.06 153	85.09 258	95.06 239

v16		88.69 250	87.50 249	92.26 248	94.19 238	88.11 204	96.81 143	95.95 226	87.01 231	80.71 295	89.80 295	83.08 138	96.20 290	84.61 232	75.34 310	92.48 299

v18		88.71 249	87.52 248	92.27 246	94.16 241	88.11 204	96.82 142	95.96 225	87.03 229	80.76 293	89.81 294	83.15 132	96.22 289	84.69 229	75.31 311	92.49 297

v8		91.29 195	90.53 191	93.57 210	94.15 242	88.12 202	97.34 94	97.06 166	88.99 175	88.32 225	94.26 234	83.08 138	98.01 209	87.62 183	83.92 277	94.57 265

v6		91.69 174	91.00 173	93.75 195	94.14 243	88.12 202	97.20 108	96.98 175	89.19 164	89.90 182	94.42 220	83.04 142	98.07 192	89.07 152	85.10 257	95.07 236

v17		88.67 251	87.47 251	92.26 248	94.13 244	88.09 206	96.81 143	95.95 226	87.02 230	80.72 294	89.75 296	83.11 135	96.20 290	84.61 232	75.15 313	92.49 297

v7		91.47 186	90.73 185	93.68 203	94.13 244	88.16 198	97.09 117	97.05 167	88.38 196	89.80 188	94.52 211	82.21 170	98.01 209	88.00 170	85.42 251	94.87 248

V14		88.52 254	87.30 254	92.17 253	94.12 246	87.99 209	96.72 154	95.91 229	86.98 233	80.50 299	89.63 297	83.03 143	96.12 294	84.23 238	74.60 316	92.40 304

v10		91.04 203	90.23 201	93.49 212	94.12 246	88.16 198	97.32 97	97.08 163	88.26 200	88.29 227	94.22 235	82.17 172	97.97 216	86.45 203	84.12 273	94.33 272

V9		88.49 257	87.26 255	92.18 252	94.12 246	87.97 212	96.73 151	95.90 230	86.95 235	80.40 301	89.61 298	82.98 147	96.13 292	84.14 239	74.55 317	92.44 301

v12		88.46 258	87.23 258	92.17 253	94.10 249	87.99 209	96.71 156	95.90 230	86.91 236	80.34 303	89.58 301	82.92 151	96.11 296	84.09 240	74.50 319	92.42 302

v15		88.53 253	87.31 253	92.20 251	94.09 250	88.05 207	96.72 154	95.90 230	87.01 231	80.53 298	89.60 300	83.02 144	96.13 292	84.29 237	74.64 314	92.41 303

Patchmtry		88.64 252	87.25 256	92.78 237	94.09 250	86.64 239	89.82 317	95.68 242	80.81 300	87.63 239	92.36 277	80.91 193	97.03 278	78.86 288	85.12 256	94.67 262

v13		88.45 259	87.22 259	92.16 255	94.08 252	87.95 213	96.71 156	95.90 230	86.86 240	80.27 305	89.55 302	82.92 151	96.12 294	84.02 242	74.63 315	92.40 304

v11		88.41 260	87.19 262	92.08 258	94.08 252	87.77 218	96.75 149	95.85 236	86.74 241	80.50 299	89.50 303	82.49 163	96.08 297	83.55 248	75.20 312	92.38 306

PatchT		88.87 247	87.42 252	93.22 225	94.08 252	85.10 257	89.51 318	94.64 285	81.92 289	92.36 128	88.15 313	80.05 209	97.01 280	72.43 307	93.65 166	97.54 147

V42		91.58 180	90.87 177	93.73 198	94.05 255	88.50 184	97.32 97	96.97 178	88.80 186	89.71 193	94.33 225	82.54 161	98.05 200	89.01 155	85.07 260	94.64 264

v1141		91.61 176	90.89 174	93.78 192	94.01 256	88.24 191	96.96 126	96.96 179	89.17 168	89.75 191	94.29 228	82.99 146	98.03 205	88.85 159	85.00 263	95.07 236

divwei89l23v2f112		91.61 176	90.89 174	93.78 192	94.01 256	88.22 193	96.96 126	96.96 179	89.17 168	89.75 191	94.28 230	83.02 144	98.03 205	88.86 158	84.98 265	95.08 234

v1		91.61 176	90.89 174	93.78 192	94.01 256	88.21 194	96.96 126	96.96 179	89.17 168	89.78 190	94.29 228	82.97 148	98.05 200	88.85 159	84.99 264	95.08 234

DTE-MVSNet		90.56 219	89.75 220	93.01 230	93.95 259	87.25 226	97.64 69	97.65 104	90.74 128	87.12 248	95.68 166	79.97 210	97.00 281	83.33 251	81.66 293	94.78 259

tpm		90.25 225	89.74 221	91.76 269	93.92 260	79.73 302	93.98 272	93.54 311	88.28 199	91.99 136	93.25 262	77.51 249	97.44 262	87.30 191	87.94 233	98.12 120

PS-MVSNAJss		93.74 104	93.51 94	94.44 162	93.91 261	89.28 170	97.75 53	97.56 113	92.50 82	89.94 181	96.54 125	88.65 68	98.18 178	93.83 77	90.90 204	95.86 191

v1144		91.37 191	90.60 189	93.68 203	93.89 262	88.23 192	96.84 138	97.03 172	88.37 197	89.69 195	94.39 221	82.04 173	97.98 213	87.80 175	85.37 252	94.84 250

v2v482		91.59 179	90.85 179	93.80 190	93.87 263	88.17 197	96.94 132	96.88 189	89.54 155	89.53 201	94.90 196	81.70 181	98.02 208	89.25 147	85.04 262	95.20 231

v148		90.99 204	90.38 194	92.81 236	93.83 264	85.80 248	96.78 148	96.68 200	89.45 158	88.75 218	93.93 242	82.96 150	97.82 236	87.83 174	83.25 283	94.80 256

Baseline_NR-MVSNet		91.20 197	90.62 188	92.95 232	93.83 264	88.03 208	97.01 123	95.12 267	88.42 195	89.70 194	95.13 191	83.47 127	97.44 262	89.66 139	83.24 284	93.37 286

EPNet_dtu		91.71 169	91.28 164	92.99 231	93.76 266	83.71 271	96.69 161	95.28 258	93.15 62	87.02 252	95.95 147	83.37 129	97.38 268	79.46 285	96.84 113	97.88 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1192		91.07 201	90.23 201	93.58 209	93.70 267	87.82 217	96.73 151	97.07 164	87.77 213	89.58 198	94.32 226	80.90 196	97.97 216	86.52 201	85.48 249	94.95 242

GG-mvs-BLEND				93.62 205	93.69 268	89.20 172	92.39 300	83.33 338		87.98 233	89.84 293	71.00 285	96.87 282	82.08 264	95.40 139	94.80 256

v144192		91.06 202	90.28 197	93.39 217	93.66 269	87.23 228	96.83 139	97.07 164	87.43 220	89.69 195	94.28 230	81.48 182	98.00 212	87.18 194	84.92 266	94.93 246

v1921920		90.85 208	90.03 208	93.29 222	93.55 270	86.96 236	96.74 150	97.04 170	87.36 222	89.52 202	94.34 224	80.23 207	97.97 216	86.27 204	85.21 255	94.94 244

v7n		90.76 210	89.86 214	93.45 216	93.54 271	87.60 222	97.70 61	97.37 138	88.85 180	87.65 238	94.08 238	81.08 187	98.10 185	84.68 230	83.79 280	94.66 263

JIA-IIPM		88.26 262	87.04 263	91.91 261	93.52 272	81.42 287	89.38 319	94.38 293	80.84 299	90.93 158	80.74 325	79.22 220	97.92 226	82.76 257	91.62 192	96.38 176

v1240		90.70 216	89.85 215	93.23 224	93.51 273	86.80 237	96.61 169	97.02 173	87.16 227	89.58 198	94.31 227	79.55 216	97.98 213	85.52 219	85.44 250	94.90 247

test_djsdf		93.07 124	92.76 112	94.00 178	93.49 274	88.70 180	98.22 26	97.57 110	91.42 113	90.08 179	95.55 172	82.85 154	97.92 226	94.07 68	91.58 193	95.40 217

SixPastTwentyTwo		89.15 243	88.54 240	90.98 278	93.49 274	80.28 298	96.70 159	94.70 282	90.78 127	84.15 274	95.57 170	71.78 280	97.71 245	84.63 231	85.07 260	94.94 244

mvs_tets		92.31 153	91.76 142	93.94 186	93.41 276	88.29 187	97.63 70	97.53 114	92.04 98	88.76 217	96.45 129	74.62 266	98.09 187	93.91 73	91.48 195	95.45 212

OurMVSNet-221017-0		90.51 221	90.19 204	91.44 274	93.41 276	81.25 288	96.98 125	96.28 213	91.68 106	86.55 256	96.30 134	74.20 269	97.98 213	88.96 156	87.40 240	95.09 233

pmmvs4		90.93 206	89.85 215	94.17 171	93.34 278	90.79 121	94.60 257	96.02 224	84.62 266	87.45 240	95.15 189	81.88 178	97.45 261	87.70 177	87.87 234	94.27 275

DI_MVS_plusplus_test		92.01 163	90.77 182	95.73 109	93.34 278	89.78 144	96.14 206	96.18 220	90.58 139	81.80 286	93.50 255	74.95 264	98.90 126	93.51 81	96.94 112	98.51 98

jajsoiax		92.42 148	91.89 140	94.03 177	93.33 280	88.50 184	97.73 56	97.53 114	92.00 100	88.85 216	96.50 127	75.62 259	98.11 184	93.88 75	91.56 194	95.48 208

v748		90.34 223	89.54 225	92.75 238	93.25 281	85.71 250	97.61 71	97.17 151	88.54 193	87.20 247	93.54 253	81.02 188	98.01 209	85.73 217	81.80 290	94.52 266

test_normal		92.01 163	90.75 184	95.80 104	93.24 282	89.97 138	95.93 218	96.24 217	90.62 135	81.63 287	93.45 258	74.98 263	98.89 128	93.61 79	97.04 111	98.55 93

v52		90.70 216	90.00 209	92.82 233	93.24 282	87.03 232	97.60 72	97.14 155	88.21 201	87.69 236	93.94 241	80.91 193	98.07 192	87.39 187	83.87 279	93.36 287

gm-plane-assit					93.22 284	78.89 308			84.82 264		93.52 254		98.64 145	87.72 176

V4		90.71 215	90.00 209	92.82 233	93.21 285	87.03 232	97.59 74	97.16 154	88.21 201	87.69 236	93.92 243	80.93 192	98.06 197	87.39 187	83.90 278	93.39 285

LP		84.13 287	81.85 292	90.97 279	93.20 286	82.12 283	87.68 324	94.27 299	76.80 314	81.93 284	88.52 308	72.97 277	95.95 299	59.53 326	81.73 291	94.84 250

MVP-Stereo		90.74 213	90.08 205	92.71 239	93.19 287	88.20 195	95.86 220	96.27 214	86.07 249	84.86 267	94.76 203	77.84 246	97.75 242	83.88 246	98.01 85	92.17 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		88.72 248	88.90 234	88.20 298	93.15 288	74.21 315	96.63 168	94.22 300	85.18 257	87.32 245	95.97 145	76.16 254	94.98 311	85.27 222	86.17 244	95.41 213

MDA-MVSNet-bldmvs		85.00 284	82.95 286	91.17 277	93.13 289	83.33 275	94.56 259	95.00 272	84.57 267	65.13 327	92.65 268	70.45 288	95.85 300	73.57 305	77.49 303	94.33 272

K. test v3		87.64 267	86.75 265	90.32 290	93.02 290	79.48 304	96.61 169	92.08 320	90.66 133	80.25 306	94.09 237	67.21 303	96.65 284	85.96 213	80.83 297	94.83 252

pmmvs5		89.86 235	88.87 235	92.82 233	92.86 291	86.23 245	96.26 198	95.39 251	84.24 269	87.12 248	94.51 212	74.27 268	97.36 269	87.61 184	87.57 236	94.86 249

testgi		87.97 263	87.21 260	90.24 291	92.86 291	80.76 290	96.67 163	94.97 274	91.74 104	85.52 263	95.83 153	62.66 313	94.47 313	76.25 297	88.36 231	95.48 208

EPNet		95.20 66	94.56 71	97.14 53	92.80 293	92.68 63	97.85 47	94.87 281	96.64 1	92.46 124	97.80 61	86.23 97	99.65 39	93.72 78	98.62 72	99.10 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		78.73 298	78.71 297	78.79 316	92.80 293	46.50 342	94.14 269	43.71 346	78.61 309	80.83 290	91.66 287	74.94 265	96.36 286	67.24 316	84.45 271	93.50 282

EG-PatchMatch MVS		87.02 272	85.44 273	91.76 269	92.67 295	85.00 258	96.08 210	96.45 208	83.41 280	79.52 308	93.49 256	57.10 321	97.72 244	79.34 287	90.87 205	92.56 295

Gipuma		67.86 307	65.41 308	75.18 320	92.66 296	73.45 317	66.50 337	94.52 290	53.33 332	57.80 331	66.07 334	30.81 336	89.20 330	48.15 335	78.88 301	62.90 334
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		92.16 160	91.55 154	93.97 181	92.58 297	89.55 151	97.51 79	97.42 133	89.42 159	88.40 223	94.84 199	80.66 198	97.88 231	91.87 109	91.28 199	94.48 267

test0.0.03 1		89.37 242	88.70 236	91.41 275	92.47 298	85.63 251	95.22 250	92.70 317	91.11 122	86.91 254	93.65 250	79.02 224	93.19 319	78.00 291	89.18 222	95.41 213

YYNet1		85.87 280	84.23 282	90.78 285	92.38 299	82.46 280	93.17 286	95.14 266	82.12 288	67.69 322	92.36 277	78.16 238	95.50 308	77.31 294	79.73 299	94.39 270

MDA-MVSNet_test_wron		85.87 280	84.23 282	90.80 284	92.38 299	82.57 278	93.17 286	95.15 265	82.15 287	67.65 323	92.33 280	78.20 235	95.51 307	77.33 293	79.74 298	94.31 274

LF4IMVS		87.94 264	87.25 256	89.98 293	92.38 299	80.05 301	94.38 262	95.25 261	87.59 218	84.34 270	94.74 205	64.31 310	97.66 249	84.83 226	87.45 237	92.23 308

lessismore_v0				90.45 288	91.96 302	79.09 307		87.19 334		80.32 304	94.39 221	66.31 305	97.55 255	84.00 244	76.84 305	94.70 261

testpf		80.97 295	81.40 293	79.65 314	91.53 303	72.43 319	73.47 335	89.55 329	78.63 308	80.81 291	89.06 305	61.36 315	91.36 325	83.34 250	84.89 267	75.15 330

pmmvs6		87.81 266	86.19 268	92.69 240	91.32 304	86.30 244	97.34 94	96.41 209	80.59 302	84.05 276	94.37 223	67.37 302	97.67 247	84.75 228	79.51 300	94.09 277

Anonymous20231206		87.09 271	86.14 269	89.93 294	91.22 305	80.35 295	96.11 207	95.35 254	83.57 278	84.16 273	93.02 264	73.54 275	95.61 304	72.16 308	86.14 245	93.84 280

DeepMVS_CX				74.68 321	90.84 306	64.34 331		81.61 341	65.34 328	67.47 325	88.01 314	48.60 330	80.13 337	62.33 323	73.68 322	79.58 328

Test4		89.48 239	87.50 249	95.44 124	90.76 307	89.72 145	95.78 226	97.09 161	90.28 142	77.67 312	91.74 286	55.42 325	98.08 188	91.92 106	96.83 114	98.52 96

test20.03		86.14 278	85.40 274	88.35 296	90.12 308	80.06 300	95.90 219	95.20 263	88.59 189	81.29 289	93.62 251	71.43 282	92.65 320	71.26 312	81.17 295	92.34 307

OpenMVS_ROB	81.14 20	84.42 286	82.28 287	90.83 281	90.06 309	84.05 269	95.73 227	94.04 303	73.89 322	80.17 307	91.53 288	59.15 318	97.64 250	66.92 317	89.05 223	90.80 317

UnsupCasMVSNet_eth		85.99 279	84.45 280	90.62 286	89.97 310	82.40 281	93.62 280	97.37 138	89.86 149	78.59 311	92.37 274	65.25 309	95.35 309	82.27 263	70.75 323	94.10 276

DSMNet-mixed		86.34 276	86.12 270	87.00 303	89.88 311	70.43 320	94.93 254	90.08 328	77.97 312	85.42 266	92.78 267	74.44 267	93.96 315	74.43 301	95.14 142	96.62 170

new_pmnet		82.89 290	81.12 295	88.18 299	89.63 312	80.18 299	91.77 303	92.57 318	76.79 315	75.56 315	88.23 312	61.22 316	94.48 312	71.43 310	82.92 287	89.87 319

MIMVSNet1		84.93 285	83.05 285	90.56 287	89.56 313	84.84 262	95.40 241	95.35 254	83.91 272	80.38 302	92.21 281	57.23 320	93.34 318	70.69 314	82.75 289	93.50 282

CMPMVS	62.92 21	85.62 282	84.92 277	87.74 300	89.14 314	73.12 318	94.17 268	96.80 193	73.98 321	73.65 317	94.93 194	66.36 304	97.61 252	83.95 245	91.28 199	92.48 299
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Patchmatch-RL test		87.38 268	86.24 267	90.81 282	88.74 315	78.40 309	88.12 323	93.17 313	87.11 228	82.17 282	89.29 304	81.95 176	95.60 305	88.64 164	77.02 304	98.41 109

pmmvs-eth3d		86.22 277	84.45 280	91.53 272	88.34 316	87.25 226	94.47 261	95.01 271	83.47 279	79.51 309	89.61 298	69.75 292	95.71 303	83.13 253	76.73 306	91.64 312

UnsupCasMVSNet_bld		82.13 294	79.46 296	90.14 292	88.00 317	82.47 279	90.89 311	96.62 206	78.94 307	75.61 314	84.40 323	56.63 322	96.31 287	77.30 295	66.77 329	91.63 313

PM-MVS		83.48 288	81.86 291	88.31 297	87.83 318	77.59 310	93.43 281	91.75 322	86.91 236	80.63 296	89.91 292	44.42 332	95.84 301	85.17 225	76.73 306	91.50 315

testing_2		87.33 269	85.03 276	94.22 169	87.77 319	89.32 167	94.97 253	97.11 160	89.22 163	71.64 321	88.73 307	55.16 326	97.94 222	91.95 105	88.73 228	95.41 213

testus		82.63 292	82.15 288	84.07 308	87.31 320	67.67 326	93.18 284	94.29 298	82.47 285	82.14 283	90.69 289	53.01 327	91.94 323	66.30 318	89.96 216	92.62 294

new-patchmatchnet		83.18 289	81.87 290	87.11 302	86.88 321	75.99 313	93.70 276	95.18 264	85.02 261	77.30 313	88.40 310	65.99 306	93.88 316	74.19 304	70.18 324	91.47 316

Anonymous20231211		78.22 300	75.30 301	86.99 304	86.14 322	74.16 316	95.62 232	93.88 306	66.43 326	74.44 316	87.86 315	41.39 333	95.11 310	62.49 322	69.46 326	91.71 311

test2356		82.77 291	82.14 289	84.65 307	85.77 323	70.36 321	91.22 307	93.69 310	81.58 293	81.82 285	89.00 306	60.63 317	90.77 326	64.74 319	90.80 206	92.82 290

1111		78.29 299	77.55 299	80.50 312	83.89 324	59.98 334	91.89 301	93.71 307	75.06 317	73.60 318	87.67 316	55.66 323	92.60 321	58.54 328	77.92 302	88.93 321

.test1245		65.38 308	69.22 306	53.86 328	83.89 324	59.98 334	91.89 301	93.71 307	75.06 317	73.60 318	87.67 316	55.66 323	92.60 321	58.54 328	2.96 341	9.00 339

ambc				86.56 305	83.60 326	70.00 324	85.69 327	94.97 274		80.60 297	88.45 309	37.42 334	96.84 283	82.69 259	75.44 309	92.86 289

pmmvs3		79.97 296	77.50 300	87.39 301	82.80 327	79.38 305	92.70 295	90.75 326	70.69 325	78.66 310	87.47 319	51.34 329	93.40 317	73.39 306	69.65 325	89.38 320

test1235678		79.82 297	78.53 298	83.69 309	82.55 328	67.55 327	92.50 298	94.13 301	79.28 305	72.10 320	86.45 321	57.27 319	90.68 327	61.60 324	80.90 296	92.82 290

TDRefinement		86.53 274	84.76 279	91.85 263	82.23 329	84.25 265	96.38 187	95.35 254	84.97 262	84.09 275	94.94 193	65.76 308	98.34 169	84.60 234	74.52 318	92.97 288

test12356		74.97 301	74.13 302	77.49 317	78.81 330	56.23 338	88.53 322	92.75 316	75.14 316	67.50 324	85.07 322	44.88 331	89.96 328	58.71 327	75.75 308	86.26 322

PMMVS2		70.19 305	66.92 307	80.01 313	76.35 331	65.67 329	86.22 326	87.58 333	64.83 329	62.38 328	80.29 327	26.78 341	88.49 332	63.79 320	54.07 331	85.88 324

FPMVS		71.27 304	69.85 304	75.50 319	74.64 332	59.03 336	91.30 305	91.50 323	58.80 330	57.92 330	88.28 311	29.98 339	85.53 334	53.43 332	82.84 288	81.95 326

E-PMN		53.28 313	52.56 314	55.43 326	74.43 333	47.13 341	83.63 330	76.30 342	42.23 336	42.59 335	62.22 336	28.57 340	74.40 339	31.53 338	31.51 336	44.78 335

no-one		68.12 306	63.78 309	81.13 311	74.01 334	70.22 323	87.61 325	90.71 327	72.63 324	53.13 332	71.89 331	30.29 337	91.45 324	61.53 325	32.21 335	81.72 327

PNet_i23d		59.01 310	55.87 311	68.44 323	73.98 335	51.37 339	81.36 331	82.41 339	52.37 333	42.49 336	70.39 333	11.39 344	79.99 338	49.77 334	38.71 333	73.97 331

wuyk23d		25.11 318	24.57 320	26.74 331	73.98 335	39.89 345	57.88 338	9.80 347	12.27 340	10.39 341	6.97 344	7.03 346	36.44 343	25.43 340	17.39 340	3.89 341

testmv		72.22 303	70.02 303	78.82 315	73.06 337	61.75 332	91.24 306	92.31 319	74.45 320	61.06 329	80.51 326	34.21 335	88.63 331	55.31 331	68.07 328	86.06 323

EMVS		52.08 315	51.31 315	54.39 327	72.62 338	45.39 343	83.84 329	75.51 343	41.13 337	40.77 337	59.65 337	30.08 338	73.60 340	28.31 339	29.90 338	44.18 336

LCM-MVSNet		72.55 302	69.39 305	82.03 310	70.81 339	65.42 330	90.12 316	94.36 295	55.02 331	65.88 326	81.72 324	24.16 343	89.96 328	74.32 303	68.10 327	90.71 318

MVE	50.73 23	53.25 314	48.81 317	66.58 325	65.34 340	57.50 337	72.49 336	70.94 344	40.15 338	39.28 338	63.51 335	6.89 348	73.48 341	38.29 337	42.38 332	68.76 333
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		56.92 312	51.11 316	74.38 322	62.30 341	61.47 333	80.09 332	84.87 336	49.62 334	30.80 340	57.20 338	7.03 346	82.94 335	55.69 330	32.36 334	78.72 329

ANet_high		63.94 309	59.58 310	77.02 318	61.24 342	66.06 328	85.66 328	87.93 332	78.53 310	42.94 334	71.04 332	25.42 342	80.71 336	52.60 333	30.83 337	84.28 325

PMVS	53.92 22	58.58 311	55.40 312	68.12 324	51.00 343	48.64 340	78.86 333	87.10 335	46.77 335	35.84 339	74.28 329	8.76 345	86.34 333	42.07 336	73.91 321	69.38 332
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		51.94 316	53.82 313	46.29 329	33.73 344	45.30 344	78.32 334	67.24 345	18.02 339	50.93 333	87.05 320	52.99 328	53.11 342	70.76 313	25.29 339	40.46 337

testmvs		13.36 320	16.33 321	4.48 333	5.04 345	2.26 347	93.18 284	3.28 348	2.70 341	8.24 342	21.66 340	2.29 350	2.19 344	7.58 341	2.96 341	9.00 339

test123		13.04 321	15.66 322	5.18 332	4.51 346	3.45 346	92.50 298	1.81 349	2.50 342	7.58 343	20.15 341	3.67 349	2.18 345	7.13 342	1.07 343	9.90 338

cdsmvs_eth3d_5k		23.24 319	30.99 319	0.00 334	0.00 347	0.00 348	0.00 339	97.63 106	0.00 343	0.00 344	96.88 103	84.38 120	0.00 346	0.00 343	0.00 344	0.00 342

pcd_1.5k_mvsjas		7.39 323	9.85 324	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	88.65 68	0.00 346	0.00 343	0.00 344	0.00 342

sosnet-low-res		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

sosnet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

uncertanet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

Regformer		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

ab-mvs-re		8.06 322	10.74 323	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	96.69 112	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

uanet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

ESAPD								98.25 25

sam_mvs1												82.76 156

sam_mvs												81.94 177

MTGPA								98.08 49

test_post1							92.81 294				16.58 343	80.53 200	97.68 246	86.20 206

test_post											17.58 342	81.76 179	98.08 188

patchmatchnet-post											90.45 290	82.65 160	98.10 185

MTMP								82.03 340

test9_res														94.81 60	99.38 33	99.45 28

agg_prior2														93.94 72	99.38 33	99.50 22

test_prior4						93.66 39	96.42 180

test_prior2							96.35 189		92.80 77	96.03 52	97.59 77	92.01 28		95.01 53	99.38 33

旧先验2							95.94 217		81.66 291	97.34 15			98.82 133	92.26 95

新几何2							95.79 224

无先验							95.79 224	97.87 84	83.87 275				99.65 39	87.68 179		98.89 78

原ACMM2							95.67 228

testdata2													99.67 37	85.96 213

segment_acmp												92.89 10

testdata1							95.26 249		93.10 65

plane_prior5								97.51 116					98.60 149	93.02 90	92.23 180	95.86 191

plane_prior4											96.64 115

plane_prior3						90.00 133			94.46 30	91.34 147

plane_prior2							97.74 54		94.85 17

plane_prior						89.99 135	97.24 102		94.06 38						92.16 184

n2								0.00 350

nn								0.00 350

door-mid								91.06 325

test11								97.88 82

door								91.13 324

HQP5-MVS						89.33 165

BP-MVS														92.13 101

HQP4-MVS										90.14 169			98.50 158			95.78 198

HQP3-MVS								97.39 135							92.10 185

HQP2-MVS												80.95 190

MDTV_nov1_ep13_2view						70.35 322	93.10 290		83.88 274	93.55 99		82.47 165		86.25 205		98.38 113

ACMMP++_ref															90.30 213

ACMMP++															91.02 203

Test By Simon												88.73 67

High-resolution multi-view benchmark