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.

AdaColmap		97.23 96	96.80 99	98.51 108	99.99 1	95.60 155	99.09 215	98.84 46	93.32 139	96.74 142	99.72 78	86.04 197	100.00 1	98.01 96	99.43 105	99.94 74

CNVR-MVS		99.40 1	99.26 1	99.84 3	99.98 2	99.51 3	99.98 8	98.69 54	98.20 3	99.93 1	99.98 2	96.82 18	100.00 1	99.75 19	100.00 1	99.99 17

MCST-MVS		99.32 3	99.14 4	99.86 2	99.97 3	99.59 2	99.97 16	98.64 62	98.47 2	99.13 70	99.92 10	96.38 23	100.00 1	99.74 21	100.00 1	100.00 1

mPP-MVS		98.39 51	98.20 48	98.97 79	99.97 3	96.92 111	99.95 37	98.38 134	95.04 75	98.61 93	99.80 56	93.39 100	100.00 1	98.64 74	100.00 1	99.98 48

CPTT-MVS		97.64 83	97.32 84	98.58 101	99.97 3	95.77 148	99.96 23	98.35 140	89.90 230	98.36 103	99.79 58	91.18 144	99.99 34	98.37 82	99.99 18	99.99 17

DP-MVS Recon		98.41 48	98.02 57	99.56 19	99.97 3	98.70 39	99.92 62	98.44 104	92.06 185	98.40 102	99.84 45	95.68 35	100.00 1	98.19 86	99.71 86	99.97 59

PAPR		98.52 40	98.16 50	99.58 18	99.97 3	98.77 32	99.95 37	98.43 112	95.35 68	98.03 114	99.75 73	94.03 86	99.98 40	98.11 91	99.83 74	99.99 17

HFP-MVS		98.56 36	98.37 39	99.14 61	99.96 8	97.43 93	99.95 37	98.61 68	94.77 82	99.31 59	99.85 32	94.22 79	100.00 1	98.70 68	99.98 31	99.98 48

region2R		98.54 38	98.37 39	99.05 71	99.96 8	97.18 101	99.96 23	98.55 80	94.87 80	99.45 48	99.85 32	94.07 85	100.00 1	98.67 70	100.00 1	99.98 48

#test#		98.59 34	98.41 31	99.14 61	99.96 8	97.43 93	99.95 37	98.61 68	95.00 76	99.31 59	99.85 32	94.22 79	100.00 1	98.78 65	99.98 31	99.98 48

ACMMPR		98.50 41	98.32 43	99.05 71	99.96 8	97.18 101	99.95 37	98.60 70	94.77 82	99.31 59	99.84 45	93.73 94	100.00 1	98.70 68	99.98 31	99.98 48

NCCC		99.37 2	99.25 2	99.71 8	99.96 8	99.15 13	99.97 16	98.62 66	98.02 6	99.90 2	99.95 3	97.33 12	100.00 1	99.54 29	100.00 1	100.00 1

CP-MVS		98.45 45	98.32 43	98.87 84	99.96 8	96.62 118	99.97 16	98.39 130	94.43 94	98.90 80	99.87 25	94.30 77	100.00 1	99.04 49	99.99 18	99.99 17

testtj		98.89 16	98.69 16	99.52 23	99.94 14	98.56 49	99.90 70	98.55 80	95.14 74	99.72 27	99.84 45	95.46 41	100.00 1	99.65 28	99.99 18	99.99 17

test_0728_SECOND				99.82 4	99.94 14	99.47 4	99.95 37	98.43 112					100.00 1	99.99 2	100.00 1	100.00 1

XVS		98.70 26	98.55 23	99.15 59	99.94 14	97.50 89	99.94 52	98.42 122	96.22 46	99.41 52	99.78 62	94.34 72	99.96 51	98.92 56	99.95 47	99.99 17

test_prior3		98.99 12	98.84 13	99.43 33	99.94 14	98.49 53	99.95 37	98.65 59	95.78 55	99.73 24	99.76 68	96.00 26	99.80 104	99.78 17	100.00 1	99.99 17

X-MVStestdata		93.83 182	92.06 208	99.15 59	99.94 14	97.50 89	99.94 52	98.42 122	96.22 46	99.41 52	41.37 350	94.34 72	99.96 51	98.92 56	99.95 47	99.99 17

test_prior				99.43 33	99.94 14	98.49 53		98.65 59					99.80 104			99.99 17

MSLP-MVS++		99.13 6	99.01 8	99.49 29	99.94 14	98.46 55	99.98 8	98.86 44	97.10 22	99.80 15	99.94 4	95.92 30	100.00 1	99.51 30	100.00 1	100.00 1

APDe-MVS		99.06 9	98.91 11	99.51 25	99.94 14	98.76 36	99.91 66	98.39 130	97.20 21	99.46 47	99.85 32	95.53 40	99.79 106	99.86 9	100.00 1	99.99 17

MP-MVS		98.23 60	97.97 60	99.03 73	99.94 14	97.17 104	99.95 37	98.39 130	94.70 85	98.26 109	99.81 55	91.84 135	100.00 1	98.85 60	99.97 41	99.93 75
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 30	98.36 41	99.49 29	99.94 14	98.73 37	99.87 84	98.33 143	93.97 115	99.76 22	99.87 25	94.99 54	99.75 114	98.55 77	100.00 1	99.98 48

PAPM_NR		98.12 63	97.93 64	98.70 90	99.94 14	96.13 137	99.82 109	98.43 112	94.56 89	97.52 125	99.70 82	94.40 67	99.98 40	97.00 124	99.98 31	99.99 17

MG-MVS		98.91 15	98.65 18	99.68 9	99.94 14	99.07 15	99.64 153	99.44 18	97.33 15	99.00 77	99.72 78	94.03 86	99.98 40	98.73 67	100.00 1	100.00 1

test_241102_ONE					99.93 26	99.30 6		98.43 112	97.26 19	99.80 15	99.88 21	96.71 19	100.00 1

ETH3 D test6400		98.81 20	98.54 24	99.59 16	99.93 26	98.93 19	99.93 58	98.46 101	94.56 89	99.84 8	99.92 10	94.32 76	99.86 88	99.96 5	99.98 31	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 6	99.93 26	99.29 7	99.95 37	98.32 145	97.28 16	99.83 9	99.91 12	97.22 14	100.00 1	99.99 2	100.00 1	99.89 84

test0726					99.93 26	99.29 7	99.96 23	98.42 122	97.28 16	99.86 4	99.94 4	97.22 14

MSP-MVS		99.09 7	99.12 5	98.98 78	99.93 26	97.24 98	99.95 37	98.42 122	97.50 12	99.52 45	99.88 21	97.43 11	99.71 122	99.50 31	99.98 31	100.00 1

agg_prior1		98.88 17	98.66 17	99.54 21	99.93 26	98.77 32	99.96 23	98.43 112	94.63 88	99.63 33	99.85 32	95.79 34	99.85 92	99.72 25	99.99 18	99.99 17

agg_prior					99.93 26	98.77 32		98.43 112		99.63 33			99.85 92

GST-MVS		98.27 56	97.97 60	99.17 55	99.92 33	97.57 83	99.93 58	98.39 130	94.04 113	98.80 83	99.74 75	92.98 112	100.00 1	98.16 88	99.76 82	99.93 75

TEST9					99.92 33	98.92 20	99.96 23	98.43 112	93.90 120	99.71 28	99.86 28	95.88 31	99.85 92

train_agg		98.88 17	98.65 18	99.59 16	99.92 33	98.92 20	99.96 23	98.43 112	94.35 97	99.71 28	99.86 28	95.94 28	99.85 92	99.69 27	99.98 31	99.99 17

test_8					99.92 33	98.88 23	99.96 23	98.43 112	94.35 97	99.69 30	99.85 32	95.94 28	99.85 92

PGM-MVS		98.34 52	98.13 52	98.99 77	99.92 33	97.00 107	99.75 127	99.50 16	93.90 120	99.37 56	99.76 68	93.24 107	100.00 1	97.75 109	99.96 44	99.98 48

ACMMP		97.74 80	97.44 78	98.66 93	99.92 33	96.13 137	99.18 210	99.45 17	94.84 81	96.41 152	99.71 80	91.40 138	99.99 34	97.99 98	98.03 137	99.87 87
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
HPM-MVS++		99.07 8	98.88 12	99.63 10	99.90 39	99.02 16	99.95 37	98.56 76	97.56 11	99.44 49	99.85 32	95.38 43	100.00 1	99.31 38	99.99 18	99.87 87

APD-MVS		98.62 31	98.35 42	99.41 37	99.90 39	98.51 52	99.87 84	98.36 139	94.08 108	99.74 23	99.73 77	94.08 84	99.74 118	99.42 35	99.99 18	99.99 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	96.59 1	98.81 20	98.54 24	99.62 13	99.90 39	98.85 26	99.24 206	98.47 99	98.14 4	99.08 71	99.91 12	93.09 110	100.00 1	99.04 49	99.99 18	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DPE-MVS		99.26 5	99.10 6	99.74 5	99.89 42	99.24 11	99.87 84	98.44 104	97.48 13	99.64 32	99.94 4	96.68 20	99.99 34	99.99 2	100.00 1	99.99 17

test_part2					99.89 42	99.25 10				99.49 46

CSCG		97.10 99	97.04 92	97.27 162	99.89 42	91.92 235	99.90 70	99.07 31	88.67 250	95.26 171	99.82 51	93.17 109	99.98 40	98.15 89	99.47 102	99.90 83

ZNCC-MVS		98.31 54	98.03 56	99.17 55	99.88 45	97.59 82	99.94 52	98.44 104	94.31 100	98.50 97	99.82 51	93.06 111	99.99 34	98.30 85	99.99 18	99.93 75

SR-MVS		98.46 44	98.30 45	98.93 82	99.88 45	97.04 106	99.84 102	98.35 140	94.92 77	99.32 58	99.80 56	93.35 101	99.78 108	99.30 39	99.95 47	99.96 64

9.14			98.38 37		99.87 47		99.91 66	98.33 143	93.22 142	99.78 20	99.89 18	94.57 64	99.85 92	99.84 10	99.97 41

SMA-MVS		98.76 24	98.48 27	99.62 13	99.87 47	98.87 24	99.86 95	98.38 134	93.19 143	99.77 21	99.94 4	95.54 37	100.00 1	99.74 21	99.99 18	100.00 1

PHI-MVS		98.41 48	98.21 47	99.03 73	99.86 49	97.10 105	99.98 8	98.80 49	90.78 217	99.62 35	99.78 62	95.30 44	100.00 1	99.80 15	99.93 59	99.99 17

zzz-MVS		98.33 53	98.00 58	99.30 45	99.85 50	97.93 73	99.80 115	98.28 149	95.76 57	97.18 132	99.88 21	92.74 117	100.00 1	98.67 70	99.88 69	99.99 17

MTAPA		98.29 55	97.96 63	99.30 45	99.85 50	97.93 73	99.39 189	98.28 149	95.76 57	97.18 132	99.88 21	92.74 117	100.00 1	98.67 70	99.88 69	99.99 17

Regformer-1		98.79 22	98.60 21	99.36 43	99.85 50	98.34 58	99.87 84	98.52 87	96.05 50	99.41 52	99.79 58	94.93 56	99.76 111	99.07 44	99.90 65	99.99 17

Regformer-2		98.78 23	98.59 22	99.36 43	99.85 50	98.32 59	99.87 84	98.52 87	96.04 51	99.41 52	99.79 58	94.92 57	99.76 111	99.05 45	99.90 65	99.98 48

LS3D		95.84 142	95.11 151	98.02 133	99.85 50	95.10 169	98.74 251	98.50 97	87.22 270	93.66 189	99.86 28	87.45 184	99.95 58	90.94 221	99.81 80	99.02 183

Regformer-3		98.58 35	98.41 31	99.10 67	99.84 55	97.57 83	99.66 146	98.52 87	95.79 54	99.01 75	99.77 64	94.40 67	99.75 114	98.82 61	99.83 74	99.98 48

Regformer-4		98.56 36	98.39 36	99.08 69	99.84 55	97.52 86	99.66 146	98.52 87	95.76 57	99.01 75	99.77 64	94.33 75	99.75 114	98.80 64	99.83 74	99.98 48

HPM-MVS		97.96 67	97.72 68	98.68 91	99.84 55	96.39 126	99.90 70	98.17 165	92.61 166	98.62 92	99.57 99	91.87 134	99.67 129	98.87 59	99.99 18	99.99 17
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ETH3D-3000-0.1		98.68 27	98.42 29	99.47 32	99.83 58	98.57 48	99.90 70	98.37 137	93.81 123	99.81 11	99.90 16	94.34 72	99.86 88	99.84 10	99.98 31	99.97 59

EI-MVSNet-Vis-set		98.27 56	98.11 53	98.75 89	99.83 58	96.59 120	99.40 185	98.51 93	95.29 70	98.51 96	99.76 68	93.60 98	99.71 122	98.53 78	99.52 99	99.95 72

save fliter					99.82 60	98.79 30	99.96 23	98.40 127	97.66 9

PLC	95.54 3	97.93 69	97.89 65	98.05 132	99.82 60	94.77 179	99.92 62	98.46 101	93.93 118	97.20 131	99.27 121	95.44 42	99.97 49	97.41 114	99.51 101	99.41 154
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 59	98.08 54	98.78 87	99.81 62	96.60 119	99.82 109	98.30 147	93.95 117	99.37 56	99.77 64	92.84 114	99.76 111	98.95 53	99.92 63	99.97 59

EI-MVSNet-UG-set		98.14 62	97.99 59	98.60 98	99.80 63	96.27 127	99.36 194	98.50 97	95.21 73	98.30 106	99.75 73	93.29 105	99.73 121	98.37 82	99.30 108	99.81 92

HPM-MVS_fast		97.80 77	97.50 76	98.68 91	99.79 64	96.42 123	99.88 81	98.16 168	91.75 193	98.94 79	99.54 102	91.82 136	99.65 131	97.62 111	99.99 18	99.99 17

xxxxxxxxxxxxxcwj		98.67 28	98.40 33	99.50 26	99.77 65	98.67 40	99.90 70	98.21 158	93.53 133	99.81 11	99.89 18	94.70 61	99.86 88	99.84 10	99.93 59	99.96 64

xxxxxxxxxxxx		98.67 28	98.40 33	99.50 26	99.77 65	98.67 40	99.90 70	98.21 158	93.53 133	99.81 11	99.89 18	94.70 61	99.86 88	99.84 10	99.93 59	99.96 64

旧先验1					99.76 67	97.52 86		98.64 62			99.85 32	95.63 36			99.94 53	99.99 17

OMC-MVS		97.28 93	97.23 85	97.41 155	99.76 67	93.36 205	99.65 149	97.95 187	96.03 52	97.41 128	99.70 82	89.61 162	99.51 136	96.73 131	98.25 131	99.38 156

新几何1				99.42 36	99.75 69	98.27 60		98.63 65	92.69 160	99.55 40	99.82 51	94.40 67	100.00 1	91.21 213	99.94 53	99.99 17

MP-MVS-pluss		98.07 65	97.64 71	99.38 42	99.74 70	98.41 56	99.74 130	98.18 164	93.35 138	96.45 149	99.85 32	92.64 120	99.97 49	98.91 58	99.89 67	99.77 98
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 13	98.77 14	99.41 37	99.74 70	98.67 40	99.77 121	98.38 134	96.73 32	99.88 3	99.74 75	94.89 58	99.59 133	99.80 15	99.98 31	99.97 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
1121		98.03 66	97.57 75	99.40 39	99.74 70	98.21 61	98.31 275	98.62 66	92.78 155	99.53 42	99.83 48	95.08 48	100.00 1	94.36 166	99.92 63	99.99 17

test12				99.43 33	99.74 70	98.56 49		98.40 127		99.65 31		94.76 59	99.75 114		99.98 31	99.99 17

原ACMM1				98.96 80	99.73 74	96.99 108		98.51 93	94.06 111	99.62 35	99.85 32	94.97 55	99.96 51	95.11 147	99.95 47	99.92 81

TSAR-MVS + GP.		98.60 32	98.51 26	98.86 85	99.73 74	96.63 117	99.97 16	97.92 191	98.07 5	98.76 85	99.55 100	95.00 53	99.94 66	99.91 8	97.68 141	99.99 17

CANet		98.27 56	97.82 66	99.63 10	99.72 76	99.10 14	99.98 8	98.51 93	97.00 25	98.52 95	99.71 80	87.80 180	99.95 58	99.75 19	99.38 106	99.83 90

F-COLMAP		96.93 106	96.95 95	96.87 169	99.71 77	91.74 240	99.85 98	97.95 187	93.11 146	95.72 164	99.16 130	92.35 124	99.94 66	95.32 145	99.35 107	98.92 185

SD-MVS		98.92 14	98.70 15	99.56 19	99.70 78	98.73 37	99.94 52	98.34 142	96.38 41	99.81 11	99.76 68	94.59 63	99.98 40	99.84 10	99.96 44	99.97 59
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
abl_6		97.67 82	97.34 82	98.66 93	99.68 79	96.11 141	99.68 143	98.14 171	93.80 124	99.27 63	99.70 82	88.65 176	99.98 40	97.46 113	99.72 85	99.89 84

ACMMP_NAP		98.49 42	98.14 51	99.54 21	99.66 80	98.62 47	99.85 98	98.37 137	94.68 86	99.53 42	99.83 48	92.87 113	100.00 1	98.66 73	99.84 73	99.99 17

DeepPCF-MVS	95.94 2	97.71 81	98.98 9	93.92 256	99.63 81	81.76 324	99.96 23	98.56 76	99.47 1	99.19 68	99.99 1	94.16 83	100.00 1	99.92 6	99.93 59	100.00 1

EPNet		98.49 42	98.40 33	98.77 88	99.62 82	96.80 114	99.90 70	99.51 15	97.60 10	99.20 66	99.36 117	93.71 95	99.91 72	97.99 98	98.71 120	99.61 121
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ETH3D cwj APD-0.16		98.40 50	98.07 55	99.40 39	99.59 83	98.41 56	99.86 95	98.24 154	92.18 180	99.73 24	99.87 25	93.47 99	99.85 92	99.74 21	99.95 47	99.93 75

PVSNet_BlendedMVS		96.05 137	95.82 134	96.72 174	99.59 83	96.99 108	99.95 37	99.10 28	94.06 111	98.27 107	95.80 246	89.00 171	99.95 58	99.12 42	87.53 244	93.24 302

PVSNet_Blended		97.94 68	97.64 71	98.83 86	99.59 83	96.99 108	100.00 1	99.10 28	95.38 67	98.27 107	99.08 133	89.00 171	99.95 58	99.12 42	99.25 109	99.57 132

PatchMatch-RL		96.04 138	95.40 141	97.95 134	99.59 83	95.22 167	99.52 171	99.07 31	93.96 116	96.49 148	98.35 184	82.28 223	99.82 103	90.15 235	99.22 110	98.81 192

test222					99.55 87	97.41 96	99.34 195	98.55 80	91.86 189	99.27 63	99.83 48	93.84 92			99.95 47	99.99 17

CNLPA		97.76 79	97.38 79	98.92 83	99.53 88	96.84 112	99.87 84	98.14 171	93.78 125	96.55 147	99.69 85	92.28 126	99.98 40	97.13 120	99.44 104	99.93 75

API-MVS		97.86 71	97.66 70	98.47 110	99.52 89	95.41 159	99.47 179	98.87 43	91.68 194	98.84 81	99.85 32	92.34 125	99.99 34	98.44 80	99.96 44	100.00 1

PVSNet	91.05 13	97.13 98	96.69 103	98.45 112	99.52 89	95.81 146	99.95 37	99.65 10	94.73 84	99.04 73	99.21 128	84.48 210	99.95 58	94.92 150	98.74 119	99.58 131

114514_t		97.41 90	96.83 97	99.14 61	99.51 91	97.83 75	99.89 79	98.27 152	88.48 254	99.06 72	99.66 91	90.30 155	99.64 132	96.32 134	99.97 41	99.96 64

cl-mvsnet2		93.77 186	93.25 189	95.33 204	99.49 92	94.43 183	99.61 158	98.09 175	90.38 221	89.16 245	95.61 253	90.56 153	97.34 240	91.93 205	84.45 264	94.21 247

testdata				98.42 115	99.47 93	95.33 161		98.56 76	93.78 125	99.79 19	99.85 32	93.64 97	99.94 66	94.97 149	99.94 53	100.00 1

MAR-MVS		97.43 86	97.19 86	98.15 128	99.47 93	94.79 178	99.05 226	98.76 50	92.65 164	98.66 90	99.82 51	88.52 177	99.98 40	98.12 90	99.63 90	99.67 111
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
DP-MVS		94.54 170	93.42 182	97.91 137	99.46 95	94.04 190	98.93 237	97.48 233	81.15 316	90.04 219	99.55 100	87.02 189	99.95 58	88.97 244	98.11 132	99.73 102

MVS_111021_LR		98.42 47	98.38 37	98.53 107	99.39 96	95.79 147	99.87 84	99.86 2	96.70 33	98.78 84	99.79 58	92.03 131	99.90 73	99.17 41	99.86 72	99.88 86

CHOSEN 280x420		99.01 11	99.03 7	98.95 81	99.38 97	98.87 24	98.46 268	99.42 20	97.03 24	99.02 74	99.09 132	99.35 1	98.21 209	99.73 24	99.78 81	99.77 98

MVS_111021_HR		98.72 25	98.62 20	99.01 76	99.36 98	97.18 101	99.93 58	99.90 1	96.81 30	98.67 89	99.77 64	93.92 88	99.89 77	99.27 40	99.94 53	99.96 64

DPM-MVS		98.83 19	98.46 28	99.97 1	99.33 99	99.92 1	99.96 23	98.44 104	97.96 7	99.55 40	99.94 4	97.18 16	100.00 1	93.81 180	99.94 53	99.98 48

TAPA-MVS	92.12 8	94.42 174	93.60 176	96.90 168	99.33 99	91.78 239	99.78 118	98.00 181	89.89 231	94.52 177	99.47 106	91.97 132	99.18 148	69.90 328	99.52 99	99.73 102
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_yl		97.83 74	97.37 80	99.21 49	99.18 101	97.98 70	99.64 153	99.27 25	91.43 202	97.88 118	98.99 141	95.84 32	99.84 101	98.82 61	95.32 189	99.79 94

DCV-MVSNet		97.83 74	97.37 80	99.21 49	99.18 101	97.98 70	99.64 153	99.27 25	91.43 202	97.88 118	98.99 141	95.84 32	99.84 101	98.82 61	95.32 189	99.79 94

DeepC-MVS	94.51 4	96.92 107	96.40 112	98.45 112	99.16 103	95.90 144	99.66 146	98.06 178	96.37 44	94.37 180	99.49 105	83.29 219	99.90 73	97.63 110	99.61 94	99.55 134
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		98.54 38	98.22 46	99.50 26	99.15 104	98.65 45	100.00 1	98.58 72	97.70 8	98.21 111	99.24 126	92.58 121	99.94 66	98.63 75	99.94 53	99.92 81
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
Anonymous202405211		93.10 199	91.99 209	96.40 182	99.10 105	89.65 276	98.88 241	97.93 189	83.71 306	94.00 185	98.75 164	68.79 305	99.88 83	95.08 148	91.71 212	99.68 109

HyFIR lowres test		96.66 120	96.43 111	97.36 159	99.05 106	93.91 193	99.70 140	99.80 3	90.54 219	96.26 154	98.08 189	92.15 129	98.23 208	96.84 130	95.46 186	99.93 75

LFMVS		94.75 164	93.56 179	98.30 121	99.03 107	95.70 153	98.74 251	97.98 184	87.81 263	98.47 98	99.39 114	67.43 312	99.53 134	98.01 96	95.20 191	99.67 111

AllTest		92.48 212	91.64 214	95.00 213	99.01 108	88.43 289	98.94 236	96.82 291	86.50 280	88.71 250	98.47 182	74.73 283	99.88 83	85.39 278	96.18 169	96.71 213

TestCases				95.00 213	99.01 108	88.43 289		96.82 291	86.50 280	88.71 250	98.47 182	74.73 283	99.88 83	85.39 278	96.18 169	96.71 213

COLMAP_ROB	90.47 14	92.18 220	91.49 220	94.25 243	99.00 110	88.04 295	98.42 273	96.70 297	82.30 313	88.43 257	99.01 138	76.97 264	99.85 92	86.11 275	96.50 166	94.86 220
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HY-MVS	92.50 7	97.79 78	97.17 88	99.63 10	98.98 111	99.32 5	97.49 297	99.52 13	95.69 61	98.32 105	97.41 201	93.32 103	99.77 109	98.08 94	95.75 182	99.81 92

VNet		97.21 97	96.57 107	99.13 66	98.97 112	97.82 76	99.03 228	99.21 27	94.31 100	99.18 69	98.88 154	86.26 196	99.89 77	98.93 55	94.32 197	99.69 108

thres200		96.96 104	96.21 115	99.22 48	98.97 112	98.84 27	99.85 98	99.71 5	93.17 144	96.26 154	98.88 154	89.87 160	99.51 136	94.26 170	94.91 192	99.31 164

tfpn200view9		96.79 111	95.99 120	99.19 51	98.94 114	98.82 28	99.78 118	99.71 5	92.86 149	96.02 157	98.87 156	89.33 165	99.50 138	93.84 177	94.57 193	99.27 167

thres400		96.78 112	95.99 120	99.16 57	98.94 114	98.82 28	99.78 118	99.71 5	92.86 149	96.02 157	98.87 156	89.33 165	99.50 138	93.84 177	94.57 193	99.16 174

Anonymous20231211		89.86 267	88.44 272	94.13 247	98.93 116	90.68 258	98.54 265	98.26 153	76.28 326	86.73 278	95.54 257	70.60 300	97.56 233	90.82 224	80.27 296	94.15 255

canonicalmvs		97.09 101	96.32 113	99.39 41	98.93 116	98.95 18	99.72 138	97.35 244	94.45 92	97.88 118	99.42 110	86.71 191	99.52 135	98.48 79	93.97 202	99.72 105

EPNet_dtu		95.71 146	95.39 142	96.66 176	98.92 118	93.41 203	99.57 163	98.90 40	96.19 48	97.52 125	98.56 176	92.65 119	97.36 238	77.89 314	98.33 127	99.20 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 64	97.60 73	99.60 15	98.92 118	99.28 9	99.89 79	99.52 13	95.58 64	98.24 110	99.39 114	93.33 102	99.74 118	97.98 100	95.58 185	99.78 97

CHOSEN 1792x2688		96.81 110	96.53 108	97.64 147	98.91 120	93.07 207	99.65 149	99.80 3	95.64 62	95.39 168	98.86 158	84.35 212	99.90 73	96.98 125	99.16 111	99.95 72

thres100view900		96.74 115	95.92 130	99.18 52	98.90 121	98.77 32	99.74 130	99.71 5	92.59 168	95.84 160	98.86 158	89.25 167	99.50 138	93.84 177	94.57 193	99.27 167

thres600view7		96.69 118	95.87 133	99.14 61	98.90 121	98.78 31	99.74 130	99.71 5	92.59 168	95.84 160	98.86 158	89.25 167	99.50 138	93.44 189	94.50 196	99.16 174

MSDG		94.37 175	93.36 186	97.40 156	98.88 123	93.95 192	99.37 192	97.38 242	85.75 291	90.80 212	99.17 129	84.11 214	99.88 83	86.35 272	98.43 125	98.36 197

Anonymous20240529		92.10 221	90.65 230	96.47 178	98.82 124	90.61 260	98.72 253	98.67 58	75.54 330	93.90 187	98.58 174	66.23 315	99.90 73	94.70 160	90.67 213	98.90 188

PVSNet_Blended_VisFu		97.27 94	96.81 98	98.66 93	98.81 125	96.67 116	99.92 62	98.64 62	94.51 91	96.38 153	98.49 178	89.05 170	99.88 83	97.10 122	98.34 126	99.43 152

PS-MVSNAJ		98.44 46	98.20 48	99.16 57	98.80 126	98.92 20	99.54 169	98.17 165	97.34 14	99.85 6	99.85 32	91.20 141	99.89 77	99.41 36	99.67 88	98.69 195

CANet_DTU		96.76 113	96.15 116	98.60 98	98.78 127	97.53 85	99.84 102	97.63 210	97.25 20	99.20 66	99.64 94	81.36 231	99.98 40	92.77 199	98.89 115	98.28 198

alignmvs		97.81 76	97.33 83	99.25 47	98.77 128	98.66 43	99.99 4	98.44 104	94.40 96	98.41 100	99.47 106	93.65 96	99.42 144	98.57 76	94.26 198	99.67 111

SteuartSystems-ACMMP		99.02 10	98.97 10	99.18 52	98.72 129	97.71 78	99.98 8	98.44 104	96.85 26	99.80 15	99.91 12	97.57 6	99.85 92	99.44 34	99.99 18	99.99 17
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 60	97.97 60	99.02 75	98.69 130	98.66 43	99.52 171	98.08 177	97.05 23	99.86 4	99.86 28	90.65 151	99.71 122	99.39 37	98.63 121	98.69 195

miper_enhance_ethall		94.36 177	93.98 168	95.49 199	98.68 131	95.24 165	99.73 135	97.29 249	93.28 141	89.86 224	95.97 244	94.37 71	97.05 260	92.20 203	84.45 264	94.19 248

MVSTER		95.53 149	95.22 147	96.45 180	98.56 132	97.72 77	99.91 66	97.67 208	92.38 176	91.39 207	97.14 208	97.24 13	97.30 243	94.80 155	87.85 239	94.34 238

VDD-MVS		93.77 186	92.94 191	96.27 186	98.55 133	90.22 268	98.77 250	97.79 202	90.85 215	96.82 140	99.42 110	61.18 329	99.77 109	98.95 53	94.13 199	98.82 191

tpmvs		94.28 178	93.57 178	96.40 182	98.55 133	91.50 249	95.70 320	98.55 80	87.47 265	92.15 202	94.26 303	91.42 137	98.95 156	88.15 253	95.85 178	98.76 194

UGNet		95.33 153	94.57 159	97.62 149	98.55 133	94.85 173	98.67 258	99.32 24	95.75 60	96.80 141	96.27 237	72.18 293	99.96 51	94.58 163	99.05 113	98.04 202
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
PCF-MVS	94.20 5	95.18 154	94.10 166	98.43 114	98.55 133	95.99 142	97.91 291	97.31 248	90.35 223	89.48 235	99.22 127	85.19 205	99.89 77	90.40 232	98.47 124	99.41 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-w/o		95.71 146	95.38 143	96.68 175	98.49 137	92.28 226	99.84 102	97.50 231	92.12 182	92.06 203	98.79 163	84.69 208	98.67 173	95.29 146	99.66 89	99.09 181

baseline1		95.78 143	94.86 154	98.54 105	98.47 138	98.07 65	99.06 222	97.99 182	92.68 162	94.13 184	98.62 171	93.28 106	98.69 172	93.79 182	85.76 252	98.84 190

EPMVS		96.53 123	96.01 119	98.09 130	98.43 139	96.12 140	96.36 310	99.43 19	93.53 133	97.64 123	95.04 281	94.41 66	98.38 197	91.13 215	98.11 132	99.75 100

sss		97.57 84	97.03 93	99.18 52	98.37 140	98.04 67	99.73 135	99.38 21	93.46 136	98.76 85	99.06 134	91.21 140	99.89 77	96.33 133	97.01 158	99.62 119

BH-untuned		95.18 154	94.83 155	96.22 187	98.36 141	91.22 251	99.80 115	97.32 247	90.91 213	91.08 209	98.67 167	83.51 216	98.54 180	94.23 171	99.61 94	98.92 185

ET-MVSNet_ETH3D		94.37 175	93.28 188	97.64 147	98.30 142	97.99 69	99.99 4	97.61 216	94.35 97	71.57 331	99.45 109	96.23 24	95.34 315	96.91 129	85.14 260	99.59 125

FMVSNet3		92.69 208	91.58 216	95.99 191	98.29 143	97.42 95	99.26 205	97.62 213	89.80 232	89.68 228	95.32 271	81.62 229	96.27 296	87.01 268	85.65 253	94.29 241

CS-MVS		97.84 73	97.69 69	98.31 120	98.28 144	96.27 127	100.00 1	97.52 228	95.29 70	99.25 65	99.65 93	91.18 144	98.94 157	98.96 52	99.04 114	99.73 102

PMMVS		96.76 113	96.76 101	96.76 172	98.28 144	92.10 230	99.91 66	97.98 184	94.12 106	99.53 42	99.39 114	86.93 190	98.73 168	96.95 127	97.73 139	99.45 149

PVSNet_0	88.03 19	91.80 228	90.27 239	96.38 184	98.27 146	90.46 264	99.94 52	99.61 11	93.99 114	86.26 288	97.39 203	71.13 299	99.89 77	98.77 66	67.05 334	98.79 193

PatchFormer-LS_test		97.01 102	96.79 100	97.69 145	98.26 147	94.80 176	98.66 261	98.13 173	93.70 129	97.86 121	98.80 162	95.54 37	98.67 173	94.12 173	96.00 173	99.60 123

DWT-MVSNet_test		97.31 92	97.19 86	97.66 146	98.24 148	94.67 180	98.86 245	98.20 163	93.60 132	98.09 112	98.89 152	97.51 7	98.78 163	94.04 174	97.28 150	99.55 134

UA-Net		96.54 122	95.96 127	98.27 122	98.23 149	95.71 152	98.00 289	98.45 103	93.72 128	98.41 100	99.27 121	88.71 175	99.66 130	91.19 214	97.69 140	99.44 151

GG-mvs-BLEND				98.54 105	98.21 150	98.01 68	93.87 325	98.52 87		97.92 116	97.92 194	99.02 2	97.94 223	98.17 87	99.58 96	99.67 111

mvs_anonymous		95.65 148	95.03 152	97.53 150	98.19 151	95.74 150	99.33 196	97.49 232	90.87 214	90.47 215	97.10 210	88.23 178	97.16 251	95.92 139	97.66 142	99.68 109

MVS_Test		96.46 125	95.74 135	98.61 97	98.18 152	97.23 99	99.31 199	97.15 260	91.07 210	98.84 81	97.05 214	88.17 179	98.97 155	94.39 165	97.50 144	99.61 121

BH-RMVSNet		95.18 154	94.31 163	97.80 138	98.17 153	95.23 166	99.76 126	97.53 226	92.52 172	94.27 182	99.25 125	76.84 266	98.80 161	90.89 223	99.54 98	99.35 160

RPSCF		91.80 228	92.79 194	88.83 310	98.15 154	69.87 337	98.11 285	96.60 300	83.93 304	94.33 181	99.27 121	79.60 249	99.46 143	91.99 204	93.16 209	97.18 211

ETV-MVS		97.92 70	97.80 67	98.25 123	98.14 155	96.48 121	99.98 8	97.63 210	95.61 63	99.29 62	99.46 108	92.55 122	98.82 160	99.02 51	98.54 122	99.46 147

IS-MVSNet		96.29 133	95.90 131	97.45 153	98.13 156	94.80 176	99.08 217	97.61 216	92.02 186	95.54 167	98.96 147	90.64 152	98.08 213	93.73 185	97.41 148	99.47 146

ab-mvs		94.69 165	93.42 182	98.51 108	98.07 157	96.26 129	96.49 309	98.68 55	90.31 224	94.54 176	97.00 216	76.30 271	99.71 122	95.98 138	93.38 207	99.56 133

XVG-OURS-SEG-HR		94.79 161	94.70 158	95.08 210	98.05 158	89.19 279	99.08 217	97.54 224	93.66 130	94.87 174	99.58 98	78.78 255	99.79 106	97.31 116	93.40 206	96.25 215

EIA-MVS		97.53 85	97.46 77	97.76 142	98.04 159	94.84 174	99.98 8	97.61 216	94.41 95	97.90 117	99.59 97	92.40 123	98.87 158	98.04 95	99.13 112	99.59 125

XVG-OURS		94.82 160	94.74 157	95.06 211	98.00 160	89.19 279	99.08 217	97.55 222	94.10 107	94.71 175	99.62 95	80.51 242	99.74 118	96.04 137	93.06 210	96.25 215

dp		95.05 157	94.43 161	96.91 167	97.99 161	92.73 216	96.29 312	97.98 184	89.70 233	95.93 159	94.67 294	93.83 93	98.45 186	86.91 271	96.53 165	99.54 138

tpmrst		96.27 135	95.98 122	97.13 163	97.96 162	93.15 206	96.34 311	98.17 165	92.07 183	98.71 88	95.12 279	93.91 89	98.73 168	94.91 152	96.62 163	99.50 144

TR-MVS		94.54 170	93.56 179	97.49 152	97.96 162	94.34 185	98.71 254	97.51 230	90.30 225	94.51 178	98.69 166	75.56 276	98.77 165	92.82 198	95.99 174	99.35 160

Vis-MVSNet (Re-imp)		96.32 130	95.98 122	97.35 160	97.93 164	94.82 175	99.47 179	98.15 170	91.83 190	95.09 172	99.11 131	91.37 139	97.47 236	93.47 188	97.43 145	99.74 101

MDTV_nov1_ep13			95.69 136		97.90 165	94.15 188	95.98 316	98.44 104	93.12 145	97.98 115	95.74 248	95.10 47	98.58 177	90.02 236	96.92 160

Fast-Effi-MVS+		95.02 158	94.19 164	97.52 151	97.88 166	94.55 181	99.97 16	97.08 264	88.85 247	94.47 179	97.96 193	84.59 209	98.41 189	89.84 238	97.10 155	99.59 125

ADS-MVSNet2		93.80 185	93.88 171	93.55 266	97.87 167	85.94 304	94.24 321	96.84 288	90.07 227	96.43 150	94.48 299	90.29 156	95.37 314	87.44 259	97.23 152	99.36 158

ADS-MVSNet		94.79 161	94.02 167	97.11 165	97.87 167	93.79 194	94.24 321	98.16 168	90.07 227	96.43 150	94.48 299	90.29 156	98.19 210	87.44 259	97.23 152	99.36 158

Effi-MVS+		96.30 132	95.69 136	98.16 125	97.85 169	96.26 129	97.41 298	97.21 253	90.37 222	98.65 91	98.58 174	86.61 193	98.70 171	97.11 121	97.37 149	99.52 141

PatchmatchNet		95.94 140	95.45 140	97.39 157	97.83 170	94.41 184	96.05 315	98.40 127	92.86 149	97.09 134	95.28 276	94.21 82	98.07 215	89.26 242	98.11 132	99.70 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 168	93.61 174	97.74 144	97.82 171	96.26 129	99.96 23	97.78 203	85.76 289	94.00 185	97.54 198	76.95 265	99.21 147	97.23 118	95.43 187	97.76 208

1112_ss		96.01 139	95.20 148	98.42 115	97.80 172	96.41 124	99.65 149	96.66 298	92.71 158	92.88 198	99.40 112	92.16 128	99.30 145	91.92 206	93.66 203	99.55 134

Test_1112_low_res		95.72 144	94.83 155	98.42 115	97.79 173	96.41 124	99.65 149	96.65 299	92.70 159	92.86 199	96.13 241	92.15 129	99.30 145	91.88 207	93.64 204	99.55 134

Effi-MVS+-dtu		94.53 172	95.30 145	92.22 284	97.77 174	82.54 318	99.59 160	97.06 266	94.92 77	95.29 170	95.37 269	85.81 198	97.89 224	94.80 155	97.07 156	96.23 217

mvs-test1		95.53 149	95.97 125	94.20 244	97.77 174	85.44 308	99.95 37	97.06 266	94.92 77	96.58 145	98.72 165	85.81 198	98.98 154	94.80 155	98.11 132	98.18 199

tpm cat1		93.51 192	92.52 201	96.47 178	97.77 174	91.47 250	96.13 313	98.06 178	80.98 317	92.91 197	93.78 308	89.66 161	98.87 158	87.03 267	96.39 167	99.09 181

xiu_mvs_v1_base_debu		97.43 86	97.06 89	98.55 102	97.74 177	98.14 62	99.31 199	97.86 197	96.43 38	99.62 35	99.69 85	85.56 201	99.68 126	99.05 45	98.31 128	97.83 204

xiu_mvs_v1_base		97.43 86	97.06 89	98.55 102	97.74 177	98.14 62	99.31 199	97.86 197	96.43 38	99.62 35	99.69 85	85.56 201	99.68 126	99.05 45	98.31 128	97.83 204

xiu_mvs_v1_base_debi		97.43 86	97.06 89	98.55 102	97.74 177	98.14 62	99.31 199	97.86 197	96.43 38	99.62 35	99.69 85	85.56 201	99.68 126	99.05 45	98.31 128	97.83 204

EPP-MVSNet		96.69 118	96.60 105	96.96 166	97.74 177	93.05 209	99.37 192	98.56 76	88.75 248	95.83 162	99.01 138	96.01 25	98.56 178	96.92 128	97.20 154	99.25 169

gg-mvs-nofinetune		93.51 192	91.86 213	98.47 110	97.72 181	97.96 72	92.62 329	98.51 93	74.70 332	97.33 129	69.59 341	98.91 3	97.79 226	97.77 107	99.56 97	99.67 111

IB-MVS	92.85 6	94.99 159	93.94 169	98.16 125	97.72 181	95.69 154	99.99 4	98.81 47	94.28 102	92.70 200	96.90 218	95.08 48	99.17 149	96.07 136	73.88 325	99.60 123
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
thisisatest0515		97.41 90	97.02 94	98.59 100	97.71 183	97.52 86	99.97 16	98.54 84	91.83 190	97.45 127	99.04 135	97.50 8	99.10 150	94.75 158	96.37 168	99.16 174

diffmvs		97.00 103	96.64 104	98.09 130	97.64 184	96.17 136	99.81 111	97.19 254	94.67 87	98.95 78	99.28 118	86.43 194	98.76 166	98.37 82	97.42 147	99.33 162

Vis-MVSNet		95.72 144	95.15 150	97.45 153	97.62 185	94.28 186	99.28 203	98.24 154	94.27 103	96.84 139	98.94 150	79.39 250	98.76 166	93.25 190	98.49 123	99.30 165
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 99	96.72 102	98.22 124	97.60 186	96.70 115	99.92 62	98.54 84	91.11 209	97.07 135	98.97 145	97.47 9	99.03 152	93.73 185	96.09 171	98.92 185

miper_ehance_all_eth		93.16 197	92.60 197	94.82 220	97.57 187	93.56 199	99.50 174	97.07 265	88.75 248	88.85 249	95.52 259	90.97 148	96.74 277	90.77 225	84.45 264	94.17 249

LCM-MVSNet-Re		92.31 217	92.60 197	91.43 292	97.53 188	79.27 332	99.02 229	91.83 341	92.07 183	80.31 311	94.38 302	83.50 217	95.48 312	97.22 119	97.58 143	99.54 138

GBi-Net		90.88 243	89.82 247	94.08 248	97.53 188	91.97 231	98.43 270	96.95 278	87.05 271	89.68 228	94.72 290	71.34 296	96.11 300	87.01 268	85.65 253	94.17 249

test1		90.88 243	89.82 247	94.08 248	97.53 188	91.97 231	98.43 270	96.95 278	87.05 271	89.68 228	94.72 290	71.34 296	96.11 300	87.01 268	85.65 253	94.17 249

FMVSNet2		91.02 240	89.56 251	95.41 203	97.53 188	95.74 150	98.98 231	97.41 240	87.05 271	88.43 257	95.00 284	71.34 296	96.24 298	85.12 280	85.21 258	94.25 244

tttt0517		96.85 108	96.49 109	97.92 136	97.48 192	95.89 145	99.85 98	98.54 84	90.72 218	96.63 144	98.93 151	97.47 9	99.02 153	93.03 197	95.76 181	98.85 189

cl_fuxian		92.53 211	91.87 212	94.52 231	97.40 193	92.99 210	99.40 185	96.93 282	87.86 261	88.69 252	95.44 263	89.95 159	96.44 288	90.45 229	80.69 292	94.14 258

CDS-MVSNet		96.34 129	96.07 117	97.13 163	97.37 194	94.96 171	99.53 170	97.91 192	91.55 197	95.37 169	98.32 185	95.05 50	97.13 254	93.80 181	95.75 182	99.30 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 115	96.26 114	98.16 125	97.36 195	96.48 121	99.96 23	98.29 148	91.93 187	95.77 163	98.07 190	95.54 37	98.29 203	90.55 227	98.89 115	99.70 106

miper_lstm_enhance		91.81 225	91.39 222	93.06 276	97.34 196	89.18 281	99.38 190	96.79 293	86.70 278	87.47 270	95.22 277	90.00 158	95.86 310	88.26 251	81.37 283	94.15 255

baseline		96.43 126	95.98 122	97.76 142	97.34 196	95.17 168	99.51 173	97.17 257	93.92 119	96.90 138	99.28 118	85.37 204	98.64 175	97.50 112	96.86 162	99.46 147

cl-mvsnet_		92.31 217	91.58 216	94.52 231	97.33 198	92.77 212	99.57 163	96.78 294	86.97 275	87.56 268	95.51 260	89.43 164	96.62 282	88.60 246	82.44 275	94.16 254

cl-mvsnet1		92.32 216	91.60 215	94.47 235	97.31 199	92.74 214	99.58 161	96.75 295	86.99 274	87.64 266	95.54 257	89.55 163	96.50 286	88.58 247	82.44 275	94.17 249

casdiffmvs		96.42 127	95.97 125	97.77 141	97.30 200	94.98 170	99.84 102	97.09 263	93.75 127	96.58 145	99.26 124	85.07 206	98.78 163	97.77 107	97.04 157	99.54 138

eth_miper_zixun_eth		92.41 215	91.93 210	93.84 259	97.28 201	90.68 258	98.83 247	96.97 277	88.57 253	89.19 244	95.73 250	89.24 169	96.69 280	89.97 237	81.55 281	94.15 255

MVSFormer		96.94 105	96.60 105	97.95 134	97.28 201	97.70 80	99.55 167	97.27 250	91.17 206	99.43 50	99.54 102	90.92 149	96.89 270	94.67 161	99.62 91	99.25 169

lupinMVS		97.85 72	97.60 73	98.62 96	97.28 201	97.70 80	99.99 4	97.55 222	95.50 66	99.43 50	99.67 89	90.92 149	98.71 170	98.40 81	99.62 91	99.45 149

SCA		94.69 165	93.81 173	97.33 161	97.10 204	94.44 182	98.86 245	98.32 145	93.30 140	96.17 156	95.59 255	76.48 269	97.95 221	91.06 217	97.43 145	99.59 125

TAMVS		95.85 141	95.58 138	96.65 177	97.07 205	93.50 200	99.17 211	97.82 201	91.39 205	95.02 173	98.01 191	92.20 127	97.30 243	93.75 184	95.83 179	99.14 177

Fast-Effi-MVS+-dtu		93.72 189	93.86 172	93.29 269	97.06 206	86.16 302	99.80 115	96.83 289	92.66 163	92.58 201	97.83 195	81.39 230	97.67 230	89.75 239	96.87 161	96.05 219

MVS_0304		89.28 276	88.31 274	92.21 285	97.05 207	86.53 301	97.76 294	99.57 12	85.58 294	93.86 188	92.71 316	51.04 339	96.30 295	84.49 284	92.72 211	93.79 285

CostFormer		96.10 136	95.88 132	96.78 171	97.03 208	92.55 222	97.08 302	97.83 200	90.04 229	98.72 87	94.89 288	95.01 52	98.29 203	96.54 132	95.77 180	99.50 144

test-LLR		96.47 124	96.04 118	97.78 139	97.02 209	95.44 157	99.96 23	98.21 158	94.07 109	95.55 165	96.38 233	93.90 90	98.27 206	90.42 230	98.83 117	99.64 117

test-mter		96.39 128	95.93 129	97.78 139	97.02 209	95.44 157	99.96 23	98.21 158	91.81 192	95.55 165	96.38 233	95.17 45	98.27 206	90.42 230	98.83 117	99.64 117

gm-plane-assit					96.97 211	93.76 196			91.47 200		98.96 147		98.79 162	94.92 150

QAPM		95.40 152	94.17 165	99.10 67	96.92 212	97.71 78	99.40 185	98.68 55	89.31 235	88.94 248	98.89 152	82.48 222	99.96 51	93.12 196	99.83 74	99.62 119

tpm2		95.47 151	95.18 149	96.35 185	96.91 213	91.70 244	96.96 305	97.93 189	88.04 260	98.44 99	95.40 265	93.32 103	97.97 218	94.00 175	95.61 184	99.38 156

FMVSNet5		88.32 281	87.47 282	90.88 295	96.90 214	88.39 291	97.28 300	95.68 317	82.60 312	84.67 297	92.40 319	79.83 248	91.16 335	76.39 321	81.51 282	93.09 304

3Dnovator+	91.53 11	96.31 131	95.24 146	99.52 23	96.88 215	98.64 46	99.72 138	98.24 154	95.27 72	88.42 259	98.98 143	82.76 221	99.94 66	97.10 122	99.83 74	99.96 64

Patchmatch-test		92.65 210	91.50 219	96.10 190	96.85 216	90.49 263	91.50 334	97.19 254	82.76 311	90.23 216	95.59 255	95.02 51	98.00 217	77.41 316	96.98 159	99.82 91

MVS		96.60 121	95.56 139	99.72 7	96.85 216	99.22 12	98.31 275	98.94 36	91.57 196	90.90 211	99.61 96	86.66 192	99.96 51	97.36 115	99.88 69	99.99 17

3Dnovator	91.47 12	96.28 134	95.34 144	99.08 69	96.82 218	97.47 92	99.45 182	98.81 47	95.52 65	89.39 236	99.00 140	81.97 224	99.95 58	97.27 117	99.83 74	99.84 89

EI-MVSNet		93.73 188	93.40 185	94.74 221	96.80 219	92.69 217	99.06 222	97.67 208	88.96 243	91.39 207	99.02 136	88.75 174	97.30 243	91.07 216	87.85 239	94.22 245

CVMVSNet		94.68 167	94.94 153	93.89 258	96.80 219	86.92 300	99.06 222	98.98 34	94.45 92	94.23 183	99.02 136	85.60 200	95.31 316	90.91 222	95.39 188	99.43 152

IterMVS-LS		92.69 208	92.11 206	94.43 239	96.80 219	92.74 214	99.45 182	96.89 285	88.98 241	89.65 231	95.38 268	88.77 173	96.34 292	90.98 220	82.04 278	94.22 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.91 242	90.17 242	93.12 273	96.78 222	90.42 266	98.89 239	97.05 268	89.03 239	86.49 283	95.42 264	76.59 268	95.02 318	87.22 264	84.09 267	93.93 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 109	95.96 127	99.48 31	96.74 223	98.52 51	98.31 275	98.86 44	95.82 53	89.91 222	98.98 143	87.49 183	99.96 51	97.80 104	99.73 84	99.96 64

IterMVS-SCA-FT		90.85 245	90.16 243	92.93 277	96.72 224	89.96 271	98.89 239	96.99 273	88.95 244	86.63 280	95.67 251	76.48 269	95.00 319	87.04 266	84.04 270	93.84 282

MVS-HIRNet		86.22 288	83.19 298	95.31 205	96.71 225	90.29 267	92.12 331	97.33 246	62.85 338	86.82 277	70.37 340	69.37 304	97.49 235	75.12 323	97.99 138	98.15 200

VDDNet		93.12 198	91.91 211	96.76 172	96.67 226	92.65 220	98.69 256	98.21 158	82.81 310	97.75 122	99.28 118	61.57 327	99.48 142	98.09 93	94.09 200	98.15 200

MIMVSNet		90.30 258	88.67 269	95.17 209	96.45 227	91.64 246	92.39 330	97.15 260	85.99 286	90.50 214	93.19 314	66.95 313	94.86 322	82.01 300	93.43 205	99.01 184

CR-MVSNet		93.45 195	92.62 196	95.94 192	96.29 228	92.66 218	92.01 332	96.23 307	92.62 165	96.94 136	93.31 312	91.04 146	96.03 305	79.23 310	95.96 175	99.13 179

RPMNet		89.39 273	87.20 284	95.94 192	96.29 228	92.66 218	92.01 332	97.63 210	70.19 337	96.94 136	85.87 334	87.25 186	96.03 305	62.69 337	95.96 175	99.13 179

Patchmtry		89.70 269	88.49 271	93.33 268	96.24 230	89.94 274	91.37 335	96.23 307	78.22 323	87.69 265	93.31 312	91.04 146	96.03 305	80.18 309	82.10 277	94.02 265

JIA-IIPM		91.76 231	90.70 229	94.94 215	96.11 231	87.51 297	93.16 328	98.13 173	75.79 329	97.58 124	77.68 338	92.84 114	97.97 218	88.47 250	96.54 164	99.33 162

OpenMVS	90.15 15	94.77 163	93.59 177	98.33 119	96.07 232	97.48 91	99.56 165	98.57 74	90.46 220	86.51 282	98.95 149	78.57 257	99.94 66	93.86 176	99.74 83	97.57 209

PAPM		98.60 32	98.42 29	99.14 61	96.05 233	98.96 17	99.90 70	99.35 23	96.68 34	98.35 104	99.66 91	96.45 22	98.51 181	99.45 33	99.89 67	99.96 64

CLD-MVS		94.06 180	93.90 170	94.55 230	96.02 234	90.69 257	99.98 8	97.72 205	96.62 36	91.05 210	98.85 161	77.21 262	98.47 182	98.11 91	89.51 219	94.48 225
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 255	88.75 268	95.25 206	95.99 235	90.16 269	91.22 336	97.54 224	76.80 325	97.26 130	86.01 333	91.88 133	96.07 304	66.16 334	95.91 177	99.51 142

ACMH+	89.98 16	90.35 256	89.54 252	92.78 280	95.99 235	86.12 303	98.81 248	97.18 256	89.38 234	83.14 303	97.76 196	68.42 309	98.43 187	89.11 243	86.05 251	93.78 286

DeepMVS_CX				82.92 321	95.98 237	58.66 342		96.01 312	92.72 157	78.34 318	95.51 260	58.29 332	98.08 213	82.57 296	85.29 256	92.03 315

ACMP	92.05 9	92.74 206	92.42 203	93.73 260	95.91 238	88.72 284	99.81 111	97.53 226	94.13 105	87.00 276	98.23 186	74.07 287	98.47 182	96.22 135	88.86 226	93.99 270
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP-NCC					95.78 239		99.87 84		96.82 27	93.37 190

ACMP_Plane					95.78 239		99.87 84		96.82 27	93.37 190

HQP-MVS		94.61 169	94.50 160	94.92 216	95.78 239	91.85 236	99.87 84	97.89 193	96.82 27	93.37 190	98.65 168	80.65 240	98.39 193	97.92 102	89.60 214	94.53 221

NP-MVS					95.77 242	91.79 238					98.65 168

plane_prior6					95.76 243	91.72 243						80.47 244

ACMM	91.95 10	92.88 203	92.52 201	93.98 255	95.75 244	89.08 282	99.77 121	97.52 228	93.00 147	89.95 221	97.99 192	76.17 273	98.46 185	93.63 187	88.87 225	94.39 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 182	92.84 192	96.80 170	95.73 245	93.57 198	99.88 81	97.24 252	92.57 170	92.92 196	96.66 226	78.73 256	97.67 230	87.75 257	94.06 201	99.17 173

plane_prior1					95.73 245

jason		97.24 95	96.86 96	98.38 118	95.73 245	97.32 97	99.97 16	97.40 241	95.34 69	98.60 94	99.54 102	87.70 181	98.56 178	97.94 101	99.47 102	99.25 169
jason: jason.
HQP_MVS		94.49 173	94.36 162	94.87 217	95.71 248	91.74 240	99.84 102	97.87 195	96.38 41	93.01 194	98.59 172	80.47 244	98.37 198	97.79 105	89.55 217	94.52 223

plane_prior7					95.71 248	91.59 248

ITE_SJBPF				92.38 282	95.69 250	85.14 309		95.71 316	92.81 152	89.33 239	98.11 188	70.23 302	98.42 188	85.91 276	88.16 237	93.59 294

ACMH	89.72 17	90.64 249	89.63 249	93.66 264	95.64 251	88.64 287	98.55 263	97.45 234	89.03 239	81.62 307	97.61 197	69.75 303	98.41 189	89.37 240	87.62 243	93.92 276
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 117	96.49 109	97.37 158	95.63 252	95.96 143	99.74 130	98.88 42	92.94 148	91.61 205	98.97 145	97.72 5	98.62 176	94.83 154	98.08 136	97.53 210

FMVSNet1		88.50 280	86.64 285	94.08 248	95.62 253	91.97 231	98.43 270	96.95 278	83.00 309	86.08 290	94.72 290	59.09 331	96.11 300	81.82 302	84.07 268	94.17 249

LPG-MVS_test		92.96 201	92.71 195	93.71 262	95.43 254	88.67 285	99.75 127	97.62 213	92.81 152	90.05 217	98.49 178	75.24 279	98.40 191	95.84 142	89.12 221	94.07 262

LGP-MVS_train				93.71 262	95.43 254	88.67 285		97.62 213	92.81 152	90.05 217	98.49 178	75.24 279	98.40 191	95.84 142	89.12 221	94.07 262

tpm		93.70 190	93.41 184	94.58 228	95.36 256	87.41 298	97.01 303	96.90 284	90.85 215	96.72 143	94.14 304	90.40 154	96.84 273	90.75 226	88.54 233	99.51 142

D2MVS		92.76 205	92.59 199	93.27 270	95.13 257	89.54 278	99.69 141	99.38 21	92.26 178	87.59 267	94.61 296	85.05 207	97.79 226	91.59 210	88.01 238	92.47 311

VPA-MVSNet		92.70 207	91.55 218	96.16 188	95.09 258	96.20 134	98.88 241	99.00 33	91.02 212	91.82 204	95.29 275	76.05 275	97.96 220	95.62 144	81.19 284	94.30 240

LTVRE_ROB	88.28 18	90.29 259	89.05 263	94.02 251	95.08 259	90.15 270	97.19 301	97.43 236	84.91 299	83.99 300	97.06 213	74.00 288	98.28 205	84.08 286	87.71 241	93.62 293
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
TinyColmap		87.87 284	86.51 286	91.94 288	95.05 260	85.57 306	97.65 295	94.08 335	84.40 302	81.82 306	96.85 222	62.14 326	98.33 200	80.25 308	86.37 250	91.91 317

test0.0.03 1		93.86 181	93.61 174	94.64 225	95.02 261	92.18 229	99.93 58	98.58 72	94.07 109	87.96 263	98.50 177	93.90 90	94.96 320	81.33 303	93.17 208	96.78 212

UniMVSNet (Re)		93.07 200	92.13 205	95.88 194	94.84 262	96.24 133	99.88 81	98.98 34	92.49 174	89.25 240	95.40 265	87.09 188	97.14 253	93.13 195	78.16 307	94.26 242

USDC		90.00 266	88.96 264	93.10 275	94.81 263	88.16 293	98.71 254	95.54 321	93.66 130	83.75 302	97.20 207	65.58 317	98.31 202	83.96 289	87.49 245	92.85 308

VPNet		91.81 225	90.46 233	95.85 196	94.74 264	95.54 156	98.98 231	98.59 71	92.14 181	90.77 213	97.44 200	68.73 307	97.54 234	94.89 153	77.89 309	94.46 226

FIs		94.10 179	93.43 181	96.11 189	94.70 265	96.82 113	99.58 161	98.93 39	92.54 171	89.34 238	97.31 204	87.62 182	97.10 257	94.22 172	86.58 248	94.40 232

UniMVSNet_ETH3D		90.06 265	88.58 270	94.49 234	94.67 266	88.09 294	97.81 293	97.57 221	83.91 305	88.44 255	97.41 201	57.44 333	97.62 232	91.41 211	88.59 232	97.77 207

UniMVSNet_NR-MVSNet		92.95 202	92.11 206	95.49 199	94.61 267	95.28 163	99.83 108	99.08 30	91.49 198	89.21 242	96.86 221	87.14 187	96.73 278	93.20 191	77.52 312	94.46 226

WR-MVS		92.31 217	91.25 223	95.48 202	94.45 268	95.29 162	99.60 159	98.68 55	90.10 226	88.07 262	96.89 219	80.68 239	96.80 276	93.14 194	79.67 299	94.36 234

nrg030		93.51 192	92.53 200	96.45 180	94.36 269	97.20 100	99.81 111	97.16 259	91.60 195	89.86 224	97.46 199	86.37 195	97.68 229	95.88 140	80.31 295	94.46 226

tfpnnormal		89.29 275	87.61 281	94.34 241	94.35 270	94.13 189	98.95 235	98.94 36	83.94 303	84.47 298	95.51 260	74.84 282	97.39 237	77.05 319	80.41 293	91.48 320

FC-MVSNet-test		93.81 184	93.15 190	95.80 197	94.30 271	96.20 134	99.42 184	98.89 41	92.33 177	89.03 247	97.27 206	87.39 185	96.83 274	93.20 191	86.48 249	94.36 234

MS-PatchMatch		90.65 248	90.30 238	91.71 291	94.22 272	85.50 307	98.24 279	97.70 206	88.67 250	86.42 285	96.37 235	67.82 311	98.03 216	83.62 291	99.62 91	91.60 319

WR-MVS_H		91.30 234	90.35 236	94.15 245	94.17 273	92.62 221	99.17 211	98.94 36	88.87 246	86.48 284	94.46 301	84.36 211	96.61 283	88.19 252	78.51 305	93.21 303

DU-MVS		92.46 214	91.45 221	95.49 199	94.05 274	95.28 163	99.81 111	98.74 51	92.25 179	89.21 242	96.64 228	81.66 227	96.73 278	93.20 191	77.52 312	94.46 226

NR-MVSNet		91.56 233	90.22 240	95.60 198	94.05 274	95.76 149	98.25 278	98.70 53	91.16 208	80.78 310	96.64 228	83.23 220	96.57 284	91.41 211	77.73 311	94.46 226

CP-MVSNet		91.23 237	90.22 240	94.26 242	93.96 276	92.39 225	99.09 215	98.57 74	88.95 244	86.42 285	96.57 230	79.19 252	96.37 290	90.29 233	78.95 302	94.02 265

XXY-MVS		91.82 224	90.46 233	95.88 194	93.91 277	95.40 160	98.87 244	97.69 207	88.63 252	87.87 264	97.08 211	74.38 286	97.89 224	91.66 209	84.07 268	94.35 237

PS-CasMVS		90.63 250	89.51 254	93.99 254	93.83 278	91.70 244	98.98 231	98.52 87	88.48 254	86.15 289	96.53 232	75.46 277	96.31 294	88.83 245	78.86 304	93.95 273

test_0402		85.58 290	83.94 293	90.50 299	93.81 279	85.04 310	98.55 263	95.20 327	76.01 327	79.72 314	95.13 278	64.15 322	96.26 297	66.04 335	86.88 247	90.21 328

XVG-ACMP-BASELINE		91.22 238	90.75 228	92.63 281	93.73 280	85.61 305	98.52 267	97.44 235	92.77 156	89.90 223	96.85 222	66.64 314	98.39 193	92.29 201	88.61 230	93.89 278

TranMVSNet+NR-MVSNet		91.68 232	90.61 231	94.87 217	93.69 281	93.98 191	99.69 141	98.65 59	91.03 211	88.44 255	96.83 225	80.05 247	96.18 299	90.26 234	76.89 319	94.45 231

TransMVSNet (Re)		87.25 285	85.28 289	93.16 272	93.56 282	91.03 252	98.54 265	94.05 336	83.69 307	81.09 309	96.16 239	75.32 278	96.40 289	76.69 320	68.41 331	92.06 314

v10		90.25 260	88.82 266	94.57 229	93.53 283	93.43 202	99.08 217	96.87 287	85.00 298	87.34 274	94.51 297	80.93 236	97.02 266	82.85 295	79.23 300	93.26 301

testgi		89.01 278	88.04 278	91.90 289	93.49 284	84.89 311	99.73 135	95.66 318	93.89 122	85.14 295	98.17 187	59.68 330	94.66 324	77.73 315	88.88 224	96.16 218

v8		90.54 252	89.17 259	94.66 224	93.43 285	93.40 204	99.20 208	96.94 281	85.76 289	87.56 268	94.51 297	81.96 225	97.19 249	84.94 282	78.25 306	93.38 299

V42		91.28 236	90.12 244	94.74 221	93.42 286	93.46 201	99.68 143	97.02 269	87.36 267	89.85 226	95.05 280	81.31 232	97.34 240	87.34 262	80.07 297	93.40 297

pm-mvs1		89.36 274	87.81 280	94.01 252	93.40 287	91.93 234	98.62 262	96.48 305	86.25 284	83.86 301	96.14 240	73.68 289	97.04 262	86.16 274	75.73 323	93.04 306

v1144		91.09 239	89.83 246	94.87 217	93.25 288	93.69 197	99.62 157	96.98 275	86.83 277	89.64 232	94.99 285	80.94 235	97.05 260	85.08 281	81.16 285	93.87 280

v1192		90.62 251	89.25 258	94.72 223	93.13 289	93.07 207	99.50 174	97.02 269	86.33 283	89.56 234	95.01 282	79.22 251	97.09 259	82.34 298	81.16 285	94.01 267

v2v482		91.30 234	90.07 245	95.01 212	93.13 289	93.79 194	99.77 121	97.02 269	88.05 259	89.25 240	95.37 269	80.73 238	97.15 252	87.28 263	80.04 298	94.09 261

OPM-MVS		93.21 196	92.80 193	94.44 237	93.12 291	90.85 256	99.77 121	97.61 216	96.19 48	91.56 206	98.65 168	75.16 281	98.47 182	93.78 183	89.39 220	93.99 270

v144192		90.79 246	89.52 253	94.59 227	93.11 292	92.77 212	99.56 165	96.99 273	86.38 282	89.82 227	94.95 287	80.50 243	97.10 257	83.98 288	80.41 293	93.90 277

PEN-MVS		90.19 262	89.06 262	93.57 265	93.06 293	90.90 255	99.06 222	98.47 99	88.11 258	85.91 291	96.30 236	76.67 267	95.94 309	87.07 265	76.91 318	93.89 278

v1240		90.20 261	88.79 267	94.44 237	93.05 294	92.27 227	99.38 190	96.92 283	85.89 287	89.36 237	94.87 289	77.89 261	97.03 264	80.66 306	81.08 287	94.01 267

v148		90.70 247	89.63 249	93.92 256	92.97 295	90.97 253	99.75 127	96.89 285	87.51 264	88.27 260	95.01 282	81.67 226	97.04 262	87.40 261	77.17 316	93.75 287

v1921920		90.46 253	89.12 260	94.50 233	92.96 296	92.46 223	99.49 176	96.98 275	86.10 285	89.61 233	95.30 272	78.55 258	97.03 264	82.17 299	80.89 291	94.01 267

Baseline_NR-MVSNet		90.33 257	89.51 254	92.81 279	92.84 297	89.95 272	99.77 121	93.94 337	84.69 301	89.04 246	95.66 252	81.66 227	96.52 285	90.99 219	76.98 317	91.97 316

pmmvs4		92.10 221	91.07 226	95.18 208	92.82 298	94.96 171	99.48 178	96.83 289	87.45 266	88.66 253	96.56 231	83.78 215	96.83 274	89.29 241	84.77 262	93.75 287

LF4IMVS		89.25 277	88.85 265	90.45 301	92.81 299	81.19 326	98.12 284	94.79 330	91.44 201	86.29 287	97.11 209	65.30 319	98.11 212	88.53 249	85.25 257	92.07 313

DTE-MVSNet		89.40 272	88.24 276	92.88 278	92.66 300	89.95 272	99.10 214	98.22 157	87.29 268	85.12 296	96.22 238	76.27 272	95.30 317	83.56 292	75.74 322	93.41 296

EU-MVSNet		90.14 264	90.34 237	89.54 307	92.55 301	81.06 327	98.69 256	98.04 180	91.41 204	86.59 281	96.84 224	80.83 237	93.31 333	86.20 273	81.91 279	94.26 242

our_test_3		90.39 254	89.48 256	93.12 273	92.40 302	89.57 277	99.33 196	96.35 306	87.84 262	85.30 294	94.99 285	84.14 213	96.09 303	80.38 307	84.56 263	93.71 292

ppachtmachnet_test		89.58 271	88.35 273	93.25 271	92.40 302	90.44 265	99.33 196	96.73 296	85.49 295	85.90 292	95.77 247	81.09 234	96.00 308	76.00 322	82.49 274	93.30 300

v7n		89.65 270	88.29 275	93.72 261	92.22 304	90.56 262	99.07 221	97.10 262	85.42 297	86.73 278	94.72 290	80.06 246	97.13 254	81.14 304	78.12 308	93.49 295

PS-MVSNAJss		93.64 191	93.31 187	94.61 226	92.11 305	92.19 228	99.12 213	97.38 242	92.51 173	88.45 254	96.99 217	91.20 141	97.29 246	94.36 166	87.71 241	94.36 234

pmmvs5		90.17 263	89.09 261	93.40 267	92.10 306	89.77 275	99.74 130	95.58 320	85.88 288	87.24 275	95.74 248	73.41 290	96.48 287	88.54 248	83.56 271	93.95 273

N_pmnet		80.06 307	80.78 305	77.89 322	91.94 307	45.28 348	98.80 249	56.82 353	78.10 324	80.08 313	93.33 310	77.03 263	95.76 311	68.14 331	82.81 273	92.64 309

test_djsdf		92.83 204	92.29 204	94.47 235	91.90 308	92.46 223	99.55 167	97.27 250	91.17 206	89.96 220	96.07 243	81.10 233	96.89 270	94.67 161	88.91 223	94.05 264

DI_MVS_plusplus_test		92.48 212	90.60 232	98.11 129	91.88 309	96.13 137	99.64 153	97.73 204	92.69 160	76.02 324	93.79 307	70.49 301	99.07 151	95.88 140	97.26 151	99.14 177

SixPastTwentyTwo		88.73 279	88.01 279	90.88 295	91.85 310	82.24 320	98.22 281	95.18 328	88.97 242	82.26 305	96.89 219	71.75 295	96.67 281	84.00 287	82.98 272	93.72 291

K. test v3		88.05 283	87.24 283	90.47 300	91.82 311	82.23 321	98.96 234	97.42 238	89.05 238	76.93 321	95.60 254	68.49 308	95.42 313	85.87 277	81.01 289	93.75 287

OurMVSNet-221017-0		89.81 268	89.48 256	90.83 297	91.64 312	81.21 325	98.17 283	95.38 323	91.48 199	85.65 293	97.31 204	72.66 291	97.29 246	88.15 253	84.83 261	93.97 272

mvs_tets		91.81 225	91.08 225	94.00 253	91.63 313	90.58 261	98.67 258	97.43 236	92.43 175	87.37 273	97.05 214	71.76 294	97.32 242	94.75 158	88.68 229	94.11 260

Gipuma		66.95 313	65.00 313	72.79 325	91.52 314	67.96 338	66.16 344	95.15 329	47.89 341	58.54 338	67.99 342	29.74 345	87.54 340	50.20 341	77.83 310	62.87 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
jajsoiax		91.92 223	91.18 224	94.15 245	91.35 315	90.95 254	99.00 230	97.42 238	92.61 166	87.38 272	97.08 211	72.46 292	97.36 238	94.53 164	88.77 227	94.13 259

MDA-MVSNet-bldmvs		84.09 300	81.52 304	91.81 290	91.32 316	88.00 296	98.67 258	95.92 314	80.22 319	55.60 341	93.32 311	68.29 310	93.60 331	73.76 324	76.61 320	93.82 284

MVP-Stereo		90.93 241	90.45 235	92.37 283	91.25 317	88.76 283	98.05 288	96.17 309	87.27 269	84.04 299	95.30 272	78.46 259	97.27 248	83.78 290	99.70 87	91.09 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 292	83.32 297	92.10 286	90.96 318	88.58 288	99.20 208	96.52 302	79.70 320	57.12 340	92.69 317	79.11 253	93.86 328	77.10 318	77.46 314	93.86 281

YYNet1		85.50 293	83.33 296	92.00 287	90.89 319	88.38 292	99.22 207	96.55 301	79.60 321	57.26 339	92.72 315	79.09 254	93.78 329	77.25 317	77.37 315	93.84 282

anonymousdsp		91.79 230	90.92 227	94.41 240	90.76 320	92.93 211	98.93 237	97.17 257	89.08 237	87.46 271	95.30 272	78.43 260	96.92 269	92.38 200	88.73 228	93.39 298

lessismore_v0				90.53 298	90.58 321	80.90 328		95.80 315		77.01 320	95.84 245	66.15 316	96.95 267	83.03 294	75.05 324	93.74 290

EG-PatchMatch MVS		85.35 294	83.81 295	89.99 305	90.39 322	81.89 323	98.21 282	96.09 311	81.78 315	74.73 328	93.72 309	51.56 338	97.12 256	79.16 311	88.61 230	90.96 323

CMPMVS	61.59 21	84.75 297	85.14 290	83.57 319	90.32 323	62.54 340	96.98 304	97.59 220	74.33 333	69.95 333	96.66 226	64.17 321	98.32 201	87.88 256	88.41 235	89.84 330
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 299	82.92 299	89.21 308	90.03 324	82.60 317	96.89 306	95.62 319	80.59 318	75.77 327	89.17 325	65.04 320	94.79 323	72.12 325	81.02 288	90.23 327

pmmvs6		85.69 289	83.84 294	91.26 294	90.00 325	84.41 313	97.82 292	96.15 310	75.86 328	81.29 308	95.39 267	61.21 328	96.87 272	83.52 293	73.29 326	92.50 310

DSMNet-mixed		88.28 282	88.24 276	88.42 313	89.64 326	75.38 334	98.06 287	89.86 344	85.59 293	88.20 261	92.14 320	76.15 274	91.95 334	78.46 312	96.05 172	97.92 203

UnsupCasMVSNet_eth		85.52 291	83.99 291	90.10 303	89.36 327	83.51 315	96.65 307	97.99 182	89.14 236	75.89 326	93.83 306	63.25 324	93.92 326	81.92 301	67.90 333	92.88 307

Anonymous20231206		86.32 287	85.42 288	89.02 309	89.11 328	80.53 330	99.05 226	95.28 324	85.43 296	82.82 304	93.92 305	74.40 285	93.44 332	66.99 332	81.83 280	93.08 305

OpenMVS_ROB	79.82 20	83.77 302	81.68 303	90.03 304	88.30 329	82.82 316	98.46 268	95.22 326	73.92 334	76.00 325	91.29 322	55.00 335	96.94 268	68.40 330	88.51 234	90.34 326

test20.03		84.72 298	83.99 291	86.91 315	88.19 330	80.62 329	98.88 241	95.94 313	88.36 256	78.87 315	94.62 295	68.75 306	89.11 339	66.52 333	75.82 321	91.00 322

MIMVSNet1		82.58 303	80.51 306	88.78 311	86.68 331	84.20 314	96.65 307	95.41 322	78.75 322	78.59 317	92.44 318	51.88 337	89.76 338	65.26 336	78.95 302	92.38 312

UnsupCasMVSNet_bld		79.97 308	77.03 310	88.78 311	85.62 332	81.98 322	93.66 326	97.35 244	75.51 331	70.79 332	83.05 335	48.70 340	94.91 321	78.31 313	60.29 338	89.46 333

Patchmatch-RL test		86.90 286	85.98 287	89.67 306	84.45 333	75.59 333	89.71 337	92.43 339	86.89 276	77.83 319	90.94 323	94.22 79	93.63 330	87.75 257	69.61 328	99.79 94

pmmvs-eth3d		84.03 301	81.97 301	90.20 302	84.15 334	87.09 299	98.10 286	94.73 332	83.05 308	74.10 329	87.77 328	65.56 318	94.01 325	81.08 305	69.24 330	89.49 332

PM-MVS		80.47 305	78.88 308	85.26 318	83.79 335	72.22 335	95.89 318	91.08 342	85.71 292	76.56 323	88.30 326	36.64 342	93.90 327	82.39 297	69.57 329	89.66 331

new-patchmatchnet		81.19 304	79.34 307	86.76 316	82.86 336	80.36 331	97.92 290	95.27 325	82.09 314	72.02 330	86.87 330	62.81 325	90.74 337	71.10 326	63.08 336	89.19 334

testing_2		85.10 295	81.72 302	95.22 207	82.25 337	94.16 187	97.54 296	97.01 272	88.15 257	62.23 335	86.43 332	44.43 341	97.18 250	92.28 202	85.20 259	94.31 239

pmmvs3		80.27 306	77.77 309	87.76 314	80.32 338	82.43 319	98.23 280	91.97 340	72.74 335	78.75 316	87.97 327	57.30 334	90.99 336	70.31 327	62.37 337	89.87 329

test_normal		73.75 309	69.52 311	86.45 317	78.71 339	71.20 336	52.76 345	96.50 303	86.56 279	46.73 345	66.99 343	34.02 343	96.33 293	84.42 285	78.96 301	91.81 318

ambc				83.23 320	77.17 340	62.61 339	87.38 339	94.55 334		76.72 322	86.65 331	30.16 344	96.36 291	84.85 283	69.86 327	90.73 325

TDRefinement		84.76 296	82.56 300	91.38 293	74.58 341	84.80 312	97.36 299	94.56 333	84.73 300	80.21 312	96.12 242	63.56 323	98.39 193	87.92 255	63.97 335	90.95 324

E-PMN		52.30 317	52.18 318	52.67 331	71.51 342	45.40 347	93.62 327	76.60 351	36.01 345	43.50 346	64.13 345	27.11 347	67.31 348	31.06 346	26.06 342	45.30 346

EMVS		51.44 319	51.22 320	52.11 332	70.71 343	44.97 349	94.04 323	75.66 352	35.34 347	42.40 347	61.56 348	28.93 346	65.87 349	27.64 347	24.73 343	45.49 345

PMMVS2		67.15 312	64.15 315	76.14 324	70.56 344	62.07 341	93.89 324	87.52 348	58.09 339	60.02 337	78.32 337	22.38 349	84.54 342	59.56 339	47.03 340	81.80 337

FPMVS		68.72 310	68.72 312	68.71 327	65.95 345	44.27 350	95.97 317	94.74 331	51.13 340	53.26 342	90.50 324	25.11 348	83.00 343	60.80 338	80.97 290	78.87 338

wuyk23d		20.37 323	20.84 325	18.99 335	65.34 346	27.73 352	50.43 346	7.67 356	9.50 350	8.01 351	6.34 351	6.13 355	26.24 350	23.40 348	10.69 347	2.99 347

LCM-MVSNet		67.77 311	64.73 314	76.87 323	62.95 347	56.25 344	89.37 338	93.74 338	44.53 342	61.99 336	80.74 336	20.42 350	86.53 341	69.37 329	59.50 339	87.84 335

MVE	53.74 22	51.54 318	47.86 321	62.60 329	59.56 348	50.93 345	79.41 342	77.69 350	35.69 346	36.27 348	61.76 347	5.79 356	69.63 346	37.97 345	36.61 341	67.24 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 315	52.24 317	67.66 328	49.27 349	56.82 343	83.94 340	82.02 349	70.47 336	33.28 349	64.54 344	17.23 352	69.16 347	45.59 343	23.85 344	77.02 339

tmp_tt		65.23 314	62.94 316	72.13 326	44.90 350	50.03 346	81.05 341	89.42 347	38.45 343	48.51 344	99.90 16	54.09 336	78.70 345	91.84 208	18.26 345	87.64 336

PMVS	49.05 23	53.75 316	51.34 319	60.97 330	40.80 351	34.68 351	74.82 343	89.62 346	37.55 344	28.67 350	72.12 339	7.09 354	81.63 344	43.17 344	68.21 332	66.59 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 321	39.14 323	33.31 333	19.94 352	24.83 353	98.36 274	9.75 355	15.53 349	51.31 343	87.14 329	19.62 351	17.74 351	47.10 342	3.47 348	57.36 343

testmvs		40.60 320	44.45 322	29.05 334	19.49 353	14.11 354	99.68 143	18.47 354	20.74 348	64.59 334	98.48 181	10.95 353	17.09 352	56.66 340	11.01 346	55.94 344

uanet_test		0.00 326	0.00 328	0.00 336	0.00 354	0.00 355	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 349	0.00 349	0.00 348

cdsmvs_eth3d_5k		23.43 322	31.24 324	0.00 336	0.00 354	0.00 355	0.00 347	98.09 175	0.00 351	0.00 352	99.67 89	83.37 218	0.00 353	0.00 349	0.00 349	0.00 348

pcd_1.5k_mvsjas		7.60 325	10.13 327	0.00 336	0.00 354	0.00 355	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	91.20 141	0.00 353	0.00 349	0.00 349	0.00 348

sosnet-low-res		0.00 326	0.00 328	0.00 336	0.00 354	0.00 355	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 349	0.00 349	0.00 348

sosnet		0.00 326	0.00 328	0.00 336	0.00 354	0.00 355	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 349	0.00 349	0.00 348

uncertanet		0.00 326	0.00 328	0.00 336	0.00 354	0.00 355	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 349	0.00 349	0.00 348

Regformer		0.00 326	0.00 328	0.00 336	0.00 354	0.00 355	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 349	0.00 349	0.00 348

ab-mvs-re		8.28 324	11.04 326	0.00 336	0.00 354	0.00 355	0.00 347	0.00 357	0.00 351	0.00 352	99.40 112	0.00 357	0.00 353	0.00 349	0.00 349	0.00 348

uanet		0.00 326	0.00 328	0.00 336	0.00 354	0.00 355	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 349	0.00 349	0.00 348

test_241102_TWO								98.43 112	97.27 18	99.80 15	99.94 4	97.18 16	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 37	99.83 9	99.91 12	97.87 4	100.00 1	99.92 6	100.00 1	100.00 1

GSMVS																99.59 125

test_part1				0.00 336		0.00 355	0.00 347	98.41 126				0.00 357	0.00 353	0.00 349	0.00 349	0.00 348

sam_mvs1												94.72 60				99.59 125

sam_mvs												94.25 78

MTGPA								98.28 149

test_post1							95.78 319				59.23 349	93.20 108	97.74 228	91.06 217

test_post											63.35 346	94.43 65	98.13 211

patchmatchnet-post											91.70 321	95.12 46	97.95 221

MTMP							99.87 84	96.49 304

test9_res														99.71 26	99.99 18	100.00 1

agg_prior2														99.48 32	100.00 1	100.00 1

test_prior4						98.05 66	99.94 52

test_prior2							99.95 37		95.78 55	99.73 24	99.76 68	96.00 26		99.78 17	100.00 1

旧先验2							99.46 181		94.21 104	99.85 6			99.95 58	96.96 126

新几何2							99.40 185

无先验							99.49 176	98.71 52	93.46 136				100.00 1	94.36 166		99.99 17

原ACMM2							99.90 70

testdata2													99.99 34	90.54 228

segment_acmp												96.68 20

testdata1							99.28 203		96.35 45

plane_prior5								97.87 195					98.37 198	97.79 105	89.55 217	94.52 223

plane_prior4											98.59 172

plane_prior3						91.64 246			96.63 35	93.01 194

plane_prior2							99.84 102		96.38 41

plane_prior						91.74 240	99.86 95		96.76 31						89.59 216

n2								0.00 357

nn								0.00 357

door-mid								89.69 345

test11								98.44 104

door								90.31 343

HQP5-MVS						91.85 236

BP-MVS														97.92 102

HQP4-MVS										93.37 190			98.39 193			94.53 221

HQP3-MVS								97.89 193							89.60 214

HQP2-MVS												80.65 240

MDTV_nov1_ep13_2view						96.26 129	96.11 314		91.89 188	98.06 113		94.40 67		94.30 169		99.67 111

ACMMP++_ref															87.04 246

ACMMP++															88.23 236

Test By Simon												92.82 116

High-resolution multi-view benchmark