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		97.23 128	96.80 137	98.51 131	99.99 1	95.60 197	99.09 301	98.84 64	93.32 200	96.74 210	99.72 92	86.04 256	100.00 1	98.01 150	99.43 127	99.94 85

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 20	98.69 80	98.20 9	99.93 2	99.98 2	96.82 25	100.00 1	99.75 39	100.00 1	99.99 24

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 38	98.64 89	98.47 3	99.13 103	99.92 16	96.38 35	100.00 1	99.74 41	100.00 1	100.00 1

mPP-MVS		98.39 55	98.20 53	98.97 91	99.97 3	96.92 136	99.95 70	98.38 182	95.04 121	98.61 136	99.80 57	93.39 116	100.00 1	98.64 112	100.00 1	99.98 55

CPTT-MVS		97.64 108	97.32 112	98.58 120	99.97 3	95.77 186	99.96 51	98.35 188	89.90 332	98.36 151	99.79 61	91.18 175	99.99 38	98.37 128	99.99 21	99.99 24

DP-MVS Recon		98.41 52	98.02 67	99.56 28	99.97 3	98.70 51	99.92 98	98.44 146	92.06 265	98.40 150	99.84 47	95.68 46	100.00 1	98.19 139	99.71 91	99.97 65

PAPR		98.52 42	98.16 57	99.58 27	99.97 3	98.77 45	99.95 70	98.43 154	95.35 115	98.03 165	99.75 78	94.03 101	99.98 49	98.11 144	99.83 80	99.99 24

MED-MVS test				99.60 23	99.96 8	98.79 41	99.97 38	98.88 54	96.36 87	99.07 108	99.93 11		100.00 1	99.98 9	99.96 46	99.99 24

TestfortrainingZip a		99.09 9	98.87 18	99.76 10	99.96 8	99.27 18	99.97 38	98.88 54	96.36 87	99.07 108	99.93 11	97.36 17	100.00 1	98.32 131	99.96 46	100.00 1

HFP-MVS		98.56 38	98.37 42	99.14 71	99.96 8	97.43 112	99.95 70	98.61 97	94.77 131	99.31 91	99.85 36	94.22 94	100.00 1	98.70 107	99.98 32	99.98 55

region2R		98.54 40	98.37 42	99.05 81	99.96 8	97.18 122	99.96 51	98.55 117	94.87 128	99.45 78	99.85 36	94.07 100	100.00 1	98.67 109	100.00 1	99.98 55

ACMMPR		98.50 43	98.32 46	99.05 81	99.96 8	97.18 122	99.95 70	98.60 99	94.77 131	99.31 91	99.84 47	93.73 110	100.00 1	98.70 107	99.98 32	99.98 55

NCCC		99.37 2	99.25 2	99.71 16	99.96 8	99.15 23	99.97 38	98.62 96	98.02 21	99.90 6	99.95 3	97.33 18	100.00 1	99.54 56	100.00 1	100.00 1

CP-MVS		98.45 47	98.32 46	98.87 96	99.96 8	96.62 149	99.97 38	98.39 178	94.43 148	98.90 117	99.87 30	94.30 91	100.00 1	99.04 82	99.99 21	99.99 24

test_one_0601					99.94 15	99.30 12		98.41 171	96.63 72	99.75 39	99.93 11	97.49 10

test_0728_SECOND				99.82 7	99.94 15	99.47 7	99.95 70	98.43 154					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 31	98.55 30	99.15 69	99.94 15	97.50 108	99.94 88	98.42 166	96.22 91	99.41 83	99.78 65	94.34 88	99.96 74	98.92 92	99.95 53	99.99 24

X-MVStestdata		93.83 268	92.06 303	99.15 69	99.94 15	97.50 108	99.94 88	98.42 166	96.22 91	99.41 83	41.37 477	94.34 88	99.96 74	98.92 92	99.95 53	99.99 24

test_prior				99.43 39	99.94 15	98.49 64		98.65 86					99.80 140			99.99 24

MSLP-MVS++		99.13 8	99.01 11	99.49 35	99.94 15	98.46 65	99.98 20	98.86 58	97.10 52	99.80 25	99.94 4	95.92 42	100.00 1	99.51 57	100.00 1	100.00 1

APDe-MVS		99.06 13	98.91 14	99.51 32	99.94 15	98.76 48	99.91 106	98.39 178	97.20 50	99.46 77	99.85 36	95.53 50	99.79 142	99.86 25	100.00 1	99.99 24
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVS		98.23 70	97.97 71	99.03 83	99.94 15	97.17 125	99.95 70	98.39 178	94.70 135	98.26 157	99.81 56	91.84 166	100.00 1	98.85 98	99.97 42	99.93 86
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 34	98.36 44	99.49 35	99.94 15	98.73 49	99.87 128	98.33 193	93.97 173	99.76 38	99.87 30	94.99 66	99.75 151	98.55 116	100.00 1	99.98 55

PAPM_NR		98.12 74	97.93 77	98.70 107	99.94 15	96.13 175	99.82 157	98.43 154	94.56 139	97.52 182	99.70 98	94.40 83	99.98 49	97.00 188	99.98 32	99.99 24

MG-MVS		98.91 21	98.65 26	99.68 17	99.94 15	99.07 25	99.64 218	99.44 19	97.33 43	99.00 113	99.72 92	94.03 101	99.98 49	98.73 106	100.00 1	100.00 1

ME-MVS		99.07 11	98.89 16	99.59 25	99.93 26	98.79 41	99.95 70	98.80 70	95.89 100	99.28 95	99.93 11	96.28 36	99.98 49	99.98 9	99.96 46	99.99 24

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 26	99.30 12	99.96 51	98.43 154	97.27 46	99.80 25	99.94 4	96.71 28	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 26	99.31 10		98.41 171	97.71 30	99.84 20				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 26	99.30 12		98.43 154	97.26 48	99.80 25	99.88 27	96.71 28	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 13	99.93 26	99.29 15	99.95 70	98.32 195	97.28 44	99.83 21	99.91 17	97.22 20	100.00 1	99.99 5	100.00 1	99.89 95
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					99.93 26	99.29 15	99.96 51	98.42 166	97.28 44	99.86 14	99.94 4	97.22 20

MSP-MVS		99.09 9	99.12 5	98.98 90	99.93 26	97.24 119	99.95 70	98.42 166	97.50 37	99.52 73	99.88 27	97.43 16	99.71 157	99.50 59	99.98 32	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
agg_prior					99.93 26	98.77 45		98.43 154		99.63 56			99.85 127

FOURS1					99.92 34	97.66 102	99.95 70	98.36 186	95.58 109	99.52 73

ZD-MVS					99.92 34	98.57 59		98.52 126	92.34 253	99.31 91	99.83 49	95.06 61	99.80 140	99.70 47	99.97 42

GST-MVS		98.27 62	97.97 71	99.17 64	99.92 34	97.57 104	99.93 95	98.39 178	94.04 171	98.80 122	99.74 85	92.98 132	100.00 1	98.16 141	99.76 88	99.93 86

TEST9					99.92 34	98.92 30	99.96 51	98.43 154	93.90 179	99.71 46	99.86 32	95.88 43	99.85 127

train_agg		98.88 22	98.65 26	99.59 25	99.92 34	98.92 30	99.96 51	98.43 154	94.35 153	99.71 46	99.86 32	95.94 40	99.85 127	99.69 48	99.98 32	99.99 24

test_8					99.92 34	98.88 33	99.96 51	98.43 154	94.35 153	99.69 48	99.85 36	95.94 40	99.85 127

PGM-MVS		98.34 57	98.13 59	98.99 88	99.92 34	97.00 132	99.75 181	99.50 17	93.90 179	99.37 88	99.76 70	93.24 125	100.00 1	97.75 169	99.96 46	99.98 55

ACMMP		97.74 101	97.44 105	98.66 111	99.92 34	96.13 175	99.18 294	99.45 18	94.84 129	96.41 224	99.71 95	91.40 169	99.99 38	97.99 152	98.03 187	99.87 98
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
DVP-MVS++		99.26 6	99.09 9	99.77 8	99.91 42	99.31 10	99.95 70	98.43 154	96.48 77	99.80 25	99.93 11	97.44 14	100.00 1	99.92 15	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 42	99.80 2		98.41 171					100.00 1	99.96 11	100.00 1	100.00 1

No_MVS				99.93 2	99.91 42	99.80 2		98.41 171					100.00 1	99.96 11	100.00 1	100.00 1

HPM-MVS++		99.07 11	98.88 17	99.63 18	99.90 45	99.02 26	99.95 70	98.56 111	97.56 36	99.44 79	99.85 36	95.38 54	100.00 1	99.31 69	99.99 21	99.87 98

APD-MVS		98.62 35	98.35 45	99.41 42	99.90 45	98.51 62	99.87 128	98.36 186	94.08 166	99.74 42	99.73 89	94.08 99	99.74 153	99.42 65	99.99 21	99.99 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	96.59 1	98.81 25	98.54 31	99.62 21	99.90 45	98.85 36	99.24 289	98.47 138	98.14 15	99.08 106	99.91 17	93.09 129	100.00 1	99.04 82	99.99 21	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
OPU-MVS				99.93 2	99.89 48	99.80 2	99.96 51				99.80 57	97.44 14	100.00 1	100.00 1	99.98 32	100.00 1

DPE-MVS		99.26 6	99.10 8	99.74 12	99.89 48	99.24 20	99.87 128	98.44 146	97.48 38	99.64 55	99.94 4	96.68 30	99.99 38	99.99 5	100.00 1	99.99 24
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.89 48	99.25 19				99.49 76

CSCG		97.10 134	97.04 124	97.27 226	99.89 48	91.92 315	99.90 112	99.07 37	88.67 356	95.26 256	99.82 52	93.17 128	99.98 49	98.15 142	99.47 122	99.90 94

ZNCC-MVS		98.31 59	98.03 66	99.17 64	99.88 52	97.59 103	99.94 88	98.44 146	94.31 156	98.50 143	99.82 52	93.06 130	99.99 38	98.30 133	99.99 21	99.93 86

SR-MVS		98.46 46	98.30 49	98.93 94	99.88 52	97.04 131	99.84 147	98.35 188	94.92 125	99.32 90	99.80 57	93.35 118	99.78 144	99.30 70	99.95 53	99.96 73

9.14			98.38 40		99.87 54		99.91 106	98.33 193	93.22 203	99.78 36	99.89 25	94.57 79	99.85 127	99.84 27	99.97 42

SMA-MVS		98.76 28	98.48 34	99.62 21	99.87 54	98.87 34	99.86 139	98.38 182	93.19 204	99.77 37	99.94 4	95.54 48	100.00 1	99.74 41	99.99 21	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
NormalMVS		97.90 83	97.85 83	98.04 163	99.86 56	95.39 207	99.61 225	97.78 262	96.52 75	98.61 136	99.31 154	92.73 140	99.67 165	96.77 198	99.48 119	99.06 239

lecture		98.67 32	98.46 35	99.28 51	99.86 56	97.88 90	99.97 38	99.25 30	96.07 95	99.79 34	99.70 98	92.53 148	99.98 49	99.51 57	99.48 119	99.97 65

PHI-MVS		98.41 52	98.21 52	99.03 83	99.86 56	97.10 129	99.98 20	98.80 70	90.78 312	99.62 59	99.78 65	95.30 55	100.00 1	99.80 30	99.93 64	99.99 24

MTAPA		98.29 61	97.96 74	99.30 50	99.85 59	97.93 88	99.39 267	98.28 202	95.76 103	97.18 196	99.88 27	92.74 139	100.00 1	98.67 109	99.88 76	99.99 24

LS3D		95.84 200	95.11 213	98.02 164	99.85 59	95.10 225	98.74 351	98.50 135	87.22 378	93.66 277	99.86 32	87.45 233	99.95 83	90.94 315	99.81 86	99.02 243

HPM-MVS		97.96 78	97.72 88	98.68 108	99.84 61	96.39 161	99.90 112	98.17 217	92.61 238	98.62 135	99.57 128	91.87 165	99.67 165	98.87 97	99.99 21	99.99 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set		98.27 62	98.11 61	98.75 104	99.83 62	96.59 153	99.40 263	98.51 129	95.29 117	98.51 142	99.76 70	93.60 114	99.71 157	98.53 119	99.52 112	99.95 81

save fliter					99.82 63	98.79 41	99.96 51	98.40 175	97.66 32

PLC	95.54 3	97.93 81	97.89 81	98.05 162	99.82 63	94.77 235	99.92 98	98.46 140	93.93 176	97.20 194	99.27 159	95.44 53	99.97 62	97.41 174	99.51 115	99.41 190
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 67	98.08 63	98.78 101	99.81 65	96.60 151	99.82 157	98.30 200	93.95 175	99.37 88	99.77 68	92.84 136	99.76 150	98.95 88	99.92 67	99.97 65

EI-MVSNet-UG-set		98.14 73	97.99 69	98.60 116	99.80 66	96.27 164	99.36 273	98.50 135	95.21 119	98.30 154	99.75 78	93.29 122	99.73 156	98.37 128	99.30 136	99.81 107

SR-MVS-dyc-post		98.31 59	98.17 56	98.71 106	99.79 67	96.37 162	99.76 177	98.31 197	94.43 148	99.40 85	99.75 78	93.28 123	99.78 144	98.90 95	99.92 67	99.97 65

RE-MVS-def			98.13 59		99.79 67	96.37 162	99.76 177	98.31 197	94.43 148	99.40 85	99.75 78	92.95 133		98.90 95	99.92 67	99.97 65

HPM-MVS_fast		97.80 95	97.50 101	98.68 108	99.79 67	96.42 157	99.88 125	98.16 222	91.75 276	98.94 115	99.54 131	91.82 167	99.65 169	97.62 172	99.99 21	99.99 24

SF-MVS		98.67 32	98.40 38	99.50 33	99.77 70	98.67 52	99.90 112	98.21 212	93.53 191	99.81 23	99.89 25	94.70 75	99.86 126	99.84 27	99.93 64	99.96 73

MGCNet		99.06 13	98.84 19	99.72 14	99.76 71	99.21 22	99.99 5	99.34 25	98.70 2	99.44 79	99.75 78	93.24 125	99.99 38	99.94 13	99.41 129	99.95 81

旧先验1					99.76 71	97.52 106		98.64 89			99.85 36	95.63 47			99.94 58	99.99 24

OMC-MVS		97.28 124	97.23 116	97.41 216	99.76 71	93.36 282	99.65 214	97.95 243	96.03 96	97.41 187	99.70 98	89.61 201	99.51 175	96.73 200	98.25 177	99.38 192

新几何1				99.42 41	99.75 74	98.27 69		98.63 95	92.69 233	99.55 68	99.82 52	94.40 83	100.00 1	91.21 307	99.94 58	99.99 24

MP-MVS-pluss		98.07 77	97.64 94	99.38 47	99.74 75	98.41 67	99.74 184	98.18 216	93.35 198	96.45 221	99.85 36	92.64 143	99.97 62	98.91 94	99.89 73	99.77 114
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 19	98.77 21	99.41 42	99.74 75	98.67 52	99.77 171	98.38 182	96.73 68	99.88 11	99.74 85	94.89 68	99.59 171	99.80 30	99.98 32	99.97 65
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test12				99.43 39	99.74 75	98.56 60		98.40 175		99.65 52		94.76 71	99.75 151		99.98 32	99.99 24

原ACMM1				98.96 92	99.73 78	96.99 133		98.51 129	94.06 169	99.62 59	99.85 36	94.97 67	99.96 74	95.11 228	99.95 53	99.92 91

TSAR-MVS + GP.		98.60 36	98.51 33	98.86 97	99.73 78	96.63 148	99.97 38	97.92 248	98.07 18	98.76 128	99.55 129	95.00 65	99.94 91	99.91 18	97.68 194	99.99 24

CANet		98.27 62	97.82 85	99.63 18	99.72 80	99.10 24	99.98 20	98.51 129	97.00 58	98.52 140	99.71 95	87.80 225	99.95 83	99.75 39	99.38 131	99.83 103

reproduce_model		98.75 29	98.66 25	99.03 83	99.71 81	97.10 129	99.73 191	98.23 210	97.02 57	99.18 101	99.90 21	94.54 80	99.99 38	99.77 35	99.90 72	99.99 24

F-COLMAP		96.93 146	96.95 127	96.87 239	99.71 81	91.74 320	99.85 142	97.95 243	93.11 211	95.72 245	99.16 177	92.35 154	99.94 91	95.32 224	99.35 134	98.92 251

reproduce-ours		98.78 26	98.67 23	99.09 78	99.70 83	97.30 116	99.74 184	98.25 206	97.10 52	99.10 104	99.90 21	94.59 76	99.99 38	99.77 35	99.91 70	99.99 24

our_new_method		98.78 26	98.67 23	99.09 78	99.70 83	97.30 116	99.74 184	98.25 206	97.10 52	99.10 104	99.90 21	94.59 76	99.99 38	99.77 35	99.91 70	99.99 24

SD-MVS		98.92 20	98.70 22	99.56 28	99.70 83	98.73 49	99.94 88	98.34 192	96.38 83	99.81 23	99.76 70	94.59 76	99.98 49	99.84 27	99.96 46	99.97 65
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
patch_mono-2		98.24 68	99.12 5	95.59 280	99.67 86	86.91 403	99.95 70	98.89 52	97.60 33	99.90 6	99.76 70	96.54 33	99.98 49	99.94 13	99.82 84	99.88 96

ACMMP_NAP		98.49 44	98.14 58	99.54 30	99.66 87	98.62 58	99.85 142	98.37 185	94.68 136	99.53 71	99.83 49	92.87 135	100.00 1	98.66 111	99.84 79	99.99 24

DeepPCF-MVS	95.94 2	97.71 105	98.98 12	93.92 346	99.63 88	81.76 438	99.96 51	98.56 111	99.47 1	99.19 100	99.99 1	94.16 98	100.00 1	99.92 15	99.93 64	100.00 1

EPNet		98.49 44	98.40 38	98.77 103	99.62 89	96.80 142	99.90 112	99.51 16	97.60 33	99.20 98	99.36 149	93.71 111	99.91 108	97.99 152	98.71 162	99.61 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		98.83 23	98.53 32	99.76 10	99.59 90	99.33 8	99.99 5	99.76 6	98.39 4	99.39 87	99.80 57	90.49 190	99.96 74	99.89 20	99.43 127	99.98 55

PVSNet_BlendedMVS		96.05 191	95.82 185	96.72 245	99.59 90	96.99 133	99.95 70	99.10 34	94.06 169	98.27 155	95.80 351	89.00 213	99.95 83	99.12 76	87.53 343	93.24 405

PVSNet_Blended		97.94 80	97.64 94	98.83 98	99.59 90	96.99 133	100.00 1	99.10 34	95.38 114	98.27 155	99.08 181	89.00 213	99.95 83	99.12 76	99.25 138	99.57 156

PatchMatch-RL		96.04 192	95.40 200	97.95 166	99.59 90	95.22 220	99.52 245	99.07 37	93.96 174	96.49 220	98.35 266	82.28 303	99.82 139	90.15 331	99.22 141	98.81 258

dcpmvs_2		97.42 119	98.09 62	95.42 287	99.58 94	87.24 399	99.23 290	96.95 378	94.28 159	98.93 116	99.73 89	94.39 86	99.16 203	99.89 20	99.82 84	99.86 100

test222					99.55 95	97.41 114	99.34 275	98.55 117	91.86 271	99.27 96	99.83 49	93.84 108			99.95 53	99.99 24

CNLPA		97.76 99	97.38 108	98.92 95	99.53 96	96.84 138	99.87 128	98.14 226	93.78 183	96.55 218	99.69 102	92.28 156	99.98 49	97.13 183	99.44 126	99.93 86

API-MVS		97.86 86	97.66 92	98.47 133	99.52 97	95.41 205	99.47 255	98.87 57	91.68 277	98.84 119	99.85 36	92.34 155	99.99 38	98.44 124	99.96 46	100.00 1

PVSNet	91.05 13	97.13 133	96.69 143	98.45 135	99.52 97	95.81 184	99.95 70	99.65 12	94.73 133	99.04 111	99.21 170	84.48 286	99.95 83	94.92 234	98.74 161	99.58 154

114514_t		97.41 120	96.83 134	99.14 71	99.51 99	97.83 92	99.89 122	98.27 204	88.48 360	99.06 110	99.66 113	90.30 193	99.64 170	96.32 209	99.97 42	99.96 73

cl22		93.77 273	93.25 277	95.33 291	99.49 100	94.43 244	99.61 225	98.09 229	90.38 320	89.16 347	95.61 359	90.56 188	97.34 333	91.93 298	84.45 364	94.21 350

testdata				98.42 139	99.47 101	95.33 211		98.56 111	93.78 183	99.79 34	99.85 36	93.64 113	99.94 91	94.97 232	99.94 58	100.00 1

MAR-MVS		97.43 115	97.19 118	98.15 155	99.47 101	94.79 234	99.05 312	98.76 72	92.65 236	98.66 133	99.82 52	88.52 219	99.98 49	98.12 143	99.63 97	99.67 128
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		94.54 245	93.42 267	97.91 172	99.46 103	94.04 259	98.93 330	97.48 299	81.15 432	90.04 318	99.55 129	87.02 241	99.95 83	88.97 343	98.11 183	99.73 118

MVS_111021_LR		98.42 51	98.38 40	98.53 128	99.39 104	95.79 185	99.87 128	99.86 2	96.70 69	98.78 123	99.79 61	92.03 162	99.90 110	99.17 75	99.86 78	99.88 96

CHOSEN 280x420		99.01 16	99.03 10	98.95 93	99.38 105	98.87 34	98.46 370	99.42 21	97.03 56	99.02 112	99.09 180	99.35 2	98.21 295	99.73 43	99.78 87	99.77 114

MVS_111021_HR		98.72 30	98.62 28	99.01 87	99.36 106	97.18 122	99.93 95	99.90 1	96.81 66	98.67 132	99.77 68	93.92 103	99.89 115	99.27 71	99.94 58	99.96 73

DPM-MVS		98.83 23	98.46 35	99.97 1	99.33 107	99.92 1	99.96 51	98.44 146	97.96 22	99.55 68	99.94 4	97.18 22	100.00 1	93.81 265	99.94 58	99.98 55

TAPA-MVS	92.12 8	94.42 253	93.60 259	96.90 238	99.33 107	91.78 319	99.78 166	98.00 237	89.89 333	94.52 262	99.47 135	91.97 163	99.18 200	69.90 449	99.52 112	99.73 118
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 217	95.07 215	96.32 260	99.32 109	96.60 151	99.76 177	98.85 61	96.65 71	87.83 369	96.05 348	99.52 1	98.11 300	96.58 204	81.07 393	94.25 345

fmvsm_s_conf0.5_n_9		98.15 72	98.02 67	98.55 122	99.28 110	95.84 183	99.99 5	98.57 105	98.17 12	99.93 2	99.74 85	87.04 240	99.97 62	99.86 25	99.59 106	99.83 103

SPE-MVS-test		97.88 84	97.94 76	97.70 189	99.28 110	95.20 221	99.98 20	97.15 345	95.53 111	99.62 59	99.79 61	92.08 161	98.38 278	98.75 105	99.28 137	99.52 168

test_fmvsm_n_1920		98.44 48	98.61 29	97.92 170	99.27 112	95.18 222	100.00 1	98.90 50	98.05 19	99.80 25	99.73 89	92.64 143	99.99 38	99.58 55	99.51 115	98.59 268

fmvsm_s_conf0.5_n_10		98.24 68	97.90 79	99.26 53	99.24 113	97.88 90	99.99 5	98.76 72	98.20 9	99.92 4	99.74 85	85.97 258	99.94 91	99.72 44	99.53 111	99.96 73

fmvsm_l_conf0.5_n_a		99.00 17	98.91 14	99.28 51	99.21 114	97.91 89	99.98 20	98.85 61	98.25 5	99.92 4	99.75 78	94.72 73	99.97 62	99.87 23	99.64 95	99.95 81

fmvsm_s_conf0.5_n_8		98.38 56	98.05 65	99.35 48	99.20 115	98.12 75	99.98 20	98.81 66	98.22 7	99.80 25	99.71 95	87.37 235	99.97 62	99.91 18	99.48 119	99.97 65

test_yl		97.83 90	97.37 109	99.21 58	99.18 116	97.98 84	99.64 218	99.27 27	91.43 286	97.88 172	98.99 192	95.84 44	99.84 135	98.82 99	95.32 270	99.79 110

DCV-MVSNet		97.83 90	97.37 109	99.21 58	99.18 116	97.98 84	99.64 218	99.27 27	91.43 286	97.88 172	98.99 192	95.84 44	99.84 135	98.82 99	95.32 270	99.79 110

fmvsm_l_conf0.5_n		98.94 18	98.84 19	99.25 54	99.17 118	97.81 94	99.98 20	98.86 58	98.25 5	99.90 6	99.76 70	94.21 96	99.97 62	99.87 23	99.52 112	99.98 55

DeepC-MVS	94.51 4	96.92 147	96.40 156	98.45 135	99.16 119	95.90 181	99.66 213	98.06 232	96.37 86	94.37 268	99.49 134	83.29 296	99.90 110	97.63 171	99.61 102	99.55 158
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 40	98.22 51	99.50 33	99.15 120	98.65 56	100.00 1	98.58 103	97.70 31	98.21 160	99.24 166	92.58 146	99.94 91	98.63 114	99.94 58	99.92 91
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
fmvsm_l_conf0.5_n_3		98.41 52	98.08 63	99.39 44	99.12 121	98.29 68	99.98 20	98.64 89	98.14 15	99.86 14	99.76 70	87.99 224	99.97 62	99.72 44	99.54 109	99.91 93

fmvsm_l_conf0.5_n_9		98.55 39	98.23 50	99.49 35	99.10 122	98.50 63	99.99 5	98.70 78	98.14 15	99.94 1	99.68 109	89.02 212	99.98 49	99.89 20	99.61 102	99.99 24

CS-MVS		97.79 97	97.91 78	97.43 214	99.10 122	94.42 245	99.99 5	97.10 357	95.07 120	99.68 49	99.75 78	92.95 133	98.34 282	98.38 126	99.14 143	99.54 162

Anonymous202405211		93.10 291	91.99 304	96.40 256	99.10 122	89.65 369	98.88 336	97.93 245	83.71 416	94.00 274	98.75 228	68.79 409	99.88 121	95.08 229	91.71 303	99.68 126

fmvsm_s_conf0.5_n		97.80 95	97.85 83	97.67 190	99.06 125	94.41 246	99.98 20	98.97 43	97.34 41	99.63 56	99.69 102	87.27 236	99.97 62	99.62 53	99.06 148	98.62 267

HyFIR lowres test		96.66 162	96.43 154	97.36 221	99.05 126	93.91 264	99.70 204	99.80 3	90.54 316	96.26 227	98.08 279	92.15 159	98.23 294	96.84 197	95.46 265	99.93 86

LFMVS		94.75 239	93.56 262	98.30 145	99.03 127	95.70 191	98.74 351	97.98 240	87.81 371	98.47 144	99.39 146	67.43 418	99.53 172	98.01 150	95.20 273	99.67 128

fmvsm_s_conf0.5_n_4		97.75 100	97.86 82	97.42 215	99.01 128	94.69 237	99.97 38	98.76 72	97.91 24	99.87 12	99.76 70	86.70 247	99.93 101	99.67 50	99.12 146	97.64 296

fmvsm_s_conf0.5_n_2		97.59 110	97.28 113	98.53 128	99.01 128	98.15 70	99.98 20	98.59 101	98.17 12	99.75 39	99.63 119	81.83 309	99.94 91	99.78 33	98.79 159	97.51 304

AllTest		92.48 306	91.64 309	95.00 300	99.01 128	88.43 387	98.94 328	96.82 392	86.50 387	88.71 352	98.47 261	74.73 384	99.88 121	85.39 383	96.18 240	96.71 310

TestCases				95.00 300	99.01 128	88.43 387		96.82 392	86.50 387	88.71 352	98.47 261	74.73 384	99.88 121	85.39 383	96.18 240	96.71 310

COLMAP_ROB	90.47 14	92.18 313	91.49 315	94.25 334	99.00 132	88.04 393	98.42 376	96.70 399	82.30 427	88.43 361	99.01 189	76.97 359	99.85 127	86.11 379	96.50 232	94.86 321
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_3		97.95 79	97.66 92	98.81 99	98.99 133	98.07 78	99.98 20	98.81 66	98.18 11	99.89 9	99.70 98	84.15 289	99.97 62	99.76 38	99.50 117	98.39 275

test_fmvs1		95.35 218	95.68 192	94.36 330	98.99 133	84.98 414	99.96 51	96.65 401	97.60 33	99.73 44	98.96 198	71.58 399	99.93 101	98.31 132	99.37 132	98.17 280

HY-MVS	92.50 7	97.79 97	97.17 120	99.63 18	98.98 135	99.32 9	97.49 404	99.52 14	95.69 106	98.32 153	97.41 299	93.32 120	99.77 147	98.08 147	95.75 255	99.81 107

VNet		97.21 129	96.57 148	99.13 75	98.97 136	97.82 93	99.03 315	99.21 32	94.31 156	99.18 101	98.88 210	86.26 254	99.89 115	98.93 90	94.32 283	99.69 125

thres200		96.96 143	96.21 163	99.22 57	98.97 136	98.84 37	99.85 142	99.71 7	93.17 206	96.26 227	98.88 210	89.87 198	99.51 175	94.26 253	94.91 275	99.31 209

tfpn200view9		96.79 151	95.99 170	99.19 60	98.94 138	98.82 38	99.78 166	99.71 7	92.86 221	96.02 235	98.87 217	89.33 205	99.50 177	93.84 262	94.57 279	99.27 218

thres400		96.78 153	95.99 170	99.16 67	98.94 138	98.82 38	99.78 166	99.71 7	92.86 221	96.02 235	98.87 217	89.33 205	99.50 177	93.84 262	94.57 279	99.16 227

sasdasda		97.09 136	96.32 157	99.39 44	98.93 140	98.95 28	99.72 195	97.35 312	94.45 144	97.88 172	99.42 139	86.71 245	99.52 173	98.48 121	93.97 289	99.72 120

Anonymous20231211		89.86 363	88.44 371	94.13 337	98.93 140	90.68 347	98.54 367	98.26 205	76.28 444	86.73 383	95.54 363	70.60 405	97.56 326	90.82 318	80.27 402	94.15 358

canonicalmvs		97.09 136	96.32 157	99.39 44	98.93 140	98.95 28	99.72 195	97.35 312	94.45 144	97.88 172	99.42 139	86.71 245	99.52 173	98.48 121	93.97 289	99.72 120

SDMVSNet		94.80 234	93.96 249	97.33 224	98.92 143	95.42 204	99.59 230	98.99 40	92.41 249	92.55 292	97.85 290	75.81 374	98.93 218	97.90 158	91.62 304	97.64 296

sd_testset		93.55 280	92.83 284	95.74 278	98.92 143	90.89 343	98.24 383	98.85 61	92.41 249	92.55 292	97.85 290	71.07 404	98.68 247	93.93 259	91.62 304	97.64 296

EPNet_dtu		95.71 206	95.39 201	96.66 247	98.92 143	93.41 278	99.57 235	98.90 50	96.19 93	97.52 182	98.56 251	92.65 142	97.36 331	77.89 430	98.33 172	99.20 225
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 75	97.60 96	99.60 23	98.92 143	99.28 17	99.89 122	99.52 14	95.58 109	98.24 159	99.39 146	93.33 119	99.74 153	97.98 154	95.58 264	99.78 113

CHOSEN 1792x2688		96.81 150	96.53 149	97.64 193	98.91 147	93.07 284	99.65 214	99.80 3	95.64 107	95.39 252	98.86 219	84.35 288	99.90 110	96.98 190	99.16 142	99.95 81

thres100view900		96.74 157	95.92 181	99.18 61	98.90 148	98.77 45	99.74 184	99.71 7	92.59 240	95.84 239	98.86 219	89.25 207	99.50 177	93.84 262	94.57 279	99.27 218

thres600view7		96.69 160	95.87 184	99.14 71	98.90 148	98.78 44	99.74 184	99.71 7	92.59 240	95.84 239	98.86 219	89.25 207	99.50 177	93.44 275	94.50 282	99.16 227

MSDG		94.37 255	93.36 274	97.40 217	98.88 150	93.95 263	99.37 271	97.38 308	85.75 398	90.80 311	99.17 174	84.11 291	99.88 121	86.35 375	98.43 170	98.36 277

MGCFI-Net		97.00 141	96.22 162	99.34 49	98.86 151	98.80 40	99.67 212	97.30 322	94.31 156	97.77 178	99.41 143	86.36 252	99.50 177	98.38 126	93.90 291	99.72 120

h-mvs33		94.92 231	94.36 235	96.59 249	98.85 152	91.29 335	98.93 330	98.94 44	95.90 98	98.77 125	98.42 264	90.89 183	99.77 147	97.80 162	70.76 441	98.72 264

Anonymous20240529		92.10 314	90.65 326	96.47 251	98.82 153	90.61 349	98.72 353	98.67 85	75.54 448	93.90 276	98.58 249	66.23 422	99.90 110	94.70 243	90.67 307	98.90 254

PVSNet_Blended_VisFu		97.27 125	96.81 136	98.66 111	98.81 154	96.67 147	99.92 98	98.64 89	94.51 141	96.38 225	98.49 257	89.05 211	99.88 121	97.10 185	98.34 171	99.43 187

PS-MVSNAJ		98.44 48	98.20 53	99.16 67	98.80 155	98.92 30	99.54 243	98.17 217	97.34 41	99.85 17	99.85 36	91.20 172	99.89 115	99.41 66	99.67 93	98.69 265

CANet_DTU		96.76 154	96.15 165	98.60 116	98.78 156	97.53 105	99.84 147	97.63 277	97.25 49	99.20 98	99.64 116	81.36 315	99.98 49	92.77 286	98.89 153	98.28 279

mvsany_test1		97.82 93	97.90 79	97.55 203	98.77 157	93.04 287	99.80 163	97.93 245	96.95 60	99.61 66	99.68 109	90.92 180	99.83 137	99.18 74	98.29 176	99.80 109

alignmvs		97.81 94	97.33 111	99.25 54	98.77 157	98.66 54	99.99 5	98.44 146	94.40 152	98.41 148	99.47 135	93.65 112	99.42 187	98.57 115	94.26 285	99.67 128

SymmetryMVS		97.64 108	97.46 102	98.17 151	98.74 159	95.39 207	99.61 225	99.26 29	96.52 75	98.61 136	99.31 154	92.73 140	99.67 165	96.77 198	95.63 262	99.45 183

SteuartSystems-ACMMP		99.02 15	98.97 13	99.18 61	98.72 160	97.71 97	99.98 20	98.44 146	96.85 61	99.80 25	99.91 17	97.57 8	99.85 127	99.44 64	99.99 21	99.99 24
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 70	97.97 71	99.02 86	98.69 161	98.66 54	99.52 245	98.08 231	97.05 55	99.86 14	99.86 32	90.65 185	99.71 157	99.39 68	98.63 163	98.69 265

miper_enhance_ethall		94.36 257	93.98 248	95.49 281	98.68 162	95.24 218	99.73 191	97.29 325	93.28 202	89.86 323	95.97 349	94.37 87	97.05 354	92.20 290	84.45 364	94.19 351

fmvsm_s_conf0.5_n_5		98.08 76	97.71 90	99.17 64	98.67 163	97.69 101	99.99 5	98.57 105	97.40 39	99.89 9	99.69 102	85.99 257	99.96 74	99.80 30	99.40 130	99.85 101

ETVMVS		97.03 140	96.64 144	98.20 150	98.67 163	97.12 126	99.89 122	98.57 105	91.10 298	98.17 161	98.59 246	93.86 107	98.19 296	95.64 221	95.24 272	99.28 216

test2506		97.53 112	97.19 118	98.58 120	98.66 165	96.90 137	98.81 345	99.77 5	94.93 123	97.95 167	98.96 198	92.51 149	99.20 198	94.93 233	98.15 180	99.64 134

ECVR-MVS		95.66 209	95.05 216	97.51 208	98.66 165	93.71 268	98.85 342	98.45 141	94.93 123	96.86 206	98.96 198	75.22 380	99.20 198	95.34 223	98.15 180	99.64 134

mamv4		95.24 221	96.90 129	90.25 408	98.65 167	72.11 456	98.28 381	97.64 276	89.99 331	95.93 237	98.25 274	94.74 72	99.11 204	99.01 87	99.64 95	99.53 166

balanced_conf03		98.27 62	97.99 69	99.11 76	98.64 168	98.43 66	99.47 255	97.79 260	94.56 139	99.74 42	98.35 266	94.33 90	99.25 192	99.12 76	99.96 46	99.64 134

fmvsm_s_conf0.5_n_a		97.73 103	97.72 88	97.77 184	98.63 169	94.26 253	99.96 51	98.92 49	97.18 51	99.75 39	99.69 102	87.00 242	99.97 62	99.46 62	98.89 153	99.08 237

MVSMamba_PlusPlus		97.83 90	97.45 104	98.99 88	98.60 170	98.15 70	99.58 232	97.74 267	90.34 323	99.26 97	98.32 269	94.29 92	99.23 193	99.03 85	99.89 73	99.58 154

testing222		97.08 139	96.75 139	98.06 161	98.56 171	96.82 139	99.85 142	98.61 97	92.53 244	98.84 119	98.84 223	93.36 117	98.30 286	95.84 217	94.30 284	99.05 241

test1111		95.57 212	94.98 219	97.37 219	98.56 171	93.37 281	98.86 340	98.45 141	94.95 122	96.63 212	98.95 203	75.21 381	99.11 204	95.02 230	98.14 182	99.64 134

MVSTER		95.53 213	95.22 208	96.45 254	98.56 171	97.72 96	99.91 106	97.67 272	92.38 252	91.39 302	97.14 306	97.24 19	97.30 338	94.80 239	87.85 336	94.34 340

testing3-2		97.72 104	97.43 107	98.60 116	98.55 174	97.11 128	100.00 1	99.23 31	93.78 183	97.90 169	98.73 230	95.50 51	99.69 161	98.53 119	94.63 277	98.99 245

VDD-MVS		93.77 273	92.94 282	96.27 261	98.55 174	90.22 358	98.77 350	97.79 260	90.85 304	96.82 208	99.42 139	61.18 442	99.77 147	98.95 88	94.13 286	98.82 257

tpmvs		94.28 259	93.57 261	96.40 256	98.55 174	91.50 333	95.70 441	98.55 117	87.47 373	92.15 295	94.26 414	91.42 168	98.95 217	88.15 354	95.85 251	98.76 260

UGNet		95.33 219	94.57 231	97.62 197	98.55 174	94.85 230	98.67 359	99.32 26	95.75 104	96.80 209	96.27 338	72.18 396	99.96 74	94.58 246	99.05 149	98.04 285
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	95.18 223	94.10 242	98.43 137	98.55 174	95.99 179	97.91 397	97.31 321	90.35 322	89.48 336	99.22 167	85.19 272	99.89 115	90.40 328	98.47 169	99.41 190
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 194	96.49 150	94.34 331	98.51 179	89.99 363	99.39 267	98.57 105	93.14 208	97.33 190	98.31 271	93.44 115	94.68 431	93.69 272	95.98 245	98.34 278

UWE-MVS		96.79 151	96.72 141	97.00 233	98.51 179	93.70 269	99.71 198	98.60 99	92.96 216	97.09 197	98.34 268	96.67 32	98.85 224	92.11 296	96.50 232	98.44 273

myMVS_eth3d28		97.86 86	97.59 98	98.68 108	98.50 181	97.26 118	99.92 98	98.55 117	93.79 182	98.26 157	98.75 228	95.20 56	99.48 183	98.93 90	96.40 235	99.29 214

test_vis1_n_1920		95.44 215	95.31 204	95.82 275	98.50 181	88.74 381	99.98 20	97.30 322	97.84 27	99.85 17	99.19 172	66.82 420	99.97 62	98.82 99	99.46 124	98.76 260

BH-w/o		95.71 206	95.38 202	96.68 246	98.49 183	92.28 306	99.84 147	97.50 297	92.12 262	92.06 298	98.79 226	84.69 282	98.67 249	95.29 225	99.66 94	99.09 235

baseline1		95.78 202	94.86 222	98.54 126	98.47 184	98.07 78	99.06 308	97.99 238	92.68 234	94.13 273	98.62 243	93.28 123	98.69 246	93.79 267	85.76 351	98.84 256

fmvsm_s_conf0.5_n_7		97.70 106	97.74 87	97.59 201	98.44 185	95.16 224	99.97 38	98.65 86	97.95 23	99.62 59	99.78 65	86.09 255	99.94 91	99.69 48	99.50 117	97.66 295

EPMVS		96.53 169	96.01 169	98.09 159	98.43 186	96.12 177	96.36 428	99.43 20	93.53 191	97.64 180	95.04 391	94.41 82	98.38 278	91.13 309	98.11 183	99.75 116

kuosan		93.17 288	92.60 290	94.86 307	98.40 187	89.54 371	98.44 372	98.53 124	84.46 411	88.49 357	97.92 287	90.57 187	97.05 354	83.10 400	93.49 294	97.99 286

WBMVS		94.52 248	94.03 246	95.98 267	98.38 188	96.68 146	99.92 98	97.63 277	90.75 313	89.64 331	95.25 384	96.77 26	96.90 366	94.35 251	83.57 371	94.35 338

UBG		97.84 89	97.69 91	98.29 146	98.38 188	96.59 153	99.90 112	98.53 124	93.91 178	98.52 140	98.42 264	96.77 26	99.17 201	98.54 117	96.20 239	99.11 234

sss		97.57 111	97.03 125	99.18 61	98.37 190	98.04 81	99.73 191	99.38 22	93.46 194	98.76 128	99.06 184	91.21 171	99.89 115	96.33 208	97.01 223	99.62 141

testing11		97.48 114	97.27 114	98.10 158	98.36 191	96.02 178	99.92 98	98.45 141	93.45 196	98.15 162	98.70 233	95.48 52	99.22 194	97.85 160	95.05 274	99.07 238

BH-untuned		95.18 223	94.83 223	96.22 262	98.36 191	91.22 336	99.80 163	97.32 320	90.91 302	91.08 305	98.67 235	83.51 293	98.54 260	94.23 254	99.61 102	98.92 251

testing91		97.16 131	96.90 129	97.97 165	98.35 193	95.67 194	99.91 106	98.42 166	92.91 219	97.33 190	98.72 231	94.81 70	99.21 195	96.98 190	94.63 277	99.03 242

testing99		97.17 130	96.91 128	97.95 166	98.35 193	95.70 191	99.91 106	98.43 154	92.94 217	97.36 188	98.72 231	94.83 69	99.21 195	97.00 188	94.64 276	98.95 247

ET-MVSNet_ETH3D		94.37 255	93.28 276	97.64 193	98.30 195	97.99 83	99.99 5	97.61 283	94.35 153	71.57 454	99.45 138	96.23 37	95.34 421	96.91 195	85.14 358	99.59 148

AUN-MVS		93.28 285	92.60 290	95.34 290	98.29 196	90.09 361	99.31 279	98.56 111	91.80 275	96.35 226	98.00 282	89.38 204	98.28 289	92.46 287	69.22 446	97.64 296

FMVSNet3		92.69 301	91.58 311	95.99 266	98.29 196	97.42 113	99.26 288	97.62 280	89.80 334	89.68 327	95.32 378	81.62 313	96.27 397	87.01 371	85.65 352	94.29 342

PMMVS		96.76 154	96.76 138	96.76 243	98.28 198	92.10 310	99.91 106	97.98 240	94.12 164	99.53 71	99.39 146	86.93 243	98.73 239	96.95 193	97.73 191	99.45 183

hse-mvs2		94.38 254	94.08 245	95.31 292	98.27 199	90.02 362	99.29 284	98.56 111	95.90 98	98.77 125	98.00 282	90.89 183	98.26 293	97.80 162	69.20 447	97.64 296

PVSNet_0	88.03 19	91.80 321	90.27 335	96.38 258	98.27 199	90.46 353	99.94 88	99.61 13	93.99 172	86.26 393	97.39 301	71.13 403	99.89 115	98.77 103	67.05 453	98.79 259

UA-Net		96.54 168	95.96 176	98.27 147	98.23 201	95.71 190	98.00 395	98.45 141	93.72 187	98.41 148	99.27 159	88.71 218	99.66 168	91.19 308	97.69 192	99.44 186

test_cas_vis1_n_1920		96.59 165	96.23 160	97.65 192	98.22 202	94.23 254	99.99 5	97.25 330	97.77 28	99.58 67	99.08 181	77.10 354	99.97 62	97.64 170	99.45 125	98.74 262

FE-MVS		95.70 208	95.01 218	97.79 180	98.21 203	94.57 239	95.03 442	98.69 80	88.90 350	97.50 184	96.19 340	92.60 145	99.49 182	89.99 333	97.94 189	99.31 209

GG-mvs-BLEND				98.54 126	98.21 203	98.01 82	93.87 447	98.52 126		97.92 168	97.92 287	99.02 3	97.94 313	98.17 140	99.58 107	99.67 128

mvs_anonymous		95.65 210	95.03 217	97.53 205	98.19 205	95.74 188	99.33 276	97.49 298	90.87 303	90.47 314	97.10 308	88.23 221	97.16 345	95.92 215	97.66 195	99.68 126

MVS_Test		96.46 171	95.74 188	98.61 115	98.18 206	97.23 120	99.31 279	97.15 345	91.07 299	98.84 119	97.05 312	88.17 222	98.97 214	94.39 248	97.50 197	99.61 145

BH-RMVSNet		95.18 223	94.31 238	97.80 178	98.17 207	95.23 219	99.76 177	97.53 293	92.52 245	94.27 271	99.25 165	76.84 361	98.80 229	90.89 317	99.54 109	99.35 200

dongtai		91.55 327	91.13 320	92.82 376	98.16 208	86.35 404	99.47 255	98.51 129	83.24 419	85.07 403	97.56 295	90.33 192	94.94 427	76.09 438	91.73 302	97.18 307

RPSCF		91.80 321	92.79 286	88.83 419	98.15 209	69.87 458	98.11 391	96.60 403	83.93 414	94.33 269	99.27 159	79.60 337	99.46 186	91.99 297	93.16 299	97.18 307

ETV-MVS		97.92 82	97.80 86	98.25 148	98.14 210	96.48 155	99.98 20	97.63 277	95.61 108	99.29 94	99.46 137	92.55 147	98.82 226	99.02 86	98.54 167	99.46 179

IS-MVSNet		96.29 183	95.90 182	97.45 211	98.13 211	94.80 233	99.08 303	97.61 283	92.02 267	95.54 250	98.96 198	90.64 186	98.08 302	93.73 270	97.41 201	99.47 177

test_fmvsmconf_n		98.43 50	98.32 46	98.78 101	98.12 212	96.41 158	99.99 5	98.83 65	98.22 7	99.67 50	99.64 116	91.11 176	99.94 91	99.67 50	99.62 98	99.98 55

fmvsm_s_conf0.1_n_2		97.25 126	96.85 133	98.43 137	98.08 213	98.08 77	99.92 98	97.76 266	98.05 19	99.65 52	99.58 125	80.88 322	99.93 101	99.59 54	98.17 178	97.29 305

ab-mvs		94.69 240	93.42 267	98.51 131	98.07 214	96.26 165	96.49 426	98.68 82	90.31 324	94.54 261	97.00 314	76.30 369	99.71 157	95.98 214	93.38 297	99.56 157

XVG-OURS-SEG-HR		94.79 235	94.70 230	95.08 297	98.05 215	89.19 373	99.08 303	97.54 291	93.66 188	94.87 259	99.58 125	78.78 345	99.79 142	97.31 177	93.40 296	96.25 314

EIA-MVS		97.53 112	97.46 102	97.76 186	98.04 216	94.84 231	99.98 20	97.61 283	94.41 151	97.90 169	99.59 122	92.40 153	98.87 222	98.04 149	99.13 144	99.59 148

XVG-OURS		94.82 232	94.74 229	95.06 298	98.00 217	89.19 373	99.08 303	97.55 289	94.10 165	94.71 260	99.62 120	80.51 328	99.74 153	96.04 213	93.06 301	96.25 314

mvsmamba		96.94 144	96.73 140	97.55 203	97.99 218	94.37 250	99.62 221	97.70 269	93.13 209	98.42 147	97.92 287	88.02 223	98.75 237	98.78 102	99.01 150	99.52 168

dp		95.05 226	94.43 233	96.91 236	97.99 218	92.73 295	96.29 431	97.98 240	89.70 335	95.93 237	94.67 404	93.83 109	98.45 266	86.91 374	96.53 231	99.54 162

tpmrst		96.27 185	95.98 172	97.13 228	97.96 220	93.15 283	96.34 429	98.17 217	92.07 263	98.71 131	95.12 388	93.91 104	98.73 239	94.91 236	96.62 229	99.50 174

TR-MVS		94.54 245	93.56 262	97.49 210	97.96 220	94.34 251	98.71 354	97.51 296	90.30 325	94.51 263	98.69 234	75.56 375	98.77 233	92.82 285	95.99 244	99.35 200

Vis-MVSNet (Re-imp)		96.32 180	95.98 172	97.35 223	97.93 222	94.82 232	99.47 255	98.15 225	91.83 272	95.09 257	99.11 179	91.37 170	97.47 329	93.47 274	97.43 198	99.74 117

MDTV_nov1_ep13			95.69 190		97.90 223	94.15 256	95.98 437	98.44 146	93.12 210	97.98 166	95.74 353	95.10 59	98.58 256	90.02 332	96.92 225

Fast-Effi-MVS+		95.02 228	94.19 240	97.52 207	97.88 224	94.55 240	99.97 38	97.08 361	88.85 352	94.47 264	97.96 286	84.59 283	98.41 270	89.84 335	97.10 216	99.59 148

ADS-MVSNet2		93.80 272	93.88 252	93.55 359	97.87 225	85.94 408	94.24 443	96.84 389	90.07 328	96.43 222	94.48 409	90.29 194	95.37 420	87.44 361	97.23 208	99.36 196

ADS-MVSNet		94.79 235	94.02 247	97.11 230	97.87 225	93.79 265	94.24 443	98.16 222	90.07 328	96.43 222	94.48 409	90.29 194	98.19 296	87.44 361	97.23 208	99.36 196

Effi-MVS+		96.30 182	95.69 190	98.16 152	97.85 227	96.26 165	97.41 406	97.21 337	90.37 321	98.65 134	98.58 249	86.61 249	98.70 245	97.11 184	97.37 202	99.52 168

PatchmatchNet		95.94 195	95.45 197	97.39 218	97.83 228	94.41 246	96.05 435	98.40 175	92.86 221	97.09 197	95.28 383	94.21 96	98.07 304	89.26 341	98.11 183	99.70 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 243	93.61 257	97.74 188	97.82 229	96.26 165	99.96 51	97.78 262	85.76 396	94.00 274	97.54 296	76.95 360	99.21 195	97.23 181	95.43 267	97.76 294

1112_ss		96.01 193	95.20 209	98.42 139	97.80 230	96.41 158	99.65 214	96.66 400	92.71 231	92.88 288	99.40 144	92.16 158	99.30 190	91.92 299	93.66 292	99.55 158

Test_1112_low_res		95.72 204	94.83 223	98.42 139	97.79 231	96.41 158	99.65 214	96.65 401	92.70 232	92.86 289	96.13 344	92.15 159	99.30 190	91.88 300	93.64 293	99.55 158

Effi-MVS+-dtu		94.53 247	95.30 205	92.22 384	97.77 232	82.54 431	99.59 230	97.06 365	94.92 125	95.29 254	95.37 376	85.81 259	97.89 314	94.80 239	97.07 217	96.23 316

tpm cat1		93.51 281	92.52 296	96.47 251	97.77 232	91.47 334	96.13 433	98.06 232	80.98 433	92.91 287	93.78 418	89.66 199	98.87 222	87.03 370	96.39 236	99.09 235

FA-MVS(test-final)		95.86 198	95.09 214	98.15 155	97.74 234	95.62 196	96.31 430	98.17 217	91.42 288	96.26 227	96.13 344	90.56 188	99.47 185	92.18 291	97.07 217	99.35 200

xiu_mvs_v1_base_debu		97.43 115	97.06 121	98.55 122	97.74 234	98.14 72	99.31 279	97.86 254	96.43 80	99.62 59	99.69 102	85.56 266	99.68 162	99.05 79	98.31 173	97.83 290

xiu_mvs_v1_base		97.43 115	97.06 121	98.55 122	97.74 234	98.14 72	99.31 279	97.86 254	96.43 80	99.62 59	99.69 102	85.56 266	99.68 162	99.05 79	98.31 173	97.83 290

xiu_mvs_v1_base_debi		97.43 115	97.06 121	98.55 122	97.74 234	98.14 72	99.31 279	97.86 254	96.43 80	99.62 59	99.69 102	85.56 266	99.68 162	99.05 79	98.31 173	97.83 290

EPP-MVSNet		96.69 160	96.60 146	96.96 235	97.74 234	93.05 286	99.37 271	98.56 111	88.75 354	95.83 241	99.01 189	96.01 38	98.56 258	96.92 194	97.20 210	99.25 220

gg-mvs-nofinetune		93.51 281	91.86 308	98.47 133	97.72 239	97.96 87	92.62 453	98.51 129	74.70 451	97.33 190	69.59 468	98.91 4	97.79 317	97.77 167	99.56 108	99.67 128

IB-MVS	92.85 6	94.99 229	93.94 250	98.16 152	97.72 239	95.69 193	99.99 5	98.81 66	94.28 159	92.70 290	96.90 316	95.08 60	99.17 201	96.07 212	73.88 434	99.60 147
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		97.41 120	97.02 126	98.59 119	97.71 241	97.52 106	99.97 38	98.54 121	91.83 272	97.45 185	99.04 186	97.50 9	99.10 206	94.75 241	96.37 237	99.16 227

VortexMVS		94.11 261	93.50 264	95.94 269	97.70 242	96.61 150	99.35 274	97.18 340	93.52 193	89.57 334	95.74 353	87.55 230	96.97 362	95.76 220	85.13 359	94.23 347

viewdifsd2359ckpt09		96.21 187	95.77 186	97.53 205	97.69 243	94.50 242	99.78 166	97.23 335	92.88 220	96.58 215	99.26 163	84.85 277	98.66 252	96.61 202	97.02 222	99.43 187

Syy-MVS		90.00 361	90.63 327	88.11 426	97.68 244	74.66 454	99.71 198	98.35 188	90.79 310	92.10 296	98.67 235	79.10 343	93.09 446	63.35 461	95.95 248	96.59 312

myMVS_eth3d		94.46 252	94.76 228	93.55 359	97.68 244	90.97 338	99.71 198	98.35 188	90.79 310	92.10 296	98.67 235	92.46 152	93.09 446	87.13 367	95.95 248	96.59 312

test_fmvs1_n		94.25 260	94.36 235	93.92 346	97.68 244	83.70 421	99.90 112	96.57 404	97.40 39	99.67 50	98.88 210	61.82 439	99.92 107	98.23 138	99.13 144	98.14 283

fmvsm_s_conf0.5_n_6		98.27 62	97.96 74	99.23 56	97.66 247	98.11 76	99.98 20	98.64 89	97.85 26	99.87 12	99.72 92	88.86 215	99.93 101	99.64 52	99.36 133	99.63 140

RRT-MVS		96.24 186	95.68 192	97.94 169	97.65 248	94.92 229	99.27 287	97.10 357	92.79 227	97.43 186	97.99 284	81.85 308	99.37 189	98.46 123	98.57 164	99.53 166

diffmvs		97.00 141	96.64 144	98.09 159	97.64 249	96.17 174	99.81 159	97.19 338	94.67 137	98.95 114	99.28 156	86.43 250	98.76 235	98.37 128	97.42 200	99.33 203
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		96.59 165	96.23 160	97.66 191	97.63 250	94.70 236	99.77 171	97.33 316	93.41 197	97.34 189	99.17 174	86.72 244	98.83 225	97.40 175	97.32 205	99.46 179

viewdifsd2359ckpt13		96.19 188	95.77 186	97.45 211	97.62 251	94.40 248	99.70 204	97.23 335	92.76 229	96.63 212	99.05 185	84.96 276	98.64 253	96.65 201	97.35 203	99.31 209

Vis-MVSNet		95.72 204	95.15 212	97.45 211	97.62 251	94.28 252	99.28 285	98.24 208	94.27 161	96.84 207	98.94 205	79.39 338	98.76 235	93.25 276	98.49 168	99.30 212
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 134	96.72 141	98.22 149	97.60 253	96.70 143	99.92 98	98.54 121	91.11 297	97.07 199	98.97 196	97.47 12	99.03 209	93.73 270	96.09 242	98.92 251

GDP-MVS		97.88 84	97.59 98	98.75 104	97.59 254	97.81 94	99.95 70	97.37 311	94.44 147	99.08 106	99.58 125	97.13 24	99.08 207	94.99 231	98.17 178	99.37 194

miper_ehance_all_eth		93.16 289	92.60 290	94.82 308	97.57 255	93.56 273	99.50 249	97.07 364	88.75 354	88.85 351	95.52 365	90.97 179	96.74 376	90.77 319	84.45 364	94.17 352

guyue		97.15 132	96.82 135	98.15 155	97.56 256	96.25 169	99.71 198	97.84 257	95.75 104	98.13 163	98.65 238	87.58 229	98.82 226	98.29 134	97.91 190	99.36 196

viewmanbaseed2359cas		96.45 172	96.07 166	97.59 201	97.55 257	94.59 238	99.70 204	97.33 316	93.62 190	97.00 202	99.32 151	85.57 265	98.71 242	97.26 180	97.33 204	99.47 177

testing3		93.92 266	94.23 239	92.99 373	97.54 258	90.23 357	99.99 5	99.16 33	90.57 315	91.33 304	98.63 242	92.99 131	92.52 450	82.46 404	95.39 268	96.22 317

SSM_0404		95.75 203	95.16 211	97.50 209	97.53 259	95.39 207	99.11 299	97.25 330	90.81 306	95.27 255	98.83 224	84.74 279	98.67 249	95.24 226	97.69 192	98.45 272

LCM-MVSNet-Re		92.31 310	92.60 290	91.43 393	97.53 259	79.27 448	99.02 317	91.83 463	92.07 263	80.31 427	94.38 412	83.50 294	95.48 417	97.22 182	97.58 196	99.54 162

GBi-Net		90.88 338	89.82 344	94.08 338	97.53 259	91.97 311	98.43 373	96.95 378	87.05 379	89.68 327	94.72 400	71.34 400	96.11 403	87.01 371	85.65 352	94.17 352

test1		90.88 338	89.82 344	94.08 338	97.53 259	91.97 311	98.43 373	96.95 378	87.05 379	89.68 327	94.72 400	71.34 400	96.11 403	87.01 371	85.65 352	94.17 352

FMVSNet2		91.02 335	89.56 349	95.41 288	97.53 259	95.74 188	98.98 320	97.41 306	87.05 379	88.43 361	95.00 394	71.34 400	96.24 399	85.12 386	85.21 357	94.25 345

tttt0517		96.85 148	96.49 150	97.92 170	97.48 264	95.89 182	99.85 142	98.54 121	90.72 314	96.63 212	98.93 208	97.47 12	99.02 210	93.03 283	95.76 254	98.85 255

BP-MVS1		98.33 58	98.18 55	98.81 99	97.44 265	97.98 84	99.96 51	98.17 217	94.88 127	98.77 125	99.59 122	97.59 7	99.08 207	98.24 137	98.93 152	99.36 196

casdiffmvs_mvg		96.43 173	95.94 179	97.89 174	97.44 265	95.47 200	99.86 139	97.29 325	93.35 198	96.03 234	99.19 172	85.39 270	98.72 241	97.89 159	97.04 219	99.49 176
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		97.38 122	97.24 115	97.80 178	97.41 267	95.64 195	99.99 5	97.06 365	94.59 138	99.63 56	99.32 151	89.20 210	98.14 298	98.76 104	99.23 140	99.62 141

viewdifsd2359ckpt07		95.83 201	95.42 199	97.07 231	97.40 268	93.04 287	99.60 228	97.24 333	92.39 251	96.09 233	99.14 178	83.07 299	98.93 218	97.02 187	96.87 226	99.23 223

c3_l		92.53 305	91.87 307	94.52 320	97.40 268	92.99 289	99.40 263	96.93 383	87.86 369	88.69 354	95.44 370	89.95 197	96.44 389	90.45 325	80.69 398	94.14 361

viewmambaseed2359dif		95.92 197	95.55 196	97.04 232	97.38 270	93.41 278	99.78 166	96.97 376	91.14 296	96.58 215	99.27 159	84.85 277	98.75 237	96.87 196	97.12 215	98.97 246

fmvsm_s_conf0.1_n		97.30 123	97.21 117	97.60 199	97.38 270	94.40 248	99.90 112	98.64 89	96.47 79	99.51 75	99.65 115	84.99 275	99.93 101	99.22 73	99.09 147	98.46 271

E3		96.36 178	95.95 178	97.60 199	97.37 272	94.52 241	99.71 198	97.33 316	93.18 205	97.02 200	99.07 183	85.45 269	98.82 226	97.27 178	97.14 214	99.46 179

CDS-MVSNet		96.34 179	96.07 166	97.13 228	97.37 272	94.96 227	99.53 244	97.91 249	91.55 280	95.37 253	98.32 269	95.05 62	97.13 348	93.80 266	95.75 255	99.30 212
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 157	96.26 159	98.16 152	97.36 274	96.48 155	99.96 51	98.29 201	91.93 268	95.77 242	98.07 280	95.54 48	98.29 287	90.55 323	98.89 153	99.70 123

miper_lstm_enhance		91.81 318	91.39 317	93.06 372	97.34 275	89.18 375	99.38 269	96.79 394	86.70 386	87.47 375	95.22 385	90.00 196	95.86 412	88.26 352	81.37 387	94.15 358

baseline		96.43 173	95.98 172	97.76 186	97.34 275	95.17 223	99.51 247	97.17 342	93.92 177	96.90 205	99.28 156	85.37 271	98.64 253	97.50 173	96.86 228	99.46 179

cl____		92.31 310	91.58 311	94.52 320	97.33 277	92.77 291	99.57 235	96.78 395	86.97 383	87.56 373	95.51 366	89.43 203	96.62 381	88.60 346	82.44 379	94.16 357

SD_0403		92.63 304	93.38 271	90.40 407	97.32 278	77.91 450	97.75 402	98.03 236	91.89 269	90.83 310	98.29 273	82.00 305	93.79 440	88.51 350	95.75 255	99.52 168

DIV-MVS_self_test		92.32 309	91.60 310	94.47 324	97.31 279	92.74 293	99.58 232	96.75 396	86.99 382	87.64 371	95.54 363	89.55 202	96.50 386	88.58 347	82.44 379	94.17 352

casdiffmvs		96.42 175	95.97 175	97.77 184	97.30 280	94.98 226	99.84 147	97.09 360	93.75 186	96.58 215	99.26 163	85.07 273	98.78 232	97.77 167	97.04 219	99.54 162
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		94.36 257	93.48 265	96.99 234	97.29 281	93.54 274	99.96 51	96.72 398	88.35 363	93.43 278	98.94 205	82.05 304	98.05 305	88.12 356	96.48 234	99.37 194

eth_miper_zixun_eth		92.41 308	91.93 305	93.84 350	97.28 282	90.68 347	98.83 343	96.97 376	88.57 359	89.19 346	95.73 356	89.24 209	96.69 379	89.97 334	81.55 385	94.15 358

MVSFormer		96.94 144	96.60 146	97.95 166	97.28 282	97.70 99	99.55 241	97.27 327	91.17 293	99.43 81	99.54 131	90.92 180	96.89 367	94.67 244	99.62 98	99.25 220

lupinMVS		97.85 88	97.60 96	98.62 114	97.28 282	97.70 99	99.99 5	97.55 289	95.50 113	99.43 81	99.67 111	90.92 180	98.71 242	98.40 125	99.62 98	99.45 183

diffmvs_AUTHOR		96.75 156	96.41 155	97.79 180	97.20 285	95.46 201	99.69 207	97.15 345	94.46 143	98.78 123	99.21 170	85.64 263	98.77 233	98.27 135	97.31 206	99.13 231

mamba_0408		94.98 230	94.09 243	97.64 193	97.14 286	95.31 212	93.48 450	97.08 361	90.48 317	94.40 265	98.62 243	84.49 284	98.67 249	93.99 257	97.18 211	98.93 248

SSM_04072		94.77 237	94.09 243	96.82 240	97.14 286	95.31 212	93.48 450	97.08 361	90.48 317	94.40 265	98.62 243	84.49 284	96.21 400	93.99 257	97.18 211	98.93 248

SSM_0407		95.62 211	94.95 220	97.61 198	97.14 286	95.31 212	99.00 318	97.25 330	90.81 306	94.40 265	98.83 224	84.74 279	98.58 256	95.24 226	97.18 211	98.93 248

SCA		94.69 240	93.81 254	97.33 224	97.10 289	94.44 243	98.86 340	98.32 195	93.30 201	96.17 232	95.59 361	76.48 367	97.95 311	91.06 311	97.43 198	99.59 148

viewmacassd2359aftdt		95.93 196	95.45 197	97.36 221	97.09 290	94.12 258	99.57 235	97.26 329	93.05 214	96.50 219	99.17 174	82.76 300	98.68 247	96.61 202	97.04 219	99.28 216

KinetiMVS		96.10 189	95.29 206	98.53 128	97.08 291	97.12 126	99.56 238	98.12 228	94.78 130	98.44 145	98.94 205	80.30 332	99.39 188	91.56 304	98.79 159	99.06 239

TAMVS		95.85 199	95.58 194	96.65 248	97.07 292	93.50 275	99.17 295	97.82 259	91.39 290	95.02 258	98.01 281	92.20 157	97.30 338	93.75 269	95.83 252	99.14 230

Fast-Effi-MVS+-dtu		93.72 276	93.86 253	93.29 364	97.06 293	86.16 405	99.80 163	96.83 390	92.66 235	92.58 291	97.83 292	81.39 314	97.67 322	89.75 336	96.87 226	96.05 319

CostFormer		96.10 189	95.88 183	96.78 242	97.03 294	92.55 301	97.08 415	97.83 258	90.04 330	98.72 130	94.89 398	95.01 64	98.29 287	96.54 205	95.77 253	99.50 174

test_fmvsmvis_n_1920		97.67 107	97.59 98	97.91 172	97.02 295	95.34 210	99.95 70	98.45 141	97.87 25	97.02 200	99.59 122	89.64 200	99.98 49	99.41 66	99.34 135	98.42 274

test-LLR		96.47 170	96.04 168	97.78 182	97.02 295	95.44 202	99.96 51	98.21 212	94.07 167	95.55 248	96.38 333	93.90 105	98.27 291	90.42 326	98.83 157	99.64 134

test-mter		96.39 176	95.93 180	97.78 182	97.02 295	95.44 202	99.96 51	98.21 212	91.81 274	95.55 248	96.38 333	95.17 57	98.27 291	90.42 326	98.83 157	99.64 134

icg_test_0407_2		95.04 227	94.78 227	95.84 274	96.97 298	91.64 326	98.63 362	97.12 350	92.33 254	95.60 246	98.88 210	85.65 261	96.56 384	92.12 292	95.70 258	99.32 205

IMVS_0407		95.21 222	94.80 226	96.46 253	96.97 298	91.64 326	98.81 345	97.12 350	92.33 254	95.60 246	98.88 210	85.65 261	98.42 268	92.12 292	95.70 258	99.32 205

IMVS_0404		93.83 268	93.17 278	95.80 276	96.97 298	91.64 326	97.78 401	97.12 350	92.33 254	90.87 309	98.88 210	76.78 362	96.43 390	92.12 292	95.70 258	99.32 205

IMVS_0403		95.25 220	94.81 225	96.58 250	96.97 298	91.64 326	98.97 325	97.12 350	92.33 254	95.43 251	98.88 210	85.78 260	98.79 230	92.12 292	95.70 258	99.32 205

gm-plane-assit					96.97 298	93.76 267			91.47 284		98.96 198		98.79 230	94.92 234

WB-MVSnew		92.90 295	92.77 287	93.26 366	96.95 303	93.63 271	99.71 198	98.16 222	91.49 281	94.28 270	98.14 277	81.33 316	96.48 387	79.47 421	95.46 265	89.68 447

QAPM		95.40 216	94.17 241	99.10 77	96.92 304	97.71 97	99.40 263	98.68 82	89.31 338	88.94 350	98.89 209	82.48 302	99.96 74	93.12 282	99.83 80	99.62 141

KD-MVS_2432*1600		88.00 383	86.10 387	93.70 355	96.91 305	94.04 259	97.17 412	97.12 350	84.93 406	81.96 417	92.41 431	92.48 150	94.51 433	79.23 422	52.68 467	92.56 417

miper_refine_blended		88.00 383	86.10 387	93.70 355	96.91 305	94.04 259	97.17 412	97.12 350	84.93 406	81.96 417	92.41 431	92.48 150	94.51 433	79.23 422	52.68 467	92.56 417

tpm2		95.47 214	95.18 210	96.35 259	96.91 305	91.70 324	96.96 418	97.93 245	88.04 367	98.44 145	95.40 372	93.32 120	97.97 308	94.00 256	95.61 263	99.38 192

FMVSNet5		88.32 379	87.47 381	90.88 396	96.90 308	88.39 389	97.28 409	95.68 425	82.60 426	84.67 405	92.40 433	79.83 335	91.16 455	76.39 437	81.51 386	93.09 408

3Dnovator+	91.53 11	96.31 181	95.24 207	99.52 31	96.88 309	98.64 57	99.72 195	98.24 208	95.27 118	88.42 363	98.98 194	82.76 300	99.94 91	97.10 185	99.83 80	99.96 73

Patchmatch-test		92.65 303	91.50 314	96.10 265	96.85 310	90.49 352	91.50 458	97.19 338	82.76 425	90.23 315	95.59 361	95.02 63	98.00 307	77.41 432	96.98 224	99.82 105

MVS		96.60 164	95.56 195	99.72 14	96.85 310	99.22 21	98.31 379	98.94 44	91.57 279	90.90 308	99.61 121	86.66 248	99.96 74	97.36 176	99.88 76	99.99 24

3Dnovator	91.47 12	96.28 184	95.34 203	99.08 80	96.82 312	97.47 111	99.45 260	98.81 66	95.52 112	89.39 337	99.00 191	81.97 306	99.95 83	97.27 178	99.83 80	99.84 102

EI-MVSNet		93.73 275	93.40 270	94.74 309	96.80 313	92.69 296	99.06 308	97.67 272	88.96 347	91.39 302	99.02 187	88.75 217	97.30 338	91.07 310	87.85 336	94.22 348

CVMVSNet		94.68 242	94.94 221	93.89 349	96.80 313	86.92 402	99.06 308	98.98 41	94.45 144	94.23 272	99.02 187	85.60 264	95.31 422	90.91 316	95.39 268	99.43 187

IterMVS-LS		92.69 301	92.11 301	94.43 328	96.80 313	92.74 293	99.45 260	96.89 386	88.98 345	89.65 330	95.38 375	88.77 216	96.34 394	90.98 314	82.04 382	94.22 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 167	96.46 153	96.91 236	96.79 316	92.50 302	99.90 112	97.38 308	96.02 97	97.79 177	99.32 151	86.36 252	98.99 211	98.26 136	96.33 238	99.23 223

IterMVS		90.91 337	90.17 339	93.12 369	96.78 317	90.42 355	98.89 334	97.05 368	89.03 342	86.49 388	95.42 371	76.59 365	95.02 424	87.22 366	84.09 367	93.93 379
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 149	95.96 176	99.48 38	96.74 318	98.52 61	98.31 379	98.86 58	95.82 101	89.91 321	98.98 194	87.49 232	99.96 74	97.80 162	99.73 90	99.96 73

IterMVS-SCA-FT		90.85 340	90.16 340	92.93 374	96.72 319	89.96 364	98.89 334	96.99 372	88.95 348	86.63 385	95.67 357	76.48 367	95.00 425	87.04 369	84.04 370	93.84 386

MVS-HIRNet		86.22 390	83.19 403	95.31 292	96.71 320	90.29 356	92.12 455	97.33 316	62.85 462	86.82 382	70.37 467	69.37 408	97.49 328	75.12 440	97.99 188	98.15 281

viewdifsd2359ckpt11		94.09 263	93.63 256	95.46 285	96.68 321	88.92 378	99.62 221	97.12 350	93.07 212	95.73 243	99.22 167	77.05 355	98.88 221	96.52 206	87.69 341	98.58 269

viewmsd2359difaftdt		94.09 263	93.64 255	95.46 285	96.68 321	88.92 378	99.62 221	97.13 349	93.07 212	95.73 243	99.22 167	77.05 355	98.89 220	96.52 206	87.70 340	98.58 269

VDDNet		93.12 290	91.91 306	96.76 243	96.67 323	92.65 299	98.69 357	98.21 212	82.81 424	97.75 179	99.28 156	61.57 440	99.48 183	98.09 146	94.09 287	98.15 281

dmvs_re		93.20 287	93.15 279	93.34 362	96.54 324	83.81 420	98.71 354	98.51 129	91.39 290	92.37 294	98.56 251	78.66 347	97.83 316	93.89 260	89.74 308	98.38 276

Elysia		94.50 249	93.38 271	97.85 176	96.49 325	96.70 143	98.98 320	97.78 262	90.81 306	96.19 230	98.55 253	73.63 391	98.98 212	89.41 337	98.56 165	97.88 288

StellarMVS		94.50 249	93.38 271	97.85 176	96.49 325	96.70 143	98.98 320	97.78 262	90.81 306	96.19 230	98.55 253	73.63 391	98.98 212	89.41 337	98.56 165	97.88 288

MIMVSNet		90.30 353	88.67 367	95.17 296	96.45 327	91.64 326	92.39 454	97.15 345	85.99 393	90.50 313	93.19 426	66.95 419	94.86 429	82.01 408	93.43 295	99.01 244

CR-MVSNet		93.45 284	92.62 289	95.94 269	96.29 328	92.66 297	92.01 456	96.23 412	92.62 237	96.94 203	93.31 424	91.04 177	96.03 408	79.23 422	95.96 246	99.13 231

RPMNet		89.76 365	87.28 382	97.19 227	96.29 328	92.66 297	92.01 456	98.31 197	70.19 458	96.94 203	85.87 460	87.25 237	99.78 144	62.69 462	95.96 246	99.13 231

tt0805		91.28 330	90.18 338	94.60 315	96.26 330	87.55 395	98.39 377	98.72 76	89.00 344	89.22 343	98.47 261	62.98 435	98.96 216	90.57 322	88.00 335	97.28 306

Patchmtry		89.70 366	88.49 370	93.33 363	96.24 331	89.94 367	91.37 459	96.23 412	78.22 441	87.69 370	93.31 424	91.04 177	96.03 408	80.18 420	82.10 381	94.02 369

test_vis1_rt		86.87 388	86.05 390	89.34 415	96.12 332	78.07 449	99.87 128	83.54 475	92.03 266	78.21 438	89.51 445	45.80 460	99.91 108	96.25 210	93.11 300	90.03 444

JIA-IIPM		91.76 324	90.70 325	94.94 302	96.11 333	87.51 396	93.16 452	98.13 227	75.79 447	97.58 181	77.68 465	92.84 136	97.97 308	88.47 351	96.54 230	99.33 203

OpenMVS	90.15 15	94.77 237	93.59 260	98.33 143	96.07 334	97.48 110	99.56 238	98.57 105	90.46 319	86.51 387	98.95 203	78.57 348	99.94 91	93.86 261	99.74 89	97.57 301

PAPM		98.60 36	98.42 37	99.14 71	96.05 335	98.96 27	99.90 112	99.35 24	96.68 70	98.35 152	99.66 113	96.45 34	98.51 261	99.45 63	99.89 73	99.96 73

CLD-MVS		94.06 265	93.90 251	94.55 319	96.02 336	90.69 346	99.98 20	97.72 268	96.62 74	91.05 307	98.85 222	77.21 353	98.47 262	98.11 144	89.51 314	94.48 326
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 350	88.75 366	95.25 294	95.99 337	90.16 359	91.22 460	97.54 291	76.80 443	97.26 193	86.01 459	91.88 164	96.07 407	66.16 457	95.91 250	99.51 172

ACMH+	89.98 16	90.35 351	89.54 350	92.78 378	95.99 337	86.12 406	98.81 345	97.18 340	89.38 337	83.14 413	97.76 293	68.42 413	98.43 267	89.11 342	86.05 350	93.78 389

DeepMVS_CX				82.92 437	95.98 339	58.66 468		96.01 417	92.72 230	78.34 437	95.51 366	58.29 446	98.08 302	82.57 403	85.29 355	92.03 425

ACMP	92.05 9	92.74 299	92.42 298	93.73 351	95.91 340	88.72 382	99.81 159	97.53 293	94.13 163	87.00 381	98.23 275	74.07 388	98.47 262	96.22 211	88.86 321	93.99 374
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 279	93.03 281	95.35 289	95.86 341	86.94 401	99.87 128	96.36 410	96.85 61	99.54 70	98.79 226	52.41 454	99.83 137	98.64 112	98.97 151	99.29 214

HQP-NCC					95.78 342		99.87 128		96.82 63	93.37 279

ACMP_Plane					95.78 342		99.87 128		96.82 63	93.37 279

HQP-MVS		94.61 244	94.50 232	94.92 303	95.78 342	91.85 316	99.87 128	97.89 250	96.82 63	93.37 279	98.65 238	80.65 326	98.39 274	97.92 156	89.60 309	94.53 322

NP-MVS					95.77 345	91.79 318					98.65 238

test_fmvsmconf0.1_n		97.74 101	97.44 105	98.64 113	95.76 346	96.20 171	99.94 88	98.05 234	98.17 12	98.89 118	99.42 139	87.65 227	99.90 110	99.50 59	99.60 105	99.82 105

plane_prior6					95.76 346	91.72 323						80.47 330

ACMM	91.95 10	92.88 296	92.52 296	93.98 345	95.75 348	89.08 377	99.77 171	97.52 295	93.00 215	89.95 320	97.99 284	76.17 371	98.46 265	93.63 273	88.87 320	94.39 334
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 268	92.84 283	96.80 241	95.73 349	93.57 272	99.88 125	97.24 333	92.57 242	92.92 286	96.66 325	78.73 346	97.67 322	87.75 359	94.06 288	99.17 226

plane_prior1					95.73 349

jason		97.24 127	96.86 132	98.38 142	95.73 349	97.32 115	99.97 38	97.40 307	95.34 116	98.60 139	99.54 131	87.70 226	98.56 258	97.94 155	99.47 122	99.25 220
jason: jason.
mmtdpeth		88.52 377	87.75 379	90.85 398	95.71 352	83.47 426	98.94 328	94.85 440	88.78 353	97.19 195	89.58 444	63.29 433	98.97 214	98.54 117	62.86 461	90.10 443

HQP_MVS		94.49 251	94.36 235	94.87 304	95.71 352	91.74 320	99.84 147	97.87 252	96.38 83	93.01 284	98.59 246	80.47 330	98.37 280	97.79 165	89.55 312	94.52 324

plane_prior7					95.71 352	91.59 332

ITE_SJBPF				92.38 381	95.69 355	85.14 412		95.71 424	92.81 224	89.33 340	98.11 278	70.23 406	98.42 268	85.91 381	88.16 333	93.59 397

fmvsm_s_conf0.1_n_a		97.09 136	96.90 129	97.63 196	95.65 356	94.21 255	99.83 154	98.50 135	96.27 90	99.65 52	99.64 116	84.72 281	99.93 101	99.04 82	98.84 156	98.74 262

ACMH	89.72 17	90.64 344	89.63 347	93.66 357	95.64 357	88.64 385	98.55 365	97.45 300	89.03 342	81.62 420	97.61 294	69.75 407	98.41 270	89.37 339	87.62 342	93.92 380
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 159	96.49 150	97.37 219	95.63 358	95.96 180	99.74 184	98.88 54	92.94 217	91.61 300	98.97 196	97.72 6	98.62 255	94.83 238	98.08 186	97.53 303

FMVSNet1		88.50 378	86.64 385	94.08 338	95.62 359	91.97 311	98.43 373	96.95 378	83.00 422	86.08 395	94.72 400	59.09 445	96.11 403	81.82 410	84.07 368	94.17 352

LuminaMVS		96.63 163	96.21 163	97.87 175	95.58 360	96.82 139	99.12 297	97.67 272	94.47 142	97.88 172	98.31 271	87.50 231	98.71 242	98.07 148	97.29 207	98.10 284

LPG-MVS_test		92.96 293	92.71 288	93.71 353	95.43 361	88.67 383	99.75 181	97.62 280	92.81 224	90.05 316	98.49 257	75.24 378	98.40 272	95.84 217	89.12 316	94.07 366

LGP-MVS_train				93.71 353	95.43 361	88.67 383		97.62 280	92.81 224	90.05 316	98.49 257	75.24 378	98.40 272	95.84 217	89.12 316	94.07 366

tpm		93.70 277	93.41 269	94.58 317	95.36 363	87.41 397	97.01 416	96.90 385	90.85 304	96.72 211	94.14 415	90.40 191	96.84 371	90.75 320	88.54 328	99.51 172

D2MVS		92.76 298	92.59 294	93.27 365	95.13 364	89.54 371	99.69 207	99.38 22	92.26 259	87.59 372	94.61 406	85.05 274	97.79 317	91.59 303	88.01 334	92.47 420

VPA-MVSNet		92.70 300	91.55 313	96.16 263	95.09 365	96.20 171	98.88 336	99.00 39	91.02 301	91.82 299	95.29 382	76.05 373	97.96 310	95.62 222	81.19 388	94.30 341

LTVRE_ROB	88.28 18	90.29 354	89.05 361	94.02 341	95.08 366	90.15 360	97.19 411	97.43 302	84.91 408	83.99 409	97.06 311	74.00 389	98.28 289	84.08 392	87.71 338	93.62 396
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		87.87 385	86.51 386	91.94 387	95.05 367	85.57 410	97.65 403	94.08 450	84.40 412	81.82 419	96.85 320	62.14 438	98.33 283	80.25 419	86.37 349	91.91 427

test0.0.03 1		93.86 267	93.61 257	94.64 313	95.02 368	92.18 309	99.93 95	98.58 103	94.07 167	87.96 367	98.50 256	93.90 105	94.96 426	81.33 411	93.17 298	96.78 309

UniMVSNet (Re)		93.07 292	92.13 300	95.88 271	94.84 369	96.24 170	99.88 125	98.98 41	92.49 247	89.25 341	95.40 372	87.09 239	97.14 347	93.13 281	78.16 412	94.26 343

USDC		90.00 361	88.96 362	93.10 371	94.81 370	88.16 391	98.71 354	95.54 429	93.66 188	83.75 411	97.20 305	65.58 424	98.31 285	83.96 395	87.49 344	92.85 414

VPNet		91.81 318	90.46 329	95.85 273	94.74 371	95.54 199	98.98 320	98.59 101	92.14 261	90.77 312	97.44 298	68.73 411	97.54 327	94.89 237	77.89 414	94.46 327

FIs		94.10 262	93.43 266	96.11 264	94.70 372	96.82 139	99.58 232	98.93 48	92.54 243	89.34 339	97.31 302	87.62 228	97.10 351	94.22 255	86.58 347	94.40 333

UniMVSNet_ETH3D		90.06 360	88.58 369	94.49 323	94.67 373	88.09 392	97.81 400	97.57 288	83.91 415	88.44 359	97.41 299	57.44 447	97.62 324	91.41 305	88.59 327	97.77 293

UniMVSNet_NR-MVSNet		92.95 294	92.11 301	95.49 281	94.61 374	95.28 216	99.83 154	99.08 36	91.49 281	89.21 344	96.86 319	87.14 238	96.73 377	93.20 277	77.52 417	94.46 327

test_fmvs2		89.47 370	89.70 346	88.77 422	94.54 375	75.74 451	99.83 154	94.70 446	94.71 134	91.08 305	96.82 324	54.46 450	97.78 319	92.87 284	88.27 331	92.80 415

MonoMVSNet		94.82 232	94.43 233	95.98 267	94.54 375	90.73 345	99.03 315	97.06 365	93.16 207	93.15 283	95.47 369	88.29 220	97.57 325	97.85 160	91.33 306	99.62 141

WR-MVS		92.31 310	91.25 318	95.48 284	94.45 377	95.29 215	99.60 228	98.68 82	90.10 327	88.07 366	96.89 317	80.68 325	96.80 375	93.14 280	79.67 405	94.36 335

nrg030		93.51 281	92.53 295	96.45 254	94.36 378	97.20 121	99.81 159	97.16 344	91.60 278	89.86 323	97.46 297	86.37 251	97.68 321	95.88 216	80.31 401	94.46 327

tfpnnormal		89.29 373	87.61 380	94.34 331	94.35 379	94.13 257	98.95 327	98.94 44	83.94 413	84.47 406	95.51 366	74.84 383	97.39 330	77.05 435	80.41 399	91.48 430

FC-MVSNet-test		93.81 271	93.15 279	95.80 276	94.30 380	96.20 171	99.42 262	98.89 52	92.33 254	89.03 349	97.27 304	87.39 234	96.83 373	93.20 277	86.48 348	94.36 335

SSC-MVS3.2		89.59 368	88.66 368	92.38 381	94.29 381	86.12 406	99.49 251	97.66 275	90.28 326	88.63 356	95.18 386	64.46 429	96.88 369	85.30 385	82.66 376	94.14 361

MS-PatchMatch		90.65 343	90.30 334	91.71 392	94.22 382	85.50 411	98.24 383	97.70 269	88.67 356	86.42 390	96.37 335	67.82 416	98.03 306	83.62 397	99.62 98	91.60 428

WR-MVS_H		91.30 328	90.35 332	94.15 335	94.17 383	92.62 300	99.17 295	98.94 44	88.87 351	86.48 389	94.46 411	84.36 287	96.61 382	88.19 353	78.51 410	93.21 406

DU-MVS		92.46 307	91.45 316	95.49 281	94.05 384	95.28 216	99.81 159	98.74 75	92.25 260	89.21 344	96.64 327	81.66 311	96.73 377	93.20 277	77.52 417	94.46 327

NR-MVSNet		91.56 326	90.22 336	95.60 279	94.05 384	95.76 187	98.25 382	98.70 78	91.16 295	80.78 426	96.64 327	83.23 297	96.57 383	91.41 305	77.73 416	94.46 327

CP-MVSNet		91.23 332	90.22 336	94.26 333	93.96 386	92.39 305	99.09 301	98.57 105	88.95 348	86.42 390	96.57 330	79.19 341	96.37 392	90.29 329	78.95 407	94.02 369

XXY-MVS		91.82 317	90.46 329	95.88 271	93.91 387	95.40 206	98.87 339	97.69 271	88.63 358	87.87 368	97.08 309	74.38 387	97.89 314	91.66 302	84.07 368	94.35 338

PS-CasMVS		90.63 345	89.51 352	93.99 344	93.83 388	91.70 324	98.98 320	98.52 126	88.48 360	86.15 394	96.53 332	75.46 376	96.31 396	88.83 344	78.86 409	93.95 377

test_0402		85.58 392	83.94 397	90.50 404	93.81 389	85.04 413	98.55 365	95.20 437	76.01 445	79.72 432	95.13 387	64.15 431	96.26 398	66.04 458	86.88 346	90.21 441

XVG-ACMP-BASELINE		91.22 333	90.75 324	92.63 380	93.73 390	85.61 409	98.52 369	97.44 301	92.77 228	89.90 322	96.85 320	66.64 421	98.39 274	92.29 289	88.61 325	93.89 382

TranMVSNet+NR-MVSNet		91.68 325	90.61 328	94.87 304	93.69 391	93.98 262	99.69 207	98.65 86	91.03 300	88.44 359	96.83 323	80.05 334	96.18 401	90.26 330	76.89 425	94.45 332

TransMVSNet (Re)		87.25 386	85.28 393	93.16 368	93.56 392	91.03 337	98.54 367	94.05 452	83.69 417	81.09 424	96.16 341	75.32 377	96.40 391	76.69 436	68.41 449	92.06 424

v10		90.25 355	88.82 364	94.57 318	93.53 393	93.43 277	99.08 303	96.87 388	85.00 405	87.34 379	94.51 407	80.93 321	97.02 361	82.85 402	79.23 406	93.26 404

testgi		89.01 375	88.04 376	91.90 388	93.49 394	84.89 415	99.73 191	95.66 426	93.89 181	85.14 401	98.17 276	59.68 444	94.66 432	77.73 431	88.88 319	96.16 318

v8		90.54 347	89.17 357	94.66 312	93.43 395	93.40 280	99.20 292	96.94 382	85.76 396	87.56 373	94.51 407	81.96 307	97.19 344	84.94 388	78.25 411	93.38 402

V42		91.28 330	90.12 341	94.74 309	93.42 396	93.46 276	99.68 210	97.02 369	87.36 375	89.85 325	95.05 390	81.31 317	97.34 333	87.34 364	80.07 403	93.40 400

pm-mvs1		89.36 372	87.81 378	94.01 342	93.40 397	91.93 314	98.62 363	96.48 408	86.25 391	83.86 410	96.14 343	73.68 390	97.04 357	86.16 378	75.73 430	93.04 410

v1144		91.09 334	89.83 343	94.87 304	93.25 398	93.69 270	99.62 221	96.98 374	86.83 385	89.64 331	94.99 395	80.94 320	97.05 354	85.08 387	81.16 389	93.87 384

v1192		90.62 346	89.25 356	94.72 311	93.13 399	93.07 284	99.50 249	97.02 369	86.33 390	89.56 335	95.01 392	79.22 340	97.09 353	82.34 406	81.16 389	94.01 371

v2v482		91.30 328	90.07 342	95.01 299	93.13 399	93.79 265	99.77 171	97.02 369	88.05 366	89.25 341	95.37 376	80.73 324	97.15 346	87.28 365	80.04 404	94.09 365

OPM-MVS		93.21 286	92.80 285	94.44 326	93.12 401	90.85 344	99.77 171	97.61 283	96.19 93	91.56 301	98.65 238	75.16 382	98.47 262	93.78 268	89.39 315	93.99 374
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 341	89.52 351	94.59 316	93.11 402	92.77 291	99.56 238	96.99 372	86.38 389	89.82 326	94.95 397	80.50 329	97.10 351	83.98 394	80.41 399	93.90 381

PEN-MVS		90.19 357	89.06 360	93.57 358	93.06 403	90.90 342	99.06 308	98.47 138	88.11 365	85.91 396	96.30 337	76.67 363	95.94 411	87.07 368	76.91 424	93.89 382

v1240		90.20 356	88.79 365	94.44 326	93.05 404	92.27 307	99.38 269	96.92 384	85.89 394	89.36 338	94.87 399	77.89 352	97.03 359	80.66 415	81.08 392	94.01 371

v148		90.70 342	89.63 347	93.92 346	92.97 405	90.97 338	99.75 181	96.89 386	87.51 372	88.27 364	95.01 392	81.67 310	97.04 357	87.40 363	77.17 422	93.75 390

v1921920		90.46 348	89.12 358	94.50 322	92.96 406	92.46 303	99.49 251	96.98 374	86.10 392	89.61 333	95.30 379	78.55 349	97.03 359	82.17 407	80.89 397	94.01 371

MVStest1		85.03 398	82.76 407	91.83 389	92.95 407	89.16 376	98.57 364	94.82 441	71.68 456	68.54 459	95.11 389	83.17 298	95.66 415	74.69 441	65.32 456	90.65 437

tt0320-xc		82.94 412	80.35 419	90.72 402	92.90 408	83.54 424	96.85 421	94.73 444	63.12 461	79.85 431	93.77 419	49.43 458	95.46 418	80.98 414	71.54 439	93.16 407

Baseline_NR-MVSNet		90.33 352	89.51 352	92.81 377	92.84 409	89.95 365	99.77 171	93.94 453	84.69 410	89.04 348	95.66 358	81.66 311	96.52 385	90.99 313	76.98 423	91.97 426

test_method		80.79 418	79.70 421	84.08 434	92.83 410	67.06 460	99.51 247	95.42 431	54.34 466	81.07 425	93.53 421	44.48 461	92.22 452	78.90 426	77.23 421	92.94 412

pmmvs4		92.10 314	91.07 322	95.18 295	92.82 411	94.96 227	99.48 254	96.83 390	87.45 374	88.66 355	96.56 331	83.78 292	96.83 373	89.29 340	84.77 362	93.75 390

LF4IMVS		89.25 374	88.85 363	90.45 406	92.81 412	81.19 441	98.12 390	94.79 442	91.44 285	86.29 392	97.11 307	65.30 427	98.11 300	88.53 349	85.25 356	92.07 423

tt0320		83.56 411	81.15 414	90.77 400	92.77 413	83.58 423	96.83 422	95.52 430	63.26 460	81.36 422	92.54 429	53.26 452	95.77 413	80.45 416	74.38 433	92.96 411

DTE-MVSNet		89.40 371	88.24 374	92.88 375	92.66 414	89.95 365	99.10 300	98.22 211	87.29 376	85.12 402	96.22 339	76.27 370	95.30 423	83.56 398	75.74 429	93.41 399

EU-MVSNet		90.14 359	90.34 333	89.54 414	92.55 415	81.06 442	98.69 357	98.04 235	91.41 289	86.59 386	96.84 322	80.83 323	93.31 445	86.20 377	81.91 383	94.26 343

APD_test1		81.15 416	80.92 416	81.86 438	92.45 416	59.76 467	96.04 436	93.61 456	73.29 454	77.06 441	96.64 327	44.28 462	96.16 402	72.35 445	82.52 377	89.67 448

sc_t1		85.01 399	82.46 409	92.67 379	92.44 417	83.09 427	97.39 407	95.72 423	65.06 459	85.64 399	96.16 341	49.50 457	97.34 333	84.86 389	75.39 431	97.57 301

our_test_3		90.39 349	89.48 354	93.12 369	92.40 418	89.57 370	99.33 276	96.35 411	87.84 370	85.30 400	94.99 395	84.14 290	96.09 406	80.38 417	84.56 363	93.71 395

ppachtmachnet_test		89.58 369	88.35 372	93.25 367	92.40 418	90.44 354	99.33 276	96.73 397	85.49 401	85.90 397	95.77 352	81.09 319	96.00 410	76.00 439	82.49 378	93.30 403

v7n		89.65 367	88.29 373	93.72 352	92.22 420	90.56 351	99.07 307	97.10 357	85.42 403	86.73 383	94.72 400	80.06 333	97.13 348	81.14 412	78.12 413	93.49 398

dmvs_testset		83.79 408	86.07 389	76.94 442	92.14 421	48.60 477	96.75 423	90.27 467	89.48 336	78.65 435	98.55 253	79.25 339	86.65 465	66.85 455	82.69 375	95.57 320

PS-MVSNAJss		93.64 278	93.31 275	94.61 314	92.11 422	92.19 308	99.12 297	97.38 308	92.51 246	88.45 358	96.99 315	91.20 172	97.29 341	94.36 249	87.71 338	94.36 335

pmmvs5		90.17 358	89.09 359	93.40 361	92.10 423	89.77 368	99.74 184	95.58 428	85.88 395	87.24 380	95.74 353	73.41 393	96.48 387	88.54 348	83.56 372	93.95 377

N_pmnet		80.06 421	80.78 417	77.89 441	91.94 424	45.28 479	98.80 348	56.82 481	78.10 442	80.08 429	93.33 422	77.03 357	95.76 414	68.14 453	82.81 374	92.64 416

test_djsdf		92.83 297	92.29 299	94.47 324	91.90 425	92.46 303	99.55 241	97.27 327	91.17 293	89.96 319	96.07 347	81.10 318	96.89 367	94.67 244	88.91 318	94.05 368

SixPastTwentyTwo		88.73 376	88.01 377	90.88 396	91.85 426	82.24 433	98.22 387	95.18 438	88.97 346	82.26 416	96.89 317	71.75 398	96.67 380	84.00 393	82.98 373	93.72 394

K. test v3		88.05 382	87.24 383	90.47 405	91.82 427	82.23 434	98.96 326	97.42 304	89.05 341	76.93 443	95.60 360	68.49 412	95.42 419	85.87 382	81.01 395	93.75 390

OurMVSNet-221017-0		89.81 364	89.48 354	90.83 399	91.64 428	81.21 440	98.17 389	95.38 433	91.48 283	85.65 398	97.31 302	72.66 394	97.29 341	88.15 354	84.83 361	93.97 376

mvs_tets		91.81 318	91.08 321	94.00 343	91.63 429	90.58 350	98.67 359	97.43 302	92.43 248	87.37 378	97.05 312	71.76 397	97.32 336	94.75 241	88.68 324	94.11 364

Gipuma		66.95 434	65.00 434	72.79 447	91.52 430	67.96 459	66.16 471	95.15 439	47.89 468	58.54 465	67.99 470	29.74 466	87.54 464	50.20 469	77.83 415	62.87 470
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 176	95.74 188	98.32 144	91.47 431	95.56 198	99.84 147	97.30 322	97.74 29	97.89 171	99.35 150	79.62 336	99.85 127	99.25 72	99.24 139	99.55 158

jajsoiax		91.92 316	91.18 319	94.15 335	91.35 432	90.95 341	99.00 318	97.42 304	92.61 238	87.38 377	97.08 309	72.46 395	97.36 331	94.53 247	88.77 322	94.13 363

MDA-MVSNet-bldmvs		84.09 406	81.52 413	91.81 390	91.32 433	88.00 394	98.67 359	95.92 419	80.22 436	55.60 468	93.32 423	68.29 414	93.60 443	73.76 442	76.61 426	93.82 388

MVP-Stereo		90.93 336	90.45 331	92.37 383	91.25 434	88.76 380	98.05 394	96.17 414	87.27 377	84.04 407	95.30 379	78.46 350	97.27 343	83.78 396	99.70 92	91.09 431
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 394	83.32 402	92.10 385	90.96 435	88.58 386	99.20 292	96.52 406	79.70 438	57.12 467	92.69 428	79.11 342	93.86 439	77.10 434	77.46 419	93.86 385

YYNet1		85.50 395	83.33 401	92.00 386	90.89 436	88.38 390	99.22 291	96.55 405	79.60 439	57.26 466	92.72 427	79.09 344	93.78 441	77.25 433	77.37 420	93.84 386

anonymousdsp		91.79 323	90.92 323	94.41 329	90.76 437	92.93 290	98.93 330	97.17 342	89.08 340	87.46 376	95.30 379	78.43 351	96.92 365	92.38 288	88.73 323	93.39 401

lessismore_v0				90.53 403	90.58 438	80.90 443		95.80 420		77.01 442	95.84 350	66.15 423	96.95 363	83.03 401	75.05 432	93.74 393

EG-PatchMatch MVS		85.35 396	83.81 399	89.99 412	90.39 439	81.89 436	98.21 388	96.09 416	81.78 429	74.73 449	93.72 420	51.56 456	97.12 350	79.16 425	88.61 325	90.96 434

EGC-MVSNET		69.38 427	63.76 437	86.26 431	90.32 440	81.66 439	96.24 432	93.85 454	0.99 478	3.22 479	92.33 434	52.44 453	92.92 448	59.53 465	84.90 360	84.21 459

CMPMVS	61.59 21	84.75 402	85.14 394	83.57 435	90.32 440	62.54 463	96.98 417	97.59 287	74.33 452	69.95 456	96.66 325	64.17 430	98.32 284	87.88 358	88.41 330	89.84 446
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 405	82.92 405	89.21 416	90.03 442	82.60 430	96.89 420	95.62 427	80.59 434	75.77 448	89.17 446	65.04 428	94.79 430	72.12 446	81.02 394	90.23 440

pmmvs6		85.69 391	83.84 398	91.26 395	90.00 443	84.41 418	97.82 399	96.15 415	75.86 446	81.29 423	95.39 374	61.21 441	96.87 370	83.52 399	73.29 435	92.50 419

ttmdpeth		88.23 381	87.06 384	91.75 391	89.91 444	87.35 398	98.92 333	95.73 422	87.92 368	84.02 408	96.31 336	68.23 415	96.84 371	86.33 376	76.12 427	91.06 432

DSMNet-mixed		88.28 380	88.24 374	88.42 424	89.64 445	75.38 453	98.06 393	89.86 468	85.59 400	88.20 365	92.14 435	76.15 372	91.95 453	78.46 428	96.05 243	97.92 287

UnsupCasMVSNet_eth		85.52 393	83.99 395	90.10 410	89.36 446	83.51 425	96.65 424	97.99 238	89.14 339	75.89 447	93.83 417	63.25 434	93.92 437	81.92 409	67.90 452	92.88 413

Anonymous20231206		86.32 389	85.42 392	89.02 418	89.11 447	80.53 446	99.05 312	95.28 434	85.43 402	82.82 414	93.92 416	74.40 386	93.44 444	66.99 454	81.83 384	93.08 409

Anonymous20240521		85.15 397	83.81 399	89.16 417	88.32 448	82.69 429	98.80 348	95.74 421	79.72 437	81.53 421	90.99 438	65.38 426	94.16 435	72.69 444	81.11 391	90.63 438

OpenMVS_ROB	79.82 20	83.77 409	81.68 412	90.03 411	88.30 449	82.82 428	98.46 370	95.22 436	73.92 453	76.00 446	91.29 437	55.00 449	96.94 364	68.40 452	88.51 329	90.34 439

test20.03		84.72 403	83.99 395	86.91 429	88.19 450	80.62 445	98.88 336	95.94 418	88.36 362	78.87 433	94.62 405	68.75 410	89.11 460	66.52 456	75.82 428	91.00 433

KD-MVS_self_test		83.59 410	82.06 410	88.20 425	86.93 451	80.70 444	97.21 410	96.38 409	82.87 423	82.49 415	88.97 447	67.63 417	92.32 451	73.75 443	62.30 463	91.58 429

MIMVSNet1		82.58 413	80.51 418	88.78 420	86.68 452	84.20 419	96.65 424	95.41 432	78.75 440	78.59 436	92.44 430	51.88 455	89.76 459	65.26 459	78.95 407	92.38 422

CL-MVSNet_self_test		84.50 404	83.15 404	88.53 423	86.00 453	81.79 437	98.82 344	97.35 312	85.12 404	83.62 412	90.91 440	76.66 364	91.40 454	69.53 450	60.36 464	92.40 421

UnsupCasMVSNet_bld		79.97 423	77.03 428	88.78 420	85.62 454	81.98 435	93.66 448	97.35 312	75.51 449	70.79 455	83.05 462	48.70 459	94.91 428	78.31 429	60.29 465	89.46 451

mvs5depth		84.87 400	82.90 406	90.77 400	85.59 455	84.84 416	91.10 461	93.29 458	83.14 420	85.07 403	94.33 413	62.17 437	97.32 336	78.83 427	72.59 438	90.14 442

Patchmatch-RL test		86.90 387	85.98 391	89.67 413	84.45 456	75.59 452	89.71 464	92.43 460	86.89 384	77.83 440	90.94 439	94.22 94	93.63 442	87.75 359	69.61 443	99.79 110

pmmvs-eth3d		84.03 407	81.97 411	90.20 409	84.15 457	87.09 400	98.10 392	94.73 444	83.05 421	74.10 452	87.77 453	65.56 425	94.01 436	81.08 413	69.24 445	89.49 450

test_fmvs3		79.99 422	80.17 420	79.45 440	84.02 458	62.83 461	99.05 312	93.49 457	88.29 364	80.06 430	86.65 457	28.09 468	88.00 461	88.63 345	73.27 436	87.54 457

PM-MVS		80.47 419	78.88 423	85.26 432	83.79 459	72.22 455	95.89 439	91.08 465	85.71 399	76.56 445	88.30 449	36.64 464	93.90 438	82.39 405	69.57 444	89.66 449

new-patchmatchnet		81.19 415	79.34 422	86.76 430	82.86 460	80.36 447	97.92 396	95.27 435	82.09 428	72.02 453	86.87 456	62.81 436	90.74 457	71.10 447	63.08 460	89.19 453

FE-MVSNET		81.05 417	78.81 424	87.79 427	81.98 461	83.70 421	98.23 385	91.78 464	81.27 431	74.29 451	87.44 454	60.92 443	90.67 458	64.92 460	68.43 448	89.01 454

mvsany_test3		82.12 414	81.14 415	85.06 433	81.87 462	70.41 457	97.09 414	92.14 461	91.27 292	77.84 439	88.73 448	39.31 463	95.49 416	90.75 320	71.24 440	89.29 452

WB-MVS		76.28 425	77.28 427	73.29 446	81.18 463	54.68 471	97.87 398	94.19 449	81.30 430	69.43 457	90.70 441	77.02 358	82.06 469	35.71 474	68.11 451	83.13 460

test_f		78.40 424	77.59 426	80.81 439	80.82 464	62.48 464	96.96 418	93.08 459	83.44 418	74.57 450	84.57 461	27.95 469	92.63 449	84.15 391	72.79 437	87.32 458

SSC-MVS		75.42 426	76.40 429	72.49 450	80.68 465	53.62 472	97.42 405	94.06 451	80.42 435	68.75 458	90.14 443	76.54 366	81.66 470	33.25 475	66.34 455	82.19 461

pmmvs3		80.27 420	77.77 425	87.76 428	80.32 466	82.43 432	98.23 385	91.97 462	72.74 455	78.75 434	87.97 452	57.30 448	90.99 456	70.31 448	62.37 462	89.87 445

testf1		68.38 430	66.92 431	72.78 448	78.80 467	50.36 474	90.95 462	87.35 473	55.47 464	58.95 463	88.14 450	20.64 473	87.60 462	57.28 466	64.69 457	80.39 463

APD_test2		68.38 430	66.92 431	72.78 448	78.80 467	50.36 474	90.95 462	87.35 473	55.47 464	58.95 463	88.14 450	20.64 473	87.60 462	57.28 466	64.69 457	80.39 463

ambc				83.23 436	77.17 469	62.61 462	87.38 466	94.55 448		76.72 444	86.65 457	30.16 465	96.36 393	84.85 390	69.86 442	90.73 436

test_vis3_rt		68.82 428	66.69 433	75.21 445	76.24 470	60.41 466	96.44 427	68.71 480	75.13 450	50.54 471	69.52 469	16.42 478	96.32 395	80.27 418	66.92 454	68.89 467

TDRefinement		84.76 401	82.56 408	91.38 394	74.58 471	84.80 417	97.36 408	94.56 447	84.73 409	80.21 428	96.12 346	63.56 432	98.39 274	87.92 357	63.97 459	90.95 435

E-PMN		52.30 438	52.18 440	52.67 456	71.51 472	45.40 478	93.62 449	76.60 478	36.01 472	43.50 473	64.13 472	27.11 470	67.31 475	31.06 476	26.06 471	45.30 474

EMVS		51.44 440	51.22 442	52.11 457	70.71 473	44.97 480	94.04 445	75.66 479	35.34 474	42.40 474	61.56 475	28.93 467	65.87 476	27.64 477	24.73 472	45.49 473

PMMVS2		67.15 433	64.15 436	76.14 444	70.56 474	62.07 465	93.89 446	87.52 472	58.09 463	60.02 462	78.32 464	22.38 472	84.54 467	59.56 464	47.03 469	81.80 462

FPMVS		68.72 429	68.72 430	68.71 452	65.95 475	44.27 481	95.97 438	94.74 443	51.13 467	53.26 469	90.50 442	25.11 471	83.00 468	60.80 463	80.97 396	78.87 465

wuyk23d		20.37 444	20.84 447	18.99 460	65.34 476	27.73 483	50.43 472	7.67 484	9.50 477	8.01 478	6.34 478	6.13 481	26.24 477	23.40 478	10.69 476	2.99 475

LCM-MVSNet		67.77 432	64.73 435	76.87 443	62.95 477	56.25 470	89.37 465	93.74 455	44.53 469	61.99 461	80.74 463	20.42 475	86.53 466	69.37 451	59.50 466	87.84 455

MVE	53.74 22	51.54 439	47.86 443	62.60 454	59.56 478	50.93 473	79.41 469	77.69 477	35.69 473	36.27 475	61.76 474	5.79 482	69.63 473	37.97 473	36.61 470	67.24 468
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 436	52.24 439	67.66 453	49.27 479	56.82 469	83.94 467	82.02 476	70.47 457	33.28 476	64.54 471	17.23 477	69.16 474	45.59 471	23.85 473	77.02 466

tmp_tt		65.23 435	62.94 438	72.13 451	44.90 480	50.03 476	81.05 468	89.42 471	38.45 470	48.51 472	99.90 21	54.09 451	78.70 472	91.84 301	18.26 474	87.64 456

PMVS	49.05 23	53.75 437	51.34 441	60.97 455	40.80 481	34.68 482	74.82 470	89.62 470	37.55 471	28.67 477	72.12 466	7.09 480	81.63 471	43.17 472	68.21 450	66.59 469
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 442	39.14 445	33.31 458	19.94 482	24.83 484	98.36 378	9.75 483	15.53 476	51.31 470	87.14 455	19.62 476	17.74 478	47.10 470	3.47 477	57.36 471

testmvs		40.60 441	44.45 444	29.05 459	19.49 483	14.11 485	99.68 210	18.47 482	20.74 475	64.59 460	98.48 260	10.95 479	17.09 479	56.66 468	11.01 475	55.94 472

mmdepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

monomultidepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

test_blank		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.02 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

eth-test2					0.00 484

eth-test					0.00 484

uanet_test		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

DCPMVS		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

cdsmvs_eth3d_5k		23.43 443	31.24 446	0.00 461	0.00 484	0.00 486	0.00 473	98.09 229	0.00 479	0.00 480	99.67 111	83.37 295	0.00 480	0.00 479	0.00 478	0.00 476

pcd_1.5k_mvsjas		7.60 446	10.13 449	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	91.20 172	0.00 480	0.00 479	0.00 478	0.00 476

sosnet-low-res		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

sosnet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uncertanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

Regformer		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

ab-mvs-re		8.28 445	11.04 448	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	99.40 144	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 480	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

TestfortrainingZip							99.97 38

WAC-MVS						90.97 338								86.10 380

PC_three_1452									96.96 59	99.80 25	99.79 61	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 154	97.27 46	99.80 25	99.94 4	97.18 22	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 77	99.83 21	99.91 17	97.87 5	100.00 1	99.92 15	100.00 1	100.00 1

GSMVS																99.59 148

sam_mvs1												94.72 73				99.59 148

sam_mvs												94.25 93

MTGPA								98.28 202

test_post1							95.78 440				59.23 476	93.20 127	97.74 320	91.06 311

test_post											63.35 473	94.43 81	98.13 299

patchmatchnet-post											91.70 436	95.12 58	97.95 311

MTMP							99.87 128	96.49 407

test9_res														99.71 46	99.99 21	100.00 1

agg_prior2														99.48 61	100.00 1	100.00 1

test_prior4						98.05 80	99.94 88

test_prior2							99.95 70		95.78 102	99.73 44	99.76 70	96.00 39		99.78 33	100.00 1

旧先验2							99.46 259		94.21 162	99.85 17			99.95 83	96.96 192

新几何2							99.40 263

无先验							99.49 251	98.71 77	93.46 194				100.00 1	94.36 249		99.99 24

原ACMM2							99.90 112

testdata2													99.99 38	90.54 324

segment_acmp												96.68 30

testdata1							99.28 285		96.35 89

plane_prior5								97.87 252					98.37 280	97.79 165	89.55 312	94.52 324

plane_prior4											98.59 246

plane_prior3						91.64 326			96.63 72	93.01 284

plane_prior2							99.84 147		96.38 83

plane_prior						91.74 320	99.86 139		96.76 67						89.59 311

n2								0.00 485

nn								0.00 485

door-mid								89.69 469

test11								98.44 146

door								90.31 466

HQP5-MVS						91.85 316

BP-MVS														97.92 156

HQP4-MVS										93.37 279			98.39 274			94.53 322

HQP3-MVS								97.89 250							89.60 309

HQP2-MVS												80.65 326

MDTV_nov1_ep13_2view						96.26 165	96.11 434		91.89 269	98.06 164		94.40 83		94.30 252		99.67 128

ACMMP++_ref															87.04 345

ACMMP++															88.23 332

Test By Simon												92.82 138

High-resolution multi-view benchmark