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	99.09 300	98.84 64	93.32 200	96.74 209	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	89.90 331	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	92.06 264	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		93.83 267	92.06 302	99.15 69	99.94 15	97.50 108	99.94 88	98.42 166	96.22 91	99.41 83	41.37 476	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	97.00 187	99.98 32	99.99 24

MG-MVS		98.91 21	98.65 26	99.68 17	99.94 15	99.07 25	99.64 217	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	92.34 252	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	99.18 293	99.45 18	94.84 129	96.41 223	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	99.24 288	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	97.27 225	99.89 48	91.92 314	99.90 112	99.07 37	88.67 355	95.26 255	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	99.61 224	97.78 262	96.52 75	98.61 136	99.31 154	92.73 140	99.67 165	96.77 197	99.48 119	99.06 238

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	90.78 311	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	99.39 266	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		95.84 199	95.11 212	98.02 164	99.85 59	95.10 225	98.74 350	98.50 135	87.22 377	93.66 276	99.86 32	87.45 233	99.95 83	90.94 314	99.81 86	99.02 242

HPM-MVS		97.96 78	97.72 88	98.68 108	99.84 61	96.39 161	99.90 112	98.17 217	92.61 237	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	99.40 262	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	99.41 189
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	99.36 272	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	91.75 275	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	97.41 215	99.76 71	93.36 281	99.65 213	97.95 243	96.03 96	97.41 187	99.70 98	89.61 201	99.51 175	96.73 199	98.25 177	99.38 191

新几何1				99.42 41	99.75 74	98.27 69		98.63 95	92.69 232	99.55 68	99.82 52	94.40 83	100.00 1	91.21 306	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	96.45 220	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	95.11 227	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	96.87 238	99.71 81	91.74 319	99.85 142	97.95 243	93.11 210	95.72 244	99.16 177	92.35 154	99.94 91	95.32 223	99.35 134	98.92 250

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	95.59 279	99.67 86	86.91 402	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	93.92 345	99.63 88	81.76 437	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		96.05 190	95.82 184	96.72 244	99.59 90	96.99 133	99.95 70	99.10 34	94.06 169	98.27 155	95.80 350	89.00 213	99.95 83	99.12 76	87.53 342	93.24 404

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		96.04 191	95.40 199	97.95 166	99.59 90	95.22 220	99.52 244	99.07 37	93.96 174	96.49 219	98.35 265	82.28 302	99.82 139	90.15 330	99.22 141	98.81 257

dcpmvs_2		97.42 119	98.09 62	95.42 286	99.58 94	87.24 398	99.23 289	96.95 377	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	99.34 274	98.55 117	91.86 270	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	96.55 217	99.69 102	92.28 156	99.98 49	97.13 182	99.44 126	99.93 86

API-MVS		97.86 86	97.66 92	98.47 133	99.52 97	95.41 205	99.47 254	98.87 57	91.68 276	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	84.48 285	99.95 83	94.92 233	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	88.48 359	99.06 110	99.66 113	90.30 193	99.64 170	96.32 208	99.97 42	99.96 73

cl22		93.77 272	93.25 276	95.33 290	99.49 100	94.43 243	99.61 224	98.09 229	90.38 319	89.16 346	95.61 358	90.56 188	97.34 332	91.93 297	84.45 363	94.21 349

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	94.97 231	99.94 58	100.00 1

MAR-MVS		97.43 115	97.19 118	98.15 155	99.47 101	94.79 234	99.05 311	98.76 72	92.65 235	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		94.54 244	93.42 266	97.91 172	99.46 103	94.04 258	98.93 329	97.48 299	81.15 431	90.04 317	99.55 129	87.02 241	99.95 83	88.97 342	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	98.46 369	99.42 21	97.03 56	99.02 112	99.09 180	99.35 2	98.21 294	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	93.81 264	99.94 58	99.98 55

TAPA-MVS	92.12 8	94.42 252	93.60 258	96.90 237	99.33 107	91.78 318	99.78 166	98.00 237	89.89 332	94.52 261	99.47 135	91.97 163	99.18 200	69.90 448	99.52 112	99.73 118
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 216	95.07 214	96.32 259	99.32 109	96.60 151	99.76 177	98.85 61	96.65 71	87.83 368	96.05 347	99.52 1	98.11 299	96.58 203	81.07 392	94.25 344

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	97.15 344	95.53 111	99.62 59	99.79 61	92.08 161	98.38 277	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	98.59 267

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	99.64 217	99.27 27	91.43 285	97.88 172	98.99 191	95.84 44	99.84 135	98.82 99	95.32 269	99.79 110

DCV-MVSNet		97.83 90	97.37 109	99.21 58	99.18 116	97.98 84	99.64 217	99.27 27	91.43 285	97.88 172	98.99 191	95.84 44	99.84 135	98.82 99	95.32 269	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	99.66 212	98.06 232	96.37 86	94.37 267	99.49 134	83.29 295	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	97.43 213	99.10 122	94.42 244	99.99 5	97.10 356	95.07 120	99.68 49	99.75 78	92.95 133	98.34 281	98.38 126	99.14 143	99.54 162

Anonymous202405211		93.10 290	91.99 303	96.40 255	99.10 122	89.65 368	98.88 335	97.93 245	83.71 415	94.00 273	98.75 227	68.79 408	99.88 121	95.08 228	91.71 302	99.68 126

fmvsm_s_conf0.5_n		97.80 95	97.85 83	97.67 190	99.06 125	94.41 245	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	98.62 266

HyFIR lowres test		96.66 162	96.43 154	97.36 220	99.05 126	93.91 263	99.70 203	99.80 3	90.54 315	96.26 226	98.08 278	92.15 159	98.23 293	96.84 196	95.46 264	99.93 86

LFMVS		94.75 238	93.56 261	98.30 145	99.03 127	95.70 191	98.74 350	97.98 240	87.81 370	98.47 144	99.39 146	67.43 417	99.53 172	98.01 150	95.20 272	99.67 128

fmvsm_s_conf0.5_n_4		97.75 100	97.86 82	97.42 214	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	97.64 295

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	81.83 308	99.94 91	99.78 33	98.79 159	97.51 303

AllTest		92.48 305	91.64 308	95.00 299	99.01 128	88.43 386	98.94 327	96.82 391	86.50 386	88.71 351	98.47 260	74.73 383	99.88 121	85.39 382	96.18 239	96.71 309

TestCases				95.00 299	99.01 128	88.43 386		96.82 391	86.50 386	88.71 351	98.47 260	74.73 383	99.88 121	85.39 382	96.18 239	96.71 309

COLMAP_ROB	90.47 14	92.18 312	91.49 314	94.25 333	99.00 132	88.04 392	98.42 375	96.70 398	82.30 426	88.43 360	99.01 188	76.97 358	99.85 127	86.11 378	96.50 231	94.86 320
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	84.15 288	99.97 62	99.76 38	99.50 117	98.39 274

test_fmvs1		95.35 217	95.68 191	94.36 329	98.99 133	84.98 413	99.96 51	96.65 400	97.60 33	99.73 44	98.96 197	71.58 398	99.93 101	98.31 132	99.37 132	98.17 279

HY-MVS	92.50 7	97.79 97	97.17 120	99.63 18	98.98 135	99.32 9	97.49 403	99.52 14	95.69 106	98.32 153	97.41 298	93.32 120	99.77 147	98.08 147	95.75 254	99.81 107

VNet		97.21 129	96.57 148	99.13 75	98.97 136	97.82 93	99.03 314	99.21 32	94.31 156	99.18 101	98.88 209	86.26 254	99.89 115	98.93 90	94.32 282	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	93.17 205	96.26 226	98.88 209	89.87 198	99.51 175	94.26 252	94.91 274	99.31 208

tfpn200view9		96.79 151	95.99 170	99.19 60	98.94 138	98.82 38	99.78 166	99.71 7	92.86 220	96.02 234	98.87 216	89.33 205	99.50 177	93.84 261	94.57 278	99.27 217

thres400		96.78 153	95.99 170	99.16 67	98.94 138	98.82 38	99.78 166	99.71 7	92.86 220	96.02 234	98.87 216	89.33 205	99.50 177	93.84 261	94.57 278	99.16 226

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	93.97 288	99.72 120

Anonymous20231211		89.86 362	88.44 370	94.13 336	98.93 140	90.68 346	98.54 366	98.26 205	76.28 443	86.73 382	95.54 362	70.60 404	97.56 325	90.82 317	80.27 401	94.15 357

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	93.97 288	99.72 120

SDMVSNet		94.80 233	93.96 248	97.33 223	98.92 143	95.42 204	99.59 229	98.99 40	92.41 248	92.55 291	97.85 289	75.81 373	98.93 218	97.90 158	91.62 303	97.64 295

sd_testset		93.55 279	92.83 283	95.74 277	98.92 143	90.89 342	98.24 382	98.85 61	92.41 248	92.55 291	97.85 289	71.07 403	98.68 246	93.93 258	91.62 303	97.64 295

EPNet_dtu		95.71 205	95.39 200	96.66 246	98.92 143	93.41 277	99.57 234	98.90 50	96.19 93	97.52 182	98.56 250	92.65 142	97.36 330	77.89 429	98.33 172	99.20 224
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	95.58 263	99.78 113

CHOSEN 1792x2688		96.81 150	96.53 149	97.64 193	98.91 147	93.07 283	99.65 213	99.80 3	95.64 107	95.39 251	98.86 218	84.35 287	99.90 110	96.98 189	99.16 142	99.95 81

thres100view900		96.74 157	95.92 180	99.18 61	98.90 148	98.77 45	99.74 184	99.71 7	92.59 239	95.84 238	98.86 218	89.25 207	99.50 177	93.84 261	94.57 278	99.27 217

thres600view7		96.69 160	95.87 183	99.14 71	98.90 148	98.78 44	99.74 184	99.71 7	92.59 239	95.84 238	98.86 218	89.25 207	99.50 177	93.44 274	94.50 281	99.16 226

MSDG		94.37 254	93.36 273	97.40 216	98.88 150	93.95 262	99.37 270	97.38 308	85.75 397	90.80 310	99.17 174	84.11 290	99.88 121	86.35 374	98.43 170	98.36 276

MGCFI-Net		97.00 141	96.22 162	99.34 49	98.86 151	98.80 40	99.67 211	97.30 321	94.31 156	97.77 178	99.41 143	86.36 252	99.50 177	98.38 126	93.90 290	99.72 120

h-mvs33		94.92 230	94.36 234	96.59 248	98.85 152	91.29 334	98.93 329	98.94 44	95.90 98	98.77 125	98.42 263	90.89 183	99.77 147	97.80 162	70.76 440	98.72 263

Anonymous20240529		92.10 313	90.65 325	96.47 250	98.82 153	90.61 348	98.72 352	98.67 85	75.54 447	93.90 275	98.58 248	66.23 421	99.90 110	94.70 242	90.67 306	98.90 253

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	96.38 224	98.49 256	89.05 211	99.88 121	97.10 184	98.34 171	99.43 186

PS-MVSNAJ		98.44 48	98.20 53	99.16 67	98.80 155	98.92 30	99.54 242	98.17 217	97.34 41	99.85 17	99.85 36	91.20 172	99.89 115	99.41 66	99.67 93	98.69 264

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	81.36 314	99.98 49	92.77 285	98.89 153	98.28 278

mvsany_test1		97.82 93	97.90 79	97.55 202	98.77 157	93.04 286	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	94.26 284	99.67 128

SymmetryMVS		97.64 108	97.46 102	98.17 151	98.74 159	95.39 207	99.61 224	99.26 29	96.52 75	98.61 136	99.31 154	92.73 140	99.67 165	96.77 197	95.63 261	99.45 182

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	99.52 244	98.08 231	97.05 55	99.86 14	99.86 32	90.65 185	99.71 157	99.39 68	98.63 163	98.69 264

miper_enhance_ethall		94.36 256	93.98 247	95.49 280	98.68 162	95.24 218	99.73 191	97.29 324	93.28 202	89.86 322	95.97 348	94.37 87	97.05 353	92.20 289	84.45 363	94.19 350

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	91.10 297	98.17 161	98.59 245	93.86 107	98.19 295	95.64 220	95.24 271	99.28 215

test2506		97.53 112	97.19 118	98.58 120	98.66 165	96.90 137	98.81 344	99.77 5	94.93 123	97.95 167	98.96 197	92.51 149	99.20 198	94.93 232	98.15 180	99.64 134

ECVR-MVS		95.66 208	95.05 215	97.51 207	98.66 165	93.71 267	98.85 341	98.45 141	94.93 123	96.86 205	98.96 197	75.22 379	99.20 198	95.34 222	98.15 180	99.64 134

mamv4		95.24 220	96.90 129	90.25 407	98.65 167	72.11 455	98.28 380	97.64 276	89.99 330	95.93 236	98.25 273	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	99.47 254	97.79 260	94.56 139	99.74 42	98.35 265	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	94.26 252	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	99.08 236

MVSMamba_PlusPlus		97.83 90	97.45 104	98.99 88	98.60 170	98.15 70	99.58 231	97.74 267	90.34 322	99.26 97	98.32 268	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	92.53 243	98.84 119	98.84 222	93.36 117	98.30 285	95.84 216	94.30 283	99.05 240

test1111		95.57 211	94.98 218	97.37 218	98.56 171	93.37 280	98.86 339	98.45 141	94.95 122	96.63 211	98.95 202	75.21 380	99.11 204	95.02 229	98.14 182	99.64 134

MVSTER		95.53 212	95.22 207	96.45 253	98.56 171	97.72 96	99.91 106	97.67 272	92.38 251	91.39 301	97.14 305	97.24 19	97.30 337	94.80 238	87.85 335	94.34 339

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	98.73 229	95.50 51	99.69 161	98.53 119	94.63 276	98.99 244

VDD-MVS		93.77 272	92.94 281	96.27 260	98.55 174	90.22 357	98.77 349	97.79 260	90.85 303	96.82 207	99.42 139	61.18 441	99.77 147	98.95 88	94.13 285	98.82 256

tpmvs		94.28 258	93.57 260	96.40 255	98.55 174	91.50 332	95.70 440	98.55 117	87.47 372	92.15 294	94.26 413	91.42 168	98.95 217	88.15 353	95.85 250	98.76 259

UGNet		95.33 218	94.57 230	97.62 197	98.55 174	94.85 230	98.67 358	99.32 26	95.75 104	96.80 208	96.27 337	72.18 395	99.96 74	94.58 245	99.05 149	98.04 284
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	95.18 222	94.10 241	98.43 137	98.55 174	95.99 179	97.91 396	97.31 320	90.35 321	89.48 335	99.22 167	85.19 271	99.89 115	90.40 327	98.47 169	99.41 189
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 193	96.49 150	94.34 330	98.51 179	89.99 362	99.39 266	98.57 105	93.14 207	97.33 190	98.31 270	93.44 115	94.68 430	93.69 271	95.98 244	98.34 277

UWE-MVS		96.79 151	96.72 141	97.00 232	98.51 179	93.70 268	99.71 198	98.60 99	92.96 215	97.09 197	98.34 267	96.67 32	98.85 224	92.11 295	96.50 231	98.44 272

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	98.75 227	95.20 56	99.48 183	98.93 90	96.40 234	99.29 213

test_vis1_n_1920		95.44 214	95.31 203	95.82 274	98.50 181	88.74 380	99.98 20	97.30 321	97.84 27	99.85 17	99.19 172	66.82 419	99.97 62	98.82 99	99.46 124	98.76 259

BH-w/o		95.71 205	95.38 201	96.68 245	98.49 183	92.28 305	99.84 147	97.50 297	92.12 261	92.06 297	98.79 225	84.69 281	98.67 248	95.29 224	99.66 94	99.09 234

baseline1		95.78 201	94.86 221	98.54 126	98.47 184	98.07 78	99.06 307	97.99 238	92.68 233	94.13 272	98.62 242	93.28 123	98.69 245	93.79 266	85.76 350	98.84 255

fmvsm_s_conf0.5_n_7		97.70 106	97.74 87	97.59 200	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	97.66 294

EPMVS		96.53 169	96.01 169	98.09 159	98.43 186	96.12 177	96.36 427	99.43 20	93.53 191	97.64 180	95.04 390	94.41 82	98.38 277	91.13 308	98.11 183	99.75 116

kuosan		93.17 287	92.60 289	94.86 306	98.40 187	89.54 370	98.44 371	98.53 124	84.46 410	88.49 356	97.92 286	90.57 187	97.05 353	83.10 399	93.49 293	97.99 285

WBMVS		94.52 247	94.03 245	95.98 266	98.38 188	96.68 146	99.92 98	97.63 277	90.75 312	89.64 330	95.25 383	96.77 26	96.90 365	94.35 250	83.57 370	94.35 337

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	98.42 263	96.77 26	99.17 201	98.54 117	96.20 238	99.11 233

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	99.06 183	91.21 171	99.89 115	96.33 207	97.01 222	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	98.70 232	95.48 52	99.22 194	97.85 160	95.05 273	99.07 237

BH-untuned		95.18 222	94.83 222	96.22 261	98.36 191	91.22 335	99.80 163	97.32 319	90.91 301	91.08 304	98.67 234	83.51 292	98.54 259	94.23 253	99.61 102	98.92 250

testing91		97.16 131	96.90 129	97.97 165	98.35 193	95.67 194	99.91 106	98.42 166	92.91 218	97.33 190	98.72 230	94.81 70	99.21 195	96.98 189	94.63 276	99.03 241

testing99		97.17 130	96.91 128	97.95 166	98.35 193	95.70 191	99.91 106	98.43 154	92.94 216	97.36 188	98.72 230	94.83 69	99.21 195	97.00 187	94.64 275	98.95 246

ET-MVSNet_ETH3D		94.37 254	93.28 275	97.64 193	98.30 195	97.99 83	99.99 5	97.61 283	94.35 153	71.57 453	99.45 138	96.23 37	95.34 420	96.91 194	85.14 357	99.59 148

AUN-MVS		93.28 284	92.60 289	95.34 289	98.29 196	90.09 360	99.31 278	98.56 111	91.80 274	96.35 225	98.00 281	89.38 204	98.28 288	92.46 286	69.22 445	97.64 295

FMVSNet3		92.69 300	91.58 310	95.99 265	98.29 196	97.42 113	99.26 287	97.62 280	89.80 333	89.68 326	95.32 377	81.62 312	96.27 396	87.01 370	85.65 351	94.29 341

PMMVS		96.76 154	96.76 138	96.76 242	98.28 198	92.10 309	99.91 106	97.98 240	94.12 164	99.53 71	99.39 146	86.93 243	98.73 238	96.95 192	97.73 191	99.45 182

hse-mvs2		94.38 253	94.08 244	95.31 291	98.27 199	90.02 361	99.29 283	98.56 111	95.90 98	98.77 125	98.00 281	90.89 183	98.26 292	97.80 162	69.20 446	97.64 295

PVSNet_0	88.03 19	91.80 320	90.27 334	96.38 257	98.27 199	90.46 352	99.94 88	99.61 13	93.99 172	86.26 392	97.39 300	71.13 402	99.89 115	98.77 103	67.05 452	98.79 258

UA-Net		96.54 168	95.96 176	98.27 147	98.23 201	95.71 190	98.00 394	98.45 141	93.72 187	98.41 148	99.27 159	88.71 218	99.66 168	91.19 307	97.69 192	99.44 185

test_cas_vis1_n_1920		96.59 165	96.23 160	97.65 192	98.22 202	94.23 253	99.99 5	97.25 329	97.77 28	99.58 67	99.08 181	77.10 353	99.97 62	97.64 170	99.45 125	98.74 261

FE-MVS		95.70 207	95.01 217	97.79 180	98.21 203	94.57 239	95.03 441	98.69 80	88.90 349	97.50 184	96.19 339	92.60 145	99.49 182	89.99 332	97.94 189	99.31 208

GG-mvs-BLEND				98.54 126	98.21 203	98.01 82	93.87 446	98.52 126		97.92 168	97.92 286	99.02 3	97.94 312	98.17 140	99.58 107	99.67 128

mvs_anonymous		95.65 209	95.03 216	97.53 204	98.19 205	95.74 188	99.33 275	97.49 298	90.87 302	90.47 313	97.10 307	88.23 221	97.16 344	95.92 214	97.66 195	99.68 126

MVS_Test		96.46 171	95.74 187	98.61 115	98.18 206	97.23 120	99.31 278	97.15 344	91.07 298	98.84 119	97.05 311	88.17 222	98.97 214	94.39 247	97.50 197	99.61 145

BH-RMVSNet		95.18 222	94.31 237	97.80 178	98.17 207	95.23 219	99.76 177	97.53 293	92.52 244	94.27 270	99.25 165	76.84 360	98.80 228	90.89 316	99.54 109	99.35 199

dongtai		91.55 326	91.13 319	92.82 375	98.16 208	86.35 403	99.47 254	98.51 129	83.24 418	85.07 402	97.56 294	90.33 192	94.94 426	76.09 437	91.73 301	97.18 306

RPSCF		91.80 320	92.79 285	88.83 418	98.15 209	69.87 457	98.11 390	96.60 402	83.93 413	94.33 268	99.27 159	79.60 336	99.46 186	91.99 296	93.16 298	97.18 306

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		96.29 182	95.90 181	97.45 210	98.13 211	94.80 233	99.08 302	97.61 283	92.02 266	95.54 249	98.96 197	90.64 186	98.08 301	93.73 269	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	80.88 321	99.93 101	99.59 54	98.17 178	97.29 304

ab-mvs		94.69 239	93.42 266	98.51 131	98.07 214	96.26 165	96.49 425	98.68 82	90.31 323	94.54 260	97.00 313	76.30 368	99.71 157	95.98 213	93.38 296	99.56 157

XVG-OURS-SEG-HR		94.79 234	94.70 229	95.08 296	98.05 215	89.19 372	99.08 302	97.54 291	93.66 188	94.87 258	99.58 125	78.78 344	99.79 142	97.31 177	93.40 295	96.25 313

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		94.82 231	94.74 228	95.06 297	98.00 217	89.19 372	99.08 302	97.55 289	94.10 165	94.71 259	99.62 120	80.51 327	99.74 153	96.04 212	93.06 300	96.25 313

mvsmamba		96.94 144	96.73 140	97.55 202	97.99 218	94.37 249	99.62 220	97.70 269	93.13 208	98.42 147	97.92 286	88.02 223	98.75 236	98.78 102	99.01 150	99.52 168

dp		95.05 225	94.43 232	96.91 235	97.99 218	92.73 294	96.29 430	97.98 240	89.70 334	95.93 236	94.67 403	93.83 109	98.45 265	86.91 373	96.53 230	99.54 162

tpmrst		96.27 184	95.98 172	97.13 227	97.96 220	93.15 282	96.34 428	98.17 217	92.07 262	98.71 131	95.12 387	93.91 104	98.73 238	94.91 235	96.62 228	99.50 174

TR-MVS		94.54 244	93.56 261	97.49 209	97.96 220	94.34 250	98.71 353	97.51 296	90.30 324	94.51 262	98.69 233	75.56 374	98.77 232	92.82 284	95.99 243	99.35 199

Vis-MVSNet (Re-imp)		96.32 179	95.98 172	97.35 222	97.93 222	94.82 232	99.47 254	98.15 225	91.83 271	95.09 256	99.11 179	91.37 170	97.47 328	93.47 273	97.43 198	99.74 117

MDTV_nov1_ep13			95.69 189		97.90 223	94.15 255	95.98 436	98.44 146	93.12 209	97.98 166	95.74 352	95.10 59	98.58 255	90.02 331	96.92 224

Fast-Effi-MVS+		95.02 227	94.19 239	97.52 206	97.88 224	94.55 240	99.97 38	97.08 360	88.85 351	94.47 263	97.96 285	84.59 282	98.41 269	89.84 334	97.10 215	99.59 148

ADS-MVSNet2		93.80 271	93.88 251	93.55 358	97.87 225	85.94 407	94.24 442	96.84 388	90.07 327	96.43 221	94.48 408	90.29 194	95.37 419	87.44 360	97.23 208	99.36 195

ADS-MVSNet		94.79 234	94.02 246	97.11 229	97.87 225	93.79 264	94.24 442	98.16 222	90.07 327	96.43 221	94.48 408	90.29 194	98.19 295	87.44 360	97.23 208	99.36 195

Effi-MVS+		96.30 181	95.69 189	98.16 152	97.85 227	96.26 165	97.41 405	97.21 336	90.37 320	98.65 134	98.58 248	86.61 249	98.70 244	97.11 183	97.37 202	99.52 168

PatchmatchNet		95.94 194	95.45 196	97.39 217	97.83 228	94.41 245	96.05 434	98.40 175	92.86 220	97.09 197	95.28 382	94.21 96	98.07 303	89.26 340	98.11 183	99.70 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 242	93.61 256	97.74 188	97.82 229	96.26 165	99.96 51	97.78 262	85.76 395	94.00 273	97.54 295	76.95 359	99.21 195	97.23 180	95.43 266	97.76 293

1112_ss		96.01 192	95.20 208	98.42 139	97.80 230	96.41 158	99.65 213	96.66 399	92.71 230	92.88 287	99.40 144	92.16 158	99.30 190	91.92 298	93.66 291	99.55 158

Test_1112_low_res		95.72 203	94.83 222	98.42 139	97.79 231	96.41 158	99.65 213	96.65 400	92.70 231	92.86 288	96.13 343	92.15 159	99.30 190	91.88 299	93.64 292	99.55 158

Effi-MVS+-dtu		94.53 246	95.30 204	92.22 383	97.77 232	82.54 430	99.59 229	97.06 364	94.92 125	95.29 253	95.37 375	85.81 259	97.89 313	94.80 238	97.07 216	96.23 315

tpm cat1		93.51 280	92.52 295	96.47 250	97.77 232	91.47 333	96.13 432	98.06 232	80.98 432	92.91 286	93.78 417	89.66 199	98.87 222	87.03 369	96.39 235	99.09 234

FA-MVS(test-final)		95.86 197	95.09 213	98.15 155	97.74 234	95.62 196	96.31 429	98.17 217	91.42 287	96.26 226	96.13 343	90.56 188	99.47 185	92.18 290	97.07 216	99.35 199

xiu_mvs_v1_base_debu		97.43 115	97.06 121	98.55 122	97.74 234	98.14 72	99.31 278	97.86 254	96.43 80	99.62 59	99.69 102	85.56 266	99.68 162	99.05 79	98.31 173	97.83 289

xiu_mvs_v1_base		97.43 115	97.06 121	98.55 122	97.74 234	98.14 72	99.31 278	97.86 254	96.43 80	99.62 59	99.69 102	85.56 266	99.68 162	99.05 79	98.31 173	97.83 289

xiu_mvs_v1_base_debi		97.43 115	97.06 121	98.55 122	97.74 234	98.14 72	99.31 278	97.86 254	96.43 80	99.62 59	99.69 102	85.56 266	99.68 162	99.05 79	98.31 173	97.83 289

EPP-MVSNet		96.69 160	96.60 146	96.96 234	97.74 234	93.05 285	99.37 270	98.56 111	88.75 353	95.83 240	99.01 188	96.01 38	98.56 257	96.92 193	97.20 210	99.25 219

gg-mvs-nofinetune		93.51 280	91.86 307	98.47 133	97.72 239	97.96 87	92.62 452	98.51 129	74.70 450	97.33 190	69.59 467	98.91 4	97.79 316	97.77 167	99.56 108	99.67 128

IB-MVS	92.85 6	94.99 228	93.94 249	98.16 152	97.72 239	95.69 193	99.99 5	98.81 66	94.28 159	92.70 289	96.90 315	95.08 60	99.17 201	96.07 211	73.88 433	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	91.83 271	97.45 185	99.04 185	97.50 9	99.10 206	94.75 240	96.37 236	99.16 226

VortexMVS		94.11 260	93.50 263	95.94 268	97.70 242	96.61 150	99.35 273	97.18 339	93.52 193	89.57 333	95.74 352	87.55 230	96.97 361	95.76 219	85.13 358	94.23 346

viewdifsd2359ckpt09		96.21 186	95.77 185	97.53 204	97.69 243	94.50 241	99.78 166	97.23 334	92.88 219	96.58 214	99.26 163	84.85 276	98.66 251	96.61 201	97.02 221	99.43 186

Syy-MVS		90.00 360	90.63 326	88.11 425	97.68 244	74.66 453	99.71 198	98.35 188	90.79 309	92.10 295	98.67 234	79.10 342	93.09 445	63.35 460	95.95 247	96.59 311

myMVS_eth3d		94.46 251	94.76 227	93.55 358	97.68 244	90.97 337	99.71 198	98.35 188	90.79 309	92.10 295	98.67 234	92.46 152	93.09 445	87.13 366	95.95 247	96.59 311

test_fmvs1_n		94.25 259	94.36 234	93.92 345	97.68 244	83.70 420	99.90 112	96.57 403	97.40 39	99.67 50	98.88 209	61.82 438	99.92 107	98.23 138	99.13 144	98.14 282

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		96.24 185	95.68 191	97.94 169	97.65 248	94.92 229	99.27 286	97.10 356	92.79 226	97.43 186	97.99 283	81.85 307	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	97.19 337	94.67 137	98.95 114	99.28 156	86.43 250	98.76 234	98.37 128	97.42 200	99.33 202
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		96.19 187	95.77 185	97.45 210	97.62 251	94.40 247	99.70 203	97.23 334	92.76 228	96.63 211	99.05 184	84.96 275	98.64 252	96.65 200	97.35 203	99.31 208

Vis-MVSNet		95.72 203	95.15 211	97.45 210	97.62 251	94.28 251	99.28 284	98.24 208	94.27 161	96.84 206	98.94 204	79.39 337	98.76 234	93.25 275	98.49 168	99.30 211
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	91.11 296	97.07 199	98.97 195	97.47 12	99.03 209	93.73 269	96.09 241	98.92 250

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	94.99 230	98.17 178	99.37 193

miper_ehance_all_eth		93.16 288	92.60 289	94.82 307	97.57 255	93.56 272	99.50 248	97.07 363	88.75 353	88.85 350	95.52 364	90.97 179	96.74 375	90.77 318	84.45 363	94.17 351

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	98.65 237	87.58 229	98.82 226	98.29 134	97.91 190	99.36 195

viewmanbaseed2359cas		96.45 172	96.07 166	97.59 200	97.55 257	94.59 238	99.70 203	97.33 316	93.62 190	97.00 201	99.32 151	85.57 265	98.71 241	97.26 179	97.33 204	99.47 177

testing3		93.92 265	94.23 238	92.99 372	97.54 258	90.23 356	99.99 5	99.16 33	90.57 314	91.33 303	98.63 241	92.99 131	92.52 449	82.46 403	95.39 267	96.22 316

SSM_0404		95.75 202	95.16 210	97.50 208	97.53 259	95.39 207	99.11 298	97.25 329	90.81 305	95.27 254	98.83 223	84.74 278	98.67 248	95.24 225	97.69 192	98.45 271

LCM-MVSNet-Re		92.31 309	92.60 289	91.43 392	97.53 259	79.27 447	99.02 316	91.83 462	92.07 262	80.31 426	94.38 411	83.50 293	95.48 416	97.22 181	97.58 196	99.54 162

GBi-Net		90.88 337	89.82 343	94.08 337	97.53 259	91.97 310	98.43 372	96.95 377	87.05 378	89.68 326	94.72 399	71.34 399	96.11 402	87.01 370	85.65 351	94.17 351

test1		90.88 337	89.82 343	94.08 337	97.53 259	91.97 310	98.43 372	96.95 377	87.05 378	89.68 326	94.72 399	71.34 399	96.11 402	87.01 370	85.65 351	94.17 351

FMVSNet2		91.02 334	89.56 348	95.41 287	97.53 259	95.74 188	98.98 319	97.41 306	87.05 378	88.43 360	95.00 393	71.34 399	96.24 398	85.12 385	85.21 356	94.25 344

tttt0517		96.85 148	96.49 150	97.92 170	97.48 264	95.89 182	99.85 142	98.54 121	90.72 313	96.63 211	98.93 207	97.47 12	99.02 210	93.03 282	95.76 253	98.85 254

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	99.36 195

casdiffmvs_mvg		96.43 173	95.94 178	97.89 174	97.44 265	95.47 200	99.86 139	97.29 324	93.35 198	96.03 233	99.19 172	85.39 269	98.72 240	97.89 159	97.04 218	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	97.06 364	94.59 138	99.63 56	99.32 151	89.20 210	98.14 297	98.76 104	99.23 140	99.62 141

viewdifsd2359ckpt07		95.83 200	95.42 198	97.07 230	97.40 268	93.04 286	99.60 227	97.24 332	92.39 250	96.09 232	99.14 178	83.07 298	98.93 218	97.02 186	96.87 225	99.23 222

c3_l		92.53 304	91.87 306	94.52 319	97.40 268	92.99 288	99.40 262	96.93 382	87.86 368	88.69 353	95.44 369	89.95 197	96.44 388	90.45 324	80.69 397	94.14 360

viewmambaseed2359dif		95.92 196	95.55 195	97.04 231	97.38 270	93.41 277	99.78 166	96.97 375	91.14 295	96.58 214	99.27 159	84.85 276	98.75 236	96.87 195	97.12 214	98.97 245

fmvsm_s_conf0.1_n		97.30 123	97.21 117	97.60 199	97.38 270	94.40 247	99.90 112	98.64 89	96.47 79	99.51 75	99.65 115	84.99 274	99.93 101	99.22 73	99.09 147	98.46 270

CDS-MVSNet		96.34 178	96.07 166	97.13 227	97.37 272	94.96 227	99.53 243	97.91 249	91.55 279	95.37 252	98.32 268	95.05 62	97.13 347	93.80 265	95.75 254	99.30 211
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	97.36 273	96.48 155	99.96 51	98.29 201	91.93 267	95.77 241	98.07 279	95.54 48	98.29 286	90.55 322	98.89 153	99.70 123

miper_lstm_enhance		91.81 317	91.39 316	93.06 371	97.34 274	89.18 374	99.38 268	96.79 393	86.70 385	87.47 374	95.22 384	90.00 196	95.86 411	88.26 351	81.37 386	94.15 357

baseline		96.43 173	95.98 172	97.76 186	97.34 274	95.17 223	99.51 246	97.17 341	93.92 177	96.90 204	99.28 156	85.37 270	98.64 252	97.50 173	96.86 227	99.46 179

cl____		92.31 309	91.58 310	94.52 319	97.33 276	92.77 290	99.57 234	96.78 394	86.97 382	87.56 372	95.51 365	89.43 203	96.62 380	88.60 345	82.44 378	94.16 356

SD_0403		92.63 303	93.38 270	90.40 406	97.32 277	77.91 449	97.75 401	98.03 236	91.89 268	90.83 309	98.29 272	82.00 304	93.79 439	88.51 349	95.75 254	99.52 168

DIV-MVS_self_test		92.32 308	91.60 309	94.47 323	97.31 278	92.74 292	99.58 231	96.75 395	86.99 381	87.64 370	95.54 362	89.55 202	96.50 385	88.58 346	82.44 378	94.17 351

casdiffmvs		96.42 175	95.97 175	97.77 184	97.30 279	94.98 226	99.84 147	97.09 359	93.75 186	96.58 214	99.26 163	85.07 272	98.78 231	97.77 167	97.04 218	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		94.36 256	93.48 264	96.99 233	97.29 280	93.54 273	99.96 51	96.72 397	88.35 362	93.43 277	98.94 204	82.05 303	98.05 304	88.12 355	96.48 233	99.37 193

eth_miper_zixun_eth		92.41 307	91.93 304	93.84 349	97.28 281	90.68 346	98.83 342	96.97 375	88.57 358	89.19 345	95.73 355	89.24 209	96.69 378	89.97 333	81.55 384	94.15 357

MVSFormer		96.94 144	96.60 146	97.95 166	97.28 281	97.70 99	99.55 240	97.27 326	91.17 292	99.43 81	99.54 131	90.92 180	96.89 366	94.67 243	99.62 98	99.25 219

lupinMVS		97.85 88	97.60 96	98.62 114	97.28 281	97.70 99	99.99 5	97.55 289	95.50 113	99.43 81	99.67 111	90.92 180	98.71 241	98.40 125	99.62 98	99.45 182

diffmvs_AUTHOR		96.75 156	96.41 155	97.79 180	97.20 284	95.46 201	99.69 206	97.15 344	94.46 143	98.78 123	99.21 170	85.64 263	98.77 232	98.27 135	97.31 206	99.13 230

mamba_0408		94.98 229	94.09 242	97.64 193	97.14 285	95.31 212	93.48 449	97.08 360	90.48 316	94.40 264	98.62 242	84.49 283	98.67 248	93.99 256	97.18 211	98.93 247

SSM_04072		94.77 236	94.09 242	96.82 239	97.14 285	95.31 212	93.48 449	97.08 360	90.48 316	94.40 264	98.62 242	84.49 283	96.21 399	93.99 256	97.18 211	98.93 247

SSM_0407		95.62 210	94.95 219	97.61 198	97.14 285	95.31 212	99.00 317	97.25 329	90.81 305	94.40 264	98.83 223	84.74 278	98.58 255	95.24 225	97.18 211	98.93 247

SCA		94.69 239	93.81 253	97.33 223	97.10 288	94.44 242	98.86 339	98.32 195	93.30 201	96.17 231	95.59 360	76.48 366	97.95 310	91.06 310	97.43 198	99.59 148

viewmacassd2359aftdt		95.93 195	95.45 196	97.36 220	97.09 289	94.12 257	99.57 234	97.26 328	93.05 213	96.50 218	99.17 174	82.76 299	98.68 246	96.61 201	97.04 218	99.28 215

KinetiMVS		96.10 188	95.29 205	98.53 128	97.08 290	97.12 126	99.56 237	98.12 228	94.78 130	98.44 145	98.94 204	80.30 331	99.39 188	91.56 303	98.79 159	99.06 238

TAMVS		95.85 198	95.58 193	96.65 247	97.07 291	93.50 274	99.17 294	97.82 259	91.39 289	95.02 257	98.01 280	92.20 157	97.30 337	93.75 268	95.83 251	99.14 229

Fast-Effi-MVS+-dtu		93.72 275	93.86 252	93.29 363	97.06 292	86.16 404	99.80 163	96.83 389	92.66 234	92.58 290	97.83 291	81.39 313	97.67 321	89.75 335	96.87 225	96.05 318

CostFormer		96.10 188	95.88 182	96.78 241	97.03 293	92.55 300	97.08 414	97.83 258	90.04 329	98.72 130	94.89 397	95.01 64	98.29 286	96.54 204	95.77 252	99.50 174

test_fmvsmvis_n_1920		97.67 107	97.59 98	97.91 172	97.02 294	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	98.42 273

test-LLR		96.47 170	96.04 168	97.78 182	97.02 294	95.44 202	99.96 51	98.21 212	94.07 167	95.55 247	96.38 332	93.90 105	98.27 290	90.42 325	98.83 157	99.64 134

test-mter		96.39 176	95.93 179	97.78 182	97.02 294	95.44 202	99.96 51	98.21 212	91.81 273	95.55 247	96.38 332	95.17 57	98.27 290	90.42 325	98.83 157	99.64 134

icg_test_0407_2		95.04 226	94.78 226	95.84 273	96.97 297	91.64 325	98.63 361	97.12 349	92.33 253	95.60 245	98.88 209	85.65 261	96.56 383	92.12 291	95.70 257	99.32 204

IMVS_0407		95.21 221	94.80 225	96.46 252	96.97 297	91.64 325	98.81 344	97.12 349	92.33 253	95.60 245	98.88 209	85.65 261	98.42 267	92.12 291	95.70 257	99.32 204

IMVS_0404		93.83 267	93.17 277	95.80 275	96.97 297	91.64 325	97.78 400	97.12 349	92.33 253	90.87 308	98.88 209	76.78 361	96.43 389	92.12 291	95.70 257	99.32 204

IMVS_0403		95.25 219	94.81 224	96.58 249	96.97 297	91.64 325	98.97 324	97.12 349	92.33 253	95.43 250	98.88 209	85.78 260	98.79 229	92.12 291	95.70 257	99.32 204

gm-plane-assit					96.97 297	93.76 266			91.47 283		98.96 197		98.79 229	94.92 233

WB-MVSnew		92.90 294	92.77 286	93.26 365	96.95 302	93.63 270	99.71 198	98.16 222	91.49 280	94.28 269	98.14 276	81.33 315	96.48 386	79.47 420	95.46 264	89.68 446

QAPM		95.40 215	94.17 240	99.10 77	96.92 303	97.71 97	99.40 262	98.68 82	89.31 337	88.94 349	98.89 208	82.48 301	99.96 74	93.12 281	99.83 80	99.62 141

KD-MVS_2432*1600		88.00 382	86.10 386	93.70 354	96.91 304	94.04 258	97.17 411	97.12 349	84.93 405	81.96 416	92.41 430	92.48 150	94.51 432	79.23 421	52.68 466	92.56 416

miper_refine_blended		88.00 382	86.10 386	93.70 354	96.91 304	94.04 258	97.17 411	97.12 349	84.93 405	81.96 416	92.41 430	92.48 150	94.51 432	79.23 421	52.68 466	92.56 416

tpm2		95.47 213	95.18 209	96.35 258	96.91 304	91.70 323	96.96 417	97.93 245	88.04 366	98.44 145	95.40 371	93.32 120	97.97 307	94.00 255	95.61 262	99.38 191

FMVSNet5		88.32 378	87.47 380	90.88 395	96.90 307	88.39 388	97.28 408	95.68 424	82.60 425	84.67 404	92.40 432	79.83 334	91.16 454	76.39 436	81.51 385	93.09 407

3Dnovator+	91.53 11	96.31 180	95.24 206	99.52 31	96.88 308	98.64 57	99.72 195	98.24 208	95.27 118	88.42 362	98.98 193	82.76 299	99.94 91	97.10 184	99.83 80	99.96 73

Patchmatch-test		92.65 302	91.50 313	96.10 264	96.85 309	90.49 351	91.50 457	97.19 337	82.76 424	90.23 314	95.59 360	95.02 63	98.00 306	77.41 431	96.98 223	99.82 105

MVS		96.60 164	95.56 194	99.72 14	96.85 309	99.22 21	98.31 378	98.94 44	91.57 278	90.90 307	99.61 121	86.66 248	99.96 74	97.36 176	99.88 76	99.99 24

3Dnovator	91.47 12	96.28 183	95.34 202	99.08 80	96.82 311	97.47 111	99.45 259	98.81 66	95.52 112	89.39 336	99.00 190	81.97 305	99.95 83	97.27 178	99.83 80	99.84 102

EI-MVSNet		93.73 274	93.40 269	94.74 308	96.80 312	92.69 295	99.06 307	97.67 272	88.96 346	91.39 301	99.02 186	88.75 217	97.30 337	91.07 309	87.85 335	94.22 347

CVMVSNet		94.68 241	94.94 220	93.89 348	96.80 312	86.92 401	99.06 307	98.98 41	94.45 144	94.23 271	99.02 186	85.60 264	95.31 421	90.91 315	95.39 267	99.43 186

IterMVS-LS		92.69 300	92.11 300	94.43 327	96.80 312	92.74 292	99.45 259	96.89 385	88.98 344	89.65 329	95.38 374	88.77 216	96.34 393	90.98 313	82.04 381	94.22 347
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 167	96.46 153	96.91 235	96.79 315	92.50 301	99.90 112	97.38 308	96.02 97	97.79 177	99.32 151	86.36 252	98.99 211	98.26 136	96.33 237	99.23 222

IterMVS		90.91 336	90.17 338	93.12 368	96.78 316	90.42 354	98.89 333	97.05 367	89.03 341	86.49 387	95.42 370	76.59 364	95.02 423	87.22 365	84.09 366	93.93 378
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	96.74 317	98.52 61	98.31 378	98.86 58	95.82 101	89.91 320	98.98 193	87.49 232	99.96 74	97.80 162	99.73 90	99.96 73

IterMVS-SCA-FT		90.85 339	90.16 339	92.93 373	96.72 318	89.96 363	98.89 333	96.99 371	88.95 347	86.63 384	95.67 356	76.48 366	95.00 424	87.04 368	84.04 369	93.84 385

MVS-HIRNet		86.22 389	83.19 402	95.31 291	96.71 319	90.29 355	92.12 454	97.33 316	62.85 461	86.82 381	70.37 466	69.37 407	97.49 327	75.12 439	97.99 188	98.15 280

viewdifsd2359ckpt11		94.09 262	93.63 255	95.46 284	96.68 320	88.92 377	99.62 220	97.12 349	93.07 211	95.73 242	99.22 167	77.05 354	98.88 221	96.52 205	87.69 340	98.58 268

viewmsd2359difaftdt		94.09 262	93.64 254	95.46 284	96.68 320	88.92 377	99.62 220	97.13 348	93.07 211	95.73 242	99.22 167	77.05 354	98.89 220	96.52 205	87.70 339	98.58 268

VDDNet		93.12 289	91.91 305	96.76 242	96.67 322	92.65 298	98.69 356	98.21 212	82.81 423	97.75 179	99.28 156	61.57 439	99.48 183	98.09 146	94.09 286	98.15 280

dmvs_re		93.20 286	93.15 278	93.34 361	96.54 323	83.81 419	98.71 353	98.51 129	91.39 289	92.37 293	98.56 250	78.66 346	97.83 315	93.89 259	89.74 307	98.38 275

Elysia		94.50 248	93.38 270	97.85 176	96.49 324	96.70 143	98.98 319	97.78 262	90.81 305	96.19 229	98.55 252	73.63 390	98.98 212	89.41 336	98.56 165	97.88 287

StellarMVS		94.50 248	93.38 270	97.85 176	96.49 324	96.70 143	98.98 319	97.78 262	90.81 305	96.19 229	98.55 252	73.63 390	98.98 212	89.41 336	98.56 165	97.88 287

MIMVSNet		90.30 352	88.67 366	95.17 295	96.45 326	91.64 325	92.39 453	97.15 344	85.99 392	90.50 312	93.19 425	66.95 418	94.86 428	82.01 407	93.43 294	99.01 243

CR-MVSNet		93.45 283	92.62 288	95.94 268	96.29 327	92.66 296	92.01 455	96.23 411	92.62 236	96.94 202	93.31 423	91.04 177	96.03 407	79.23 421	95.96 245	99.13 230

RPMNet		89.76 364	87.28 381	97.19 226	96.29 327	92.66 296	92.01 455	98.31 197	70.19 457	96.94 202	85.87 459	87.25 237	99.78 144	62.69 461	95.96 245	99.13 230

tt0805		91.28 329	90.18 337	94.60 314	96.26 329	87.55 394	98.39 376	98.72 76	89.00 343	89.22 342	98.47 260	62.98 434	98.96 216	90.57 321	88.00 334	97.28 305

Patchmtry		89.70 365	88.49 369	93.33 362	96.24 330	89.94 366	91.37 458	96.23 411	78.22 440	87.69 369	93.31 423	91.04 177	96.03 407	80.18 419	82.10 380	94.02 368

test_vis1_rt		86.87 387	86.05 389	89.34 414	96.12 331	78.07 448	99.87 128	83.54 474	92.03 265	78.21 437	89.51 444	45.80 459	99.91 108	96.25 209	93.11 299	90.03 443

JIA-IIPM		91.76 323	90.70 324	94.94 301	96.11 332	87.51 395	93.16 451	98.13 227	75.79 446	97.58 181	77.68 464	92.84 136	97.97 307	88.47 350	96.54 229	99.33 202

OpenMVS	90.15 15	94.77 236	93.59 259	98.33 143	96.07 333	97.48 110	99.56 237	98.57 105	90.46 318	86.51 386	98.95 202	78.57 347	99.94 91	93.86 260	99.74 89	97.57 300

PAPM		98.60 36	98.42 37	99.14 71	96.05 334	98.96 27	99.90 112	99.35 24	96.68 70	98.35 152	99.66 113	96.45 34	98.51 260	99.45 63	99.89 73	99.96 73

CLD-MVS		94.06 264	93.90 250	94.55 318	96.02 335	90.69 345	99.98 20	97.72 268	96.62 74	91.05 306	98.85 221	77.21 352	98.47 261	98.11 144	89.51 313	94.48 325
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 349	88.75 365	95.25 293	95.99 336	90.16 358	91.22 459	97.54 291	76.80 442	97.26 193	86.01 458	91.88 164	96.07 406	66.16 456	95.91 249	99.51 172

ACMH+	89.98 16	90.35 350	89.54 349	92.78 377	95.99 336	86.12 405	98.81 344	97.18 339	89.38 336	83.14 412	97.76 292	68.42 412	98.43 266	89.11 341	86.05 349	93.78 388

DeepMVS_CX				82.92 436	95.98 338	58.66 467		96.01 416	92.72 229	78.34 436	95.51 365	58.29 445	98.08 301	82.57 402	85.29 354	92.03 424

ACMP	92.05 9	92.74 298	92.42 297	93.73 350	95.91 339	88.72 381	99.81 159	97.53 293	94.13 163	87.00 380	98.23 274	74.07 387	98.47 261	96.22 210	88.86 320	93.99 373
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 278	93.03 280	95.35 288	95.86 340	86.94 400	99.87 128	96.36 409	96.85 61	99.54 70	98.79 225	52.41 453	99.83 137	98.64 112	98.97 151	99.29 213

HQP-NCC					95.78 341		99.87 128		96.82 63	93.37 278

ACMP_Plane					95.78 341		99.87 128		96.82 63	93.37 278

HQP-MVS		94.61 243	94.50 231	94.92 302	95.78 341	91.85 315	99.87 128	97.89 250	96.82 63	93.37 278	98.65 237	80.65 325	98.39 273	97.92 156	89.60 308	94.53 321

NP-MVS					95.77 344	91.79 317					98.65 237

test_fmvsmconf0.1_n		97.74 101	97.44 105	98.64 113	95.76 345	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					95.76 345	91.72 322						80.47 329

ACMM	91.95 10	92.88 295	92.52 295	93.98 344	95.75 347	89.08 376	99.77 171	97.52 295	93.00 214	89.95 319	97.99 283	76.17 370	98.46 264	93.63 272	88.87 319	94.39 333
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 267	92.84 282	96.80 240	95.73 348	93.57 271	99.88 125	97.24 332	92.57 241	92.92 285	96.66 324	78.73 345	97.67 321	87.75 358	94.06 287	99.17 225

plane_prior1					95.73 348

jason		97.24 127	96.86 132	98.38 142	95.73 348	97.32 115	99.97 38	97.40 307	95.34 116	98.60 139	99.54 131	87.70 226	98.56 257	97.94 155	99.47 122	99.25 219
jason: jason.
mmtdpeth		88.52 376	87.75 378	90.85 397	95.71 351	83.47 425	98.94 327	94.85 439	88.78 352	97.19 195	89.58 443	63.29 432	98.97 214	98.54 117	62.86 460	90.10 442

HQP_MVS		94.49 250	94.36 234	94.87 303	95.71 351	91.74 319	99.84 147	97.87 252	96.38 83	93.01 283	98.59 245	80.47 329	98.37 279	97.79 165	89.55 311	94.52 323

plane_prior7					95.71 351	91.59 331

ITE_SJBPF				92.38 380	95.69 354	85.14 411		95.71 423	92.81 223	89.33 339	98.11 277	70.23 405	98.42 267	85.91 380	88.16 332	93.59 396

fmvsm_s_conf0.1_n_a		97.09 136	96.90 129	97.63 196	95.65 355	94.21 254	99.83 154	98.50 135	96.27 90	99.65 52	99.64 116	84.72 280	99.93 101	99.04 82	98.84 156	98.74 261

ACMH	89.72 17	90.64 343	89.63 346	93.66 356	95.64 356	88.64 384	98.55 364	97.45 300	89.03 341	81.62 419	97.61 293	69.75 406	98.41 269	89.37 338	87.62 341	93.92 379
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 159	96.49 150	97.37 218	95.63 357	95.96 180	99.74 184	98.88 54	92.94 216	91.61 299	98.97 195	97.72 6	98.62 254	94.83 237	98.08 186	97.53 302

FMVSNet1		88.50 377	86.64 384	94.08 337	95.62 358	91.97 310	98.43 372	96.95 377	83.00 421	86.08 394	94.72 399	59.09 444	96.11 402	81.82 409	84.07 367	94.17 351

LuminaMVS		96.63 163	96.21 163	97.87 175	95.58 359	96.82 139	99.12 296	97.67 272	94.47 142	97.88 172	98.31 270	87.50 231	98.71 241	98.07 148	97.29 207	98.10 283

LPG-MVS_test		92.96 292	92.71 287	93.71 352	95.43 360	88.67 382	99.75 181	97.62 280	92.81 223	90.05 315	98.49 256	75.24 377	98.40 271	95.84 216	89.12 315	94.07 365

LGP-MVS_train				93.71 352	95.43 360	88.67 382		97.62 280	92.81 223	90.05 315	98.49 256	75.24 377	98.40 271	95.84 216	89.12 315	94.07 365

tpm		93.70 276	93.41 268	94.58 316	95.36 362	87.41 396	97.01 415	96.90 384	90.85 303	96.72 210	94.14 414	90.40 191	96.84 370	90.75 319	88.54 327	99.51 172

D2MVS		92.76 297	92.59 293	93.27 364	95.13 363	89.54 370	99.69 206	99.38 22	92.26 258	87.59 371	94.61 405	85.05 273	97.79 316	91.59 302	88.01 333	92.47 419

VPA-MVSNet		92.70 299	91.55 312	96.16 262	95.09 364	96.20 171	98.88 335	99.00 39	91.02 300	91.82 298	95.29 381	76.05 372	97.96 309	95.62 221	81.19 387	94.30 340

LTVRE_ROB	88.28 18	90.29 353	89.05 360	94.02 340	95.08 365	90.15 359	97.19 410	97.43 302	84.91 407	83.99 408	97.06 310	74.00 388	98.28 288	84.08 391	87.71 337	93.62 395
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		87.87 384	86.51 385	91.94 386	95.05 366	85.57 409	97.65 402	94.08 449	84.40 411	81.82 418	96.85 319	62.14 437	98.33 282	80.25 418	86.37 348	91.91 426

test0.0.03 1		93.86 266	93.61 256	94.64 312	95.02 367	92.18 308	99.93 95	98.58 103	94.07 167	87.96 366	98.50 255	93.90 105	94.96 425	81.33 410	93.17 297	96.78 308

UniMVSNet (Re)		93.07 291	92.13 299	95.88 270	94.84 368	96.24 170	99.88 125	98.98 41	92.49 246	89.25 340	95.40 371	87.09 239	97.14 346	93.13 280	78.16 411	94.26 342

USDC		90.00 360	88.96 361	93.10 370	94.81 369	88.16 390	98.71 353	95.54 428	93.66 188	83.75 410	97.20 304	65.58 423	98.31 284	83.96 394	87.49 343	92.85 413

VPNet		91.81 317	90.46 328	95.85 272	94.74 370	95.54 199	98.98 319	98.59 101	92.14 260	90.77 311	97.44 297	68.73 410	97.54 326	94.89 236	77.89 413	94.46 326

FIs		94.10 261	93.43 265	96.11 263	94.70 371	96.82 139	99.58 231	98.93 48	92.54 242	89.34 338	97.31 301	87.62 228	97.10 350	94.22 254	86.58 346	94.40 332

UniMVSNet_ETH3D		90.06 359	88.58 368	94.49 322	94.67 372	88.09 391	97.81 399	97.57 288	83.91 414	88.44 358	97.41 298	57.44 446	97.62 323	91.41 304	88.59 326	97.77 292

UniMVSNet_NR-MVSNet		92.95 293	92.11 300	95.49 280	94.61 373	95.28 216	99.83 154	99.08 36	91.49 280	89.21 343	96.86 318	87.14 238	96.73 376	93.20 276	77.52 416	94.46 326

test_fmvs2		89.47 369	89.70 345	88.77 421	94.54 374	75.74 450	99.83 154	94.70 445	94.71 134	91.08 304	96.82 323	54.46 449	97.78 318	92.87 283	88.27 330	92.80 414

MonoMVSNet		94.82 231	94.43 232	95.98 266	94.54 374	90.73 344	99.03 314	97.06 364	93.16 206	93.15 282	95.47 368	88.29 220	97.57 324	97.85 160	91.33 305	99.62 141

WR-MVS		92.31 309	91.25 317	95.48 283	94.45 376	95.29 215	99.60 227	98.68 82	90.10 326	88.07 365	96.89 316	80.68 324	96.80 374	93.14 279	79.67 404	94.36 334

nrg030		93.51 280	92.53 294	96.45 253	94.36 377	97.20 121	99.81 159	97.16 343	91.60 277	89.86 322	97.46 296	86.37 251	97.68 320	95.88 215	80.31 400	94.46 326

tfpnnormal		89.29 372	87.61 379	94.34 330	94.35 378	94.13 256	98.95 326	98.94 44	83.94 412	84.47 405	95.51 365	74.84 382	97.39 329	77.05 434	80.41 398	91.48 429

FC-MVSNet-test		93.81 270	93.15 278	95.80 275	94.30 379	96.20 171	99.42 261	98.89 52	92.33 253	89.03 348	97.27 303	87.39 234	96.83 372	93.20 276	86.48 347	94.36 334

SSC-MVS3.2		89.59 367	88.66 367	92.38 380	94.29 380	86.12 405	99.49 250	97.66 275	90.28 325	88.63 355	95.18 385	64.46 428	96.88 368	85.30 384	82.66 375	94.14 360

MS-PatchMatch		90.65 342	90.30 333	91.71 391	94.22 381	85.50 410	98.24 382	97.70 269	88.67 355	86.42 389	96.37 334	67.82 415	98.03 305	83.62 396	99.62 98	91.60 427

WR-MVS_H		91.30 327	90.35 331	94.15 334	94.17 382	92.62 299	99.17 294	98.94 44	88.87 350	86.48 388	94.46 410	84.36 286	96.61 381	88.19 352	78.51 409	93.21 405

DU-MVS		92.46 306	91.45 315	95.49 280	94.05 383	95.28 216	99.81 159	98.74 75	92.25 259	89.21 343	96.64 326	81.66 310	96.73 376	93.20 276	77.52 416	94.46 326

NR-MVSNet		91.56 325	90.22 335	95.60 278	94.05 383	95.76 187	98.25 381	98.70 78	91.16 294	80.78 425	96.64 326	83.23 296	96.57 382	91.41 304	77.73 415	94.46 326

CP-MVSNet		91.23 331	90.22 335	94.26 332	93.96 385	92.39 304	99.09 300	98.57 105	88.95 347	86.42 389	96.57 329	79.19 340	96.37 391	90.29 328	78.95 406	94.02 368

XXY-MVS		91.82 316	90.46 328	95.88 270	93.91 386	95.40 206	98.87 338	97.69 271	88.63 357	87.87 367	97.08 308	74.38 386	97.89 313	91.66 301	84.07 367	94.35 337

PS-CasMVS		90.63 344	89.51 351	93.99 343	93.83 387	91.70 323	98.98 319	98.52 126	88.48 359	86.15 393	96.53 331	75.46 375	96.31 395	88.83 343	78.86 408	93.95 376

test_0402		85.58 391	83.94 396	90.50 403	93.81 388	85.04 412	98.55 364	95.20 436	76.01 444	79.72 431	95.13 386	64.15 430	96.26 397	66.04 457	86.88 345	90.21 440

XVG-ACMP-BASELINE		91.22 332	90.75 323	92.63 379	93.73 389	85.61 408	98.52 368	97.44 301	92.77 227	89.90 321	96.85 319	66.64 420	98.39 273	92.29 288	88.61 324	93.89 381

TranMVSNet+NR-MVSNet		91.68 324	90.61 327	94.87 303	93.69 390	93.98 261	99.69 206	98.65 86	91.03 299	88.44 358	96.83 322	80.05 333	96.18 400	90.26 329	76.89 424	94.45 331

TransMVSNet (Re)		87.25 385	85.28 392	93.16 367	93.56 391	91.03 336	98.54 366	94.05 451	83.69 416	81.09 423	96.16 340	75.32 376	96.40 390	76.69 435	68.41 448	92.06 423

v10		90.25 354	88.82 363	94.57 317	93.53 392	93.43 276	99.08 302	96.87 387	85.00 404	87.34 378	94.51 406	80.93 320	97.02 360	82.85 401	79.23 405	93.26 403

testgi		89.01 374	88.04 375	91.90 387	93.49 393	84.89 414	99.73 191	95.66 425	93.89 181	85.14 400	98.17 275	59.68 443	94.66 431	77.73 430	88.88 318	96.16 317

v8		90.54 346	89.17 356	94.66 311	93.43 394	93.40 279	99.20 291	96.94 381	85.76 395	87.56 372	94.51 406	81.96 306	97.19 343	84.94 387	78.25 410	93.38 401

V42		91.28 329	90.12 340	94.74 308	93.42 395	93.46 275	99.68 209	97.02 368	87.36 374	89.85 324	95.05 389	81.31 316	97.34 332	87.34 363	80.07 402	93.40 399

pm-mvs1		89.36 371	87.81 377	94.01 341	93.40 396	91.93 313	98.62 362	96.48 407	86.25 390	83.86 409	96.14 342	73.68 389	97.04 356	86.16 377	75.73 429	93.04 409

v1144		91.09 333	89.83 342	94.87 303	93.25 397	93.69 269	99.62 220	96.98 373	86.83 384	89.64 330	94.99 394	80.94 319	97.05 353	85.08 386	81.16 388	93.87 383

v1192		90.62 345	89.25 355	94.72 310	93.13 398	93.07 283	99.50 248	97.02 368	86.33 389	89.56 334	95.01 391	79.22 339	97.09 352	82.34 405	81.16 388	94.01 370

v2v482		91.30 327	90.07 341	95.01 298	93.13 398	93.79 264	99.77 171	97.02 368	88.05 365	89.25 340	95.37 375	80.73 323	97.15 345	87.28 364	80.04 403	94.09 364

OPM-MVS		93.21 285	92.80 284	94.44 325	93.12 400	90.85 343	99.77 171	97.61 283	96.19 93	91.56 300	98.65 237	75.16 381	98.47 261	93.78 267	89.39 314	93.99 373
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 340	89.52 350	94.59 315	93.11 401	92.77 290	99.56 237	96.99 371	86.38 388	89.82 325	94.95 396	80.50 328	97.10 350	83.98 393	80.41 398	93.90 380

PEN-MVS		90.19 356	89.06 359	93.57 357	93.06 402	90.90 341	99.06 307	98.47 138	88.11 364	85.91 395	96.30 336	76.67 362	95.94 410	87.07 367	76.91 423	93.89 381

v1240		90.20 355	88.79 364	94.44 325	93.05 403	92.27 306	99.38 268	96.92 383	85.89 393	89.36 337	94.87 398	77.89 351	97.03 358	80.66 414	81.08 391	94.01 370

v148		90.70 341	89.63 346	93.92 345	92.97 404	90.97 337	99.75 181	96.89 385	87.51 371	88.27 363	95.01 391	81.67 309	97.04 356	87.40 362	77.17 421	93.75 389

v1921920		90.46 347	89.12 357	94.50 321	92.96 405	92.46 302	99.49 250	96.98 373	86.10 391	89.61 332	95.30 378	78.55 348	97.03 358	82.17 406	80.89 396	94.01 370

MVStest1		85.03 397	82.76 406	91.83 388	92.95 406	89.16 375	98.57 363	94.82 440	71.68 455	68.54 458	95.11 388	83.17 297	95.66 414	74.69 440	65.32 455	90.65 436

tt0320-xc		82.94 411	80.35 418	90.72 401	92.90 407	83.54 423	96.85 420	94.73 443	63.12 460	79.85 430	93.77 418	49.43 457	95.46 417	80.98 413	71.54 438	93.16 406

Baseline_NR-MVSNet		90.33 351	89.51 351	92.81 376	92.84 408	89.95 364	99.77 171	93.94 452	84.69 409	89.04 347	95.66 357	81.66 310	96.52 384	90.99 312	76.98 422	91.97 425

test_method		80.79 417	79.70 420	84.08 433	92.83 409	67.06 459	99.51 246	95.42 430	54.34 465	81.07 424	93.53 420	44.48 460	92.22 451	78.90 425	77.23 420	92.94 411

pmmvs4		92.10 313	91.07 321	95.18 294	92.82 410	94.96 227	99.48 253	96.83 389	87.45 373	88.66 354	96.56 330	83.78 291	96.83 372	89.29 339	84.77 361	93.75 389

LF4IMVS		89.25 373	88.85 362	90.45 405	92.81 411	81.19 440	98.12 389	94.79 441	91.44 284	86.29 391	97.11 306	65.30 426	98.11 299	88.53 348	85.25 355	92.07 422

tt0320		83.56 410	81.15 413	90.77 399	92.77 412	83.58 422	96.83 421	95.52 429	63.26 459	81.36 421	92.54 428	53.26 451	95.77 412	80.45 415	74.38 432	92.96 410

DTE-MVSNet		89.40 370	88.24 373	92.88 374	92.66 413	89.95 364	99.10 299	98.22 211	87.29 375	85.12 401	96.22 338	76.27 369	95.30 422	83.56 397	75.74 428	93.41 398

EU-MVSNet		90.14 358	90.34 332	89.54 413	92.55 414	81.06 441	98.69 356	98.04 235	91.41 288	86.59 385	96.84 321	80.83 322	93.31 444	86.20 376	81.91 382	94.26 342

APD_test1		81.15 415	80.92 415	81.86 437	92.45 415	59.76 466	96.04 435	93.61 455	73.29 453	77.06 440	96.64 326	44.28 461	96.16 401	72.35 444	82.52 376	89.67 447

sc_t1		85.01 398	82.46 408	92.67 378	92.44 416	83.09 426	97.39 406	95.72 422	65.06 458	85.64 398	96.16 340	49.50 456	97.34 332	84.86 388	75.39 430	97.57 300

our_test_3		90.39 348	89.48 353	93.12 368	92.40 417	89.57 369	99.33 275	96.35 410	87.84 369	85.30 399	94.99 394	84.14 289	96.09 405	80.38 416	84.56 362	93.71 394

ppachtmachnet_test		89.58 368	88.35 371	93.25 366	92.40 417	90.44 353	99.33 275	96.73 396	85.49 400	85.90 396	95.77 351	81.09 318	96.00 409	76.00 438	82.49 377	93.30 402

v7n		89.65 366	88.29 372	93.72 351	92.22 419	90.56 350	99.07 306	97.10 356	85.42 402	86.73 382	94.72 399	80.06 332	97.13 347	81.14 411	78.12 412	93.49 397

dmvs_testset		83.79 407	86.07 388	76.94 441	92.14 420	48.60 476	96.75 422	90.27 466	89.48 335	78.65 434	98.55 252	79.25 338	86.65 464	66.85 454	82.69 374	95.57 319

PS-MVSNAJss		93.64 277	93.31 274	94.61 313	92.11 421	92.19 307	99.12 296	97.38 308	92.51 245	88.45 357	96.99 314	91.20 172	97.29 340	94.36 248	87.71 337	94.36 334

pmmvs5		90.17 357	89.09 358	93.40 360	92.10 422	89.77 367	99.74 184	95.58 427	85.88 394	87.24 379	95.74 352	73.41 392	96.48 386	88.54 347	83.56 371	93.95 376

N_pmnet		80.06 420	80.78 416	77.89 440	91.94 423	45.28 478	98.80 347	56.82 480	78.10 441	80.08 428	93.33 421	77.03 356	95.76 413	68.14 452	82.81 373	92.64 415

test_djsdf		92.83 296	92.29 298	94.47 323	91.90 424	92.46 302	99.55 240	97.27 326	91.17 292	89.96 318	96.07 346	81.10 317	96.89 366	94.67 243	88.91 317	94.05 367

SixPastTwentyTwo		88.73 375	88.01 376	90.88 395	91.85 425	82.24 432	98.22 386	95.18 437	88.97 345	82.26 415	96.89 316	71.75 397	96.67 379	84.00 392	82.98 372	93.72 393

K. test v3		88.05 381	87.24 382	90.47 404	91.82 426	82.23 433	98.96 325	97.42 304	89.05 340	76.93 442	95.60 359	68.49 411	95.42 418	85.87 381	81.01 394	93.75 389

OurMVSNet-221017-0		89.81 363	89.48 353	90.83 398	91.64 427	81.21 439	98.17 388	95.38 432	91.48 282	85.65 397	97.31 301	72.66 393	97.29 340	88.15 353	84.83 360	93.97 375

mvs_tets		91.81 317	91.08 320	94.00 342	91.63 428	90.58 349	98.67 358	97.43 302	92.43 247	87.37 377	97.05 311	71.76 396	97.32 335	94.75 240	88.68 323	94.11 363

Gipuma		66.95 433	65.00 433	72.79 446	91.52 429	67.96 458	66.16 470	95.15 438	47.89 467	58.54 464	67.99 469	29.74 465	87.54 463	50.20 468	77.83 414	62.87 469
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 176	95.74 187	98.32 144	91.47 430	95.56 198	99.84 147	97.30 321	97.74 29	97.89 171	99.35 150	79.62 335	99.85 127	99.25 72	99.24 139	99.55 158

jajsoiax		91.92 315	91.18 318	94.15 334	91.35 431	90.95 340	99.00 317	97.42 304	92.61 237	87.38 376	97.08 308	72.46 394	97.36 330	94.53 246	88.77 321	94.13 362

MDA-MVSNet-bldmvs		84.09 405	81.52 412	91.81 389	91.32 432	88.00 393	98.67 358	95.92 418	80.22 435	55.60 467	93.32 422	68.29 413	93.60 442	73.76 441	76.61 425	93.82 387

MVP-Stereo		90.93 335	90.45 330	92.37 382	91.25 433	88.76 379	98.05 393	96.17 413	87.27 376	84.04 406	95.30 378	78.46 349	97.27 342	83.78 395	99.70 92	91.09 430
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 393	83.32 401	92.10 384	90.96 434	88.58 385	99.20 291	96.52 405	79.70 437	57.12 466	92.69 427	79.11 341	93.86 438	77.10 433	77.46 418	93.86 384

YYNet1		85.50 394	83.33 400	92.00 385	90.89 435	88.38 389	99.22 290	96.55 404	79.60 438	57.26 465	92.72 426	79.09 343	93.78 440	77.25 432	77.37 419	93.84 385

anonymousdsp		91.79 322	90.92 322	94.41 328	90.76 436	92.93 289	98.93 329	97.17 341	89.08 339	87.46 375	95.30 378	78.43 350	96.92 364	92.38 287	88.73 322	93.39 400

lessismore_v0				90.53 402	90.58 437	80.90 442		95.80 419		77.01 441	95.84 349	66.15 422	96.95 362	83.03 400	75.05 431	93.74 392

EG-PatchMatch MVS		85.35 395	83.81 398	89.99 411	90.39 438	81.89 435	98.21 387	96.09 415	81.78 428	74.73 448	93.72 419	51.56 455	97.12 349	79.16 424	88.61 324	90.96 433

EGC-MVSNET		69.38 426	63.76 436	86.26 430	90.32 439	81.66 438	96.24 431	93.85 453	0.99 477	3.22 478	92.33 433	52.44 452	92.92 447	59.53 464	84.90 359	84.21 458

CMPMVS	61.59 21	84.75 401	85.14 393	83.57 434	90.32 439	62.54 462	96.98 416	97.59 287	74.33 451	69.95 455	96.66 324	64.17 429	98.32 283	87.88 357	88.41 329	89.84 445
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 404	82.92 404	89.21 415	90.03 441	82.60 429	96.89 419	95.62 426	80.59 433	75.77 447	89.17 445	65.04 427	94.79 429	72.12 445	81.02 393	90.23 439

pmmvs6		85.69 390	83.84 397	91.26 394	90.00 442	84.41 417	97.82 398	96.15 414	75.86 445	81.29 422	95.39 373	61.21 440	96.87 369	83.52 398	73.29 434	92.50 418

ttmdpeth		88.23 380	87.06 383	91.75 390	89.91 443	87.35 397	98.92 332	95.73 421	87.92 367	84.02 407	96.31 335	68.23 414	96.84 370	86.33 375	76.12 426	91.06 431

DSMNet-mixed		88.28 379	88.24 373	88.42 423	89.64 444	75.38 452	98.06 392	89.86 467	85.59 399	88.20 364	92.14 434	76.15 371	91.95 452	78.46 427	96.05 242	97.92 286

UnsupCasMVSNet_eth		85.52 392	83.99 394	90.10 409	89.36 445	83.51 424	96.65 423	97.99 238	89.14 338	75.89 446	93.83 416	63.25 433	93.92 436	81.92 408	67.90 451	92.88 412

Anonymous20231206		86.32 388	85.42 391	89.02 417	89.11 446	80.53 445	99.05 311	95.28 433	85.43 401	82.82 413	93.92 415	74.40 385	93.44 443	66.99 453	81.83 383	93.08 408

Anonymous20240521		85.15 396	83.81 398	89.16 416	88.32 447	82.69 428	98.80 347	95.74 420	79.72 436	81.53 420	90.99 437	65.38 425	94.16 434	72.69 443	81.11 390	90.63 437

OpenMVS_ROB	79.82 20	83.77 408	81.68 411	90.03 410	88.30 448	82.82 427	98.46 369	95.22 435	73.92 452	76.00 445	91.29 436	55.00 448	96.94 363	68.40 451	88.51 328	90.34 438

test20.03		84.72 402	83.99 394	86.91 428	88.19 449	80.62 444	98.88 335	95.94 417	88.36 361	78.87 432	94.62 404	68.75 409	89.11 459	66.52 455	75.82 427	91.00 432

KD-MVS_self_test		83.59 409	82.06 409	88.20 424	86.93 450	80.70 443	97.21 409	96.38 408	82.87 422	82.49 414	88.97 446	67.63 416	92.32 450	73.75 442	62.30 462	91.58 428

MIMVSNet1		82.58 412	80.51 417	88.78 419	86.68 451	84.20 418	96.65 423	95.41 431	78.75 439	78.59 435	92.44 429	51.88 454	89.76 458	65.26 458	78.95 406	92.38 421

CL-MVSNet_self_test		84.50 403	83.15 403	88.53 422	86.00 452	81.79 436	98.82 343	97.35 312	85.12 403	83.62 411	90.91 439	76.66 363	91.40 453	69.53 449	60.36 463	92.40 420

UnsupCasMVSNet_bld		79.97 422	77.03 427	88.78 419	85.62 453	81.98 434	93.66 447	97.35 312	75.51 448	70.79 454	83.05 461	48.70 458	94.91 427	78.31 428	60.29 464	89.46 450

mvs5depth		84.87 399	82.90 405	90.77 399	85.59 454	84.84 415	91.10 460	93.29 457	83.14 419	85.07 402	94.33 412	62.17 436	97.32 335	78.83 426	72.59 437	90.14 441

Patchmatch-RL test		86.90 386	85.98 390	89.67 412	84.45 455	75.59 451	89.71 463	92.43 459	86.89 383	77.83 439	90.94 438	94.22 94	93.63 441	87.75 358	69.61 442	99.79 110

pmmvs-eth3d		84.03 406	81.97 410	90.20 408	84.15 456	87.09 399	98.10 391	94.73 443	83.05 420	74.10 451	87.77 452	65.56 424	94.01 435	81.08 412	69.24 444	89.49 449

test_fmvs3		79.99 421	80.17 419	79.45 439	84.02 457	62.83 460	99.05 311	93.49 456	88.29 363	80.06 429	86.65 456	28.09 467	88.00 460	88.63 344	73.27 435	87.54 456

PM-MVS		80.47 418	78.88 422	85.26 431	83.79 458	72.22 454	95.89 438	91.08 464	85.71 398	76.56 444	88.30 448	36.64 463	93.90 437	82.39 404	69.57 443	89.66 448

new-patchmatchnet		81.19 414	79.34 421	86.76 429	82.86 459	80.36 446	97.92 395	95.27 434	82.09 427	72.02 452	86.87 455	62.81 435	90.74 456	71.10 446	63.08 459	89.19 452

FE-MVSNET		81.05 416	78.81 423	87.79 426	81.98 460	83.70 420	98.23 384	91.78 463	81.27 430	74.29 450	87.44 453	60.92 442	90.67 457	64.92 459	68.43 447	89.01 453

mvsany_test3		82.12 413	81.14 414	85.06 432	81.87 461	70.41 456	97.09 413	92.14 460	91.27 291	77.84 438	88.73 447	39.31 462	95.49 415	90.75 319	71.24 439	89.29 451

WB-MVS		76.28 424	77.28 426	73.29 445	81.18 462	54.68 470	97.87 397	94.19 448	81.30 429	69.43 456	90.70 440	77.02 357	82.06 468	35.71 473	68.11 450	83.13 459

test_f		78.40 423	77.59 425	80.81 438	80.82 463	62.48 463	96.96 417	93.08 458	83.44 417	74.57 449	84.57 460	27.95 468	92.63 448	84.15 390	72.79 436	87.32 457

SSC-MVS		75.42 425	76.40 428	72.49 449	80.68 464	53.62 471	97.42 404	94.06 450	80.42 434	68.75 457	90.14 442	76.54 365	81.66 469	33.25 474	66.34 454	82.19 460

pmmvs3		80.27 419	77.77 424	87.76 427	80.32 465	82.43 431	98.23 384	91.97 461	72.74 454	78.75 433	87.97 451	57.30 447	90.99 455	70.31 447	62.37 461	89.87 444

testf1		68.38 429	66.92 430	72.78 447	78.80 466	50.36 473	90.95 461	87.35 472	55.47 463	58.95 462	88.14 449	20.64 472	87.60 461	57.28 465	64.69 456	80.39 462

APD_test2		68.38 429	66.92 430	72.78 447	78.80 466	50.36 473	90.95 461	87.35 472	55.47 463	58.95 462	88.14 449	20.64 472	87.60 461	57.28 465	64.69 456	80.39 462

ambc				83.23 435	77.17 468	62.61 461	87.38 465	94.55 447		76.72 443	86.65 456	30.16 464	96.36 392	84.85 389	69.86 441	90.73 435

test_vis3_rt		68.82 427	66.69 432	75.21 444	76.24 469	60.41 465	96.44 426	68.71 479	75.13 449	50.54 470	69.52 468	16.42 477	96.32 394	80.27 417	66.92 453	68.89 466

TDRefinement		84.76 400	82.56 407	91.38 393	74.58 470	84.80 416	97.36 407	94.56 446	84.73 408	80.21 427	96.12 345	63.56 431	98.39 273	87.92 356	63.97 458	90.95 434

E-PMN		52.30 437	52.18 439	52.67 455	71.51 471	45.40 477	93.62 448	76.60 477	36.01 471	43.50 472	64.13 471	27.11 469	67.31 474	31.06 475	26.06 470	45.30 473

EMVS		51.44 439	51.22 441	52.11 456	70.71 472	44.97 479	94.04 444	75.66 478	35.34 473	42.40 473	61.56 474	28.93 466	65.87 475	27.64 476	24.73 471	45.49 472

PMMVS2		67.15 432	64.15 435	76.14 443	70.56 473	62.07 464	93.89 445	87.52 471	58.09 462	60.02 461	78.32 463	22.38 471	84.54 466	59.56 463	47.03 468	81.80 461

FPMVS		68.72 428	68.72 429	68.71 451	65.95 474	44.27 480	95.97 437	94.74 442	51.13 466	53.26 468	90.50 441	25.11 470	83.00 467	60.80 462	80.97 395	78.87 464

wuyk23d		20.37 443	20.84 446	18.99 459	65.34 475	27.73 482	50.43 471	7.67 483	9.50 476	8.01 477	6.34 477	6.13 480	26.24 476	23.40 477	10.69 475	2.99 474

LCM-MVSNet		67.77 431	64.73 434	76.87 442	62.95 476	56.25 469	89.37 464	93.74 454	44.53 468	61.99 460	80.74 462	20.42 474	86.53 465	69.37 450	59.50 465	87.84 454

MVE	53.74 22	51.54 438	47.86 442	62.60 453	59.56 477	50.93 472	79.41 468	77.69 476	35.69 472	36.27 474	61.76 473	5.79 481	69.63 472	37.97 472	36.61 469	67.24 467
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 435	52.24 438	67.66 452	49.27 478	56.82 468	83.94 466	82.02 475	70.47 456	33.28 475	64.54 470	17.23 476	69.16 473	45.59 470	23.85 472	77.02 465

tmp_tt		65.23 434	62.94 437	72.13 450	44.90 479	50.03 475	81.05 467	89.42 470	38.45 469	48.51 471	99.90 21	54.09 450	78.70 471	91.84 300	18.26 473	87.64 455

PMVS	49.05 23	53.75 436	51.34 440	60.97 454	40.80 480	34.68 481	74.82 469	89.62 469	37.55 470	28.67 476	72.12 465	7.09 479	81.63 470	43.17 471	68.21 449	66.59 468
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 441	39.14 444	33.31 457	19.94 481	24.83 483	98.36 377	9.75 482	15.53 475	51.31 469	87.14 454	19.62 475	17.74 477	47.10 469	3.47 476	57.36 470

testmvs		40.60 440	44.45 443	29.05 458	19.49 482	14.11 484	99.68 209	18.47 481	20.74 474	64.59 459	98.48 259	10.95 478	17.09 478	56.66 467	11.01 474	55.94 471

mmdepth		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

monomultidepth		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

test_blank		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.02 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

eth-test2					0.00 483

eth-test					0.00 483

uanet_test		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

DCPMVS		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

cdsmvs_eth3d_5k		23.43 442	31.24 445	0.00 460	0.00 483	0.00 485	0.00 472	98.09 229	0.00 478	0.00 479	99.67 111	83.37 294	0.00 479	0.00 478	0.00 477	0.00 475

pcd_1.5k_mvsjas		7.60 445	10.13 448	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	91.20 172	0.00 479	0.00 478	0.00 477	0.00 475

sosnet-low-res		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

sosnet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uncertanet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

Regformer		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

ab-mvs-re		8.28 444	11.04 447	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	99.40 144	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uanet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 479	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

TestfortrainingZip							99.97 38

WAC-MVS						90.97 337								86.10 379

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							95.78 439				59.23 475	93.20 127	97.74 319	91.06 310

test_post											63.35 472	94.43 81	98.13 298

patchmatchnet-post											91.70 435	95.12 58	97.95 310

MTMP							99.87 128	96.49 406

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							99.46 258		94.21 162	99.85 17			99.95 83	96.96 191

新几何2							99.40 262

无先验							99.49 250	98.71 77	93.46 194				100.00 1	94.36 248		99.99 24

原ACMM2							99.90 112

testdata2													99.99 38	90.54 323

segment_acmp												96.68 30

testdata1							99.28 284		96.35 89

plane_prior5								97.87 252					98.37 279	97.79 165	89.55 311	94.52 323

plane_prior4											98.59 245

plane_prior3						91.64 325			96.63 72	93.01 283

plane_prior2							99.84 147		96.38 83

plane_prior						91.74 319	99.86 139		96.76 67						89.59 310

n2								0.00 484

nn								0.00 484

door-mid								89.69 468

test11								98.44 146

door								90.31 465

HQP5-MVS						91.85 315

BP-MVS														97.92 156

HQP4-MVS										93.37 278			98.39 273			94.53 321

HQP3-MVS								97.89 250							89.60 308

HQP2-MVS												80.65 325

MDTV_nov1_ep13_2view						96.26 165	96.11 433		91.89 268	98.06 164		94.40 83		94.30 251		99.67 128

ACMMP++_ref															87.04 344

ACMMP++															88.23 331

Test By Simon												92.82 138

High-resolution multi-view benchmark