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		97.23 130	96.80 139	98.51 132	99.99 1	95.60 200	99.09 310	98.84 65	93.32 203	96.74 214	99.72 94	86.04 260	100.00 1	98.01 152	99.43 129	99.94 86

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 21	98.69 81	98.20 9	99.93 2	99.98 2	96.82 26	100.00 1	99.75 41	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 39	98.64 90	98.47 3	99.13 104	99.92 17	96.38 36	100.00 1	99.74 43	100.00 1	100.00 1

mPP-MVS		98.39 56	98.20 54	98.97 92	99.97 3	96.92 138	99.95 72	98.38 183	95.04 123	98.61 138	99.80 58	93.39 117	100.00 1	98.64 114	100.00 1	99.98 56

CPTT-MVS		97.64 110	97.32 114	98.58 121	99.97 3	95.77 189	99.96 53	98.35 189	89.90 340	98.36 153	99.79 62	91.18 177	99.99 39	98.37 130	99.99 21	99.99 24

DP-MVS Recon		98.41 53	98.02 68	99.56 29	99.97 3	98.70 52	99.92 100	98.44 147	92.06 272	98.40 152	99.84 48	95.68 47	100.00 1	98.19 141	99.71 92	99.97 66

PAPR		98.52 43	98.16 58	99.58 28	99.97 3	98.77 46	99.95 72	98.43 155	95.35 117	98.03 167	99.75 80	94.03 102	99.98 50	98.11 146	99.83 81	99.99 24

MED-MVS test				99.60 23	99.96 8	98.79 41	99.97 39	98.88 54	96.36 88	99.07 109	99.93 11		100.00 1	99.98 9	99.96 46	99.99 24

MED-MVS		99.15 8	99.00 12	99.60 23	99.96 8	98.79 41	99.97 39	98.88 54	95.89 101	99.07 109	99.93 11	97.36 17	100.00 1	99.98 9	99.96 46	99.99 24

TestfortrainingZip a		99.09 10	98.87 19	99.76 10	99.96 8	99.27 18	99.97 39	98.88 54	96.36 88	99.07 109	99.93 11	97.36 17	100.00 1	98.32 133	99.96 46	100.00 1

HFP-MVS		98.56 39	98.37 43	99.14 72	99.96 8	97.43 114	99.95 72	98.61 98	94.77 133	99.31 92	99.85 37	94.22 95	100.00 1	98.70 109	99.98 32	99.98 56

region2R		98.54 41	98.37 43	99.05 82	99.96 8	97.18 124	99.96 53	98.55 118	94.87 130	99.45 79	99.85 37	94.07 101	100.00 1	98.67 111	100.00 1	99.98 56

ACMMPR		98.50 44	98.32 47	99.05 82	99.96 8	97.18 124	99.95 72	98.60 100	94.77 133	99.31 92	99.84 48	93.73 111	100.00 1	98.70 109	99.98 32	99.98 56

NCCC		99.37 2	99.25 2	99.71 16	99.96 8	99.15 23	99.97 39	98.62 97	98.02 22	99.90 6	99.95 3	97.33 19	100.00 1	99.54 58	100.00 1	100.00 1

CP-MVS		98.45 48	98.32 47	98.87 97	99.96 8	96.62 152	99.97 39	98.39 179	94.43 150	98.90 119	99.87 31	94.30 92	100.00 1	99.04 84	99.99 21	99.99 24

test_one_0601					99.94 16	99.30 12		98.41 172	96.63 73	99.75 40	99.93 11	97.49 10

test_0728_SECOND				99.82 7	99.94 16	99.47 7	99.95 72	98.43 155					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 32	98.55 31	99.15 70	99.94 16	97.50 110	99.94 90	98.42 167	96.22 92	99.41 84	99.78 66	94.34 89	99.96 75	98.92 94	99.95 54	99.99 24

X-MVStestdata		93.83 275	92.06 310	99.15 70	99.94 16	97.50 110	99.94 90	98.42 167	96.22 92	99.41 84	41.37 488	94.34 89	99.96 75	98.92 94	99.95 54	99.99 24

test_prior				99.43 40	99.94 16	98.49 65		98.65 87					99.80 142			99.99 24

MSLP-MVS++		99.13 9	99.01 11	99.49 36	99.94 16	98.46 66	99.98 21	98.86 59	97.10 53	99.80 26	99.94 4	95.92 43	100.00 1	99.51 59	100.00 1	100.00 1

APDe-MVS		99.06 14	98.91 15	99.51 33	99.94 16	98.76 49	99.91 108	98.39 179	97.20 51	99.46 78	99.85 37	95.53 51	99.79 144	99.86 27	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 71	97.97 72	99.03 84	99.94 16	97.17 127	99.95 72	98.39 179	94.70 137	98.26 159	99.81 57	91.84 168	100.00 1	98.85 100	99.97 42	99.93 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 35	98.36 45	99.49 36	99.94 16	98.73 50	99.87 130	98.33 194	93.97 175	99.76 39	99.87 31	94.99 67	99.75 153	98.55 118	100.00 1	99.98 56

PAPM_NR		98.12 75	97.93 78	98.70 108	99.94 16	96.13 178	99.82 160	98.43 155	94.56 141	97.52 184	99.70 100	94.40 84	99.98 50	97.00 192	99.98 32	99.99 24

MG-MVS		98.91 22	98.65 27	99.68 17	99.94 16	99.07 25	99.64 224	99.44 19	97.33 44	99.00 115	99.72 94	94.03 102	99.98 50	98.73 108	100.00 1	100.00 1

ME-MVS		99.07 12	98.89 17	99.59 26	99.93 27	98.79 41	99.95 72	98.80 71	95.89 101	99.28 96	99.93 11	96.28 37	99.98 50	99.98 9	99.96 46	99.99 24

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 27	99.30 12	99.96 53	98.43 155	97.27 47	99.80 26	99.94 4	96.71 29	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 27	99.31 10		98.41 172	97.71 31	99.84 21				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 27	99.30 12		98.43 155	97.26 49	99.80 26	99.88 28	96.71 29	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 13	99.93 27	99.29 15	99.95 72	98.32 196	97.28 45	99.83 22	99.91 18	97.22 21	100.00 1	99.99 5	100.00 1	99.89 96
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 27	99.29 15	99.96 53	98.42 167	97.28 45	99.86 15	99.94 4	97.22 21

MSP-MVS		99.09 10	99.12 5	98.98 91	99.93 27	97.24 121	99.95 72	98.42 167	97.50 38	99.52 74	99.88 28	97.43 16	99.71 159	99.50 61	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 27	98.77 46		98.43 155		99.63 57			99.85 129

FOURS1					99.92 35	97.66 104	99.95 72	98.36 187	95.58 111	99.52 74

ZD-MVS					99.92 35	98.57 60		98.52 127	92.34 260	99.31 92	99.83 50	95.06 62	99.80 142	99.70 49	99.97 42

GST-MVS		98.27 63	97.97 72	99.17 65	99.92 35	97.57 106	99.93 97	98.39 179	94.04 173	98.80 124	99.74 87	92.98 133	100.00 1	98.16 143	99.76 89	99.93 87

TEST9					99.92 35	98.92 30	99.96 53	98.43 155	93.90 181	99.71 47	99.86 33	95.88 44	99.85 129

train_agg		98.88 23	98.65 27	99.59 26	99.92 35	98.92 30	99.96 53	98.43 155	94.35 155	99.71 47	99.86 33	95.94 41	99.85 129	99.69 50	99.98 32	99.99 24

test_8					99.92 35	98.88 33	99.96 53	98.43 155	94.35 155	99.69 49	99.85 37	95.94 41	99.85 129

PGM-MVS		98.34 58	98.13 60	98.99 89	99.92 35	97.00 134	99.75 185	99.50 17	93.90 181	99.37 89	99.76 72	93.24 126	100.00 1	97.75 171	99.96 46	99.98 56

ACMMP		97.74 103	97.44 107	98.66 112	99.92 35	96.13 178	99.18 303	99.45 18	94.84 131	96.41 231	99.71 97	91.40 171	99.99 39	97.99 154	98.03 189	99.87 99
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 43	99.31 10	99.95 72	98.43 155	96.48 78	99.80 26	99.93 11	97.44 14	100.00 1	99.92 16	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 43	99.80 2		98.41 172					100.00 1	99.96 12	100.00 1	100.00 1

No_MVS				99.93 2	99.91 43	99.80 2		98.41 172					100.00 1	99.96 12	100.00 1	100.00 1

HPM-MVS++		99.07 12	98.88 18	99.63 18	99.90 46	99.02 26	99.95 72	98.56 112	97.56 37	99.44 80	99.85 37	95.38 55	100.00 1	99.31 71	99.99 21	99.87 99

APD-MVS		98.62 36	98.35 46	99.41 43	99.90 46	98.51 63	99.87 130	98.36 187	94.08 168	99.74 43	99.73 91	94.08 100	99.74 155	99.42 67	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 26	98.54 32	99.62 21	99.90 46	98.85 36	99.24 298	98.47 139	98.14 16	99.08 107	99.91 18	93.09 130	100.00 1	99.04 84	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 49	99.80 2	99.96 53				99.80 58	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 49	99.24 20	99.87 130	98.44 147	97.48 39	99.64 56	99.94 4	96.68 31	99.99 39	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 49	99.25 19				99.49 77

CSCG		97.10 136	97.04 126	97.27 231	99.89 49	91.92 323	99.90 114	99.07 37	88.67 364	95.26 263	99.82 53	93.17 129	99.98 50	98.15 144	99.47 124	99.90 95

ZNCC-MVS		98.31 60	98.03 67	99.17 65	99.88 53	97.59 105	99.94 90	98.44 147	94.31 158	98.50 145	99.82 53	93.06 131	99.99 39	98.30 135	99.99 21	99.93 87

SR-MVS		98.46 47	98.30 50	98.93 95	99.88 53	97.04 133	99.84 149	98.35 189	94.92 127	99.32 91	99.80 58	93.35 119	99.78 146	99.30 72	99.95 54	99.96 74

9.14			98.38 41		99.87 55		99.91 108	98.33 194	93.22 206	99.78 37	99.89 26	94.57 80	99.85 129	99.84 29	99.97 42

SMA-MVS		98.76 29	98.48 35	99.62 21	99.87 55	98.87 34	99.86 141	98.38 183	93.19 208	99.77 38	99.94 4	95.54 49	100.00 1	99.74 43	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 85	97.85 85	98.04 165	99.86 57	95.39 210	99.61 231	97.78 265	96.52 76	98.61 138	99.31 156	92.73 141	99.67 167	96.77 205	99.48 121	99.06 243

lecture		98.67 33	98.46 36	99.28 52	99.86 57	97.88 91	99.97 39	99.25 30	96.07 96	99.79 35	99.70 100	92.53 149	99.98 50	99.51 59	99.48 121	99.97 66

PHI-MVS		98.41 53	98.21 53	99.03 84	99.86 57	97.10 131	99.98 21	98.80 71	90.78 319	99.62 60	99.78 66	95.30 56	100.00 1	99.80 32	99.93 65	99.99 24

MTAPA		98.29 62	97.96 75	99.30 51	99.85 60	97.93 89	99.39 274	98.28 203	95.76 105	97.18 199	99.88 28	92.74 140	100.00 1	98.67 111	99.88 77	99.99 24

LS3D		95.84 205	95.11 220	98.02 166	99.85 60	95.10 228	98.74 360	98.50 136	87.22 386	93.66 284	99.86 33	87.45 236	99.95 84	90.94 322	99.81 87	99.02 249

HPM-MVS		97.96 80	97.72 90	98.68 109	99.84 62	96.39 164	99.90 114	98.17 218	92.61 243	98.62 137	99.57 130	91.87 167	99.67 167	98.87 99	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 63	98.11 62	98.75 105	99.83 63	96.59 156	99.40 270	98.51 130	95.29 119	98.51 144	99.76 72	93.60 115	99.71 159	98.53 121	99.52 114	99.95 82

save fliter					99.82 64	98.79 41	99.96 53	98.40 176	97.66 33

PLC	95.54 3	97.93 83	97.89 82	98.05 164	99.82 64	94.77 239	99.92 100	98.46 141	93.93 178	97.20 197	99.27 162	95.44 54	99.97 63	97.41 177	99.51 117	99.41 193
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 68	98.08 64	98.78 102	99.81 66	96.60 154	99.82 160	98.30 201	93.95 177	99.37 89	99.77 70	92.84 137	99.76 152	98.95 90	99.92 68	99.97 66

EI-MVSNet-UG-set		98.14 74	97.99 70	98.60 117	99.80 67	96.27 167	99.36 280	98.50 136	95.21 121	98.30 156	99.75 80	93.29 123	99.73 158	98.37 130	99.30 138	99.81 108

SR-MVS-dyc-post		98.31 60	98.17 57	98.71 107	99.79 68	96.37 165	99.76 181	98.31 198	94.43 150	99.40 86	99.75 80	93.28 124	99.78 146	98.90 97	99.92 68	99.97 66

RE-MVS-def			98.13 60		99.79 68	96.37 165	99.76 181	98.31 198	94.43 150	99.40 86	99.75 80	92.95 134		98.90 97	99.92 68	99.97 66

HPM-MVS_fast		97.80 97	97.50 103	98.68 109	99.79 68	96.42 160	99.88 127	98.16 223	91.75 283	98.94 117	99.54 133	91.82 169	99.65 171	97.62 174	99.99 21	99.99 24

SF-MVS		98.67 33	98.40 39	99.50 34	99.77 71	98.67 53	99.90 114	98.21 213	93.53 193	99.81 24	99.89 26	94.70 76	99.86 128	99.84 29	99.93 65	99.96 74

MGCNet		99.06 14	98.84 20	99.72 14	99.76 72	99.21 22	99.99 5	99.34 25	98.70 2	99.44 80	99.75 80	93.24 126	99.99 39	99.94 14	99.41 131	99.95 82

旧先验1					99.76 72	97.52 108		98.64 90			99.85 37	95.63 48			99.94 59	99.99 24

OMC-MVS		97.28 126	97.23 118	97.41 221	99.76 72	93.36 290	99.65 220	97.95 245	96.03 97	97.41 190	99.70 100	89.61 203	99.51 177	96.73 207	98.25 179	99.38 195

新几何1				99.42 42	99.75 75	98.27 70		98.63 96	92.69 238	99.55 69	99.82 53	94.40 84	100.00 1	91.21 314	99.94 59	99.99 24

MP-MVS-pluss		98.07 78	97.64 96	99.38 48	99.74 76	98.41 68	99.74 189	98.18 217	93.35 201	96.45 226	99.85 37	92.64 144	99.97 63	98.91 96	99.89 74	99.77 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 20	98.77 22	99.41 43	99.74 76	98.67 53	99.77 175	98.38 183	96.73 69	99.88 12	99.74 87	94.89 69	99.59 173	99.80 32	99.98 32	99.97 66
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 40	99.74 76	98.56 61		98.40 176		99.65 53		94.76 72	99.75 153		99.98 32	99.99 24

原ACMM1				98.96 93	99.73 79	96.99 135		98.51 130	94.06 171	99.62 60	99.85 37	94.97 68	99.96 75	95.11 235	99.95 54	99.92 92

TSAR-MVS + GP.		98.60 37	98.51 34	98.86 98	99.73 79	96.63 151	99.97 39	97.92 250	98.07 19	98.76 130	99.55 131	95.00 66	99.94 93	99.91 19	97.68 196	99.99 24

CANet		98.27 63	97.82 87	99.63 18	99.72 81	99.10 24	99.98 21	98.51 130	97.00 59	98.52 142	99.71 97	87.80 227	99.95 84	99.75 41	99.38 133	99.83 104

reproduce_model		98.75 30	98.66 26	99.03 84	99.71 82	97.10 131	99.73 196	98.23 211	97.02 58	99.18 102	99.90 22	94.54 81	99.99 39	99.77 37	99.90 73	99.99 24

F-COLMAP		96.93 148	96.95 129	96.87 246	99.71 82	91.74 330	99.85 144	97.95 245	93.11 215	95.72 252	99.16 180	92.35 155	99.94 93	95.32 231	99.35 136	98.92 257

reproduce-ours		98.78 27	98.67 24	99.09 79	99.70 84	97.30 118	99.74 189	98.25 207	97.10 53	99.10 105	99.90 22	94.59 77	99.99 39	99.77 37	99.91 71	99.99 24

our_new_method		98.78 27	98.67 24	99.09 79	99.70 84	97.30 118	99.74 189	98.25 207	97.10 53	99.10 105	99.90 22	94.59 77	99.99 39	99.77 37	99.91 71	99.99 24

SD-MVS		98.92 21	98.70 23	99.56 29	99.70 84	98.73 50	99.94 90	98.34 193	96.38 84	99.81 24	99.76 72	94.59 77	99.98 50	99.84 29	99.96 46	99.97 66
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 69	99.12 5	95.59 288	99.67 87	86.91 414	99.95 72	98.89 52	97.60 34	99.90 6	99.76 72	96.54 34	99.98 50	99.94 14	99.82 85	99.88 97

ACMMP_NAP		98.49 45	98.14 59	99.54 31	99.66 88	98.62 59	99.85 144	98.37 186	94.68 138	99.53 72	99.83 50	92.87 136	100.00 1	98.66 113	99.84 80	99.99 24

DeepPCF-MVS	95.94 2	97.71 107	98.98 13	93.92 356	99.63 89	81.76 449	99.96 53	98.56 112	99.47 1	99.19 101	99.99 1	94.16 99	100.00 1	99.92 16	99.93 65	100.00 1

EPNet		98.49 45	98.40 39	98.77 104	99.62 90	96.80 145	99.90 114	99.51 16	97.60 34	99.20 99	99.36 151	93.71 112	99.91 110	97.99 154	98.71 164	99.61 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		98.83 24	98.53 33	99.76 10	99.59 91	99.33 8	99.99 5	99.76 6	98.39 4	99.39 88	99.80 58	90.49 192	99.96 75	99.89 21	99.43 129	99.98 56

PVSNet_BlendedMVS		96.05 195	95.82 189	96.72 252	99.59 91	96.99 135	99.95 72	99.10 34	94.06 171	98.27 157	95.80 358	89.00 215	99.95 84	99.12 78	87.53 350	93.24 415

PVSNet_Blended		97.94 82	97.64 96	98.83 99	99.59 91	96.99 135	100.00 1	99.10 34	95.38 116	98.27 157	99.08 184	89.00 215	99.95 84	99.12 78	99.25 140	99.57 157

PatchMatch-RL		96.04 196	95.40 205	97.95 168	99.59 91	95.22 223	99.52 252	99.07 37	93.96 176	96.49 224	98.35 273	82.28 312	99.82 141	90.15 338	99.22 143	98.81 264

dcpmvs_2		97.42 121	98.09 63	95.42 295	99.58 95	87.24 410	99.23 299	96.95 387	94.28 161	98.93 118	99.73 91	94.39 87	99.16 205	99.89 21	99.82 85	99.86 101

test222					99.55 96	97.41 116	99.34 282	98.55 118	91.86 278	99.27 97	99.83 50	93.84 109			99.95 54	99.99 24

CNLPA		97.76 101	97.38 110	98.92 96	99.53 97	96.84 140	99.87 130	98.14 227	93.78 185	96.55 222	99.69 104	92.28 157	99.98 50	97.13 187	99.44 128	99.93 87

API-MVS		97.86 88	97.66 94	98.47 134	99.52 98	95.41 208	99.47 262	98.87 58	91.68 284	98.84 121	99.85 37	92.34 156	99.99 39	98.44 126	99.96 46	100.00 1

PVSNet	91.05 13	97.13 135	96.69 145	98.45 137	99.52 98	95.81 187	99.95 72	99.65 12	94.73 135	99.04 113	99.21 173	84.48 292	99.95 84	94.92 241	98.74 163	99.58 155

114514_t		97.41 122	96.83 136	99.14 72	99.51 100	97.83 93	99.89 124	98.27 205	88.48 368	99.06 112	99.66 115	90.30 195	99.64 172	96.32 216	99.97 42	99.96 74

cl22		93.77 280	93.25 284	95.33 299	99.49 101	94.43 249	99.61 231	98.09 230	90.38 328	89.16 355	95.61 366	90.56 190	97.34 340	91.93 305	84.45 372	94.21 358

testdata				98.42 141	99.47 102	95.33 214		98.56 112	93.78 185	99.79 35	99.85 37	93.64 114	99.94 93	94.97 239	99.94 59	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 157	99.47 102	94.79 238	99.05 321	98.76 73	92.65 241	98.66 135	99.82 53	88.52 221	99.98 50	98.12 145	99.63 99	99.67 129
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		94.54 252	93.42 274	97.91 174	99.46 104	94.04 265	98.93 339	97.48 302	81.15 441	90.04 326	99.55 131	87.02 244	99.95 84	88.97 352	98.11 185	99.73 119

MVS_111021_LR		98.42 52	98.38 41	98.53 129	99.39 105	95.79 188	99.87 130	99.86 2	96.70 70	98.78 125	99.79 62	92.03 164	99.90 112	99.17 77	99.86 79	99.88 97

CHOSEN 280x420		99.01 17	99.03 10	98.95 94	99.38 106	98.87 34	98.46 379	99.42 21	97.03 57	99.02 114	99.09 183	99.35 2	98.21 302	99.73 45	99.78 88	99.77 115

MVS_111021_HR		98.72 31	98.62 29	99.01 88	99.36 107	97.18 124	99.93 97	99.90 1	96.81 67	98.67 134	99.77 70	93.92 104	99.89 117	99.27 73	99.94 59	99.96 74

fmvsm_s_conf0.5_n_11		98.03 79	97.89 82	98.46 136	99.35 108	97.76 97	99.99 5	98.04 236	98.20 9	99.90 6	99.78 66	86.21 258	99.95 84	99.89 21	99.68 94	97.65 302

DPM-MVS		98.83 24	98.46 36	99.97 1	99.33 109	99.92 1	99.96 53	98.44 147	97.96 23	99.55 69	99.94 4	97.18 23	100.00 1	93.81 272	99.94 59	99.98 56

TAPA-MVS	92.12 8	94.42 260	93.60 266	96.90 245	99.33 109	91.78 329	99.78 170	98.00 239	89.89 341	94.52 269	99.47 137	91.97 165	99.18 202	69.90 460	99.52 114	99.73 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 224	95.07 222	96.32 267	99.32 111	96.60 154	99.76 181	98.85 62	96.65 72	87.83 379	96.05 355	99.52 1	98.11 307	96.58 211	81.07 401	94.25 352

fmvsm_s_conf0.5_n_9		98.15 73	98.02 68	98.55 123	99.28 112	95.84 186	99.99 5	98.57 106	98.17 13	99.93 2	99.74 87	87.04 243	99.97 63	99.86 27	99.59 108	99.83 104

SPE-MVS-test		97.88 86	97.94 77	97.70 192	99.28 112	95.20 224	99.98 21	97.15 353	95.53 113	99.62 60	99.79 62	92.08 163	98.38 285	98.75 107	99.28 139	99.52 169

test_fmvsm_n_1920		98.44 49	98.61 30	97.92 172	99.27 114	95.18 225	100.00 1	98.90 50	98.05 20	99.80 26	99.73 91	92.64 144	99.99 39	99.58 57	99.51 117	98.59 274

fmvsm_s_conf0.5_n_10		98.24 69	97.90 80	99.26 54	99.24 115	97.88 91	99.99 5	98.76 73	98.20 9	99.92 4	99.74 87	85.97 262	99.94 93	99.72 46	99.53 113	99.96 74

fmvsm_l_conf0.5_n_a		99.00 18	98.91 15	99.28 52	99.21 116	97.91 90	99.98 21	98.85 62	98.25 5	99.92 4	99.75 80	94.72 74	99.97 63	99.87 25	99.64 97	99.95 82

fmvsm_s_conf0.5_n_8		98.38 57	98.05 66	99.35 49	99.20 117	98.12 76	99.98 21	98.81 67	98.22 7	99.80 26	99.71 97	87.37 238	99.97 63	99.91 19	99.48 121	99.97 66

test_yl		97.83 92	97.37 111	99.21 59	99.18 118	97.98 85	99.64 224	99.27 27	91.43 293	97.88 174	98.99 199	95.84 45	99.84 137	98.82 101	95.32 277	99.79 111

DCV-MVSNet		97.83 92	97.37 111	99.21 59	99.18 118	97.98 85	99.64 224	99.27 27	91.43 293	97.88 174	98.99 199	95.84 45	99.84 137	98.82 101	95.32 277	99.79 111

fmvsm_l_conf0.5_n		98.94 19	98.84 20	99.25 55	99.17 120	97.81 95	99.98 21	98.86 59	98.25 5	99.90 6	99.76 72	94.21 97	99.97 63	99.87 25	99.52 114	99.98 56

DeepC-MVS	94.51 4	96.92 149	96.40 159	98.45 137	99.16 121	95.90 184	99.66 219	98.06 233	96.37 87	94.37 275	99.49 136	83.29 305	99.90 112	97.63 173	99.61 104	99.55 159
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 41	98.22 52	99.50 34	99.15 122	98.65 57	100.00 1	98.58 104	97.70 32	98.21 162	99.24 169	92.58 147	99.94 93	98.63 116	99.94 59	99.92 92
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 53	98.08 64	99.39 45	99.12 123	98.29 69	99.98 21	98.64 90	98.14 16	99.86 15	99.76 72	87.99 226	99.97 63	99.72 46	99.54 111	99.91 94

fmvsm_l_conf0.5_n_9		98.55 40	98.23 51	99.49 36	99.10 124	98.50 64	99.99 5	98.70 79	98.14 16	99.94 1	99.68 111	89.02 214	99.98 50	99.89 21	99.61 104	99.99 24

CS-MVS		97.79 99	97.91 79	97.43 219	99.10 124	94.42 250	99.99 5	97.10 365	95.07 122	99.68 50	99.75 80	92.95 134	98.34 289	98.38 128	99.14 145	99.54 163

Anonymous202405211		93.10 298	91.99 311	96.40 263	99.10 124	89.65 379	98.88 345	97.93 247	83.71 425	94.00 281	98.75 235	68.79 419	99.88 123	95.08 236	91.71 310	99.68 127

fmvsm_s_conf0.5_n		97.80 97	97.85 85	97.67 193	99.06 127	94.41 251	99.98 21	98.97 43	97.34 42	99.63 57	99.69 104	87.27 239	99.97 63	99.62 55	99.06 150	98.62 273

HyFIR lowres test		96.66 165	96.43 156	97.36 226	99.05 128	93.91 270	99.70 210	99.80 3	90.54 324	96.26 234	98.08 286	92.15 161	98.23 301	96.84 202	95.46 272	99.93 87

LFMVS		94.75 246	93.56 269	98.30 147	99.03 129	95.70 194	98.74 360	97.98 242	87.81 379	98.47 146	99.39 148	67.43 428	99.53 174	98.01 152	95.20 280	99.67 129

fmvsm_s_conf0.5_n_4		97.75 102	97.86 84	97.42 220	99.01 130	94.69 241	99.97 39	98.76 73	97.91 25	99.87 13	99.76 72	86.70 250	99.93 103	99.67 52	99.12 148	97.64 303

fmvsm_s_conf0.5_n_2		97.59 112	97.28 115	98.53 129	99.01 130	98.15 71	99.98 21	98.59 102	98.17 13	99.75 40	99.63 121	81.83 318	99.94 93	99.78 35	98.79 161	97.51 311

AllTest		92.48 314	91.64 317	95.00 308	99.01 130	88.43 397	98.94 337	96.82 401	86.50 395	88.71 360	98.47 268	74.73 394	99.88 123	85.39 393	96.18 247	96.71 317

TestCases				95.00 308	99.01 130	88.43 397		96.82 401	86.50 395	88.71 360	98.47 268	74.73 394	99.88 123	85.39 393	96.18 247	96.71 317

COLMAP_ROB	90.47 14	92.18 321	91.49 323	94.25 342	99.00 134	88.04 403	98.42 385	96.70 408	82.30 436	88.43 370	99.01 194	76.97 369	99.85 129	86.11 389	96.50 239	94.86 328
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 81	97.66 94	98.81 100	98.99 135	98.07 79	99.98 21	98.81 67	98.18 12	99.89 10	99.70 100	84.15 295	99.97 63	99.76 40	99.50 119	98.39 281

test_fmvs1		95.35 225	95.68 196	94.36 338	98.99 135	84.98 425	99.96 53	96.65 410	97.60 34	99.73 45	98.96 205	71.58 409	99.93 103	98.31 134	99.37 134	98.17 286

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 18	98.98 137	99.32 9	97.49 414	99.52 14	95.69 108	98.32 155	97.41 306	93.32 121	99.77 149	98.08 149	95.75 262	99.81 108

VNet		97.21 131	96.57 150	99.13 76	98.97 138	97.82 94	99.03 324	99.21 32	94.31 158	99.18 102	98.88 217	86.26 257	99.89 117	98.93 92	94.32 290	99.69 126

thres200		96.96 145	96.21 166	99.22 58	98.97 138	98.84 37	99.85 144	99.71 7	93.17 210	96.26 234	98.88 217	89.87 200	99.51 177	94.26 260	94.91 282	99.31 212

tfpn200view9		96.79 153	95.99 173	99.19 61	98.94 140	98.82 38	99.78 170	99.71 7	92.86 225	96.02 242	98.87 224	89.33 207	99.50 179	93.84 269	94.57 286	99.27 221

thres400		96.78 155	95.99 173	99.16 68	98.94 140	98.82 38	99.78 170	99.71 7	92.86 225	96.02 242	98.87 224	89.33 207	99.50 179	93.84 269	94.57 286	99.16 231

sasdasda		97.09 138	96.32 160	99.39 45	98.93 142	98.95 28	99.72 200	97.35 315	94.45 146	97.88 174	99.42 141	86.71 248	99.52 175	98.48 123	93.97 296	99.72 121

Anonymous20231211		89.86 372	88.44 380	94.13 347	98.93 142	90.68 357	98.54 376	98.26 206	76.28 454	86.73 393	95.54 370	70.60 415	97.56 333	90.82 325	80.27 410	94.15 367

canonicalmvs		97.09 138	96.32 160	99.39 45	98.93 142	98.95 28	99.72 200	97.35 315	94.45 146	97.88 174	99.42 141	86.71 248	99.52 175	98.48 123	93.97 296	99.72 121

SDMVSNet		94.80 241	93.96 256	97.33 229	98.92 145	95.42 207	99.59 236	98.99 40	92.41 256	92.55 299	97.85 297	75.81 384	98.93 220	97.90 160	91.62 311	97.64 303

sd_testset		93.55 287	92.83 291	95.74 286	98.92 145	90.89 353	98.24 393	98.85 62	92.41 256	92.55 299	97.85 297	71.07 414	98.68 254	93.93 266	91.62 311	97.64 303

EPNet_dtu		95.71 213	95.39 206	96.66 254	98.92 145	93.41 286	99.57 241	98.90 50	96.19 94	97.52 184	98.56 258	92.65 143	97.36 338	77.89 441	98.33 174	99.20 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 76	97.60 98	99.60 23	98.92 145	99.28 17	99.89 124	99.52 14	95.58 111	98.24 161	99.39 148	93.33 120	99.74 155	97.98 156	95.58 271	99.78 114

CHOSEN 1792x2688		96.81 152	96.53 151	97.64 196	98.91 149	93.07 292	99.65 220	99.80 3	95.64 109	95.39 259	98.86 226	84.35 294	99.90 112	96.98 194	99.16 144	99.95 82

thres100view900		96.74 160	95.92 185	99.18 62	98.90 150	98.77 46	99.74 189	99.71 7	92.59 245	95.84 246	98.86 226	89.25 209	99.50 179	93.84 269	94.57 286	99.27 221

thres600view7		96.69 163	95.87 188	99.14 72	98.90 150	98.78 45	99.74 189	99.71 7	92.59 245	95.84 246	98.86 226	89.25 209	99.50 179	93.44 282	94.50 289	99.16 231

MSDG		94.37 262	93.36 281	97.40 222	98.88 152	93.95 269	99.37 278	97.38 311	85.75 406	90.80 318	99.17 177	84.11 297	99.88 123	86.35 385	98.43 172	98.36 283

MGCFI-Net		97.00 143	96.22 165	99.34 50	98.86 153	98.80 40	99.67 218	97.30 327	94.31 158	97.77 180	99.41 145	86.36 255	99.50 179	98.38 128	93.90 298	99.72 121

h-mvs33		94.92 238	94.36 242	96.59 256	98.85 154	91.29 345	98.93 339	98.94 44	95.90 99	98.77 127	98.42 271	90.89 185	99.77 149	97.80 164	70.76 450	98.72 270

Anonymous20240529		92.10 322	90.65 334	96.47 258	98.82 155	90.61 359	98.72 362	98.67 86	75.54 458	93.90 283	98.58 256	66.23 432	99.90 112	94.70 250	90.67 314	98.90 260

PVSNet_Blended_VisFu		97.27 127	96.81 138	98.66 112	98.81 156	96.67 150	99.92 100	98.64 90	94.51 143	96.38 232	98.49 264	89.05 213	99.88 123	97.10 189	98.34 173	99.43 190

PS-MVSNAJ		98.44 49	98.20 54	99.16 68	98.80 157	98.92 30	99.54 250	98.17 218	97.34 42	99.85 18	99.85 37	91.20 174	99.89 117	99.41 68	99.67 95	98.69 271

CANet_DTU		96.76 156	96.15 168	98.60 117	98.78 158	97.53 107	99.84 149	97.63 280	97.25 50	99.20 99	99.64 118	81.36 324	99.98 50	92.77 293	98.89 155	98.28 285

mvsany_test1		97.82 95	97.90 80	97.55 207	98.77 159	93.04 295	99.80 166	97.93 247	96.95 61	99.61 67	99.68 111	90.92 182	99.83 139	99.18 76	98.29 178	99.80 110

alignmvs		97.81 96	97.33 113	99.25 55	98.77 159	98.66 55	99.99 5	98.44 147	94.40 154	98.41 150	99.47 137	93.65 113	99.42 189	98.57 117	94.26 292	99.67 129

SymmetryMVS		97.64 110	97.46 104	98.17 153	98.74 161	95.39 210	99.61 231	99.26 29	96.52 76	98.61 138	99.31 156	92.73 141	99.67 167	96.77 205	95.63 269	99.45 186

SteuartSystems-ACMMP		99.02 16	98.97 14	99.18 62	98.72 162	97.71 99	99.98 21	98.44 147	96.85 62	99.80 26	99.91 18	97.57 8	99.85 129	99.44 66	99.99 21	99.99 24
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 71	97.97 72	99.02 87	98.69 163	98.66 55	99.52 252	98.08 232	97.05 56	99.86 15	99.86 33	90.65 187	99.71 159	99.39 70	98.63 165	98.69 271

miper_enhance_ethall		94.36 264	93.98 255	95.49 289	98.68 164	95.24 221	99.73 196	97.29 333	93.28 205	89.86 331	95.97 356	94.37 88	97.05 361	92.20 297	84.45 372	94.19 359

fmvsm_s_conf0.5_n_5		98.08 77	97.71 92	99.17 65	98.67 165	97.69 103	99.99 5	98.57 106	97.40 40	99.89 10	99.69 104	85.99 261	99.96 75	99.80 32	99.40 132	99.85 102

ETVMVS		97.03 142	96.64 146	98.20 152	98.67 165	97.12 128	99.89 124	98.57 106	91.10 305	98.17 163	98.59 253	93.86 108	98.19 303	95.64 228	95.24 279	99.28 219

test2506		97.53 114	97.19 120	98.58 121	98.66 167	96.90 139	98.81 354	99.77 5	94.93 125	97.95 169	98.96 205	92.51 150	99.20 200	94.93 240	98.15 182	99.64 135

ECVR-MVS		95.66 216	95.05 223	97.51 212	98.66 167	93.71 274	98.85 351	98.45 142	94.93 125	96.86 210	98.96 205	75.22 390	99.20 200	95.34 230	98.15 182	99.64 135

mamv4		95.24 228	96.90 131	90.25 418	98.65 169	72.11 467	98.28 390	97.64 279	89.99 339	95.93 244	98.25 281	94.74 73	99.11 206	99.01 89	99.64 97	99.53 167

balanced_conf03		98.27 63	97.99 70	99.11 77	98.64 170	98.43 67	99.47 262	97.79 262	94.56 141	99.74 43	98.35 273	94.33 91	99.25 194	99.12 78	99.96 46	99.64 135

fmvsm_s_conf0.5_n_a		97.73 105	97.72 90	97.77 186	98.63 171	94.26 258	99.96 53	98.92 49	97.18 52	99.75 40	99.69 104	87.00 245	99.97 63	99.46 64	98.89 155	99.08 241

MVSMamba_PlusPlus		97.83 92	97.45 106	98.99 89	98.60 172	98.15 71	99.58 238	97.74 270	90.34 331	99.26 98	98.32 276	94.29 93	99.23 195	99.03 87	99.89 74	99.58 155

testing222		97.08 141	96.75 141	98.06 163	98.56 173	96.82 141	99.85 144	98.61 98	92.53 251	98.84 121	98.84 230	93.36 118	98.30 293	95.84 224	94.30 291	99.05 245

test1111		95.57 219	94.98 226	97.37 224	98.56 173	93.37 289	98.86 349	98.45 142	94.95 124	96.63 216	98.95 210	75.21 391	99.11 206	95.02 237	98.14 184	99.64 135

MVSTER		95.53 220	95.22 215	96.45 261	98.56 173	97.72 98	99.91 108	97.67 275	92.38 259	91.39 309	97.14 313	97.24 20	97.30 345	94.80 246	87.85 343	94.34 347

testing3-2		97.72 106	97.43 109	98.60 117	98.55 176	97.11 130	100.00 1	99.23 31	93.78 185	97.90 171	98.73 237	95.50 52	99.69 163	98.53 121	94.63 284	98.99 251

VDD-MVS		93.77 280	92.94 289	96.27 268	98.55 176	90.22 368	98.77 359	97.79 262	90.85 311	96.82 212	99.42 141	61.18 452	99.77 149	98.95 90	94.13 293	98.82 263

tpmvs		94.28 266	93.57 268	96.40 263	98.55 176	91.50 343	95.70 452	98.55 118	87.47 381	92.15 302	94.26 422	91.42 170	98.95 219	88.15 364	95.85 258	98.76 266

UGNet		95.33 226	94.57 238	97.62 200	98.55 176	94.85 233	98.67 368	99.32 26	95.75 106	96.80 213	96.27 345	72.18 406	99.96 75	94.58 253	99.05 151	98.04 291
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	95.18 230	94.10 249	98.43 139	98.55 176	95.99 182	97.91 407	97.31 326	90.35 330	89.48 344	99.22 170	85.19 277	99.89 117	90.40 335	98.47 171	99.41 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 199	96.49 152	94.34 339	98.51 181	89.99 373	99.39 274	98.57 106	93.14 212	97.33 193	98.31 278	93.44 116	94.68 441	93.69 279	95.98 252	98.34 284

UWE-MVS		96.79 153	96.72 143	97.00 240	98.51 181	93.70 275	99.71 203	98.60 100	92.96 220	97.09 200	98.34 275	96.67 33	98.85 226	92.11 303	96.50 239	98.44 279

myMVS_eth3d28		97.86 88	97.59 100	98.68 109	98.50 183	97.26 120	99.92 100	98.55 118	93.79 184	98.26 159	98.75 235	95.20 57	99.48 185	98.93 92	96.40 242	99.29 217

test_vis1_n_1920		95.44 222	95.31 211	95.82 283	98.50 183	88.74 391	99.98 21	97.30 327	97.84 28	99.85 18	99.19 175	66.82 430	99.97 63	98.82 101	99.46 126	98.76 266

BH-w/o		95.71 213	95.38 209	96.68 253	98.49 185	92.28 314	99.84 149	97.50 300	92.12 269	92.06 305	98.79 233	84.69 288	98.67 256	95.29 232	99.66 96	99.09 239

baseline1		95.78 209	94.86 229	98.54 127	98.47 186	98.07 79	99.06 317	97.99 240	92.68 239	94.13 280	98.62 250	93.28 124	98.69 253	93.79 274	85.76 359	98.84 262

fmvsm_s_conf0.5_n_7		97.70 108	97.74 89	97.59 205	98.44 187	95.16 227	99.97 39	98.65 87	97.95 24	99.62 60	99.78 66	86.09 259	99.94 93	99.69 50	99.50 119	97.66 301

EPMVS		96.53 172	96.01 172	98.09 161	98.43 188	96.12 180	96.36 439	99.43 20	93.53 193	97.64 182	95.04 398	94.41 83	98.38 285	91.13 316	98.11 185	99.75 117

kuosan		93.17 295	92.60 297	94.86 315	98.40 189	89.54 381	98.44 381	98.53 125	84.46 420	88.49 366	97.92 294	90.57 189	97.05 361	83.10 410	93.49 301	97.99 292

WBMVS		94.52 255	94.03 253	95.98 274	98.38 190	96.68 149	99.92 100	97.63 280	90.75 320	89.64 339	95.25 391	96.77 27	96.90 373	94.35 258	83.57 379	94.35 345

UBG		97.84 91	97.69 93	98.29 148	98.38 190	96.59 156	99.90 114	98.53 125	93.91 180	98.52 142	98.42 271	96.77 27	99.17 203	98.54 119	96.20 246	99.11 238

sss		97.57 113	97.03 127	99.18 62	98.37 192	98.04 82	99.73 196	99.38 22	93.46 197	98.76 130	99.06 188	91.21 173	99.89 117	96.33 215	97.01 227	99.62 142

testing11		97.48 116	97.27 116	98.10 160	98.36 193	96.02 181	99.92 100	98.45 142	93.45 199	98.15 164	98.70 240	95.48 53	99.22 196	97.85 162	95.05 281	99.07 242

BH-untuned		95.18 230	94.83 230	96.22 269	98.36 193	91.22 346	99.80 166	97.32 325	90.91 309	91.08 312	98.67 242	83.51 300	98.54 267	94.23 261	99.61 104	98.92 257

testing91		97.16 133	96.90 131	97.97 167	98.35 195	95.67 197	99.91 108	98.42 167	92.91 223	97.33 193	98.72 238	94.81 71	99.21 197	96.98 194	94.63 284	99.03 248

testing99		97.17 132	96.91 130	97.95 168	98.35 195	95.70 194	99.91 108	98.43 155	92.94 221	97.36 191	98.72 238	94.83 70	99.21 197	97.00 192	94.64 283	98.95 253

ET-MVSNet_ETH3D		94.37 262	93.28 283	97.64 196	98.30 197	97.99 84	99.99 5	97.61 286	94.35 155	71.57 465	99.45 140	96.23 38	95.34 431	96.91 200	85.14 366	99.59 149

AUN-MVS		93.28 292	92.60 297	95.34 298	98.29 198	90.09 371	99.31 287	98.56 112	91.80 282	96.35 233	98.00 289	89.38 206	98.28 296	92.46 294	69.22 456	97.64 303

FMVSNet3		92.69 309	91.58 319	95.99 273	98.29 198	97.42 115	99.26 297	97.62 283	89.80 342	89.68 335	95.32 385	81.62 322	96.27 407	87.01 381	85.65 360	94.29 349

PMMVS		96.76 156	96.76 140	96.76 250	98.28 200	92.10 318	99.91 108	97.98 242	94.12 166	99.53 72	99.39 148	86.93 246	98.73 246	96.95 197	97.73 193	99.45 186

hse-mvs2		94.38 261	94.08 252	95.31 300	98.27 201	90.02 372	99.29 293	98.56 112	95.90 99	98.77 127	98.00 289	90.89 185	98.26 300	97.80 164	69.20 457	97.64 303

PVSNet_0	88.03 19	91.80 329	90.27 343	96.38 265	98.27 201	90.46 363	99.94 90	99.61 13	93.99 174	86.26 403	97.39 308	71.13 413	99.89 117	98.77 105	67.05 463	98.79 265

UA-Net		96.54 171	95.96 179	98.27 149	98.23 203	95.71 193	98.00 405	98.45 142	93.72 189	98.41 150	99.27 162	88.71 220	99.66 170	91.19 315	97.69 194	99.44 189

test_cas_vis1_n_1920		96.59 168	96.23 163	97.65 195	98.22 204	94.23 259	99.99 5	97.25 338	97.77 29	99.58 68	99.08 184	77.10 364	99.97 63	97.64 172	99.45 127	98.74 268

FE-MVS		95.70 215	95.01 225	97.79 182	98.21 205	94.57 243	95.03 453	98.69 81	88.90 358	97.50 186	96.19 347	92.60 146	99.49 184	89.99 340	97.94 191	99.31 212

GG-mvs-BLEND				98.54 127	98.21 205	98.01 83	93.87 458	98.52 127		97.92 170	97.92 294	99.02 3	97.94 320	98.17 142	99.58 109	99.67 129

mvs_anonymous		95.65 217	95.03 224	97.53 209	98.19 207	95.74 191	99.33 283	97.49 301	90.87 310	90.47 321	97.10 315	88.23 223	97.16 352	95.92 222	97.66 197	99.68 127

MVS_Test		96.46 174	95.74 192	98.61 116	98.18 208	97.23 122	99.31 287	97.15 353	91.07 306	98.84 121	97.05 319	88.17 224	98.97 216	94.39 255	97.50 199	99.61 146

BH-RMVSNet		95.18 230	94.31 245	97.80 180	98.17 209	95.23 222	99.76 181	97.53 296	92.52 252	94.27 278	99.25 168	76.84 371	98.80 236	90.89 324	99.54 111	99.35 203

dongtai		91.55 335	91.13 328	92.82 386	98.16 210	86.35 415	99.47 262	98.51 130	83.24 428	85.07 413	97.56 302	90.33 194	94.94 437	76.09 449	91.73 309	97.18 314

RPSCF		91.80 329	92.79 293	88.83 430	98.15 211	69.87 469	98.11 401	96.60 412	83.93 423	94.33 276	99.27 162	79.60 346	99.46 188	91.99 304	93.16 306	97.18 314

ETV-MVS		97.92 84	97.80 88	98.25 150	98.14 212	96.48 158	99.98 21	97.63 280	95.61 110	99.29 95	99.46 139	92.55 148	98.82 230	99.02 88	98.54 169	99.46 181

IS-MVSNet		96.29 187	95.90 186	97.45 215	98.13 213	94.80 237	99.08 312	97.61 286	92.02 274	95.54 257	98.96 205	90.64 188	98.08 309	93.73 277	97.41 203	99.47 179

test_fmvsmconf_n		98.43 51	98.32 47	98.78 102	98.12 214	96.41 161	99.99 5	98.83 66	98.22 7	99.67 51	99.64 118	91.11 178	99.94 93	99.67 52	99.62 100	99.98 56

fmvsm_s_conf0.1_n_2		97.25 128	96.85 135	98.43 139	98.08 215	98.08 78	99.92 100	97.76 269	98.05 20	99.65 53	99.58 127	80.88 331	99.93 103	99.59 56	98.17 180	97.29 312

ab-mvs		94.69 247	93.42 274	98.51 132	98.07 216	96.26 168	96.49 437	98.68 83	90.31 332	94.54 268	97.00 321	76.30 379	99.71 159	95.98 221	93.38 304	99.56 158

XVG-OURS-SEG-HR		94.79 242	94.70 237	95.08 305	98.05 217	89.19 383	99.08 312	97.54 294	93.66 190	94.87 266	99.58 127	78.78 354	99.79 144	97.31 180	93.40 303	96.25 321

EIA-MVS		97.53 114	97.46 104	97.76 188	98.04 218	94.84 234	99.98 21	97.61 286	94.41 153	97.90 171	99.59 124	92.40 154	98.87 224	98.04 151	99.13 146	99.59 149

XVG-OURS		94.82 239	94.74 236	95.06 306	98.00 219	89.19 383	99.08 312	97.55 292	94.10 167	94.71 267	99.62 122	80.51 337	99.74 155	96.04 220	93.06 308	96.25 321

mvsmamba		96.94 146	96.73 142	97.55 207	97.99 220	94.37 255	99.62 227	97.70 272	93.13 213	98.42 149	97.92 294	88.02 225	98.75 244	98.78 104	99.01 152	99.52 169

dp		95.05 233	94.43 240	96.91 243	97.99 220	92.73 303	96.29 442	97.98 242	89.70 343	95.93 244	94.67 412	93.83 110	98.45 273	86.91 384	96.53 238	99.54 163

tpmrst		96.27 189	95.98 175	97.13 235	97.96 222	93.15 291	96.34 440	98.17 218	92.07 270	98.71 133	95.12 395	93.91 105	98.73 246	94.91 243	96.62 236	99.50 175

TR-MVS		94.54 252	93.56 269	97.49 214	97.96 222	94.34 256	98.71 363	97.51 299	90.30 333	94.51 270	98.69 241	75.56 385	98.77 240	92.82 292	95.99 251	99.35 203

Vis-MVSNet (Re-imp)		96.32 184	95.98 175	97.35 228	97.93 224	94.82 236	99.47 262	98.15 226	91.83 279	95.09 264	99.11 182	91.37 172	97.47 336	93.47 281	97.43 200	99.74 118

MDTV_nov1_ep13			95.69 194		97.90 225	94.15 262	95.98 448	98.44 147	93.12 214	97.98 168	95.74 360	95.10 60	98.58 263	90.02 339	96.92 229

Fast-Effi-MVS+		95.02 235	94.19 247	97.52 211	97.88 226	94.55 244	99.97 39	97.08 369	88.85 360	94.47 271	97.96 293	84.59 289	98.41 277	89.84 342	97.10 220	99.59 149

ADS-MVSNet2		93.80 279	93.88 259	93.55 369	97.87 227	85.94 419	94.24 454	96.84 398	90.07 336	96.43 229	94.48 417	90.29 196	95.37 430	87.44 371	97.23 211	99.36 199

ADS-MVSNet		94.79 242	94.02 254	97.11 237	97.87 227	93.79 271	94.24 454	98.16 223	90.07 336	96.43 229	94.48 417	90.29 196	98.19 303	87.44 371	97.23 211	99.36 199

Effi-MVS+		96.30 186	95.69 194	98.16 154	97.85 229	96.26 168	97.41 417	97.21 345	90.37 329	98.65 136	98.58 256	86.61 252	98.70 252	97.11 188	97.37 205	99.52 169

PatchmatchNet		95.94 200	95.45 202	97.39 223	97.83 230	94.41 251	96.05 446	98.40 176	92.86 225	97.09 200	95.28 390	94.21 97	98.07 311	89.26 350	98.11 185	99.70 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 250	93.61 264	97.74 190	97.82 231	96.26 168	99.96 53	97.78 265	85.76 404	94.00 281	97.54 303	76.95 370	99.21 197	97.23 185	95.43 274	97.76 300

1112_ss		96.01 197	95.20 216	98.42 141	97.80 232	96.41 161	99.65 220	96.66 409	92.71 236	92.88 295	99.40 146	92.16 160	99.30 192	91.92 306	93.66 299	99.55 159

E3new		96.75 158	96.43 156	97.71 191	97.79 233	94.83 235	99.80 166	97.33 319	93.52 195	97.49 187	99.31 156	87.73 228	98.83 227	97.52 175	97.40 204	99.48 178

Test_1112_low_res		95.72 211	94.83 230	98.42 141	97.79 233	96.41 161	99.65 220	96.65 410	92.70 237	92.86 296	96.13 351	92.15 161	99.30 192	91.88 307	93.64 300	99.55 159

Effi-MVS+-dtu		94.53 254	95.30 212	92.22 394	97.77 235	82.54 442	99.59 236	97.06 374	94.92 127	95.29 261	95.37 383	85.81 263	97.89 321	94.80 246	97.07 221	96.23 323

tpm cat1		93.51 288	92.52 303	96.47 258	97.77 235	91.47 344	96.13 444	98.06 233	80.98 442	92.91 294	93.78 426	89.66 201	98.87 224	87.03 380	96.39 243	99.09 239

FA-MVS(test-final)		95.86 203	95.09 221	98.15 157	97.74 237	95.62 199	96.31 441	98.17 218	91.42 295	96.26 234	96.13 351	90.56 190	99.47 187	92.18 298	97.07 221	99.35 203

xiu_mvs_v1_base_debu		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 287	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 296

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 287	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 296

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 287	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 296

EPP-MVSNet		96.69 163	96.60 148	96.96 242	97.74 237	93.05 294	99.37 278	98.56 112	88.75 362	95.83 248	99.01 194	96.01 39	98.56 265	96.92 198	97.20 213	99.25 224

gg-mvs-nofinetune		93.51 288	91.86 315	98.47 134	97.72 242	97.96 88	92.62 464	98.51 130	74.70 461	97.33 193	69.59 479	98.91 4	97.79 324	97.77 169	99.56 110	99.67 129

IB-MVS	92.85 6	94.99 236	93.94 257	98.16 154	97.72 242	95.69 196	99.99 5	98.81 67	94.28 161	92.70 297	96.90 323	95.08 61	99.17 203	96.07 219	73.88 442	99.60 148
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
thisisatest0515		97.41 122	97.02 128	98.59 120	97.71 244	97.52 108	99.97 39	98.54 122	91.83 279	97.45 188	99.04 190	97.50 9	99.10 208	94.75 248	96.37 244	99.16 231

VortexMVS		94.11 268	93.50 271	95.94 276	97.70 245	96.61 153	99.35 281	97.18 348	93.52 195	89.57 342	95.74 360	87.55 233	96.97 369	95.76 227	85.13 367	94.23 354

viewdifsd2359ckpt09		96.21 191	95.77 190	97.53 209	97.69 246	94.50 247	99.78 170	97.23 343	92.88 224	96.58 219	99.26 166	84.85 282	98.66 259	96.61 209	97.02 226	99.43 190

Syy-MVS		90.00 370	90.63 335	88.11 437	97.68 247	74.66 465	99.71 203	98.35 189	90.79 317	92.10 303	98.67 242	79.10 352	93.09 457	63.35 472	95.95 255	96.59 319

myMVS_eth3d		94.46 259	94.76 235	93.55 369	97.68 247	90.97 348	99.71 203	98.35 189	90.79 317	92.10 303	98.67 242	92.46 153	93.09 457	87.13 377	95.95 255	96.59 319

test_fmvs1_n		94.25 267	94.36 242	93.92 356	97.68 247	83.70 432	99.90 114	96.57 413	97.40 40	99.67 51	98.88 217	61.82 449	99.92 109	98.23 140	99.13 146	98.14 289

fmvsm_s_conf0.5_n_6		98.27 63	97.96 75	99.23 57	97.66 250	98.11 77	99.98 21	98.64 90	97.85 27	99.87 13	99.72 94	88.86 217	99.93 103	99.64 54	99.36 135	99.63 141

RRT-MVS		96.24 190	95.68 196	97.94 171	97.65 251	94.92 232	99.27 296	97.10 365	92.79 231	97.43 189	97.99 291	81.85 317	99.37 191	98.46 125	98.57 166	99.53 167

diffmvs		97.00 143	96.64 146	98.09 161	97.64 252	96.17 177	99.81 162	97.19 346	94.67 139	98.95 116	99.28 159	86.43 253	98.76 242	98.37 130	97.42 202	99.33 206
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		96.59 168	96.23 163	97.66 194	97.63 253	94.70 240	99.77 175	97.33 319	93.41 200	97.34 192	99.17 177	86.72 247	98.83 227	97.40 178	97.32 208	99.46 181

viewdifsd2359ckpt13		96.19 192	95.77 190	97.45 215	97.62 254	94.40 253	99.70 210	97.23 343	92.76 233	96.63 216	99.05 189	84.96 281	98.64 260	96.65 208	97.35 206	99.31 212

Vis-MVSNet		95.72 211	95.15 219	97.45 215	97.62 254	94.28 257	99.28 294	98.24 209	94.27 163	96.84 211	98.94 212	79.39 347	98.76 242	93.25 283	98.49 170	99.30 215
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 136	96.72 143	98.22 151	97.60 256	96.70 146	99.92 100	98.54 122	91.11 304	97.07 202	98.97 203	97.47 12	99.03 211	93.73 277	96.09 249	98.92 257

GDP-MVS		97.88 86	97.59 100	98.75 105	97.59 257	97.81 95	99.95 72	97.37 314	94.44 149	99.08 107	99.58 127	97.13 25	99.08 209	94.99 238	98.17 180	99.37 197

miper_ehance_all_eth		93.16 296	92.60 297	94.82 316	97.57 258	93.56 281	99.50 256	97.07 373	88.75 362	88.85 359	95.52 372	90.97 181	96.74 383	90.77 326	84.45 372	94.17 361

guyue		97.15 134	96.82 137	98.15 157	97.56 259	96.25 172	99.71 203	97.84 259	95.75 106	98.13 165	98.65 245	87.58 232	98.82 230	98.29 136	97.91 192	99.36 199

viewmanbaseed2359cas		96.45 175	96.07 169	97.59 205	97.55 260	94.59 242	99.70 210	97.33 319	93.62 192	97.00 206	99.32 153	85.57 269	98.71 249	97.26 184	97.33 207	99.47 179

testing3		93.92 273	94.23 246	92.99 383	97.54 261	90.23 367	99.99 5	99.16 33	90.57 323	91.33 311	98.63 249	92.99 132	92.52 461	82.46 414	95.39 275	96.22 324

SSM_0404		95.75 210	95.16 218	97.50 213	97.53 262	95.39 210	99.11 308	97.25 338	90.81 313	95.27 262	98.83 231	84.74 285	98.67 256	95.24 233	97.69 194	98.45 278

LCM-MVSNet-Re		92.31 318	92.60 297	91.43 403	97.53 262	79.27 459	99.02 326	91.83 474	92.07 270	80.31 437	94.38 420	83.50 301	95.48 427	97.22 186	97.58 198	99.54 163

GBi-Net		90.88 346	89.82 352	94.08 348	97.53 262	91.97 319	98.43 382	96.95 387	87.05 387	89.68 335	94.72 408	71.34 410	96.11 413	87.01 381	85.65 360	94.17 361

test1		90.88 346	89.82 352	94.08 348	97.53 262	91.97 319	98.43 382	96.95 387	87.05 387	89.68 335	94.72 408	71.34 410	96.11 413	87.01 381	85.65 360	94.17 361

FMVSNet2		91.02 343	89.56 357	95.41 296	97.53 262	95.74 191	98.98 329	97.41 309	87.05 387	88.43 370	95.00 402	71.34 410	96.24 409	85.12 396	85.21 365	94.25 352

tttt0517		96.85 150	96.49 152	97.92 172	97.48 267	95.89 185	99.85 144	98.54 122	90.72 321	96.63 216	98.93 215	97.47 12	99.02 212	93.03 290	95.76 261	98.85 261

BP-MVS1		98.33 59	98.18 56	98.81 100	97.44 268	97.98 85	99.96 53	98.17 218	94.88 129	98.77 127	99.59 124	97.59 7	99.08 209	98.24 139	98.93 154	99.36 199

casdiffmvs_mvg		96.43 176	95.94 183	97.89 176	97.44 268	95.47 203	99.86 141	97.29 333	93.35 201	96.03 241	99.19 175	85.39 274	98.72 248	97.89 161	97.04 223	99.49 177
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E2		96.36 181	95.95 181	97.60 202	97.41 270	94.52 245	99.71 203	97.33 319	93.20 207	97.02 203	99.07 186	85.37 275	98.82 230	97.27 181	97.14 217	99.46 181

EC-MVSNet		97.38 124	97.24 117	97.80 180	97.41 270	95.64 198	99.99 5	97.06 374	94.59 140	99.63 57	99.32 153	89.20 212	98.14 305	98.76 106	99.23 142	99.62 142

viewdifsd2359ckpt07		95.83 206	95.42 204	97.07 238	97.40 272	93.04 295	99.60 234	97.24 341	92.39 258	96.09 240	99.14 181	83.07 308	98.93 220	97.02 191	96.87 230	99.23 227

c3_l		92.53 313	91.87 314	94.52 328	97.40 272	92.99 297	99.40 270	96.93 392	87.86 377	88.69 362	95.44 377	89.95 199	96.44 399	90.45 332	80.69 406	94.14 370

viewmambaseed2359dif		95.92 202	95.55 200	97.04 239	97.38 274	93.41 286	99.78 170	96.97 385	91.14 303	96.58 219	99.27 162	84.85 282	98.75 244	96.87 201	97.12 219	98.97 252

fmvsm_s_conf0.1_n		97.30 125	97.21 119	97.60 202	97.38 274	94.40 253	99.90 114	98.64 90	96.47 80	99.51 76	99.65 117	84.99 280	99.93 103	99.22 75	99.09 149	98.46 277

E3		96.36 181	95.95 181	97.60 202	97.37 276	94.52 245	99.71 203	97.33 319	93.18 209	97.02 203	99.07 186	85.45 273	98.82 230	97.27 181	97.14 217	99.46 181

CDS-MVSNet		96.34 183	96.07 169	97.13 235	97.37 276	94.96 230	99.53 251	97.91 251	91.55 287	95.37 260	98.32 276	95.05 63	97.13 355	93.80 273	95.75 262	99.30 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 160	96.26 162	98.16 154	97.36 278	96.48 158	99.96 53	98.29 202	91.93 275	95.77 249	98.07 287	95.54 49	98.29 294	90.55 330	98.89 155	99.70 124

miper_lstm_enhance		91.81 326	91.39 325	93.06 382	97.34 279	89.18 385	99.38 276	96.79 403	86.70 394	87.47 385	95.22 392	90.00 198	95.86 422	88.26 361	81.37 395	94.15 367

baseline		96.43 176	95.98 175	97.76 188	97.34 279	95.17 226	99.51 254	97.17 350	93.92 179	96.90 209	99.28 159	85.37 275	98.64 260	97.50 176	96.86 232	99.46 181

cl____		92.31 318	91.58 319	94.52 328	97.33 281	92.77 299	99.57 241	96.78 404	86.97 391	87.56 383	95.51 373	89.43 205	96.62 389	88.60 355	82.44 387	94.16 366

SD_0403		92.63 312	93.38 278	90.40 417	97.32 282	77.91 461	97.75 412	98.03 238	91.89 276	90.83 317	98.29 280	82.00 314	93.79 450	88.51 359	95.75 262	99.52 169

DIV-MVS_self_test		92.32 317	91.60 318	94.47 332	97.31 283	92.74 301	99.58 238	96.75 405	86.99 390	87.64 381	95.54 370	89.55 204	96.50 394	88.58 356	82.44 387	94.17 361

casdiffmvs		96.42 178	95.97 178	97.77 186	97.30 284	94.98 229	99.84 149	97.09 368	93.75 188	96.58 219	99.26 166	85.07 278	98.78 239	97.77 169	97.04 223	99.54 163
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		94.36 264	93.48 272	96.99 241	97.29 285	93.54 282	99.96 53	96.72 407	88.35 371	93.43 285	98.94 212	82.05 313	98.05 312	88.12 366	96.48 241	99.37 197

eth_miper_zixun_eth		92.41 316	91.93 312	93.84 360	97.28 286	90.68 357	98.83 352	96.97 385	88.57 367	89.19 354	95.73 363	89.24 211	96.69 387	89.97 341	81.55 393	94.15 367

MVSFormer		96.94 146	96.60 148	97.95 168	97.28 286	97.70 101	99.55 248	97.27 335	91.17 300	99.43 82	99.54 133	90.92 182	96.89 374	94.67 251	99.62 100	99.25 224

lupinMVS		97.85 90	97.60 98	98.62 115	97.28 286	97.70 101	99.99 5	97.55 292	95.50 115	99.43 82	99.67 113	90.92 182	98.71 249	98.40 127	99.62 100	99.45 186

diffmvs_AUTHOR		96.75 158	96.41 158	97.79 182	97.20 289	95.46 204	99.69 213	97.15 353	94.46 145	98.78 125	99.21 173	85.64 267	98.77 240	98.27 137	97.31 209	99.13 235

mamba_0408		94.98 237	94.09 250	97.64 196	97.14 290	95.31 215	93.48 461	97.08 369	90.48 325	94.40 272	98.62 250	84.49 290	98.67 256	93.99 264	97.18 214	98.93 254

SSM_04072		94.77 244	94.09 250	96.82 247	97.14 290	95.31 215	93.48 461	97.08 369	90.48 325	94.40 272	98.62 250	84.49 290	96.21 410	93.99 264	97.18 214	98.93 254

SSM_0407		95.62 218	94.95 227	97.61 201	97.14 290	95.31 215	99.00 327	97.25 338	90.81 313	94.40 272	98.83 231	84.74 285	98.58 263	95.24 233	97.18 214	98.93 254

SCA		94.69 247	93.81 261	97.33 229	97.10 293	94.44 248	98.86 349	98.32 196	93.30 204	96.17 239	95.59 368	76.48 377	97.95 318	91.06 318	97.43 200	99.59 149

viewmacassd2359aftdt		95.93 201	95.45 202	97.36 226	97.09 294	94.12 264	99.57 241	97.26 337	93.05 218	96.50 223	99.17 177	82.76 309	98.68 254	96.61 209	97.04 223	99.28 219

KinetiMVS		96.10 193	95.29 213	98.53 129	97.08 295	97.12 128	99.56 245	98.12 229	94.78 132	98.44 147	98.94 212	80.30 341	99.39 190	91.56 311	98.79 161	99.06 243

TAMVS		95.85 204	95.58 198	96.65 255	97.07 296	93.50 283	99.17 304	97.82 261	91.39 297	95.02 265	98.01 288	92.20 159	97.30 345	93.75 276	95.83 259	99.14 234

Fast-Effi-MVS+-dtu		93.72 283	93.86 260	93.29 374	97.06 297	86.16 416	99.80 166	96.83 399	92.66 240	92.58 298	97.83 299	81.39 323	97.67 329	89.75 343	96.87 230	96.05 326

E4		96.01 197	95.53 201	97.44 218	97.05 298	94.23 259	99.57 241	97.30 327	92.72 234	96.47 225	99.03 191	83.98 298	98.83 227	96.92 198	96.77 233	99.27 221

E5		95.83 206	95.39 206	97.15 233	97.03 299	93.59 278	99.32 286	97.30 327	92.58 247	96.45 226	99.00 197	83.37 303	98.81 234	96.81 203	96.65 235	99.04 246

CostFormer		96.10 193	95.88 187	96.78 249	97.03 299	92.55 309	97.08 426	97.83 260	90.04 338	98.72 132	94.89 406	95.01 65	98.29 294	96.54 212	95.77 260	99.50 175

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 174	97.02 301	95.34 213	99.95 72	98.45 142	97.87 26	97.02 203	99.59 124	89.64 202	99.98 50	99.41 68	99.34 137	98.42 280

test-LLR		96.47 173	96.04 171	97.78 184	97.02 301	95.44 205	99.96 53	98.21 213	94.07 169	95.55 255	96.38 340	93.90 106	98.27 298	90.42 333	98.83 159	99.64 135

test-mter		96.39 179	95.93 184	97.78 184	97.02 301	95.44 205	99.96 53	98.21 213	91.81 281	95.55 255	96.38 340	95.17 58	98.27 298	90.42 333	98.83 159	99.64 135

E6		95.83 206	95.39 206	97.14 234	97.00 304	93.58 279	99.31 287	97.30 327	92.57 248	96.45 226	99.01 194	83.44 302	98.81 234	96.80 204	96.66 234	99.04 246

icg_test_0407_2		95.04 234	94.78 234	95.84 282	96.97 305	91.64 336	98.63 371	97.12 358	92.33 261	95.60 253	98.88 217	85.65 265	96.56 392	92.12 299	95.70 265	99.32 208

IMVS_0407		95.21 229	94.80 233	96.46 260	96.97 305	91.64 336	98.81 354	97.12 358	92.33 261	95.60 253	98.88 217	85.65 265	98.42 275	92.12 299	95.70 265	99.32 208

IMVS_0404		93.83 275	93.17 285	95.80 284	96.97 305	91.64 336	97.78 411	97.12 358	92.33 261	90.87 316	98.88 217	76.78 372	96.43 400	92.12 299	95.70 265	99.32 208

IMVS_0403		95.25 227	94.81 232	96.58 257	96.97 305	91.64 336	98.97 334	97.12 358	92.33 261	95.43 258	98.88 217	85.78 264	98.79 237	92.12 299	95.70 265	99.32 208

gm-plane-assit					96.97 305	93.76 273			91.47 291		98.96 205		98.79 237	94.92 241

WB-MVSnew		92.90 302	92.77 294	93.26 376	96.95 310	93.63 277	99.71 203	98.16 223	91.49 288	94.28 277	98.14 284	81.33 325	96.48 397	79.47 431	95.46 272	89.68 458

QAPM		95.40 223	94.17 248	99.10 78	96.92 311	97.71 99	99.40 270	98.68 83	89.31 346	88.94 358	98.89 216	82.48 311	99.96 75	93.12 289	99.83 81	99.62 142

KD-MVS_2432*1600		88.00 392	86.10 396	93.70 365	96.91 312	94.04 265	97.17 423	97.12 358	84.93 415	81.96 427	92.41 440	92.48 151	94.51 443	79.23 432	52.68 478	92.56 427

miper_refine_blended		88.00 392	86.10 396	93.70 365	96.91 312	94.04 265	97.17 423	97.12 358	84.93 415	81.96 427	92.41 440	92.48 151	94.51 443	79.23 432	52.68 478	92.56 427

tpm2		95.47 221	95.18 217	96.35 266	96.91 312	91.70 334	96.96 429	97.93 247	88.04 375	98.44 147	95.40 379	93.32 121	97.97 315	94.00 263	95.61 270	99.38 195

FMVSNet5		88.32 388	87.47 390	90.88 406	96.90 315	88.39 399	97.28 420	95.68 435	82.60 435	84.67 415	92.40 442	79.83 344	91.16 466	76.39 448	81.51 394	93.09 418

3Dnovator+	91.53 11	96.31 185	95.24 214	99.52 32	96.88 316	98.64 58	99.72 200	98.24 209	95.27 120	88.42 372	98.98 201	82.76 309	99.94 93	97.10 189	99.83 81	99.96 74

Patchmatch-test		92.65 311	91.50 322	96.10 272	96.85 317	90.49 362	91.50 469	97.19 346	82.76 434	90.23 322	95.59 368	95.02 64	98.00 314	77.41 443	96.98 228	99.82 106

MVS		96.60 167	95.56 199	99.72 14	96.85 317	99.22 21	98.31 388	98.94 44	91.57 286	90.90 315	99.61 123	86.66 251	99.96 75	97.36 179	99.88 77	99.99 24

3Dnovator	91.47 12	96.28 188	95.34 210	99.08 81	96.82 319	97.47 113	99.45 267	98.81 67	95.52 114	89.39 345	99.00 197	81.97 315	99.95 84	97.27 181	99.83 81	99.84 103

EI-MVSNet		93.73 282	93.40 277	94.74 317	96.80 320	92.69 304	99.06 317	97.67 275	88.96 355	91.39 309	99.02 192	88.75 219	97.30 345	91.07 317	87.85 343	94.22 356

CVMVSNet		94.68 249	94.94 228	93.89 359	96.80 320	86.92 413	99.06 317	98.98 41	94.45 146	94.23 279	99.02 192	85.60 268	95.31 432	90.91 323	95.39 275	99.43 190

IterMVS-LS		92.69 309	92.11 308	94.43 336	96.80 320	92.74 301	99.45 267	96.89 395	88.98 353	89.65 338	95.38 382	88.77 218	96.34 404	90.98 321	82.04 390	94.22 356
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 170	96.46 155	96.91 243	96.79 323	92.50 310	99.90 114	97.38 311	96.02 98	97.79 179	99.32 153	86.36 255	98.99 213	98.26 138	96.33 245	99.23 227

IterMVS		90.91 345	90.17 347	93.12 379	96.78 324	90.42 365	98.89 343	97.05 377	89.03 350	86.49 398	95.42 378	76.59 375	95.02 434	87.22 376	84.09 375	93.93 389
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 151	95.96 179	99.48 39	96.74 325	98.52 62	98.31 388	98.86 59	95.82 103	89.91 329	98.98 201	87.49 235	99.96 75	97.80 164	99.73 91	99.96 74

IterMVS-SCA-FT		90.85 348	90.16 348	92.93 384	96.72 326	89.96 374	98.89 343	96.99 381	88.95 356	86.63 395	95.67 364	76.48 377	95.00 435	87.04 379	84.04 378	93.84 396

MVS-HIRNet		86.22 400	83.19 413	95.31 300	96.71 327	90.29 366	92.12 466	97.33 319	62.85 473	86.82 392	70.37 478	69.37 418	97.49 335	75.12 451	97.99 190	98.15 287

viewdifsd2359ckpt11		94.09 270	93.63 263	95.46 293	96.68 328	88.92 388	99.62 227	97.12 358	93.07 216	95.73 250	99.22 170	77.05 365	98.88 223	96.52 213	87.69 348	98.58 275

viewmsd2359difaftdt		94.09 270	93.64 262	95.46 293	96.68 328	88.92 388	99.62 227	97.13 357	93.07 216	95.73 250	99.22 170	77.05 365	98.89 222	96.52 213	87.70 347	98.58 275

VDDNet		93.12 297	91.91 313	96.76 250	96.67 330	92.65 307	98.69 366	98.21 213	82.81 433	97.75 181	99.28 159	61.57 450	99.48 185	98.09 148	94.09 294	98.15 287

dmvs_re		93.20 294	93.15 286	93.34 372	96.54 331	83.81 431	98.71 363	98.51 130	91.39 297	92.37 301	98.56 258	78.66 356	97.83 323	93.89 267	89.74 315	98.38 282

Elysia		94.50 256	93.38 278	97.85 178	96.49 332	96.70 146	98.98 329	97.78 265	90.81 313	96.19 237	98.55 260	73.63 401	98.98 214	89.41 344	98.56 167	97.88 294

StellarMVS		94.50 256	93.38 278	97.85 178	96.49 332	96.70 146	98.98 329	97.78 265	90.81 313	96.19 237	98.55 260	73.63 401	98.98 214	89.41 344	98.56 167	97.88 294

MIMVSNet		90.30 361	88.67 376	95.17 304	96.45 334	91.64 336	92.39 465	97.15 353	85.99 401	90.50 320	93.19 434	66.95 429	94.86 439	82.01 418	93.43 302	99.01 250

CR-MVSNet		93.45 291	92.62 296	95.94 276	96.29 335	92.66 305	92.01 467	96.23 421	92.62 242	96.94 207	93.31 432	91.04 179	96.03 418	79.23 432	95.96 253	99.13 235

RPMNet		89.76 374	87.28 391	97.19 232	96.29 335	92.66 305	92.01 467	98.31 198	70.19 468	96.94 207	85.87 471	87.25 240	99.78 146	62.69 473	95.96 253	99.13 235

tt0805		91.28 338	90.18 346	94.60 323	96.26 337	87.55 406	98.39 386	98.72 77	89.00 352	89.22 351	98.47 268	62.98 445	98.96 218	90.57 329	88.00 342	97.28 313

Patchmtry		89.70 375	88.49 379	93.33 373	96.24 338	89.94 377	91.37 470	96.23 421	78.22 451	87.69 380	93.31 432	91.04 179	96.03 418	80.18 430	82.10 389	94.02 379

test_vis1_rt		86.87 397	86.05 399	89.34 426	96.12 339	78.07 460	99.87 130	83.54 486	92.03 273	78.21 448	89.51 455	45.80 471	99.91 110	96.25 217	93.11 307	90.03 454

JIA-IIPM		91.76 332	90.70 333	94.94 310	96.11 340	87.51 407	93.16 463	98.13 228	75.79 457	97.58 183	77.68 476	92.84 137	97.97 315	88.47 360	96.54 237	99.33 206

OpenMVS	90.15 15	94.77 244	93.59 267	98.33 145	96.07 341	97.48 112	99.56 245	98.57 106	90.46 327	86.51 397	98.95 210	78.57 357	99.94 93	93.86 268	99.74 90	97.57 308

PAPM		98.60 37	98.42 38	99.14 72	96.05 342	98.96 27	99.90 114	99.35 24	96.68 71	98.35 154	99.66 115	96.45 35	98.51 268	99.45 65	99.89 74	99.96 74

CLD-MVS		94.06 272	93.90 258	94.55 327	96.02 343	90.69 356	99.98 21	97.72 271	96.62 75	91.05 314	98.85 229	77.21 363	98.47 269	98.11 146	89.51 321	94.48 333
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 358	88.75 375	95.25 302	95.99 344	90.16 369	91.22 471	97.54 294	76.80 453	97.26 196	86.01 470	91.88 166	96.07 417	66.16 468	95.91 257	99.51 173

ACMH+	89.98 16	90.35 359	89.54 358	92.78 388	95.99 344	86.12 417	98.81 354	97.18 348	89.38 345	83.14 423	97.76 300	68.42 423	98.43 274	89.11 351	86.05 358	93.78 399

DeepMVS_CX				82.92 448	95.98 346	58.66 479		96.01 427	92.72 234	78.34 447	95.51 373	58.29 457	98.08 309	82.57 413	85.29 363	92.03 435

ACMP	92.05 9	92.74 307	92.42 305	93.73 361	95.91 347	88.72 392	99.81 162	97.53 296	94.13 165	87.00 391	98.23 282	74.07 398	98.47 269	96.22 218	88.86 328	93.99 384
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 286	93.03 288	95.35 297	95.86 348	86.94 412	99.87 130	96.36 419	96.85 62	99.54 71	98.79 233	52.41 465	99.83 139	98.64 114	98.97 153	99.29 217

HQP-NCC					95.78 349		99.87 130		96.82 64	93.37 286

ACMP_Plane					95.78 349		99.87 130		96.82 64	93.37 286

HQP-MVS		94.61 251	94.50 239	94.92 311	95.78 349	91.85 324	99.87 130	97.89 252	96.82 64	93.37 286	98.65 245	80.65 335	98.39 281	97.92 158	89.60 316	94.53 329

NP-MVS					95.77 352	91.79 328					98.65 245

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 114	95.76 353	96.20 174	99.94 90	98.05 235	98.17 13	98.89 120	99.42 141	87.65 230	99.90 112	99.50 61	99.60 107	99.82 106

plane_prior6					95.76 353	91.72 333						80.47 339

ACMM	91.95 10	92.88 303	92.52 303	93.98 355	95.75 355	89.08 387	99.77 175	97.52 298	93.00 219	89.95 328	97.99 291	76.17 381	98.46 272	93.63 280	88.87 327	94.39 341
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 275	92.84 290	96.80 248	95.73 356	93.57 280	99.88 127	97.24 341	92.57 248	92.92 293	96.66 332	78.73 355	97.67 329	87.75 369	94.06 295	99.17 230

plane_prior1					95.73 356

jason		97.24 129	96.86 134	98.38 144	95.73 356	97.32 117	99.97 39	97.40 310	95.34 118	98.60 141	99.54 133	87.70 229	98.56 265	97.94 157	99.47 124	99.25 224
jason: jason.
mmtdpeth		88.52 386	87.75 388	90.85 408	95.71 359	83.47 437	98.94 337	94.85 451	88.78 361	97.19 198	89.58 454	63.29 443	98.97 216	98.54 119	62.86 471	90.10 453

HQP_MVS		94.49 258	94.36 242	94.87 312	95.71 359	91.74 330	99.84 149	97.87 254	96.38 84	93.01 291	98.59 253	80.47 339	98.37 287	97.79 167	89.55 319	94.52 331

plane_prior7					95.71 359	91.59 342

ITE_SJBPF				92.38 391	95.69 362	85.14 423		95.71 434	92.81 228	89.33 348	98.11 285	70.23 416	98.42 275	85.91 391	88.16 340	93.59 407

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 199	95.65 363	94.21 261	99.83 156	98.50 136	96.27 91	99.65 53	99.64 118	84.72 287	99.93 103	99.04 84	98.84 158	98.74 268

ACMH	89.72 17	90.64 352	89.63 355	93.66 367	95.64 364	88.64 395	98.55 374	97.45 303	89.03 350	81.62 430	97.61 301	69.75 417	98.41 277	89.37 346	87.62 349	93.92 390
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 162	96.49 152	97.37 224	95.63 365	95.96 183	99.74 189	98.88 54	92.94 221	91.61 307	98.97 203	97.72 6	98.62 262	94.83 245	98.08 188	97.53 310

FMVSNet1		88.50 387	86.64 394	94.08 348	95.62 366	91.97 319	98.43 382	96.95 387	83.00 431	86.08 405	94.72 408	59.09 456	96.11 413	81.82 420	84.07 376	94.17 361

LuminaMVS		96.63 166	96.21 166	97.87 177	95.58 367	96.82 141	99.12 306	97.67 275	94.47 144	97.88 174	98.31 278	87.50 234	98.71 249	98.07 150	97.29 210	98.10 290

LPG-MVS_test		92.96 300	92.71 295	93.71 363	95.43 368	88.67 393	99.75 185	97.62 283	92.81 228	90.05 324	98.49 264	75.24 388	98.40 279	95.84 224	89.12 323	94.07 376

LGP-MVS_train				93.71 363	95.43 368	88.67 393		97.62 283	92.81 228	90.05 324	98.49 264	75.24 388	98.40 279	95.84 224	89.12 323	94.07 376

tpm		93.70 284	93.41 276	94.58 325	95.36 370	87.41 408	97.01 427	96.90 394	90.85 311	96.72 215	94.14 423	90.40 193	96.84 378	90.75 327	88.54 335	99.51 173

D2MVS		92.76 306	92.59 301	93.27 375	95.13 371	89.54 381	99.69 213	99.38 22	92.26 266	87.59 382	94.61 414	85.05 279	97.79 324	91.59 310	88.01 341	92.47 430

VPA-MVSNet		92.70 308	91.55 321	96.16 270	95.09 372	96.20 174	98.88 345	99.00 39	91.02 308	91.82 306	95.29 389	76.05 383	97.96 317	95.62 229	81.19 396	94.30 348

LTVRE_ROB	88.28 18	90.29 362	89.05 369	94.02 351	95.08 373	90.15 370	97.19 422	97.43 305	84.91 417	83.99 419	97.06 318	74.00 399	98.28 296	84.08 402	87.71 345	93.62 406
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		87.87 394	86.51 395	91.94 397	95.05 374	85.57 421	97.65 413	94.08 461	84.40 421	81.82 429	96.85 327	62.14 448	98.33 290	80.25 429	86.37 356	91.91 437

test0.0.03 1		93.86 274	93.61 264	94.64 321	95.02 375	92.18 317	99.93 97	98.58 104	94.07 169	87.96 377	98.50 263	93.90 106	94.96 436	81.33 421	93.17 305	96.78 316

UniMVSNet (Re)		93.07 299	92.13 307	95.88 279	94.84 376	96.24 173	99.88 127	98.98 41	92.49 254	89.25 349	95.40 379	87.09 242	97.14 354	93.13 288	78.16 420	94.26 350

USDC		90.00 370	88.96 370	93.10 381	94.81 377	88.16 401	98.71 363	95.54 439	93.66 190	83.75 421	97.20 312	65.58 434	98.31 292	83.96 405	87.49 351	92.85 424

VPNet		91.81 326	90.46 337	95.85 281	94.74 378	95.54 202	98.98 329	98.59 102	92.14 268	90.77 319	97.44 305	68.73 421	97.54 334	94.89 244	77.89 422	94.46 334

FIs		94.10 269	93.43 273	96.11 271	94.70 379	96.82 141	99.58 238	98.93 48	92.54 250	89.34 347	97.31 309	87.62 231	97.10 358	94.22 262	86.58 354	94.40 340

UniMVSNet_ETH3D		90.06 369	88.58 378	94.49 331	94.67 380	88.09 402	97.81 410	97.57 291	83.91 424	88.44 368	97.41 306	57.44 458	97.62 331	91.41 312	88.59 334	97.77 299

UniMVSNet_NR-MVSNet		92.95 301	92.11 308	95.49 289	94.61 381	95.28 219	99.83 156	99.08 36	91.49 288	89.21 352	96.86 326	87.14 241	96.73 384	93.20 284	77.52 425	94.46 334

test_fmvs2		89.47 379	89.70 354	88.77 433	94.54 382	75.74 462	99.83 156	94.70 457	94.71 136	91.08 312	96.82 331	54.46 461	97.78 326	92.87 291	88.27 338	92.80 425

MonoMVSNet		94.82 239	94.43 240	95.98 274	94.54 382	90.73 355	99.03 324	97.06 374	93.16 211	93.15 290	95.47 376	88.29 222	97.57 332	97.85 162	91.33 313	99.62 142

WR-MVS		92.31 318	91.25 326	95.48 292	94.45 384	95.29 218	99.60 234	98.68 83	90.10 335	88.07 376	96.89 324	80.68 334	96.80 382	93.14 287	79.67 413	94.36 342

nrg030		93.51 288	92.53 302	96.45 261	94.36 385	97.20 123	99.81 162	97.16 352	91.60 285	89.86 331	97.46 304	86.37 254	97.68 328	95.88 223	80.31 409	94.46 334

tfpnnormal		89.29 382	87.61 389	94.34 339	94.35 386	94.13 263	98.95 336	98.94 44	83.94 422	84.47 416	95.51 373	74.84 393	97.39 337	77.05 446	80.41 407	91.48 440

FC-MVSNet-test		93.81 278	93.15 286	95.80 284	94.30 387	96.20 174	99.42 269	98.89 52	92.33 261	89.03 357	97.27 311	87.39 237	96.83 380	93.20 284	86.48 355	94.36 342

SSC-MVS3.2		89.59 377	88.66 377	92.38 391	94.29 388	86.12 417	99.49 258	97.66 278	90.28 334	88.63 365	95.18 393	64.46 439	96.88 376	85.30 395	82.66 384	94.14 370

MS-PatchMatch		90.65 351	90.30 342	91.71 402	94.22 389	85.50 422	98.24 393	97.70 272	88.67 364	86.42 400	96.37 342	67.82 426	98.03 313	83.62 407	99.62 100	91.60 438

WR-MVS_H		91.30 336	90.35 340	94.15 345	94.17 390	92.62 308	99.17 304	98.94 44	88.87 359	86.48 399	94.46 419	84.36 293	96.61 390	88.19 363	78.51 418	93.21 416

DU-MVS		92.46 315	91.45 324	95.49 289	94.05 391	95.28 219	99.81 162	98.74 76	92.25 267	89.21 352	96.64 334	81.66 320	96.73 384	93.20 284	77.52 425	94.46 334

NR-MVSNet		91.56 334	90.22 344	95.60 287	94.05 391	95.76 190	98.25 392	98.70 79	91.16 302	80.78 436	96.64 334	83.23 306	96.57 391	91.41 312	77.73 424	94.46 334

CP-MVSNet		91.23 340	90.22 344	94.26 341	93.96 393	92.39 313	99.09 310	98.57 106	88.95 356	86.42 400	96.57 337	79.19 350	96.37 402	90.29 336	78.95 415	94.02 379

XXY-MVS		91.82 325	90.46 337	95.88 279	93.91 394	95.40 209	98.87 348	97.69 274	88.63 366	87.87 378	97.08 316	74.38 397	97.89 321	91.66 309	84.07 376	94.35 345

PS-CasMVS		90.63 353	89.51 360	93.99 354	93.83 395	91.70 334	98.98 329	98.52 127	88.48 368	86.15 404	96.53 339	75.46 386	96.31 406	88.83 353	78.86 417	93.95 387

test_0402		85.58 402	83.94 407	90.50 414	93.81 396	85.04 424	98.55 374	95.20 448	76.01 455	79.72 442	95.13 394	64.15 441	96.26 408	66.04 469	86.88 353	90.21 451

XVG-ACMP-BASELINE		91.22 341	90.75 332	92.63 390	93.73 397	85.61 420	98.52 378	97.44 304	92.77 232	89.90 330	96.85 327	66.64 431	98.39 281	92.29 296	88.61 332	93.89 392

TranMVSNet+NR-MVSNet		91.68 333	90.61 336	94.87 312	93.69 398	93.98 268	99.69 213	98.65 87	91.03 307	88.44 368	96.83 330	80.05 343	96.18 411	90.26 337	76.89 433	94.45 339

TransMVSNet (Re)		87.25 395	85.28 402	93.16 378	93.56 399	91.03 347	98.54 376	94.05 463	83.69 426	81.09 434	96.16 348	75.32 387	96.40 401	76.69 447	68.41 459	92.06 434

v10		90.25 363	88.82 372	94.57 326	93.53 400	93.43 285	99.08 312	96.87 397	85.00 414	87.34 389	94.51 415	80.93 330	97.02 368	82.85 412	79.23 414	93.26 414

testgi		89.01 384	88.04 385	91.90 398	93.49 401	84.89 426	99.73 196	95.66 436	93.89 183	85.14 411	98.17 283	59.68 454	94.66 442	77.73 442	88.88 326	96.16 325

v8		90.54 355	89.17 365	94.66 320	93.43 402	93.40 288	99.20 301	96.94 391	85.76 404	87.56 383	94.51 415	81.96 316	97.19 351	84.94 398	78.25 419	93.38 412

V42		91.28 338	90.12 349	94.74 317	93.42 403	93.46 284	99.68 216	97.02 378	87.36 383	89.85 333	95.05 397	81.31 326	97.34 340	87.34 374	80.07 411	93.40 410

pm-mvs1		89.36 381	87.81 387	94.01 352	93.40 404	91.93 322	98.62 372	96.48 417	86.25 399	83.86 420	96.14 350	73.68 400	97.04 364	86.16 388	75.73 438	93.04 420

v1144		91.09 342	89.83 351	94.87 312	93.25 405	93.69 276	99.62 227	96.98 383	86.83 393	89.64 339	94.99 403	80.94 329	97.05 361	85.08 397	81.16 397	93.87 394

v1192		90.62 354	89.25 364	94.72 319	93.13 406	93.07 292	99.50 256	97.02 378	86.33 398	89.56 343	95.01 400	79.22 349	97.09 360	82.34 416	81.16 397	94.01 381

v2v482		91.30 336	90.07 350	95.01 307	93.13 406	93.79 271	99.77 175	97.02 378	88.05 374	89.25 349	95.37 383	80.73 333	97.15 353	87.28 375	80.04 412	94.09 375

OPM-MVS		93.21 293	92.80 292	94.44 334	93.12 408	90.85 354	99.77 175	97.61 286	96.19 94	91.56 308	98.65 245	75.16 392	98.47 269	93.78 275	89.39 322	93.99 384
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 349	89.52 359	94.59 324	93.11 409	92.77 299	99.56 245	96.99 381	86.38 397	89.82 334	94.95 405	80.50 338	97.10 358	83.98 404	80.41 407	93.90 391

PEN-MVS		90.19 365	89.06 368	93.57 368	93.06 410	90.90 352	99.06 317	98.47 139	88.11 373	85.91 406	96.30 344	76.67 373	95.94 421	87.07 378	76.91 432	93.89 392

v1240		90.20 364	88.79 373	94.44 334	93.05 411	92.27 315	99.38 276	96.92 393	85.89 402	89.36 346	94.87 407	77.89 362	97.03 366	80.66 425	81.08 400	94.01 381

FE-MVSNET3		92.78 305	91.73 316	95.92 278	93.03 412	96.82 141	99.83 156	97.79 262	90.58 322	90.09 323	95.04 398	84.75 284	96.72 386	88.20 362	86.23 357	94.23 354

v148		90.70 350	89.63 355	93.92 356	92.97 413	90.97 348	99.75 185	96.89 395	87.51 380	88.27 374	95.01 400	81.67 319	97.04 364	87.40 373	77.17 430	93.75 400

v1921920		90.46 356	89.12 366	94.50 330	92.96 414	92.46 311	99.49 258	96.98 383	86.10 400	89.61 341	95.30 386	78.55 358	97.03 366	82.17 417	80.89 405	94.01 381

MVStest1		85.03 408	82.76 417	91.83 399	92.95 415	89.16 386	98.57 373	94.82 452	71.68 466	68.54 470	95.11 396	83.17 307	95.66 425	74.69 452	65.32 466	90.65 447

tt0320-xc		82.94 423	80.35 430	90.72 412	92.90 416	83.54 435	96.85 432	94.73 455	63.12 472	79.85 441	93.77 427	49.43 469	95.46 428	80.98 424	71.54 448	93.16 417

Baseline_NR-MVSNet		90.33 360	89.51 360	92.81 387	92.84 417	89.95 375	99.77 175	93.94 464	84.69 419	89.04 356	95.66 365	81.66 320	96.52 393	90.99 320	76.98 431	91.97 436

test_method		80.79 429	79.70 432	84.08 445	92.83 418	67.06 471	99.51 254	95.42 441	54.34 477	81.07 435	93.53 429	44.48 472	92.22 463	78.90 437	77.23 429	92.94 422

pmmvs4		92.10 322	91.07 330	95.18 303	92.82 419	94.96 230	99.48 261	96.83 399	87.45 382	88.66 364	96.56 338	83.78 299	96.83 380	89.29 349	84.77 370	93.75 400

LF4IMVS		89.25 383	88.85 371	90.45 416	92.81 420	81.19 452	98.12 400	94.79 453	91.44 292	86.29 402	97.11 314	65.30 437	98.11 307	88.53 358	85.25 364	92.07 433

tt0320		83.56 422	81.15 425	90.77 410	92.77 421	83.58 434	96.83 433	95.52 440	63.26 471	81.36 432	92.54 437	53.26 463	95.77 423	80.45 426	74.38 441	92.96 421

DTE-MVSNet		89.40 380	88.24 383	92.88 385	92.66 422	89.95 375	99.10 309	98.22 212	87.29 384	85.12 412	96.22 346	76.27 380	95.30 433	83.56 408	75.74 437	93.41 409

EU-MVSNet		90.14 367	90.34 341	89.54 425	92.55 423	81.06 453	98.69 366	98.04 236	91.41 296	86.59 396	96.84 329	80.83 332	93.31 455	86.20 387	81.91 391	94.26 350

APD_test1		81.15 427	80.92 427	81.86 449	92.45 424	59.76 478	96.04 447	93.61 467	73.29 464	77.06 451	96.64 334	44.28 473	96.16 412	72.35 456	82.52 385	89.67 459

sc_t1		85.01 409	82.46 419	92.67 389	92.44 425	83.09 438	97.39 418	95.72 433	65.06 470	85.64 409	96.16 348	49.50 468	97.34 340	84.86 399	75.39 439	97.57 308

our_test_3		90.39 357	89.48 362	93.12 379	92.40 426	89.57 380	99.33 283	96.35 420	87.84 378	85.30 410	94.99 403	84.14 296	96.09 416	80.38 427	84.56 371	93.71 405

ppachtmachnet_test		89.58 378	88.35 381	93.25 377	92.40 426	90.44 364	99.33 283	96.73 406	85.49 409	85.90 407	95.77 359	81.09 328	96.00 420	76.00 450	82.49 386	93.30 413

v7n		89.65 376	88.29 382	93.72 362	92.22 428	90.56 361	99.07 316	97.10 365	85.42 411	86.73 393	94.72 408	80.06 342	97.13 355	81.14 422	78.12 421	93.49 408

dmvs_testset		83.79 418	86.07 398	76.94 453	92.14 429	48.60 488	96.75 434	90.27 478	89.48 344	78.65 445	98.55 260	79.25 348	86.65 476	66.85 466	82.69 383	95.57 327

PS-MVSNAJss		93.64 285	93.31 282	94.61 322	92.11 430	92.19 316	99.12 306	97.38 311	92.51 253	88.45 367	96.99 322	91.20 174	97.29 348	94.36 256	87.71 345	94.36 342

pmmvs5		90.17 366	89.09 367	93.40 371	92.10 431	89.77 378	99.74 189	95.58 438	85.88 403	87.24 390	95.74 360	73.41 403	96.48 397	88.54 357	83.56 380	93.95 387

N_pmnet		80.06 432	80.78 428	77.89 452	91.94 432	45.28 490	98.80 357	56.82 492	78.10 452	80.08 439	93.33 430	77.03 367	95.76 424	68.14 464	82.81 382	92.64 426

test_djsdf		92.83 304	92.29 306	94.47 332	91.90 433	92.46 311	99.55 248	97.27 335	91.17 300	89.96 327	96.07 354	81.10 327	96.89 374	94.67 251	88.91 325	94.05 378

SixPastTwentyTwo		88.73 385	88.01 386	90.88 406	91.85 434	82.24 444	98.22 397	95.18 449	88.97 354	82.26 426	96.89 324	71.75 408	96.67 388	84.00 403	82.98 381	93.72 404

K. test v3		88.05 391	87.24 392	90.47 415	91.82 435	82.23 445	98.96 335	97.42 307	89.05 349	76.93 453	95.60 367	68.49 422	95.42 429	85.87 392	81.01 403	93.75 400

OurMVSNet-221017-0		89.81 373	89.48 362	90.83 409	91.64 436	81.21 451	98.17 399	95.38 443	91.48 290	85.65 408	97.31 309	72.66 404	97.29 348	88.15 364	84.83 369	93.97 386

mvs_tets		91.81 326	91.08 329	94.00 353	91.63 437	90.58 360	98.67 368	97.43 305	92.43 255	87.37 388	97.05 319	71.76 407	97.32 343	94.75 248	88.68 331	94.11 374

Gipuma		66.95 445	65.00 445	72.79 458	91.52 438	67.96 470	66.16 482	95.15 450	47.89 479	58.54 476	67.99 481	29.74 477	87.54 475	50.20 480	77.83 423	62.87 481
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 179	95.74 192	98.32 146	91.47 439	95.56 201	99.84 149	97.30 327	97.74 30	97.89 173	99.35 152	79.62 345	99.85 129	99.25 74	99.24 141	99.55 159

jajsoiax		91.92 324	91.18 327	94.15 345	91.35 440	90.95 351	99.00 327	97.42 307	92.61 243	87.38 387	97.08 316	72.46 405	97.36 338	94.53 254	88.77 329	94.13 373

MDA-MVSNet-bldmvs		84.09 416	81.52 423	91.81 400	91.32 441	88.00 404	98.67 368	95.92 429	80.22 445	55.60 479	93.32 431	68.29 424	93.60 453	73.76 453	76.61 434	93.82 398

MVP-Stereo		90.93 344	90.45 339	92.37 393	91.25 442	88.76 390	98.05 404	96.17 423	87.27 385	84.04 417	95.30 386	78.46 359	97.27 350	83.78 406	99.70 93	91.09 441
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 404	83.32 412	92.10 395	90.96 443	88.58 396	99.20 301	96.52 415	79.70 447	57.12 478	92.69 436	79.11 351	93.86 449	77.10 445	77.46 427	93.86 395

YYNet1		85.50 405	83.33 411	92.00 396	90.89 444	88.38 400	99.22 300	96.55 414	79.60 448	57.26 477	92.72 435	79.09 353	93.78 451	77.25 444	77.37 428	93.84 396

anonymousdsp		91.79 331	90.92 331	94.41 337	90.76 445	92.93 298	98.93 339	97.17 350	89.08 348	87.46 386	95.30 386	78.43 360	96.92 372	92.38 295	88.73 330	93.39 411

lessismore_v0				90.53 413	90.58 446	80.90 454		95.80 430		77.01 452	95.84 357	66.15 433	96.95 370	83.03 411	75.05 440	93.74 403

EG-PatchMatch MVS		85.35 406	83.81 409	89.99 423	90.39 447	81.89 447	98.21 398	96.09 425	81.78 438	74.73 459	93.72 428	51.56 467	97.12 357	79.16 435	88.61 332	90.96 444

EGC-MVSNET		69.38 438	63.76 448	86.26 442	90.32 448	81.66 450	96.24 443	93.85 465	0.99 489	3.22 490	92.33 444	52.44 464	92.92 459	59.53 476	84.90 368	84.21 470

CMPMVS	61.59 21	84.75 412	85.14 403	83.57 446	90.32 448	62.54 474	96.98 428	97.59 290	74.33 462	69.95 467	96.66 332	64.17 440	98.32 291	87.88 368	88.41 337	89.84 456
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 415	82.92 415	89.21 427	90.03 450	82.60 441	96.89 431	95.62 437	80.59 443	75.77 458	89.17 456	65.04 438	94.79 440	72.12 457	81.02 402	90.23 450

pmmvs6		85.69 401	83.84 408	91.26 405	90.00 451	84.41 429	97.82 409	96.15 424	75.86 456	81.29 433	95.39 381	61.21 451	96.87 377	83.52 409	73.29 443	92.50 429

ttmdpeth		88.23 390	87.06 393	91.75 401	89.91 452	87.35 409	98.92 342	95.73 432	87.92 376	84.02 418	96.31 343	68.23 425	96.84 378	86.33 386	76.12 435	91.06 442

DSMNet-mixed		88.28 389	88.24 383	88.42 435	89.64 453	75.38 464	98.06 403	89.86 479	85.59 408	88.20 375	92.14 445	76.15 382	91.95 464	78.46 439	96.05 250	97.92 293

UnsupCasMVSNet_eth		85.52 403	83.99 405	90.10 421	89.36 454	83.51 436	96.65 435	97.99 240	89.14 347	75.89 457	93.83 425	63.25 444	93.92 447	81.92 419	67.90 462	92.88 423

Anonymous20231206		86.32 399	85.42 401	89.02 429	89.11 455	80.53 457	99.05 321	95.28 444	85.43 410	82.82 424	93.92 424	74.40 396	93.44 454	66.99 465	81.83 392	93.08 419

Anonymous20240521		85.15 407	83.81 409	89.16 428	88.32 456	82.69 440	98.80 357	95.74 431	79.72 446	81.53 431	90.99 448	65.38 436	94.16 445	72.69 455	81.11 399	90.63 448

OpenMVS_ROB	79.82 20	83.77 419	81.68 422	90.03 422	88.30 457	82.82 439	98.46 379	95.22 447	73.92 463	76.00 456	91.29 447	55.00 460	96.94 371	68.40 463	88.51 336	90.34 449

test20.03		84.72 413	83.99 405	86.91 440	88.19 458	80.62 456	98.88 345	95.94 428	88.36 370	78.87 443	94.62 413	68.75 420	89.11 471	66.52 467	75.82 436	91.00 443

blend_shiyan4		90.13 368	88.79 373	94.17 343	87.12 459	91.83 326	99.75 185	97.08 369	79.27 449	88.69 362	92.53 438	92.25 158	96.50 394	89.35 347	73.04 445	94.18 360

KD-MVS_self_test		83.59 420	82.06 420	88.20 436	86.93 460	80.70 455	97.21 421	96.38 418	82.87 432	82.49 425	88.97 457	67.63 427	92.32 462	73.75 454	62.30 473	91.58 439

MIMVSNet1		82.58 424	80.51 429	88.78 431	86.68 461	84.20 430	96.65 435	95.41 442	78.75 450	78.59 446	92.44 439	51.88 466	89.76 470	65.26 470	78.95 415	92.38 432

usedtu_blend_shiyan5		86.75 398	84.29 404	94.16 344	86.66 462	91.83 326	97.42 415	95.23 446	69.94 469	88.37 373	92.36 443	78.01 361	96.50 394	89.35 347	61.26 474	94.14 370

CL-MVSNet_self_test		84.50 414	83.15 414	88.53 434	86.00 463	81.79 448	98.82 353	97.35 315	85.12 413	83.62 422	90.91 450	76.66 374	91.40 465	69.53 461	60.36 475	92.40 431

UnsupCasMVSNet_bld		79.97 434	77.03 439	88.78 431	85.62 464	81.98 446	93.66 459	97.35 315	75.51 459	70.79 466	83.05 473	48.70 470	94.91 438	78.31 440	60.29 476	89.46 462

mvs5depth		84.87 410	82.90 416	90.77 410	85.59 465	84.84 427	91.10 472	93.29 469	83.14 429	85.07 413	94.33 421	62.17 447	97.32 343	78.83 438	72.59 447	90.14 452

Patchmatch-RL test		86.90 396	85.98 400	89.67 424	84.45 466	75.59 463	89.71 475	92.43 471	86.89 392	77.83 450	90.94 449	94.22 95	93.63 452	87.75 369	69.61 453	99.79 111

pmmvs-eth3d		84.03 417	81.97 421	90.20 419	84.15 467	87.09 411	98.10 402	94.73 455	83.05 430	74.10 463	87.77 463	65.56 435	94.01 446	81.08 423	69.24 455	89.49 461

test_fmvs3		79.99 433	80.17 431	79.45 451	84.02 468	62.83 472	99.05 321	93.49 468	88.29 372	80.06 440	86.65 468	28.09 479	88.00 472	88.63 354	73.27 444	87.54 468

PM-MVS		80.47 430	78.88 434	85.26 443	83.79 469	72.22 466	95.89 450	91.08 476	85.71 407	76.56 455	88.30 459	36.64 475	93.90 448	82.39 415	69.57 454	89.66 460

new-patchmatchnet		81.19 426	79.34 433	86.76 441	82.86 470	80.36 458	97.92 406	95.27 445	82.09 437	72.02 464	86.87 467	62.81 446	90.74 468	71.10 458	63.08 470	89.19 464

FE-MVSNET2		83.57 421	81.36 424	90.20 419	82.83 471	87.59 405	98.28 390	96.04 426	85.33 412	74.13 462	87.45 464	59.16 455	93.26 456	79.12 436	69.91 451	89.77 457

FE-MVSNET		81.05 428	78.81 435	87.79 438	81.98 472	83.70 432	98.23 395	91.78 475	81.27 440	74.29 461	87.44 465	60.92 453	90.67 469	64.92 471	68.43 458	89.01 465

mvsany_test3		82.12 425	81.14 426	85.06 444	81.87 473	70.41 468	97.09 425	92.14 472	91.27 299	77.84 449	88.73 458	39.31 474	95.49 426	90.75 327	71.24 449	89.29 463

WB-MVS		76.28 436	77.28 438	73.29 457	81.18 474	54.68 482	97.87 408	94.19 460	81.30 439	69.43 468	90.70 451	77.02 368	82.06 480	35.71 485	68.11 461	83.13 471

test_f		78.40 435	77.59 437	80.81 450	80.82 475	62.48 475	96.96 429	93.08 470	83.44 427	74.57 460	84.57 472	27.95 480	92.63 460	84.15 401	72.79 446	87.32 469

SSC-MVS		75.42 437	76.40 440	72.49 461	80.68 476	53.62 483	97.42 415	94.06 462	80.42 444	68.75 469	90.14 453	76.54 376	81.66 481	33.25 486	66.34 465	82.19 472

pmmvs3		80.27 431	77.77 436	87.76 439	80.32 477	82.43 443	98.23 395	91.97 473	72.74 465	78.75 444	87.97 462	57.30 459	90.99 467	70.31 459	62.37 472	89.87 455

testf1		68.38 441	66.92 442	72.78 459	78.80 478	50.36 485	90.95 473	87.35 484	55.47 475	58.95 474	88.14 460	20.64 484	87.60 473	57.28 477	64.69 467	80.39 474

APD_test2		68.38 441	66.92 442	72.78 459	78.80 478	50.36 485	90.95 473	87.35 484	55.47 475	58.95 474	88.14 460	20.64 484	87.60 473	57.28 477	64.69 467	80.39 474

ambc				83.23 447	77.17 480	62.61 473	87.38 477	94.55 459		76.72 454	86.65 468	30.16 476	96.36 403	84.85 400	69.86 452	90.73 446

test_vis3_rt		68.82 439	66.69 444	75.21 456	76.24 481	60.41 477	96.44 438	68.71 491	75.13 460	50.54 482	69.52 480	16.42 489	96.32 405	80.27 428	66.92 464	68.89 478

TDRefinement		84.76 411	82.56 418	91.38 404	74.58 482	84.80 428	97.36 419	94.56 458	84.73 418	80.21 438	96.12 353	63.56 442	98.39 281	87.92 367	63.97 469	90.95 445

E-PMN		52.30 449	52.18 451	52.67 467	71.51 483	45.40 489	93.62 460	76.60 489	36.01 483	43.50 484	64.13 483	27.11 481	67.31 486	31.06 487	26.06 482	45.30 485

EMVS		51.44 451	51.22 453	52.11 468	70.71 484	44.97 491	94.04 456	75.66 490	35.34 485	42.40 485	61.56 486	28.93 478	65.87 487	27.64 488	24.73 483	45.49 484

PMMVS2		67.15 444	64.15 447	76.14 455	70.56 485	62.07 476	93.89 457	87.52 483	58.09 474	60.02 473	78.32 475	22.38 483	84.54 478	59.56 475	47.03 480	81.80 473

FPMVS		68.72 440	68.72 441	68.71 463	65.95 486	44.27 492	95.97 449	94.74 454	51.13 478	53.26 480	90.50 452	25.11 482	83.00 479	60.80 474	80.97 404	78.87 476

wuyk23d		20.37 455	20.84 458	18.99 471	65.34 487	27.73 494	50.43 483	7.67 495	9.50 488	8.01 489	6.34 489	6.13 492	26.24 488	23.40 489	10.69 487	2.99 486

LCM-MVSNet		67.77 443	64.73 446	76.87 454	62.95 488	56.25 481	89.37 476	93.74 466	44.53 480	61.99 472	80.74 474	20.42 486	86.53 477	69.37 462	59.50 477	87.84 466

MVE	53.74 22	51.54 450	47.86 454	62.60 465	59.56 489	50.93 484	79.41 480	77.69 488	35.69 484	36.27 486	61.76 485	5.79 493	69.63 484	37.97 484	36.61 481	67.24 479
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 447	52.24 450	67.66 464	49.27 490	56.82 480	83.94 478	82.02 487	70.47 467	33.28 487	64.54 482	17.23 488	69.16 485	45.59 482	23.85 484	77.02 477

tmp_tt		65.23 446	62.94 449	72.13 462	44.90 491	50.03 487	81.05 479	89.42 482	38.45 481	48.51 483	99.90 22	54.09 462	78.70 483	91.84 308	18.26 485	87.64 467

PMVS	49.05 23	53.75 448	51.34 452	60.97 466	40.80 492	34.68 493	74.82 481	89.62 481	37.55 482	28.67 488	72.12 477	7.09 491	81.63 482	43.17 483	68.21 460	66.59 480
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 453	39.14 456	33.31 469	19.94 493	24.83 495	98.36 387	9.75 494	15.53 487	51.31 481	87.14 466	19.62 487	17.74 489	47.10 481	3.47 488	57.36 482

testmvs		40.60 452	44.45 455	29.05 470	19.49 494	14.11 496	99.68 216	18.47 493	20.74 486	64.59 471	98.48 267	10.95 490	17.09 490	56.66 479	11.01 486	55.94 483

mmdepth		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

monomultidepth		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

test_blank		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.02 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

eth-test2					0.00 495

eth-test					0.00 495

uanet_test		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

DCPMVS		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

cdsmvs_eth3d_5k		23.43 454	31.24 457	0.00 472	0.00 495	0.00 497	0.00 484	98.09 230	0.00 490	0.00 491	99.67 113	83.37 303	0.00 491	0.00 490	0.00 489	0.00 487

pcd_1.5k_mvsjas		7.60 457	10.13 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	91.20 174	0.00 491	0.00 490	0.00 489	0.00 487

sosnet-low-res		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

sosnet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

uncertanet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

Regformer		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

ab-mvs-re		8.28 456	11.04 459	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	99.40 146	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

uanet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 491	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

TestfortrainingZip							99.97 39

WAC-MVS						90.97 348								86.10 390

PC_three_1452									96.96 60	99.80 26	99.79 62	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 155	97.27 47	99.80 26	99.94 4	97.18 23	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 78	99.83 22	99.91 18	97.87 5	100.00 1	99.92 16	100.00 1	100.00 1

GSMVS																99.59 149

sam_mvs1												94.72 74				99.59 149

sam_mvs												94.25 94

MTGPA								98.28 203

test_post1							95.78 451				59.23 487	93.20 128	97.74 327	91.06 318

test_post											63.35 484	94.43 82	98.13 306

patchmatchnet-post											91.70 446	95.12 59	97.95 318

MTMP							99.87 130	96.49 416

test9_res														99.71 48	99.99 21	100.00 1

agg_prior2														99.48 63	100.00 1	100.00 1

test_prior4						98.05 81	99.94 90

test_prior2							99.95 72		95.78 104	99.73 45	99.76 72	96.00 40		99.78 35	100.00 1

旧先验2							99.46 266		94.21 164	99.85 18			99.95 84	96.96 196

新几何2							99.40 270

无先验							99.49 258	98.71 78	93.46 197				100.00 1	94.36 256		99.99 24

原ACMM2							99.90 114

testdata2													99.99 39	90.54 331

segment_acmp												96.68 31

testdata1							99.28 294		96.35 90

plane_prior5								97.87 254					98.37 287	97.79 167	89.55 319	94.52 331

plane_prior4											98.59 253

plane_prior3						91.64 336			96.63 73	93.01 291

plane_prior2							99.84 149		96.38 84

plane_prior						91.74 330	99.86 141		96.76 68						89.59 318

n2								0.00 496

nn								0.00 496

door-mid								89.69 480

test11								98.44 147

door								90.31 477

HQP5-MVS						91.85 324

BP-MVS														97.92 158

HQP4-MVS										93.37 286			98.39 281			94.53 329

HQP3-MVS								97.89 252							89.60 316

HQP2-MVS												80.65 335

MDTV_nov1_ep13_2view						96.26 168	96.11 445		91.89 276	98.06 166		94.40 84		94.30 259		99.67 129

ACMMP++_ref															87.04 352

ACMMP++															88.23 339

Test By Simon												92.82 139

High-resolution multi-view benchmark