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	99.09 308	98.84 65	93.32 203	96.74 214	99.72 94	86.04 259	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	89.90 339	98.36 153	99.79 62	91.18 176	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	92.06 271	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		93.83 274	92.06 309	99.15 70	99.94 16	97.50 110	99.94 90	98.42 167	96.22 92	99.41 84	41.37 485	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	91.84 167	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	99.64 223	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	92.34 259	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	99.18 301	99.45 18	94.84 131	96.41 230	99.71 97	91.40 170	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	99.24 296	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	91.92 322	99.90 114	99.07 37	88.67 363	95.26 262	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	99.61 230	97.78 265	96.52 76	98.61 138	99.31 156	92.73 141	99.67 167	96.77 204	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	90.78 318	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	99.39 273	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	95.11 219	98.02 166	99.85 60	95.10 228	98.74 358	98.50 136	87.22 385	93.66 283	99.86 33	87.45 235	99.95 84	90.94 321	99.81 87	99.02 248

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	91.87 166	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	99.40 269	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	99.36 279	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	91.75 282	98.94 117	99.54 133	91.82 168	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	93.36 289	99.65 219	97.95 245	96.03 97	97.41 190	99.70 100	89.61 202	99.51 177	96.73 206	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	91.21 313	99.94 59	99.99 24

MP-MVS-pluss		98.07 78	97.64 96	99.38 48	99.74 76	98.41 68	99.74 188	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	95.11 234	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	87.80 226	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	99.73 195	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	96.87 245	99.71 82	91.74 327	99.85 144	97.95 245	93.11 215	95.72 251	99.16 180	92.35 155	99.94 93	95.32 230	99.35 136	98.92 256

reproduce-ours		98.78 27	98.67 24	99.09 79	99.70 84	97.30 118	99.74 188	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	99.74 188	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	95.59 287	99.67 87	86.91 411	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	93.92 353	99.63 89	81.76 446	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	90.49 191	99.96 75	99.89 21	99.43 129	99.98 56

PVSNet_BlendedMVS		96.05 195	95.82 189	96.72 251	99.59 91	96.99 135	99.95 72	99.10 34	94.06 171	98.27 157	95.80 357	89.00 214	99.95 84	99.12 78	87.53 349	93.24 412

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	89.00 214	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	99.52 251	99.07 37	93.96 176	96.49 224	98.35 272	82.28 310	99.82 141	90.15 337	99.22 143	98.81 263

dcpmvs_2		97.42 121	98.09 63	95.42 294	99.58 95	87.24 407	99.23 297	96.95 385	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	99.34 281	98.55 118	91.86 277	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	99.47 261	98.87 58	91.68 283	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	84.48 291	99.95 84	94.92 240	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	88.48 367	99.06 112	99.66 115	90.30 194	99.64 172	96.32 215	99.97 42	99.96 74

cl22		93.77 279	93.25 283	95.33 298	99.49 101	94.43 249	99.61 230	98.09 230	90.38 327	89.16 354	95.61 365	90.56 189	97.34 339	91.93 304	84.45 371	94.21 357

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	94.97 238	99.94 59	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 157	99.47 102	94.79 238	99.05 319	98.76 73	92.65 241	98.66 135	99.82 53	88.52 220	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		94.54 251	93.42 273	97.91 174	99.46 104	94.04 265	98.93 337	97.48 302	81.15 440	90.04 325	99.55 131	87.02 243	99.95 84	88.97 349	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	92.03 163	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	98.46 377	99.42 21	97.03 57	99.02 114	99.09 183	99.35 2	98.21 301	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	86.21 257	99.95 84	99.89 21	99.68 94	97.65 301

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	93.81 271	99.94 59	99.98 56

TAPA-MVS	92.12 8	94.42 259	93.60 265	96.90 244	99.33 109	91.78 326	99.78 170	98.00 239	89.89 340	94.52 268	99.47 137	91.97 164	99.18 202	69.90 457	99.52 114	99.73 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 223	95.07 221	96.32 266	99.32 111	96.60 154	99.76 181	98.85 62	96.65 72	87.83 376	96.05 354	99.52 1	98.11 306	96.58 210	81.07 400	94.25 351

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	87.04 242	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	97.15 352	95.53 113	99.62 60	99.79 62	92.08 162	98.38 284	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	98.59 273

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	85.97 261	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	87.37 237	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	99.64 223	99.27 27	91.43 292	97.88 174	98.99 198	95.84 45	99.84 137	98.82 101	95.32 276	99.79 111

DCV-MVSNet		97.83 92	97.37 111	99.21 59	99.18 118	97.98 85	99.64 223	99.27 27	91.43 292	97.88 174	98.99 198	95.84 45	99.84 137	98.82 101	95.32 276	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	99.66 218	98.06 233	96.37 87	94.37 274	99.49 136	83.29 303	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	87.99 225	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	89.02 213	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	97.10 364	95.07 122	99.68 50	99.75 80	92.95 134	98.34 288	98.38 128	99.14 145	99.54 163

Anonymous202405211		93.10 297	91.99 310	96.40 262	99.10 124	89.65 376	98.88 343	97.93 247	83.71 424	94.00 280	98.75 234	68.79 416	99.88 123	95.08 235	91.71 309	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	87.27 238	99.97 63	99.62 55	99.06 150	98.62 272

HyFIR lowres test		96.66 165	96.43 156	97.36 226	99.05 128	93.91 270	99.70 209	99.80 3	90.54 323	96.26 233	98.08 285	92.15 160	98.23 300	96.84 202	95.46 271	99.93 87

LFMVS		94.75 245	93.56 268	98.30 147	99.03 129	95.70 194	98.74 358	97.98 242	87.81 378	98.47 146	99.39 148	67.43 425	99.53 174	98.01 152	95.20 279	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	86.70 249	99.93 103	99.67 52	99.12 148	97.64 302

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	81.83 316	99.94 93	99.78 35	98.79 161	97.51 310

AllTest		92.48 313	91.64 316	95.00 307	99.01 130	88.43 394	98.94 335	96.82 399	86.50 394	88.71 359	98.47 267	74.73 391	99.88 123	85.39 390	96.18 246	96.71 316

TestCases				95.00 307	99.01 130	88.43 394		96.82 399	86.50 394	88.71 359	98.47 267	74.73 391	99.88 123	85.39 390	96.18 246	96.71 316

COLMAP_ROB	90.47 14	92.18 320	91.49 322	94.25 341	99.00 134	88.04 400	98.42 383	96.70 406	82.30 435	88.43 368	99.01 194	76.97 366	99.85 129	86.11 386	96.50 238	94.86 327
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	84.15 294	99.97 63	99.76 40	99.50 119	98.39 280

test_fmvs1		95.35 224	95.68 196	94.36 337	98.99 135	84.98 422	99.96 53	96.65 408	97.60 34	99.73 45	98.96 204	71.58 406	99.93 103	98.31 134	99.37 134	98.17 285

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 18	98.98 137	99.32 9	97.49 412	99.52 14	95.69 108	98.32 155	97.41 305	93.32 121	99.77 149	98.08 149	95.75 261	99.81 108

VNet		97.21 131	96.57 150	99.13 76	98.97 138	97.82 94	99.03 322	99.21 32	94.31 158	99.18 102	98.88 216	86.26 256	99.89 117	98.93 92	94.32 289	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	96.26 233	98.88 216	89.87 199	99.51 177	94.26 259	94.91 281	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	96.02 241	98.87 223	89.33 206	99.50 179	93.84 268	94.57 285	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	96.02 241	98.87 223	89.33 206	99.50 179	93.84 268	94.57 285	99.16 231

sasdasda		97.09 138	96.32 160	99.39 45	98.93 142	98.95 28	99.72 199	97.35 315	94.45 146	97.88 174	99.42 141	86.71 247	99.52 175	98.48 123	93.97 295	99.72 121

Anonymous20231211		89.86 370	88.44 378	94.13 344	98.93 142	90.68 354	98.54 374	98.26 206	76.28 452	86.73 390	95.54 369	70.60 412	97.56 332	90.82 324	80.27 409	94.15 365

canonicalmvs		97.09 138	96.32 160	99.39 45	98.93 142	98.95 28	99.72 199	97.35 315	94.45 146	97.88 174	99.42 141	86.71 247	99.52 175	98.48 123	93.97 295	99.72 121

SDMVSNet		94.80 240	93.96 255	97.33 229	98.92 145	95.42 207	99.59 235	98.99 40	92.41 255	92.55 298	97.85 296	75.81 381	98.93 220	97.90 160	91.62 310	97.64 302

sd_testset		93.55 286	92.83 290	95.74 285	98.92 145	90.89 350	98.24 391	98.85 62	92.41 255	92.55 298	97.85 296	71.07 411	98.68 253	93.93 265	91.62 310	97.64 302

EPNet_dtu		95.71 212	95.39 206	96.66 253	98.92 145	93.41 285	99.57 240	98.90 50	96.19 94	97.52 184	98.56 257	92.65 143	97.36 337	77.89 438	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	95.58 270	99.78 114

CHOSEN 1792x2688		96.81 152	96.53 151	97.64 196	98.91 149	93.07 291	99.65 219	99.80 3	95.64 109	95.39 258	98.86 225	84.35 293	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	99.74 188	99.71 7	92.59 245	95.84 245	98.86 225	89.25 208	99.50 179	93.84 268	94.57 285	99.27 221

thres600view7		96.69 163	95.87 188	99.14 72	98.90 150	98.78 45	99.74 188	99.71 7	92.59 245	95.84 245	98.86 225	89.25 208	99.50 179	93.44 281	94.50 288	99.16 231

MSDG		94.37 261	93.36 280	97.40 222	98.88 152	93.95 269	99.37 277	97.38 311	85.75 405	90.80 317	99.17 177	84.11 296	99.88 123	86.35 382	98.43 172	98.36 282

MGCFI-Net		97.00 143	96.22 165	99.34 50	98.86 153	98.80 40	99.67 217	97.30 327	94.31 158	97.77 180	99.41 145	86.36 254	99.50 179	98.38 128	93.90 297	99.72 121

h-mvs33		94.92 237	94.36 241	96.59 255	98.85 154	91.29 342	98.93 337	98.94 44	95.90 99	98.77 127	98.42 270	90.89 184	99.77 149	97.80 164	70.76 448	98.72 269

Anonymous20240529		92.10 321	90.65 333	96.47 257	98.82 155	90.61 356	98.72 360	98.67 86	75.54 456	93.90 282	98.58 255	66.23 429	99.90 112	94.70 249	90.67 313	98.90 259

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	96.38 231	98.49 263	89.05 212	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	99.54 249	98.17 218	97.34 42	99.85 18	99.85 37	91.20 173	99.89 117	99.41 68	99.67 95	98.69 270

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	81.36 322	99.98 50	92.77 292	98.89 155	98.28 284

mvsany_test1		97.82 95	97.90 80	97.55 207	98.77 159	93.04 294	99.80 166	97.93 247	96.95 61	99.61 67	99.68 111	90.92 181	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	94.26 291	99.67 129

SymmetryMVS		97.64 110	97.46 104	98.17 153	98.74 161	95.39 210	99.61 230	99.26 29	96.52 76	98.61 138	99.31 156	92.73 141	99.67 167	96.77 204	95.63 268	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	99.52 251	98.08 232	97.05 56	99.86 15	99.86 33	90.65 186	99.71 159	99.39 70	98.63 165	98.69 270

miper_enhance_ethall		94.36 263	93.98 254	95.49 288	98.68 164	95.24 221	99.73 195	97.29 332	93.28 205	89.86 330	95.97 355	94.37 88	97.05 360	92.20 296	84.45 371	94.19 358

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	85.99 260	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	91.10 304	98.17 163	98.59 252	93.86 108	98.19 302	95.64 227	95.24 278	99.28 219

test2506		97.53 114	97.19 120	98.58 121	98.66 167	96.90 139	98.81 352	99.77 5	94.93 125	97.95 169	98.96 204	92.51 150	99.20 200	94.93 239	98.15 182	99.64 135

ECVR-MVS		95.66 215	95.05 222	97.51 212	98.66 167	93.71 274	98.85 349	98.45 142	94.93 125	96.86 210	98.96 204	75.22 387	99.20 200	95.34 229	98.15 182	99.64 135

mamv4		95.24 227	96.90 131	90.25 415	98.65 169	72.11 464	98.28 388	97.64 279	89.99 338	95.93 243	98.25 280	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	99.47 261	97.79 262	94.56 141	99.74 43	98.35 272	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	87.00 244	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	99.58 237	97.74 270	90.34 330	99.26 98	98.32 275	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	92.53 250	98.84 121	98.84 229	93.36 118	98.30 292	95.84 223	94.30 290	99.05 245

test1111		95.57 218	94.98 225	97.37 224	98.56 173	93.37 288	98.86 347	98.45 142	94.95 124	96.63 216	98.95 209	75.21 388	99.11 206	95.02 236	98.14 184	99.64 135

MVSTER		95.53 219	95.22 214	96.45 260	98.56 173	97.72 98	99.91 108	97.67 275	92.38 258	91.39 308	97.14 312	97.24 20	97.30 344	94.80 245	87.85 342	94.34 346

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	98.73 236	95.50 52	99.69 163	98.53 121	94.63 283	98.99 250

VDD-MVS		93.77 279	92.94 288	96.27 267	98.55 176	90.22 365	98.77 357	97.79 262	90.85 310	96.82 212	99.42 141	61.18 449	99.77 149	98.95 90	94.13 292	98.82 262

tpmvs		94.28 265	93.57 267	96.40 262	98.55 176	91.50 340	95.70 449	98.55 118	87.47 380	92.15 301	94.26 421	91.42 169	98.95 219	88.15 361	95.85 257	98.76 265

UGNet		95.33 225	94.57 237	97.62 200	98.55 176	94.85 233	98.67 366	99.32 26	95.75 106	96.80 213	96.27 344	72.18 403	99.96 75	94.58 252	99.05 151	98.04 290
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	95.18 229	94.10 248	98.43 139	98.55 176	95.99 182	97.91 405	97.31 326	90.35 329	89.48 343	99.22 170	85.19 276	99.89 117	90.40 334	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	94.34 338	98.51 181	89.99 370	99.39 273	98.57 106	93.14 212	97.33 193	98.31 277	93.44 116	94.68 438	93.69 278	95.98 251	98.34 283

UWE-MVS		96.79 153	96.72 143	97.00 239	98.51 181	93.70 275	99.71 202	98.60 100	92.96 220	97.09 200	98.34 274	96.67 33	98.85 226	92.11 302	96.50 238	98.44 278

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	98.75 234	95.20 57	99.48 185	98.93 92	96.40 241	99.29 217

test_vis1_n_1920		95.44 221	95.31 210	95.82 282	98.50 183	88.74 388	99.98 21	97.30 327	97.84 28	99.85 18	99.19 175	66.82 427	99.97 63	98.82 101	99.46 126	98.76 265

BH-w/o		95.71 212	95.38 208	96.68 252	98.49 185	92.28 313	99.84 149	97.50 300	92.12 268	92.06 304	98.79 232	84.69 287	98.67 255	95.29 231	99.66 96	99.09 239

baseline1		95.78 208	94.86 228	98.54 127	98.47 186	98.07 79	99.06 315	97.99 240	92.68 239	94.13 279	98.62 249	93.28 124	98.69 252	93.79 273	85.76 358	98.84 261

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	86.09 258	99.94 93	99.69 50	99.50 119	97.66 300

EPMVS		96.53 172	96.01 172	98.09 161	98.43 188	96.12 180	96.36 436	99.43 20	93.53 193	97.64 182	95.04 397	94.41 83	98.38 284	91.13 315	98.11 185	99.75 117

kuosan		93.17 294	92.60 296	94.86 314	98.40 189	89.54 378	98.44 379	98.53 125	84.46 419	88.49 364	97.92 293	90.57 188	97.05 360	83.10 407	93.49 300	97.99 291

WBMVS		94.52 254	94.03 252	95.98 273	98.38 190	96.68 149	99.92 100	97.63 280	90.75 319	89.64 338	95.25 390	96.77 27	96.90 372	94.35 257	83.57 378	94.35 344

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	98.42 270	96.77 27	99.17 203	98.54 119	96.20 245	99.11 238

sss		97.57 113	97.03 127	99.18 62	98.37 192	98.04 82	99.73 195	99.38 22	93.46 197	98.76 130	99.06 188	91.21 172	99.89 117	96.33 214	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	98.70 239	95.48 53	99.22 196	97.85 162	95.05 280	99.07 242

BH-untuned		95.18 229	94.83 229	96.22 268	98.36 193	91.22 343	99.80 166	97.32 325	90.91 308	91.08 311	98.67 241	83.51 299	98.54 266	94.23 260	99.61 104	98.92 256

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	98.72 237	94.81 71	99.21 197	96.98 194	94.63 283	99.03 247

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	98.72 237	94.83 70	99.21 197	97.00 192	94.64 282	98.95 252

ET-MVSNet_ETH3D		94.37 261	93.28 282	97.64 196	98.30 197	97.99 84	99.99 5	97.61 286	94.35 155	71.57 462	99.45 140	96.23 38	95.34 428	96.91 200	85.14 365	99.59 149

AUN-MVS		93.28 291	92.60 296	95.34 297	98.29 198	90.09 368	99.31 285	98.56 112	91.80 281	96.35 232	98.00 288	89.38 205	98.28 295	92.46 293	69.22 454	97.64 302

FMVSNet3		92.69 308	91.58 318	95.99 272	98.29 198	97.42 115	99.26 295	97.62 283	89.80 341	89.68 334	95.32 384	81.62 320	96.27 404	87.01 378	85.65 359	94.29 348

PMMVS		96.76 156	96.76 140	96.76 249	98.28 200	92.10 317	99.91 108	97.98 242	94.12 166	99.53 72	99.39 148	86.93 245	98.73 245	96.95 197	97.73 193	99.45 186

hse-mvs2		94.38 260	94.08 251	95.31 299	98.27 201	90.02 369	99.29 291	98.56 112	95.90 99	98.77 127	98.00 288	90.89 184	98.26 299	97.80 164	69.20 455	97.64 302

PVSNet_0	88.03 19	91.80 328	90.27 342	96.38 264	98.27 201	90.46 360	99.94 90	99.61 13	93.99 174	86.26 400	97.39 307	71.13 410	99.89 117	98.77 105	67.05 461	98.79 264

UA-Net		96.54 171	95.96 179	98.27 149	98.23 203	95.71 193	98.00 403	98.45 142	93.72 189	98.41 150	99.27 162	88.71 219	99.66 170	91.19 314	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	97.25 337	97.77 29	99.58 68	99.08 184	77.10 361	99.97 63	97.64 172	99.45 127	98.74 267

FE-MVS		95.70 214	95.01 224	97.79 182	98.21 205	94.57 243	95.03 450	98.69 81	88.90 357	97.50 186	96.19 346	92.60 146	99.49 184	89.99 339	97.94 191	99.31 212

GG-mvs-BLEND				98.54 127	98.21 205	98.01 83	93.87 455	98.52 127		97.92 170	97.92 293	99.02 3	97.94 319	98.17 142	99.58 109	99.67 129

mvs_anonymous		95.65 216	95.03 223	97.53 209	98.19 207	95.74 191	99.33 282	97.49 301	90.87 309	90.47 320	97.10 314	88.23 222	97.16 351	95.92 221	97.66 197	99.68 127

MVS_Test		96.46 174	95.74 192	98.61 116	98.18 208	97.23 122	99.31 285	97.15 352	91.07 305	98.84 121	97.05 318	88.17 223	98.97 216	94.39 254	97.50 199	99.61 146

BH-RMVSNet		95.18 229	94.31 244	97.80 180	98.17 209	95.23 222	99.76 181	97.53 296	92.52 251	94.27 277	99.25 168	76.84 368	98.80 235	90.89 323	99.54 111	99.35 203

dongtai		91.55 334	91.13 327	92.82 383	98.16 210	86.35 412	99.47 261	98.51 130	83.24 427	85.07 410	97.56 301	90.33 193	94.94 434	76.09 446	91.73 308	97.18 313

RPSCF		91.80 328	92.79 292	88.83 427	98.15 211	69.87 466	98.11 399	96.60 410	83.93 422	94.33 275	99.27 162	79.60 344	99.46 188	91.99 303	93.16 305	97.18 313

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	99.08 310	97.61 286	92.02 273	95.54 256	98.96 204	90.64 187	98.08 308	93.73 276	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	91.11 177	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	80.88 329	99.93 103	99.59 56	98.17 180	97.29 311

ab-mvs		94.69 246	93.42 273	98.51 132	98.07 216	96.26 168	96.49 434	98.68 83	90.31 331	94.54 267	97.00 320	76.30 376	99.71 159	95.98 220	93.38 303	99.56 158

XVG-OURS-SEG-HR		94.79 241	94.70 236	95.08 304	98.05 217	89.19 380	99.08 310	97.54 294	93.66 190	94.87 265	99.58 127	78.78 352	99.79 144	97.31 180	93.40 302	96.25 320

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		94.82 238	94.74 235	95.06 305	98.00 219	89.19 380	99.08 310	97.55 292	94.10 167	94.71 266	99.62 122	80.51 335	99.74 155	96.04 219	93.06 307	96.25 320

mvsmamba		96.94 146	96.73 142	97.55 207	97.99 220	94.37 255	99.62 226	97.70 272	93.13 213	98.42 149	97.92 293	88.02 224	98.75 243	98.78 104	99.01 152	99.52 169

dp		95.05 232	94.43 239	96.91 242	97.99 220	92.73 302	96.29 439	97.98 242	89.70 342	95.93 243	94.67 411	93.83 110	98.45 272	86.91 381	96.53 237	99.54 163

tpmrst		96.27 189	95.98 175	97.13 234	97.96 222	93.15 290	96.34 437	98.17 218	92.07 269	98.71 133	95.12 394	93.91 105	98.73 245	94.91 242	96.62 235	99.50 175

TR-MVS		94.54 251	93.56 268	97.49 214	97.96 222	94.34 256	98.71 361	97.51 299	90.30 332	94.51 269	98.69 240	75.56 382	98.77 239	92.82 291	95.99 250	99.35 203

Vis-MVSNet (Re-imp)		96.32 184	95.98 175	97.35 228	97.93 224	94.82 236	99.47 261	98.15 226	91.83 278	95.09 263	99.11 182	91.37 171	97.47 335	93.47 280	97.43 200	99.74 118

MDTV_nov1_ep13			95.69 194		97.90 225	94.15 262	95.98 445	98.44 147	93.12 214	97.98 168	95.74 359	95.10 60	98.58 262	90.02 338	96.92 229

Fast-Effi-MVS+		95.02 234	94.19 246	97.52 211	97.88 226	94.55 244	99.97 39	97.08 368	88.85 359	94.47 270	97.96 292	84.59 288	98.41 276	89.84 341	97.10 220	99.59 149

ADS-MVSNet2		93.80 278	93.88 258	93.55 366	97.87 227	85.94 416	94.24 451	96.84 396	90.07 335	96.43 228	94.48 416	90.29 195	95.37 427	87.44 368	97.23 211	99.36 199

ADS-MVSNet		94.79 241	94.02 253	97.11 236	97.87 227	93.79 271	94.24 451	98.16 223	90.07 335	96.43 228	94.48 416	90.29 195	98.19 302	87.44 368	97.23 211	99.36 199

Effi-MVS+		96.30 186	95.69 194	98.16 154	97.85 229	96.26 168	97.41 414	97.21 344	90.37 328	98.65 136	98.58 255	86.61 251	98.70 251	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	96.05 443	98.40 176	92.86 225	97.09 200	95.28 389	94.21 97	98.07 310	89.26 347	98.11 185	99.70 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 249	93.61 263	97.74 190	97.82 231	96.26 168	99.96 53	97.78 265	85.76 403	94.00 280	97.54 302	76.95 367	99.21 197	97.23 185	95.43 273	97.76 299

1112_ss		96.01 197	95.20 215	98.42 141	97.80 232	96.41 161	99.65 219	96.66 407	92.71 236	92.88 294	99.40 146	92.16 159	99.30 192	91.92 305	93.66 298	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	87.73 227	98.83 227	97.52 175	97.40 204	99.48 178

Test_1112_low_res		95.72 210	94.83 229	98.42 141	97.79 233	96.41 161	99.65 219	96.65 408	92.70 237	92.86 295	96.13 350	92.15 160	99.30 192	91.88 306	93.64 299	99.55 159

Effi-MVS+-dtu		94.53 253	95.30 211	92.22 391	97.77 235	82.54 439	99.59 235	97.06 372	94.92 127	95.29 260	95.37 382	85.81 262	97.89 320	94.80 245	97.07 221	96.23 322

tpm cat1		93.51 287	92.52 302	96.47 257	97.77 235	91.47 341	96.13 441	98.06 233	80.98 441	92.91 293	93.78 425	89.66 200	98.87 224	87.03 377	96.39 242	99.09 239

FA-MVS(test-final)		95.86 203	95.09 220	98.15 157	97.74 237	95.62 199	96.31 438	98.17 218	91.42 294	96.26 233	96.13 350	90.56 189	99.47 187	92.18 297	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	99.31 285	97.86 256	96.43 81	99.62 60	99.69 104	85.56 269	99.68 164	99.05 81	98.31 175	97.83 295

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 285	97.86 256	96.43 81	99.62 60	99.69 104	85.56 269	99.68 164	99.05 81	98.31 175	97.83 295

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 285	97.86 256	96.43 81	99.62 60	99.69 104	85.56 269	99.68 164	99.05 81	98.31 175	97.83 295

EPP-MVSNet		96.69 163	96.60 148	96.96 241	97.74 237	93.05 293	99.37 277	98.56 112	88.75 361	95.83 247	99.01 194	96.01 39	98.56 264	96.92 198	97.20 213	99.25 224

gg-mvs-nofinetune		93.51 287	91.86 314	98.47 134	97.72 242	97.96 88	92.62 461	98.51 130	74.70 459	97.33 193	69.59 476	98.91 4	97.79 323	97.77 169	99.56 110	99.67 129

IB-MVS	92.85 6	94.99 235	93.94 256	98.16 154	97.72 242	95.69 196	99.99 5	98.81 67	94.28 161	92.70 296	96.90 322	95.08 61	99.17 203	96.07 218	73.88 441	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	91.83 278	97.45 188	99.04 190	97.50 9	99.10 208	94.75 247	96.37 243	99.16 231

VortexMVS		94.11 267	93.50 270	95.94 275	97.70 245	96.61 153	99.35 280	97.18 347	93.52 195	89.57 341	95.74 359	87.55 232	96.97 368	95.76 226	85.13 366	94.23 353

viewdifsd2359ckpt09		96.21 191	95.77 190	97.53 209	97.69 246	94.50 247	99.78 170	97.23 342	92.88 224	96.58 219	99.26 166	84.85 281	98.66 258	96.61 208	97.02 226	99.43 190

Syy-MVS		90.00 368	90.63 334	88.11 434	97.68 247	74.66 462	99.71 202	98.35 189	90.79 316	92.10 302	98.67 241	79.10 350	93.09 454	63.35 469	95.95 254	96.59 318

myMVS_eth3d		94.46 258	94.76 234	93.55 366	97.68 247	90.97 345	99.71 202	98.35 189	90.79 316	92.10 302	98.67 241	92.46 153	93.09 454	87.13 374	95.95 254	96.59 318

test_fmvs1_n		94.25 266	94.36 241	93.92 353	97.68 247	83.70 429	99.90 114	96.57 411	97.40 40	99.67 51	98.88 216	61.82 446	99.92 109	98.23 140	99.13 146	98.14 288

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	88.86 216	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	99.27 294	97.10 364	92.79 231	97.43 189	97.99 290	81.85 315	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	97.19 345	94.67 139	98.95 116	99.28 159	86.43 252	98.76 241	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	86.72 246	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	99.70 209	97.23 342	92.76 233	96.63 216	99.05 189	84.96 280	98.64 259	96.65 207	97.35 206	99.31 212

Vis-MVSNet		95.72 210	95.15 218	97.45 215	97.62 254	94.28 257	99.28 292	98.24 209	94.27 163	96.84 211	98.94 211	79.39 345	98.76 241	93.25 282	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	91.11 303	97.07 202	98.97 202	97.47 12	99.03 211	93.73 276	96.09 248	98.92 256

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	94.99 237	98.17 180	99.37 197

miper_ehance_all_eth		93.16 295	92.60 296	94.82 315	97.57 258	93.56 280	99.50 255	97.07 371	88.75 361	88.85 358	95.52 371	90.97 180	96.74 382	90.77 325	84.45 371	94.17 359

guyue		97.15 134	96.82 137	98.15 157	97.56 259	96.25 172	99.71 202	97.84 259	95.75 106	98.13 165	98.65 244	87.58 231	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	99.70 209	97.33 319	93.62 192	97.00 206	99.32 153	85.57 268	98.71 248	97.26 184	97.33 207	99.47 179

testing3		93.92 272	94.23 245	92.99 380	97.54 261	90.23 364	99.99 5	99.16 33	90.57 322	91.33 310	98.63 248	92.99 132	92.52 458	82.46 411	95.39 274	96.22 323

SSM_0404		95.75 209	95.16 217	97.50 213	97.53 262	95.39 210	99.11 306	97.25 337	90.81 312	95.27 261	98.83 230	84.74 284	98.67 255	95.24 232	97.69 194	98.45 277

LCM-MVSNet-Re		92.31 317	92.60 296	91.43 400	97.53 262	79.27 456	99.02 324	91.83 471	92.07 269	80.31 434	94.38 419	83.50 300	95.48 424	97.22 186	97.58 198	99.54 163

GBi-Net		90.88 345	89.82 351	94.08 345	97.53 262	91.97 318	98.43 380	96.95 385	87.05 386	89.68 334	94.72 407	71.34 407	96.11 410	87.01 378	85.65 359	94.17 359

test1		90.88 345	89.82 351	94.08 345	97.53 262	91.97 318	98.43 380	96.95 385	87.05 386	89.68 334	94.72 407	71.34 407	96.11 410	87.01 378	85.65 359	94.17 359

FMVSNet2		91.02 342	89.56 356	95.41 295	97.53 262	95.74 191	98.98 327	97.41 309	87.05 386	88.43 368	95.00 401	71.34 407	96.24 406	85.12 393	85.21 364	94.25 351

tttt0517		96.85 150	96.49 152	97.92 172	97.48 267	95.89 185	99.85 144	98.54 122	90.72 320	96.63 216	98.93 214	97.47 12	99.02 212	93.03 289	95.76 260	98.85 260

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	97.29 332	93.35 201	96.03 240	99.19 175	85.39 273	98.72 247	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	99.71 202	97.33 319	93.20 207	97.02 203	99.07 186	85.37 274	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	97.06 372	94.59 140	99.63 57	99.32 153	89.20 211	98.14 304	98.76 106	99.23 142	99.62 142

viewdifsd2359ckpt07		95.83 206	95.42 204	97.07 237	97.40 272	93.04 294	99.60 233	97.24 340	92.39 257	96.09 239	99.14 181	83.07 306	98.93 220	97.02 191	96.87 230	99.23 227

c3_l		92.53 312	91.87 313	94.52 327	97.40 272	92.99 296	99.40 269	96.93 390	87.86 376	88.69 361	95.44 376	89.95 198	96.44 396	90.45 331	80.69 405	94.14 368

viewmambaseed2359dif		95.92 202	95.55 200	97.04 238	97.38 274	93.41 285	99.78 170	96.97 383	91.14 302	96.58 219	99.27 162	84.85 281	98.75 243	96.87 201	97.12 219	98.97 251

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	84.99 279	99.93 103	99.22 75	99.09 149	98.46 276

E3		96.36 181	95.95 181	97.60 202	97.37 276	94.52 245	99.71 202	97.33 319	93.18 209	97.02 203	99.07 186	85.45 272	98.82 230	97.27 181	97.14 217	99.46 181

CDS-MVSNet		96.34 183	96.07 169	97.13 234	97.37 276	94.96 230	99.53 250	97.91 251	91.55 286	95.37 259	98.32 275	95.05 63	97.13 354	93.80 272	95.75 261	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	91.93 274	95.77 248	98.07 286	95.54 49	98.29 293	90.55 329	98.89 155	99.70 124

miper_lstm_enhance		91.81 325	91.39 324	93.06 379	97.34 279	89.18 382	99.38 275	96.79 401	86.70 393	87.47 382	95.22 391	90.00 197	95.86 419	88.26 358	81.37 394	94.15 365

baseline		96.43 176	95.98 175	97.76 188	97.34 279	95.17 226	99.51 253	97.17 349	93.92 179	96.90 209	99.28 159	85.37 274	98.64 259	97.50 176	96.86 232	99.46 181

cl____		92.31 317	91.58 318	94.52 327	97.33 281	92.77 298	99.57 240	96.78 402	86.97 390	87.56 380	95.51 372	89.43 204	96.62 388	88.60 352	82.44 386	94.16 364

SD_0403		92.63 311	93.38 277	90.40 414	97.32 282	77.91 458	97.75 410	98.03 238	91.89 275	90.83 316	98.29 279	82.00 312	93.79 447	88.51 356	95.75 261	99.52 169

DIV-MVS_self_test		92.32 316	91.60 317	94.47 331	97.31 283	92.74 300	99.58 237	96.75 403	86.99 389	87.64 378	95.54 369	89.55 203	96.50 393	88.58 353	82.44 386	94.17 359

casdiffmvs		96.42 178	95.97 178	97.77 186	97.30 284	94.98 229	99.84 149	97.09 367	93.75 188	96.58 219	99.26 166	85.07 277	98.78 238	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		94.36 263	93.48 271	96.99 240	97.29 285	93.54 281	99.96 53	96.72 405	88.35 370	93.43 284	98.94 211	82.05 311	98.05 311	88.12 363	96.48 240	99.37 197

eth_miper_zixun_eth		92.41 315	91.93 311	93.84 357	97.28 286	90.68 354	98.83 350	96.97 383	88.57 366	89.19 353	95.73 362	89.24 210	96.69 386	89.97 340	81.55 392	94.15 365

MVSFormer		96.94 146	96.60 148	97.95 168	97.28 286	97.70 101	99.55 247	97.27 334	91.17 299	99.43 82	99.54 133	90.92 181	96.89 373	94.67 250	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	90.92 181	98.71 248	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	99.69 212	97.15 352	94.46 145	98.78 125	99.21 173	85.64 266	98.77 239	98.27 137	97.31 209	99.13 235

mamba_0408		94.98 236	94.09 249	97.64 196	97.14 290	95.31 215	93.48 458	97.08 368	90.48 324	94.40 271	98.62 249	84.49 289	98.67 255	93.99 263	97.18 214	98.93 253

SSM_04072		94.77 243	94.09 249	96.82 246	97.14 290	95.31 215	93.48 458	97.08 368	90.48 324	94.40 271	98.62 249	84.49 289	96.21 407	93.99 263	97.18 214	98.93 253

SSM_0407		95.62 217	94.95 226	97.61 201	97.14 290	95.31 215	99.00 325	97.25 337	90.81 312	94.40 271	98.83 230	84.74 284	98.58 262	95.24 232	97.18 214	98.93 253

SCA		94.69 246	93.81 260	97.33 229	97.10 293	94.44 248	98.86 347	98.32 196	93.30 204	96.17 238	95.59 367	76.48 374	97.95 317	91.06 317	97.43 200	99.59 149

viewmacassd2359aftdt		95.93 201	95.45 202	97.36 226	97.09 294	94.12 264	99.57 240	97.26 336	93.05 218	96.50 223	99.17 177	82.76 307	98.68 253	96.61 208	97.04 223	99.28 219

KinetiMVS		96.10 193	95.29 212	98.53 129	97.08 295	97.12 128	99.56 244	98.12 229	94.78 132	98.44 147	98.94 211	80.30 339	99.39 190	91.56 310	98.79 161	99.06 243

TAMVS		95.85 204	95.58 198	96.65 254	97.07 296	93.50 282	99.17 302	97.82 261	91.39 296	95.02 264	98.01 287	92.20 158	97.30 344	93.75 275	95.83 258	99.14 234

Fast-Effi-MVS+-dtu		93.72 282	93.86 259	93.29 371	97.06 297	86.16 413	99.80 166	96.83 397	92.66 240	92.58 297	97.83 298	81.39 321	97.67 328	89.75 342	96.87 230	96.05 325

E4		96.01 197	95.53 201	97.44 218	97.05 298	94.23 259	99.57 240	97.30 327	92.72 234	96.47 225	99.03 191	83.98 297	98.83 227	96.92 198	96.77 233	99.27 221

CostFormer		96.10 193	95.88 187	96.78 248	97.03 299	92.55 308	97.08 423	97.83 260	90.04 337	98.72 132	94.89 405	95.01 65	98.29 293	96.54 211	95.77 259	99.50 175

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 174	97.02 300	95.34 213	99.95 72	98.45 142	97.87 26	97.02 203	99.59 124	89.64 201	99.98 50	99.41 68	99.34 137	98.42 279

test-LLR		96.47 173	96.04 171	97.78 184	97.02 300	95.44 205	99.96 53	98.21 213	94.07 169	95.55 254	96.38 339	93.90 106	98.27 297	90.42 332	98.83 159	99.64 135

test-mter		96.39 179	95.93 184	97.78 184	97.02 300	95.44 205	99.96 53	98.21 213	91.81 280	95.55 254	96.38 339	95.17 58	98.27 297	90.42 332	98.83 159	99.64 135

E6		95.83 206	95.39 206	97.14 233	97.00 303	93.58 278	99.31 285	97.30 327	92.57 247	96.45 226	99.01 194	83.44 301	98.81 234	96.80 203	96.66 234	99.04 246

icg_test_0407_2		95.04 233	94.78 233	95.84 281	96.97 304	91.64 333	98.63 369	97.12 357	92.33 260	95.60 252	98.88 216	85.65 264	96.56 391	92.12 298	95.70 264	99.32 208

IMVS_0407		95.21 228	94.80 232	96.46 259	96.97 304	91.64 333	98.81 352	97.12 357	92.33 260	95.60 252	98.88 216	85.65 264	98.42 274	92.12 298	95.70 264	99.32 208

IMVS_0404		93.83 274	93.17 284	95.80 283	96.97 304	91.64 333	97.78 409	97.12 357	92.33 260	90.87 315	98.88 216	76.78 369	96.43 397	92.12 298	95.70 264	99.32 208

IMVS_0403		95.25 226	94.81 231	96.58 256	96.97 304	91.64 333	98.97 332	97.12 357	92.33 260	95.43 257	98.88 216	85.78 263	98.79 236	92.12 298	95.70 264	99.32 208

gm-plane-assit					96.97 304	93.76 273			91.47 290		98.96 204		98.79 236	94.92 240

WB-MVSnew		92.90 301	92.77 293	93.26 373	96.95 309	93.63 277	99.71 202	98.16 223	91.49 287	94.28 276	98.14 283	81.33 323	96.48 394	79.47 428	95.46 271	89.68 455

QAPM		95.40 222	94.17 247	99.10 78	96.92 310	97.71 99	99.40 269	98.68 83	89.31 345	88.94 357	98.89 215	82.48 309	99.96 75	93.12 288	99.83 81	99.62 142

KD-MVS_2432*1600		88.00 390	86.10 394	93.70 362	96.91 311	94.04 265	97.17 420	97.12 357	84.93 414	81.96 424	92.41 438	92.48 151	94.51 440	79.23 429	52.68 475	92.56 424

miper_refine_blended		88.00 390	86.10 394	93.70 362	96.91 311	94.04 265	97.17 420	97.12 357	84.93 414	81.96 424	92.41 438	92.48 151	94.51 440	79.23 429	52.68 475	92.56 424

tpm2		95.47 220	95.18 216	96.35 265	96.91 311	91.70 331	96.96 426	97.93 247	88.04 374	98.44 147	95.40 378	93.32 121	97.97 314	94.00 262	95.61 269	99.38 195

FMVSNet5		88.32 386	87.47 388	90.88 403	96.90 314	88.39 396	97.28 417	95.68 433	82.60 434	84.67 412	92.40 440	79.83 342	91.16 463	76.39 445	81.51 393	93.09 415

3Dnovator+	91.53 11	96.31 185	95.24 213	99.52 32	96.88 315	98.64 58	99.72 199	98.24 209	95.27 120	88.42 370	98.98 200	82.76 307	99.94 93	97.10 189	99.83 81	99.96 74

Patchmatch-test		92.65 310	91.50 321	96.10 271	96.85 316	90.49 359	91.50 466	97.19 345	82.76 433	90.23 321	95.59 367	95.02 64	98.00 313	77.41 440	96.98 228	99.82 106

MVS		96.60 167	95.56 199	99.72 14	96.85 316	99.22 21	98.31 386	98.94 44	91.57 285	90.90 314	99.61 123	86.66 250	99.96 75	97.36 179	99.88 77	99.99 24

3Dnovator	91.47 12	96.28 188	95.34 209	99.08 81	96.82 318	97.47 113	99.45 266	98.81 67	95.52 114	89.39 344	99.00 197	81.97 313	99.95 84	97.27 181	99.83 81	99.84 103

EI-MVSNet		93.73 281	93.40 276	94.74 316	96.80 319	92.69 303	99.06 315	97.67 275	88.96 354	91.39 308	99.02 192	88.75 218	97.30 344	91.07 316	87.85 342	94.22 355

CVMVSNet		94.68 248	94.94 227	93.89 356	96.80 319	86.92 410	99.06 315	98.98 41	94.45 146	94.23 278	99.02 192	85.60 267	95.31 429	90.91 322	95.39 274	99.43 190

IterMVS-LS		92.69 308	92.11 307	94.43 335	96.80 319	92.74 300	99.45 266	96.89 393	88.98 352	89.65 337	95.38 381	88.77 217	96.34 401	90.98 320	82.04 389	94.22 355
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 170	96.46 155	96.91 242	96.79 322	92.50 309	99.90 114	97.38 311	96.02 98	97.79 179	99.32 153	86.36 254	98.99 213	98.26 138	96.33 244	99.23 227

IterMVS		90.91 344	90.17 346	93.12 376	96.78 323	90.42 362	98.89 341	97.05 375	89.03 349	86.49 395	95.42 377	76.59 372	95.02 431	87.22 373	84.09 374	93.93 386
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	96.74 324	98.52 62	98.31 386	98.86 59	95.82 103	89.91 328	98.98 200	87.49 234	99.96 75	97.80 164	99.73 91	99.96 74

IterMVS-SCA-FT		90.85 347	90.16 347	92.93 381	96.72 325	89.96 371	98.89 341	96.99 379	88.95 355	86.63 392	95.67 363	76.48 374	95.00 432	87.04 376	84.04 377	93.84 393

MVS-HIRNet		86.22 397	83.19 410	95.31 299	96.71 326	90.29 363	92.12 463	97.33 319	62.85 470	86.82 389	70.37 475	69.37 415	97.49 334	75.12 448	97.99 190	98.15 286

viewdifsd2359ckpt11		94.09 269	93.63 262	95.46 292	96.68 327	88.92 385	99.62 226	97.12 357	93.07 216	95.73 249	99.22 170	77.05 362	98.88 223	96.52 212	87.69 347	98.58 274

viewmsd2359difaftdt		94.09 269	93.64 261	95.46 292	96.68 327	88.92 385	99.62 226	97.13 356	93.07 216	95.73 249	99.22 170	77.05 362	98.89 222	96.52 212	87.70 346	98.58 274

VDDNet		93.12 296	91.91 312	96.76 249	96.67 329	92.65 306	98.69 364	98.21 213	82.81 432	97.75 181	99.28 159	61.57 447	99.48 185	98.09 148	94.09 293	98.15 286

dmvs_re		93.20 293	93.15 285	93.34 369	96.54 330	83.81 428	98.71 361	98.51 130	91.39 296	92.37 300	98.56 257	78.66 354	97.83 322	93.89 266	89.74 314	98.38 281

Elysia		94.50 255	93.38 277	97.85 178	96.49 331	96.70 146	98.98 327	97.78 265	90.81 312	96.19 236	98.55 259	73.63 398	98.98 214	89.41 343	98.56 167	97.88 293

StellarMVS		94.50 255	93.38 277	97.85 178	96.49 331	96.70 146	98.98 327	97.78 265	90.81 312	96.19 236	98.55 259	73.63 398	98.98 214	89.41 343	98.56 167	97.88 293

MIMVSNet		90.30 360	88.67 374	95.17 303	96.45 333	91.64 333	92.39 462	97.15 352	85.99 400	90.50 319	93.19 433	66.95 426	94.86 436	82.01 415	93.43 301	99.01 249

CR-MVSNet		93.45 290	92.62 295	95.94 275	96.29 334	92.66 304	92.01 464	96.23 419	92.62 242	96.94 207	93.31 431	91.04 178	96.03 415	79.23 429	95.96 252	99.13 235

RPMNet		89.76 372	87.28 389	97.19 232	96.29 334	92.66 304	92.01 464	98.31 198	70.19 466	96.94 207	85.87 468	87.25 239	99.78 146	62.69 470	95.96 252	99.13 235

tt0805		91.28 337	90.18 345	94.60 322	96.26 336	87.55 403	98.39 384	98.72 77	89.00 351	89.22 350	98.47 267	62.98 442	98.96 218	90.57 328	88.00 341	97.28 312

Patchmtry		89.70 373	88.49 377	93.33 370	96.24 337	89.94 374	91.37 467	96.23 419	78.22 449	87.69 377	93.31 431	91.04 178	96.03 415	80.18 427	82.10 388	94.02 376

test_vis1_rt		86.87 395	86.05 397	89.34 423	96.12 338	78.07 457	99.87 130	83.54 483	92.03 272	78.21 445	89.51 452	45.80 468	99.91 110	96.25 216	93.11 306	90.03 451

JIA-IIPM		91.76 331	90.70 332	94.94 309	96.11 339	87.51 404	93.16 460	98.13 228	75.79 455	97.58 183	77.68 473	92.84 137	97.97 314	88.47 357	96.54 236	99.33 206

OpenMVS	90.15 15	94.77 243	93.59 266	98.33 145	96.07 340	97.48 112	99.56 244	98.57 106	90.46 326	86.51 394	98.95 209	78.57 355	99.94 93	93.86 267	99.74 90	97.57 307

PAPM		98.60 37	98.42 38	99.14 72	96.05 341	98.96 27	99.90 114	99.35 24	96.68 71	98.35 154	99.66 115	96.45 35	98.51 267	99.45 65	99.89 74	99.96 74

CLD-MVS		94.06 271	93.90 257	94.55 326	96.02 342	90.69 353	99.98 21	97.72 271	96.62 75	91.05 313	98.85 228	77.21 360	98.47 268	98.11 146	89.51 320	94.48 332
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 357	88.75 373	95.25 301	95.99 343	90.16 366	91.22 468	97.54 294	76.80 451	97.26 196	86.01 467	91.88 165	96.07 414	66.16 465	95.91 256	99.51 173

ACMH+	89.98 16	90.35 358	89.54 357	92.78 385	95.99 343	86.12 414	98.81 352	97.18 347	89.38 344	83.14 420	97.76 299	68.42 420	98.43 273	89.11 348	86.05 357	93.78 396

DeepMVS_CX				82.92 445	95.98 345	58.66 476		96.01 425	92.72 234	78.34 444	95.51 372	58.29 454	98.08 308	82.57 410	85.29 362	92.03 432

ACMP	92.05 9	92.74 306	92.42 304	93.73 358	95.91 346	88.72 389	99.81 162	97.53 296	94.13 165	87.00 388	98.23 281	74.07 395	98.47 268	96.22 217	88.86 327	93.99 381
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 285	93.03 287	95.35 296	95.86 347	86.94 409	99.87 130	96.36 417	96.85 62	99.54 71	98.79 232	52.41 462	99.83 139	98.64 114	98.97 153	99.29 217

HQP-NCC					95.78 348		99.87 130		96.82 64	93.37 285

ACMP_Plane					95.78 348		99.87 130		96.82 64	93.37 285

HQP-MVS		94.61 250	94.50 238	94.92 310	95.78 348	91.85 323	99.87 130	97.89 252	96.82 64	93.37 285	98.65 244	80.65 333	98.39 280	97.92 158	89.60 315	94.53 328

NP-MVS					95.77 351	91.79 325					98.65 244

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 114	95.76 352	96.20 174	99.94 90	98.05 235	98.17 13	98.89 120	99.42 141	87.65 229	99.90 112	99.50 61	99.60 107	99.82 106

plane_prior6					95.76 352	91.72 330						80.47 337

ACMM	91.95 10	92.88 302	92.52 302	93.98 352	95.75 354	89.08 384	99.77 175	97.52 298	93.00 219	89.95 327	97.99 290	76.17 378	98.46 271	93.63 279	88.87 326	94.39 340
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 274	92.84 289	96.80 247	95.73 355	93.57 279	99.88 127	97.24 340	92.57 247	92.92 292	96.66 331	78.73 353	97.67 328	87.75 366	94.06 294	99.17 230

plane_prior1					95.73 355

jason		97.24 129	96.86 134	98.38 144	95.73 355	97.32 117	99.97 39	97.40 310	95.34 118	98.60 141	99.54 133	87.70 228	98.56 264	97.94 157	99.47 124	99.25 224
jason: jason.
mmtdpeth		88.52 384	87.75 386	90.85 405	95.71 358	83.47 434	98.94 335	94.85 448	88.78 360	97.19 198	89.58 451	63.29 440	98.97 216	98.54 119	62.86 469	90.10 450

HQP_MVS		94.49 257	94.36 241	94.87 311	95.71 358	91.74 327	99.84 149	97.87 254	96.38 84	93.01 290	98.59 252	80.47 337	98.37 286	97.79 167	89.55 318	94.52 330

plane_prior7					95.71 358	91.59 339

ITE_SJBPF				92.38 388	95.69 361	85.14 420		95.71 432	92.81 228	89.33 347	98.11 284	70.23 413	98.42 274	85.91 388	88.16 339	93.59 404

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 199	95.65 362	94.21 261	99.83 156	98.50 136	96.27 91	99.65 53	99.64 118	84.72 286	99.93 103	99.04 84	98.84 158	98.74 267

ACMH	89.72 17	90.64 351	89.63 354	93.66 364	95.64 363	88.64 392	98.55 372	97.45 303	89.03 349	81.62 427	97.61 300	69.75 414	98.41 276	89.37 345	87.62 348	93.92 387
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	95.63 364	95.96 183	99.74 188	98.88 54	92.94 221	91.61 306	98.97 202	97.72 6	98.62 261	94.83 244	98.08 188	97.53 309

FMVSNet1		88.50 385	86.64 392	94.08 345	95.62 365	91.97 318	98.43 380	96.95 385	83.00 430	86.08 402	94.72 407	59.09 453	96.11 410	81.82 417	84.07 375	94.17 359

LuminaMVS		96.63 166	96.21 166	97.87 177	95.58 366	96.82 141	99.12 304	97.67 275	94.47 144	97.88 174	98.31 277	87.50 233	98.71 248	98.07 150	97.29 210	98.10 289

LPG-MVS_test		92.96 299	92.71 294	93.71 360	95.43 367	88.67 390	99.75 185	97.62 283	92.81 228	90.05 323	98.49 263	75.24 385	98.40 278	95.84 223	89.12 322	94.07 373

LGP-MVS_train				93.71 360	95.43 367	88.67 390		97.62 283	92.81 228	90.05 323	98.49 263	75.24 385	98.40 278	95.84 223	89.12 322	94.07 373

tpm		93.70 283	93.41 275	94.58 324	95.36 369	87.41 405	97.01 424	96.90 392	90.85 310	96.72 215	94.14 422	90.40 192	96.84 377	90.75 326	88.54 334	99.51 173

D2MVS		92.76 305	92.59 300	93.27 372	95.13 370	89.54 378	99.69 212	99.38 22	92.26 265	87.59 379	94.61 413	85.05 278	97.79 323	91.59 309	88.01 340	92.47 427

VPA-MVSNet		92.70 307	91.55 320	96.16 269	95.09 371	96.20 174	98.88 343	99.00 39	91.02 307	91.82 305	95.29 388	76.05 380	97.96 316	95.62 228	81.19 395	94.30 347

LTVRE_ROB	88.28 18	90.29 361	89.05 368	94.02 348	95.08 372	90.15 367	97.19 419	97.43 305	84.91 416	83.99 416	97.06 317	74.00 396	98.28 295	84.08 399	87.71 344	93.62 403
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		87.87 392	86.51 393	91.94 394	95.05 373	85.57 418	97.65 411	94.08 458	84.40 420	81.82 426	96.85 326	62.14 445	98.33 289	80.25 426	86.37 355	91.91 434

test0.0.03 1		93.86 273	93.61 263	94.64 320	95.02 374	92.18 316	99.93 97	98.58 104	94.07 169	87.96 374	98.50 262	93.90 106	94.96 433	81.33 418	93.17 304	96.78 315

UniMVSNet (Re)		93.07 298	92.13 306	95.88 278	94.84 375	96.24 173	99.88 127	98.98 41	92.49 253	89.25 348	95.40 378	87.09 241	97.14 353	93.13 287	78.16 419	94.26 349

USDC		90.00 368	88.96 369	93.10 378	94.81 376	88.16 398	98.71 361	95.54 437	93.66 190	83.75 418	97.20 311	65.58 431	98.31 291	83.96 402	87.49 350	92.85 421

VPNet		91.81 325	90.46 336	95.85 280	94.74 377	95.54 202	98.98 327	98.59 102	92.14 267	90.77 318	97.44 304	68.73 418	97.54 333	94.89 243	77.89 421	94.46 333

FIs		94.10 268	93.43 272	96.11 270	94.70 378	96.82 141	99.58 237	98.93 48	92.54 249	89.34 346	97.31 308	87.62 230	97.10 357	94.22 261	86.58 353	94.40 339

UniMVSNet_ETH3D		90.06 367	88.58 376	94.49 330	94.67 379	88.09 399	97.81 408	97.57 291	83.91 423	88.44 366	97.41 305	57.44 455	97.62 330	91.41 311	88.59 333	97.77 298

UniMVSNet_NR-MVSNet		92.95 300	92.11 307	95.49 288	94.61 380	95.28 219	99.83 156	99.08 36	91.49 287	89.21 351	96.86 325	87.14 240	96.73 383	93.20 283	77.52 424	94.46 333

test_fmvs2		89.47 377	89.70 353	88.77 430	94.54 381	75.74 459	99.83 156	94.70 454	94.71 136	91.08 311	96.82 330	54.46 458	97.78 325	92.87 290	88.27 337	92.80 422

MonoMVSNet		94.82 238	94.43 239	95.98 273	94.54 381	90.73 352	99.03 322	97.06 372	93.16 211	93.15 289	95.47 375	88.29 221	97.57 331	97.85 162	91.33 312	99.62 142

WR-MVS		92.31 317	91.25 325	95.48 291	94.45 383	95.29 218	99.60 233	98.68 83	90.10 334	88.07 373	96.89 323	80.68 332	96.80 381	93.14 286	79.67 412	94.36 341

nrg030		93.51 287	92.53 301	96.45 260	94.36 384	97.20 123	99.81 162	97.16 351	91.60 284	89.86 330	97.46 303	86.37 253	97.68 327	95.88 222	80.31 408	94.46 333

tfpnnormal		89.29 380	87.61 387	94.34 338	94.35 385	94.13 263	98.95 334	98.94 44	83.94 421	84.47 413	95.51 372	74.84 390	97.39 336	77.05 443	80.41 406	91.48 437

FC-MVSNet-test		93.81 277	93.15 285	95.80 283	94.30 386	96.20 174	99.42 268	98.89 52	92.33 260	89.03 356	97.27 310	87.39 236	96.83 379	93.20 283	86.48 354	94.36 341

SSC-MVS3.2		89.59 375	88.66 375	92.38 388	94.29 387	86.12 414	99.49 257	97.66 278	90.28 333	88.63 363	95.18 392	64.46 436	96.88 375	85.30 392	82.66 383	94.14 368

MS-PatchMatch		90.65 350	90.30 341	91.71 399	94.22 388	85.50 419	98.24 391	97.70 272	88.67 363	86.42 397	96.37 341	67.82 423	98.03 312	83.62 404	99.62 100	91.60 435

WR-MVS_H		91.30 335	90.35 339	94.15 342	94.17 389	92.62 307	99.17 302	98.94 44	88.87 358	86.48 396	94.46 418	84.36 292	96.61 389	88.19 360	78.51 417	93.21 413

DU-MVS		92.46 314	91.45 323	95.49 288	94.05 390	95.28 219	99.81 162	98.74 76	92.25 266	89.21 351	96.64 333	81.66 318	96.73 383	93.20 283	77.52 424	94.46 333

NR-MVSNet		91.56 333	90.22 343	95.60 286	94.05 390	95.76 190	98.25 390	98.70 79	91.16 301	80.78 433	96.64 333	83.23 304	96.57 390	91.41 311	77.73 423	94.46 333

CP-MVSNet		91.23 339	90.22 343	94.26 340	93.96 392	92.39 312	99.09 308	98.57 106	88.95 355	86.42 397	96.57 336	79.19 348	96.37 399	90.29 335	78.95 414	94.02 376

XXY-MVS		91.82 324	90.46 336	95.88 278	93.91 393	95.40 209	98.87 346	97.69 274	88.63 365	87.87 375	97.08 315	74.38 394	97.89 320	91.66 308	84.07 375	94.35 344

PS-CasMVS		90.63 352	89.51 359	93.99 351	93.83 394	91.70 331	98.98 327	98.52 127	88.48 367	86.15 401	96.53 338	75.46 383	96.31 403	88.83 350	78.86 416	93.95 384

test_0402		85.58 399	83.94 404	90.50 411	93.81 395	85.04 421	98.55 372	95.20 445	76.01 453	79.72 439	95.13 393	64.15 438	96.26 405	66.04 466	86.88 352	90.21 448

XVG-ACMP-BASELINE		91.22 340	90.75 331	92.63 387	93.73 396	85.61 417	98.52 376	97.44 304	92.77 232	89.90 329	96.85 326	66.64 428	98.39 280	92.29 295	88.61 331	93.89 389

TranMVSNet+NR-MVSNet		91.68 332	90.61 335	94.87 311	93.69 397	93.98 268	99.69 212	98.65 87	91.03 306	88.44 366	96.83 329	80.05 341	96.18 408	90.26 336	76.89 432	94.45 338

TransMVSNet (Re)		87.25 393	85.28 400	93.16 375	93.56 398	91.03 344	98.54 374	94.05 460	83.69 425	81.09 431	96.16 347	75.32 384	96.40 398	76.69 444	68.41 457	92.06 431

v10		90.25 362	88.82 371	94.57 325	93.53 399	93.43 284	99.08 310	96.87 395	85.00 413	87.34 386	94.51 414	80.93 328	97.02 367	82.85 409	79.23 413	93.26 411

testgi		89.01 382	88.04 383	91.90 395	93.49 400	84.89 423	99.73 195	95.66 434	93.89 183	85.14 408	98.17 282	59.68 451	94.66 439	77.73 439	88.88 325	96.16 324

v8		90.54 354	89.17 364	94.66 319	93.43 401	93.40 287	99.20 299	96.94 389	85.76 403	87.56 380	94.51 414	81.96 314	97.19 350	84.94 395	78.25 418	93.38 409

V42		91.28 337	90.12 348	94.74 316	93.42 402	93.46 283	99.68 215	97.02 376	87.36 382	89.85 332	95.05 396	81.31 324	97.34 339	87.34 371	80.07 410	93.40 407

pm-mvs1		89.36 379	87.81 385	94.01 349	93.40 403	91.93 321	98.62 370	96.48 415	86.25 398	83.86 417	96.14 349	73.68 397	97.04 363	86.16 385	75.73 437	93.04 417

v1144		91.09 341	89.83 350	94.87 311	93.25 404	93.69 276	99.62 226	96.98 381	86.83 392	89.64 338	94.99 402	80.94 327	97.05 360	85.08 394	81.16 396	93.87 391

v1192		90.62 353	89.25 363	94.72 318	93.13 405	93.07 291	99.50 255	97.02 376	86.33 397	89.56 342	95.01 399	79.22 347	97.09 359	82.34 413	81.16 396	94.01 378

v2v482		91.30 335	90.07 349	95.01 306	93.13 405	93.79 271	99.77 175	97.02 376	88.05 373	89.25 348	95.37 382	80.73 331	97.15 352	87.28 372	80.04 411	94.09 372

OPM-MVS		93.21 292	92.80 291	94.44 333	93.12 407	90.85 351	99.77 175	97.61 286	96.19 94	91.56 307	98.65 244	75.16 389	98.47 268	93.78 274	89.39 321	93.99 381
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 348	89.52 358	94.59 323	93.11 408	92.77 298	99.56 244	96.99 379	86.38 396	89.82 333	94.95 404	80.50 336	97.10 357	83.98 401	80.41 406	93.90 388

PEN-MVS		90.19 364	89.06 367	93.57 365	93.06 409	90.90 349	99.06 315	98.47 139	88.11 372	85.91 403	96.30 343	76.67 370	95.94 418	87.07 375	76.91 431	93.89 389

v1240		90.20 363	88.79 372	94.44 333	93.05 410	92.27 314	99.38 275	96.92 391	85.89 401	89.36 345	94.87 406	77.89 359	97.03 365	80.66 422	81.08 399	94.01 378

FE-MVSNET3		92.78 304	91.73 315	95.92 277	93.03 411	96.82 141	99.83 156	97.79 262	90.58 321	90.09 322	95.04 397	84.75 283	96.72 385	88.20 359	86.23 356	94.23 353

v148		90.70 349	89.63 354	93.92 353	92.97 412	90.97 345	99.75 185	96.89 393	87.51 379	88.27 371	95.01 399	81.67 317	97.04 363	87.40 370	77.17 429	93.75 397

v1921920		90.46 355	89.12 365	94.50 329	92.96 413	92.46 310	99.49 257	96.98 381	86.10 399	89.61 340	95.30 385	78.55 356	97.03 365	82.17 414	80.89 404	94.01 378

MVStest1		85.03 405	82.76 414	91.83 396	92.95 414	89.16 383	98.57 371	94.82 449	71.68 464	68.54 467	95.11 395	83.17 305	95.66 422	74.69 449	65.32 464	90.65 444

tt0320-xc		82.94 420	80.35 427	90.72 409	92.90 415	83.54 432	96.85 429	94.73 452	63.12 469	79.85 438	93.77 426	49.43 466	95.46 425	80.98 421	71.54 446	93.16 414

Baseline_NR-MVSNet		90.33 359	89.51 359	92.81 384	92.84 416	89.95 372	99.77 175	93.94 461	84.69 418	89.04 355	95.66 364	81.66 318	96.52 392	90.99 319	76.98 430	91.97 433

test_method		80.79 426	79.70 429	84.08 442	92.83 417	67.06 468	99.51 253	95.42 439	54.34 474	81.07 432	93.53 428	44.48 469	92.22 460	78.90 434	77.23 428	92.94 419

pmmvs4		92.10 321	91.07 329	95.18 302	92.82 418	94.96 230	99.48 260	96.83 397	87.45 381	88.66 362	96.56 337	83.78 298	96.83 379	89.29 346	84.77 369	93.75 397

LF4IMVS		89.25 381	88.85 370	90.45 413	92.81 419	81.19 449	98.12 398	94.79 450	91.44 291	86.29 399	97.11 313	65.30 434	98.11 306	88.53 355	85.25 363	92.07 430

tt0320		83.56 419	81.15 422	90.77 407	92.77 420	83.58 431	96.83 430	95.52 438	63.26 468	81.36 429	92.54 436	53.26 460	95.77 420	80.45 423	74.38 440	92.96 418

DTE-MVSNet		89.40 378	88.24 381	92.88 382	92.66 421	89.95 372	99.10 307	98.22 212	87.29 383	85.12 409	96.22 345	76.27 377	95.30 430	83.56 405	75.74 436	93.41 406

EU-MVSNet		90.14 366	90.34 340	89.54 422	92.55 422	81.06 450	98.69 364	98.04 236	91.41 295	86.59 393	96.84 328	80.83 330	93.31 452	86.20 384	81.91 390	94.26 349

APD_test1		81.15 424	80.92 424	81.86 446	92.45 423	59.76 475	96.04 444	93.61 464	73.29 462	77.06 448	96.64 333	44.28 470	96.16 409	72.35 453	82.52 384	89.67 456

sc_t1		85.01 406	82.46 416	92.67 386	92.44 424	83.09 435	97.39 415	95.72 431	65.06 467	85.64 406	96.16 347	49.50 465	97.34 339	84.86 396	75.39 438	97.57 307

our_test_3		90.39 356	89.48 361	93.12 376	92.40 425	89.57 377	99.33 282	96.35 418	87.84 377	85.30 407	94.99 402	84.14 295	96.09 413	80.38 424	84.56 370	93.71 402

ppachtmachnet_test		89.58 376	88.35 379	93.25 374	92.40 425	90.44 361	99.33 282	96.73 404	85.49 408	85.90 404	95.77 358	81.09 326	96.00 417	76.00 447	82.49 385	93.30 410

v7n		89.65 374	88.29 380	93.72 359	92.22 427	90.56 358	99.07 314	97.10 364	85.42 410	86.73 390	94.72 407	80.06 340	97.13 354	81.14 419	78.12 420	93.49 405

dmvs_testset		83.79 415	86.07 396	76.94 450	92.14 428	48.60 485	96.75 431	90.27 475	89.48 343	78.65 442	98.55 259	79.25 346	86.65 473	66.85 463	82.69 382	95.57 326

PS-MVSNAJss		93.64 284	93.31 281	94.61 321	92.11 429	92.19 315	99.12 304	97.38 311	92.51 252	88.45 365	96.99 321	91.20 173	97.29 347	94.36 255	87.71 344	94.36 341

pmmvs5		90.17 365	89.09 366	93.40 368	92.10 430	89.77 375	99.74 188	95.58 436	85.88 402	87.24 387	95.74 359	73.41 400	96.48 394	88.54 354	83.56 379	93.95 384

N_pmnet		80.06 429	80.78 425	77.89 449	91.94 431	45.28 487	98.80 355	56.82 489	78.10 450	80.08 436	93.33 429	77.03 364	95.76 421	68.14 461	82.81 381	92.64 423

test_djsdf		92.83 303	92.29 305	94.47 331	91.90 432	92.46 310	99.55 247	97.27 334	91.17 299	89.96 326	96.07 353	81.10 325	96.89 373	94.67 250	88.91 324	94.05 375

SixPastTwentyTwo		88.73 383	88.01 384	90.88 403	91.85 433	82.24 441	98.22 395	95.18 446	88.97 353	82.26 423	96.89 323	71.75 405	96.67 387	84.00 400	82.98 380	93.72 401

K. test v3		88.05 389	87.24 390	90.47 412	91.82 434	82.23 442	98.96 333	97.42 307	89.05 348	76.93 450	95.60 366	68.49 419	95.42 426	85.87 389	81.01 402	93.75 397

OurMVSNet-221017-0		89.81 371	89.48 361	90.83 406	91.64 435	81.21 448	98.17 397	95.38 441	91.48 289	85.65 405	97.31 308	72.66 401	97.29 347	88.15 361	84.83 368	93.97 383

mvs_tets		91.81 325	91.08 328	94.00 350	91.63 436	90.58 357	98.67 366	97.43 305	92.43 254	87.37 385	97.05 318	71.76 404	97.32 342	94.75 247	88.68 330	94.11 371

Gipuma		66.95 442	65.00 442	72.79 455	91.52 437	67.96 467	66.16 479	95.15 447	47.89 476	58.54 473	67.99 478	29.74 474	87.54 472	50.20 477	77.83 422	62.87 478
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	91.47 438	95.56 201	99.84 149	97.30 327	97.74 30	97.89 173	99.35 152	79.62 343	99.85 129	99.25 74	99.24 141	99.55 159

jajsoiax		91.92 323	91.18 326	94.15 342	91.35 439	90.95 348	99.00 325	97.42 307	92.61 243	87.38 384	97.08 315	72.46 402	97.36 337	94.53 253	88.77 328	94.13 370

MDA-MVSNet-bldmvs		84.09 413	81.52 420	91.81 397	91.32 440	88.00 401	98.67 366	95.92 427	80.22 444	55.60 476	93.32 430	68.29 421	93.60 450	73.76 450	76.61 433	93.82 395

MVP-Stereo		90.93 343	90.45 338	92.37 390	91.25 441	88.76 387	98.05 402	96.17 421	87.27 384	84.04 414	95.30 385	78.46 357	97.27 349	83.78 403	99.70 93	91.09 438
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 401	83.32 409	92.10 392	90.96 442	88.58 393	99.20 299	96.52 413	79.70 446	57.12 475	92.69 435	79.11 349	93.86 446	77.10 442	77.46 426	93.86 392

YYNet1		85.50 402	83.33 408	92.00 393	90.89 443	88.38 397	99.22 298	96.55 412	79.60 447	57.26 474	92.72 434	79.09 351	93.78 448	77.25 441	77.37 427	93.84 393

anonymousdsp		91.79 330	90.92 330	94.41 336	90.76 444	92.93 297	98.93 337	97.17 349	89.08 347	87.46 383	95.30 385	78.43 358	96.92 371	92.38 294	88.73 329	93.39 408

lessismore_v0				90.53 410	90.58 445	80.90 451		95.80 428		77.01 449	95.84 356	66.15 430	96.95 369	83.03 408	75.05 439	93.74 400

EG-PatchMatch MVS		85.35 403	83.81 406	89.99 420	90.39 446	81.89 444	98.21 396	96.09 423	81.78 437	74.73 456	93.72 427	51.56 464	97.12 356	79.16 432	88.61 331	90.96 441

EGC-MVSNET		69.38 435	63.76 445	86.26 439	90.32 447	81.66 447	96.24 440	93.85 462	0.99 486	3.22 487	92.33 441	52.44 461	92.92 456	59.53 473	84.90 367	84.21 467

CMPMVS	61.59 21	84.75 409	85.14 401	83.57 443	90.32 447	62.54 471	96.98 425	97.59 290	74.33 460	69.95 464	96.66 331	64.17 437	98.32 290	87.88 365	88.41 336	89.84 453
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 412	82.92 412	89.21 424	90.03 449	82.60 438	96.89 428	95.62 435	80.59 442	75.77 455	89.17 453	65.04 435	94.79 437	72.12 454	81.02 401	90.23 447

pmmvs6		85.69 398	83.84 405	91.26 402	90.00 450	84.41 426	97.82 407	96.15 422	75.86 454	81.29 430	95.39 380	61.21 448	96.87 376	83.52 406	73.29 442	92.50 426

ttmdpeth		88.23 388	87.06 391	91.75 398	89.91 451	87.35 406	98.92 340	95.73 430	87.92 375	84.02 415	96.31 342	68.23 422	96.84 377	86.33 383	76.12 434	91.06 439

DSMNet-mixed		88.28 387	88.24 381	88.42 432	89.64 452	75.38 461	98.06 401	89.86 476	85.59 407	88.20 372	92.14 442	76.15 379	91.95 461	78.46 436	96.05 249	97.92 292

UnsupCasMVSNet_eth		85.52 400	83.99 402	90.10 418	89.36 453	83.51 433	96.65 432	97.99 240	89.14 346	75.89 454	93.83 424	63.25 441	93.92 444	81.92 416	67.90 460	92.88 420

Anonymous20231206		86.32 396	85.42 399	89.02 426	89.11 454	80.53 454	99.05 319	95.28 442	85.43 409	82.82 421	93.92 423	74.40 393	93.44 451	66.99 462	81.83 391	93.08 416

Anonymous20240521		85.15 404	83.81 406	89.16 425	88.32 455	82.69 437	98.80 355	95.74 429	79.72 445	81.53 428	90.99 445	65.38 433	94.16 442	72.69 452	81.11 398	90.63 445

OpenMVS_ROB	79.82 20	83.77 416	81.68 419	90.03 419	88.30 456	82.82 436	98.46 377	95.22 444	73.92 461	76.00 453	91.29 444	55.00 457	96.94 370	68.40 460	88.51 335	90.34 446

test20.03		84.72 410	83.99 402	86.91 437	88.19 457	80.62 453	98.88 343	95.94 426	88.36 369	78.87 440	94.62 412	68.75 417	89.11 468	66.52 464	75.82 435	91.00 440

KD-MVS_self_test		83.59 417	82.06 417	88.20 433	86.93 458	80.70 452	97.21 418	96.38 416	82.87 431	82.49 422	88.97 454	67.63 424	92.32 459	73.75 451	62.30 471	91.58 436

MIMVSNet1		82.58 421	80.51 426	88.78 428	86.68 459	84.20 427	96.65 432	95.41 440	78.75 448	78.59 443	92.44 437	51.88 463	89.76 467	65.26 467	78.95 414	92.38 429

CL-MVSNet_self_test		84.50 411	83.15 411	88.53 431	86.00 460	81.79 445	98.82 351	97.35 315	85.12 412	83.62 419	90.91 447	76.66 371	91.40 462	69.53 458	60.36 472	92.40 428

UnsupCasMVSNet_bld		79.97 431	77.03 436	88.78 428	85.62 461	81.98 443	93.66 456	97.35 315	75.51 457	70.79 463	83.05 470	48.70 467	94.91 435	78.31 437	60.29 473	89.46 459

mvs5depth		84.87 407	82.90 413	90.77 407	85.59 462	84.84 424	91.10 469	93.29 466	83.14 428	85.07 410	94.33 420	62.17 444	97.32 342	78.83 435	72.59 445	90.14 449

Patchmatch-RL test		86.90 394	85.98 398	89.67 421	84.45 463	75.59 460	89.71 472	92.43 468	86.89 391	77.83 447	90.94 446	94.22 95	93.63 449	87.75 366	69.61 451	99.79 111

pmmvs-eth3d		84.03 414	81.97 418	90.20 416	84.15 464	87.09 408	98.10 400	94.73 452	83.05 429	74.10 460	87.77 460	65.56 432	94.01 443	81.08 420	69.24 453	89.49 458

test_fmvs3		79.99 430	80.17 428	79.45 448	84.02 465	62.83 469	99.05 319	93.49 465	88.29 371	80.06 437	86.65 465	28.09 476	88.00 469	88.63 351	73.27 443	87.54 465

PM-MVS		80.47 427	78.88 431	85.26 440	83.79 466	72.22 463	95.89 447	91.08 473	85.71 406	76.56 452	88.30 456	36.64 472	93.90 445	82.39 412	69.57 452	89.66 457

new-patchmatchnet		81.19 423	79.34 430	86.76 438	82.86 467	80.36 455	97.92 404	95.27 443	82.09 436	72.02 461	86.87 464	62.81 443	90.74 465	71.10 455	63.08 468	89.19 461

FE-MVSNET2		83.57 418	81.36 421	90.20 416	82.83 468	87.59 402	98.28 388	96.04 424	85.33 411	74.13 459	87.45 461	59.16 452	93.26 453	79.12 433	69.91 449	89.77 454

FE-MVSNET		81.05 425	78.81 432	87.79 435	81.98 469	83.70 429	98.23 393	91.78 472	81.27 439	74.29 458	87.44 462	60.92 450	90.67 466	64.92 468	68.43 456	89.01 462

mvsany_test3		82.12 422	81.14 423	85.06 441	81.87 470	70.41 465	97.09 422	92.14 469	91.27 298	77.84 446	88.73 455	39.31 471	95.49 423	90.75 326	71.24 447	89.29 460

WB-MVS		76.28 433	77.28 435	73.29 454	81.18 471	54.68 479	97.87 406	94.19 457	81.30 438	69.43 465	90.70 448	77.02 365	82.06 477	35.71 482	68.11 459	83.13 468

test_f		78.40 432	77.59 434	80.81 447	80.82 472	62.48 472	96.96 426	93.08 467	83.44 426	74.57 457	84.57 469	27.95 477	92.63 457	84.15 398	72.79 444	87.32 466

SSC-MVS		75.42 434	76.40 437	72.49 458	80.68 473	53.62 480	97.42 413	94.06 459	80.42 443	68.75 466	90.14 450	76.54 373	81.66 478	33.25 483	66.34 463	82.19 469

pmmvs3		80.27 428	77.77 433	87.76 436	80.32 474	82.43 440	98.23 393	91.97 470	72.74 463	78.75 441	87.97 459	57.30 456	90.99 464	70.31 456	62.37 470	89.87 452

testf1		68.38 438	66.92 439	72.78 456	78.80 475	50.36 482	90.95 470	87.35 481	55.47 472	58.95 471	88.14 457	20.64 481	87.60 470	57.28 474	64.69 465	80.39 471

APD_test2		68.38 438	66.92 439	72.78 456	78.80 475	50.36 482	90.95 470	87.35 481	55.47 472	58.95 471	88.14 457	20.64 481	87.60 470	57.28 474	64.69 465	80.39 471

ambc				83.23 444	77.17 477	62.61 470	87.38 474	94.55 456		76.72 451	86.65 465	30.16 473	96.36 400	84.85 397	69.86 450	90.73 443

test_vis3_rt		68.82 436	66.69 441	75.21 453	76.24 478	60.41 474	96.44 435	68.71 488	75.13 458	50.54 479	69.52 477	16.42 486	96.32 402	80.27 425	66.92 462	68.89 475

TDRefinement		84.76 408	82.56 415	91.38 401	74.58 479	84.80 425	97.36 416	94.56 455	84.73 417	80.21 435	96.12 352	63.56 439	98.39 280	87.92 364	63.97 467	90.95 442

E-PMN		52.30 446	52.18 448	52.67 464	71.51 480	45.40 486	93.62 457	76.60 486	36.01 480	43.50 481	64.13 480	27.11 478	67.31 483	31.06 484	26.06 479	45.30 482

EMVS		51.44 448	51.22 450	52.11 465	70.71 481	44.97 488	94.04 453	75.66 487	35.34 482	42.40 482	61.56 483	28.93 475	65.87 484	27.64 485	24.73 480	45.49 481

PMMVS2		67.15 441	64.15 444	76.14 452	70.56 482	62.07 473	93.89 454	87.52 480	58.09 471	60.02 470	78.32 472	22.38 480	84.54 475	59.56 472	47.03 477	81.80 470

FPMVS		68.72 437	68.72 438	68.71 460	65.95 483	44.27 489	95.97 446	94.74 451	51.13 475	53.26 477	90.50 449	25.11 479	83.00 476	60.80 471	80.97 403	78.87 473

wuyk23d		20.37 452	20.84 455	18.99 468	65.34 484	27.73 491	50.43 480	7.67 492	9.50 485	8.01 486	6.34 486	6.13 489	26.24 485	23.40 486	10.69 484	2.99 483

LCM-MVSNet		67.77 440	64.73 443	76.87 451	62.95 485	56.25 478	89.37 473	93.74 463	44.53 477	61.99 469	80.74 471	20.42 483	86.53 474	69.37 459	59.50 474	87.84 463

MVE	53.74 22	51.54 447	47.86 451	62.60 462	59.56 486	50.93 481	79.41 477	77.69 485	35.69 481	36.27 483	61.76 482	5.79 490	69.63 481	37.97 481	36.61 478	67.24 476
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 444	52.24 447	67.66 461	49.27 487	56.82 477	83.94 475	82.02 484	70.47 465	33.28 484	64.54 479	17.23 485	69.16 482	45.59 479	23.85 481	77.02 474

tmp_tt		65.23 443	62.94 446	72.13 459	44.90 488	50.03 484	81.05 476	89.42 479	38.45 478	48.51 480	99.90 22	54.09 459	78.70 480	91.84 307	18.26 482	87.64 464

PMVS	49.05 23	53.75 445	51.34 449	60.97 463	40.80 489	34.68 490	74.82 478	89.62 478	37.55 479	28.67 485	72.12 474	7.09 488	81.63 479	43.17 480	68.21 458	66.59 477
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 450	39.14 453	33.31 466	19.94 490	24.83 492	98.36 385	9.75 491	15.53 484	51.31 478	87.14 463	19.62 484	17.74 486	47.10 478	3.47 485	57.36 479

testmvs		40.60 449	44.45 452	29.05 467	19.49 491	14.11 493	99.68 215	18.47 490	20.74 483	64.59 468	98.48 266	10.95 487	17.09 487	56.66 476	11.01 483	55.94 480

mmdepth		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

monomultidepth		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

test_blank		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.02 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

eth-test2					0.00 492

eth-test					0.00 492

uanet_test		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

DCPMVS		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

cdsmvs_eth3d_5k		23.43 451	31.24 454	0.00 469	0.00 492	0.00 494	0.00 481	98.09 230	0.00 487	0.00 488	99.67 113	83.37 302	0.00 488	0.00 487	0.00 486	0.00 484

pcd_1.5k_mvsjas		7.60 454	10.13 457	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	91.20 173	0.00 488	0.00 487	0.00 486	0.00 484

sosnet-low-res		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

sosnet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

uncertanet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

Regformer		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

ab-mvs-re		8.28 453	11.04 456	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	99.40 146	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

uanet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 488	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

TestfortrainingZip							99.97 39

WAC-MVS						90.97 345								86.10 387

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							95.78 448				59.23 484	93.20 128	97.74 326	91.06 317

test_post											63.35 481	94.43 82	98.13 305

patchmatchnet-post											91.70 443	95.12 59	97.95 317

MTMP							99.87 130	96.49 414

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							99.46 265		94.21 164	99.85 18			99.95 84	96.96 196

新几何2							99.40 269

无先验							99.49 257	98.71 78	93.46 197				100.00 1	94.36 255		99.99 24

原ACMM2							99.90 114

testdata2													99.99 39	90.54 330

segment_acmp												96.68 31

testdata1							99.28 292		96.35 90

plane_prior5								97.87 254					98.37 286	97.79 167	89.55 318	94.52 330

plane_prior4											98.59 252

plane_prior3						91.64 333			96.63 73	93.01 290

plane_prior2							99.84 149		96.38 84

plane_prior						91.74 327	99.86 141		96.76 68						89.59 317

n2								0.00 493

nn								0.00 493

door-mid								89.69 477

test11								98.44 147

door								90.31 474

HQP5-MVS						91.85 323

BP-MVS														97.92 158

HQP4-MVS										93.37 285			98.39 280			94.53 328

HQP3-MVS								97.89 252							89.60 315

HQP2-MVS												80.65 333

MDTV_nov1_ep13_2view						96.26 168	96.11 442		91.89 275	98.06 166		94.40 84		94.30 258		99.67 129

ACMMP++_ref															87.04 351

ACMMP++															88.23 338

Test By Simon												92.82 139

High-resolution multi-view benchmark