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	99.09 309	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	89.90 339	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	92.06 271	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	41.37 486	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	92.34 259	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	99.18 302	99.45 18	94.84 131	96.41 230	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	99.24 297	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	99.61 231	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	99.39 274	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	95.11 219	98.02 166	99.85 60	95.10 228	98.74 359	98.50 136	87.22 385	93.66 283	99.86 33	87.45 236	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	91.87 167	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	91.75 282	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	93.36 289	99.65 220	97.95 245	96.03 97	97.41 190	99.70 100	89.61 203	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	99.74 189	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	95.11 234	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	87.80 227	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	96.87 245	99.71 82	91.74 328	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	99.74 189	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	95.59 287	99.67 87	86.91 412	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	93.92 354	99.63 89	81.76 447	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	96.72 251	99.59 91	96.99 135	99.95 72	99.10 34	94.06 171	98.27 157	95.80 357	89.00 215	99.95 84	99.12 78	87.53 349	93.24 413

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	98.35 272	82.28 311	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	87.24 408	99.23 298	96.95 386	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	91.86 277	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	99.47 262	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	84.48 292	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	90.30 195	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	99.61 231	98.09 230	90.38 327	89.16 354	95.61 365	90.56 190	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	99.05 320	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		94.54 251	93.42 273	97.91 174	99.46 104	94.04 265	98.93 338	97.48 302	81.15 440	90.04 325	99.55 131	87.02 244	99.95 84	88.97 350	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	98.46 378	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	86.21 258	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	91.78 327	99.78 170	98.00 239	89.89 340	94.52 268	99.47 137	91.97 165	99.18 202	69.90 458	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	87.83 377	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	87.04 243	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	97.15 352	95.53 113	99.62 60	99.79 62	92.08 163	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	85.97 262	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	91.43 292	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	99.64 224	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	99.66 219	98.06 233	96.37 87	94.37 274	99.49 136	83.29 304	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	97.10 364	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	89.65 377	98.88 344	97.93 247	83.71 424	94.00 280	98.75 234	68.79 417	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	87.27 239	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	99.70 210	99.80 3	90.54 323	96.26 233	98.08 285	92.15 161	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	98.74 359	97.98 242	87.81 378	98.47 146	99.39 148	67.43 426	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	86.70 250	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	81.83 317	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	88.43 395	98.94 336	96.82 400	86.50 394	88.71 359	98.47 267	74.73 392	99.88 123	85.39 391	96.18 246	96.71 316

TestCases				95.00 307	99.01 130	88.43 395		96.82 400	86.50 394	88.71 359	98.47 267	74.73 392	99.88 123	85.39 391	96.18 246	96.71 316

COLMAP_ROB	90.47 14	92.18 320	91.49 322	94.25 341	99.00 134	88.04 401	98.42 384	96.70 407	82.30 435	88.43 369	99.01 194	76.97 367	99.85 129	86.11 387	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	84.15 295	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	84.98 423	99.96 53	96.65 409	97.60 34	99.73 45	98.96 204	71.58 407	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	97.49 413	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	99.03 323	99.21 32	94.31 158	99.18 102	98.88 216	86.26 257	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	89.87 200	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	89.33 207	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	89.33 207	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	99.72 200	97.35 315	94.45 146	97.88 174	99.42 141	86.71 248	99.52 175	98.48 123	93.97 295	99.72 121

Anonymous20231211		89.86 371	88.44 379	94.13 345	98.93 142	90.68 355	98.54 375	98.26 206	76.28 453	86.73 391	95.54 369	70.60 413	97.56 332	90.82 324	80.27 409	94.15 366

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	93.97 295	99.72 121

SDMVSNet		94.80 240	93.96 255	97.33 229	98.92 145	95.42 207	99.59 236	98.99 40	92.41 255	92.55 298	97.85 296	75.81 382	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	90.89 351	98.24 392	98.85 62	92.41 255	92.55 298	97.85 296	71.07 412	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	99.57 241	98.90 50	96.19 94	97.52 184	98.56 257	92.65 143	97.36 337	77.89 439	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	99.65 220	99.80 3	95.64 109	95.39 258	98.86 225	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	95.84 245	98.86 225	89.25 209	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	99.74 189	99.71 7	92.59 245	95.84 245	98.86 225	89.25 209	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	99.37 278	97.38 311	85.75 405	90.80 317	99.17 177	84.11 297	99.88 123	86.35 383	98.43 172	98.36 282

MGCFI-Net		97.00 143	96.22 165	99.34 50	98.86 153	98.80 40	99.67 218	97.30 327	94.31 158	97.77 180	99.41 145	86.36 255	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	91.29 343	98.93 338	98.94 44	95.90 99	98.77 127	98.42 270	90.89 185	99.77 149	97.80 164	70.76 449	98.72 269

Anonymous20240529		92.10 321	90.65 333	96.47 257	98.82 155	90.61 357	98.72 361	98.67 86	75.54 457	93.90 282	98.58 255	66.23 430	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	89.05 213	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	98.69 270

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	81.36 323	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	90.92 182	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	94.26 291	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	96.77 204	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	99.52 252	98.08 232	97.05 56	99.86 15	99.86 33	90.65 187	99.71 159	99.39 70	98.63 165	98.69 270

miper_enhance_ethall		94.36 263	93.98 254	95.49 288	98.68 164	95.24 221	99.73 196	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	85.99 261	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	91.10 304	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	98.81 353	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	98.85 350	98.45 142	94.93 125	96.86 210	98.96 204	75.22 388	99.20 200	95.34 229	98.15 182	99.64 135

mamv4		95.24 227	96.90 131	90.25 416	98.65 169	72.11 465	98.28 389	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	99.47 262	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	87.00 245	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	90.34 330	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	98.86 348	98.45 142	94.95 124	96.63 216	98.95 209	75.21 389	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	90.22 366	98.77 358	97.79 262	90.85 310	96.82 212	99.42 141	61.18 450	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	91.50 341	95.70 450	98.55 118	87.47 380	92.15 301	94.26 421	91.42 170	98.95 219	88.15 362	95.85 257	98.76 265

UGNet		95.33 225	94.57 237	97.62 200	98.55 176	94.85 233	98.67 367	99.32 26	95.75 106	96.80 213	96.27 344	72.18 404	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	97.91 406	97.31 326	90.35 329	89.48 343	99.22 170	85.19 277	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	89.99 371	99.39 274	98.57 106	93.14 212	97.33 193	98.31 277	93.44 116	94.68 439	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	99.71 203	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	88.74 389	99.98 21	97.30 327	97.84 28	99.85 18	99.19 175	66.82 428	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	84.69 288	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	99.06 316	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	86.09 259	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	96.36 437	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	89.54 379	98.44 380	98.53 125	84.46 419	88.49 365	97.92 293	90.57 189	97.05 360	83.10 408	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	99.73 196	99.38 22	93.46 197	98.76 130	99.06 188	91.21 173	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	91.22 344	99.80 166	97.32 325	90.91 308	91.08 311	98.67 241	83.51 300	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	71.57 463	99.45 140	96.23 38	95.34 429	96.91 200	85.14 365	99.59 149

AUN-MVS		93.28 291	92.60 296	95.34 297	98.29 198	90.09 369	99.31 286	98.56 112	91.80 281	96.35 232	98.00 288	89.38 206	98.28 295	92.46 293	69.22 455	97.64 302

FMVSNet3		92.69 308	91.58 318	95.99 272	98.29 198	97.42 115	99.26 296	97.62 283	89.80 341	89.68 334	95.32 384	81.62 321	96.27 405	87.01 379	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	86.93 246	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	90.02 370	99.29 292	98.56 112	95.90 99	98.77 127	98.00 288	90.89 185	98.26 299	97.80 164	69.20 456	97.64 302

PVSNet_0	88.03 19	91.80 328	90.27 342	96.38 264	98.27 201	90.46 361	99.94 90	99.61 13	93.99 174	86.26 401	97.39 307	71.13 411	99.89 117	98.77 105	67.05 462	98.79 264

UA-Net		96.54 171	95.96 179	98.27 149	98.23 203	95.71 193	98.00 404	98.45 142	93.72 189	98.41 150	99.27 162	88.71 220	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	77.10 362	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	95.03 451	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	93.87 456	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	99.33 283	97.49 301	90.87 309	90.47 320	97.10 314	88.23 223	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	99.31 286	97.15 352	91.07 305	98.84 121	97.05 318	88.17 224	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	76.84 369	98.80 235	90.89 323	99.54 111	99.35 203

dongtai		91.55 334	91.13 327	92.82 384	98.16 210	86.35 413	99.47 262	98.51 130	83.24 427	85.07 411	97.56 301	90.33 194	94.94 435	76.09 447	91.73 308	97.18 313

RPSCF		91.80 328	92.79 292	88.83 428	98.15 211	69.87 467	98.11 400	96.60 411	83.93 422	94.33 275	99.27 162	79.60 345	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	99.08 311	97.61 286	92.02 273	95.54 256	98.96 204	90.64 188	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	91.11 178	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	80.88 330	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	96.49 435	98.68 83	90.31 331	94.54 267	97.00 320	76.30 377	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	89.19 381	99.08 311	97.54 294	93.66 190	94.87 265	99.58 127	78.78 353	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	89.19 381	99.08 311	97.55 292	94.10 167	94.71 266	99.62 122	80.51 336	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	99.62 227	97.70 272	93.13 213	98.42 149	97.92 293	88.02 225	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	96.29 440	97.98 242	89.70 342	95.93 243	94.67 411	93.83 110	98.45 272	86.91 382	96.53 237	99.54 163

tpmrst		96.27 189	95.98 175	97.13 234	97.96 222	93.15 290	96.34 438	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	98.71 362	97.51 299	90.30 332	94.51 269	98.69 240	75.56 383	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	99.47 262	98.15 226	91.83 278	95.09 263	99.11 182	91.37 172	97.47 335	93.47 280	97.43 200	99.74 118

MDTV_nov1_ep13			95.69 194		97.90 225	94.15 262	95.98 446	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	84.59 289	98.41 276	89.84 341	97.10 220	99.59 149

ADS-MVSNet2		93.80 278	93.88 258	93.55 367	97.87 227	85.94 417	94.24 452	96.84 397	90.07 335	96.43 228	94.48 416	90.29 196	95.37 428	87.44 369	97.23 211	99.36 199

ADS-MVSNet		94.79 241	94.02 253	97.11 236	97.87 227	93.79 271	94.24 452	98.16 223	90.07 335	96.43 228	94.48 416	90.29 196	98.19 302	87.44 369	97.23 211	99.36 199

Effi-MVS+		96.30 186	95.69 194	98.16 154	97.85 229	96.26 168	97.41 415	97.21 344	90.37 328	98.65 136	98.58 255	86.61 252	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	96.05 444	98.40 176	92.86 225	97.09 200	95.28 389	94.21 97	98.07 310	89.26 348	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	76.95 368	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	99.65 220	96.66 408	92.71 236	92.88 294	99.40 146	92.16 160	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	87.73 228	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	99.65 220	96.65 409	92.70 237	92.86 295	96.13 350	92.15 161	99.30 192	91.88 306	93.64 299	99.55 159

Effi-MVS+-dtu		94.53 253	95.30 211	92.22 392	97.77 235	82.54 440	99.59 236	97.06 373	94.92 127	95.29 260	95.37 382	85.81 263	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	91.47 342	96.13 442	98.06 233	80.98 441	92.91 293	93.78 425	89.66 201	98.87 224	87.03 378	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	96.31 439	98.17 218	91.42 294	96.26 233	96.13 350	90.56 190	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	99.31 286	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 295

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 286	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 295

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 286	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 295

EPP-MVSNet		96.69 163	96.60 148	96.96 241	97.74 237	93.05 293	99.37 278	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	92.62 462	98.51 130	74.70 460	97.33 193	69.59 477	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	99.35 281	97.18 347	93.52 195	89.57 341	95.74 359	87.55 233	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	84.85 282	98.66 258	96.61 208	97.02 226	99.43 190

Syy-MVS		90.00 369	90.63 334	88.11 435	97.68 247	74.66 463	99.71 203	98.35 189	90.79 316	92.10 302	98.67 241	79.10 351	93.09 455	63.35 470	95.95 254	96.59 318

myMVS_eth3d		94.46 258	94.76 234	93.55 367	97.68 247	90.97 346	99.71 203	98.35 189	90.79 316	92.10 302	98.67 241	92.46 153	93.09 455	87.13 375	95.95 254	96.59 318

test_fmvs1_n		94.25 266	94.36 241	93.92 354	97.68 247	83.70 430	99.90 114	96.57 412	97.40 40	99.67 51	98.88 216	61.82 447	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	88.86 217	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	99.27 295	97.10 364	92.79 231	97.43 189	97.99 290	81.85 316	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	86.43 253	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	86.72 247	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	97.23 342	92.76 233	96.63 216	99.05 189	84.96 281	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	99.28 293	98.24 209	94.27 163	96.84 211	98.94 211	79.39 346	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	99.50 256	97.07 372	88.75 361	88.85 358	95.52 371	90.97 181	96.74 382	90.77 325	84.45 371	94.17 360

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	98.65 244	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	98.71 248	97.26 184	97.33 207	99.47 179

testing3		93.92 272	94.23 245	92.99 381	97.54 261	90.23 365	99.99 5	99.16 33	90.57 322	91.33 310	98.63 248	92.99 132	92.52 459	82.46 412	95.39 274	96.22 323

SSM_0404		95.75 209	95.16 217	97.50 213	97.53 262	95.39 210	99.11 307	97.25 337	90.81 312	95.27 261	98.83 230	84.74 285	98.67 255	95.24 232	97.69 194	98.45 277

LCM-MVSNet-Re		92.31 317	92.60 296	91.43 401	97.53 262	79.27 457	99.02 325	91.83 472	92.07 269	80.31 435	94.38 419	83.50 301	95.48 425	97.22 186	97.58 198	99.54 163

GBi-Net		90.88 345	89.82 351	94.08 346	97.53 262	91.97 318	98.43 381	96.95 386	87.05 386	89.68 334	94.72 407	71.34 408	96.11 411	87.01 379	85.65 359	94.17 360

test1		90.88 345	89.82 351	94.08 346	97.53 262	91.97 318	98.43 381	96.95 386	87.05 386	89.68 334	94.72 407	71.34 408	96.11 411	87.01 379	85.65 359	94.17 360

FMVSNet2		91.02 342	89.56 356	95.41 295	97.53 262	95.74 191	98.98 328	97.41 309	87.05 386	88.43 369	95.00 401	71.34 408	96.24 407	85.12 394	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	85.39 274	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	99.71 203	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	97.06 373	94.59 140	99.63 57	99.32 153	89.20 212	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	99.60 234	97.24 340	92.39 257	96.09 239	99.14 181	83.07 307	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	99.40 270	96.93 391	87.86 376	88.69 361	95.44 376	89.95 199	96.44 397	90.45 331	80.69 405	94.14 369

viewmambaseed2359dif		95.92 202	95.55 200	97.04 238	97.38 274	93.41 285	99.78 170	96.97 384	91.14 302	96.58 219	99.27 162	84.85 282	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	84.99 280	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	99.71 203	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	97.13 234	97.37 276	94.96 230	99.53 251	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	93.06 380	97.34 279	89.18 383	99.38 276	96.79 402	86.70 393	87.47 383	95.22 391	90.00 198	95.86 420	88.26 359	81.37 394	94.15 366

baseline		96.43 176	95.98 175	97.76 188	97.34 279	95.17 226	99.51 254	97.17 349	93.92 179	96.90 209	99.28 159	85.37 275	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	99.57 241	96.78 403	86.97 390	87.56 381	95.51 372	89.43 205	96.62 388	88.60 353	82.44 386	94.16 365

SD_0403		92.63 311	93.38 277	90.40 415	97.32 282	77.91 459	97.75 411	98.03 238	91.89 275	90.83 316	98.29 279	82.00 313	93.79 448	88.51 357	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	99.58 238	96.75 404	86.99 389	87.64 379	95.54 369	89.55 204	96.50 393	88.58 354	82.44 386	94.17 360

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	85.07 278	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	96.72 406	88.35 370	93.43 284	98.94 211	82.05 312	98.05 311	88.12 364	96.48 240	99.37 197

eth_miper_zixun_eth		92.41 315	91.93 311	93.84 358	97.28 286	90.68 355	98.83 351	96.97 384	88.57 366	89.19 353	95.73 362	89.24 211	96.69 386	89.97 340	81.55 392	94.15 366

MVSFormer		96.94 146	96.60 148	97.95 168	97.28 286	97.70 101	99.55 248	97.27 334	91.17 299	99.43 82	99.54 133	90.92 182	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	90.92 182	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	99.69 213	97.15 352	94.46 145	98.78 125	99.21 173	85.64 267	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	93.48 459	97.08 368	90.48 324	94.40 271	98.62 249	84.49 290	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	93.48 459	97.08 368	90.48 324	94.40 271	98.62 249	84.49 290	96.21 408	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	99.00 326	97.25 337	90.81 312	94.40 271	98.83 230	84.74 285	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	98.86 348	98.32 196	93.30 204	96.17 238	95.59 367	76.48 375	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	99.57 241	97.26 336	93.05 218	96.50 223	99.17 177	82.76 308	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	99.56 245	98.12 229	94.78 132	98.44 147	98.94 211	80.30 340	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	99.17 303	97.82 261	91.39 296	95.02 264	98.01 287	92.20 159	97.30 344	93.75 275	95.83 258	99.14 234

Fast-Effi-MVS+-dtu		93.72 282	93.86 259	93.29 372	97.06 297	86.16 414	99.80 166	96.83 398	92.66 240	92.58 297	97.83 298	81.39 322	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	99.57 241	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

CostFormer		96.10 193	95.88 187	96.78 248	97.03 299	92.55 308	97.08 424	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	89.64 202	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	99.31 286	97.30 327	92.57 247	96.45 226	99.01 194	83.44 302	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	91.64 334	98.63 370	97.12 357	92.33 260	95.60 252	98.88 216	85.65 265	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	91.64 334	98.81 353	97.12 357	92.33 260	95.60 252	98.88 216	85.65 265	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	91.64 334	97.78 410	97.12 357	92.33 260	90.87 315	98.88 216	76.78 370	96.43 398	92.12 298	95.70 264	99.32 208

IMVS_0403		95.25 226	94.81 231	96.58 256	96.97 304	91.64 334	98.97 333	97.12 357	92.33 260	95.43 257	98.88 216	85.78 264	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	93.26 374	96.95 309	93.63 277	99.71 203	98.16 223	91.49 287	94.28 276	98.14 283	81.33 324	96.48 395	79.47 429	95.46 271	89.68 456

QAPM		95.40 222	94.17 247	99.10 78	96.92 310	97.71 99	99.40 270	98.68 83	89.31 345	88.94 357	98.89 215	82.48 310	99.96 75	93.12 288	99.83 81	99.62 142

KD-MVS_2432*1600		88.00 391	86.10 395	93.70 363	96.91 311	94.04 265	97.17 421	97.12 357	84.93 414	81.96 425	92.41 439	92.48 151	94.51 441	79.23 430	52.68 476	92.56 425

miper_refine_blended		88.00 391	86.10 395	93.70 363	96.91 311	94.04 265	97.17 421	97.12 357	84.93 414	81.96 425	92.41 439	92.48 151	94.51 441	79.23 430	52.68 476	92.56 425

tpm2		95.47 220	95.18 216	96.35 265	96.91 311	91.70 332	96.96 427	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		88.32 387	87.47 389	90.88 404	96.90 314	88.39 397	97.28 418	95.68 434	82.60 434	84.67 413	92.40 441	79.83 343	91.16 464	76.39 446	81.51 393	93.09 416

3Dnovator+	91.53 11	96.31 185	95.24 213	99.52 32	96.88 315	98.64 58	99.72 200	98.24 209	95.27 120	88.42 371	98.98 200	82.76 308	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	90.49 360	91.50 467	97.19 345	82.76 433	90.23 321	95.59 367	95.02 64	98.00 313	77.41 441	96.98 228	99.82 106

MVS		96.60 167	95.56 199	99.72 14	96.85 316	99.22 21	98.31 387	98.94 44	91.57 285	90.90 314	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	95.34 209	99.08 81	96.82 318	97.47 113	99.45 267	98.81 67	95.52 114	89.39 344	99.00 197	81.97 314	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	99.06 316	97.67 275	88.96 354	91.39 308	99.02 192	88.75 219	97.30 344	91.07 316	87.85 342	94.22 355

CVMVSNet		94.68 248	94.94 227	93.89 357	96.80 319	86.92 411	99.06 316	98.98 41	94.45 146	94.23 278	99.02 192	85.60 268	95.31 430	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	99.45 267	96.89 394	88.98 352	89.65 337	95.38 381	88.77 218	96.34 402	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	86.36 255	98.99 213	98.26 138	96.33 244	99.23 227

IterMVS		90.91 344	90.17 346	93.12 377	96.78 323	90.42 363	98.89 342	97.05 376	89.03 349	86.49 396	95.42 377	76.59 373	95.02 432	87.22 374	84.09 374	93.93 387
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	98.31 387	98.86 59	95.82 103	89.91 328	98.98 200	87.49 235	99.96 75	97.80 164	99.73 91	99.96 74

IterMVS-SCA-FT		90.85 347	90.16 347	92.93 382	96.72 325	89.96 372	98.89 342	96.99 380	88.95 355	86.63 393	95.67 363	76.48 375	95.00 433	87.04 377	84.04 377	93.84 394

MVS-HIRNet		86.22 398	83.19 411	95.31 299	96.71 326	90.29 364	92.12 464	97.33 319	62.85 471	86.82 390	70.37 476	69.37 416	97.49 334	75.12 449	97.99 190	98.15 286

viewdifsd2359ckpt11		94.09 269	93.63 262	95.46 292	96.68 327	88.92 386	99.62 227	97.12 357	93.07 216	95.73 249	99.22 170	77.05 363	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	88.92 386	99.62 227	97.13 356	93.07 216	95.73 249	99.22 170	77.05 363	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	98.69 365	98.21 213	82.81 432	97.75 181	99.28 159	61.57 448	99.48 185	98.09 148	94.09 293	98.15 286

dmvs_re		93.20 293	93.15 285	93.34 370	96.54 330	83.81 429	98.71 362	98.51 130	91.39 296	92.37 300	98.56 257	78.66 355	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	98.98 328	97.78 265	90.81 312	96.19 236	98.55 259	73.63 399	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	98.98 328	97.78 265	90.81 312	96.19 236	98.55 259	73.63 399	98.98 214	89.41 343	98.56 167	97.88 293

MIMVSNet		90.30 360	88.67 375	95.17 303	96.45 333	91.64 334	92.39 463	97.15 352	85.99 400	90.50 319	93.19 433	66.95 427	94.86 437	82.01 416	93.43 301	99.01 249

CR-MVSNet		93.45 290	92.62 295	95.94 275	96.29 334	92.66 304	92.01 465	96.23 420	92.62 242	96.94 207	93.31 431	91.04 179	96.03 416	79.23 430	95.96 252	99.13 235

RPMNet		89.76 373	87.28 390	97.19 232	96.29 334	92.66 304	92.01 465	98.31 198	70.19 467	96.94 207	85.87 469	87.25 240	99.78 146	62.69 471	95.96 252	99.13 235

tt0805		91.28 337	90.18 345	94.60 322	96.26 336	87.55 404	98.39 385	98.72 77	89.00 351	89.22 350	98.47 267	62.98 443	98.96 218	90.57 328	88.00 341	97.28 312

Patchmtry		89.70 374	88.49 378	93.33 371	96.24 337	89.94 375	91.37 468	96.23 420	78.22 450	87.69 378	93.31 431	91.04 179	96.03 416	80.18 428	82.10 388	94.02 377

test_vis1_rt		86.87 396	86.05 398	89.34 424	96.12 338	78.07 458	99.87 130	83.54 484	92.03 272	78.21 446	89.51 453	45.80 469	99.91 110	96.25 216	93.11 306	90.03 452

JIA-IIPM		91.76 331	90.70 332	94.94 309	96.11 339	87.51 405	93.16 461	98.13 228	75.79 456	97.58 183	77.68 474	92.84 137	97.97 314	88.47 358	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	99.56 245	98.57 106	90.46 326	86.51 395	98.95 209	78.57 356	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	90.69 354	99.98 21	97.72 271	96.62 75	91.05 313	98.85 228	77.21 361	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	88.75 374	95.25 301	95.99 343	90.16 367	91.22 469	97.54 294	76.80 452	97.26 196	86.01 468	91.88 166	96.07 415	66.16 466	95.91 256	99.51 173

ACMH+	89.98 16	90.35 358	89.54 357	92.78 386	95.99 343	86.12 415	98.81 353	97.18 347	89.38 344	83.14 421	97.76 299	68.42 421	98.43 273	89.11 349	86.05 357	93.78 397

DeepMVS_CX				82.92 446	95.98 345	58.66 477		96.01 426	92.72 234	78.34 445	95.51 372	58.29 455	98.08 308	82.57 411	85.29 362	92.03 433

ACMP	92.05 9	92.74 306	92.42 304	93.73 359	95.91 346	88.72 390	99.81 162	97.53 296	94.13 165	87.00 389	98.23 281	74.07 396	98.47 268	96.22 217	88.86 327	93.99 382
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	86.94 410	99.87 130	96.36 418	96.85 62	99.54 71	98.79 232	52.41 463	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	80.65 334	98.39 280	97.92 158	89.60 315	94.53 328

NP-MVS					95.77 351	91.79 326					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	87.65 230	99.90 112	99.50 61	99.60 107	99.82 106

plane_prior6					95.76 352	91.72 331						80.47 338

ACMM	91.95 10	92.88 302	92.52 302	93.98 353	95.75 354	89.08 385	99.77 175	97.52 298	93.00 219	89.95 327	97.99 290	76.17 379	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	78.73 354	97.67 328	87.75 367	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	87.70 229	98.56 264	97.94 157	99.47 124	99.25 224
jason: jason.
mmtdpeth		88.52 385	87.75 387	90.85 406	95.71 358	83.47 435	98.94 336	94.85 449	88.78 360	97.19 198	89.58 452	63.29 441	98.97 216	98.54 119	62.86 470	90.10 451

HQP_MVS		94.49 257	94.36 241	94.87 311	95.71 358	91.74 328	99.84 149	97.87 254	96.38 84	93.01 290	98.59 252	80.47 338	98.37 286	97.79 167	89.55 318	94.52 330

plane_prior7					95.71 358	91.59 340

ITE_SJBPF				92.38 389	95.69 361	85.14 421		95.71 433	92.81 228	89.33 347	98.11 284	70.23 414	98.42 274	85.91 389	88.16 339	93.59 405

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	84.72 287	99.93 103	99.04 84	98.84 158	98.74 267

ACMH	89.72 17	90.64 351	89.63 354	93.66 365	95.64 363	88.64 393	98.55 373	97.45 303	89.03 349	81.62 428	97.61 300	69.75 415	98.41 276	89.37 345	87.62 348	93.92 388
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	99.74 189	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		88.50 386	86.64 393	94.08 346	95.62 365	91.97 318	98.43 381	96.95 386	83.00 430	86.08 403	94.72 407	59.09 454	96.11 411	81.82 418	84.07 375	94.17 360

LuminaMVS		96.63 166	96.21 166	97.87 177	95.58 366	96.82 141	99.12 305	97.67 275	94.47 144	97.88 174	98.31 277	87.50 234	98.71 248	98.07 150	97.29 210	98.10 289

LPG-MVS_test		92.96 299	92.71 294	93.71 361	95.43 367	88.67 391	99.75 185	97.62 283	92.81 228	90.05 323	98.49 263	75.24 386	98.40 278	95.84 223	89.12 322	94.07 374

LGP-MVS_train				93.71 361	95.43 367	88.67 391		97.62 283	92.81 228	90.05 323	98.49 263	75.24 386	98.40 278	95.84 223	89.12 322	94.07 374

tpm		93.70 283	93.41 275	94.58 324	95.36 369	87.41 406	97.01 425	96.90 393	90.85 310	96.72 215	94.14 422	90.40 193	96.84 377	90.75 326	88.54 334	99.51 173

D2MVS		92.76 305	92.59 300	93.27 373	95.13 370	89.54 379	99.69 213	99.38 22	92.26 265	87.59 380	94.61 413	85.05 279	97.79 323	91.59 309	88.01 340	92.47 428

VPA-MVSNet		92.70 307	91.55 320	96.16 269	95.09 371	96.20 174	98.88 344	99.00 39	91.02 307	91.82 305	95.29 388	76.05 381	97.96 316	95.62 228	81.19 395	94.30 347

LTVRE_ROB	88.28 18	90.29 361	89.05 368	94.02 349	95.08 372	90.15 368	97.19 420	97.43 305	84.91 416	83.99 417	97.06 317	74.00 397	98.28 295	84.08 400	87.71 344	93.62 404
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		87.87 393	86.51 394	91.94 395	95.05 373	85.57 419	97.65 412	94.08 459	84.40 420	81.82 427	96.85 326	62.14 446	98.33 289	80.25 427	86.37 355	91.91 435

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	87.96 375	98.50 262	93.90 106	94.96 434	81.33 419	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	87.09 242	97.14 353	93.13 287	78.16 419	94.26 349

USDC		90.00 369	88.96 369	93.10 379	94.81 376	88.16 399	98.71 362	95.54 438	93.66 190	83.75 419	97.20 311	65.58 432	98.31 291	83.96 403	87.49 350	92.85 422

VPNet		91.81 325	90.46 336	95.85 280	94.74 377	95.54 202	98.98 328	98.59 102	92.14 267	90.77 318	97.44 304	68.73 419	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	99.58 238	98.93 48	92.54 249	89.34 346	97.31 308	87.62 231	97.10 357	94.22 261	86.58 353	94.40 339

UniMVSNet_ETH3D		90.06 368	88.58 377	94.49 330	94.67 379	88.09 400	97.81 409	97.57 291	83.91 423	88.44 367	97.41 305	57.44 456	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	87.14 241	96.73 383	93.20 283	77.52 424	94.46 333

test_fmvs2		89.47 378	89.70 353	88.77 431	94.54 381	75.74 460	99.83 156	94.70 455	94.71 136	91.08 311	96.82 330	54.46 459	97.78 325	92.87 290	88.27 337	92.80 423

MonoMVSNet		94.82 238	94.43 239	95.98 273	94.54 381	90.73 353	99.03 323	97.06 373	93.16 211	93.15 289	95.47 375	88.29 222	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	99.60 234	98.68 83	90.10 334	88.07 374	96.89 323	80.68 333	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	86.37 254	97.68 327	95.88 222	80.31 408	94.46 333

tfpnnormal		89.29 381	87.61 388	94.34 338	94.35 385	94.13 263	98.95 335	98.94 44	83.94 421	84.47 414	95.51 372	74.84 391	97.39 336	77.05 444	80.41 406	91.48 438

FC-MVSNet-test		93.81 277	93.15 285	95.80 283	94.30 386	96.20 174	99.42 269	98.89 52	92.33 260	89.03 356	97.27 310	87.39 237	96.83 379	93.20 283	86.48 354	94.36 341

SSC-MVS3.2		89.59 376	88.66 376	92.38 389	94.29 387	86.12 415	99.49 258	97.66 278	90.28 333	88.63 364	95.18 392	64.46 437	96.88 375	85.30 393	82.66 383	94.14 369

MS-PatchMatch		90.65 350	90.30 341	91.71 400	94.22 388	85.50 420	98.24 392	97.70 272	88.67 363	86.42 398	96.37 341	67.82 424	98.03 312	83.62 405	99.62 100	91.60 436

WR-MVS_H		91.30 335	90.35 339	94.15 343	94.17 389	92.62 307	99.17 303	98.94 44	88.87 358	86.48 397	94.46 418	84.36 293	96.61 389	88.19 361	78.51 417	93.21 414

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	81.66 319	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	98.25 391	98.70 79	91.16 301	80.78 434	96.64 333	83.23 305	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	99.09 309	98.57 106	88.95 355	86.42 398	96.57 336	79.19 349	96.37 400	90.29 335	78.95 414	94.02 377

XXY-MVS		91.82 324	90.46 336	95.88 278	93.91 393	95.40 209	98.87 347	97.69 274	88.63 365	87.87 376	97.08 315	74.38 395	97.89 320	91.66 308	84.07 375	94.35 344

PS-CasMVS		90.63 352	89.51 359	93.99 352	93.83 394	91.70 332	98.98 328	98.52 127	88.48 367	86.15 402	96.53 338	75.46 384	96.31 404	88.83 351	78.86 416	93.95 385

test_0402		85.58 400	83.94 405	90.50 412	93.81 395	85.04 422	98.55 373	95.20 446	76.01 454	79.72 440	95.13 393	64.15 439	96.26 406	66.04 467	86.88 352	90.21 449

XVG-ACMP-BASELINE		91.22 340	90.75 331	92.63 388	93.73 396	85.61 418	98.52 377	97.44 304	92.77 232	89.90 329	96.85 326	66.64 429	98.39 280	92.29 295	88.61 331	93.89 390

TranMVSNet+NR-MVSNet		91.68 332	90.61 335	94.87 311	93.69 397	93.98 268	99.69 213	98.65 87	91.03 306	88.44 367	96.83 329	80.05 342	96.18 409	90.26 336	76.89 432	94.45 338

TransMVSNet (Re)		87.25 394	85.28 401	93.16 376	93.56 398	91.03 345	98.54 375	94.05 461	83.69 425	81.09 432	96.16 347	75.32 385	96.40 399	76.69 445	68.41 458	92.06 432

v10		90.25 362	88.82 371	94.57 325	93.53 399	93.43 284	99.08 311	96.87 396	85.00 413	87.34 387	94.51 414	80.93 329	97.02 367	82.85 410	79.23 413	93.26 412

testgi		89.01 383	88.04 384	91.90 396	93.49 400	84.89 424	99.73 196	95.66 435	93.89 183	85.14 409	98.17 282	59.68 452	94.66 440	77.73 440	88.88 325	96.16 324

v8		90.54 354	89.17 364	94.66 319	93.43 401	93.40 287	99.20 300	96.94 390	85.76 403	87.56 381	94.51 414	81.96 315	97.19 350	84.94 396	78.25 418	93.38 410

V42		91.28 337	90.12 348	94.74 316	93.42 402	93.46 283	99.68 216	97.02 377	87.36 382	89.85 332	95.05 396	81.31 325	97.34 339	87.34 372	80.07 410	93.40 408

pm-mvs1		89.36 380	87.81 386	94.01 350	93.40 403	91.93 321	98.62 371	96.48 416	86.25 398	83.86 418	96.14 349	73.68 398	97.04 363	86.16 386	75.73 437	93.04 418

v1144		91.09 341	89.83 350	94.87 311	93.25 404	93.69 276	99.62 227	96.98 382	86.83 392	89.64 338	94.99 402	80.94 328	97.05 360	85.08 395	81.16 396	93.87 392

v1192		90.62 353	89.25 363	94.72 318	93.13 405	93.07 291	99.50 256	97.02 377	86.33 397	89.56 342	95.01 399	79.22 348	97.09 359	82.34 414	81.16 396	94.01 379

v2v482		91.30 335	90.07 349	95.01 306	93.13 405	93.79 271	99.77 175	97.02 377	88.05 373	89.25 348	95.37 382	80.73 332	97.15 352	87.28 373	80.04 411	94.09 373

OPM-MVS		93.21 292	92.80 291	94.44 333	93.12 407	90.85 352	99.77 175	97.61 286	96.19 94	91.56 307	98.65 244	75.16 390	98.47 268	93.78 274	89.39 321	93.99 382
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	99.56 245	96.99 380	86.38 396	89.82 333	94.95 404	80.50 337	97.10 357	83.98 402	80.41 406	93.90 389

PEN-MVS		90.19 364	89.06 367	93.57 366	93.06 409	90.90 350	99.06 316	98.47 139	88.11 372	85.91 404	96.30 343	76.67 371	95.94 419	87.07 376	76.91 431	93.89 390

v1240		90.20 363	88.79 372	94.44 333	93.05 410	92.27 314	99.38 276	96.92 392	85.89 401	89.36 345	94.87 406	77.89 360	97.03 365	80.66 423	81.08 399	94.01 379

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	84.75 284	96.72 385	88.20 360	86.23 356	94.23 353

v148		90.70 349	89.63 354	93.92 354	92.97 412	90.97 346	99.75 185	96.89 394	87.51 379	88.27 372	95.01 399	81.67 318	97.04 363	87.40 371	77.17 429	93.75 398

v1921920		90.46 355	89.12 365	94.50 329	92.96 413	92.46 310	99.49 258	96.98 382	86.10 399	89.61 340	95.30 385	78.55 357	97.03 365	82.17 415	80.89 404	94.01 379

MVStest1		85.03 406	82.76 415	91.83 397	92.95 414	89.16 384	98.57 372	94.82 450	71.68 465	68.54 468	95.11 395	83.17 306	95.66 423	74.69 450	65.32 465	90.65 445

tt0320-xc		82.94 421	80.35 428	90.72 410	92.90 415	83.54 433	96.85 430	94.73 453	63.12 470	79.85 439	93.77 426	49.43 467	95.46 426	80.98 422	71.54 447	93.16 415

Baseline_NR-MVSNet		90.33 359	89.51 359	92.81 385	92.84 416	89.95 373	99.77 175	93.94 462	84.69 418	89.04 355	95.66 364	81.66 319	96.52 392	90.99 319	76.98 430	91.97 434

test_method		80.79 427	79.70 430	84.08 443	92.83 417	67.06 469	99.51 254	95.42 440	54.34 475	81.07 433	93.53 428	44.48 470	92.22 461	78.90 435	77.23 428	92.94 420

pmmvs4		92.10 321	91.07 329	95.18 302	92.82 418	94.96 230	99.48 261	96.83 398	87.45 381	88.66 363	96.56 337	83.78 299	96.83 379	89.29 347	84.77 369	93.75 398

LF4IMVS		89.25 382	88.85 370	90.45 414	92.81 419	81.19 450	98.12 399	94.79 451	91.44 291	86.29 400	97.11 313	65.30 435	98.11 306	88.53 356	85.25 363	92.07 431

tt0320		83.56 420	81.15 423	90.77 408	92.77 420	83.58 432	96.83 431	95.52 439	63.26 469	81.36 430	92.54 436	53.26 461	95.77 421	80.45 424	74.38 440	92.96 419

DTE-MVSNet		89.40 379	88.24 382	92.88 383	92.66 421	89.95 373	99.10 308	98.22 212	87.29 383	85.12 410	96.22 345	76.27 378	95.30 431	83.56 406	75.74 436	93.41 407

EU-MVSNet		90.14 366	90.34 340	89.54 423	92.55 422	81.06 451	98.69 365	98.04 236	91.41 295	86.59 394	96.84 328	80.83 331	93.31 453	86.20 385	81.91 390	94.26 349

APD_test1		81.15 425	80.92 425	81.86 447	92.45 423	59.76 476	96.04 445	93.61 465	73.29 463	77.06 449	96.64 333	44.28 471	96.16 410	72.35 454	82.52 384	89.67 457

sc_t1		85.01 407	82.46 417	92.67 387	92.44 424	83.09 436	97.39 416	95.72 432	65.06 468	85.64 407	96.16 347	49.50 466	97.34 339	84.86 397	75.39 438	97.57 307

our_test_3		90.39 356	89.48 361	93.12 377	92.40 425	89.57 378	99.33 283	96.35 419	87.84 377	85.30 408	94.99 402	84.14 296	96.09 414	80.38 425	84.56 370	93.71 403

ppachtmachnet_test		89.58 377	88.35 380	93.25 375	92.40 425	90.44 362	99.33 283	96.73 405	85.49 408	85.90 405	95.77 358	81.09 327	96.00 418	76.00 448	82.49 385	93.30 411

v7n		89.65 375	88.29 381	93.72 360	92.22 427	90.56 359	99.07 315	97.10 364	85.42 410	86.73 391	94.72 407	80.06 341	97.13 354	81.14 420	78.12 420	93.49 406

dmvs_testset		83.79 416	86.07 397	76.94 451	92.14 428	48.60 486	96.75 432	90.27 476	89.48 343	78.65 443	98.55 259	79.25 347	86.65 474	66.85 464	82.69 382	95.57 326

PS-MVSNAJss		93.64 284	93.31 281	94.61 321	92.11 429	92.19 315	99.12 305	97.38 311	92.51 252	88.45 366	96.99 321	91.20 174	97.29 347	94.36 255	87.71 344	94.36 341

pmmvs5		90.17 365	89.09 366	93.40 369	92.10 430	89.77 376	99.74 189	95.58 437	85.88 402	87.24 388	95.74 359	73.41 401	96.48 395	88.54 355	83.56 379	93.95 385

N_pmnet		80.06 430	80.78 426	77.89 450	91.94 431	45.28 488	98.80 356	56.82 490	78.10 451	80.08 437	93.33 429	77.03 365	95.76 422	68.14 462	82.81 381	92.64 424

test_djsdf		92.83 303	92.29 305	94.47 331	91.90 432	92.46 310	99.55 248	97.27 334	91.17 299	89.96 326	96.07 353	81.10 326	96.89 373	94.67 250	88.91 324	94.05 376

SixPastTwentyTwo		88.73 384	88.01 385	90.88 404	91.85 433	82.24 442	98.22 396	95.18 447	88.97 353	82.26 424	96.89 323	71.75 406	96.67 387	84.00 401	82.98 380	93.72 402

K. test v3		88.05 390	87.24 391	90.47 413	91.82 434	82.23 443	98.96 334	97.42 307	89.05 348	76.93 451	95.60 366	68.49 420	95.42 427	85.87 390	81.01 402	93.75 398

OurMVSNet-221017-0		89.81 372	89.48 361	90.83 407	91.64 435	81.21 449	98.17 398	95.38 442	91.48 289	85.65 406	97.31 308	72.66 402	97.29 347	88.15 362	84.83 368	93.97 384

mvs_tets		91.81 325	91.08 328	94.00 351	91.63 436	90.58 358	98.67 367	97.43 305	92.43 254	87.37 386	97.05 318	71.76 405	97.32 342	94.75 247	88.68 330	94.11 372

Gipuma		66.95 443	65.00 443	72.79 456	91.52 437	67.96 468	66.16 480	95.15 448	47.89 477	58.54 474	67.99 479	29.74 475	87.54 473	50.20 478	77.83 422	62.87 479
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	79.62 344	99.85 129	99.25 74	99.24 141	99.55 159

jajsoiax		91.92 323	91.18 326	94.15 343	91.35 439	90.95 349	99.00 326	97.42 307	92.61 243	87.38 385	97.08 315	72.46 403	97.36 337	94.53 253	88.77 328	94.13 371

MDA-MVSNet-bldmvs		84.09 414	81.52 421	91.81 398	91.32 440	88.00 402	98.67 367	95.92 428	80.22 444	55.60 477	93.32 430	68.29 422	93.60 451	73.76 451	76.61 433	93.82 396

MVP-Stereo		90.93 343	90.45 338	92.37 391	91.25 441	88.76 388	98.05 403	96.17 422	87.27 384	84.04 415	95.30 385	78.46 358	97.27 349	83.78 404	99.70 93	91.09 439
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 402	83.32 410	92.10 393	90.96 442	88.58 394	99.20 300	96.52 414	79.70 446	57.12 476	92.69 435	79.11 350	93.86 447	77.10 443	77.46 426	93.86 393

YYNet1		85.50 403	83.33 409	92.00 394	90.89 443	88.38 398	99.22 299	96.55 413	79.60 447	57.26 475	92.72 434	79.09 352	93.78 449	77.25 442	77.37 427	93.84 394

anonymousdsp		91.79 330	90.92 330	94.41 336	90.76 444	92.93 297	98.93 338	97.17 349	89.08 347	87.46 384	95.30 385	78.43 359	96.92 371	92.38 294	88.73 329	93.39 409

lessismore_v0				90.53 411	90.58 445	80.90 452		95.80 429		77.01 450	95.84 356	66.15 431	96.95 369	83.03 409	75.05 439	93.74 401

EG-PatchMatch MVS		85.35 404	83.81 407	89.99 421	90.39 446	81.89 445	98.21 397	96.09 424	81.78 437	74.73 457	93.72 427	51.56 465	97.12 356	79.16 433	88.61 331	90.96 442

EGC-MVSNET		69.38 436	63.76 446	86.26 440	90.32 447	81.66 448	96.24 441	93.85 463	0.99 487	3.22 488	92.33 442	52.44 462	92.92 457	59.53 474	84.90 367	84.21 468

CMPMVS	61.59 21	84.75 410	85.14 402	83.57 444	90.32 447	62.54 472	96.98 426	97.59 290	74.33 461	69.95 465	96.66 331	64.17 438	98.32 290	87.88 366	88.41 336	89.84 454
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 413	82.92 413	89.21 425	90.03 449	82.60 439	96.89 429	95.62 436	80.59 442	75.77 456	89.17 454	65.04 436	94.79 438	72.12 455	81.02 401	90.23 448

pmmvs6		85.69 399	83.84 406	91.26 403	90.00 450	84.41 427	97.82 408	96.15 423	75.86 455	81.29 431	95.39 380	61.21 449	96.87 376	83.52 407	73.29 442	92.50 427

ttmdpeth		88.23 389	87.06 392	91.75 399	89.91 451	87.35 407	98.92 341	95.73 431	87.92 375	84.02 416	96.31 342	68.23 423	96.84 377	86.33 384	76.12 434	91.06 440

DSMNet-mixed		88.28 388	88.24 382	88.42 433	89.64 452	75.38 462	98.06 402	89.86 477	85.59 407	88.20 373	92.14 443	76.15 380	91.95 462	78.46 437	96.05 249	97.92 292

UnsupCasMVSNet_eth		85.52 401	83.99 403	90.10 419	89.36 453	83.51 434	96.65 433	97.99 240	89.14 346	75.89 455	93.83 424	63.25 442	93.92 445	81.92 417	67.90 461	92.88 421

Anonymous20231206		86.32 397	85.42 400	89.02 427	89.11 454	80.53 455	99.05 320	95.28 443	85.43 409	82.82 422	93.92 423	74.40 394	93.44 452	66.99 463	81.83 391	93.08 417

Anonymous20240521		85.15 405	83.81 407	89.16 426	88.32 455	82.69 438	98.80 356	95.74 430	79.72 445	81.53 429	90.99 446	65.38 434	94.16 443	72.69 453	81.11 398	90.63 446

OpenMVS_ROB	79.82 20	83.77 417	81.68 420	90.03 420	88.30 456	82.82 437	98.46 378	95.22 445	73.92 462	76.00 454	91.29 445	55.00 458	96.94 370	68.40 461	88.51 335	90.34 447

test20.03		84.72 411	83.99 403	86.91 438	88.19 457	80.62 454	98.88 344	95.94 427	88.36 369	78.87 441	94.62 412	68.75 418	89.11 469	66.52 465	75.82 435	91.00 441

blend_shiyan4		90.13 367	88.79 372	94.17 342	87.12 458	91.83 325	99.75 185	97.08 368	79.27 448	88.69 361	92.53 437	92.25 158	96.50 393	89.35 346	73.04 444	94.18 359

KD-MVS_self_test		83.59 418	82.06 418	88.20 434	86.93 459	80.70 453	97.21 419	96.38 417	82.87 431	82.49 423	88.97 455	67.63 425	92.32 460	73.75 452	62.30 472	91.58 437

MIMVSNet1		82.58 422	80.51 427	88.78 429	86.68 460	84.20 428	96.65 433	95.41 441	78.75 449	78.59 444	92.44 438	51.88 464	89.76 468	65.26 468	78.95 414	92.38 430

CL-MVSNet_self_test		84.50 412	83.15 412	88.53 432	86.00 461	81.79 446	98.82 352	97.35 315	85.12 412	83.62 420	90.91 448	76.66 372	91.40 463	69.53 459	60.36 473	92.40 429

UnsupCasMVSNet_bld		79.97 432	77.03 437	88.78 429	85.62 462	81.98 444	93.66 457	97.35 315	75.51 458	70.79 464	83.05 471	48.70 468	94.91 436	78.31 438	60.29 474	89.46 460

mvs5depth		84.87 408	82.90 414	90.77 408	85.59 463	84.84 425	91.10 470	93.29 467	83.14 428	85.07 411	94.33 420	62.17 445	97.32 342	78.83 436	72.59 446	90.14 450

Patchmatch-RL test		86.90 395	85.98 399	89.67 422	84.45 464	75.59 461	89.71 473	92.43 469	86.89 391	77.83 448	90.94 447	94.22 95	93.63 450	87.75 367	69.61 452	99.79 111

pmmvs-eth3d		84.03 415	81.97 419	90.20 417	84.15 465	87.09 409	98.10 401	94.73 453	83.05 429	74.10 461	87.77 461	65.56 433	94.01 444	81.08 421	69.24 454	89.49 459

test_fmvs3		79.99 431	80.17 429	79.45 449	84.02 466	62.83 470	99.05 320	93.49 466	88.29 371	80.06 438	86.65 466	28.09 477	88.00 470	88.63 352	73.27 443	87.54 466

PM-MVS		80.47 428	78.88 432	85.26 441	83.79 467	72.22 464	95.89 448	91.08 474	85.71 406	76.56 453	88.30 457	36.64 473	93.90 446	82.39 413	69.57 453	89.66 458

new-patchmatchnet		81.19 424	79.34 431	86.76 439	82.86 468	80.36 456	97.92 405	95.27 444	82.09 436	72.02 462	86.87 465	62.81 444	90.74 466	71.10 456	63.08 469	89.19 462

FE-MVSNET2		83.57 419	81.36 422	90.20 417	82.83 469	87.59 403	98.28 389	96.04 425	85.33 411	74.13 460	87.45 462	59.16 453	93.26 454	79.12 434	69.91 450	89.77 455

FE-MVSNET		81.05 426	78.81 433	87.79 436	81.98 470	83.70 430	98.23 394	91.78 473	81.27 439	74.29 459	87.44 463	60.92 451	90.67 467	64.92 469	68.43 457	89.01 463

mvsany_test3		82.12 423	81.14 424	85.06 442	81.87 471	70.41 466	97.09 423	92.14 470	91.27 298	77.84 447	88.73 456	39.31 472	95.49 424	90.75 326	71.24 448	89.29 461

WB-MVS		76.28 434	77.28 436	73.29 455	81.18 472	54.68 480	97.87 407	94.19 458	81.30 438	69.43 466	90.70 449	77.02 366	82.06 478	35.71 483	68.11 460	83.13 469

test_f		78.40 433	77.59 435	80.81 448	80.82 473	62.48 473	96.96 427	93.08 468	83.44 426	74.57 458	84.57 470	27.95 478	92.63 458	84.15 399	72.79 445	87.32 467

SSC-MVS		75.42 435	76.40 438	72.49 459	80.68 474	53.62 481	97.42 414	94.06 460	80.42 443	68.75 467	90.14 451	76.54 374	81.66 479	33.25 484	66.34 464	82.19 470

pmmvs3		80.27 429	77.77 434	87.76 437	80.32 475	82.43 441	98.23 394	91.97 471	72.74 464	78.75 442	87.97 460	57.30 457	90.99 465	70.31 457	62.37 471	89.87 453

testf1		68.38 439	66.92 440	72.78 457	78.80 476	50.36 483	90.95 471	87.35 482	55.47 473	58.95 472	88.14 458	20.64 482	87.60 471	57.28 475	64.69 466	80.39 472

APD_test2		68.38 439	66.92 440	72.78 457	78.80 476	50.36 483	90.95 471	87.35 482	55.47 473	58.95 472	88.14 458	20.64 482	87.60 471	57.28 475	64.69 466	80.39 472

ambc				83.23 445	77.17 478	62.61 471	87.38 475	94.55 457		76.72 452	86.65 466	30.16 474	96.36 401	84.85 398	69.86 451	90.73 444

test_vis3_rt		68.82 437	66.69 442	75.21 454	76.24 479	60.41 475	96.44 436	68.71 489	75.13 459	50.54 480	69.52 478	16.42 487	96.32 403	80.27 426	66.92 463	68.89 476

TDRefinement		84.76 409	82.56 416	91.38 402	74.58 480	84.80 426	97.36 417	94.56 456	84.73 417	80.21 436	96.12 352	63.56 440	98.39 280	87.92 365	63.97 468	90.95 443

E-PMN		52.30 447	52.18 449	52.67 465	71.51 481	45.40 487	93.62 458	76.60 487	36.01 481	43.50 482	64.13 481	27.11 479	67.31 484	31.06 485	26.06 480	45.30 483

EMVS		51.44 449	51.22 451	52.11 466	70.71 482	44.97 489	94.04 454	75.66 488	35.34 483	42.40 483	61.56 484	28.93 476	65.87 485	27.64 486	24.73 481	45.49 482

PMMVS2		67.15 442	64.15 445	76.14 453	70.56 483	62.07 474	93.89 455	87.52 481	58.09 472	60.02 471	78.32 473	22.38 481	84.54 476	59.56 473	47.03 478	81.80 471

FPMVS		68.72 438	68.72 439	68.71 461	65.95 484	44.27 490	95.97 447	94.74 452	51.13 476	53.26 478	90.50 450	25.11 480	83.00 477	60.80 472	80.97 403	78.87 474

wuyk23d		20.37 453	20.84 456	18.99 469	65.34 485	27.73 492	50.43 481	7.67 493	9.50 486	8.01 487	6.34 487	6.13 490	26.24 486	23.40 487	10.69 485	2.99 484

LCM-MVSNet		67.77 441	64.73 444	76.87 452	62.95 486	56.25 479	89.37 474	93.74 464	44.53 478	61.99 470	80.74 472	20.42 484	86.53 475	69.37 460	59.50 475	87.84 464

MVE	53.74 22	51.54 448	47.86 452	62.60 463	59.56 487	50.93 482	79.41 478	77.69 486	35.69 482	36.27 484	61.76 483	5.79 491	69.63 482	37.97 482	36.61 479	67.24 477
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 445	52.24 448	67.66 462	49.27 488	56.82 478	83.94 476	82.02 485	70.47 466	33.28 485	64.54 480	17.23 486	69.16 483	45.59 480	23.85 482	77.02 475

tmp_tt		65.23 444	62.94 447	72.13 460	44.90 489	50.03 485	81.05 477	89.42 480	38.45 479	48.51 481	99.90 22	54.09 460	78.70 481	91.84 307	18.26 483	87.64 465

PMVS	49.05 23	53.75 446	51.34 450	60.97 464	40.80 490	34.68 491	74.82 479	89.62 479	37.55 480	28.67 486	72.12 475	7.09 489	81.63 480	43.17 481	68.21 459	66.59 478
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 451	39.14 454	33.31 467	19.94 491	24.83 493	98.36 386	9.75 492	15.53 485	51.31 479	87.14 464	19.62 485	17.74 487	47.10 479	3.47 486	57.36 480

testmvs		40.60 450	44.45 453	29.05 468	19.49 492	14.11 494	99.68 216	18.47 491	20.74 484	64.59 469	98.48 266	10.95 488	17.09 488	56.66 477	11.01 484	55.94 481

mmdepth		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

monomultidepth		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

test_blank		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.02 488	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

eth-test2					0.00 493

eth-test					0.00 493

uanet_test		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

DCPMVS		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

cdsmvs_eth3d_5k		23.43 452	31.24 455	0.00 470	0.00 493	0.00 495	0.00 482	98.09 230	0.00 488	0.00 489	99.67 113	83.37 303	0.00 489	0.00 488	0.00 487	0.00 485

pcd_1.5k_mvsjas		7.60 455	10.13 458	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	91.20 174	0.00 489	0.00 488	0.00 487	0.00 485

sosnet-low-res		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

sosnet		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

uncertanet		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

Regformer		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

ab-mvs-re		8.28 454	11.04 457	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	99.40 146	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

uanet		0.00 456	0.00 459	0.00 470	0.00 493	0.00 495	0.00 482	0.00 494	0.00 488	0.00 489	0.00 489	0.00 492	0.00 489	0.00 488	0.00 487	0.00 485

TestfortrainingZip							99.97 39

WAC-MVS						90.97 346								86.10 388

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							95.78 449				59.23 485	93.20 128	97.74 326	91.06 317

test_post											63.35 482	94.43 82	98.13 305

patchmatchnet-post											91.70 444	95.12 59	97.95 317

MTMP							99.87 130	96.49 415

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	94.36 255		99.99 24

原ACMM2							99.90 114

testdata2													99.99 39	90.54 330

segment_acmp												96.68 31

testdata1							99.28 293		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						91.64 334			96.63 73	93.01 290

plane_prior2							99.84 149		96.38 84

plane_prior						91.74 328	99.86 141		96.76 68						89.59 317

n2								0.00 494

nn								0.00 494

door-mid								89.69 478

test11								98.44 147

door								90.31 475

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												80.65 334

MDTV_nov1_ep13_2view						96.26 168	96.11 443		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