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	99.09 307	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	89.90 338	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	92.06 270	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		93.83 273	92.06 308	99.15 70	99.94 16	97.50 110	99.94 90	98.42 167	96.22 92	99.41 84	41.37 484	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	92.34 258	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	99.18 300	99.45 18	94.84 131	96.41 229	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	99.24 295	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	91.92 321	99.90 114	99.07 37	88.67 362	95.26 261	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	96.77 203	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	90.78 317	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	95.11 218	98.02 166	99.85 60	95.10 228	98.74 357	98.50 136	87.22 384	93.66 282	99.86 33	87.45 235	99.95 84	90.94 320	99.81 87	99.02 247

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	91.75 281	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	93.36 288	99.65 219	97.95 245	96.03 97	97.41 190	99.70 100	89.61 202	99.51 177	96.73 205	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	91.21 312	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	95.11 233	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	96.87 244	99.71 82	91.74 326	99.85 144	97.95 245	93.11 215	95.72 250	99.16 180	92.35 155	99.94 93	95.32 229	99.35 136	98.92 255

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	95.59 286	99.67 87	86.91 410	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	93.92 352	99.63 89	81.76 445	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	96.72 250	99.59 91	96.99 135	99.95 72	99.10 34	94.06 171	98.27 157	95.80 356	89.00 214	99.95 84	99.12 78	87.53 348	93.24 411

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	98.35 271	82.28 309	99.82 141	90.15 336	99.22 143	98.81 262

dcpmvs_2		97.42 121	98.09 63	95.42 293	99.58 95	87.24 406	99.23 296	96.95 384	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	91.86 276	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	91.68 282	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	94.92 239	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	88.48 366	99.06 112	99.66 115	90.30 194	99.64 172	96.32 214	99.97 42	99.96 74

cl22		93.77 278	93.25 282	95.33 297	99.49 101	94.43 249	99.61 230	98.09 230	90.38 326	89.16 353	95.61 364	90.56 189	97.34 338	91.93 303	84.45 370	94.21 356

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	94.97 237	99.94 59	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 157	99.47 102	94.79 238	99.05 318	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		94.54 250	93.42 272	97.91 174	99.46 104	94.04 265	98.93 336	97.48 302	81.15 439	90.04 324	99.55 131	87.02 243	99.95 84	88.97 348	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	98.46 376	99.42 21	97.03 57	99.02 114	99.09 183	99.35 2	98.21 300	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	97.65 300

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	93.81 270	99.94 59	99.98 56

TAPA-MVS	92.12 8	94.42 258	93.60 264	96.90 243	99.33 109	91.78 325	99.78 170	98.00 239	89.89 339	94.52 267	99.47 137	91.97 164	99.18 202	69.90 456	99.52 114	99.73 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 222	95.07 220	96.32 265	99.32 111	96.60 154	99.76 181	98.85 62	96.65 72	87.83 375	96.05 353	99.52 1	98.11 305	96.58 209	81.07 399	94.25 350

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	97.15 351	95.53 113	99.62 60	99.79 62	92.08 162	98.38 283	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	98.59 272

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	91.43 291	97.88 174	98.99 197	95.84 45	99.84 137	98.82 101	95.32 275	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	91.43 291	97.88 174	98.99 197	95.84 45	99.84 137	98.82 101	95.32 275	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	94.37 273	99.49 136	83.29 302	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	97.10 363	95.07 122	99.68 50	99.75 80	92.95 134	98.34 287	98.38 128	99.14 145	99.54 163

Anonymous202405211		93.10 296	91.99 309	96.40 261	99.10 124	89.65 375	98.88 342	97.93 247	83.71 423	94.00 279	98.75 233	68.79 415	99.88 123	95.08 234	91.71 308	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	98.62 271

HyFIR lowres test		96.66 165	96.43 156	97.36 226	99.05 128	93.91 270	99.70 209	99.80 3	90.54 322	96.26 232	98.08 284	92.15 160	98.23 299	96.84 202	95.46 270	99.93 87

LFMVS		94.75 244	93.56 267	98.30 147	99.03 129	95.70 194	98.74 357	97.98 242	87.81 377	98.47 146	99.39 148	67.43 424	99.53 174	98.01 152	95.20 278	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	97.64 301

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	81.83 315	99.94 93	99.78 35	98.79 161	97.51 309

AllTest		92.48 312	91.64 315	95.00 306	99.01 130	88.43 393	98.94 334	96.82 398	86.50 393	88.71 358	98.47 266	74.73 390	99.88 123	85.39 389	96.18 245	96.71 315

TestCases				95.00 306	99.01 130	88.43 393		96.82 398	86.50 393	88.71 358	98.47 266	74.73 390	99.88 123	85.39 389	96.18 245	96.71 315

COLMAP_ROB	90.47 14	92.18 319	91.49 321	94.25 340	99.00 134	88.04 399	98.42 382	96.70 405	82.30 434	88.43 367	99.01 194	76.97 365	99.85 129	86.11 385	96.50 237	94.86 326
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	98.39 279

test_fmvs1		95.35 223	95.68 196	94.36 336	98.99 135	84.98 421	99.96 53	96.65 407	97.60 34	99.73 45	98.96 203	71.58 405	99.93 103	98.31 134	99.37 134	98.17 284

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 18	98.98 137	99.32 9	97.49 411	99.52 14	95.69 108	98.32 155	97.41 304	93.32 121	99.77 149	98.08 149	95.75 260	99.81 108

VNet		97.21 131	96.57 150	99.13 76	98.97 138	97.82 94	99.03 321	99.21 32	94.31 158	99.18 102	98.88 215	86.26 256	99.89 117	98.93 92	94.32 288	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	96.26 232	98.88 215	89.87 199	99.51 177	94.26 258	94.91 280	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	96.02 240	98.87 222	89.33 206	99.50 179	93.84 267	94.57 284	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	96.02 240	98.87 222	89.33 206	99.50 179	93.84 267	94.57 284	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	93.97 294	99.72 121

Anonymous20231211		89.86 369	88.44 377	94.13 343	98.93 142	90.68 353	98.54 373	98.26 206	76.28 451	86.73 389	95.54 368	70.60 411	97.56 331	90.82 323	80.27 408	94.15 364

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	93.97 294	99.72 121

SDMVSNet		94.80 239	93.96 254	97.33 229	98.92 145	95.42 207	99.59 235	98.99 40	92.41 254	92.55 297	97.85 295	75.81 380	98.93 220	97.90 160	91.62 309	97.64 301

sd_testset		93.55 285	92.83 289	95.74 284	98.92 145	90.89 349	98.24 390	98.85 62	92.41 254	92.55 297	97.85 295	71.07 410	98.68 252	93.93 264	91.62 309	97.64 301

EPNet_dtu		95.71 211	95.39 206	96.66 252	98.92 145	93.41 284	99.57 240	98.90 50	96.19 94	97.52 184	98.56 256	92.65 143	97.36 336	77.89 437	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	95.58 269	99.78 114

CHOSEN 1792x2688		96.81 152	96.53 151	97.64 196	98.91 149	93.07 290	99.65 219	99.80 3	95.64 109	95.39 257	98.86 224	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	95.84 244	98.86 224	89.25 208	99.50 179	93.84 267	94.57 284	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	95.84 244	98.86 224	89.25 208	99.50 179	93.44 280	94.50 287	99.16 231

MSDG		94.37 260	93.36 279	97.40 222	98.88 152	93.95 269	99.37 277	97.38 311	85.75 404	90.80 316	99.17 177	84.11 296	99.88 123	86.35 381	98.43 172	98.36 281

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	93.90 296	99.72 121

h-mvs33		94.92 236	94.36 240	96.59 254	98.85 154	91.29 341	98.93 336	98.94 44	95.90 99	98.77 127	98.42 269	90.89 184	99.77 149	97.80 164	70.76 447	98.72 268

Anonymous20240529		92.10 320	90.65 332	96.47 256	98.82 155	90.61 355	98.72 359	98.67 86	75.54 455	93.90 281	98.58 254	66.23 428	99.90 112	94.70 248	90.67 312	98.90 258

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	96.38 230	98.49 262	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	98.69 269

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	81.36 321	99.98 50	92.77 291	98.89 155	98.28 283

mvsany_test1		97.82 95	97.90 80	97.55 207	98.77 159	93.04 293	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	94.26 290	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	96.77 203	95.63 267	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	98.69 269

miper_enhance_ethall		94.36 262	93.98 253	95.49 287	98.68 164	95.24 221	99.73 195	97.29 331	93.28 205	89.86 329	95.97 354	94.37 88	97.05 359	92.20 295	84.45 370	94.19 357

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	91.10 303	98.17 163	98.59 251	93.86 108	98.19 301	95.64 226	95.24 277	99.28 219

test2506		97.53 114	97.19 120	98.58 121	98.66 167	96.90 139	98.81 351	99.77 5	94.93 125	97.95 169	98.96 203	92.51 150	99.20 200	94.93 238	98.15 182	99.64 135

ECVR-MVS		95.66 214	95.05 221	97.51 212	98.66 167	93.71 274	98.85 348	98.45 142	94.93 125	96.86 210	98.96 203	75.22 386	99.20 200	95.34 228	98.15 182	99.64 135

mamv4		95.24 226	96.90 131	90.25 414	98.65 169	72.11 463	98.28 387	97.64 279	89.99 337	95.93 242	98.25 279	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	98.35 271	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	90.34 329	99.26 98	98.32 274	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	92.53 249	98.84 121	98.84 228	93.36 118	98.30 291	95.84 222	94.30 289	99.05 245

test1111		95.57 217	94.98 224	97.37 224	98.56 173	93.37 287	98.86 346	98.45 142	94.95 124	96.63 216	98.95 208	75.21 387	99.11 206	95.02 235	98.14 184	99.64 135

MVSTER		95.53 218	95.22 213	96.45 259	98.56 173	97.72 98	99.91 108	97.67 275	92.38 257	91.39 307	97.14 311	97.24 20	97.30 343	94.80 244	87.85 341	94.34 345

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	98.73 235	95.50 52	99.69 163	98.53 121	94.63 282	98.99 249

VDD-MVS		93.77 278	92.94 287	96.27 266	98.55 176	90.22 364	98.77 356	97.79 262	90.85 309	96.82 212	99.42 141	61.18 448	99.77 149	98.95 90	94.13 291	98.82 261

tpmvs		94.28 264	93.57 266	96.40 261	98.55 176	91.50 339	95.70 448	98.55 118	87.47 379	92.15 300	94.26 420	91.42 169	98.95 219	88.15 360	95.85 256	98.76 264

UGNet		95.33 224	94.57 236	97.62 200	98.55 176	94.85 233	98.67 365	99.32 26	95.75 106	96.80 213	96.27 343	72.18 402	99.96 75	94.58 251	99.05 151	98.04 289
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	95.18 228	94.10 247	98.43 139	98.55 176	95.99 182	97.91 404	97.31 326	90.35 328	89.48 342	99.22 170	85.19 276	99.89 117	90.40 333	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	94.34 337	98.51 181	89.99 369	99.39 273	98.57 106	93.14 212	97.33 193	98.31 276	93.44 116	94.68 437	93.69 277	95.98 250	98.34 282

UWE-MVS		96.79 153	96.72 143	97.00 238	98.51 181	93.70 275	99.71 202	98.60 100	92.96 220	97.09 200	98.34 273	96.67 33	98.85 226	92.11 301	96.50 237	98.44 277

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	98.75 233	95.20 57	99.48 185	98.93 92	96.40 240	99.29 217

test_vis1_n_1920		95.44 220	95.31 209	95.82 281	98.50 183	88.74 387	99.98 21	97.30 327	97.84 28	99.85 18	99.19 175	66.82 426	99.97 63	98.82 101	99.46 126	98.76 264

BH-w/o		95.71 211	95.38 207	96.68 251	98.49 185	92.28 312	99.84 149	97.50 300	92.12 267	92.06 303	98.79 231	84.69 287	98.67 254	95.29 230	99.66 96	99.09 239

baseline1		95.78 207	94.86 227	98.54 127	98.47 186	98.07 79	99.06 314	97.99 240	92.68 239	94.13 278	98.62 248	93.28 124	98.69 251	93.79 272	85.76 357	98.84 260

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	97.66 299

EPMVS		96.53 172	96.01 172	98.09 161	98.43 188	96.12 180	96.36 435	99.43 20	93.53 193	97.64 182	95.04 396	94.41 83	98.38 283	91.13 314	98.11 185	99.75 117

kuosan		93.17 293	92.60 295	94.86 313	98.40 189	89.54 377	98.44 378	98.53 125	84.46 418	88.49 363	97.92 292	90.57 188	97.05 359	83.10 406	93.49 299	97.99 290

WBMVS		94.52 253	94.03 251	95.98 272	98.38 190	96.68 149	99.92 100	97.63 280	90.75 318	89.64 337	95.25 389	96.77 27	96.90 371	94.35 256	83.57 377	94.35 343

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	98.42 269	96.77 27	99.17 203	98.54 119	96.20 244	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	96.33 213	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	98.70 238	95.48 53	99.22 196	97.85 162	95.05 279	99.07 242

BH-untuned		95.18 228	94.83 228	96.22 267	98.36 193	91.22 342	99.80 166	97.32 325	90.91 307	91.08 310	98.67 240	83.51 299	98.54 265	94.23 259	99.61 104	98.92 255

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	98.72 236	94.81 71	99.21 197	96.98 194	94.63 282	99.03 246

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	98.72 236	94.83 70	99.21 197	97.00 192	94.64 281	98.95 251

ET-MVSNet_ETH3D		94.37 260	93.28 281	97.64 196	98.30 197	97.99 84	99.99 5	97.61 286	94.35 155	71.57 461	99.45 140	96.23 38	95.34 427	96.91 200	85.14 364	99.59 149

AUN-MVS		93.28 290	92.60 295	95.34 296	98.29 198	90.09 367	99.31 285	98.56 112	91.80 280	96.35 231	98.00 287	89.38 205	98.28 294	92.46 292	69.22 453	97.64 301

FMVSNet3		92.69 307	91.58 317	95.99 271	98.29 198	97.42 115	99.26 294	97.62 283	89.80 340	89.68 333	95.32 383	81.62 319	96.27 403	87.01 377	85.65 358	94.29 347

PMMVS		96.76 156	96.76 140	96.76 248	98.28 200	92.10 316	99.91 108	97.98 242	94.12 166	99.53 72	99.39 148	86.93 245	98.73 244	96.95 197	97.73 193	99.45 186

hse-mvs2		94.38 259	94.08 250	95.31 298	98.27 201	90.02 368	99.29 290	98.56 112	95.90 99	98.77 127	98.00 287	90.89 184	98.26 298	97.80 164	69.20 454	97.64 301

PVSNet_0	88.03 19	91.80 327	90.27 341	96.38 263	98.27 201	90.46 359	99.94 90	99.61 13	93.99 174	86.26 399	97.39 306	71.13 409	99.89 117	98.77 105	67.05 460	98.79 263

UA-Net		96.54 171	95.96 179	98.27 149	98.23 203	95.71 193	98.00 402	98.45 142	93.72 189	98.41 150	99.27 162	88.71 219	99.66 170	91.19 313	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	97.25 336	97.77 29	99.58 68	99.08 184	77.10 360	99.97 63	97.64 172	99.45 127	98.74 266

FE-MVS		95.70 213	95.01 223	97.79 182	98.21 205	94.57 243	95.03 449	98.69 81	88.90 356	97.50 186	96.19 345	92.60 146	99.49 184	89.99 338	97.94 191	99.31 212

GG-mvs-BLEND				98.54 127	98.21 205	98.01 83	93.87 454	98.52 127		97.92 170	97.92 292	99.02 3	97.94 318	98.17 142	99.58 109	99.67 129

mvs_anonymous		95.65 215	95.03 222	97.53 209	98.19 207	95.74 191	99.33 282	97.49 301	90.87 308	90.47 319	97.10 313	88.23 222	97.16 350	95.92 220	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	97.15 351	91.07 304	98.84 121	97.05 317	88.17 223	98.97 216	94.39 253	97.50 199	99.61 146

BH-RMVSNet		95.18 228	94.31 243	97.80 180	98.17 209	95.23 222	99.76 181	97.53 296	92.52 250	94.27 276	99.25 168	76.84 367	98.80 234	90.89 322	99.54 111	99.35 203

dongtai		91.55 333	91.13 326	92.82 382	98.16 210	86.35 411	99.47 261	98.51 130	83.24 426	85.07 409	97.56 300	90.33 193	94.94 433	76.09 445	91.73 307	97.18 312

RPSCF		91.80 327	92.79 291	88.83 426	98.15 211	69.87 465	98.11 398	96.60 409	83.93 421	94.33 274	99.27 162	79.60 343	99.46 188	91.99 302	93.16 304	97.18 312

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	99.08 309	97.61 286	92.02 272	95.54 255	98.96 203	90.64 187	98.08 307	93.73 275	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	80.88 328	99.93 103	99.59 56	98.17 180	97.29 310

ab-mvs		94.69 245	93.42 272	98.51 132	98.07 216	96.26 168	96.49 433	98.68 83	90.31 330	94.54 266	97.00 319	76.30 375	99.71 159	95.98 219	93.38 302	99.56 158

XVG-OURS-SEG-HR		94.79 240	94.70 235	95.08 303	98.05 217	89.19 379	99.08 309	97.54 294	93.66 190	94.87 264	99.58 127	78.78 351	99.79 144	97.31 180	93.40 301	96.25 319

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		94.82 237	94.74 234	95.06 304	98.00 219	89.19 379	99.08 309	97.55 292	94.10 167	94.71 265	99.62 122	80.51 334	99.74 155	96.04 218	93.06 306	96.25 319

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	97.92 292	88.02 224	98.75 242	98.78 104	99.01 152	99.52 169

dp		95.05 231	94.43 238	96.91 241	97.99 220	92.73 301	96.29 438	97.98 242	89.70 341	95.93 242	94.67 410	93.83 110	98.45 271	86.91 380	96.53 236	99.54 163

tpmrst		96.27 189	95.98 175	97.13 233	97.96 222	93.15 289	96.34 436	98.17 218	92.07 268	98.71 133	95.12 393	93.91 105	98.73 244	94.91 241	96.62 234	99.50 175

TR-MVS		94.54 250	93.56 267	97.49 214	97.96 222	94.34 256	98.71 360	97.51 299	90.30 331	94.51 268	98.69 239	75.56 381	98.77 238	92.82 290	95.99 249	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	91.83 277	95.09 262	99.11 182	91.37 171	97.47 334	93.47 279	97.43 200	99.74 118

MDTV_nov1_ep13			95.69 194		97.90 225	94.15 262	95.98 444	98.44 147	93.12 214	97.98 168	95.74 358	95.10 60	98.58 261	90.02 337	96.92 229

Fast-Effi-MVS+		95.02 233	94.19 245	97.52 211	97.88 226	94.55 244	99.97 39	97.08 367	88.85 358	94.47 269	97.96 291	84.59 288	98.41 275	89.84 340	97.10 220	99.59 149

ADS-MVSNet2		93.80 277	93.88 257	93.55 365	97.87 227	85.94 415	94.24 450	96.84 395	90.07 334	96.43 227	94.48 415	90.29 195	95.37 426	87.44 367	97.23 211	99.36 199

ADS-MVSNet		94.79 240	94.02 252	97.11 235	97.87 227	93.79 271	94.24 450	98.16 223	90.07 334	96.43 227	94.48 415	90.29 195	98.19 301	87.44 367	97.23 211	99.36 199

Effi-MVS+		96.30 186	95.69 194	98.16 154	97.85 229	96.26 168	97.41 413	97.21 343	90.37 327	98.65 136	98.58 254	86.61 251	98.70 250	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	96.05 442	98.40 176	92.86 225	97.09 200	95.28 388	94.21 97	98.07 309	89.26 346	98.11 185	99.70 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 248	93.61 262	97.74 190	97.82 231	96.26 168	99.96 53	97.78 265	85.76 402	94.00 279	97.54 301	76.95 366	99.21 197	97.23 185	95.43 272	97.76 298

1112_ss		96.01 197	95.20 214	98.42 141	97.80 232	96.41 161	99.65 219	96.66 406	92.71 236	92.88 293	99.40 146	92.16 159	99.30 192	91.92 304	93.66 297	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		95.72 209	94.83 228	98.42 141	97.79 233	96.41 161	99.65 219	96.65 407	92.70 237	92.86 294	96.13 349	92.15 160	99.30 192	91.88 305	93.64 298	99.55 159

Effi-MVS+-dtu		94.53 252	95.30 210	92.22 390	97.77 235	82.54 438	99.59 235	97.06 371	94.92 127	95.29 259	95.37 381	85.81 262	97.89 319	94.80 244	97.07 221	96.23 321

tpm cat1		93.51 286	92.52 301	96.47 256	97.77 235	91.47 340	96.13 440	98.06 233	80.98 440	92.91 292	93.78 424	89.66 200	98.87 224	87.03 376	96.39 241	99.09 239

FA-MVS(test-final)		95.86 203	95.09 219	98.15 157	97.74 237	95.62 199	96.31 437	98.17 218	91.42 293	96.26 232	96.13 349	90.56 189	99.47 187	92.18 296	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	97.83 294

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	97.83 294

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	97.83 294

EPP-MVSNet		96.69 163	96.60 148	96.96 240	97.74 237	93.05 292	99.37 277	98.56 112	88.75 360	95.83 246	99.01 194	96.01 39	98.56 263	96.92 198	97.20 213	99.25 224

gg-mvs-nofinetune		93.51 286	91.86 313	98.47 134	97.72 242	97.96 88	92.62 460	98.51 130	74.70 458	97.33 193	69.59 475	98.91 4	97.79 322	97.77 169	99.56 110	99.67 129

IB-MVS	92.85 6	94.99 234	93.94 255	98.16 154	97.72 242	95.69 196	99.99 5	98.81 67	94.28 161	92.70 295	96.90 321	95.08 61	99.17 203	96.07 217	73.88 440	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	91.83 277	97.45 188	99.04 190	97.50 9	99.10 208	94.75 246	96.37 242	99.16 231

VortexMVS		94.11 266	93.50 269	95.94 274	97.70 245	96.61 153	99.35 280	97.18 346	93.52 195	89.57 340	95.74 358	87.55 232	96.97 367	95.76 225	85.13 365	94.23 352

viewdifsd2359ckpt09		96.21 191	95.77 190	97.53 209	97.69 246	94.50 247	99.78 170	97.23 341	92.88 224	96.58 219	99.26 166	84.85 281	98.66 257	96.61 207	97.02 226	99.43 190

Syy-MVS		90.00 367	90.63 333	88.11 433	97.68 247	74.66 461	99.71 202	98.35 189	90.79 315	92.10 301	98.67 240	79.10 349	93.09 453	63.35 468	95.95 253	96.59 317

myMVS_eth3d		94.46 257	94.76 233	93.55 365	97.68 247	90.97 344	99.71 202	98.35 189	90.79 315	92.10 301	98.67 240	92.46 153	93.09 453	87.13 373	95.95 253	96.59 317

test_fmvs1_n		94.25 265	94.36 240	93.92 352	97.68 247	83.70 428	99.90 114	96.57 410	97.40 40	99.67 51	98.88 215	61.82 445	99.92 109	98.23 140	99.13 146	98.14 287

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	99.27 293	97.10 363	92.79 231	97.43 189	97.99 289	81.85 314	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	97.19 344	94.67 139	98.95 116	99.28 159	86.43 252	98.76 240	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	97.23 341	92.76 233	96.63 216	99.05 189	84.96 280	98.64 258	96.65 206	97.35 206	99.31 212

Vis-MVSNet		95.72 209	95.15 217	97.45 215	97.62 254	94.28 257	99.28 291	98.24 209	94.27 163	96.84 211	98.94 210	79.39 344	98.76 240	93.25 281	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	91.11 302	97.07 202	98.97 201	97.47 12	99.03 211	93.73 275	96.09 247	98.92 255

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	94.99 236	98.17 180	99.37 197

miper_ehance_all_eth		93.16 294	92.60 295	94.82 314	97.57 258	93.56 279	99.50 255	97.07 370	88.75 360	88.85 357	95.52 370	90.97 180	96.74 381	90.77 324	84.45 370	94.17 358

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	98.65 243	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	98.71 247	97.26 184	97.33 207	99.47 179

testing3		93.92 271	94.23 244	92.99 379	97.54 261	90.23 363	99.99 5	99.16 33	90.57 321	91.33 309	98.63 247	92.99 132	92.52 457	82.46 410	95.39 273	96.22 322

SSM_0404		95.75 208	95.16 216	97.50 213	97.53 262	95.39 210	99.11 305	97.25 336	90.81 311	95.27 260	98.83 229	84.74 284	98.67 254	95.24 231	97.69 194	98.45 276

LCM-MVSNet-Re		92.31 316	92.60 295	91.43 399	97.53 262	79.27 455	99.02 323	91.83 470	92.07 268	80.31 433	94.38 418	83.50 300	95.48 423	97.22 186	97.58 198	99.54 163

GBi-Net		90.88 344	89.82 350	94.08 344	97.53 262	91.97 317	98.43 379	96.95 384	87.05 385	89.68 333	94.72 406	71.34 406	96.11 409	87.01 377	85.65 358	94.17 358

test1		90.88 344	89.82 350	94.08 344	97.53 262	91.97 317	98.43 379	96.95 384	87.05 385	89.68 333	94.72 406	71.34 406	96.11 409	87.01 377	85.65 358	94.17 358

FMVSNet2		91.02 341	89.56 355	95.41 294	97.53 262	95.74 191	98.98 326	97.41 309	87.05 385	88.43 367	95.00 400	71.34 406	96.24 405	85.12 392	85.21 363	94.25 350

tttt0517		96.85 150	96.49 152	97.92 172	97.48 267	95.89 185	99.85 144	98.54 122	90.72 319	96.63 216	98.93 213	97.47 12	99.02 212	93.03 288	95.76 259	98.85 259

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	97.29 331	93.35 201	96.03 239	99.19 175	85.39 273	98.72 246	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	97.06 371	94.59 140	99.63 57	99.32 153	89.20 211	98.14 303	98.76 106	99.23 142	99.62 142

viewdifsd2359ckpt07		95.83 206	95.42 204	97.07 236	97.40 272	93.04 293	99.60 233	97.24 339	92.39 256	96.09 238	99.14 181	83.07 305	98.93 220	97.02 191	96.87 230	99.23 227

c3_l		92.53 311	91.87 312	94.52 326	97.40 272	92.99 295	99.40 269	96.93 389	87.86 375	88.69 360	95.44 375	89.95 198	96.44 395	90.45 330	80.69 404	94.14 367

viewmambaseed2359dif		95.92 202	95.55 200	97.04 237	97.38 274	93.41 284	99.78 170	96.97 382	91.14 301	96.58 219	99.27 162	84.85 281	98.75 242	96.87 201	97.12 219	98.97 250

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	98.46 275

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	97.13 233	97.37 276	94.96 230	99.53 250	97.91 251	91.55 285	95.37 258	98.32 274	95.05 63	97.13 353	93.80 271	95.75 260	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	91.93 273	95.77 247	98.07 285	95.54 49	98.29 292	90.55 328	98.89 155	99.70 124

miper_lstm_enhance		91.81 324	91.39 323	93.06 378	97.34 279	89.18 381	99.38 275	96.79 400	86.70 392	87.47 381	95.22 390	90.00 197	95.86 418	88.26 357	81.37 393	94.15 364

baseline		96.43 176	95.98 175	97.76 188	97.34 279	95.17 226	99.51 253	97.17 348	93.92 179	96.90 209	99.28 159	85.37 274	98.64 258	97.50 176	96.86 232	99.46 181

cl____		92.31 316	91.58 317	94.52 326	97.33 281	92.77 297	99.57 240	96.78 401	86.97 389	87.56 379	95.51 371	89.43 204	96.62 387	88.60 351	82.44 385	94.16 363

SD_0403		92.63 310	93.38 276	90.40 413	97.32 282	77.91 457	97.75 409	98.03 238	91.89 274	90.83 315	98.29 278	82.00 311	93.79 446	88.51 355	95.75 260	99.52 169

DIV-MVS_self_test		92.32 315	91.60 316	94.47 330	97.31 283	92.74 299	99.58 237	96.75 402	86.99 388	87.64 377	95.54 368	89.55 203	96.50 392	88.58 352	82.44 385	94.17 358

casdiffmvs		96.42 178	95.97 178	97.77 186	97.30 284	94.98 229	99.84 149	97.09 366	93.75 188	96.58 219	99.26 166	85.07 277	98.78 237	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		94.36 262	93.48 270	96.99 239	97.29 285	93.54 280	99.96 53	96.72 404	88.35 369	93.43 283	98.94 210	82.05 310	98.05 310	88.12 362	96.48 239	99.37 197

eth_miper_zixun_eth		92.41 314	91.93 310	93.84 356	97.28 286	90.68 353	98.83 349	96.97 382	88.57 365	89.19 352	95.73 361	89.24 210	96.69 385	89.97 339	81.55 391	94.15 364

MVSFormer		96.94 146	96.60 148	97.95 168	97.28 286	97.70 101	99.55 247	97.27 333	91.17 298	99.43 82	99.54 133	90.92 181	96.89 372	94.67 249	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	98.71 247	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	97.15 351	94.46 145	98.78 125	99.21 173	85.64 266	98.77 238	98.27 137	97.31 209	99.13 235

mamba_0408		94.98 235	94.09 248	97.64 196	97.14 290	95.31 215	93.48 457	97.08 367	90.48 323	94.40 270	98.62 248	84.49 289	98.67 254	93.99 262	97.18 214	98.93 252

SSM_04072		94.77 242	94.09 248	96.82 245	97.14 290	95.31 215	93.48 457	97.08 367	90.48 323	94.40 270	98.62 248	84.49 289	96.21 406	93.99 262	97.18 214	98.93 252

SSM_0407		95.62 216	94.95 225	97.61 201	97.14 290	95.31 215	99.00 324	97.25 336	90.81 311	94.40 270	98.83 229	84.74 284	98.58 261	95.24 231	97.18 214	98.93 252

SCA		94.69 245	93.81 259	97.33 229	97.10 293	94.44 248	98.86 346	98.32 196	93.30 204	96.17 237	95.59 366	76.48 373	97.95 316	91.06 316	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	97.26 335	93.05 218	96.50 223	99.17 177	82.76 306	98.68 252	96.61 207	97.04 223	99.28 219

KinetiMVS		96.10 193	95.29 211	98.53 129	97.08 295	97.12 128	99.56 244	98.12 229	94.78 132	98.44 147	98.94 210	80.30 338	99.39 190	91.56 309	98.79 161	99.06 243

TAMVS		95.85 204	95.58 198	96.65 253	97.07 296	93.50 281	99.17 301	97.82 261	91.39 295	95.02 263	98.01 286	92.20 158	97.30 343	93.75 274	95.83 257	99.14 234

Fast-Effi-MVS+-dtu		93.72 281	93.86 258	93.29 370	97.06 297	86.16 412	99.80 166	96.83 396	92.66 240	92.58 296	97.83 297	81.39 320	97.67 327	89.75 341	96.87 230	96.05 324

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	96.78 247	97.03 299	92.55 307	97.08 422	97.83 260	90.04 336	98.72 132	94.89 404	95.01 65	98.29 292	96.54 210	95.77 258	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	98.42 278

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	95.55 253	96.38 338	93.90 106	98.27 296	90.42 331	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	91.81 279	95.55 253	96.38 338	95.17 58	98.27 296	90.42 331	98.83 159	99.64 135

icg_test_0407_2		95.04 232	94.78 232	95.84 280	96.97 303	91.64 332	98.63 368	97.12 356	92.33 259	95.60 251	98.88 215	85.65 264	96.56 390	92.12 297	95.70 263	99.32 208

IMVS_0407		95.21 227	94.80 231	96.46 258	96.97 303	91.64 332	98.81 351	97.12 356	92.33 259	95.60 251	98.88 215	85.65 264	98.42 273	92.12 297	95.70 263	99.32 208

IMVS_0404		93.83 273	93.17 283	95.80 282	96.97 303	91.64 332	97.78 408	97.12 356	92.33 259	90.87 314	98.88 215	76.78 368	96.43 396	92.12 297	95.70 263	99.32 208

IMVS_0403		95.25 225	94.81 230	96.58 255	96.97 303	91.64 332	98.97 331	97.12 356	92.33 259	95.43 256	98.88 215	85.78 263	98.79 235	92.12 297	95.70 263	99.32 208

gm-plane-assit					96.97 303	93.76 273			91.47 289		98.96 203		98.79 235	94.92 239

WB-MVSnew		92.90 300	92.77 292	93.26 372	96.95 308	93.63 277	99.71 202	98.16 223	91.49 286	94.28 275	98.14 282	81.33 322	96.48 393	79.47 427	95.46 270	89.68 454

QAPM		95.40 221	94.17 246	99.10 78	96.92 309	97.71 99	99.40 269	98.68 83	89.31 344	88.94 356	98.89 214	82.48 308	99.96 75	93.12 287	99.83 81	99.62 142

KD-MVS_2432*1600		88.00 389	86.10 393	93.70 361	96.91 310	94.04 265	97.17 419	97.12 356	84.93 413	81.96 423	92.41 437	92.48 151	94.51 439	79.23 428	52.68 474	92.56 423

miper_refine_blended		88.00 389	86.10 393	93.70 361	96.91 310	94.04 265	97.17 419	97.12 356	84.93 413	81.96 423	92.41 437	92.48 151	94.51 439	79.23 428	52.68 474	92.56 423

tpm2		95.47 219	95.18 215	96.35 264	96.91 310	91.70 330	96.96 425	97.93 247	88.04 373	98.44 147	95.40 377	93.32 121	97.97 313	94.00 261	95.61 268	99.38 195

FMVSNet5		88.32 385	87.47 387	90.88 402	96.90 313	88.39 395	97.28 416	95.68 432	82.60 433	84.67 411	92.40 439	79.83 341	91.16 462	76.39 444	81.51 392	93.09 414

3Dnovator+	91.53 11	96.31 185	95.24 212	99.52 32	96.88 314	98.64 58	99.72 199	98.24 209	95.27 120	88.42 369	98.98 199	82.76 306	99.94 93	97.10 189	99.83 81	99.96 74

Patchmatch-test		92.65 309	91.50 320	96.10 270	96.85 315	90.49 358	91.50 465	97.19 344	82.76 432	90.23 320	95.59 366	95.02 64	98.00 312	77.41 439	96.98 228	99.82 106

MVS		96.60 167	95.56 199	99.72 14	96.85 315	99.22 21	98.31 385	98.94 44	91.57 284	90.90 313	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	95.34 208	99.08 81	96.82 317	97.47 113	99.45 266	98.81 67	95.52 114	89.39 343	99.00 196	81.97 312	99.95 84	97.27 181	99.83 81	99.84 103

EI-MVSNet		93.73 280	93.40 275	94.74 315	96.80 318	92.69 302	99.06 314	97.67 275	88.96 353	91.39 307	99.02 192	88.75 218	97.30 343	91.07 315	87.85 341	94.22 354

CVMVSNet		94.68 247	94.94 226	93.89 355	96.80 318	86.92 409	99.06 314	98.98 41	94.45 146	94.23 277	99.02 192	85.60 267	95.31 428	90.91 321	95.39 273	99.43 190

IterMVS-LS		92.69 307	92.11 306	94.43 334	96.80 318	92.74 299	99.45 266	96.89 392	88.98 351	89.65 336	95.38 380	88.77 217	96.34 400	90.98 319	82.04 388	94.22 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 170	96.46 155	96.91 241	96.79 321	92.50 308	99.90 114	97.38 311	96.02 98	97.79 179	99.32 153	86.36 254	98.99 213	98.26 138	96.33 243	99.23 227

IterMVS		90.91 343	90.17 345	93.12 375	96.78 322	90.42 361	98.89 340	97.05 374	89.03 348	86.49 394	95.42 376	76.59 371	95.02 430	87.22 372	84.09 373	93.93 385
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	96.74 323	98.52 62	98.31 385	98.86 59	95.82 103	89.91 327	98.98 199	87.49 234	99.96 75	97.80 164	99.73 91	99.96 74

IterMVS-SCA-FT		90.85 346	90.16 346	92.93 380	96.72 324	89.96 370	98.89 340	96.99 378	88.95 354	86.63 391	95.67 362	76.48 373	95.00 431	87.04 375	84.04 376	93.84 392

MVS-HIRNet		86.22 396	83.19 409	95.31 298	96.71 325	90.29 362	92.12 462	97.33 319	62.85 469	86.82 388	70.37 474	69.37 414	97.49 333	75.12 447	97.99 190	98.15 285

viewdifsd2359ckpt11		94.09 268	93.63 261	95.46 291	96.68 326	88.92 384	99.62 226	97.12 356	93.07 216	95.73 248	99.22 170	77.05 361	98.88 223	96.52 211	87.69 346	98.58 273

viewmsd2359difaftdt		94.09 268	93.64 260	95.46 291	96.68 326	88.92 384	99.62 226	97.13 355	93.07 216	95.73 248	99.22 170	77.05 361	98.89 222	96.52 211	87.70 345	98.58 273

VDDNet		93.12 295	91.91 311	96.76 248	96.67 328	92.65 305	98.69 363	98.21 213	82.81 431	97.75 181	99.28 159	61.57 446	99.48 185	98.09 148	94.09 292	98.15 285

dmvs_re		93.20 292	93.15 284	93.34 368	96.54 329	83.81 427	98.71 360	98.51 130	91.39 295	92.37 299	98.56 256	78.66 353	97.83 321	93.89 265	89.74 313	98.38 280

Elysia		94.50 254	93.38 276	97.85 178	96.49 330	96.70 146	98.98 326	97.78 265	90.81 311	96.19 235	98.55 258	73.63 397	98.98 214	89.41 342	98.56 167	97.88 292

StellarMVS		94.50 254	93.38 276	97.85 178	96.49 330	96.70 146	98.98 326	97.78 265	90.81 311	96.19 235	98.55 258	73.63 397	98.98 214	89.41 342	98.56 167	97.88 292

MIMVSNet		90.30 359	88.67 373	95.17 302	96.45 332	91.64 332	92.39 461	97.15 351	85.99 399	90.50 318	93.19 432	66.95 425	94.86 435	82.01 414	93.43 300	99.01 248

CR-MVSNet		93.45 289	92.62 294	95.94 274	96.29 333	92.66 303	92.01 463	96.23 418	92.62 242	96.94 207	93.31 430	91.04 178	96.03 414	79.23 428	95.96 251	99.13 235

RPMNet		89.76 371	87.28 388	97.19 232	96.29 333	92.66 303	92.01 463	98.31 198	70.19 465	96.94 207	85.87 467	87.25 239	99.78 146	62.69 469	95.96 251	99.13 235

tt0805		91.28 336	90.18 344	94.60 321	96.26 335	87.55 402	98.39 383	98.72 77	89.00 350	89.22 349	98.47 266	62.98 441	98.96 218	90.57 327	88.00 340	97.28 311

Patchmtry		89.70 372	88.49 376	93.33 369	96.24 336	89.94 373	91.37 466	96.23 418	78.22 448	87.69 376	93.31 430	91.04 178	96.03 414	80.18 426	82.10 387	94.02 375

test_vis1_rt		86.87 394	86.05 396	89.34 422	96.12 337	78.07 456	99.87 130	83.54 482	92.03 271	78.21 444	89.51 451	45.80 467	99.91 110	96.25 215	93.11 305	90.03 450

JIA-IIPM		91.76 330	90.70 331	94.94 308	96.11 338	87.51 403	93.16 459	98.13 228	75.79 454	97.58 183	77.68 472	92.84 137	97.97 313	88.47 356	96.54 235	99.33 206

OpenMVS	90.15 15	94.77 242	93.59 265	98.33 145	96.07 339	97.48 112	99.56 244	98.57 106	90.46 325	86.51 393	98.95 208	78.57 354	99.94 93	93.86 266	99.74 90	97.57 306

PAPM		98.60 37	98.42 38	99.14 72	96.05 340	98.96 27	99.90 114	99.35 24	96.68 71	98.35 154	99.66 115	96.45 35	98.51 266	99.45 65	99.89 74	99.96 74

CLD-MVS		94.06 270	93.90 256	94.55 325	96.02 341	90.69 352	99.98 21	97.72 271	96.62 75	91.05 312	98.85 227	77.21 359	98.47 267	98.11 146	89.51 319	94.48 331
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 356	88.75 372	95.25 300	95.99 342	90.16 365	91.22 467	97.54 294	76.80 450	97.26 196	86.01 466	91.88 165	96.07 413	66.16 464	95.91 255	99.51 173

ACMH+	89.98 16	90.35 357	89.54 356	92.78 384	95.99 342	86.12 413	98.81 351	97.18 346	89.38 343	83.14 419	97.76 298	68.42 419	98.43 272	89.11 347	86.05 356	93.78 395

DeepMVS_CX				82.92 444	95.98 344	58.66 475		96.01 424	92.72 234	78.34 443	95.51 371	58.29 453	98.08 307	82.57 409	85.29 361	92.03 431

ACMP	92.05 9	92.74 305	92.42 303	93.73 357	95.91 345	88.72 388	99.81 162	97.53 296	94.13 165	87.00 387	98.23 280	74.07 394	98.47 267	96.22 216	88.86 326	93.99 380
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 284	93.03 286	95.35 295	95.86 346	86.94 408	99.87 130	96.36 416	96.85 62	99.54 71	98.79 231	52.41 461	99.83 139	98.64 114	98.97 153	99.29 217

HQP-NCC					95.78 347		99.87 130		96.82 64	93.37 284

ACMP_Plane					95.78 347		99.87 130		96.82 64	93.37 284

HQP-MVS		94.61 249	94.50 237	94.92 309	95.78 347	91.85 322	99.87 130	97.89 252	96.82 64	93.37 284	98.65 243	80.65 332	98.39 279	97.92 158	89.60 314	94.53 327

NP-MVS					95.77 350	91.79 324					98.65 243

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 114	95.76 351	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					95.76 351	91.72 329						80.47 336

ACMM	91.95 10	92.88 301	92.52 301	93.98 351	95.75 353	89.08 383	99.77 175	97.52 298	93.00 219	89.95 326	97.99 289	76.17 377	98.46 270	93.63 278	88.87 325	94.39 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 273	92.84 288	96.80 246	95.73 354	93.57 278	99.88 127	97.24 339	92.57 247	92.92 291	96.66 330	78.73 352	97.67 327	87.75 365	94.06 293	99.17 230

plane_prior1					95.73 354

jason		97.24 129	96.86 134	98.38 144	95.73 354	97.32 117	99.97 39	97.40 310	95.34 118	98.60 141	99.54 133	87.70 228	98.56 263	97.94 157	99.47 124	99.25 224
jason: jason.
mmtdpeth		88.52 383	87.75 385	90.85 404	95.71 357	83.47 433	98.94 334	94.85 447	88.78 359	97.19 198	89.58 450	63.29 439	98.97 216	98.54 119	62.86 468	90.10 449

HQP_MVS		94.49 256	94.36 240	94.87 310	95.71 357	91.74 326	99.84 149	97.87 254	96.38 84	93.01 289	98.59 251	80.47 336	98.37 285	97.79 167	89.55 317	94.52 329

plane_prior7					95.71 357	91.59 338

ITE_SJBPF				92.38 387	95.69 360	85.14 419		95.71 431	92.81 228	89.33 346	98.11 283	70.23 412	98.42 273	85.91 387	88.16 338	93.59 403

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 199	95.65 361	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	98.74 266

ACMH	89.72 17	90.64 350	89.63 353	93.66 363	95.64 362	88.64 391	98.55 371	97.45 303	89.03 348	81.62 426	97.61 299	69.75 413	98.41 275	89.37 344	87.62 347	93.92 386
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	95.63 363	95.96 183	99.74 188	98.88 54	92.94 221	91.61 305	98.97 201	97.72 6	98.62 260	94.83 243	98.08 188	97.53 308

FMVSNet1		88.50 384	86.64 391	94.08 344	95.62 364	91.97 317	98.43 379	96.95 384	83.00 429	86.08 401	94.72 406	59.09 452	96.11 409	81.82 416	84.07 374	94.17 358

LuminaMVS		96.63 166	96.21 166	97.87 177	95.58 365	96.82 141	99.12 303	97.67 275	94.47 144	97.88 174	98.31 276	87.50 233	98.71 247	98.07 150	97.29 210	98.10 288

LPG-MVS_test		92.96 298	92.71 293	93.71 359	95.43 366	88.67 389	99.75 185	97.62 283	92.81 228	90.05 322	98.49 262	75.24 384	98.40 277	95.84 222	89.12 321	94.07 372

LGP-MVS_train				93.71 359	95.43 366	88.67 389		97.62 283	92.81 228	90.05 322	98.49 262	75.24 384	98.40 277	95.84 222	89.12 321	94.07 372

tpm		93.70 282	93.41 274	94.58 323	95.36 368	87.41 404	97.01 423	96.90 391	90.85 309	96.72 215	94.14 421	90.40 192	96.84 376	90.75 325	88.54 333	99.51 173

D2MVS		92.76 304	92.59 299	93.27 371	95.13 369	89.54 377	99.69 212	99.38 22	92.26 264	87.59 378	94.61 412	85.05 278	97.79 322	91.59 308	88.01 339	92.47 426

VPA-MVSNet		92.70 306	91.55 319	96.16 268	95.09 370	96.20 174	98.88 342	99.00 39	91.02 306	91.82 304	95.29 387	76.05 379	97.96 315	95.62 227	81.19 394	94.30 346

LTVRE_ROB	88.28 18	90.29 360	89.05 367	94.02 347	95.08 371	90.15 366	97.19 418	97.43 305	84.91 415	83.99 415	97.06 316	74.00 395	98.28 294	84.08 398	87.71 343	93.62 402
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		87.87 391	86.51 392	91.94 393	95.05 372	85.57 417	97.65 410	94.08 457	84.40 419	81.82 425	96.85 325	62.14 444	98.33 288	80.25 425	86.37 354	91.91 433

test0.0.03 1		93.86 272	93.61 262	94.64 319	95.02 373	92.18 315	99.93 97	98.58 104	94.07 169	87.96 373	98.50 261	93.90 106	94.96 432	81.33 417	93.17 303	96.78 314

UniMVSNet (Re)		93.07 297	92.13 305	95.88 277	94.84 374	96.24 173	99.88 127	98.98 41	92.49 252	89.25 347	95.40 377	87.09 241	97.14 352	93.13 286	78.16 418	94.26 348

USDC		90.00 367	88.96 368	93.10 377	94.81 375	88.16 397	98.71 360	95.54 436	93.66 190	83.75 417	97.20 310	65.58 430	98.31 290	83.96 401	87.49 349	92.85 420

VPNet		91.81 324	90.46 335	95.85 279	94.74 376	95.54 202	98.98 326	98.59 102	92.14 266	90.77 317	97.44 303	68.73 417	97.54 332	94.89 242	77.89 420	94.46 332

FIs		94.10 267	93.43 271	96.11 269	94.70 377	96.82 141	99.58 237	98.93 48	92.54 248	89.34 345	97.31 307	87.62 230	97.10 356	94.22 260	86.58 352	94.40 338

UniMVSNet_ETH3D		90.06 366	88.58 375	94.49 329	94.67 378	88.09 398	97.81 407	97.57 291	83.91 422	88.44 365	97.41 304	57.44 454	97.62 329	91.41 310	88.59 332	97.77 297

UniMVSNet_NR-MVSNet		92.95 299	92.11 306	95.49 287	94.61 379	95.28 219	99.83 156	99.08 36	91.49 286	89.21 350	96.86 324	87.14 240	96.73 382	93.20 282	77.52 423	94.46 332

test_fmvs2		89.47 376	89.70 352	88.77 429	94.54 380	75.74 458	99.83 156	94.70 453	94.71 136	91.08 310	96.82 329	54.46 457	97.78 324	92.87 289	88.27 336	92.80 421

MonoMVSNet		94.82 237	94.43 238	95.98 272	94.54 380	90.73 351	99.03 321	97.06 371	93.16 211	93.15 288	95.47 374	88.29 221	97.57 330	97.85 162	91.33 311	99.62 142

WR-MVS		92.31 316	91.25 324	95.48 290	94.45 382	95.29 218	99.60 233	98.68 83	90.10 333	88.07 372	96.89 322	80.68 331	96.80 380	93.14 285	79.67 411	94.36 340

nrg030		93.51 286	92.53 300	96.45 259	94.36 383	97.20 123	99.81 162	97.16 350	91.60 283	89.86 329	97.46 302	86.37 253	97.68 326	95.88 221	80.31 407	94.46 332

tfpnnormal		89.29 379	87.61 386	94.34 337	94.35 384	94.13 263	98.95 333	98.94 44	83.94 420	84.47 412	95.51 371	74.84 389	97.39 335	77.05 442	80.41 405	91.48 436

FC-MVSNet-test		93.81 276	93.15 284	95.80 282	94.30 385	96.20 174	99.42 268	98.89 52	92.33 259	89.03 355	97.27 309	87.39 236	96.83 378	93.20 282	86.48 353	94.36 340

SSC-MVS3.2		89.59 374	88.66 374	92.38 387	94.29 386	86.12 413	99.49 257	97.66 278	90.28 332	88.63 362	95.18 391	64.46 435	96.88 374	85.30 391	82.66 382	94.14 367

MS-PatchMatch		90.65 349	90.30 340	91.71 398	94.22 387	85.50 418	98.24 390	97.70 272	88.67 362	86.42 396	96.37 340	67.82 422	98.03 311	83.62 403	99.62 100	91.60 434

WR-MVS_H		91.30 334	90.35 338	94.15 341	94.17 388	92.62 306	99.17 301	98.94 44	88.87 357	86.48 395	94.46 417	84.36 292	96.61 388	88.19 359	78.51 416	93.21 412

DU-MVS		92.46 313	91.45 322	95.49 287	94.05 389	95.28 219	99.81 162	98.74 76	92.25 265	89.21 350	96.64 332	81.66 317	96.73 382	93.20 282	77.52 423	94.46 332

NR-MVSNet		91.56 332	90.22 342	95.60 285	94.05 389	95.76 190	98.25 389	98.70 79	91.16 300	80.78 432	96.64 332	83.23 303	96.57 389	91.41 310	77.73 422	94.46 332

CP-MVSNet		91.23 338	90.22 342	94.26 339	93.96 391	92.39 311	99.09 307	98.57 106	88.95 354	86.42 396	96.57 335	79.19 347	96.37 398	90.29 334	78.95 413	94.02 375

XXY-MVS		91.82 323	90.46 335	95.88 277	93.91 392	95.40 209	98.87 345	97.69 274	88.63 364	87.87 374	97.08 314	74.38 393	97.89 319	91.66 307	84.07 374	94.35 343

PS-CasMVS		90.63 351	89.51 358	93.99 350	93.83 393	91.70 330	98.98 326	98.52 127	88.48 366	86.15 400	96.53 337	75.46 382	96.31 402	88.83 349	78.86 415	93.95 383

test_0402		85.58 398	83.94 403	90.50 410	93.81 394	85.04 420	98.55 371	95.20 444	76.01 452	79.72 438	95.13 392	64.15 437	96.26 404	66.04 465	86.88 351	90.21 447

XVG-ACMP-BASELINE		91.22 339	90.75 330	92.63 386	93.73 395	85.61 416	98.52 375	97.44 304	92.77 232	89.90 328	96.85 325	66.64 427	98.39 279	92.29 294	88.61 330	93.89 388

TranMVSNet+NR-MVSNet		91.68 331	90.61 334	94.87 310	93.69 396	93.98 268	99.69 212	98.65 87	91.03 305	88.44 365	96.83 328	80.05 340	96.18 407	90.26 335	76.89 431	94.45 337

TransMVSNet (Re)		87.25 392	85.28 399	93.16 374	93.56 397	91.03 343	98.54 373	94.05 459	83.69 424	81.09 430	96.16 346	75.32 383	96.40 397	76.69 443	68.41 456	92.06 430

v10		90.25 361	88.82 370	94.57 324	93.53 398	93.43 283	99.08 309	96.87 394	85.00 412	87.34 385	94.51 413	80.93 327	97.02 366	82.85 408	79.23 412	93.26 410

testgi		89.01 381	88.04 382	91.90 394	93.49 399	84.89 422	99.73 195	95.66 433	93.89 183	85.14 407	98.17 281	59.68 450	94.66 438	77.73 438	88.88 324	96.16 323

v8		90.54 353	89.17 363	94.66 318	93.43 400	93.40 286	99.20 298	96.94 388	85.76 402	87.56 379	94.51 413	81.96 313	97.19 349	84.94 394	78.25 417	93.38 408

V42		91.28 336	90.12 347	94.74 315	93.42 401	93.46 282	99.68 215	97.02 375	87.36 381	89.85 331	95.05 395	81.31 323	97.34 338	87.34 370	80.07 409	93.40 406

pm-mvs1		89.36 378	87.81 384	94.01 348	93.40 402	91.93 320	98.62 369	96.48 414	86.25 397	83.86 416	96.14 348	73.68 396	97.04 362	86.16 384	75.73 436	93.04 416

v1144		91.09 340	89.83 349	94.87 310	93.25 403	93.69 276	99.62 226	96.98 380	86.83 391	89.64 337	94.99 401	80.94 326	97.05 359	85.08 393	81.16 395	93.87 390

v1192		90.62 352	89.25 362	94.72 317	93.13 404	93.07 290	99.50 255	97.02 375	86.33 396	89.56 341	95.01 398	79.22 346	97.09 358	82.34 412	81.16 395	94.01 377

v2v482		91.30 334	90.07 348	95.01 305	93.13 404	93.79 271	99.77 175	97.02 375	88.05 372	89.25 347	95.37 381	80.73 330	97.15 351	87.28 371	80.04 410	94.09 371

OPM-MVS		93.21 291	92.80 290	94.44 332	93.12 406	90.85 350	99.77 175	97.61 286	96.19 94	91.56 306	98.65 243	75.16 388	98.47 267	93.78 273	89.39 320	93.99 380
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 347	89.52 357	94.59 322	93.11 407	92.77 297	99.56 244	96.99 378	86.38 395	89.82 332	94.95 403	80.50 335	97.10 356	83.98 400	80.41 405	93.90 387

PEN-MVS		90.19 363	89.06 366	93.57 364	93.06 408	90.90 348	99.06 314	98.47 139	88.11 371	85.91 402	96.30 342	76.67 369	95.94 417	87.07 374	76.91 430	93.89 388

v1240		90.20 362	88.79 371	94.44 332	93.05 409	92.27 313	99.38 275	96.92 390	85.89 400	89.36 344	94.87 405	77.89 358	97.03 364	80.66 421	81.08 398	94.01 377

FE-MVSNET3		92.78 303	91.73 314	95.92 276	93.03 410	96.82 141	99.83 156	97.79 262	90.58 320	90.09 321	95.04 396	84.75 283	96.72 384	88.20 358	86.23 355	94.23 352

v148		90.70 348	89.63 353	93.92 352	92.97 411	90.97 344	99.75 185	96.89 392	87.51 378	88.27 370	95.01 398	81.67 316	97.04 362	87.40 369	77.17 428	93.75 396

v1921920		90.46 354	89.12 364	94.50 328	92.96 412	92.46 309	99.49 257	96.98 380	86.10 398	89.61 339	95.30 384	78.55 355	97.03 364	82.17 413	80.89 403	94.01 377

MVStest1		85.03 404	82.76 413	91.83 395	92.95 413	89.16 382	98.57 370	94.82 448	71.68 463	68.54 466	95.11 394	83.17 304	95.66 421	74.69 448	65.32 463	90.65 443

tt0320-xc		82.94 419	80.35 426	90.72 408	92.90 414	83.54 431	96.85 428	94.73 451	63.12 468	79.85 437	93.77 425	49.43 465	95.46 424	80.98 420	71.54 445	93.16 413

Baseline_NR-MVSNet		90.33 358	89.51 358	92.81 383	92.84 415	89.95 371	99.77 175	93.94 460	84.69 417	89.04 354	95.66 363	81.66 317	96.52 391	90.99 318	76.98 429	91.97 432

test_method		80.79 425	79.70 428	84.08 441	92.83 416	67.06 467	99.51 253	95.42 438	54.34 473	81.07 431	93.53 427	44.48 468	92.22 459	78.90 433	77.23 427	92.94 418

pmmvs4		92.10 320	91.07 328	95.18 301	92.82 417	94.96 230	99.48 260	96.83 396	87.45 380	88.66 361	96.56 336	83.78 298	96.83 378	89.29 345	84.77 368	93.75 396

LF4IMVS		89.25 380	88.85 369	90.45 412	92.81 418	81.19 448	98.12 397	94.79 449	91.44 290	86.29 398	97.11 312	65.30 433	98.11 305	88.53 354	85.25 362	92.07 429

tt0320		83.56 418	81.15 421	90.77 406	92.77 419	83.58 430	96.83 429	95.52 437	63.26 467	81.36 428	92.54 435	53.26 459	95.77 419	80.45 422	74.38 439	92.96 417

DTE-MVSNet		89.40 377	88.24 380	92.88 381	92.66 420	89.95 371	99.10 306	98.22 212	87.29 382	85.12 408	96.22 344	76.27 376	95.30 429	83.56 404	75.74 435	93.41 405

EU-MVSNet		90.14 365	90.34 339	89.54 421	92.55 421	81.06 449	98.69 363	98.04 236	91.41 294	86.59 392	96.84 327	80.83 329	93.31 451	86.20 383	81.91 389	94.26 348

APD_test1		81.15 423	80.92 423	81.86 445	92.45 422	59.76 474	96.04 443	93.61 463	73.29 461	77.06 447	96.64 332	44.28 469	96.16 408	72.35 452	82.52 383	89.67 455

sc_t1		85.01 405	82.46 415	92.67 385	92.44 423	83.09 434	97.39 414	95.72 430	65.06 466	85.64 405	96.16 346	49.50 464	97.34 338	84.86 395	75.39 437	97.57 306

our_test_3		90.39 355	89.48 360	93.12 375	92.40 424	89.57 376	99.33 282	96.35 417	87.84 376	85.30 406	94.99 401	84.14 295	96.09 412	80.38 423	84.56 369	93.71 401

ppachtmachnet_test		89.58 375	88.35 378	93.25 373	92.40 424	90.44 360	99.33 282	96.73 403	85.49 407	85.90 403	95.77 357	81.09 325	96.00 416	76.00 446	82.49 384	93.30 409

v7n		89.65 373	88.29 379	93.72 358	92.22 426	90.56 357	99.07 313	97.10 363	85.42 409	86.73 389	94.72 406	80.06 339	97.13 353	81.14 418	78.12 419	93.49 404

dmvs_testset		83.79 414	86.07 395	76.94 449	92.14 427	48.60 484	96.75 430	90.27 474	89.48 342	78.65 441	98.55 258	79.25 345	86.65 472	66.85 462	82.69 381	95.57 325

PS-MVSNAJss		93.64 283	93.31 280	94.61 320	92.11 428	92.19 314	99.12 303	97.38 311	92.51 251	88.45 364	96.99 320	91.20 173	97.29 346	94.36 254	87.71 343	94.36 340

pmmvs5		90.17 364	89.09 365	93.40 367	92.10 429	89.77 374	99.74 188	95.58 435	85.88 401	87.24 386	95.74 358	73.41 399	96.48 393	88.54 353	83.56 378	93.95 383

N_pmnet		80.06 428	80.78 424	77.89 448	91.94 430	45.28 486	98.80 354	56.82 488	78.10 449	80.08 435	93.33 428	77.03 363	95.76 420	68.14 460	82.81 380	92.64 422

test_djsdf		92.83 302	92.29 304	94.47 330	91.90 431	92.46 309	99.55 247	97.27 333	91.17 298	89.96 325	96.07 352	81.10 324	96.89 372	94.67 249	88.91 323	94.05 374

SixPastTwentyTwo		88.73 382	88.01 383	90.88 402	91.85 432	82.24 440	98.22 394	95.18 445	88.97 352	82.26 422	96.89 322	71.75 404	96.67 386	84.00 399	82.98 379	93.72 400

K. test v3		88.05 388	87.24 389	90.47 411	91.82 433	82.23 441	98.96 332	97.42 307	89.05 347	76.93 449	95.60 365	68.49 418	95.42 425	85.87 388	81.01 401	93.75 396

OurMVSNet-221017-0		89.81 370	89.48 360	90.83 405	91.64 434	81.21 447	98.17 396	95.38 440	91.48 288	85.65 404	97.31 307	72.66 400	97.29 346	88.15 360	84.83 367	93.97 382

mvs_tets		91.81 324	91.08 327	94.00 349	91.63 435	90.58 356	98.67 365	97.43 305	92.43 253	87.37 384	97.05 317	71.76 403	97.32 341	94.75 246	88.68 329	94.11 370

Gipuma		66.95 441	65.00 441	72.79 454	91.52 436	67.96 466	66.16 478	95.15 446	47.89 475	58.54 472	67.99 477	29.74 473	87.54 471	50.20 476	77.83 421	62.87 477
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	91.47 437	95.56 201	99.84 149	97.30 327	97.74 30	97.89 173	99.35 152	79.62 342	99.85 129	99.25 74	99.24 141	99.55 159

jajsoiax		91.92 322	91.18 325	94.15 341	91.35 438	90.95 347	99.00 324	97.42 307	92.61 243	87.38 383	97.08 314	72.46 401	97.36 336	94.53 252	88.77 327	94.13 369

MDA-MVSNet-bldmvs		84.09 412	81.52 419	91.81 396	91.32 439	88.00 400	98.67 365	95.92 426	80.22 443	55.60 475	93.32 429	68.29 420	93.60 449	73.76 449	76.61 432	93.82 394

MVP-Stereo		90.93 342	90.45 337	92.37 389	91.25 440	88.76 386	98.05 401	96.17 420	87.27 383	84.04 413	95.30 384	78.46 356	97.27 348	83.78 402	99.70 93	91.09 437
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 400	83.32 408	92.10 391	90.96 441	88.58 392	99.20 298	96.52 412	79.70 445	57.12 474	92.69 434	79.11 348	93.86 445	77.10 441	77.46 425	93.86 391

YYNet1		85.50 401	83.33 407	92.00 392	90.89 442	88.38 396	99.22 297	96.55 411	79.60 446	57.26 473	92.72 433	79.09 350	93.78 447	77.25 440	77.37 426	93.84 392

anonymousdsp		91.79 329	90.92 329	94.41 335	90.76 443	92.93 296	98.93 336	97.17 348	89.08 346	87.46 382	95.30 384	78.43 357	96.92 370	92.38 293	88.73 328	93.39 407

lessismore_v0				90.53 409	90.58 444	80.90 450		95.80 427		77.01 448	95.84 355	66.15 429	96.95 368	83.03 407	75.05 438	93.74 399

EG-PatchMatch MVS		85.35 402	83.81 405	89.99 419	90.39 445	81.89 443	98.21 395	96.09 422	81.78 436	74.73 455	93.72 426	51.56 463	97.12 355	79.16 431	88.61 330	90.96 440

EGC-MVSNET		69.38 434	63.76 444	86.26 438	90.32 446	81.66 446	96.24 439	93.85 461	0.99 485	3.22 486	92.33 440	52.44 460	92.92 455	59.53 472	84.90 366	84.21 466

CMPMVS	61.59 21	84.75 408	85.14 400	83.57 442	90.32 446	62.54 470	96.98 424	97.59 290	74.33 459	69.95 463	96.66 330	64.17 436	98.32 289	87.88 364	88.41 335	89.84 452
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 411	82.92 411	89.21 423	90.03 448	82.60 437	96.89 427	95.62 434	80.59 441	75.77 454	89.17 452	65.04 434	94.79 436	72.12 453	81.02 400	90.23 446

pmmvs6		85.69 397	83.84 404	91.26 401	90.00 449	84.41 425	97.82 406	96.15 421	75.86 453	81.29 429	95.39 379	61.21 447	96.87 375	83.52 405	73.29 441	92.50 425

ttmdpeth		88.23 387	87.06 390	91.75 397	89.91 450	87.35 405	98.92 339	95.73 429	87.92 374	84.02 414	96.31 341	68.23 421	96.84 376	86.33 382	76.12 433	91.06 438

DSMNet-mixed		88.28 386	88.24 380	88.42 431	89.64 451	75.38 460	98.06 400	89.86 475	85.59 406	88.20 371	92.14 441	76.15 378	91.95 460	78.46 435	96.05 248	97.92 291

UnsupCasMVSNet_eth		85.52 399	83.99 401	90.10 417	89.36 452	83.51 432	96.65 431	97.99 240	89.14 345	75.89 453	93.83 423	63.25 440	93.92 443	81.92 415	67.90 459	92.88 419

Anonymous20231206		86.32 395	85.42 398	89.02 425	89.11 453	80.53 453	99.05 318	95.28 441	85.43 408	82.82 420	93.92 422	74.40 392	93.44 450	66.99 461	81.83 390	93.08 415

Anonymous20240521		85.15 403	83.81 405	89.16 424	88.32 454	82.69 436	98.80 354	95.74 428	79.72 444	81.53 427	90.99 444	65.38 432	94.16 441	72.69 451	81.11 397	90.63 444

OpenMVS_ROB	79.82 20	83.77 415	81.68 418	90.03 418	88.30 455	82.82 435	98.46 376	95.22 443	73.92 460	76.00 452	91.29 443	55.00 456	96.94 369	68.40 459	88.51 334	90.34 445

test20.03		84.72 409	83.99 401	86.91 436	88.19 456	80.62 452	98.88 342	95.94 425	88.36 368	78.87 439	94.62 411	68.75 416	89.11 467	66.52 463	75.82 434	91.00 439

KD-MVS_self_test		83.59 416	82.06 416	88.20 432	86.93 457	80.70 451	97.21 417	96.38 415	82.87 430	82.49 421	88.97 453	67.63 423	92.32 458	73.75 450	62.30 470	91.58 435

MIMVSNet1		82.58 420	80.51 425	88.78 427	86.68 458	84.20 426	96.65 431	95.41 439	78.75 447	78.59 442	92.44 436	51.88 462	89.76 466	65.26 466	78.95 413	92.38 428

CL-MVSNet_self_test		84.50 410	83.15 410	88.53 430	86.00 459	81.79 444	98.82 350	97.35 315	85.12 411	83.62 418	90.91 446	76.66 370	91.40 461	69.53 457	60.36 471	92.40 427

UnsupCasMVSNet_bld		79.97 430	77.03 435	88.78 427	85.62 460	81.98 442	93.66 455	97.35 315	75.51 456	70.79 462	83.05 469	48.70 466	94.91 434	78.31 436	60.29 472	89.46 458

mvs5depth		84.87 406	82.90 412	90.77 406	85.59 461	84.84 423	91.10 468	93.29 465	83.14 427	85.07 409	94.33 419	62.17 443	97.32 341	78.83 434	72.59 444	90.14 448

Patchmatch-RL test		86.90 393	85.98 397	89.67 420	84.45 462	75.59 459	89.71 471	92.43 467	86.89 390	77.83 446	90.94 445	94.22 95	93.63 448	87.75 365	69.61 450	99.79 111

pmmvs-eth3d		84.03 413	81.97 417	90.20 415	84.15 463	87.09 407	98.10 399	94.73 451	83.05 428	74.10 459	87.77 459	65.56 431	94.01 442	81.08 419	69.24 452	89.49 457

test_fmvs3		79.99 429	80.17 427	79.45 447	84.02 464	62.83 468	99.05 318	93.49 464	88.29 370	80.06 436	86.65 464	28.09 475	88.00 468	88.63 350	73.27 442	87.54 464

PM-MVS		80.47 426	78.88 430	85.26 439	83.79 465	72.22 462	95.89 446	91.08 472	85.71 405	76.56 451	88.30 455	36.64 471	93.90 444	82.39 411	69.57 451	89.66 456

new-patchmatchnet		81.19 422	79.34 429	86.76 437	82.86 466	80.36 454	97.92 403	95.27 442	82.09 435	72.02 460	86.87 463	62.81 442	90.74 464	71.10 454	63.08 467	89.19 460

FE-MVSNET2		83.57 417	81.36 420	90.20 415	82.83 467	87.59 401	98.28 387	96.04 423	85.33 410	74.13 458	87.45 460	59.16 451	93.26 452	79.12 432	69.91 448	89.77 453

FE-MVSNET		81.05 424	78.81 431	87.79 434	81.98 468	83.70 428	98.23 392	91.78 471	81.27 438	74.29 457	87.44 461	60.92 449	90.67 465	64.92 467	68.43 455	89.01 461

mvsany_test3		82.12 421	81.14 422	85.06 440	81.87 469	70.41 464	97.09 421	92.14 468	91.27 297	77.84 445	88.73 454	39.31 470	95.49 422	90.75 325	71.24 446	89.29 459

WB-MVS		76.28 432	77.28 434	73.29 453	81.18 470	54.68 478	97.87 405	94.19 456	81.30 437	69.43 464	90.70 447	77.02 364	82.06 476	35.71 481	68.11 458	83.13 467

test_f		78.40 431	77.59 433	80.81 446	80.82 471	62.48 471	96.96 425	93.08 466	83.44 425	74.57 456	84.57 468	27.95 476	92.63 456	84.15 397	72.79 443	87.32 465

SSC-MVS		75.42 433	76.40 436	72.49 457	80.68 472	53.62 479	97.42 412	94.06 458	80.42 442	68.75 465	90.14 449	76.54 372	81.66 477	33.25 482	66.34 462	82.19 468

pmmvs3		80.27 427	77.77 432	87.76 435	80.32 473	82.43 439	98.23 392	91.97 469	72.74 462	78.75 440	87.97 458	57.30 455	90.99 463	70.31 455	62.37 469	89.87 451

testf1		68.38 437	66.92 438	72.78 455	78.80 474	50.36 481	90.95 469	87.35 480	55.47 471	58.95 470	88.14 456	20.64 480	87.60 469	57.28 473	64.69 464	80.39 470

APD_test2		68.38 437	66.92 438	72.78 455	78.80 474	50.36 481	90.95 469	87.35 480	55.47 471	58.95 470	88.14 456	20.64 480	87.60 469	57.28 473	64.69 464	80.39 470

ambc				83.23 443	77.17 476	62.61 469	87.38 473	94.55 455		76.72 450	86.65 464	30.16 472	96.36 399	84.85 396	69.86 449	90.73 442

test_vis3_rt		68.82 435	66.69 440	75.21 452	76.24 477	60.41 473	96.44 434	68.71 487	75.13 457	50.54 478	69.52 476	16.42 485	96.32 401	80.27 424	66.92 461	68.89 474

TDRefinement		84.76 407	82.56 414	91.38 400	74.58 478	84.80 424	97.36 415	94.56 454	84.73 416	80.21 434	96.12 351	63.56 438	98.39 279	87.92 363	63.97 466	90.95 441

E-PMN		52.30 445	52.18 447	52.67 463	71.51 479	45.40 485	93.62 456	76.60 485	36.01 479	43.50 480	64.13 479	27.11 477	67.31 482	31.06 483	26.06 478	45.30 481

EMVS		51.44 447	51.22 449	52.11 464	70.71 480	44.97 487	94.04 452	75.66 486	35.34 481	42.40 481	61.56 482	28.93 474	65.87 483	27.64 484	24.73 479	45.49 480

PMMVS2		67.15 440	64.15 443	76.14 451	70.56 481	62.07 472	93.89 453	87.52 479	58.09 470	60.02 469	78.32 471	22.38 479	84.54 474	59.56 471	47.03 476	81.80 469

FPMVS		68.72 436	68.72 437	68.71 459	65.95 482	44.27 488	95.97 445	94.74 450	51.13 474	53.26 476	90.50 448	25.11 478	83.00 475	60.80 470	80.97 402	78.87 472

wuyk23d		20.37 451	20.84 454	18.99 467	65.34 483	27.73 490	50.43 479	7.67 491	9.50 484	8.01 485	6.34 485	6.13 488	26.24 484	23.40 485	10.69 483	2.99 482

LCM-MVSNet		67.77 439	64.73 442	76.87 450	62.95 484	56.25 477	89.37 472	93.74 462	44.53 476	61.99 468	80.74 470	20.42 482	86.53 473	69.37 458	59.50 473	87.84 462

MVE	53.74 22	51.54 446	47.86 450	62.60 461	59.56 485	50.93 480	79.41 476	77.69 484	35.69 480	36.27 482	61.76 481	5.79 489	69.63 480	37.97 480	36.61 477	67.24 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 443	52.24 446	67.66 460	49.27 486	56.82 476	83.94 474	82.02 483	70.47 464	33.28 483	64.54 478	17.23 484	69.16 481	45.59 478	23.85 480	77.02 473

tmp_tt		65.23 442	62.94 445	72.13 458	44.90 487	50.03 483	81.05 475	89.42 478	38.45 477	48.51 479	99.90 22	54.09 458	78.70 479	91.84 306	18.26 481	87.64 463

PMVS	49.05 23	53.75 444	51.34 448	60.97 462	40.80 488	34.68 489	74.82 477	89.62 477	37.55 478	28.67 484	72.12 473	7.09 487	81.63 478	43.17 479	68.21 457	66.59 476
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 449	39.14 452	33.31 465	19.94 489	24.83 491	98.36 384	9.75 490	15.53 483	51.31 477	87.14 462	19.62 483	17.74 485	47.10 477	3.47 484	57.36 478

testmvs		40.60 448	44.45 451	29.05 466	19.49 490	14.11 492	99.68 215	18.47 489	20.74 482	64.59 467	98.48 265	10.95 486	17.09 486	56.66 475	11.01 482	55.94 479

mmdepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

monomultidepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

test_blank		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.02 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

eth-test2					0.00 491

eth-test					0.00 491

uanet_test		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

DCPMVS		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

cdsmvs_eth3d_5k		23.43 450	31.24 453	0.00 468	0.00 491	0.00 493	0.00 480	98.09 230	0.00 486	0.00 487	99.67 113	83.37 301	0.00 487	0.00 486	0.00 485	0.00 483

pcd_1.5k_mvsjas		7.60 453	10.13 456	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	91.20 173	0.00 487	0.00 486	0.00 485	0.00 483

sosnet-low-res		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uncertanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

Regformer		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

ab-mvs-re		8.28 452	11.04 455	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	99.40 146	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 487	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

TestfortrainingZip							99.97 39

WAC-MVS						90.97 344								86.10 386

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							95.78 447				59.23 483	93.20 128	97.74 325	91.06 316

test_post											63.35 480	94.43 82	98.13 304

patchmatchnet-post											91.70 442	95.12 59	97.95 316

MTMP							99.87 130	96.49 413

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	94.36 254		99.99 24

原ACMM2							99.90 114

testdata2													99.99 39	90.54 329

segment_acmp												96.68 31

testdata1							99.28 291		96.35 90

plane_prior5								97.87 254					98.37 285	97.79 167	89.55 317	94.52 329

plane_prior4											98.59 251

plane_prior3						91.64 332			96.63 73	93.01 289

plane_prior2							99.84 149		96.38 84

plane_prior						91.74 326	99.86 141		96.76 68						89.59 316

n2								0.00 492

nn								0.00 492

door-mid								89.69 476

test11								98.44 147

door								90.31 473

HQP5-MVS						91.85 322

BP-MVS														97.92 158

HQP4-MVS										93.37 284			98.39 279			94.53 327

HQP3-MVS								97.89 252							89.60 314

HQP2-MVS												80.65 332

MDTV_nov1_ep13_2view						96.26 168	96.11 441		91.89 274	98.06 166		94.40 84		94.30 257		99.67 129

ACMMP++_ref															87.04 350

ACMMP++															88.23 337

Test By Simon												92.82 139

High-resolution multi-view benchmark