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	95.60 201	99.09 317	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	95.77 190	99.96 53	98.35 189	89.90 343	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	92.06 274	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	96.62 153	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		93.83 277	92.06 312	99.15 70	99.94 16	97.50 110	99.94 90	98.42 167	96.22 92	99.41 84	41.37 496	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	96.13 179	99.82 161	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	99.64 229	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	92.34 262	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	99.75 188	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	96.13 179	99.18 310	99.45 18	94.84 131	96.41 233	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	99.24 305	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	91.92 326	99.90 114	99.07 37	88.67 367	95.26 265	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	95.39 211	99.61 236	97.78 266	96.52 76	98.61 138	99.31 156	92.73 141	99.67 167	96.77 207	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	90.78 321	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	99.39 279	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	95.11 222	98.02 166	99.85 60	95.10 229	98.74 367	98.50 136	87.22 389	93.66 286	99.86 33	87.45 236	99.95 84	90.94 324	99.81 87	99.02 251

HPM-MVS		97.96 80	97.72 90	98.68 109	99.84 62	96.39 165	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	96.59 157	99.40 275	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	94.77 240	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	96.60 155	99.82 161	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	96.27 168	99.36 285	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	96.37 166	99.76 182	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	96.37 166	99.76 182	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	96.42 161	99.88 127	98.16 223	91.75 285	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	93.36 293	99.65 225	97.95 245	96.03 97	97.41 190	99.70 100	89.61 203	99.51 177	96.73 209	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	91.21 316	99.94 59	99.99 24

MP-MVS-pluss		98.07 78	97.64 96	99.38 48	99.74 76	98.41 68	99.74 192	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	99.77 176	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	95.11 237	99.95 54	99.92 92

TSAR-MVS + GP.		98.60 37	98.51 34	98.86 98	99.73 79	96.63 152	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	99.73 199	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	96.87 248	99.71 82	91.74 336	99.85 144	97.95 245	93.11 215	95.72 254	99.16 180	92.35 155	99.94 93	95.32 233	99.35 136	98.92 259

reproduce-ours		98.78 27	98.67 24	99.09 79	99.70 84	97.30 118	99.74 192	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	99.74 192	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	95.59 291	99.67 87	86.91 421	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	93.92 363	99.63 89	81.76 456	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	96.80 146	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	96.72 254	99.59 91	96.99 135	99.95 72	99.10 34	94.06 171	98.27 157	95.80 360	89.00 215	99.95 84	99.12 78	87.53 352	93.24 422

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	95.22 224	99.52 257	99.07 37	93.96 176	96.49 224	98.35 275	82.28 315	99.82 141	90.15 340	99.22 143	98.81 266

dcpmvs_2		97.42 121	98.09 63	95.42 298	99.58 95	87.24 417	99.23 306	96.95 394	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	99.34 287	98.55 118	91.86 280	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	95.41 209	99.47 267	98.87 58	91.68 286	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	95.81 188	99.95 72	99.65 12	94.73 135	99.04 113	99.21 173	84.48 293	99.95 84	94.92 243	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	88.48 371	99.06 112	99.66 115	90.30 195	99.64 172	96.32 218	99.97 42	99.96 74

cl22		93.77 282	93.25 286	95.33 302	99.49 101	94.43 250	99.61 236	98.09 230	90.38 331	89.16 358	95.61 368	90.56 190	97.34 342	91.93 307	84.45 375	94.21 361

testdata				98.42 141	99.47 102	95.33 215		98.56 112	93.78 185	99.79 35	99.85 37	93.64 114	99.94 93	94.97 241	99.94 59	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 157	99.47 102	94.79 239	99.05 328	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		94.54 254	93.42 276	97.91 174	99.46 104	94.04 266	98.93 346	97.48 303	81.15 444	90.04 329	99.55 131	87.02 244	99.95 84	88.97 354	98.11 185	99.73 119

MVS_111021_LR		98.42 52	98.38 41	98.53 129	99.39 105	95.79 189	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	98.46 386	99.42 21	97.03 57	99.02 114	99.09 183	99.35 2	98.21 304	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	97.65 304

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	93.81 274	99.94 59	99.98 56

TAPA-MVS	92.12 8	94.42 262	93.60 268	96.90 247	99.33 109	91.78 335	99.78 171	98.00 239	89.89 344	94.52 271	99.47 137	91.97 165	99.18 202	69.90 467	99.52 114	99.73 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 226	95.07 224	96.32 269	99.32 111	96.60 155	99.76 182	98.85 62	96.65 72	87.83 386	96.05 357	99.52 1	98.11 309	96.58 213	81.07 404	94.25 354

fmvsm_s_conf0.5_n_9		98.15 73	98.02 68	98.55 123	99.28 112	95.84 187	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	95.20 225	99.98 21	97.15 356	95.53 113	99.62 60	99.79 62	92.08 163	98.38 287	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	95.18 226	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	98.59 276

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	99.64 229	99.27 27	91.43 295	97.88 174	98.99 201	95.84 45	99.84 137	98.82 101	95.32 279	99.79 111

DCV-MVSNet		97.83 92	97.37 111	99.21 59	99.18 118	97.98 85	99.64 229	99.27 27	91.43 295	97.88 174	98.99 201	95.84 45	99.84 137	98.82 101	95.32 279	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	95.90 185	99.66 224	98.06 233	96.37 87	94.37 277	99.49 136	83.29 308	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	94.42 251	99.99 5	97.10 368	95.07 122	99.68 50	99.75 80	92.95 134	98.34 291	98.38 128	99.14 145	99.54 163

Anonymous202405211		93.10 300	91.99 313	96.40 265	99.10 124	89.65 386	98.88 352	97.93 247	83.71 428	94.00 283	98.75 237	68.79 426	99.88 123	95.08 238	91.71 312	99.68 127

fmvsm_s_conf0.5_n		97.80 97	97.85 85	97.67 193	99.06 127	94.41 252	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	98.62 275

HyFIR lowres test		96.66 165	96.43 156	97.36 226	99.05 128	93.91 271	99.70 215	99.80 3	90.54 327	96.26 236	98.08 288	92.15 161	98.23 303	96.84 202	95.46 274	99.93 87

LFMVS		94.75 248	93.56 271	98.30 147	99.03 129	95.70 195	98.74 367	97.98 242	87.81 382	98.47 146	99.39 148	67.43 435	99.53 174	98.01 152	95.20 282	99.67 129

fmvsm_s_conf0.5_n_4		97.75 102	97.86 84	97.42 220	99.01 130	94.69 242	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	97.64 305

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	81.83 321	99.94 93	99.78 35	98.79 161	97.51 313

AllTest		92.48 317	91.64 320	95.00 311	99.01 130	88.43 404	98.94 344	96.82 408	86.50 398	88.71 363	98.47 270	74.73 401	99.88 123	85.39 400	96.18 249	96.71 319

TestCases				95.00 311	99.01 130	88.43 404		96.82 408	86.50 398	88.71 363	98.47 270	74.73 401	99.88 123	85.39 400	96.18 249	96.71 319

COLMAP_ROB	90.47 14	92.18 324	91.49 326	94.25 345	99.00 134	88.04 410	98.42 392	96.70 415	82.30 439	88.43 374	99.01 194	76.97 376	99.85 129	86.11 396	96.50 241	94.86 330
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	84.15 296	99.97 63	99.76 40	99.50 119	98.39 283

test_fmvs1		95.35 227	95.68 196	94.36 341	98.99 135	84.98 432	99.96 53	96.65 417	97.60 34	99.73 45	98.96 207	71.58 416	99.93 103	98.31 134	99.37 134	98.17 288

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 18	98.98 137	99.32 9	97.49 421	99.52 14	95.69 108	98.32 155	97.41 308	93.32 121	99.77 149	98.08 149	95.75 264	99.81 108

VNet		97.21 131	96.57 150	99.13 76	98.97 138	97.82 94	99.03 331	99.21 32	94.31 158	99.18 102	98.88 219	86.26 257	99.89 117	98.93 92	94.32 292	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	96.26 236	98.88 219	89.87 200	99.51 177	94.26 262	94.91 284	99.31 212

tfpn200view9		96.79 153	95.99 173	99.19 61	98.94 140	98.82 38	99.78 171	99.71 7	92.86 225	96.02 244	98.87 226	89.33 207	99.50 179	93.84 271	94.57 288	99.27 221

thres400		96.78 155	95.99 173	99.16 68	98.94 140	98.82 38	99.78 171	99.71 7	92.86 225	96.02 244	98.87 226	89.33 207	99.50 179	93.84 271	94.57 288	99.16 231

sasdasda		97.09 138	96.32 160	99.39 45	98.93 142	98.95 28	99.72 203	97.35 316	94.45 146	97.88 174	99.42 141	86.71 248	99.52 175	98.48 123	93.97 298	99.72 121

Anonymous20231211		89.86 375	88.44 383	94.13 353	98.93 142	90.68 364	98.54 383	98.26 206	76.28 461	86.73 400	95.54 372	70.60 422	97.56 335	90.82 327	80.27 413	94.15 370

canonicalmvs		97.09 138	96.32 160	99.39 45	98.93 142	98.95 28	99.72 203	97.35 316	94.45 146	97.88 174	99.42 141	86.71 248	99.52 175	98.48 123	93.97 298	99.72 121

SDMVSNet		94.80 243	93.96 258	97.33 229	98.92 145	95.42 208	99.59 241	98.99 40	92.41 258	92.55 301	97.85 299	75.81 391	98.93 220	97.90 160	91.62 313	97.64 305

sd_testset		93.55 289	92.83 293	95.74 289	98.92 145	90.89 360	98.24 400	98.85 62	92.41 258	92.55 301	97.85 299	71.07 421	98.68 256	93.93 268	91.62 313	97.64 305

EPNet_dtu		95.71 215	95.39 206	96.66 256	98.92 145	93.41 289	99.57 246	98.90 50	96.19 94	97.52 184	98.56 260	92.65 143	97.36 340	77.89 448	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	95.58 273	99.78 114

CHOSEN 1792x2688		96.81 152	96.53 151	97.64 196	98.91 149	93.07 295	99.65 225	99.80 3	95.64 109	95.39 261	98.86 228	84.35 295	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	99.74 192	99.71 7	92.59 245	95.84 248	98.86 228	89.25 209	99.50 179	93.84 271	94.57 288	99.27 221

thres600view7		96.69 163	95.87 188	99.14 72	98.90 150	98.78 45	99.74 192	99.71 7	92.59 245	95.84 248	98.86 228	89.25 209	99.50 179	93.44 284	94.50 291	99.16 231

MSDG		94.37 264	93.36 283	97.40 222	98.88 152	93.95 270	99.37 283	97.38 312	85.75 409	90.80 320	99.17 177	84.11 298	99.88 123	86.35 392	98.43 172	98.36 285

MGCFI-Net		97.00 143	96.22 165	99.34 50	98.86 153	98.80 40	99.67 223	97.30 328	94.31 158	97.77 180	99.41 145	86.36 255	99.50 179	98.38 128	93.90 300	99.72 121

h-mvs33		94.92 240	94.36 244	96.59 258	98.85 154	91.29 352	98.93 346	98.94 44	95.90 99	98.77 127	98.42 273	90.89 185	99.77 149	97.80 164	70.76 453	98.72 272

Anonymous20240529		92.10 325	90.65 337	96.47 260	98.82 155	90.61 366	98.72 369	98.67 86	75.54 465	93.90 285	98.58 258	66.23 439	99.90 112	94.70 252	90.67 316	98.90 262

PVSNet_Blended_VisFu		97.27 127	96.81 138	98.66 112	98.81 156	96.67 151	99.92 100	98.64 90	94.51 143	96.38 234	98.49 266	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	99.54 255	98.17 218	97.34 42	99.85 18	99.85 37	91.20 174	99.89 117	99.41 68	99.67 95	98.69 273

CANet_DTU		96.76 156	96.15 168	98.60 117	98.78 158	97.53 107	99.84 149	97.63 281	97.25 50	99.20 99	99.64 118	81.36 327	99.98 50	92.77 295	98.89 155	98.28 287

mvsany_test1		97.82 95	97.90 80	97.55 207	98.77 159	93.04 298	99.80 167	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	94.26 294	99.67 129

SymmetryMVS		97.64 110	97.46 104	98.17 153	98.74 161	95.39 211	99.61 236	99.26 29	96.52 76	98.61 138	99.31 156	92.73 141	99.67 167	96.77 207	95.63 271	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	99.52 257	98.08 232	97.05 56	99.86 15	99.86 33	90.65 187	99.71 159	99.39 70	98.63 165	98.69 273

miper_enhance_ethall		94.36 266	93.98 257	95.49 292	98.68 164	95.24 222	99.73 199	97.29 336	93.28 205	89.86 334	95.97 358	94.37 88	97.05 363	92.20 299	84.45 375	94.19 362

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	91.10 307	98.17 163	98.59 255	93.86 108	98.19 305	95.64 230	95.24 281	99.28 219

test2506		97.53 114	97.19 120	98.58 121	98.66 167	96.90 139	98.81 361	99.77 5	94.93 125	97.95 169	98.96 207	92.51 150	99.20 200	94.93 242	98.15 182	99.64 135

ECVR-MVS		95.66 218	95.05 225	97.51 212	98.66 167	93.71 275	98.85 358	98.45 142	94.93 125	96.86 210	98.96 207	75.22 397	99.20 200	95.34 232	98.15 182	99.64 135

mamv4		95.24 230	96.90 131	90.25 425	98.65 169	72.11 475	98.28 397	97.64 280	89.99 342	95.93 246	98.25 283	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	99.47 267	97.79 262	94.56 141	99.74 43	98.35 275	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	94.26 259	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	99.58 243	97.74 271	90.34 334	99.26 98	98.32 278	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	92.53 253	98.84 121	98.84 232	93.36 118	98.30 295	95.84 226	94.30 293	99.05 245

test1111		95.57 221	94.98 228	97.37 224	98.56 173	93.37 292	98.86 356	98.45 142	94.95 124	96.63 216	98.95 212	75.21 398	99.11 206	95.02 239	98.14 184	99.64 135

MVSTER		95.53 222	95.22 217	96.45 263	98.56 173	97.72 98	99.91 108	97.67 276	92.38 261	91.39 311	97.14 315	97.24 20	97.30 347	94.80 248	87.85 345	94.34 349

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	98.73 239	95.50 52	99.69 163	98.53 121	94.63 286	98.99 253

VDD-MVS		93.77 282	92.94 291	96.27 270	98.55 176	90.22 375	98.77 366	97.79 262	90.85 313	96.82 212	99.42 141	61.18 459	99.77 149	98.95 90	94.13 295	98.82 265

tpmvs		94.28 268	93.57 270	96.40 265	98.55 176	91.50 350	95.70 460	98.55 118	87.47 384	92.15 304	94.26 425	91.42 170	98.95 219	88.15 371	95.85 260	98.76 268

UGNet		95.33 228	94.57 240	97.62 200	98.55 176	94.85 234	98.67 375	99.32 26	95.75 106	96.80 213	96.27 347	72.18 413	99.96 75	94.58 255	99.05 151	98.04 293
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	95.18 232	94.10 251	98.43 139	98.55 176	95.99 183	97.91 414	97.31 327	90.35 333	89.48 347	99.22 170	85.19 277	99.89 117	90.40 337	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	94.34 342	98.51 181	89.99 380	99.39 279	98.57 106	93.14 212	97.33 193	98.31 280	93.44 116	94.68 448	93.69 281	95.98 254	98.34 286

UWE-MVS		96.79 153	96.72 143	97.00 242	98.51 181	93.70 276	99.71 208	98.60 100	92.96 220	97.09 200	98.34 277	96.67 33	98.85 226	92.11 305	96.50 241	98.44 281

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	98.75 237	95.20 57	99.48 185	98.93 92	96.40 244	99.29 217

test_vis1_n_1920		95.44 224	95.31 213	95.82 286	98.50 183	88.74 398	99.98 21	97.30 328	97.84 28	99.85 18	99.19 175	66.82 437	99.97 63	98.82 101	99.46 126	98.76 268

BH-w/o		95.71 215	95.38 211	96.68 255	98.49 185	92.28 317	99.84 149	97.50 301	92.12 271	92.06 307	98.79 235	84.69 289	98.67 258	95.29 234	99.66 96	99.09 239

baseline1		95.78 211	94.86 231	98.54 127	98.47 186	98.07 79	99.06 324	97.99 240	92.68 239	94.13 282	98.62 252	93.28 124	98.69 255	93.79 276	85.76 362	98.84 264

fmvsm_s_conf0.5_n_7		97.70 108	97.74 89	97.59 205	98.44 187	95.16 228	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	97.66 303

EPMVS		96.53 172	96.01 172	98.09 161	98.43 188	96.12 181	96.36 447	99.43 20	93.53 193	97.64 182	95.04 400	94.41 83	98.38 287	91.13 318	98.11 185	99.75 117

kuosan		93.17 297	92.60 299	94.86 318	98.40 189	89.54 388	98.44 388	98.53 125	84.46 423	88.49 369	97.92 296	90.57 189	97.05 363	83.10 417	93.49 303	97.99 294

WBMVS		94.52 257	94.03 255	95.98 276	98.38 190	96.68 150	99.92 100	97.63 281	90.75 322	89.64 342	95.25 393	96.77 27	96.90 375	94.35 260	83.57 382	94.35 347

UBG		97.84 91	97.69 93	98.29 148	98.38 190	96.59 157	99.90 114	98.53 125	93.91 180	98.52 142	98.42 273	96.77 27	99.17 203	98.54 119	96.20 248	99.11 238

sss		97.57 113	97.03 127	99.18 62	98.37 192	98.04 82	99.73 199	99.38 22	93.46 197	98.76 130	99.06 188	91.21 173	99.89 117	96.33 217	97.01 227	99.62 142

testing11		97.48 116	97.27 116	98.10 160	98.36 193	96.02 182	99.92 100	98.45 142	93.45 199	98.15 164	98.70 242	95.48 53	99.22 196	97.85 162	95.05 283	99.07 242

BH-untuned		95.18 232	94.83 232	96.22 271	98.36 193	91.22 353	99.80 167	97.32 326	90.91 311	91.08 314	98.67 244	83.51 301	98.54 269	94.23 263	99.61 104	98.92 259

testing91		97.16 133	96.90 131	97.97 167	98.35 195	95.67 198	99.91 108	98.42 167	92.91 223	97.33 193	98.72 240	94.81 71	99.21 197	96.98 194	94.63 286	99.03 250

testing99		97.17 132	96.91 130	97.95 168	98.35 195	95.70 195	99.91 108	98.43 155	92.94 221	97.36 191	98.72 240	94.83 70	99.21 197	97.00 192	94.64 285	98.95 255

ET-MVSNet_ETH3D		94.37 264	93.28 285	97.64 196	98.30 197	97.99 84	99.99 5	97.61 287	94.35 155	71.57 473	99.45 140	96.23 38	95.34 438	96.91 200	85.14 369	99.59 149

AUN-MVS		93.28 294	92.60 299	95.34 301	98.29 198	90.09 378	99.31 293	98.56 112	91.80 284	96.35 235	98.00 291	89.38 206	98.28 298	92.46 296	69.22 459	97.64 305

FMVSNet3		92.69 312	91.58 322	95.99 275	98.29 198	97.42 115	99.26 304	97.62 284	89.80 345	89.68 338	95.32 387	81.62 325	96.27 414	87.01 388	85.65 363	94.29 351

PMMVS		96.76 156	96.76 140	96.76 252	98.28 200	92.10 321	99.91 108	97.98 242	94.12 166	99.53 72	99.39 148	86.93 246	98.73 248	96.95 197	97.73 193	99.45 186

hse-mvs2		94.38 263	94.08 254	95.31 303	98.27 201	90.02 379	99.29 300	98.56 112	95.90 99	98.77 127	98.00 291	90.89 185	98.26 302	97.80 164	69.20 460	97.64 305

PVSNet_0	88.03 19	91.80 332	90.27 346	96.38 267	98.27 201	90.46 370	99.94 90	99.61 13	93.99 174	86.26 410	97.39 310	71.13 420	99.89 117	98.77 105	67.05 466	98.79 267

UA-Net		96.54 171	95.96 179	98.27 149	98.23 203	95.71 194	98.00 412	98.45 142	93.72 189	98.41 150	99.27 162	88.71 220	99.66 170	91.19 317	97.69 194	99.44 189

test_cas_vis1_n_1920		96.59 168	96.23 163	97.65 195	98.22 204	94.23 260	99.99 5	97.25 341	97.77 29	99.58 68	99.08 184	77.10 371	99.97 63	97.64 172	99.45 127	98.74 270

FE-MVS		95.70 217	95.01 227	97.79 182	98.21 205	94.57 244	95.03 461	98.69 81	88.90 361	97.50 186	96.19 349	92.60 146	99.49 184	89.99 342	97.94 191	99.31 212

GG-mvs-BLEND				98.54 127	98.21 205	98.01 83	93.87 466	98.52 127		97.92 170	97.92 296	99.02 3	97.94 322	98.17 142	99.58 109	99.67 129

mvs_anonymous		95.65 219	95.03 226	97.53 209	98.19 207	95.74 192	99.33 288	97.49 302	90.87 312	90.47 323	97.10 317	88.23 223	97.16 354	95.92 224	97.66 197	99.68 127

MVS_Test		96.46 174	95.74 192	98.61 116	98.18 208	97.23 122	99.31 293	97.15 356	91.07 308	98.84 121	97.05 321	88.17 224	98.97 216	94.39 257	97.50 199	99.61 146

BH-RMVSNet		95.18 232	94.31 247	97.80 180	98.17 209	95.23 223	99.76 182	97.53 297	92.52 254	94.27 280	99.25 168	76.84 378	98.80 238	90.89 326	99.54 111	99.35 203

dongtai		91.55 338	91.13 331	92.82 393	98.16 210	86.35 422	99.47 267	98.51 130	83.24 431	85.07 420	97.56 304	90.33 194	94.94 444	76.09 456	91.73 311	97.18 316

RPSCF		91.80 332	92.79 295	88.83 437	98.15 211	69.87 477	98.11 408	96.60 419	83.93 426	94.33 278	99.27 162	79.60 349	99.46 188	91.99 306	93.16 308	97.18 316

ETV-MVS		97.92 84	97.80 88	98.25 150	98.14 212	96.48 159	99.98 21	97.63 281	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	94.80 238	99.08 319	97.61 287	92.02 276	95.54 259	98.96 207	90.64 188	98.08 311	93.73 279	97.41 203	99.47 179

test_fmvsmconf_n		98.43 51	98.32 47	98.78 102	98.12 214	96.41 162	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	97.76 270	98.05 20	99.65 53	99.58 127	80.88 334	99.93 103	99.59 56	98.17 180	97.29 314

ab-mvs		94.69 249	93.42 276	98.51 132	98.07 216	96.26 169	96.49 445	98.68 83	90.31 335	94.54 270	97.00 323	76.30 386	99.71 159	95.98 223	93.38 306	99.56 158

XVG-OURS-SEG-HR		94.79 244	94.70 239	95.08 308	98.05 217	89.19 390	99.08 319	97.54 295	93.66 190	94.87 268	99.58 127	78.78 357	99.79 144	97.31 180	93.40 305	96.25 323

EIA-MVS		97.53 114	97.46 104	97.76 188	98.04 218	94.84 235	99.98 21	97.61 287	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		94.82 241	94.74 238	95.06 309	98.00 219	89.19 390	99.08 319	97.55 293	94.10 167	94.71 269	99.62 122	80.51 340	99.74 155	96.04 222	93.06 310	96.25 323

mvsmamba		96.94 146	96.73 142	97.55 207	97.99 220	94.37 256	99.62 232	97.70 273	93.13 213	98.42 149	97.92 296	88.02 225	98.75 246	98.78 104	99.01 152	99.52 169

dp		95.05 235	94.43 242	96.91 245	97.99 220	92.73 306	96.29 450	97.98 242	89.70 346	95.93 246	94.67 415	93.83 110	98.45 275	86.91 391	96.53 240	99.54 163

tpmrst		96.27 189	95.98 175	97.13 237	97.96 222	93.15 294	96.34 448	98.17 218	92.07 272	98.71 133	95.12 397	93.91 105	98.73 248	94.91 245	96.62 238	99.50 175

TR-MVS		94.54 254	93.56 271	97.49 214	97.96 222	94.34 257	98.71 370	97.51 300	90.30 336	94.51 272	98.69 243	75.56 392	98.77 242	92.82 294	95.99 253	99.35 203

Vis-MVSNet (Re-imp)		96.32 184	95.98 175	97.35 228	97.93 224	94.82 237	99.47 267	98.15 226	91.83 281	95.09 266	99.11 182	91.37 172	97.47 338	93.47 283	97.43 200	99.74 118

MDTV_nov1_ep13			95.69 194		97.90 225	94.15 263	95.98 456	98.44 147	93.12 214	97.98 168	95.74 362	95.10 60	98.58 265	90.02 341	96.92 229

Fast-Effi-MVS+		95.02 237	94.19 249	97.52 211	97.88 226	94.55 245	99.97 39	97.08 372	88.85 363	94.47 273	97.96 295	84.59 290	98.41 279	89.84 344	97.10 220	99.59 149

ADS-MVSNet2		93.80 281	93.88 261	93.55 376	97.87 227	85.94 426	94.24 462	96.84 405	90.07 339	96.43 231	94.48 420	90.29 196	95.37 437	87.44 378	97.23 211	99.36 199

ADS-MVSNet		94.79 244	94.02 256	97.11 239	97.87 227	93.79 272	94.24 462	98.16 223	90.07 339	96.43 231	94.48 420	90.29 196	98.19 305	87.44 378	97.23 211	99.36 199

Effi-MVS+		96.30 186	95.69 194	98.16 154	97.85 229	96.26 169	97.41 424	97.21 348	90.37 332	98.65 136	98.58 258	86.61 252	98.70 254	97.11 188	97.37 205	99.52 169

PatchmatchNet		95.94 200	95.45 202	97.39 223	97.83 230	94.41 252	96.05 454	98.40 176	92.86 225	97.09 200	95.28 392	94.21 97	98.07 313	89.26 352	98.11 185	99.70 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 252	93.61 266	97.74 190	97.82 231	96.26 169	99.96 53	97.78 266	85.76 407	94.00 283	97.54 305	76.95 377	99.21 197	97.23 185	95.43 276	97.76 302

1112_ss		96.01 197	95.20 218	98.42 141	97.80 232	96.41 162	99.65 225	96.66 416	92.71 236	92.88 297	99.40 146	92.16 160	99.30 192	91.92 308	93.66 301	99.55 159

E3new		96.75 158	96.43 156	97.71 191	97.79 233	94.83 236	99.80 167	97.33 320	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		95.72 213	94.83 232	98.42 141	97.79 233	96.41 162	99.65 225	96.65 417	92.70 237	92.86 298	96.13 353	92.15 161	99.30 192	91.88 309	93.64 302	99.55 159

Effi-MVS+-dtu		94.53 256	95.30 214	92.22 401	97.77 235	82.54 449	99.59 241	97.06 381	94.92 127	95.29 263	95.37 385	85.81 263	97.89 323	94.80 248	97.07 221	96.23 325

tpm cat1		93.51 290	92.52 305	96.47 260	97.77 235	91.47 351	96.13 452	98.06 233	80.98 445	92.91 296	93.78 429	89.66 201	98.87 224	87.03 387	96.39 245	99.09 239

FA-MVS(test-final)		95.86 203	95.09 223	98.15 157	97.74 237	95.62 200	96.31 449	98.17 218	91.42 297	96.26 236	96.13 353	90.56 190	99.47 187	92.18 300	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	99.31 293	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 298

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 293	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 298

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 293	97.86 256	96.43 81	99.62 60	99.69 104	85.56 270	99.68 164	99.05 81	98.31 175	97.83 298

EPP-MVSNet		96.69 163	96.60 148	96.96 244	97.74 237	93.05 297	99.37 283	98.56 112	88.75 365	95.83 250	99.01 194	96.01 39	98.56 267	96.92 198	97.20 213	99.25 224

gg-mvs-nofinetune		93.51 290	91.86 317	98.47 134	97.72 242	97.96 88	92.62 472	98.51 130	74.70 468	97.33 193	69.59 487	98.91 4	97.79 326	97.77 169	99.56 110	99.67 129

IB-MVS	92.85 6	94.99 238	93.94 259	98.16 154	97.72 242	95.69 197	99.99 5	98.81 67	94.28 161	92.70 299	96.90 325	95.08 61	99.17 203	96.07 221	73.88 445	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	91.83 281	97.45 188	99.04 190	97.50 9	99.10 208	94.75 250	96.37 246	99.16 231

VortexMVS		94.11 270	93.50 273	95.94 278	97.70 245	96.61 154	99.35 286	97.18 351	93.52 195	89.57 345	95.74 362	87.55 233	96.97 371	95.76 229	85.13 370	94.23 356

viewdifsd2359ckpt09		96.21 191	95.77 190	97.53 209	97.69 246	94.50 248	99.78 171	97.23 346	92.88 224	96.58 219	99.26 166	84.85 282	98.66 261	96.61 211	97.02 226	99.43 190

Syy-MVS		90.00 373	90.63 338	88.11 444	97.68 247	74.66 473	99.71 208	98.35 189	90.79 319	92.10 305	98.67 244	79.10 355	93.09 464	63.35 480	95.95 257	96.59 321

myMVS_eth3d		94.46 261	94.76 237	93.55 376	97.68 247	90.97 355	99.71 208	98.35 189	90.79 319	92.10 305	98.67 244	92.46 153	93.09 464	87.13 384	95.95 257	96.59 321

test_fmvs1_n		94.25 269	94.36 244	93.92 363	97.68 247	83.70 439	99.90 114	96.57 420	97.40 40	99.67 51	98.88 219	61.82 456	99.92 109	98.23 140	99.13 146	98.14 291

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	94.92 233	99.27 303	97.10 368	92.79 231	97.43 189	97.99 293	81.85 320	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	96.17 178	99.81 163	97.19 349	94.67 139	98.95 116	99.28 159	86.43 253	98.76 244	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	94.70 241	99.77 176	97.33 320	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	94.40 254	99.70 215	97.23 346	92.76 233	96.63 216	99.05 189	84.96 281	98.64 262	96.65 210	97.35 206	99.31 212

Vis-MVSNet		95.72 213	95.15 221	97.45 215	97.62 254	94.28 258	99.28 301	98.24 209	94.27 163	96.84 211	98.94 214	79.39 350	98.76 244	93.25 285	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	96.70 147	99.92 100	98.54 122	91.11 306	97.07 202	98.97 205	97.47 12	99.03 211	93.73 279	96.09 251	98.92 259

GDP-MVS		97.88 86	97.59 100	98.75 105	97.59 257	97.81 95	99.95 72	97.37 315	94.44 149	99.08 107	99.58 127	97.13 25	99.08 209	94.99 240	98.17 180	99.37 197

miper_ehance_all_eth		93.16 298	92.60 299	94.82 319	97.57 258	93.56 284	99.50 261	97.07 380	88.75 365	88.85 362	95.52 374	90.97 181	96.74 385	90.77 328	84.45 375	94.17 364

guyue		97.15 134	96.82 137	98.15 157	97.56 259	96.25 173	99.71 208	97.84 259	95.75 106	98.13 165	98.65 247	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	94.59 243	99.70 215	97.33 320	93.62 192	97.00 206	99.32 153	85.57 269	98.71 251	97.26 184	97.33 207	99.47 179

testing3		93.92 275	94.23 248	92.99 390	97.54 261	90.23 374	99.99 5	99.16 33	90.57 326	91.33 313	98.63 251	92.99 132	92.52 468	82.46 421	95.39 277	96.22 326

SSM_0404		95.75 212	95.16 220	97.50 213	97.53 262	95.39 211	99.11 315	97.25 341	90.81 315	95.27 264	98.83 233	84.74 286	98.67 258	95.24 235	97.69 194	98.45 280

LCM-MVSNet-Re		92.31 321	92.60 299	91.43 410	97.53 262	79.27 466	99.02 333	91.83 482	92.07 272	80.31 444	94.38 423	83.50 302	95.48 434	97.22 186	97.58 198	99.54 163

GBi-Net		90.88 349	89.82 355	94.08 355	97.53 262	91.97 322	98.43 389	96.95 394	87.05 390	89.68 338	94.72 411	71.34 417	96.11 420	87.01 388	85.65 363	94.17 364

test1		90.88 349	89.82 355	94.08 355	97.53 262	91.97 322	98.43 389	96.95 394	87.05 390	89.68 338	94.72 411	71.34 417	96.11 420	87.01 388	85.65 363	94.17 364

FMVSNet2		91.02 346	89.56 360	95.41 299	97.53 262	95.74 192	98.98 336	97.41 310	87.05 390	88.43 374	95.00 405	71.34 417	96.24 416	85.12 403	85.21 368	94.25 354

tttt0517		96.85 150	96.49 152	97.92 172	97.48 267	95.89 186	99.85 144	98.54 122	90.72 323	96.63 216	98.93 217	97.47 12	99.02 212	93.03 292	95.76 263	98.85 263

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	95.47 204	99.86 141	97.29 336	93.35 201	96.03 243	99.19 175	85.39 274	98.72 250	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	94.52 246	99.71 208	97.33 320	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	95.64 199	99.99 5	97.06 381	94.59 140	99.63 57	99.32 153	89.20 212	98.14 307	98.76 106	99.23 142	99.62 142

viewdifsd2359ckpt07		95.83 206	95.42 204	97.07 240	97.40 272	93.04 298	99.60 239	97.24 344	92.39 260	96.09 242	99.14 181	83.07 311	98.93 220	97.02 191	96.87 230	99.23 227

c3_l		92.53 316	91.87 316	94.52 331	97.40 272	92.99 300	99.40 275	96.93 399	87.86 380	88.69 365	95.44 379	89.95 199	96.44 403	90.45 334	80.69 409	94.14 374

viewmambaseed2359dif		95.92 202	95.55 200	97.04 241	97.38 274	93.41 289	99.78 171	96.97 392	91.14 305	96.58 219	99.27 162	84.85 282	98.75 246	96.87 201	97.12 219	98.97 254

fmvsm_s_conf0.1_n		97.30 125	97.21 119	97.60 202	97.38 274	94.40 254	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	98.46 279

E3		96.36 181	95.95 181	97.60 202	97.37 276	94.52 246	99.71 208	97.33 320	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	97.13 237	97.37 276	94.96 231	99.53 256	97.91 251	91.55 289	95.37 262	98.32 278	95.05 63	97.13 357	93.80 275	95.75 264	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	96.48 159	99.96 53	98.29 202	91.93 277	95.77 251	98.07 289	95.54 49	98.29 296	90.55 332	98.89 155	99.70 124

miper_lstm_enhance		91.81 329	91.39 328	93.06 389	97.34 279	89.18 392	99.38 281	96.79 410	86.70 397	87.47 392	95.22 394	90.00 198	95.86 429	88.26 367	81.37 398	94.15 370

baseline		96.43 176	95.98 175	97.76 188	97.34 279	95.17 227	99.51 259	97.17 353	93.92 179	96.90 209	99.28 159	85.37 275	98.64 262	97.50 176	96.86 232	99.46 181

cl____		92.31 321	91.58 322	94.52 331	97.33 281	92.77 302	99.57 246	96.78 411	86.97 394	87.56 390	95.51 375	89.43 205	96.62 392	88.60 357	82.44 390	94.16 369

SD_0403		92.63 315	93.38 280	90.40 424	97.32 282	77.91 468	97.75 419	98.03 238	91.89 278	90.83 319	98.29 282	82.00 317	93.79 457	88.51 362	95.75 264	99.52 169

DIV-MVS_self_test		92.32 320	91.60 321	94.47 335	97.31 283	92.74 304	99.58 243	96.75 412	86.99 393	87.64 388	95.54 372	89.55 204	96.50 398	88.58 358	82.44 390	94.17 364

casdiffmvs		96.42 178	95.97 178	97.77 186	97.30 284	94.98 230	99.84 149	97.09 371	93.75 188	96.58 219	99.26 166	85.07 278	98.78 241	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		94.36 266	93.48 274	96.99 243	97.29 285	93.54 285	99.96 53	96.72 414	88.35 374	93.43 287	98.94 214	82.05 316	98.05 314	88.12 373	96.48 243	99.37 197

eth_miper_zixun_eth		92.41 319	91.93 314	93.84 367	97.28 286	90.68 364	98.83 359	96.97 392	88.57 370	89.19 357	95.73 365	89.24 211	96.69 390	89.97 343	81.55 396	94.15 370

MVSFormer		96.94 146	96.60 148	97.95 168	97.28 286	97.70 101	99.55 253	97.27 338	91.17 302	99.43 82	99.54 133	90.92 182	96.89 376	94.67 253	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	97.55 293	95.50 115	99.43 82	99.67 113	90.92 182	98.71 251	98.40 127	99.62 100	99.45 186

diffmvs_AUTHOR		96.75 158	96.41 158	97.79 182	97.20 289	95.46 205	99.69 218	97.15 356	94.46 145	98.78 125	99.21 173	85.64 267	98.77 242	98.27 137	97.31 209	99.13 235

mamba_0408		94.98 239	94.09 252	97.64 196	97.14 290	95.31 216	93.48 469	97.08 372	90.48 328	94.40 274	98.62 252	84.49 291	98.67 258	93.99 266	97.18 214	98.93 256

SSM_04072		94.77 246	94.09 252	96.82 249	97.14 290	95.31 216	93.48 469	97.08 372	90.48 328	94.40 274	98.62 252	84.49 291	96.21 417	93.99 266	97.18 214	98.93 256

SSM_0407		95.62 220	94.95 229	97.61 201	97.14 290	95.31 216	99.00 334	97.25 341	90.81 315	94.40 274	98.83 233	84.74 286	98.58 265	95.24 235	97.18 214	98.93 256

SCA		94.69 249	93.81 263	97.33 229	97.10 293	94.44 249	98.86 356	98.32 196	93.30 204	96.17 241	95.59 370	76.48 384	97.95 320	91.06 320	97.43 200	99.59 149

viewmacassd2359aftdt		95.93 201	95.45 202	97.36 226	97.09 294	94.12 265	99.57 246	97.26 340	93.05 218	96.50 223	99.17 177	82.76 312	98.68 256	96.61 211	97.04 223	99.28 219

KinetiMVS		96.10 193	95.29 215	98.53 129	97.08 295	97.12 128	99.56 250	98.12 229	94.78 132	98.44 147	98.94 214	80.30 344	99.39 190	91.56 313	98.79 161	99.06 243

TAMVS		95.85 204	95.58 198	96.65 257	97.07 296	93.50 286	99.17 311	97.82 261	91.39 299	95.02 267	98.01 290	92.20 159	97.30 347	93.75 278	95.83 261	99.14 234

Fast-Effi-MVS+-dtu		93.72 285	93.86 262	93.29 381	97.06 297	86.16 423	99.80 167	96.83 406	92.66 240	92.58 300	97.83 301	81.39 326	97.67 331	89.75 345	96.87 230	96.05 328

E4		96.01 197	95.53 201	97.44 218	97.05 298	94.23 260	99.57 246	97.30 328	92.72 234	96.47 225	99.03 191	83.98 299	98.83 227	96.92 198	96.77 233	99.27 221

E5new		95.83 206	95.39 206	97.15 233	97.03 299	93.59 279	99.32 291	97.30 328	92.58 247	96.45 226	99.00 198	83.37 305	98.81 234	96.81 203	96.65 236	99.04 246

E5		95.83 206	95.39 206	97.15 233	97.03 299	93.59 279	99.32 291	97.30 328	92.58 247	96.45 226	99.00 198	83.37 305	98.81 234	96.81 203	96.65 236	99.04 246

CostFormer		96.10 193	95.88 187	96.78 251	97.03 299	92.55 312	97.08 433	97.83 260	90.04 341	98.72 132	94.89 409	95.01 65	98.29 296	96.54 214	95.77 262	99.50 175

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 174	97.02 302	95.34 214	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	98.42 282

test-LLR		96.47 173	96.04 171	97.78 184	97.02 302	95.44 206	99.96 53	98.21 213	94.07 169	95.55 257	96.38 342	93.90 106	98.27 300	90.42 335	98.83 159	99.64 135

test-mter		96.39 179	95.93 184	97.78 184	97.02 302	95.44 206	99.96 53	98.21 213	91.81 283	95.55 257	96.38 342	95.17 58	98.27 300	90.42 335	98.83 159	99.64 135

E6new		95.83 206	95.39 206	97.14 235	97.00 305	93.58 281	99.31 293	97.30 328	92.57 249	96.45 226	99.01 194	83.44 303	98.81 234	96.80 205	96.66 234	99.04 246

E6		95.83 206	95.39 206	97.14 235	97.00 305	93.58 281	99.31 293	97.30 328	92.57 249	96.45 226	99.01 194	83.44 303	98.81 234	96.80 205	96.66 234	99.04 246

icg_test_0407_2		95.04 236	94.78 236	95.84 285	96.97 307	91.64 343	98.63 378	97.12 361	92.33 263	95.60 255	98.88 219	85.65 265	96.56 395	92.12 301	95.70 267	99.32 208

IMVS_0407		95.21 231	94.80 235	96.46 262	96.97 307	91.64 343	98.81 361	97.12 361	92.33 263	95.60 255	98.88 219	85.65 265	98.42 277	92.12 301	95.70 267	99.32 208

IMVS_0404		93.83 277	93.17 287	95.80 287	96.97 307	91.64 343	97.78 418	97.12 361	92.33 263	90.87 318	98.88 219	76.78 379	96.43 404	92.12 301	95.70 267	99.32 208

IMVS_0403		95.25 229	94.81 234	96.58 259	96.97 307	91.64 343	98.97 341	97.12 361	92.33 263	95.43 260	98.88 219	85.78 264	98.79 239	92.12 301	95.70 267	99.32 208

gm-plane-assit					96.97 307	93.76 274			91.47 293		98.96 207		98.79 239	94.92 243

WB-MVSnew		92.90 304	92.77 296	93.26 383	96.95 312	93.63 278	99.71 208	98.16 223	91.49 290	94.28 279	98.14 286	81.33 328	96.48 401	79.47 438	95.46 274	89.68 466

QAPM		95.40 225	94.17 250	99.10 78	96.92 313	97.71 99	99.40 275	98.68 83	89.31 349	88.94 361	98.89 218	82.48 314	99.96 75	93.12 291	99.83 81	99.62 142

KD-MVS_2432*1600		88.00 395	86.10 399	93.70 372	96.91 314	94.04 266	97.17 430	97.12 361	84.93 418	81.96 434	92.41 443	92.48 151	94.51 450	79.23 439	52.68 486	92.56 434

miper_refine_blended		88.00 395	86.10 399	93.70 372	96.91 314	94.04 266	97.17 430	97.12 361	84.93 418	81.96 434	92.41 443	92.48 151	94.51 450	79.23 439	52.68 486	92.56 434

tpm2		95.47 223	95.18 219	96.35 268	96.91 314	91.70 341	96.96 436	97.93 247	88.04 378	98.44 147	95.40 381	93.32 121	97.97 317	94.00 265	95.61 272	99.38 195

FMVSNet5		88.32 391	87.47 393	90.88 413	96.90 317	88.39 406	97.28 427	95.68 442	82.60 438	84.67 422	92.40 445	79.83 347	91.16 473	76.39 455	81.51 397	93.09 425

3Dnovator+	91.53 11	96.31 185	95.24 216	99.52 32	96.88 318	98.64 58	99.72 203	98.24 209	95.27 120	88.42 376	98.98 203	82.76 312	99.94 93	97.10 189	99.83 81	99.96 74

Patchmatch-test		92.65 314	91.50 325	96.10 274	96.85 319	90.49 369	91.50 477	97.19 349	82.76 437	90.23 324	95.59 370	95.02 64	98.00 316	77.41 450	96.98 228	99.82 106

MVS		96.60 167	95.56 199	99.72 14	96.85 319	99.22 21	98.31 395	98.94 44	91.57 288	90.90 317	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	95.34 212	99.08 81	96.82 321	97.47 113	99.45 272	98.81 67	95.52 114	89.39 348	99.00 198	81.97 318	99.95 84	97.27 181	99.83 81	99.84 103

EI-MVSNet		93.73 284	93.40 279	94.74 320	96.80 322	92.69 307	99.06 324	97.67 276	88.96 358	91.39 311	99.02 192	88.75 219	97.30 347	91.07 319	87.85 345	94.22 359

CVMVSNet		94.68 251	94.94 230	93.89 366	96.80 322	86.92 420	99.06 324	98.98 41	94.45 146	94.23 281	99.02 192	85.60 268	95.31 439	90.91 325	95.39 277	99.43 190

IterMVS-LS		92.69 312	92.11 310	94.43 339	96.80 322	92.74 304	99.45 272	96.89 402	88.98 356	89.65 341	95.38 384	88.77 218	96.34 411	90.98 323	82.04 393	94.22 359
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 170	96.46 155	96.91 245	96.79 325	92.50 313	99.90 114	97.38 312	96.02 98	97.79 179	99.32 153	86.36 255	98.99 213	98.26 138	96.33 247	99.23 227

IterMVS		90.91 348	90.17 350	93.12 386	96.78 326	90.42 372	98.89 350	97.05 384	89.03 353	86.49 405	95.42 380	76.59 382	95.02 441	87.22 383	84.09 378	93.93 396
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	96.74 327	98.52 62	98.31 395	98.86 59	95.82 103	89.91 332	98.98 203	87.49 235	99.96 75	97.80 164	99.73 91	99.96 74

IterMVS-SCA-FT		90.85 351	90.16 351	92.93 391	96.72 328	89.96 381	98.89 350	96.99 388	88.95 359	86.63 402	95.67 366	76.48 384	95.00 442	87.04 386	84.04 381	93.84 403

MVS-HIRNet		86.22 407	83.19 420	95.31 303	96.71 329	90.29 373	92.12 474	97.33 320	62.85 481	86.82 399	70.37 486	69.37 425	97.49 337	75.12 458	97.99 190	98.15 289

viewdifsd2359ckpt11		94.09 272	93.63 265	95.46 296	96.68 330	88.92 395	99.62 232	97.12 361	93.07 216	95.73 252	99.22 170	77.05 372	98.88 223	96.52 215	87.69 350	98.58 277

viewmsd2359difaftdt		94.09 272	93.64 264	95.46 296	96.68 330	88.92 395	99.62 232	97.13 360	93.07 216	95.73 252	99.22 170	77.05 372	98.89 222	96.52 215	87.70 349	98.58 277

VDDNet		93.12 299	91.91 315	96.76 252	96.67 332	92.65 310	98.69 373	98.21 213	82.81 436	97.75 181	99.28 159	61.57 457	99.48 185	98.09 148	94.09 296	98.15 289

dmvs_re		93.20 296	93.15 288	93.34 379	96.54 333	83.81 438	98.71 370	98.51 130	91.39 299	92.37 303	98.56 260	78.66 359	97.83 325	93.89 269	89.74 317	98.38 284

Elysia		94.50 258	93.38 280	97.85 178	96.49 334	96.70 147	98.98 336	97.78 266	90.81 315	96.19 239	98.55 262	73.63 408	98.98 214	89.41 346	98.56 167	97.88 296

StellarMVS		94.50 258	93.38 280	97.85 178	96.49 334	96.70 147	98.98 336	97.78 266	90.81 315	96.19 239	98.55 262	73.63 408	98.98 214	89.41 346	98.56 167	97.88 296

MIMVSNet		90.30 364	88.67 379	95.17 307	96.45 336	91.64 343	92.39 473	97.15 356	85.99 404	90.50 322	93.19 437	66.95 436	94.86 446	82.01 425	93.43 304	99.01 252

CR-MVSNet		93.45 293	92.62 298	95.94 278	96.29 337	92.66 308	92.01 475	96.23 428	92.62 242	96.94 207	93.31 435	91.04 179	96.03 425	79.23 439	95.96 255	99.13 235

RPMNet		89.76 377	87.28 394	97.19 232	96.29 337	92.66 308	92.01 475	98.31 198	70.19 475	96.94 207	85.87 479	87.25 240	99.78 146	62.69 481	95.96 255	99.13 235

tt0805		91.28 341	90.18 349	94.60 326	96.26 339	87.55 413	98.39 393	98.72 77	89.00 355	89.22 354	98.47 270	62.98 452	98.96 218	90.57 331	88.00 344	97.28 315

Patchmtry		89.70 378	88.49 382	93.33 380	96.24 340	89.94 384	91.37 478	96.23 428	78.22 458	87.69 387	93.31 435	91.04 179	96.03 425	80.18 437	82.10 392	94.02 386

test_vis1_rt		86.87 404	86.05 402	89.34 433	96.12 341	78.07 467	99.87 130	83.54 494	92.03 275	78.21 455	89.51 462	45.80 478	99.91 110	96.25 219	93.11 309	90.03 462

JIA-IIPM		91.76 335	90.70 336	94.94 313	96.11 342	87.51 414	93.16 471	98.13 228	75.79 464	97.58 183	77.68 484	92.84 137	97.97 317	88.47 363	96.54 239	99.33 206

OpenMVS	90.15 15	94.77 246	93.59 269	98.33 145	96.07 343	97.48 112	99.56 250	98.57 106	90.46 330	86.51 404	98.95 212	78.57 360	99.94 93	93.86 270	99.74 90	97.57 310

PAPM		98.60 37	98.42 38	99.14 72	96.05 344	98.96 27	99.90 114	99.35 24	96.68 71	98.35 154	99.66 115	96.45 35	98.51 270	99.45 65	99.89 74	99.96 74

CLD-MVS		94.06 274	93.90 260	94.55 330	96.02 345	90.69 363	99.98 21	97.72 272	96.62 75	91.05 316	98.85 231	77.21 370	98.47 271	98.11 146	89.51 323	94.48 335
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 361	88.75 378	95.25 305	95.99 346	90.16 376	91.22 479	97.54 295	76.80 460	97.26 196	86.01 478	91.88 166	96.07 424	66.16 475	95.91 259	99.51 173

ACMH+	89.98 16	90.35 362	89.54 361	92.78 395	95.99 346	86.12 424	98.81 361	97.18 351	89.38 348	83.14 430	97.76 302	68.42 430	98.43 276	89.11 353	86.05 361	93.78 406

DeepMVS_CX				82.92 456	95.98 348	58.66 487		96.01 434	92.72 234	78.34 454	95.51 375	58.29 464	98.08 311	82.57 420	85.29 366	92.03 443

ACMP	92.05 9	92.74 310	92.42 307	93.73 368	95.91 349	88.72 399	99.81 163	97.53 297	94.13 165	87.00 398	98.23 284	74.07 405	98.47 271	96.22 220	88.86 330	93.99 391
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 288	93.03 290	95.35 300	95.86 350	86.94 419	99.87 130	96.36 426	96.85 62	99.54 71	98.79 235	52.41 472	99.83 139	98.64 114	98.97 153	99.29 217

HQP-NCC					95.78 351		99.87 130		96.82 64	93.37 288

ACMP_Plane					95.78 351		99.87 130		96.82 64	93.37 288

HQP-MVS		94.61 253	94.50 241	94.92 314	95.78 351	91.85 329	99.87 130	97.89 252	96.82 64	93.37 288	98.65 247	80.65 338	98.39 283	97.92 158	89.60 318	94.53 331

NP-MVS					95.77 354	91.79 333					98.65 247

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 114	95.76 355	96.20 175	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					95.76 355	91.72 340						80.47 342

ACMM	91.95 10	92.88 305	92.52 305	93.98 362	95.75 357	89.08 394	99.77 176	97.52 299	93.00 219	89.95 331	97.99 293	76.17 388	98.46 274	93.63 282	88.87 329	94.39 343
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 277	92.84 292	96.80 250	95.73 358	93.57 283	99.88 127	97.24 344	92.57 249	92.92 295	96.66 334	78.73 358	97.67 331	87.75 376	94.06 297	99.17 230

plane_prior1					95.73 358

jason		97.24 129	96.86 134	98.38 144	95.73 358	97.32 117	99.97 39	97.40 311	95.34 118	98.60 141	99.54 133	87.70 229	98.56 267	97.94 157	99.47 124	99.25 224
jason: jason.
mmtdpeth		88.52 389	87.75 391	90.85 415	95.71 361	83.47 444	98.94 344	94.85 458	88.78 364	97.19 198	89.58 461	63.29 450	98.97 216	98.54 119	62.86 474	90.10 461

HQP_MVS		94.49 260	94.36 244	94.87 315	95.71 361	91.74 336	99.84 149	97.87 254	96.38 84	93.01 293	98.59 255	80.47 342	98.37 289	97.79 167	89.55 321	94.52 333

plane_prior7					95.71 361	91.59 349

ITE_SJBPF				92.38 398	95.69 364	85.14 430		95.71 441	92.81 228	89.33 351	98.11 287	70.23 423	98.42 277	85.91 398	88.16 342	93.59 414

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 199	95.65 365	94.21 262	99.83 156	98.50 136	96.27 91	99.65 53	99.64 118	84.72 288	99.93 103	99.04 84	98.84 158	98.74 270

ACMH	89.72 17	90.64 355	89.63 358	93.66 374	95.64 366	88.64 402	98.55 381	97.45 304	89.03 353	81.62 437	97.61 303	69.75 424	98.41 279	89.37 348	87.62 351	93.92 397
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	95.63 367	95.96 184	99.74 192	98.88 54	92.94 221	91.61 309	98.97 205	97.72 6	98.62 264	94.83 247	98.08 188	97.53 312

FMVSNet1		88.50 390	86.64 397	94.08 355	95.62 368	91.97 322	98.43 389	96.95 394	83.00 434	86.08 412	94.72 411	59.09 463	96.11 420	81.82 427	84.07 379	94.17 364

LuminaMVS		96.63 166	96.21 166	97.87 177	95.58 369	96.82 141	99.12 313	97.67 276	94.47 144	97.88 174	98.31 280	87.50 234	98.71 251	98.07 150	97.29 210	98.10 292

LPG-MVS_test		92.96 302	92.71 297	93.71 370	95.43 370	88.67 400	99.75 188	97.62 284	92.81 228	90.05 327	98.49 266	75.24 395	98.40 281	95.84 226	89.12 325	94.07 383

LGP-MVS_train				93.71 370	95.43 370	88.67 400		97.62 284	92.81 228	90.05 327	98.49 266	75.24 395	98.40 281	95.84 226	89.12 325	94.07 383

tpm		93.70 286	93.41 278	94.58 328	95.36 372	87.41 415	97.01 434	96.90 401	90.85 313	96.72 215	94.14 426	90.40 193	96.84 380	90.75 329	88.54 337	99.51 173

D2MVS		92.76 309	92.59 303	93.27 382	95.13 373	89.54 388	99.69 218	99.38 22	92.26 268	87.59 389	94.61 417	85.05 279	97.79 326	91.59 312	88.01 343	92.47 438

VPA-MVSNet		92.70 311	91.55 324	96.16 272	95.09 374	96.20 175	98.88 352	99.00 39	91.02 310	91.82 308	95.29 391	76.05 390	97.96 319	95.62 231	81.19 399	94.30 350

LTVRE_ROB	88.28 18	90.29 365	89.05 372	94.02 358	95.08 375	90.15 377	97.19 429	97.43 306	84.91 420	83.99 426	97.06 320	74.00 406	98.28 298	84.08 409	87.71 347	93.62 413
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		87.87 397	86.51 398	91.94 404	95.05 376	85.57 428	97.65 420	94.08 468	84.40 424	81.82 436	96.85 329	62.14 455	98.33 292	80.25 436	86.37 358	91.91 445

test0.0.03 1		93.86 276	93.61 266	94.64 324	95.02 377	92.18 320	99.93 97	98.58 104	94.07 169	87.96 384	98.50 265	93.90 106	94.96 443	81.33 428	93.17 307	96.78 318

UniMVSNet (Re)		93.07 301	92.13 309	95.88 282	94.84 378	96.24 174	99.88 127	98.98 41	92.49 256	89.25 352	95.40 381	87.09 242	97.14 356	93.13 290	78.16 423	94.26 352

USDC		90.00 373	88.96 373	93.10 388	94.81 379	88.16 408	98.71 370	95.54 446	93.66 190	83.75 428	97.20 314	65.58 441	98.31 294	83.96 412	87.49 353	92.85 431

VPNet		91.81 329	90.46 340	95.85 284	94.74 380	95.54 203	98.98 336	98.59 102	92.14 270	90.77 321	97.44 307	68.73 428	97.54 336	94.89 246	77.89 425	94.46 336

FIs		94.10 271	93.43 275	96.11 273	94.70 381	96.82 141	99.58 243	98.93 48	92.54 252	89.34 350	97.31 311	87.62 231	97.10 360	94.22 264	86.58 356	94.40 342

UniMVSNet_ETH3D		90.06 372	88.58 381	94.49 334	94.67 382	88.09 409	97.81 417	97.57 292	83.91 427	88.44 371	97.41 308	57.44 465	97.62 333	91.41 314	88.59 336	97.77 301

UniMVSNet_NR-MVSNet		92.95 303	92.11 310	95.49 292	94.61 383	95.28 220	99.83 156	99.08 36	91.49 290	89.21 355	96.86 328	87.14 241	96.73 386	93.20 286	77.52 428	94.46 336

test_fmvs2		89.47 382	89.70 357	88.77 440	94.54 384	75.74 469	99.83 156	94.70 464	94.71 136	91.08 314	96.82 333	54.46 468	97.78 328	92.87 293	88.27 340	92.80 432

MonoMVSNet		94.82 241	94.43 242	95.98 276	94.54 384	90.73 362	99.03 331	97.06 381	93.16 211	93.15 292	95.47 378	88.29 222	97.57 334	97.85 162	91.33 315	99.62 142

WR-MVS		92.31 321	91.25 329	95.48 295	94.45 386	95.29 219	99.60 239	98.68 83	90.10 338	88.07 383	96.89 326	80.68 337	96.80 384	93.14 289	79.67 416	94.36 344

nrg030		93.51 290	92.53 304	96.45 263	94.36 387	97.20 123	99.81 163	97.16 355	91.60 287	89.86 334	97.46 306	86.37 254	97.68 330	95.88 225	80.31 412	94.46 336

tfpnnormal		89.29 385	87.61 392	94.34 342	94.35 388	94.13 264	98.95 343	98.94 44	83.94 425	84.47 423	95.51 375	74.84 400	97.39 339	77.05 453	80.41 410	91.48 448

FC-MVSNet-test		93.81 280	93.15 288	95.80 287	94.30 389	96.20 175	99.42 274	98.89 52	92.33 263	89.03 360	97.27 313	87.39 237	96.83 382	93.20 286	86.48 357	94.36 344

SSC-MVS3.2		89.59 380	88.66 380	92.38 398	94.29 390	86.12 424	99.49 263	97.66 279	90.28 337	88.63 368	95.18 395	64.46 446	96.88 378	85.30 402	82.66 387	94.14 374

MS-PatchMatch		90.65 354	90.30 345	91.71 409	94.22 391	85.50 429	98.24 400	97.70 273	88.67 367	86.42 407	96.37 344	67.82 433	98.03 315	83.62 414	99.62 100	91.60 446

WR-MVS_H		91.30 339	90.35 343	94.15 349	94.17 392	92.62 311	99.17 311	98.94 44	88.87 362	86.48 406	94.46 422	84.36 294	96.61 393	88.19 369	78.51 421	93.21 423

DU-MVS		92.46 318	91.45 327	95.49 292	94.05 393	95.28 220	99.81 163	98.74 76	92.25 269	89.21 355	96.64 336	81.66 323	96.73 386	93.20 286	77.52 428	94.46 336

NR-MVSNet		91.56 337	90.22 347	95.60 290	94.05 393	95.76 191	98.25 399	98.70 79	91.16 304	80.78 443	96.64 336	83.23 309	96.57 394	91.41 314	77.73 427	94.46 336

CP-MVSNet		91.23 343	90.22 347	94.26 344	93.96 395	92.39 316	99.09 317	98.57 106	88.95 359	86.42 407	96.57 339	79.19 353	96.37 409	90.29 338	78.95 418	94.02 386

XXY-MVS		91.82 328	90.46 340	95.88 282	93.91 396	95.40 210	98.87 355	97.69 275	88.63 369	87.87 385	97.08 318	74.38 404	97.89 323	91.66 311	84.07 379	94.35 347

PS-CasMVS		90.63 356	89.51 363	93.99 361	93.83 397	91.70 341	98.98 336	98.52 127	88.48 371	86.15 411	96.53 341	75.46 393	96.31 413	88.83 355	78.86 420	93.95 394

test_0402		85.58 409	83.94 414	90.50 421	93.81 398	85.04 431	98.55 381	95.20 455	76.01 462	79.72 449	95.13 396	64.15 448	96.26 415	66.04 476	86.88 355	90.21 459

XVG-ACMP-BASELINE		91.22 344	90.75 335	92.63 397	93.73 399	85.61 427	98.52 385	97.44 305	92.77 232	89.90 333	96.85 329	66.64 438	98.39 283	92.29 298	88.61 334	93.89 399

TranMVSNet+NR-MVSNet		91.68 336	90.61 339	94.87 315	93.69 400	93.98 269	99.69 218	98.65 87	91.03 309	88.44 371	96.83 332	80.05 346	96.18 418	90.26 339	76.89 436	94.45 341

TransMVSNet (Re)		87.25 402	85.28 409	93.16 385	93.56 401	91.03 354	98.54 383	94.05 470	83.69 429	81.09 441	96.16 350	75.32 394	96.40 408	76.69 454	68.41 462	92.06 442

v10		90.25 366	88.82 375	94.57 329	93.53 402	93.43 288	99.08 319	96.87 404	85.00 417	87.34 396	94.51 418	80.93 333	97.02 370	82.85 419	79.23 417	93.26 421

testgi		89.01 387	88.04 388	91.90 405	93.49 403	84.89 433	99.73 199	95.66 443	93.89 183	85.14 418	98.17 285	59.68 461	94.66 449	77.73 449	88.88 328	96.16 327

v8		90.54 358	89.17 368	94.66 323	93.43 404	93.40 291	99.20 308	96.94 398	85.76 407	87.56 390	94.51 418	81.96 319	97.19 353	84.94 405	78.25 422	93.38 419

V42		91.28 341	90.12 352	94.74 320	93.42 405	93.46 287	99.68 221	97.02 385	87.36 386	89.85 336	95.05 399	81.31 329	97.34 342	87.34 381	80.07 414	93.40 417

pm-mvs1		89.36 384	87.81 390	94.01 359	93.40 406	91.93 325	98.62 379	96.48 424	86.25 402	83.86 427	96.14 352	73.68 407	97.04 366	86.16 395	75.73 441	93.04 427

v1144		91.09 345	89.83 354	94.87 315	93.25 407	93.69 277	99.62 232	96.98 390	86.83 396	89.64 342	94.99 406	80.94 332	97.05 363	85.08 404	81.16 400	93.87 401

v1192		90.62 357	89.25 367	94.72 322	93.13 408	93.07 295	99.50 261	97.02 385	86.33 401	89.56 346	95.01 403	79.22 352	97.09 362	82.34 423	81.16 400	94.01 388

v2v482		91.30 339	90.07 353	95.01 310	93.13 408	93.79 272	99.77 176	97.02 385	88.05 377	89.25 352	95.37 385	80.73 336	97.15 355	87.28 382	80.04 415	94.09 382

OPM-MVS		93.21 295	92.80 294	94.44 337	93.12 410	90.85 361	99.77 176	97.61 287	96.19 94	91.56 310	98.65 247	75.16 399	98.47 271	93.78 277	89.39 324	93.99 391
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 352	89.52 362	94.59 327	93.11 411	92.77 302	99.56 250	96.99 388	86.38 400	89.82 337	94.95 408	80.50 341	97.10 360	83.98 411	80.41 410	93.90 398

PEN-MVS		90.19 368	89.06 371	93.57 375	93.06 412	90.90 359	99.06 324	98.47 139	88.11 376	85.91 413	96.30 346	76.67 380	95.94 428	87.07 385	76.91 435	93.89 399

v1240		90.20 367	88.79 376	94.44 337	93.05 413	92.27 318	99.38 281	96.92 400	85.89 405	89.36 349	94.87 410	77.89 367	97.03 368	80.66 432	81.08 403	94.01 388

usedtu_dtu_shiyan1		92.78 307	91.73 318	95.92 280	93.03 414	96.82 141	99.83 156	97.79 262	90.58 324	90.09 325	95.04 400	84.75 284	96.72 388	88.19 369	86.23 359	94.23 356

FE-MVSNET3		92.78 307	91.73 318	95.92 280	93.03 414	96.82 141	99.83 156	97.79 262	90.58 324	90.09 325	95.04 400	84.75 284	96.72 388	88.20 368	86.23 359	94.23 356

v148		90.70 353	89.63 358	93.92 363	92.97 416	90.97 355	99.75 188	96.89 402	87.51 383	88.27 380	95.01 403	81.67 322	97.04 366	87.40 380	77.17 433	93.75 407

v1921920		90.46 359	89.12 369	94.50 333	92.96 417	92.46 314	99.49 263	96.98 390	86.10 403	89.61 344	95.30 388	78.55 361	97.03 368	82.17 424	80.89 408	94.01 388

MVStest1		85.03 415	82.76 424	91.83 406	92.95 418	89.16 393	98.57 380	94.82 459	71.68 473	68.54 478	95.11 398	83.17 310	95.66 432	74.69 459	65.32 469	90.65 455

tt0320-xc		82.94 430	80.35 437	90.72 419	92.90 419	83.54 442	96.85 439	94.73 462	63.12 480	79.85 448	93.77 430	49.43 476	95.46 435	80.98 431	71.54 451	93.16 424

Baseline_NR-MVSNet		90.33 363	89.51 363	92.81 394	92.84 420	89.95 382	99.77 176	93.94 471	84.69 422	89.04 359	95.66 367	81.66 323	96.52 397	90.99 322	76.98 434	91.97 444

test_method		80.79 436	79.70 439	84.08 453	92.83 421	67.06 479	99.51 259	95.42 448	54.34 485	81.07 442	93.53 432	44.48 479	92.22 470	78.90 444	77.23 432	92.94 429

pmmvs4		92.10 325	91.07 333	95.18 306	92.82 422	94.96 231	99.48 266	96.83 406	87.45 385	88.66 367	96.56 340	83.78 300	96.83 382	89.29 351	84.77 373	93.75 407

LF4IMVS		89.25 386	88.85 374	90.45 423	92.81 423	81.19 459	98.12 407	94.79 460	91.44 294	86.29 409	97.11 316	65.30 444	98.11 309	88.53 360	85.25 367	92.07 441

tt0320		83.56 429	81.15 432	90.77 417	92.77 424	83.58 441	96.83 440	95.52 447	63.26 479	81.36 439	92.54 440	53.26 470	95.77 430	80.45 433	74.38 444	92.96 428

DTE-MVSNet		89.40 383	88.24 386	92.88 392	92.66 425	89.95 382	99.10 316	98.22 212	87.29 387	85.12 419	96.22 348	76.27 387	95.30 440	83.56 415	75.74 440	93.41 416

EU-MVSNet		90.14 370	90.34 344	89.54 432	92.55 426	81.06 460	98.69 373	98.04 236	91.41 298	86.59 403	96.84 331	80.83 335	93.31 462	86.20 394	81.91 394	94.26 352

APD_test1		81.15 434	80.92 434	81.86 457	92.45 427	59.76 486	96.04 455	93.61 475	73.29 471	77.06 458	96.64 336	44.28 480	96.16 419	72.35 463	82.52 388	89.67 467

sc_t1		85.01 416	82.46 426	92.67 396	92.44 428	83.09 445	97.39 425	95.72 440	65.06 477	85.64 416	96.16 350	49.50 475	97.34 342	84.86 406	75.39 442	97.57 310

our_test_3		90.39 360	89.48 365	93.12 386	92.40 429	89.57 387	99.33 288	96.35 427	87.84 381	85.30 417	94.99 406	84.14 297	96.09 423	80.38 434	84.56 374	93.71 412

ppachtmachnet_test		89.58 381	88.35 384	93.25 384	92.40 429	90.44 371	99.33 288	96.73 413	85.49 412	85.90 414	95.77 361	81.09 331	96.00 427	76.00 457	82.49 389	93.30 420

v7n		89.65 379	88.29 385	93.72 369	92.22 431	90.56 368	99.07 323	97.10 368	85.42 414	86.73 400	94.72 411	80.06 345	97.13 357	81.14 429	78.12 424	93.49 415

dmvs_testset		83.79 425	86.07 401	76.94 461	92.14 432	48.60 496	96.75 441	90.27 486	89.48 347	78.65 452	98.55 262	79.25 351	86.65 484	66.85 473	82.69 386	95.57 329

PS-MVSNAJss		93.64 287	93.31 284	94.61 325	92.11 433	92.19 319	99.12 313	97.38 312	92.51 255	88.45 370	96.99 324	91.20 174	97.29 350	94.36 258	87.71 347	94.36 344

pmmvs5		90.17 369	89.09 370	93.40 378	92.10 434	89.77 385	99.74 192	95.58 445	85.88 406	87.24 397	95.74 362	73.41 410	96.48 401	88.54 359	83.56 383	93.95 394

N_pmnet		80.06 439	80.78 435	77.89 460	91.94 435	45.28 498	98.80 364	56.82 500	78.10 459	80.08 446	93.33 433	77.03 374	95.76 431	68.14 471	82.81 385	92.64 433

test_djsdf		92.83 306	92.29 308	94.47 335	91.90 436	92.46 314	99.55 253	97.27 338	91.17 302	89.96 330	96.07 356	81.10 330	96.89 376	94.67 253	88.91 327	94.05 385

SixPastTwentyTwo		88.73 388	88.01 389	90.88 413	91.85 437	82.24 451	98.22 404	95.18 456	88.97 357	82.26 433	96.89 326	71.75 415	96.67 391	84.00 410	82.98 384	93.72 411

K. test v3		88.05 394	87.24 395	90.47 422	91.82 438	82.23 452	98.96 342	97.42 308	89.05 352	76.93 460	95.60 369	68.49 429	95.42 436	85.87 399	81.01 406	93.75 407

OurMVSNet-221017-0		89.81 376	89.48 365	90.83 416	91.64 439	81.21 458	98.17 406	95.38 450	91.48 292	85.65 415	97.31 311	72.66 411	97.29 350	88.15 371	84.83 372	93.97 393

mvs_tets		91.81 329	91.08 332	94.00 360	91.63 440	90.58 367	98.67 375	97.43 306	92.43 257	87.37 395	97.05 321	71.76 414	97.32 345	94.75 250	88.68 333	94.11 381

Gipuma		66.95 453	65.00 453	72.79 466	91.52 441	67.96 478	66.16 490	95.15 457	47.89 487	58.54 484	67.99 489	29.74 485	87.54 483	50.20 488	77.83 426	62.87 489
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	91.47 442	95.56 202	99.84 149	97.30 328	97.74 30	97.89 173	99.35 152	79.62 348	99.85 129	99.25 74	99.24 141	99.55 159

jajsoiax		91.92 327	91.18 330	94.15 349	91.35 443	90.95 358	99.00 334	97.42 308	92.61 243	87.38 394	97.08 318	72.46 412	97.36 340	94.53 256	88.77 331	94.13 379

MDA-MVSNet-bldmvs		84.09 423	81.52 430	91.81 407	91.32 444	88.00 411	98.67 375	95.92 436	80.22 448	55.60 487	93.32 434	68.29 431	93.60 460	73.76 460	76.61 437	93.82 405

MVP-Stereo		90.93 347	90.45 342	92.37 400	91.25 445	88.76 397	98.05 411	96.17 430	87.27 388	84.04 424	95.30 388	78.46 362	97.27 352	83.78 413	99.70 93	91.09 449
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 411	83.32 419	92.10 402	90.96 446	88.58 403	99.20 308	96.52 422	79.70 450	57.12 486	92.69 439	79.11 354	93.86 456	77.10 452	77.46 430	93.86 402

YYNet1		85.50 412	83.33 418	92.00 403	90.89 447	88.38 407	99.22 307	96.55 421	79.60 451	57.26 485	92.72 438	79.09 356	93.78 458	77.25 451	77.37 431	93.84 403

anonymousdsp		91.79 334	90.92 334	94.41 340	90.76 448	92.93 301	98.93 346	97.17 353	89.08 351	87.46 393	95.30 388	78.43 363	96.92 374	92.38 297	88.73 332	93.39 418

lessismore_v0				90.53 420	90.58 449	80.90 461		95.80 437		77.01 459	95.84 359	66.15 440	96.95 372	83.03 418	75.05 443	93.74 410

EG-PatchMatch MVS		85.35 413	83.81 416	89.99 430	90.39 450	81.89 454	98.21 405	96.09 432	81.78 441	74.73 466	93.72 431	51.56 474	97.12 359	79.16 442	88.61 334	90.96 452

EGC-MVSNET		69.38 446	63.76 456	86.26 450	90.32 451	81.66 457	96.24 451	93.85 472	0.99 497	3.22 498	92.33 450	52.44 471	92.92 466	59.53 484	84.90 371	84.21 478

CMPMVS	61.59 21	84.75 419	85.14 410	83.57 454	90.32 451	62.54 482	96.98 435	97.59 291	74.33 469	69.95 475	96.66 334	64.17 447	98.32 293	87.88 375	88.41 339	89.84 464
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 422	82.92 422	89.21 434	90.03 453	82.60 448	96.89 438	95.62 444	80.59 446	75.77 465	89.17 463	65.04 445	94.79 447	72.12 464	81.02 405	90.23 458

pmmvs6		85.69 408	83.84 415	91.26 412	90.00 454	84.41 436	97.82 416	96.15 431	75.86 463	81.29 440	95.39 383	61.21 458	96.87 379	83.52 416	73.29 446	92.50 437

ttmdpeth		88.23 393	87.06 396	91.75 408	89.91 455	87.35 416	98.92 349	95.73 439	87.92 379	84.02 425	96.31 345	68.23 432	96.84 380	86.33 393	76.12 438	91.06 450

DSMNet-mixed		88.28 392	88.24 386	88.42 442	89.64 456	75.38 472	98.06 410	89.86 487	85.59 411	88.20 382	92.14 452	76.15 389	91.95 471	78.46 446	96.05 252	97.92 295

UnsupCasMVSNet_eth		85.52 410	83.99 412	90.10 428	89.36 457	83.51 443	96.65 442	97.99 240	89.14 350	75.89 464	93.83 428	63.25 451	93.92 454	81.92 426	67.90 465	92.88 430

Anonymous20231206		86.32 406	85.42 408	89.02 436	89.11 458	80.53 464	99.05 328	95.28 451	85.43 413	82.82 431	93.92 427	74.40 403	93.44 461	66.99 472	81.83 395	93.08 426

Anonymous20240521		85.15 414	83.81 416	89.16 435	88.32 459	82.69 447	98.80 364	95.74 438	79.72 449	81.53 438	90.99 455	65.38 443	94.16 452	72.69 462	81.11 402	90.63 456

OpenMVS_ROB	79.82 20	83.77 426	81.68 429	90.03 429	88.30 460	82.82 446	98.46 386	95.22 454	73.92 470	76.00 463	91.29 454	55.00 467	96.94 373	68.40 470	88.51 338	90.34 457

test20.03		84.72 420	83.99 412	86.91 447	88.19 461	80.62 463	98.88 352	95.94 435	88.36 373	78.87 450	94.62 416	68.75 427	89.11 479	66.52 474	75.82 439	91.00 451

blend_shiyan4		90.13 371	88.79 376	94.17 346	87.12 462	91.83 331	99.75 188	97.08 372	79.27 456	88.69 365	92.53 441	92.25 158	96.50 398	89.35 349	73.04 448	94.18 363

KD-MVS_self_test		83.59 427	82.06 427	88.20 443	86.93 463	80.70 462	97.21 428	96.38 425	82.87 435	82.49 432	88.97 464	67.63 434	92.32 469	73.75 461	62.30 476	91.58 447

MIMVSNet1		82.58 431	80.51 436	88.78 438	86.68 464	84.20 437	96.65 442	95.41 449	78.75 457	78.59 453	92.44 442	51.88 473	89.76 478	65.26 477	78.95 418	92.38 440

wanda-best-256-512		87.82 398	85.71 404	94.15 349	86.66 465	91.88 327	99.76 182	97.08 372	79.46 452	88.37 377	92.36 446	78.01 364	96.43 404	88.39 364	61.26 478	94.14 374

FE-blended-shiyan7		87.82 398	85.71 404	94.15 349	86.66 465	91.88 327	99.76 182	97.08 372	79.46 452	88.37 377	92.36 446	78.01 364	96.43 404	88.39 364	61.26 478	94.14 374

usedtu_blend_shiyan5		86.75 405	84.29 411	94.16 347	86.66 465	91.83 331	97.42 422	95.23 453	69.94 476	88.37 377	92.36 446	78.01 364	96.50 398	89.35 349	61.26 478	94.14 374

blended_shiyan8		87.82 398	85.71 404	94.16 347	86.54 468	91.79 333	99.72 203	97.08 372	79.32 454	88.44 371	92.35 449	77.88 368	96.56 395	88.53 360	61.51 477	94.15 370

blended_shiyan6		87.74 401	85.62 407	94.09 354	86.53 469	91.73 339	99.72 203	97.08 372	79.32 454	88.22 381	92.31 451	77.82 369	96.43 404	88.31 366	61.26 478	94.13 379

CL-MVSNet_self_test		84.50 421	83.15 421	88.53 441	86.00 470	81.79 455	98.82 360	97.35 316	85.12 416	83.62 429	90.91 457	76.66 381	91.40 472	69.53 468	60.36 482	92.40 439

UnsupCasMVSNet_bld		79.97 441	77.03 446	88.78 438	85.62 471	81.98 453	93.66 467	97.35 316	75.51 466	70.79 474	83.05 481	48.70 477	94.91 445	78.31 447	60.29 483	89.46 470

mvs5depth		84.87 417	82.90 423	90.77 417	85.59 472	84.84 434	91.10 480	93.29 477	83.14 432	85.07 420	94.33 424	62.17 454	97.32 345	78.83 445	72.59 450	90.14 460

Patchmatch-RL test		86.90 403	85.98 403	89.67 431	84.45 473	75.59 470	89.71 483	92.43 479	86.89 395	77.83 457	90.94 456	94.22 95	93.63 459	87.75 376	69.61 456	99.79 111

pmmvs-eth3d		84.03 424	81.97 428	90.20 426	84.15 474	87.09 418	98.10 409	94.73 462	83.05 433	74.10 470	87.77 471	65.56 442	94.01 453	81.08 430	69.24 458	89.49 469

test_fmvs3		79.99 440	80.17 438	79.45 459	84.02 475	62.83 480	99.05 328	93.49 476	88.29 375	80.06 447	86.65 476	28.09 487	88.00 480	88.63 356	73.27 447	87.54 476

PM-MVS		80.47 437	78.88 441	85.26 451	83.79 476	72.22 474	95.89 458	91.08 484	85.71 410	76.56 462	88.30 467	36.64 483	93.90 455	82.39 422	69.57 457	89.66 468

new-patchmatchnet		81.19 433	79.34 440	86.76 448	82.86 477	80.36 465	97.92 413	95.27 452	82.09 440	72.02 472	86.87 475	62.81 453	90.74 476	71.10 465	63.08 473	89.19 472

FE-MVSNET2		83.57 428	81.36 431	90.20 426	82.83 478	87.59 412	98.28 397	96.04 433	85.33 415	74.13 469	87.45 472	59.16 462	93.26 463	79.12 443	69.91 454	89.77 465

FE-MVSNET		81.05 435	78.81 442	87.79 445	81.98 479	83.70 439	98.23 402	91.78 483	81.27 443	74.29 468	87.44 473	60.92 460	90.67 477	64.92 478	68.43 461	89.01 473

mvsany_test3		82.12 432	81.14 433	85.06 452	81.87 480	70.41 476	97.09 432	92.14 480	91.27 301	77.84 456	88.73 465	39.31 481	95.49 433	90.75 329	71.24 452	89.29 471

WB-MVS		76.28 443	77.28 445	73.29 465	81.18 481	54.68 490	97.87 415	94.19 467	81.30 442	69.43 476	90.70 458	77.02 375	82.06 488	35.71 493	68.11 464	83.13 479

test_f		78.40 442	77.59 444	80.81 458	80.82 482	62.48 483	96.96 436	93.08 478	83.44 430	74.57 467	84.57 480	27.95 488	92.63 467	84.15 408	72.79 449	87.32 477

SSC-MVS		75.42 445	76.40 447	72.49 469	80.68 483	53.62 491	97.42 422	94.06 469	80.42 447	68.75 477	90.14 460	76.54 383	81.66 489	33.25 494	66.34 468	82.19 480

pmmvs3		80.27 438	77.77 443	87.76 446	80.32 484	82.43 450	98.23 402	91.97 481	72.74 472	78.75 451	87.97 470	57.30 466	90.99 475	70.31 466	62.37 475	89.87 463

testf1		68.38 449	66.92 450	72.78 467	78.80 485	50.36 493	90.95 481	87.35 492	55.47 483	58.95 482	88.14 468	20.64 492	87.60 481	57.28 485	64.69 470	80.39 482

APD_test2		68.38 449	66.92 450	72.78 467	78.80 485	50.36 493	90.95 481	87.35 492	55.47 483	58.95 482	88.14 468	20.64 492	87.60 481	57.28 485	64.69 470	80.39 482

ambc				83.23 455	77.17 487	62.61 481	87.38 485	94.55 466		76.72 461	86.65 476	30.16 484	96.36 410	84.85 407	69.86 455	90.73 454

test_vis3_rt		68.82 447	66.69 452	75.21 464	76.24 488	60.41 485	96.44 446	68.71 499	75.13 467	50.54 490	69.52 488	16.42 497	96.32 412	80.27 435	66.92 467	68.89 486

usedtu_dtu_shiyan2		75.87 444	72.37 448	86.39 449	76.18 489	75.49 471	96.53 444	93.82 473	64.74 478	72.53 471	88.48 466	37.67 482	91.12 474	64.13 479	57.22 485	92.56 434

TDRefinement		84.76 418	82.56 425	91.38 411	74.58 490	84.80 435	97.36 426	94.56 465	84.73 421	80.21 445	96.12 355	63.56 449	98.39 283	87.92 374	63.97 472	90.95 453

E-PMN		52.30 457	52.18 459	52.67 475	71.51 491	45.40 497	93.62 468	76.60 497	36.01 491	43.50 492	64.13 491	27.11 489	67.31 494	31.06 495	26.06 490	45.30 493

EMVS		51.44 459	51.22 461	52.11 476	70.71 492	44.97 499	94.04 464	75.66 498	35.34 493	42.40 493	61.56 494	28.93 486	65.87 495	27.64 496	24.73 491	45.49 492

PMMVS2		67.15 452	64.15 455	76.14 463	70.56 493	62.07 484	93.89 465	87.52 491	58.09 482	60.02 481	78.32 483	22.38 491	84.54 486	59.56 483	47.03 488	81.80 481

FPMVS		68.72 448	68.72 449	68.71 471	65.95 494	44.27 500	95.97 457	94.74 461	51.13 486	53.26 488	90.50 459	25.11 490	83.00 487	60.80 482	80.97 407	78.87 484

wuyk23d		20.37 463	20.84 466	18.99 479	65.34 495	27.73 502	50.43 491	7.67 503	9.50 496	8.01 497	6.34 497	6.13 500	26.24 496	23.40 497	10.69 495	2.99 494

LCM-MVSNet		67.77 451	64.73 454	76.87 462	62.95 496	56.25 489	89.37 484	93.74 474	44.53 488	61.99 480	80.74 482	20.42 494	86.53 485	69.37 469	59.50 484	87.84 474

MVE	53.74 22	51.54 458	47.86 462	62.60 473	59.56 497	50.93 492	79.41 488	77.69 496	35.69 492	36.27 494	61.76 493	5.79 501	69.63 492	37.97 492	36.61 489	67.24 487
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 455	52.24 458	67.66 472	49.27 498	56.82 488	83.94 486	82.02 495	70.47 474	33.28 495	64.54 490	17.23 496	69.16 493	45.59 490	23.85 492	77.02 485

tmp_tt		65.23 454	62.94 457	72.13 470	44.90 499	50.03 495	81.05 487	89.42 490	38.45 489	48.51 491	99.90 22	54.09 469	78.70 491	91.84 310	18.26 493	87.64 475

PMVS	49.05 23	53.75 456	51.34 460	60.97 474	40.80 500	34.68 501	74.82 489	89.62 489	37.55 490	28.67 496	72.12 485	7.09 499	81.63 490	43.17 491	68.21 463	66.59 488
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 461	39.14 464	33.31 477	19.94 501	24.83 503	98.36 394	9.75 502	15.53 495	51.31 489	87.14 474	19.62 495	17.74 497	47.10 489	3.47 496	57.36 490

testmvs		40.60 460	44.45 463	29.05 478	19.49 502	14.11 504	99.68 221	18.47 501	20.74 494	64.59 479	98.48 269	10.95 498	17.09 498	56.66 487	11.01 494	55.94 491

mmdepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

monomultidepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

test_blank		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.02 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

eth-test2					0.00 503

eth-test					0.00 503

uanet_test		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

DCPMVS		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

cdsmvs_eth3d_5k		23.43 462	31.24 465	0.00 480	0.00 503	0.00 505	0.00 492	98.09 230	0.00 498	0.00 499	99.67 113	83.37 305	0.00 499	0.00 498	0.00 497	0.00 495

pcd_1.5k_mvsjas		7.60 465	10.13 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	91.20 174	0.00 499	0.00 498	0.00 497	0.00 495

sosnet-low-res		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

sosnet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uncertanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

Regformer		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

ab-mvs-re		8.28 464	11.04 467	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	99.40 146	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 499	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

TestfortrainingZip							99.97 39

WAC-MVS						90.97 355								86.10 397

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							95.78 459				59.23 495	93.20 128	97.74 329	91.06 320

test_post											63.35 492	94.43 82	98.13 308

patchmatchnet-post											91.70 453	95.12 59	97.95 320

MTMP							99.87 130	96.49 423

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							99.46 271		94.21 164	99.85 18			99.95 84	96.96 196

新几何2							99.40 275

无先验							99.49 263	98.71 78	93.46 197				100.00 1	94.36 258		99.99 24

原ACMM2							99.90 114

testdata2													99.99 39	90.54 333

segment_acmp												96.68 31

testdata1							99.28 301		96.35 90

plane_prior5								97.87 254					98.37 289	97.79 167	89.55 321	94.52 333

plane_prior4											98.59 255

plane_prior3						91.64 343			96.63 73	93.01 293

plane_prior2							99.84 149		96.38 84

plane_prior						91.74 336	99.86 141		96.76 68						89.59 320

n2								0.00 504

nn								0.00 504

door-mid								89.69 488

test11								98.44 147

door								90.31 485

HQP5-MVS						91.85 329

BP-MVS														97.92 158

HQP4-MVS										93.37 288			98.39 283			94.53 331

HQP3-MVS								97.89 252							89.60 318

HQP2-MVS												80.65 338

MDTV_nov1_ep13_2view						96.26 169	96.11 453		91.89 278	98.06 166		94.40 84		94.30 261		99.67 129

ACMMP++_ref															87.04 354

ACMMP++															88.23 341

Test By Simon												92.82 139

High-resolution multi-view benchmark