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		97.23 124	96.80 133	98.51 126	99.99 1	95.60 192	99.09 283	98.84 62	93.32 192	96.74 201	99.72 88	86.04 252	100.00 1	98.01 144	99.43 122	99.94 80

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 18	98.69 75	98.20 9	99.93 1	99.98 2	96.82 24	100.00 1	99.75 36	100.00 1	99.99 23

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 36	98.64 84	98.47 3	99.13 100	99.92 13	96.38 34	100.00 1	99.74 38	100.00 1	100.00 1

mPP-MVS		98.39 52	98.20 50	98.97 86	99.97 3	96.92 131	99.95 65	98.38 177	95.04 116	98.61 130	99.80 54	93.39 114	100.00 1	98.64 108	100.00 1	99.98 51

CPTT-MVS		97.64 104	97.32 108	98.58 115	99.97 3	95.77 181	99.96 46	98.35 183	89.90 307	98.36 145	99.79 58	91.18 173	99.99 36	98.37 124	99.99 21	99.99 23

DP-MVS Recon		98.41 49	98.02 64	99.56 25	99.97 3	98.70 48	99.92 92	98.44 141	92.06 246	98.40 144	99.84 44	95.68 44	100.00 1	98.19 133	99.71 88	99.97 61

PAPR		98.52 39	98.16 54	99.58 24	99.97 3	98.77 42	99.95 65	98.43 149	95.35 110	98.03 159	99.75 75	94.03 99	99.98 47	98.11 138	99.83 77	99.99 23

HFP-MVS		98.56 36	98.37 40	99.14 66	99.96 8	97.43 107	99.95 65	98.61 92	94.77 126	99.31 89	99.85 33	94.22 92	100.00 1	98.70 103	99.98 32	99.98 51

region2R		98.54 37	98.37 40	99.05 76	99.96 8	97.18 117	99.96 46	98.55 112	94.87 123	99.45 76	99.85 33	94.07 98	100.00 1	98.67 105	100.00 1	99.98 51

ACMMPR		98.50 40	98.32 44	99.05 76	99.96 8	97.18 117	99.95 65	98.60 94	94.77 126	99.31 89	99.84 44	93.73 108	100.00 1	98.70 103	99.98 32	99.98 51

NCCC		99.37 2	99.25 2	99.71 15	99.96 8	99.15 22	99.97 36	98.62 91	98.02 19	99.90 4	99.95 3	97.33 17	100.00 1	99.54 52	100.00 1	100.00 1

CP-MVS		98.45 44	98.32 44	98.87 91	99.96 8	96.62 144	99.97 36	98.39 173	94.43 142	98.90 112	99.87 27	94.30 89	100.00 1	99.04 78	99.99 21	99.99 23

test_one_0601					99.94 13	99.30 12		98.41 166	96.63 70	99.75 37	99.93 11	97.49 10

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 65	98.43 149					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 29	98.55 28	99.15 64	99.94 13	97.50 103	99.94 82	98.42 161	96.22 87	99.41 81	99.78 62	94.34 86	99.96 70	98.92 88	99.95 50	99.99 23

X-MVStestdata		93.83 246	92.06 279	99.15 64	99.94 13	97.50 103	99.94 82	98.42 161	96.22 87	99.41 81	41.37 451	94.34 86	99.96 70	98.92 88	99.95 50	99.99 23

test_prior				99.43 35	99.94 13	98.49 60		98.65 81					99.80 135			99.99 23

MSLP-MVS++		99.13 8	99.01 11	99.49 32	99.94 13	98.46 61	99.98 18	98.86 56	97.10 50	99.80 23	99.94 4	95.92 40	100.00 1	99.51 53	100.00 1	100.00 1

APDe-MVS		99.06 11	98.91 14	99.51 29	99.94 13	98.76 45	99.91 100	98.39 173	97.20 48	99.46 75	99.85 33	95.53 48	99.79 137	99.86 22	100.00 1	99.99 23
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVS		98.23 66	97.97 68	99.03 78	99.94 13	97.17 120	99.95 65	98.39 173	94.70 130	98.26 151	99.81 53	91.84 164	100.00 1	98.85 94	99.97 42	99.93 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 32	98.36 42	99.49 32	99.94 13	98.73 46	99.87 122	98.33 188	93.97 167	99.76 36	99.87 27	94.99 64	99.75 146	98.55 112	100.00 1	99.98 51

PAPM_NR		98.12 70	97.93 74	98.70 102	99.94 13	96.13 170	99.82 151	98.43 149	94.56 134	97.52 176	99.70 94	94.40 81	99.98 47	97.00 178	99.98 32	99.99 23

MG-MVS		98.91 19	98.65 24	99.68 16	99.94 13	99.07 24	99.64 205	99.44 19	97.33 41	99.00 108	99.72 88	94.03 99	99.98 47	98.73 102	100.00 1	100.00 1

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 24	99.30 12	99.96 46	98.43 149	97.27 44	99.80 23	99.94 4	96.71 27	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 24	99.31 10		98.41 166	97.71 28	99.84 18				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 12		98.43 149	97.26 46	99.80 23	99.88 24	96.71 27	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 12	99.93 24	99.29 15	99.95 65	98.32 190	97.28 42	99.83 19	99.91 14	97.22 19	100.00 1	99.99 5	100.00 1	99.89 90
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 24	99.29 15	99.96 46	98.42 161	97.28 42	99.86 12	99.94 4	97.22 19

MSP-MVS		99.09 9	99.12 5	98.98 85	99.93 24	97.24 114	99.95 65	98.42 161	97.50 35	99.52 71	99.88 24	97.43 16	99.71 152	99.50 55	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 24	98.77 42		98.43 149		99.63 54			99.85 122

FOURS1					99.92 31	97.66 97	99.95 65	98.36 181	95.58 104	99.52 71

ZD-MVS					99.92 31	98.57 56		98.52 121	92.34 238	99.31 89	99.83 46	95.06 59	99.80 135	99.70 43	99.97 42

GST-MVS		98.27 59	97.97 68	99.17 59	99.92 31	97.57 99	99.93 89	98.39 173	94.04 165	98.80 117	99.74 82	92.98 130	100.00 1	98.16 135	99.76 85	99.93 81

TEST9					99.92 31	98.92 29	99.96 46	98.43 149	93.90 173	99.71 44	99.86 29	95.88 41	99.85 122

train_agg		98.88 20	98.65 24	99.59 23	99.92 31	98.92 29	99.96 46	98.43 149	94.35 147	99.71 44	99.86 29	95.94 38	99.85 122	99.69 44	99.98 32	99.99 23

test_8					99.92 31	98.88 32	99.96 46	98.43 149	94.35 147	99.69 46	99.85 33	95.94 38	99.85 122

PGM-MVS		98.34 54	98.13 56	98.99 83	99.92 31	97.00 127	99.75 172	99.50 17	93.90 173	99.37 86	99.76 67	93.24 123	100.00 1	97.75 163	99.96 46	99.98 51

ACMMP		97.74 97	97.44 101	98.66 106	99.92 31	96.13 170	99.18 277	99.45 18	94.84 124	96.41 211	99.71 91	91.40 167	99.99 36	97.99 146	98.03 182	99.87 93
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 39	99.31 10	99.95 65	98.43 149	96.48 75	99.80 23	99.93 11	97.44 14	100.00 1	99.92 13	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 39	99.80 2		98.41 166					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 166					100.00 1	99.96 9	100.00 1	100.00 1

HPM-MVS++		99.07 10	98.88 16	99.63 17	99.90 42	99.02 25	99.95 65	98.56 106	97.56 34	99.44 77	99.85 33	95.38 52	100.00 1	99.31 65	99.99 21	99.87 93

APD-MVS		98.62 33	98.35 43	99.41 38	99.90 42	98.51 59	99.87 122	98.36 181	94.08 160	99.74 40	99.73 85	94.08 97	99.74 148	99.42 61	99.99 21	99.99 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	96.59 1	98.81 23	98.54 29	99.62 20	99.90 42	98.85 35	99.24 272	98.47 133	98.14 14	99.08 103	99.91 14	93.09 127	100.00 1	99.04 78	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 45	99.80 2	99.96 46				99.80 54	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 11	99.89 45	99.24 19	99.87 122	98.44 141	97.48 36	99.64 53	99.94 4	96.68 29	99.99 36	99.99 5	100.00 1	99.99 23
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.89 45	99.25 18				99.49 74

CSCG		97.10 130	97.04 120	97.27 211	99.89 45	91.92 297	99.90 106	99.07 37	88.67 331	95.26 236	99.82 49	93.17 126	99.98 47	98.15 136	99.47 117	99.90 89

ZNCC-MVS		98.31 56	98.03 63	99.17 59	99.88 49	97.59 98	99.94 82	98.44 141	94.31 150	98.50 137	99.82 49	93.06 128	99.99 36	98.30 128	99.99 21	99.93 81

SR-MVS		98.46 43	98.30 47	98.93 89	99.88 49	97.04 126	99.84 141	98.35 183	94.92 120	99.32 88	99.80 54	93.35 116	99.78 139	99.30 66	99.95 50	99.96 69

9.14			98.38 38		99.87 51		99.91 100	98.33 188	93.22 195	99.78 34	99.89 22	94.57 77	99.85 122	99.84 24	99.97 42

SMA-MVS		98.76 26	98.48 32	99.62 20	99.87 51	98.87 33	99.86 133	98.38 177	93.19 196	99.77 35	99.94 4	95.54 46	100.00 1	99.74 38	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 79	97.85 79	98.04 158	99.86 53	95.39 201	99.61 210	97.78 256	96.52 73	98.61 130	99.31 148	92.73 138	99.67 160	96.77 187	99.48 114	99.06 221

lecture		98.67 30	98.46 33	99.28 47	99.86 53	97.88 86	99.97 36	99.25 30	96.07 91	99.79 32	99.70 94	92.53 146	99.98 47	99.51 53	99.48 114	99.97 61

PHI-MVS		98.41 49	98.21 49	99.03 78	99.86 53	97.10 124	99.98 18	98.80 68	90.78 289	99.62 57	99.78 62	95.30 53	100.00 1	99.80 27	99.93 61	99.99 23

MTAPA		98.29 58	97.96 71	99.30 46	99.85 56	97.93 84	99.39 250	98.28 197	95.76 98	97.18 189	99.88 24	92.74 137	100.00 1	98.67 105	99.88 73	99.99 23

LS3D		95.84 188	95.11 199	98.02 159	99.85 56	95.10 215	98.74 330	98.50 130	87.22 353	93.66 254	99.86 29	87.45 230	99.95 79	90.94 291	99.81 83	99.02 225

HPM-MVS		97.96 74	97.72 84	98.68 103	99.84 58	96.39 156	99.90 106	98.17 212	92.61 224	98.62 129	99.57 123	91.87 163	99.67 160	98.87 93	99.99 21	99.99 23
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 59	98.11 58	98.75 99	99.83 59	96.59 148	99.40 246	98.51 124	95.29 112	98.51 136	99.76 67	93.60 112	99.71 152	98.53 115	99.52 107	99.95 76

save fliter					99.82 60	98.79 40	99.96 46	98.40 170	97.66 30

PLC	95.54 3	97.93 77	97.89 77	98.05 157	99.82 60	94.77 225	99.92 92	98.46 135	93.93 170	97.20 187	99.27 153	95.44 51	99.97 58	97.41 168	99.51 110	99.41 180
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 64	98.08 60	98.78 96	99.81 62	96.60 146	99.82 151	98.30 195	93.95 169	99.37 86	99.77 65	92.84 134	99.76 145	98.95 84	99.92 64	99.97 61

EI-MVSNet-UG-set		98.14 69	97.99 66	98.60 111	99.80 63	96.27 159	99.36 256	98.50 130	95.21 114	98.30 148	99.75 75	93.29 120	99.73 151	98.37 124	99.30 131	99.81 102

SR-MVS-dyc-post		98.31 56	98.17 53	98.71 101	99.79 64	96.37 157	99.76 168	98.31 192	94.43 142	99.40 83	99.75 75	93.28 121	99.78 139	98.90 91	99.92 64	99.97 61

RE-MVS-def			98.13 56		99.79 64	96.37 157	99.76 168	98.31 192	94.43 142	99.40 83	99.75 75	92.95 131		98.90 91	99.92 64	99.97 61

HPM-MVS_fast		97.80 91	97.50 97	98.68 103	99.79 64	96.42 152	99.88 119	98.16 217	91.75 256	98.94 110	99.54 126	91.82 165	99.65 164	97.62 166	99.99 21	99.99 23

SF-MVS		98.67 30	98.40 36	99.50 30	99.77 67	98.67 49	99.90 106	98.21 207	93.53 184	99.81 21	99.89 22	94.70 73	99.86 121	99.84 24	99.93 61	99.96 69

MVS_0304		99.06 11	98.84 17	99.72 13	99.76 68	99.21 21	99.99 5	99.34 25	98.70 2	99.44 77	99.75 75	93.24 123	99.99 36	99.94 11	99.41 124	99.95 76

旧先验1					99.76 68	97.52 101		98.64 84			99.85 33	95.63 45			99.94 55	99.99 23

OMC-MVS		97.28 120	97.23 112	97.41 202	99.76 68	93.36 265	99.65 201	97.95 237	96.03 92	97.41 181	99.70 94	89.61 199	99.51 170	96.73 189	98.25 172	99.38 182

新几何1				99.42 37	99.75 71	98.27 65		98.63 90	92.69 219	99.55 66	99.82 49	94.40 81	100.00 1	91.21 283	99.94 55	99.99 23

MP-MVS-pluss		98.07 73	97.64 90	99.38 43	99.74 72	98.41 63	99.74 175	98.18 211	93.35 190	96.45 208	99.85 33	92.64 141	99.97 58	98.91 90	99.89 70	99.77 109
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 17	98.77 19	99.41 38	99.74 72	98.67 49	99.77 163	98.38 177	96.73 66	99.88 9	99.74 82	94.89 66	99.59 166	99.80 27	99.98 32	99.97 61
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 35	99.74 72	98.56 57		98.40 170		99.65 50		94.76 69	99.75 146		99.98 32	99.99 23

原ACMM1				98.96 87	99.73 75	96.99 128		98.51 124	94.06 163	99.62 57	99.85 33	94.97 65	99.96 70	95.11 210	99.95 50	99.92 86

TSAR-MVS + GP.		98.60 34	98.51 31	98.86 92	99.73 75	96.63 143	99.97 36	97.92 242	98.07 16	98.76 122	99.55 124	95.00 63	99.94 87	99.91 16	97.68 188	99.99 23

CANet		98.27 59	97.82 81	99.63 17	99.72 77	99.10 23	99.98 18	98.51 124	97.00 56	98.52 134	99.71 91	87.80 222	99.95 79	99.75 36	99.38 126	99.83 98

reproduce_model		98.75 27	98.66 23	99.03 78	99.71 78	97.10 124	99.73 182	98.23 205	97.02 55	99.18 98	99.90 18	94.54 78	99.99 36	99.77 32	99.90 69	99.99 23

F-COLMAP		96.93 142	96.95 123	96.87 222	99.71 78	91.74 302	99.85 136	97.95 237	93.11 202	95.72 229	99.16 164	92.35 152	99.94 87	95.32 208	99.35 129	98.92 229

reproduce-ours		98.78 24	98.67 21	99.09 73	99.70 80	97.30 111	99.74 175	98.25 201	97.10 50	99.10 101	99.90 18	94.59 74	99.99 36	99.77 32	99.91 67	99.99 23

our_new_method		98.78 24	98.67 21	99.09 73	99.70 80	97.30 111	99.74 175	98.25 201	97.10 50	99.10 101	99.90 18	94.59 74	99.99 36	99.77 32	99.91 67	99.99 23

SD-MVS		98.92 18	98.70 20	99.56 25	99.70 80	98.73 46	99.94 82	98.34 187	96.38 81	99.81 21	99.76 67	94.59 74	99.98 47	99.84 24	99.96 46	99.97 61
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 65	99.12 5	95.59 258	99.67 83	86.91 379	99.95 65	98.89 52	97.60 31	99.90 4	99.76 67	96.54 32	99.98 47	99.94 11	99.82 81	99.88 91

ACMMP_NAP		98.49 41	98.14 55	99.54 27	99.66 84	98.62 55	99.85 136	98.37 180	94.68 131	99.53 69	99.83 46	92.87 133	100.00 1	98.66 107	99.84 76	99.99 23

DeepPCF-MVS	95.94 2	97.71 101	98.98 12	93.92 322	99.63 85	81.76 413	99.96 46	98.56 106	99.47 1	99.19 97	99.99 1	94.16 96	100.00 1	99.92 13	99.93 61	100.00 1

EPNet		98.49 41	98.40 36	98.77 98	99.62 86	96.80 137	99.90 106	99.51 16	97.60 31	99.20 95	99.36 144	93.71 109	99.91 103	97.99 146	98.71 157	99.61 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		98.83 21	98.53 30	99.76 10	99.59 87	99.33 8	99.99 5	99.76 6	98.39 4	99.39 85	99.80 54	90.49 188	99.96 70	99.89 18	99.43 122	99.98 51

PVSNet_BlendedMVS		96.05 181	95.82 177	96.72 227	99.59 87	96.99 128	99.95 65	99.10 34	94.06 163	98.27 149	95.80 326	89.00 210	99.95 79	99.12 72	87.53 318	93.24 380

PVSNet_Blended		97.94 76	97.64 90	98.83 93	99.59 87	96.99 128	100.00 1	99.10 34	95.38 109	98.27 149	99.08 167	89.00 210	99.95 79	99.12 72	99.25 133	99.57 151

PatchMatch-RL		96.04 182	95.40 187	97.95 161	99.59 87	95.22 210	99.52 228	99.07 37	93.96 168	96.49 207	98.35 242	82.28 283	99.82 134	90.15 307	99.22 136	98.81 236

dcpmvs_2		97.42 115	98.09 59	95.42 263	99.58 91	87.24 375	99.23 273	96.95 353	94.28 153	98.93 111	99.73 85	94.39 84	99.16 198	99.89 18	99.82 81	99.86 95

test222					99.55 92	97.41 109	99.34 258	98.55 112	91.86 251	99.27 93	99.83 46	93.84 106			99.95 50	99.99 23

CNLPA		97.76 95	97.38 104	98.92 90	99.53 93	96.84 133	99.87 122	98.14 221	93.78 177	96.55 206	99.69 98	92.28 154	99.98 47	97.13 174	99.44 121	99.93 81

API-MVS		97.86 82	97.66 88	98.47 128	99.52 94	95.41 199	99.47 238	98.87 55	91.68 257	98.84 114	99.85 33	92.34 153	99.99 36	98.44 120	99.96 46	100.00 1

PVSNet	91.05 13	97.13 129	96.69 139	98.45 130	99.52 94	95.81 179	99.95 65	99.65 12	94.73 128	99.04 106	99.21 160	84.48 268	99.95 79	94.92 216	98.74 156	99.58 149

114514_t		97.41 116	96.83 130	99.14 66	99.51 96	97.83 87	99.89 116	98.27 199	88.48 335	99.06 105	99.66 108	90.30 191	99.64 165	96.32 193	99.97 42	99.96 69

cl22		93.77 250	93.25 254	95.33 267	99.49 97	94.43 230	99.61 210	98.09 224	90.38 295	89.16 322	95.61 334	90.56 186	97.34 312	91.93 274	84.45 339	94.21 325

testdata				98.42 134	99.47 98	95.33 204		98.56 106	93.78 177	99.79 32	99.85 33	93.64 111	99.94 87	94.97 214	99.94 55	100.00 1

MAR-MVS		97.43 111	97.19 114	98.15 150	99.47 98	94.79 224	99.05 294	98.76 69	92.65 222	98.66 127	99.82 49	88.52 216	99.98 47	98.12 137	99.63 94	99.67 123
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		94.54 225	93.42 245	97.91 167	99.46 100	94.04 243	98.93 310	97.48 293	81.15 406	90.04 293	99.55 124	87.02 238	99.95 79	88.97 319	98.11 178	99.73 113

MVS_111021_LR		98.42 48	98.38 38	98.53 123	99.39 101	95.79 180	99.87 122	99.86 2	96.70 67	98.78 118	99.79 58	92.03 160	99.90 105	99.17 71	99.86 75	99.88 91

CHOSEN 280x420		99.01 14	99.03 10	98.95 88	99.38 102	98.87 33	98.46 348	99.42 21	97.03 54	99.02 107	99.09 166	99.35 2	98.21 274	99.73 40	99.78 84	99.77 109

MVS_111021_HR		98.72 28	98.62 26	99.01 82	99.36 103	97.18 117	99.93 89	99.90 1	96.81 64	98.67 126	99.77 65	93.92 101	99.89 110	99.27 67	99.94 55	99.96 69

DPM-MVS		98.83 21	98.46 33	99.97 1	99.33 104	99.92 1	99.96 46	98.44 141	97.96 20	99.55 66	99.94 4	97.18 21	100.00 1	93.81 245	99.94 55	99.98 51

TAPA-MVS	92.12 8	94.42 233	93.60 237	96.90 221	99.33 104	91.78 301	99.78 160	98.00 231	89.89 308	94.52 242	99.47 130	91.97 161	99.18 195	69.90 424	99.52 107	99.73 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 202	95.07 201	96.32 240	99.32 106	96.60 146	99.76 168	98.85 59	96.65 69	87.83 344	96.05 323	99.52 1	98.11 279	96.58 190	81.07 368	94.25 320

fmvsm_s_conf0.5_n_9		98.15 68	98.02 64	98.55 117	99.28 107	95.84 178	99.99 5	98.57 100	98.17 11	99.93 1	99.74 82	87.04 237	99.97 58	99.86 22	99.59 102	99.83 98

SPE-MVS-test		97.88 80	97.94 73	97.70 183	99.28 107	95.20 211	99.98 18	97.15 330	95.53 106	99.62 57	99.79 58	92.08 159	98.38 257	98.75 101	99.28 132	99.52 163

test_fmvsm_n_1920		98.44 45	98.61 27	97.92 165	99.27 109	95.18 212	100.00 1	98.90 50	98.05 17	99.80 23	99.73 85	92.64 141	99.99 36	99.58 51	99.51 110	98.59 246

fmvsm_l_conf0.5_n_a		99.00 15	98.91 14	99.28 47	99.21 110	97.91 85	99.98 18	98.85 59	98.25 5	99.92 3	99.75 75	94.72 71	99.97 58	99.87 20	99.64 92	99.95 76

fmvsm_s_conf0.5_n_8		98.38 53	98.05 62	99.35 44	99.20 111	98.12 71	99.98 18	98.81 64	98.22 7	99.80 23	99.71 91	87.37 232	99.97 58	99.91 16	99.48 114	99.97 61

test_yl		97.83 86	97.37 105	99.21 53	99.18 112	97.98 80	99.64 205	99.27 27	91.43 266	97.88 166	98.99 176	95.84 42	99.84 130	98.82 95	95.32 247	99.79 105

DCV-MVSNet		97.83 86	97.37 105	99.21 53	99.18 112	97.98 80	99.64 205	99.27 27	91.43 266	97.88 166	98.99 176	95.84 42	99.84 130	98.82 95	95.32 247	99.79 105

fmvsm_l_conf0.5_n		98.94 16	98.84 17	99.25 49	99.17 114	97.81 89	99.98 18	98.86 56	98.25 5	99.90 4	99.76 67	94.21 94	99.97 58	99.87 20	99.52 107	99.98 51

DeepC-MVS	94.51 4	96.92 143	96.40 151	98.45 130	99.16 115	95.90 176	99.66 200	98.06 227	96.37 84	94.37 245	99.49 129	83.29 278	99.90 105	97.63 165	99.61 99	99.55 153
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 37	98.22 48	99.50 30	99.15 116	98.65 53	100.00 1	98.58 98	97.70 29	98.21 154	99.24 158	92.58 144	99.94 87	98.63 110	99.94 55	99.92 86
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 49	98.08 60	99.39 40	99.12 117	98.29 64	99.98 18	98.64 84	98.14 14	99.86 12	99.76 67	87.99 221	99.97 58	99.72 41	99.54 105	99.91 88

CS-MVS		97.79 93	97.91 75	97.43 200	99.10 118	94.42 231	99.99 5	97.10 335	95.07 115	99.68 47	99.75 75	92.95 131	98.34 261	98.38 122	99.14 138	99.54 157

Anonymous202405211		93.10 268	91.99 280	96.40 236	99.10 118	89.65 347	98.88 316	97.93 239	83.71 391	94.00 251	98.75 206	68.79 385	99.88 116	95.08 211	91.71 280	99.68 121

fmvsm_s_conf0.5_n		97.80 91	97.85 79	97.67 184	99.06 120	94.41 232	99.98 18	98.97 43	97.34 39	99.63 54	99.69 98	87.27 233	99.97 58	99.62 49	99.06 143	98.62 245

HyFIR lowres test		96.66 157	96.43 150	97.36 207	99.05 121	93.91 248	99.70 194	99.80 3	90.54 293	96.26 214	98.08 254	92.15 157	98.23 273	96.84 186	95.46 242	99.93 81

LFMVS		94.75 219	93.56 240	98.30 140	99.03 122	95.70 186	98.74 330	97.98 234	87.81 346	98.47 138	99.39 141	67.43 394	99.53 167	98.01 144	95.20 250	99.67 123

fmvsm_s_conf0.5_n_4		97.75 96	97.86 78	97.42 201	99.01 123	94.69 226	99.97 36	98.76 69	97.91 22	99.87 10	99.76 67	86.70 243	99.93 96	99.67 46	99.12 141	97.64 271

fmvsm_s_conf0.5_n_2		97.59 106	97.28 109	98.53 123	99.01 123	98.15 66	99.98 18	98.59 96	98.17 11	99.75 37	99.63 114	81.83 288	99.94 87	99.78 30	98.79 154	97.51 279

AllTest		92.48 282	91.64 285	95.00 276	99.01 123	88.43 363	98.94 308	96.82 367	86.50 362	88.71 327	98.47 237	74.73 360	99.88 116	85.39 358	96.18 222	96.71 285

TestCases				95.00 276	99.01 123	88.43 363		96.82 367	86.50 362	88.71 327	98.47 237	74.73 360	99.88 116	85.39 358	96.18 222	96.71 285

COLMAP_ROB	90.47 14	92.18 289	91.49 291	94.25 310	99.00 127	88.04 369	98.42 354	96.70 374	82.30 402	88.43 336	99.01 173	76.97 336	99.85 122	86.11 354	96.50 214	94.86 296
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 75	97.66 88	98.81 94	98.99 128	98.07 74	99.98 18	98.81 64	98.18 10	99.89 7	99.70 94	84.15 271	99.97 58	99.76 35	99.50 112	98.39 250

test_fmvs1		95.35 203	95.68 182	94.36 306	98.99 128	84.98 390	99.96 46	96.65 376	97.60 31	99.73 42	98.96 182	71.58 375	99.93 96	98.31 127	99.37 127	98.17 255

HY-MVS	92.50 7	97.79 93	97.17 116	99.63 17	98.98 130	99.32 9	97.49 379	99.52 14	95.69 101	98.32 147	97.41 274	93.32 118	99.77 142	98.08 141	95.75 237	99.81 102

VNet		97.21 125	96.57 144	99.13 70	98.97 131	97.82 88	99.03 297	99.21 32	94.31 150	99.18 98	98.88 194	86.26 250	99.89 110	98.93 86	94.32 260	99.69 120

thres200		96.96 139	96.21 157	99.22 52	98.97 131	98.84 36	99.85 136	99.71 7	93.17 197	96.26 214	98.88 194	89.87 196	99.51 170	94.26 235	94.91 252	99.31 195

tfpn200view9		96.79 147	95.99 163	99.19 55	98.94 133	98.82 37	99.78 160	99.71 7	92.86 208	96.02 221	98.87 197	89.33 203	99.50 172	93.84 242	94.57 256	99.27 202

thres400		96.78 149	95.99 163	99.16 62	98.94 133	98.82 37	99.78 160	99.71 7	92.86 208	96.02 221	98.87 197	89.33 203	99.50 172	93.84 242	94.57 256	99.16 210

sasdasda		97.09 132	96.32 152	99.39 40	98.93 135	98.95 27	99.72 186	97.35 306	94.45 138	97.88 166	99.42 134	86.71 241	99.52 168	98.48 117	93.97 266	99.72 115

Anonymous20231211		89.86 339	88.44 347	94.13 313	98.93 135	90.68 325	98.54 345	98.26 200	76.28 418	86.73 358	95.54 338	70.60 381	97.56 305	90.82 294	80.27 377	94.15 333

canonicalmvs		97.09 132	96.32 152	99.39 40	98.93 135	98.95 27	99.72 186	97.35 306	94.45 138	97.88 166	99.42 134	86.71 241	99.52 168	98.48 117	93.97 266	99.72 115

SDMVSNet		94.80 215	93.96 229	97.33 209	98.92 138	95.42 198	99.59 214	98.99 40	92.41 235	92.55 269	97.85 265	75.81 350	98.93 213	97.90 152	91.62 281	97.64 271

sd_testset		93.55 257	92.83 260	95.74 256	98.92 138	90.89 321	98.24 361	98.85 59	92.41 235	92.55 269	97.85 265	71.07 380	98.68 233	93.93 239	91.62 281	97.64 271

EPNet_dtu		95.71 192	95.39 188	96.66 229	98.92 138	93.41 262	99.57 219	98.90 50	96.19 89	97.52 176	98.56 227	92.65 140	97.36 310	77.89 405	98.33 167	99.20 208
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 71	97.60 92	99.60 22	98.92 138	99.28 17	99.89 116	99.52 14	95.58 104	98.24 153	99.39 141	93.33 117	99.74 148	97.98 148	95.58 241	99.78 108

CHOSEN 1792x2688		96.81 146	96.53 145	97.64 186	98.91 142	93.07 267	99.65 201	99.80 3	95.64 102	95.39 233	98.86 199	84.35 270	99.90 105	96.98 180	99.16 137	99.95 76

thres100view900		96.74 152	95.92 173	99.18 56	98.90 143	98.77 42	99.74 175	99.71 7	92.59 226	95.84 225	98.86 199	89.25 205	99.50 172	93.84 242	94.57 256	99.27 202

thres600view7		96.69 155	95.87 176	99.14 66	98.90 143	98.78 41	99.74 175	99.71 7	92.59 226	95.84 225	98.86 199	89.25 205	99.50 172	93.44 255	94.50 259	99.16 210

MSDG		94.37 235	93.36 251	97.40 203	98.88 145	93.95 247	99.37 254	97.38 302	85.75 373	90.80 286	99.17 163	84.11 273	99.88 116	86.35 350	98.43 165	98.36 252

MGCFI-Net		97.00 137	96.22 156	99.34 45	98.86 146	98.80 39	99.67 199	97.30 313	94.31 150	97.77 172	99.41 138	86.36 248	99.50 172	98.38 122	93.90 268	99.72 115

h-mvs33		94.92 212	94.36 217	96.59 231	98.85 147	91.29 313	98.93 310	98.94 44	95.90 94	98.77 119	98.42 240	90.89 181	99.77 142	97.80 156	70.76 416	98.72 242

Anonymous20240529		92.10 290	90.65 302	96.47 232	98.82 148	90.61 327	98.72 332	98.67 80	75.54 422	93.90 253	98.58 225	66.23 398	99.90 105	94.70 225	90.67 284	98.90 232

PVSNet_Blended_VisFu		97.27 121	96.81 132	98.66 106	98.81 149	96.67 142	99.92 92	98.64 84	94.51 136	96.38 212	98.49 233	89.05 209	99.88 116	97.10 176	98.34 166	99.43 178

PS-MVSNAJ		98.44 45	98.20 50	99.16 62	98.80 150	98.92 29	99.54 226	98.17 212	97.34 39	99.85 15	99.85 33	91.20 170	99.89 110	99.41 62	99.67 90	98.69 243

CANet_DTU		96.76 150	96.15 159	98.60 111	98.78 151	97.53 100	99.84 141	97.63 271	97.25 47	99.20 95	99.64 111	81.36 294	99.98 47	92.77 266	98.89 148	98.28 254

mvsany_test1		97.82 89	97.90 76	97.55 192	98.77 152	93.04 270	99.80 157	97.93 239	96.95 58	99.61 64	99.68 105	90.92 178	99.83 132	99.18 70	98.29 171	99.80 104

alignmvs		97.81 90	97.33 107	99.25 49	98.77 152	98.66 51	99.99 5	98.44 141	94.40 146	98.41 142	99.47 130	93.65 110	99.42 182	98.57 111	94.26 262	99.67 123

SymmetryMVS		97.64 104	97.46 98	98.17 146	98.74 154	95.39 201	99.61 210	99.26 29	96.52 73	98.61 130	99.31 148	92.73 138	99.67 160	96.77 187	95.63 239	99.45 174

SteuartSystems-ACMMP		99.02 13	98.97 13	99.18 56	98.72 155	97.71 92	99.98 18	98.44 141	96.85 59	99.80 23	99.91 14	97.57 8	99.85 122	99.44 60	99.99 21	99.99 23
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 66	97.97 68	99.02 81	98.69 156	98.66 51	99.52 228	98.08 226	97.05 53	99.86 12	99.86 29	90.65 183	99.71 152	99.39 64	98.63 158	98.69 243

miper_enhance_ethall		94.36 237	93.98 228	95.49 259	98.68 157	95.24 208	99.73 182	97.29 316	93.28 194	89.86 298	95.97 324	94.37 85	97.05 333	92.20 270	84.45 339	94.19 326

fmvsm_s_conf0.5_n_5		98.08 72	97.71 86	99.17 59	98.67 158	97.69 96	99.99 5	98.57 100	97.40 37	99.89 7	99.69 98	85.99 253	99.96 70	99.80 27	99.40 125	99.85 96

ETVMVS		97.03 136	96.64 140	98.20 145	98.67 158	97.12 121	99.89 116	98.57 100	91.10 277	98.17 155	98.59 222	93.86 105	98.19 275	95.64 205	95.24 249	99.28 201

test2506		97.53 108	97.19 114	98.58 115	98.66 160	96.90 132	98.81 325	99.77 5	94.93 118	97.95 161	98.96 182	92.51 147	99.20 193	94.93 215	98.15 175	99.64 129

ECVR-MVS		95.66 195	95.05 202	97.51 196	98.66 160	93.71 252	98.85 322	98.45 136	94.93 118	96.86 197	98.96 182	75.22 356	99.20 193	95.34 207	98.15 175	99.64 129

mamv4		95.24 205	96.90 125	90.25 383	98.65 162	72.11 430	98.28 359	97.64 270	89.99 306	95.93 223	98.25 249	94.74 70	99.11 199	99.01 83	99.64 92	99.53 161

balanced_conf03		98.27 59	97.99 66	99.11 71	98.64 163	98.43 62	99.47 238	97.79 254	94.56 134	99.74 40	98.35 242	94.33 88	99.25 187	99.12 72	99.96 46	99.64 129

fmvsm_s_conf0.5_n_a		97.73 99	97.72 84	97.77 178	98.63 164	94.26 238	99.96 46	98.92 49	97.18 49	99.75 37	99.69 98	87.00 239	99.97 58	99.46 58	98.89 148	99.08 219

MVSMamba_PlusPlus		97.83 86	97.45 100	98.99 83	98.60 165	98.15 66	99.58 216	97.74 261	90.34 298	99.26 94	98.32 245	94.29 90	99.23 188	99.03 81	99.89 70	99.58 149

testing222		97.08 135	96.75 135	98.06 156	98.56 166	96.82 134	99.85 136	98.61 92	92.53 230	98.84 114	98.84 203	93.36 115	98.30 265	95.84 201	94.30 261	99.05 223

test1111		95.57 197	94.98 205	97.37 205	98.56 166	93.37 264	98.86 320	98.45 136	94.95 117	96.63 203	98.95 187	75.21 357	99.11 199	95.02 212	98.14 177	99.64 129

MVSTER		95.53 198	95.22 195	96.45 234	98.56 166	97.72 91	99.91 100	97.67 266	92.38 237	91.39 279	97.14 281	97.24 18	97.30 317	94.80 221	87.85 313	94.34 315

testing3-2		97.72 100	97.43 103	98.60 111	98.55 169	97.11 123	100.00 1	99.23 31	93.78 177	97.90 163	98.73 208	95.50 49	99.69 156	98.53 115	94.63 254	98.99 227

VDD-MVS		93.77 250	92.94 258	96.27 241	98.55 169	90.22 336	98.77 329	97.79 254	90.85 283	96.82 199	99.42 134	61.18 418	99.77 142	98.95 84	94.13 263	98.82 235

tpmvs		94.28 239	93.57 239	96.40 236	98.55 169	91.50 311	95.70 416	98.55 112	87.47 348	92.15 272	94.26 389	91.42 166	98.95 212	88.15 329	95.85 233	98.76 238

UGNet		95.33 204	94.57 213	97.62 189	98.55 169	94.85 220	98.67 338	99.32 26	95.75 99	96.80 200	96.27 313	72.18 372	99.96 70	94.58 228	99.05 144	98.04 260
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	95.18 206	94.10 224	98.43 132	98.55 169	95.99 174	97.91 374	97.31 312	90.35 297	89.48 311	99.22 159	85.19 261	99.89 110	90.40 304	98.47 164	99.41 180
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 184	96.49 146	94.34 307	98.51 174	89.99 341	99.39 250	98.57 100	93.14 199	97.33 183	98.31 247	93.44 113	94.68 407	93.69 252	95.98 227	98.34 253

UWE-MVS		96.79 147	96.72 137	97.00 216	98.51 174	93.70 253	99.71 189	98.60 94	92.96 204	97.09 190	98.34 244	96.67 31	98.85 216	92.11 272	96.50 214	98.44 248

myMVS_eth3d28		97.86 82	97.59 94	98.68 103	98.50 176	97.26 113	99.92 92	98.55 112	93.79 176	98.26 151	98.75 206	95.20 54	99.48 178	98.93 86	96.40 217	99.29 199

test_vis1_n_1920		95.44 200	95.31 191	95.82 254	98.50 176	88.74 357	99.98 18	97.30 313	97.84 25	99.85 15	99.19 161	66.82 396	99.97 58	98.82 95	99.46 119	98.76 238

BH-w/o		95.71 192	95.38 189	96.68 228	98.49 178	92.28 288	99.84 141	97.50 291	92.12 243	92.06 275	98.79 204	84.69 266	98.67 234	95.29 209	99.66 91	99.09 217

baseline1		95.78 189	94.86 207	98.54 121	98.47 179	98.07 74	99.06 290	97.99 232	92.68 220	94.13 250	98.62 221	93.28 121	98.69 232	93.79 247	85.76 326	98.84 234

fmvsm_s_conf0.5_n_7		97.70 102	97.74 83	97.59 191	98.44 180	95.16 214	99.97 36	98.65 81	97.95 21	99.62 57	99.78 62	86.09 251	99.94 87	99.69 44	99.50 112	97.66 270

EPMVS		96.53 163	96.01 162	98.09 154	98.43 181	96.12 172	96.36 403	99.43 20	93.53 184	97.64 174	95.04 366	94.41 80	98.38 257	91.13 285	98.11 178	99.75 111

kuosan		93.17 265	92.60 266	94.86 283	98.40 182	89.54 349	98.44 350	98.53 119	84.46 386	88.49 332	97.92 262	90.57 185	97.05 333	83.10 375	93.49 271	97.99 261

WBMVS		94.52 228	94.03 226	95.98 247	98.38 183	96.68 141	99.92 92	97.63 271	90.75 290	89.64 306	95.25 359	96.77 25	96.90 345	94.35 233	83.57 346	94.35 313

UBG		97.84 85	97.69 87	98.29 141	98.38 183	96.59 148	99.90 106	98.53 119	93.91 172	98.52 134	98.42 240	96.77 25	99.17 196	98.54 113	96.20 221	99.11 216

sss		97.57 107	97.03 121	99.18 56	98.37 185	98.04 77	99.73 182	99.38 22	93.46 187	98.76 122	99.06 169	91.21 169	99.89 110	96.33 192	97.01 206	99.62 136

testing11		97.48 110	97.27 110	98.10 153	98.36 186	96.02 173	99.92 92	98.45 136	93.45 189	98.15 156	98.70 211	95.48 50	99.22 189	97.85 154	95.05 251	99.07 220

BH-untuned		95.18 206	94.83 208	96.22 242	98.36 186	91.22 314	99.80 157	97.32 311	90.91 281	91.08 282	98.67 213	83.51 275	98.54 240	94.23 236	99.61 99	98.92 229

testing91		97.16 127	96.90 125	97.97 160	98.35 188	95.67 189	99.91 100	98.42 161	92.91 207	97.33 183	98.72 209	94.81 68	99.21 190	96.98 180	94.63 254	99.03 224

testing99		97.17 126	96.91 124	97.95 161	98.35 188	95.70 186	99.91 100	98.43 149	92.94 205	97.36 182	98.72 209	94.83 67	99.21 190	97.00 178	94.64 253	98.95 228

ET-MVSNet_ETH3D		94.37 235	93.28 253	97.64 186	98.30 190	97.99 79	99.99 5	97.61 277	94.35 147	71.57 428	99.45 133	96.23 35	95.34 397	96.91 185	85.14 333	99.59 143

AUN-MVS		93.28 262	92.60 266	95.34 266	98.29 191	90.09 339	99.31 262	98.56 106	91.80 255	96.35 213	98.00 257	89.38 202	98.28 268	92.46 267	69.22 421	97.64 271

FMVSNet3		92.69 278	91.58 287	95.99 246	98.29 191	97.42 108	99.26 271	97.62 274	89.80 309	89.68 302	95.32 353	81.62 292	96.27 374	87.01 346	85.65 327	94.29 317

PMMVS		96.76 150	96.76 134	96.76 225	98.28 193	92.10 292	99.91 100	97.98 234	94.12 158	99.53 69	99.39 141	86.93 240	98.73 226	96.95 183	97.73 186	99.45 174

hse-mvs2		94.38 234	94.08 225	95.31 268	98.27 194	90.02 340	99.29 267	98.56 106	95.90 94	98.77 119	98.00 257	90.89 181	98.26 272	97.80 156	69.20 422	97.64 271

PVSNet_0	88.03 19	91.80 297	90.27 311	96.38 238	98.27 194	90.46 331	99.94 82	99.61 13	93.99 166	86.26 368	97.39 276	71.13 379	99.89 110	98.77 99	67.05 427	98.79 237

UA-Net		96.54 162	95.96 169	98.27 142	98.23 196	95.71 185	98.00 372	98.45 136	93.72 181	98.41 142	99.27 153	88.71 215	99.66 163	91.19 284	97.69 187	99.44 177

test_cas_vis1_n_1920		96.59 160	96.23 155	97.65 185	98.22 197	94.23 239	99.99 5	97.25 320	97.77 26	99.58 65	99.08 167	77.10 333	99.97 58	97.64 164	99.45 120	98.74 240

FE-MVS		95.70 194	95.01 204	97.79 175	98.21 198	94.57 227	95.03 417	98.69 75	88.90 325	97.50 178	96.19 315	92.60 143	99.49 177	89.99 309	97.94 184	99.31 195

GG-mvs-BLEND				98.54 121	98.21 198	98.01 78	93.87 422	98.52 121		97.92 162	97.92 262	99.02 3	97.94 292	98.17 134	99.58 103	99.67 123

mvs_anonymous		95.65 196	95.03 203	97.53 194	98.19 200	95.74 183	99.33 259	97.49 292	90.87 282	90.47 289	97.10 283	88.23 218	97.16 324	95.92 199	97.66 189	99.68 121

MVS_Test		96.46 165	95.74 178	98.61 110	98.18 201	97.23 115	99.31 262	97.15 330	91.07 278	98.84 114	97.05 287	88.17 219	98.97 209	94.39 230	97.50 191	99.61 140

BH-RMVSNet		95.18 206	94.31 220	97.80 173	98.17 202	95.23 209	99.76 168	97.53 287	92.52 231	94.27 248	99.25 157	76.84 338	98.80 219	90.89 293	99.54 105	99.35 190

dongtai		91.55 303	91.13 296	92.82 352	98.16 203	86.35 380	99.47 238	98.51 124	83.24 394	85.07 378	97.56 270	90.33 190	94.94 403	76.09 413	91.73 279	97.18 282

RPSCF		91.80 297	92.79 262	88.83 394	98.15 204	69.87 432	98.11 368	96.60 378	83.93 389	94.33 246	99.27 153	79.60 316	99.46 181	91.99 273	93.16 276	97.18 282

ETV-MVS		97.92 78	97.80 82	98.25 143	98.14 205	96.48 150	99.98 18	97.63 271	95.61 103	99.29 92	99.46 132	92.55 145	98.82 217	99.02 82	98.54 162	99.46 172

IS-MVSNet		96.29 175	95.90 174	97.45 198	98.13 206	94.80 223	99.08 285	97.61 277	92.02 248	95.54 232	98.96 182	90.64 184	98.08 281	93.73 250	97.41 195	99.47 171

test_fmvsmconf_n		98.43 47	98.32 44	98.78 96	98.12 207	96.41 153	99.99 5	98.83 63	98.22 7	99.67 48	99.64 111	91.11 174	99.94 87	99.67 46	99.62 95	99.98 51

fmvsm_s_conf0.1_n_2		97.25 122	96.85 129	98.43 132	98.08 208	98.08 73	99.92 92	97.76 260	98.05 17	99.65 50	99.58 120	80.88 301	99.93 96	99.59 50	98.17 173	97.29 280

ab-mvs		94.69 220	93.42 245	98.51 126	98.07 209	96.26 160	96.49 401	98.68 77	90.31 299	94.54 241	97.00 289	76.30 345	99.71 152	95.98 198	93.38 274	99.56 152

XVG-OURS-SEG-HR		94.79 216	94.70 212	95.08 273	98.05 210	89.19 351	99.08 285	97.54 285	93.66 182	94.87 239	99.58 120	78.78 324	99.79 137	97.31 170	93.40 273	96.25 289

EIA-MVS		97.53 108	97.46 98	97.76 180	98.04 211	94.84 221	99.98 18	97.61 277	94.41 145	97.90 163	99.59 117	92.40 151	98.87 214	98.04 143	99.13 139	99.59 143

XVG-OURS		94.82 213	94.74 211	95.06 274	98.00 212	89.19 351	99.08 285	97.55 283	94.10 159	94.71 240	99.62 115	80.51 307	99.74 148	96.04 197	93.06 278	96.25 289

mvsmamba		96.94 140	96.73 136	97.55 192	97.99 213	94.37 235	99.62 208	97.70 263	93.13 200	98.42 141	97.92 262	88.02 220	98.75 225	98.78 98	99.01 145	99.52 163

dp		95.05 209	94.43 215	96.91 219	97.99 213	92.73 277	96.29 406	97.98 234	89.70 310	95.93 223	94.67 379	93.83 107	98.45 246	86.91 349	96.53 213	99.54 157

tpmrst		96.27 177	95.98 165	97.13 213	97.96 215	93.15 266	96.34 404	98.17 212	92.07 244	98.71 125	95.12 363	93.91 102	98.73 226	94.91 218	96.62 211	99.50 168

TR-MVS		94.54 225	93.56 240	97.49 197	97.96 215	94.34 236	98.71 333	97.51 290	90.30 300	94.51 243	98.69 212	75.56 351	98.77 222	92.82 265	95.99 226	99.35 190

Vis-MVSNet (Re-imp)		96.32 172	95.98 165	97.35 208	97.93 217	94.82 222	99.47 238	98.15 220	91.83 252	95.09 237	99.11 165	91.37 168	97.47 308	93.47 254	97.43 192	99.74 112

MDTV_nov1_ep13			95.69 180		97.90 218	94.15 241	95.98 412	98.44 141	93.12 201	97.98 160	95.74 328	95.10 57	98.58 237	90.02 308	96.92 208

Fast-Effi-MVS+		95.02 210	94.19 222	97.52 195	97.88 219	94.55 228	99.97 36	97.08 339	88.85 327	94.47 244	97.96 261	84.59 267	98.41 249	89.84 311	97.10 201	99.59 143

ADS-MVSNet2		93.80 249	93.88 232	93.55 335	97.87 220	85.94 384	94.24 418	96.84 364	90.07 303	96.43 209	94.48 384	90.29 192	95.37 396	87.44 336	97.23 198	99.36 186

ADS-MVSNet		94.79 216	94.02 227	97.11 215	97.87 220	93.79 249	94.24 418	98.16 217	90.07 303	96.43 209	94.48 384	90.29 192	98.19 275	87.44 336	97.23 198	99.36 186

Effi-MVS+		96.30 174	95.69 180	98.16 147	97.85 222	96.26 160	97.41 381	97.21 322	90.37 296	98.65 128	98.58 225	86.61 245	98.70 231	97.11 175	97.37 196	99.52 163

PatchmatchNet		95.94 185	95.45 186	97.39 204	97.83 223	94.41 232	96.05 410	98.40 170	92.86 208	97.09 190	95.28 358	94.21 94	98.07 283	89.26 317	98.11 178	99.70 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 223	93.61 235	97.74 182	97.82 224	96.26 160	99.96 46	97.78 256	85.76 371	94.00 251	97.54 271	76.95 337	99.21 190	97.23 172	95.43 244	97.76 269

1112_ss		96.01 183	95.20 196	98.42 134	97.80 225	96.41 153	99.65 201	96.66 375	92.71 217	92.88 265	99.40 139	92.16 156	99.30 185	91.92 275	93.66 269	99.55 153

Test_1112_low_res		95.72 190	94.83 208	98.42 134	97.79 226	96.41 153	99.65 201	96.65 376	92.70 218	92.86 266	96.13 319	92.15 157	99.30 185	91.88 276	93.64 270	99.55 153

Effi-MVS+-dtu		94.53 227	95.30 192	92.22 360	97.77 227	82.54 406	99.59 214	97.06 341	94.92 120	95.29 235	95.37 351	85.81 254	97.89 293	94.80 221	97.07 202	96.23 291

tpm cat1		93.51 258	92.52 272	96.47 232	97.77 227	91.47 312	96.13 408	98.06 227	80.98 407	92.91 264	93.78 393	89.66 197	98.87 214	87.03 345	96.39 218	99.09 217

FA-MVS(test-final)		95.86 186	95.09 200	98.15 150	97.74 229	95.62 191	96.31 405	98.17 212	91.42 268	96.26 214	96.13 319	90.56 186	99.47 180	92.18 271	97.07 202	99.35 190

xiu_mvs_v1_base_debu		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 262	97.86 248	96.43 78	99.62 57	99.69 98	85.56 256	99.68 157	99.05 75	98.31 168	97.83 265

xiu_mvs_v1_base		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 262	97.86 248	96.43 78	99.62 57	99.69 98	85.56 256	99.68 157	99.05 75	98.31 168	97.83 265

xiu_mvs_v1_base_debi		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 262	97.86 248	96.43 78	99.62 57	99.69 98	85.56 256	99.68 157	99.05 75	98.31 168	97.83 265

EPP-MVSNet		96.69 155	96.60 142	96.96 218	97.74 229	93.05 269	99.37 254	98.56 106	88.75 329	95.83 227	99.01 173	96.01 36	98.56 238	96.92 184	97.20 200	99.25 204

gg-mvs-nofinetune		93.51 258	91.86 284	98.47 128	97.72 234	97.96 83	92.62 426	98.51 124	74.70 425	97.33 183	69.59 442	98.91 4	97.79 296	97.77 161	99.56 104	99.67 123

IB-MVS	92.85 6	94.99 211	93.94 230	98.16 147	97.72 234	95.69 188	99.99 5	98.81 64	94.28 153	92.70 267	96.90 291	95.08 58	99.17 196	96.07 196	73.88 409	99.60 142
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		97.41 116	97.02 122	98.59 114	97.71 236	97.52 101	99.97 36	98.54 116	91.83 252	97.45 179	99.04 170	97.50 9	99.10 201	94.75 223	96.37 219	99.16 210

VortexMVS		94.11 241	93.50 242	95.94 249	97.70 237	96.61 145	99.35 257	97.18 325	93.52 186	89.57 309	95.74 328	87.55 227	96.97 341	95.76 204	85.13 334	94.23 322

Syy-MVS		90.00 337	90.63 303	88.11 401	97.68 238	74.66 428	99.71 189	98.35 183	90.79 287	92.10 273	98.67 213	79.10 322	93.09 421	63.35 435	95.95 230	96.59 287

myMVS_eth3d		94.46 232	94.76 210	93.55 335	97.68 238	90.97 316	99.71 189	98.35 183	90.79 287	92.10 273	98.67 213	92.46 150	93.09 421	87.13 342	95.95 230	96.59 287

test_fmvs1_n		94.25 240	94.36 217	93.92 322	97.68 238	83.70 397	99.90 106	96.57 379	97.40 37	99.67 48	98.88 194	61.82 415	99.92 102	98.23 132	99.13 139	98.14 258

fmvsm_s_conf0.5_n_6		98.27 59	97.96 71	99.23 51	97.66 241	98.11 72	99.98 18	98.64 84	97.85 24	99.87 10	99.72 88	88.86 212	99.93 96	99.64 48	99.36 128	99.63 135

RRT-MVS		96.24 178	95.68 182	97.94 164	97.65 242	94.92 219	99.27 270	97.10 335	92.79 214	97.43 180	97.99 259	81.85 287	99.37 184	98.46 119	98.57 159	99.53 161

diffmvs		97.00 137	96.64 140	98.09 154	97.64 243	96.17 169	99.81 153	97.19 323	94.67 132	98.95 109	99.28 150	86.43 246	98.76 223	98.37 124	97.42 194	99.33 193
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		95.72 190	95.15 198	97.45 198	97.62 244	94.28 237	99.28 268	98.24 203	94.27 155	96.84 198	98.94 189	79.39 317	98.76 223	93.25 256	98.49 163	99.30 197
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 130	96.72 137	98.22 144	97.60 245	96.70 138	99.92 92	98.54 116	91.11 276	97.07 192	98.97 180	97.47 12	99.03 204	93.73 250	96.09 224	98.92 229

GDP-MVS		97.88 80	97.59 94	98.75 99	97.59 246	97.81 89	99.95 65	97.37 305	94.44 141	99.08 103	99.58 120	97.13 23	99.08 202	94.99 213	98.17 173	99.37 184

miper_ehance_all_eth		93.16 266	92.60 266	94.82 284	97.57 247	93.56 257	99.50 232	97.07 340	88.75 329	88.85 326	95.52 340	90.97 177	96.74 355	90.77 295	84.45 339	94.17 327

guyue		97.15 128	96.82 131	98.15 150	97.56 248	96.25 164	99.71 189	97.84 251	95.75 99	98.13 157	98.65 216	87.58 226	98.82 217	98.29 129	97.91 185	99.36 186

testing3		93.92 244	94.23 221	92.99 349	97.54 249	90.23 335	99.99 5	99.16 33	90.57 292	91.33 281	98.63 220	92.99 129	92.52 425	82.46 379	95.39 245	96.22 292

LCM-MVSNet-Re		92.31 286	92.60 266	91.43 369	97.53 250	79.27 423	99.02 299	91.83 438	92.07 244	80.31 402	94.38 387	83.50 276	95.48 393	97.22 173	97.58 190	99.54 157

GBi-Net		90.88 314	89.82 320	94.08 314	97.53 250	91.97 293	98.43 351	96.95 353	87.05 354	89.68 302	94.72 375	71.34 376	96.11 379	87.01 346	85.65 327	94.17 327

test1		90.88 314	89.82 320	94.08 314	97.53 250	91.97 293	98.43 351	96.95 353	87.05 354	89.68 302	94.72 375	71.34 376	96.11 379	87.01 346	85.65 327	94.17 327

FMVSNet2		91.02 311	89.56 325	95.41 264	97.53 250	95.74 183	98.98 301	97.41 300	87.05 354	88.43 336	95.00 369	71.34 376	96.24 376	85.12 361	85.21 332	94.25 320

tttt0517		96.85 144	96.49 146	97.92 165	97.48 254	95.89 177	99.85 136	98.54 116	90.72 291	96.63 203	98.93 192	97.47 12	99.02 205	93.03 263	95.76 236	98.85 233

BP-MVS1		98.33 55	98.18 52	98.81 94	97.44 255	97.98 80	99.96 46	98.17 212	94.88 122	98.77 119	99.59 117	97.59 7	99.08 202	98.24 131	98.93 147	99.36 186

casdiffmvs_mvg		96.43 166	95.94 171	97.89 169	97.44 255	95.47 195	99.86 133	97.29 316	93.35 190	96.03 220	99.19 161	85.39 259	98.72 228	97.89 153	97.04 204	99.49 170
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		97.38 118	97.24 111	97.80 173	97.41 257	95.64 190	99.99 5	97.06 341	94.59 133	99.63 54	99.32 146	89.20 208	98.14 277	98.76 100	99.23 135	99.62 136

c3_l		92.53 281	91.87 283	94.52 296	97.40 258	92.99 271	99.40 246	96.93 358	87.86 344	88.69 329	95.44 345	89.95 195	96.44 367	90.45 301	80.69 373	94.14 336

fmvsm_s_conf0.1_n		97.30 119	97.21 113	97.60 190	97.38 259	94.40 234	99.90 106	98.64 84	96.47 77	99.51 73	99.65 110	84.99 264	99.93 96	99.22 69	99.09 142	98.46 247

CDS-MVSNet		96.34 171	96.07 160	97.13 213	97.37 260	94.96 217	99.53 227	97.91 243	91.55 260	95.37 234	98.32 245	95.05 60	97.13 327	93.80 246	95.75 237	99.30 197
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 152	96.26 154	98.16 147	97.36 261	96.48 150	99.96 46	98.29 196	91.93 249	95.77 228	98.07 255	95.54 46	98.29 266	90.55 299	98.89 148	99.70 118

miper_lstm_enhance		91.81 294	91.39 293	93.06 348	97.34 262	89.18 353	99.38 252	96.79 369	86.70 361	87.47 350	95.22 360	90.00 194	95.86 388	88.26 327	81.37 362	94.15 333

baseline		96.43 166	95.98 165	97.76 180	97.34 262	95.17 213	99.51 230	97.17 327	93.92 171	96.90 196	99.28 150	85.37 260	98.64 235	97.50 167	96.86 210	99.46 172

cl____		92.31 286	91.58 287	94.52 296	97.33 264	92.77 273	99.57 219	96.78 370	86.97 358	87.56 348	95.51 341	89.43 201	96.62 360	88.60 322	82.44 354	94.16 332

DIV-MVS_self_test		92.32 285	91.60 286	94.47 300	97.31 265	92.74 275	99.58 216	96.75 371	86.99 357	87.64 346	95.54 338	89.55 200	96.50 364	88.58 323	82.44 354	94.17 327

casdiffmvs		96.42 168	95.97 168	97.77 178	97.30 266	94.98 216	99.84 141	97.09 338	93.75 180	96.58 205	99.26 156	85.07 262	98.78 221	97.77 161	97.04 204	99.54 157
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		94.36 237	93.48 243	96.99 217	97.29 267	93.54 258	99.96 46	96.72 373	88.35 338	93.43 255	98.94 189	82.05 284	98.05 284	88.12 331	96.48 216	99.37 184

eth_miper_zixun_eth		92.41 284	91.93 281	93.84 326	97.28 268	90.68 325	98.83 323	96.97 352	88.57 334	89.19 321	95.73 331	89.24 207	96.69 358	89.97 310	81.55 360	94.15 333

MVSFormer		96.94 140	96.60 142	97.95 161	97.28 268	97.70 94	99.55 224	97.27 318	91.17 273	99.43 79	99.54 126	90.92 178	96.89 346	94.67 226	99.62 95	99.25 204

lupinMVS		97.85 84	97.60 92	98.62 109	97.28 268	97.70 94	99.99 5	97.55 283	95.50 108	99.43 79	99.67 106	90.92 178	98.71 229	98.40 121	99.62 95	99.45 174

SCA		94.69 220	93.81 234	97.33 209	97.10 271	94.44 229	98.86 320	98.32 190	93.30 193	96.17 219	95.59 336	76.48 343	97.95 290	91.06 287	97.43 192	99.59 143

KinetiMVS		96.10 179	95.29 193	98.53 123	97.08 272	97.12 121	99.56 221	98.12 223	94.78 125	98.44 139	98.94 189	80.30 311	99.39 183	91.56 280	98.79 154	99.06 221

TAMVS		95.85 187	95.58 184	96.65 230	97.07 273	93.50 259	99.17 278	97.82 253	91.39 270	95.02 238	98.01 256	92.20 155	97.30 317	93.75 249	95.83 234	99.14 213

Fast-Effi-MVS+-dtu		93.72 253	93.86 233	93.29 340	97.06 274	86.16 381	99.80 157	96.83 365	92.66 221	92.58 268	97.83 267	81.39 293	97.67 301	89.75 312	96.87 209	96.05 294

CostFormer		96.10 179	95.88 175	96.78 224	97.03 275	92.55 283	97.08 390	97.83 252	90.04 305	98.72 124	94.89 373	95.01 62	98.29 266	96.54 191	95.77 235	99.50 168

test_fmvsmvis_n_1920		97.67 103	97.59 94	97.91 167	97.02 276	95.34 203	99.95 65	98.45 136	97.87 23	97.02 193	99.59 117	89.64 198	99.98 47	99.41 62	99.34 130	98.42 249

test-LLR		96.47 164	96.04 161	97.78 176	97.02 276	95.44 196	99.96 46	98.21 207	94.07 161	95.55 230	96.38 308	93.90 103	98.27 270	90.42 302	98.83 152	99.64 129

test-mter		96.39 169	95.93 172	97.78 176	97.02 276	95.44 196	99.96 46	98.21 207	91.81 254	95.55 230	96.38 308	95.17 55	98.27 270	90.42 302	98.83 152	99.64 129

gm-plane-assit					96.97 279	93.76 251			91.47 264		98.96 182		98.79 220	94.92 216

WB-MVSnew		92.90 272	92.77 263	93.26 342	96.95 280	93.63 255	99.71 189	98.16 217	91.49 261	94.28 247	98.14 252	81.33 295	96.48 365	79.47 396	95.46 242	89.68 422

QAPM		95.40 201	94.17 223	99.10 72	96.92 281	97.71 92	99.40 246	98.68 77	89.31 313	88.94 325	98.89 193	82.48 282	99.96 70	93.12 262	99.83 77	99.62 136

KD-MVS_2432*1600		88.00 359	86.10 363	93.70 331	96.91 282	94.04 243	97.17 387	97.12 333	84.93 381	81.96 392	92.41 406	92.48 148	94.51 409	79.23 397	52.68 441	92.56 392

miper_refine_blended		88.00 359	86.10 363	93.70 331	96.91 282	94.04 243	97.17 387	97.12 333	84.93 381	81.96 392	92.41 406	92.48 148	94.51 409	79.23 397	52.68 441	92.56 392

tpm2		95.47 199	95.18 197	96.35 239	96.91 282	91.70 306	96.96 393	97.93 239	88.04 342	98.44 139	95.40 347	93.32 118	97.97 287	94.00 238	95.61 240	99.38 182

FMVSNet5		88.32 355	87.47 357	90.88 372	96.90 285	88.39 365	97.28 384	95.68 400	82.60 401	84.67 380	92.40 408	79.83 314	91.16 430	76.39 412	81.51 361	93.09 383

3Dnovator+	91.53 11	96.31 173	95.24 194	99.52 28	96.88 286	98.64 54	99.72 186	98.24 203	95.27 113	88.42 338	98.98 178	82.76 281	99.94 87	97.10 176	99.83 77	99.96 69

Patchmatch-test		92.65 280	91.50 290	96.10 245	96.85 287	90.49 330	91.50 431	97.19 323	82.76 400	90.23 290	95.59 336	95.02 61	98.00 286	77.41 407	96.98 207	99.82 100

MVS		96.60 159	95.56 185	99.72 13	96.85 287	99.22 20	98.31 357	98.94 44	91.57 259	90.90 285	99.61 116	86.66 244	99.96 70	97.36 169	99.88 73	99.99 23

3Dnovator	91.47 12	96.28 176	95.34 190	99.08 75	96.82 289	97.47 106	99.45 243	98.81 64	95.52 107	89.39 312	99.00 175	81.97 285	99.95 79	97.27 171	99.83 77	99.84 97

EI-MVSNet		93.73 252	93.40 248	94.74 285	96.80 290	92.69 278	99.06 290	97.67 266	88.96 322	91.39 279	99.02 171	88.75 214	97.30 317	91.07 286	87.85 313	94.22 323

CVMVSNet		94.68 222	94.94 206	93.89 325	96.80 290	86.92 378	99.06 290	98.98 41	94.45 138	94.23 249	99.02 171	85.60 255	95.31 398	90.91 292	95.39 245	99.43 178

IterMVS-LS		92.69 278	92.11 277	94.43 304	96.80 290	92.74 275	99.45 243	96.89 361	88.98 320	89.65 305	95.38 350	88.77 213	96.34 371	90.98 290	82.04 357	94.22 323
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 161	96.46 149	96.91 219	96.79 293	92.50 284	99.90 106	97.38 302	96.02 93	97.79 171	99.32 146	86.36 248	98.99 206	98.26 130	96.33 220	99.23 207

IterMVS		90.91 313	90.17 315	93.12 345	96.78 294	90.42 333	98.89 314	97.05 344	89.03 317	86.49 363	95.42 346	76.59 341	95.02 400	87.22 341	84.09 342	93.93 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 145	95.96 169	99.48 34	96.74 295	98.52 58	98.31 357	98.86 56	95.82 96	89.91 296	98.98 178	87.49 229	99.96 70	97.80 156	99.73 87	99.96 69

IterMVS-SCA-FT		90.85 316	90.16 316	92.93 350	96.72 296	89.96 342	98.89 314	96.99 348	88.95 323	86.63 360	95.67 332	76.48 343	95.00 401	87.04 344	84.04 345	93.84 361

MVS-HIRNet		86.22 366	83.19 379	95.31 268	96.71 297	90.29 334	92.12 428	97.33 310	62.85 436	86.82 357	70.37 441	69.37 384	97.49 307	75.12 415	97.99 183	98.15 256

VDDNet		93.12 267	91.91 282	96.76 225	96.67 298	92.65 281	98.69 336	98.21 207	82.81 399	97.75 173	99.28 150	61.57 416	99.48 178	98.09 140	94.09 264	98.15 256

dmvs_re		93.20 264	93.15 255	93.34 338	96.54 299	83.81 396	98.71 333	98.51 124	91.39 270	92.37 271	98.56 227	78.66 326	97.83 295	93.89 240	89.74 285	98.38 251

Elysia		94.50 229	93.38 249	97.85 171	96.49 300	96.70 138	98.98 301	97.78 256	90.81 285	96.19 217	98.55 229	73.63 367	98.98 207	89.41 313	98.56 160	97.88 263

StellarMVS		94.50 229	93.38 249	97.85 171	96.49 300	96.70 138	98.98 301	97.78 256	90.81 285	96.19 217	98.55 229	73.63 367	98.98 207	89.41 313	98.56 160	97.88 263

MIMVSNet		90.30 329	88.67 343	95.17 272	96.45 302	91.64 308	92.39 427	97.15 330	85.99 368	90.50 288	93.19 401	66.95 395	94.86 405	82.01 383	93.43 272	99.01 226

CR-MVSNet		93.45 261	92.62 265	95.94 249	96.29 303	92.66 279	92.01 429	96.23 387	92.62 223	96.94 194	93.31 399	91.04 175	96.03 384	79.23 397	95.96 228	99.13 214

RPMNet		89.76 341	87.28 358	97.19 212	96.29 303	92.66 279	92.01 429	98.31 192	70.19 432	96.94 194	85.87 434	87.25 234	99.78 139	62.69 436	95.96 228	99.13 214

tt0805		91.28 306	90.18 314	94.60 291	96.26 305	87.55 371	98.39 355	98.72 72	89.00 319	89.22 318	98.47 237	62.98 411	98.96 211	90.57 298	88.00 312	97.28 281

Patchmtry		89.70 342	88.49 346	93.33 339	96.24 306	89.94 345	91.37 432	96.23 387	78.22 415	87.69 345	93.31 399	91.04 175	96.03 384	80.18 395	82.10 356	94.02 344

test_vis1_rt		86.87 364	86.05 366	89.34 390	96.12 307	78.07 424	99.87 122	83.54 449	92.03 247	78.21 413	89.51 420	45.80 435	99.91 103	96.25 194	93.11 277	90.03 419

JIA-IIPM		91.76 300	90.70 301	94.94 278	96.11 308	87.51 372	93.16 425	98.13 222	75.79 421	97.58 175	77.68 439	92.84 134	97.97 287	88.47 326	96.54 212	99.33 193

OpenMVS	90.15 15	94.77 218	93.59 238	98.33 138	96.07 309	97.48 105	99.56 221	98.57 100	90.46 294	86.51 362	98.95 187	78.57 327	99.94 87	93.86 241	99.74 86	97.57 276

PAPM		98.60 34	98.42 35	99.14 66	96.05 310	98.96 26	99.90 106	99.35 24	96.68 68	98.35 146	99.66 108	96.45 33	98.51 241	99.45 59	99.89 70	99.96 69

CLD-MVS		94.06 243	93.90 231	94.55 295	96.02 311	90.69 324	99.98 18	97.72 262	96.62 72	91.05 284	98.85 202	77.21 332	98.47 242	98.11 138	89.51 291	94.48 301
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 326	88.75 342	95.25 270	95.99 312	90.16 337	91.22 433	97.54 285	76.80 417	97.26 186	86.01 433	91.88 162	96.07 383	66.16 432	95.91 232	99.51 166

ACMH+	89.98 16	90.35 327	89.54 326	92.78 354	95.99 312	86.12 382	98.81 325	97.18 325	89.38 312	83.14 388	97.76 268	68.42 389	98.43 247	89.11 318	86.05 325	93.78 364

DeepMVS_CX				82.92 411	95.98 314	58.66 442		96.01 392	92.72 216	78.34 412	95.51 341	58.29 421	98.08 281	82.57 378	85.29 330	92.03 400

ACMP	92.05 9	92.74 276	92.42 274	93.73 327	95.91 315	88.72 358	99.81 153	97.53 287	94.13 157	87.00 356	98.23 250	74.07 364	98.47 242	96.22 195	88.86 298	93.99 349
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 256	93.03 257	95.35 265	95.86 316	86.94 377	99.87 122	96.36 385	96.85 59	99.54 68	98.79 204	52.41 429	99.83 132	98.64 108	98.97 146	99.29 199

HQP-NCC					95.78 317		99.87 122		96.82 61	93.37 256

ACMP_Plane					95.78 317		99.87 122		96.82 61	93.37 256

HQP-MVS		94.61 224	94.50 214	94.92 279	95.78 317	91.85 298	99.87 122	97.89 244	96.82 61	93.37 256	98.65 216	80.65 305	98.39 253	97.92 150	89.60 286	94.53 297

NP-MVS					95.77 320	91.79 300					98.65 216

test_fmvsmconf0.1_n		97.74 97	97.44 101	98.64 108	95.76 321	96.20 166	99.94 82	98.05 229	98.17 11	98.89 113	99.42 134	87.65 224	99.90 105	99.50 55	99.60 101	99.82 100

plane_prior6					95.76 321	91.72 305						80.47 309

ACMM	91.95 10	92.88 273	92.52 272	93.98 321	95.75 323	89.08 355	99.77 163	97.52 289	93.00 203	89.95 295	97.99 259	76.17 347	98.46 245	93.63 253	88.87 297	94.39 309
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 246	92.84 259	96.80 223	95.73 324	93.57 256	99.88 119	97.24 321	92.57 228	92.92 263	96.66 300	78.73 325	97.67 301	87.75 334	94.06 265	99.17 209

plane_prior1					95.73 324

jason		97.24 123	96.86 128	98.38 137	95.73 324	97.32 110	99.97 36	97.40 301	95.34 111	98.60 133	99.54 126	87.70 223	98.56 238	97.94 149	99.47 117	99.25 204
jason: jason.
mmtdpeth		88.52 353	87.75 355	90.85 374	95.71 327	83.47 401	98.94 308	94.85 415	88.78 328	97.19 188	89.58 419	63.29 409	98.97 209	98.54 113	62.86 435	90.10 418

HQP_MVS		94.49 231	94.36 217	94.87 280	95.71 327	91.74 302	99.84 141	97.87 246	96.38 81	93.01 261	98.59 222	80.47 309	98.37 259	97.79 159	89.55 289	94.52 299

plane_prior7					95.71 327	91.59 310

ITE_SJBPF				92.38 357	95.69 330	85.14 388		95.71 399	92.81 211	89.33 315	98.11 253	70.23 382	98.42 248	85.91 356	88.16 310	93.59 372

fmvsm_s_conf0.1_n_a		97.09 132	96.90 125	97.63 188	95.65 331	94.21 240	99.83 148	98.50 130	96.27 86	99.65 50	99.64 111	84.72 265	99.93 96	99.04 78	98.84 151	98.74 240

ACMH	89.72 17	90.64 320	89.63 323	93.66 333	95.64 332	88.64 361	98.55 343	97.45 294	89.03 317	81.62 395	97.61 269	69.75 383	98.41 249	89.37 315	87.62 317	93.92 355
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 154	96.49 146	97.37 205	95.63 333	95.96 175	99.74 175	98.88 54	92.94 205	91.61 277	98.97 180	97.72 6	98.62 236	94.83 220	98.08 181	97.53 278

FMVSNet1		88.50 354	86.64 361	94.08 314	95.62 334	91.97 293	98.43 351	96.95 353	83.00 397	86.08 370	94.72 375	59.09 420	96.11 379	81.82 385	84.07 343	94.17 327

LuminaMVS		96.63 158	96.21 157	97.87 170	95.58 335	96.82 134	99.12 280	97.67 266	94.47 137	97.88 166	98.31 247	87.50 228	98.71 229	98.07 142	97.29 197	98.10 259

LPG-MVS_test		92.96 270	92.71 264	93.71 329	95.43 336	88.67 359	99.75 172	97.62 274	92.81 211	90.05 291	98.49 233	75.24 354	98.40 251	95.84 201	89.12 293	94.07 341

LGP-MVS_train				93.71 329	95.43 336	88.67 359		97.62 274	92.81 211	90.05 291	98.49 233	75.24 354	98.40 251	95.84 201	89.12 293	94.07 341

tpm		93.70 254	93.41 247	94.58 293	95.36 338	87.41 373	97.01 391	96.90 360	90.85 283	96.72 202	94.14 390	90.40 189	96.84 350	90.75 296	88.54 305	99.51 166

D2MVS		92.76 275	92.59 270	93.27 341	95.13 339	89.54 349	99.69 195	99.38 22	92.26 240	87.59 347	94.61 381	85.05 263	97.79 296	91.59 279	88.01 311	92.47 395

VPA-MVSNet		92.70 277	91.55 289	96.16 243	95.09 340	96.20 166	98.88 316	99.00 39	91.02 280	91.82 276	95.29 357	76.05 349	97.96 289	95.62 206	81.19 363	94.30 316

LTVRE_ROB	88.28 18	90.29 330	89.05 337	94.02 317	95.08 341	90.15 338	97.19 386	97.43 296	84.91 383	83.99 384	97.06 286	74.00 365	98.28 268	84.08 367	87.71 315	93.62 371
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		87.87 361	86.51 362	91.94 363	95.05 342	85.57 386	97.65 378	94.08 425	84.40 387	81.82 394	96.85 295	62.14 414	98.33 262	80.25 394	86.37 324	91.91 402

test0.0.03 1		93.86 245	93.61 235	94.64 289	95.02 343	92.18 291	99.93 89	98.58 98	94.07 161	87.96 342	98.50 232	93.90 103	94.96 402	81.33 386	93.17 275	96.78 284

UniMVSNet (Re)		93.07 269	92.13 276	95.88 251	94.84 344	96.24 165	99.88 119	98.98 41	92.49 233	89.25 316	95.40 347	87.09 236	97.14 326	93.13 261	78.16 387	94.26 318

USDC		90.00 337	88.96 338	93.10 347	94.81 345	88.16 367	98.71 333	95.54 404	93.66 182	83.75 386	97.20 280	65.58 400	98.31 264	83.96 370	87.49 319	92.85 389

VPNet		91.81 294	90.46 305	95.85 253	94.74 346	95.54 194	98.98 301	98.59 96	92.14 242	90.77 287	97.44 273	68.73 387	97.54 306	94.89 219	77.89 389	94.46 302

FIs		94.10 242	93.43 244	96.11 244	94.70 347	96.82 134	99.58 216	98.93 48	92.54 229	89.34 314	97.31 277	87.62 225	97.10 330	94.22 237	86.58 322	94.40 308

UniMVSNet_ETH3D		90.06 336	88.58 345	94.49 299	94.67 348	88.09 368	97.81 377	97.57 282	83.91 390	88.44 334	97.41 274	57.44 422	97.62 303	91.41 281	88.59 304	97.77 268

UniMVSNet_NR-MVSNet		92.95 271	92.11 277	95.49 259	94.61 349	95.28 206	99.83 148	99.08 36	91.49 261	89.21 319	96.86 294	87.14 235	96.73 356	93.20 257	77.52 392	94.46 302

test_fmvs2		89.47 346	89.70 322	88.77 397	94.54 350	75.74 425	99.83 148	94.70 421	94.71 129	91.08 282	96.82 299	54.46 425	97.78 298	92.87 264	88.27 308	92.80 390

MonoMVSNet		94.82 213	94.43 215	95.98 247	94.54 350	90.73 323	99.03 297	97.06 341	93.16 198	93.15 260	95.47 344	88.29 217	97.57 304	97.85 154	91.33 283	99.62 136

WR-MVS		92.31 286	91.25 294	95.48 262	94.45 352	95.29 205	99.60 213	98.68 77	90.10 302	88.07 341	96.89 292	80.68 304	96.80 354	93.14 260	79.67 380	94.36 310

nrg030		93.51 258	92.53 271	96.45 234	94.36 353	97.20 116	99.81 153	97.16 329	91.60 258	89.86 298	97.46 272	86.37 247	97.68 300	95.88 200	80.31 376	94.46 302

tfpnnormal		89.29 349	87.61 356	94.34 307	94.35 354	94.13 242	98.95 307	98.94 44	83.94 388	84.47 381	95.51 341	74.84 359	97.39 309	77.05 410	80.41 374	91.48 405

FC-MVSNet-test		93.81 248	93.15 255	95.80 255	94.30 355	96.20 166	99.42 245	98.89 52	92.33 239	89.03 324	97.27 279	87.39 231	96.83 352	93.20 257	86.48 323	94.36 310

SSC-MVS3.2		89.59 344	88.66 344	92.38 357	94.29 356	86.12 382	99.49 234	97.66 269	90.28 301	88.63 331	95.18 361	64.46 405	96.88 348	85.30 360	82.66 351	94.14 336

MS-PatchMatch		90.65 319	90.30 310	91.71 368	94.22 357	85.50 387	98.24 361	97.70 263	88.67 331	86.42 365	96.37 310	67.82 392	98.03 285	83.62 372	99.62 95	91.60 403

WR-MVS_H		91.30 304	90.35 308	94.15 311	94.17 358	92.62 282	99.17 278	98.94 44	88.87 326	86.48 364	94.46 386	84.36 269	96.61 361	88.19 328	78.51 385	93.21 381

DU-MVS		92.46 283	91.45 292	95.49 259	94.05 359	95.28 206	99.81 153	98.74 71	92.25 241	89.21 319	96.64 302	81.66 290	96.73 356	93.20 257	77.52 392	94.46 302

NR-MVSNet		91.56 302	90.22 312	95.60 257	94.05 359	95.76 182	98.25 360	98.70 74	91.16 275	80.78 401	96.64 302	83.23 279	96.57 362	91.41 281	77.73 391	94.46 302

CP-MVSNet		91.23 308	90.22 312	94.26 309	93.96 361	92.39 287	99.09 283	98.57 100	88.95 323	86.42 365	96.57 305	79.19 320	96.37 369	90.29 305	78.95 382	94.02 344

XXY-MVS		91.82 293	90.46 305	95.88 251	93.91 362	95.40 200	98.87 319	97.69 265	88.63 333	87.87 343	97.08 284	74.38 363	97.89 293	91.66 278	84.07 343	94.35 313

PS-CasMVS		90.63 321	89.51 328	93.99 320	93.83 363	91.70 306	98.98 301	98.52 121	88.48 335	86.15 369	96.53 307	75.46 352	96.31 373	88.83 320	78.86 384	93.95 352

test_0402		85.58 368	83.94 373	90.50 380	93.81 364	85.04 389	98.55 343	95.20 412	76.01 419	79.72 407	95.13 362	64.15 407	96.26 375	66.04 433	86.88 321	90.21 416

XVG-ACMP-BASELINE		91.22 309	90.75 300	92.63 356	93.73 365	85.61 385	98.52 347	97.44 295	92.77 215	89.90 297	96.85 295	66.64 397	98.39 253	92.29 269	88.61 302	93.89 357

TranMVSNet+NR-MVSNet		91.68 301	90.61 304	94.87 280	93.69 366	93.98 246	99.69 195	98.65 81	91.03 279	88.44 334	96.83 298	80.05 313	96.18 377	90.26 306	76.89 400	94.45 307

TransMVSNet (Re)		87.25 362	85.28 369	93.16 344	93.56 367	91.03 315	98.54 345	94.05 427	83.69 392	81.09 399	96.16 316	75.32 353	96.40 368	76.69 411	68.41 423	92.06 399

v10		90.25 331	88.82 340	94.57 294	93.53 368	93.43 261	99.08 285	96.87 363	85.00 380	87.34 354	94.51 382	80.93 300	97.02 340	82.85 377	79.23 381	93.26 379

testgi		89.01 351	88.04 352	91.90 364	93.49 369	84.89 391	99.73 182	95.66 401	93.89 175	85.14 376	98.17 251	59.68 419	94.66 408	77.73 406	88.88 296	96.16 293

v8		90.54 323	89.17 333	94.66 288	93.43 370	93.40 263	99.20 275	96.94 357	85.76 371	87.56 348	94.51 382	81.96 286	97.19 323	84.94 363	78.25 386	93.38 377

V42		91.28 306	90.12 317	94.74 285	93.42 371	93.46 260	99.68 197	97.02 345	87.36 350	89.85 300	95.05 365	81.31 296	97.34 312	87.34 339	80.07 378	93.40 375

pm-mvs1		89.36 348	87.81 354	94.01 318	93.40 372	91.93 296	98.62 341	96.48 383	86.25 366	83.86 385	96.14 318	73.68 366	97.04 336	86.16 353	75.73 405	93.04 385

v1144		91.09 310	89.83 319	94.87 280	93.25 373	93.69 254	99.62 208	96.98 350	86.83 360	89.64 306	94.99 370	80.94 299	97.05 333	85.08 362	81.16 364	93.87 359

v1192		90.62 322	89.25 332	94.72 287	93.13 374	93.07 267	99.50 232	97.02 345	86.33 365	89.56 310	95.01 367	79.22 319	97.09 332	82.34 381	81.16 364	94.01 346

v2v482		91.30 304	90.07 318	95.01 275	93.13 374	93.79 249	99.77 163	97.02 345	88.05 341	89.25 316	95.37 351	80.73 303	97.15 325	87.28 340	80.04 379	94.09 340

OPM-MVS		93.21 263	92.80 261	94.44 302	93.12 376	90.85 322	99.77 163	97.61 277	96.19 89	91.56 278	98.65 216	75.16 358	98.47 242	93.78 248	89.39 292	93.99 349
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 317	89.52 327	94.59 292	93.11 377	92.77 273	99.56 221	96.99 348	86.38 364	89.82 301	94.95 372	80.50 308	97.10 330	83.98 369	80.41 374	93.90 356

PEN-MVS		90.19 333	89.06 336	93.57 334	93.06 378	90.90 320	99.06 290	98.47 133	88.11 340	85.91 371	96.30 312	76.67 339	95.94 387	87.07 343	76.91 399	93.89 357

v1240		90.20 332	88.79 341	94.44 302	93.05 379	92.27 289	99.38 252	96.92 359	85.89 369	89.36 313	94.87 374	77.89 331	97.03 338	80.66 390	81.08 367	94.01 346

v148		90.70 318	89.63 323	93.92 322	92.97 380	90.97 316	99.75 172	96.89 361	87.51 347	88.27 339	95.01 367	81.67 289	97.04 336	87.40 338	77.17 397	93.75 365

v1921920		90.46 324	89.12 334	94.50 298	92.96 381	92.46 285	99.49 234	96.98 350	86.10 367	89.61 308	95.30 354	78.55 328	97.03 338	82.17 382	80.89 372	94.01 346

MVStest1		85.03 374	82.76 383	91.83 365	92.95 382	89.16 354	98.57 342	94.82 416	71.68 430	68.54 433	95.11 364	83.17 280	95.66 391	74.69 416	65.32 430	90.65 412

tt0320-xc		82.94 388	80.35 395	90.72 378	92.90 383	83.54 399	96.85 396	94.73 419	63.12 435	79.85 406	93.77 394	49.43 433	95.46 394	80.98 389	71.54 414	93.16 382

Baseline_NR-MVSNet		90.33 328	89.51 328	92.81 353	92.84 384	89.95 343	99.77 163	93.94 428	84.69 385	89.04 323	95.66 333	81.66 290	96.52 363	90.99 289	76.98 398	91.97 401

test_method		80.79 393	79.70 397	84.08 408	92.83 385	67.06 434	99.51 230	95.42 406	54.34 440	81.07 400	93.53 396	44.48 436	92.22 427	78.90 401	77.23 396	92.94 387

pmmvs4		92.10 290	91.07 298	95.18 271	92.82 386	94.96 217	99.48 237	96.83 365	87.45 349	88.66 330	96.56 306	83.78 274	96.83 352	89.29 316	84.77 337	93.75 365

LF4IMVS		89.25 350	88.85 339	90.45 382	92.81 387	81.19 416	98.12 367	94.79 417	91.44 265	86.29 367	97.11 282	65.30 403	98.11 279	88.53 325	85.25 331	92.07 398

tt0320		83.56 387	81.15 390	90.77 376	92.77 388	83.58 398	96.83 397	95.52 405	63.26 434	81.36 397	92.54 404	53.26 427	95.77 389	80.45 391	74.38 408	92.96 386

DTE-MVSNet		89.40 347	88.24 350	92.88 351	92.66 389	89.95 343	99.10 282	98.22 206	87.29 351	85.12 377	96.22 314	76.27 346	95.30 399	83.56 373	75.74 404	93.41 374

EU-MVSNet		90.14 335	90.34 309	89.54 389	92.55 390	81.06 417	98.69 336	98.04 230	91.41 269	86.59 361	96.84 297	80.83 302	93.31 420	86.20 352	81.91 358	94.26 318

APD_test1		81.15 392	80.92 392	81.86 412	92.45 391	59.76 441	96.04 411	93.61 431	73.29 428	77.06 416	96.64 302	44.28 437	96.16 378	72.35 420	82.52 352	89.67 423

sc_t1		85.01 375	82.46 385	92.67 355	92.44 392	83.09 402	97.39 382	95.72 398	65.06 433	85.64 374	96.16 316	49.50 432	97.34 312	84.86 364	75.39 406	97.57 276

our_test_3		90.39 325	89.48 330	93.12 345	92.40 393	89.57 348	99.33 259	96.35 386	87.84 345	85.30 375	94.99 370	84.14 272	96.09 382	80.38 392	84.56 338	93.71 370

ppachtmachnet_test		89.58 345	88.35 348	93.25 343	92.40 393	90.44 332	99.33 259	96.73 372	85.49 376	85.90 372	95.77 327	81.09 298	96.00 386	76.00 414	82.49 353	93.30 378

v7n		89.65 343	88.29 349	93.72 328	92.22 395	90.56 329	99.07 289	97.10 335	85.42 378	86.73 358	94.72 375	80.06 312	97.13 327	81.14 387	78.12 388	93.49 373

dmvs_testset		83.79 384	86.07 365	76.94 416	92.14 396	48.60 451	96.75 398	90.27 441	89.48 311	78.65 410	98.55 229	79.25 318	86.65 439	66.85 430	82.69 350	95.57 295

PS-MVSNAJss		93.64 255	93.31 252	94.61 290	92.11 397	92.19 290	99.12 280	97.38 302	92.51 232	88.45 333	96.99 290	91.20 170	97.29 320	94.36 231	87.71 315	94.36 310

pmmvs5		90.17 334	89.09 335	93.40 337	92.10 398	89.77 346	99.74 175	95.58 403	85.88 370	87.24 355	95.74 328	73.41 369	96.48 365	88.54 324	83.56 347	93.95 352

N_pmnet		80.06 396	80.78 393	77.89 415	91.94 399	45.28 453	98.80 327	56.82 455	78.10 416	80.08 404	93.33 397	77.03 334	95.76 390	68.14 428	82.81 349	92.64 391

test_djsdf		92.83 274	92.29 275	94.47 300	91.90 400	92.46 285	99.55 224	97.27 318	91.17 273	89.96 294	96.07 322	81.10 297	96.89 346	94.67 226	88.91 295	94.05 343

SixPastTwentyTwo		88.73 352	88.01 353	90.88 372	91.85 401	82.24 408	98.22 364	95.18 413	88.97 321	82.26 391	96.89 292	71.75 374	96.67 359	84.00 368	82.98 348	93.72 369

K. test v3		88.05 358	87.24 359	90.47 381	91.82 402	82.23 409	98.96 306	97.42 298	89.05 316	76.93 418	95.60 335	68.49 388	95.42 395	85.87 357	81.01 370	93.75 365

OurMVSNet-221017-0		89.81 340	89.48 330	90.83 375	91.64 403	81.21 415	98.17 366	95.38 408	91.48 263	85.65 373	97.31 277	72.66 370	97.29 320	88.15 329	84.83 336	93.97 351

mvs_tets		91.81 294	91.08 297	94.00 319	91.63 404	90.58 328	98.67 338	97.43 296	92.43 234	87.37 353	97.05 287	71.76 373	97.32 315	94.75 223	88.68 301	94.11 339

Gipuma		66.95 409	65.00 409	72.79 421	91.52 405	67.96 433	66.16 444	95.15 414	47.89 442	58.54 439	67.99 444	29.74 441	87.54 438	50.20 443	77.83 390	62.87 444
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 169	95.74 178	98.32 139	91.47 406	95.56 193	99.84 141	97.30 313	97.74 27	97.89 165	99.35 145	79.62 315	99.85 122	99.25 68	99.24 134	99.55 153

jajsoiax		91.92 292	91.18 295	94.15 311	91.35 407	90.95 319	99.00 300	97.42 298	92.61 224	87.38 352	97.08 284	72.46 371	97.36 310	94.53 229	88.77 299	94.13 338

MDA-MVSNet-bldmvs		84.09 382	81.52 389	91.81 366	91.32 408	88.00 370	98.67 338	95.92 394	80.22 410	55.60 442	93.32 398	68.29 390	93.60 418	73.76 417	76.61 401	93.82 363

MVP-Stereo		90.93 312	90.45 307	92.37 359	91.25 409	88.76 356	98.05 371	96.17 389	87.27 352	84.04 382	95.30 354	78.46 329	97.27 322	83.78 371	99.70 89	91.09 406
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 370	83.32 378	92.10 361	90.96 410	88.58 362	99.20 275	96.52 381	79.70 412	57.12 441	92.69 403	79.11 321	93.86 415	77.10 409	77.46 394	93.86 360

YYNet1		85.50 371	83.33 377	92.00 362	90.89 411	88.38 366	99.22 274	96.55 380	79.60 413	57.26 440	92.72 402	79.09 323	93.78 416	77.25 408	77.37 395	93.84 361

anonymousdsp		91.79 299	90.92 299	94.41 305	90.76 412	92.93 272	98.93 310	97.17 327	89.08 315	87.46 351	95.30 354	78.43 330	96.92 344	92.38 268	88.73 300	93.39 376

lessismore_v0				90.53 379	90.58 413	80.90 418		95.80 395		77.01 417	95.84 325	66.15 399	96.95 342	83.03 376	75.05 407	93.74 368

EG-PatchMatch MVS		85.35 372	83.81 375	89.99 387	90.39 414	81.89 411	98.21 365	96.09 391	81.78 404	74.73 424	93.72 395	51.56 431	97.12 329	79.16 400	88.61 302	90.96 409

EGC-MVSNET		69.38 402	63.76 412	86.26 405	90.32 415	81.66 414	96.24 407	93.85 429	0.99 452	3.22 453	92.33 409	52.44 428	92.92 423	59.53 439	84.90 335	84.21 433

CMPMVS	61.59 21	84.75 378	85.14 370	83.57 409	90.32 415	62.54 437	96.98 392	97.59 281	74.33 426	69.95 430	96.66 300	64.17 406	98.32 263	87.88 333	88.41 307	89.84 421
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 381	82.92 381	89.21 391	90.03 417	82.60 405	96.89 395	95.62 402	80.59 408	75.77 423	89.17 421	65.04 404	94.79 406	72.12 421	81.02 369	90.23 415

pmmvs6		85.69 367	83.84 374	91.26 371	90.00 418	84.41 394	97.82 376	96.15 390	75.86 420	81.29 398	95.39 349	61.21 417	96.87 349	83.52 374	73.29 410	92.50 394

ttmdpeth		88.23 357	87.06 360	91.75 367	89.91 419	87.35 374	98.92 313	95.73 397	87.92 343	84.02 383	96.31 311	68.23 391	96.84 350	86.33 351	76.12 402	91.06 407

DSMNet-mixed		88.28 356	88.24 350	88.42 399	89.64 420	75.38 427	98.06 370	89.86 442	85.59 375	88.20 340	92.14 410	76.15 348	91.95 428	78.46 403	96.05 225	97.92 262

UnsupCasMVSNet_eth		85.52 369	83.99 371	90.10 385	89.36 421	83.51 400	96.65 399	97.99 232	89.14 314	75.89 422	93.83 392	63.25 410	93.92 413	81.92 384	67.90 426	92.88 388

Anonymous20231206		86.32 365	85.42 368	89.02 393	89.11 422	80.53 421	99.05 294	95.28 409	85.43 377	82.82 389	93.92 391	74.40 362	93.44 419	66.99 429	81.83 359	93.08 384

Anonymous20240521		85.15 373	83.81 375	89.16 392	88.32 423	82.69 404	98.80 327	95.74 396	79.72 411	81.53 396	90.99 413	65.38 402	94.16 411	72.69 419	81.11 366	90.63 413

OpenMVS_ROB	79.82 20	83.77 385	81.68 388	90.03 386	88.30 424	82.82 403	98.46 348	95.22 411	73.92 427	76.00 421	91.29 412	55.00 424	96.94 343	68.40 427	88.51 306	90.34 414

test20.03		84.72 379	83.99 371	86.91 403	88.19 425	80.62 420	98.88 316	95.94 393	88.36 337	78.87 408	94.62 380	68.75 386	89.11 434	66.52 431	75.82 403	91.00 408

KD-MVS_self_test		83.59 386	82.06 386	88.20 400	86.93 426	80.70 419	97.21 385	96.38 384	82.87 398	82.49 390	88.97 422	67.63 393	92.32 426	73.75 418	62.30 437	91.58 404

MIMVSNet1		82.58 389	80.51 394	88.78 395	86.68 427	84.20 395	96.65 399	95.41 407	78.75 414	78.59 411	92.44 405	51.88 430	89.76 433	65.26 434	78.95 382	92.38 397

CL-MVSNet_self_test		84.50 380	83.15 380	88.53 398	86.00 428	81.79 412	98.82 324	97.35 306	85.12 379	83.62 387	90.91 415	76.66 340	91.40 429	69.53 425	60.36 438	92.40 396

UnsupCasMVSNet_bld		79.97 398	77.03 403	88.78 395	85.62 429	81.98 410	93.66 423	97.35 306	75.51 423	70.79 429	83.05 436	48.70 434	94.91 404	78.31 404	60.29 439	89.46 426

mvs5depth		84.87 376	82.90 382	90.77 376	85.59 430	84.84 392	91.10 434	93.29 433	83.14 395	85.07 378	94.33 388	62.17 413	97.32 315	78.83 402	72.59 413	90.14 417

Patchmatch-RL test		86.90 363	85.98 367	89.67 388	84.45 431	75.59 426	89.71 437	92.43 435	86.89 359	77.83 415	90.94 414	94.22 92	93.63 417	87.75 334	69.61 418	99.79 105

pmmvs-eth3d		84.03 383	81.97 387	90.20 384	84.15 432	87.09 376	98.10 369	94.73 419	83.05 396	74.10 426	87.77 428	65.56 401	94.01 412	81.08 388	69.24 420	89.49 425

test_fmvs3		79.99 397	80.17 396	79.45 414	84.02 433	62.83 435	99.05 294	93.49 432	88.29 339	80.06 405	86.65 431	28.09 443	88.00 435	88.63 321	73.27 411	87.54 431

PM-MVS		80.47 394	78.88 399	85.26 406	83.79 434	72.22 429	95.89 414	91.08 439	85.71 374	76.56 420	88.30 424	36.64 439	93.90 414	82.39 380	69.57 419	89.66 424

new-patchmatchnet		81.19 391	79.34 398	86.76 404	82.86 435	80.36 422	97.92 373	95.27 410	82.09 403	72.02 427	86.87 430	62.81 412	90.74 432	71.10 422	63.08 434	89.19 428

mvsany_test3		82.12 390	81.14 391	85.06 407	81.87 436	70.41 431	97.09 389	92.14 436	91.27 272	77.84 414	88.73 423	39.31 438	95.49 392	90.75 296	71.24 415	89.29 427

WB-MVS		76.28 400	77.28 402	73.29 420	81.18 437	54.68 445	97.87 375	94.19 424	81.30 405	69.43 431	90.70 416	77.02 335	82.06 443	35.71 448	68.11 425	83.13 434

test_f		78.40 399	77.59 401	80.81 413	80.82 438	62.48 438	96.96 393	93.08 434	83.44 393	74.57 425	84.57 435	27.95 444	92.63 424	84.15 366	72.79 412	87.32 432

SSC-MVS		75.42 401	76.40 404	72.49 424	80.68 439	53.62 446	97.42 380	94.06 426	80.42 409	68.75 432	90.14 418	76.54 342	81.66 444	33.25 449	66.34 429	82.19 435

pmmvs3		80.27 395	77.77 400	87.76 402	80.32 440	82.43 407	98.23 363	91.97 437	72.74 429	78.75 409	87.97 427	57.30 423	90.99 431	70.31 423	62.37 436	89.87 420

testf1		68.38 405	66.92 406	72.78 422	78.80 441	50.36 448	90.95 435	87.35 447	55.47 438	58.95 437	88.14 425	20.64 448	87.60 436	57.28 440	64.69 431	80.39 437

APD_test2		68.38 405	66.92 406	72.78 422	78.80 441	50.36 448	90.95 435	87.35 447	55.47 438	58.95 437	88.14 425	20.64 448	87.60 436	57.28 440	64.69 431	80.39 437

ambc				83.23 410	77.17 443	62.61 436	87.38 439	94.55 423		76.72 419	86.65 431	30.16 440	96.36 370	84.85 365	69.86 417	90.73 411

test_vis3_rt		68.82 403	66.69 408	75.21 419	76.24 444	60.41 440	96.44 402	68.71 454	75.13 424	50.54 445	69.52 443	16.42 453	96.32 372	80.27 393	66.92 428	68.89 441

TDRefinement		84.76 377	82.56 384	91.38 370	74.58 445	84.80 393	97.36 383	94.56 422	84.73 384	80.21 403	96.12 321	63.56 408	98.39 253	87.92 332	63.97 433	90.95 410

E-PMN		52.30 413	52.18 415	52.67 430	71.51 446	45.40 452	93.62 424	76.60 452	36.01 446	43.50 447	64.13 446	27.11 445	67.31 449	31.06 450	26.06 445	45.30 448

EMVS		51.44 415	51.22 417	52.11 431	70.71 447	44.97 454	94.04 420	75.66 453	35.34 448	42.40 448	61.56 449	28.93 442	65.87 450	27.64 451	24.73 446	45.49 447

PMMVS2		67.15 408	64.15 411	76.14 418	70.56 448	62.07 439	93.89 421	87.52 446	58.09 437	60.02 436	78.32 438	22.38 447	84.54 441	59.56 438	47.03 443	81.80 436

FPMVS		68.72 404	68.72 405	68.71 426	65.95 449	44.27 455	95.97 413	94.74 418	51.13 441	53.26 443	90.50 417	25.11 446	83.00 442	60.80 437	80.97 371	78.87 439

wuyk23d		20.37 419	20.84 422	18.99 434	65.34 450	27.73 457	50.43 445	7.67 458	9.50 451	8.01 452	6.34 452	6.13 456	26.24 451	23.40 452	10.69 450	2.99 449

LCM-MVSNet		67.77 407	64.73 410	76.87 417	62.95 451	56.25 444	89.37 438	93.74 430	44.53 443	61.99 435	80.74 437	20.42 450	86.53 440	69.37 426	59.50 440	87.84 429

MVE	53.74 22	51.54 414	47.86 418	62.60 428	59.56 452	50.93 447	79.41 442	77.69 451	35.69 447	36.27 449	61.76 448	5.79 457	69.63 447	37.97 447	36.61 444	67.24 442
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 411	52.24 414	67.66 427	49.27 453	56.82 443	83.94 440	82.02 450	70.47 431	33.28 450	64.54 445	17.23 452	69.16 448	45.59 445	23.85 447	77.02 440

tmp_tt		65.23 410	62.94 413	72.13 425	44.90 454	50.03 450	81.05 441	89.42 445	38.45 444	48.51 446	99.90 18	54.09 426	78.70 446	91.84 277	18.26 448	87.64 430

PMVS	49.05 23	53.75 412	51.34 416	60.97 429	40.80 455	34.68 456	74.82 443	89.62 444	37.55 445	28.67 451	72.12 440	7.09 455	81.63 445	43.17 446	68.21 424	66.59 443
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 417	39.14 420	33.31 432	19.94 456	24.83 458	98.36 356	9.75 457	15.53 450	51.31 444	87.14 429	19.62 451	17.74 452	47.10 444	3.47 451	57.36 445

testmvs		40.60 416	44.45 419	29.05 433	19.49 457	14.11 459	99.68 197	18.47 456	20.74 449	64.59 434	98.48 236	10.95 454	17.09 453	56.66 442	11.01 449	55.94 446

mmdepth		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

monomultidepth		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

test_blank		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.02 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

eth-test2					0.00 458

eth-test					0.00 458

uanet_test		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

DCPMVS		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

cdsmvs_eth3d_5k		23.43 418	31.24 421	0.00 435	0.00 458	0.00 460	0.00 446	98.09 224	0.00 453	0.00 454	99.67 106	83.37 277	0.00 454	0.00 453	0.00 452	0.00 450

pcd_1.5k_mvsjas		7.60 421	10.13 424	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	91.20 170	0.00 454	0.00 453	0.00 452	0.00 450

sosnet-low-res		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

sosnet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uncertanet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

Regformer		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

ab-mvs-re		8.28 420	11.04 423	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	99.40 139	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uanet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

WAC-MVS						90.97 316								86.10 355

PC_three_1452									96.96 57	99.80 23	99.79 58	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 149	97.27 44	99.80 23	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 75	99.83 19	99.91 14	97.87 5	100.00 1	99.92 13	100.00 1	100.00 1

GSMVS																99.59 143

sam_mvs1												94.72 71				99.59 143

sam_mvs												94.25 91

MTGPA								98.28 197

test_post1							95.78 415				59.23 450	93.20 125	97.74 299	91.06 287

test_post											63.35 447	94.43 79	98.13 278

patchmatchnet-post											91.70 411	95.12 56	97.95 290

MTMP							99.87 122	96.49 382

test9_res														99.71 42	99.99 21	100.00 1

agg_prior2														99.48 57	100.00 1	100.00 1

test_prior4						98.05 76	99.94 82

test_prior2							99.95 65		95.78 97	99.73 42	99.76 67	96.00 37		99.78 30	100.00 1

旧先验2							99.46 242		94.21 156	99.85 15			99.95 79	96.96 182

新几何2							99.40 246

无先验							99.49 234	98.71 73	93.46 187				100.00 1	94.36 231		99.99 23

原ACMM2							99.90 106

testdata2													99.99 36	90.54 300

segment_acmp												96.68 29

testdata1							99.28 268		96.35 85

plane_prior5								97.87 246					98.37 259	97.79 159	89.55 289	94.52 299

plane_prior4											98.59 222

plane_prior3						91.64 308			96.63 70	93.01 261

plane_prior2							99.84 141		96.38 81

plane_prior						91.74 302	99.86 133		96.76 65						89.59 288

n2								0.00 459

nn								0.00 459

door-mid								89.69 443

test11								98.44 141

door								90.31 440

HQP5-MVS						91.85 298

BP-MVS														97.92 150

HQP4-MVS										93.37 256			98.39 253			94.53 297

HQP3-MVS								97.89 244							89.60 286

HQP2-MVS												80.65 305

MDTV_nov1_ep13_2view						96.26 160	96.11 409		91.89 250	98.06 158		94.40 81		94.30 234		99.67 123

ACMMP++_ref															87.04 320

ACMMP++															88.23 309

Test By Simon												92.82 136

High-resolution multi-view benchmark