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	99.09 284	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	89.90 313	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	92.06 249	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		93.83 250	92.06 285	99.15 64	99.94 13	97.50 103	99.94 82	98.42 161	96.22 87	99.41 81	41.37 457	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	97.27 213	99.89 45	91.92 299	99.90 106	99.07 37	88.67 337	95.26 239	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	97.78 257	96.52 73	98.61 130	99.31 148	92.73 138	99.67 160	96.77 187	99.48 114	99.06 225

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	90.78 295	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	95.11 200	98.02 159	99.85 56	95.10 217	98.74 334	98.50 130	87.22 359	93.66 258	99.86 29	87.45 230	99.95 79	90.94 296	99.81 83	99.02 229

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	94.77 227	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	99.41 181
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	91.75 260	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	97.41 204	99.76 68	93.36 267	99.65 201	97.95 238	96.03 92	97.41 181	99.70 94	89.61 199	99.51 170	96.73 189	98.25 172	99.38 183

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	91.21 288	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	95.11 212	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	97.92 243	98.07 16	98.76 122	99.55 124	95.00 63	99.94 87	99.91 16	97.68 189	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	96.87 224	99.71 78	91.74 304	99.85 136	97.95 238	93.11 202	95.72 229	99.16 164	92.35 152	99.94 87	95.32 208	99.35 129	98.92 234

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	95.59 263	99.67 83	86.91 384	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	93.92 327	99.63 85	81.76 418	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	96.72 229	99.59 87	96.99 128	99.95 65	99.10 34	94.06 163	98.27 149	95.80 332	89.00 210	99.95 79	99.12 72	87.53 324	93.24 386

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	95.22 212	99.52 228	99.07 37	93.96 168	96.49 207	98.35 247	82.28 287	99.82 134	90.15 312	99.22 136	98.81 241

dcpmvs_2		97.42 115	98.09 59	95.42 268	99.58 91	87.24 380	99.23 273	96.95 359	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	91.86 255	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	91.68 261	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	84.48 272	99.95 79	94.92 218	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	88.48 341	99.06 105	99.66 108	90.30 191	99.64 165	96.32 193	99.97 42	99.96 69

cl22		93.77 255	93.25 259	95.33 272	99.49 97	94.43 232	99.61 210	98.09 224	90.38 301	89.16 328	95.61 340	90.56 186	97.34 316	91.93 279	84.45 345	94.21 331

testdata				98.42 134	99.47 98	95.33 205		98.56 106	93.78 177	99.79 32	99.85 33	93.64 111	99.94 87	94.97 216	99.94 55	100.00 1

MAR-MVS		97.43 111	97.19 114	98.15 150	99.47 98	94.79 226	99.05 295	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		94.54 229	93.42 249	97.91 167	99.46 100	94.04 245	98.93 313	97.48 294	81.15 412	90.04 299	99.55 124	87.02 238	99.95 79	88.97 324	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	98.46 352	99.42 21	97.03 54	99.02 107	99.09 166	99.35 2	98.21 278	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	93.81 247	99.94 55	99.98 51

TAPA-MVS	92.12 8	94.42 237	93.60 241	96.90 223	99.33 104	91.78 303	99.78 160	98.00 232	89.89 314	94.52 245	99.47 130	91.97 161	99.18 195	69.90 430	99.52 107	99.73 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 204	95.07 202	96.32 244	99.32 106	96.60 146	99.76 168	98.85 59	96.65 69	87.83 350	96.05 329	99.52 1	98.11 283	96.58 190	81.07 374	94.25 326

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	95.20 213	99.98 18	97.15 333	95.53 106	99.62 57	99.79 58	92.08 159	98.38 261	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	95.18 214	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	98.59 251

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	91.43 270	97.88 166	98.99 176	95.84 42	99.84 130	98.82 95	95.32 253	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	91.43 270	97.88 166	98.99 176	95.84 42	99.84 130	98.82 95	95.32 253	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	94.37 249	99.49 129	83.29 282	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	97.43 202	99.10 118	94.42 233	99.99 5	97.10 341	95.07 115	99.68 47	99.75 75	92.95 131	98.34 265	98.38 122	99.14 138	99.54 157

Anonymous202405211		93.10 273	91.99 286	96.40 240	99.10 118	89.65 352	98.88 319	97.93 240	83.71 397	94.00 255	98.75 211	68.79 391	99.88 116	95.08 213	91.71 286	99.68 121

fmvsm_s_conf0.5_n		97.80 91	97.85 79	97.67 184	99.06 120	94.41 234	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	98.62 250

HyFIR lowres test		96.66 157	96.43 150	97.36 209	99.05 121	93.91 250	99.70 194	99.80 3	90.54 299	96.26 214	98.08 260	92.15 157	98.23 277	96.84 186	95.46 248	99.93 81

LFMVS		94.75 223	93.56 244	98.30 140	99.03 122	95.70 186	98.74 334	97.98 235	87.81 352	98.47 138	99.39 141	67.43 400	99.53 167	98.01 144	95.20 256	99.67 123

fmvsm_s_conf0.5_n_4		97.75 96	97.86 78	97.42 203	99.01 123	94.69 228	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	97.64 277

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	81.83 293	99.94 87	99.78 30	98.79 154	97.51 285

AllTest		92.48 288	91.64 291	95.00 281	99.01 123	88.43 368	98.94 311	96.82 373	86.50 368	88.71 333	98.47 242	74.73 366	99.88 116	85.39 364	96.18 224	96.71 291

TestCases				95.00 281	99.01 123	88.43 368		96.82 373	86.50 368	88.71 333	98.47 242	74.73 366	99.88 116	85.39 364	96.18 224	96.71 291

COLMAP_ROB	90.47 14	92.18 295	91.49 297	94.25 315	99.00 127	88.04 374	98.42 358	96.70 380	82.30 408	88.43 342	99.01 173	76.97 341	99.85 122	86.11 360	96.50 216	94.86 302
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	84.15 275	99.97 58	99.76 35	99.50 112	98.39 256

test_fmvs1		95.35 205	95.68 182	94.36 311	98.99 128	84.98 395	99.96 46	96.65 382	97.60 31	99.73 42	98.96 182	71.58 381	99.93 96	98.31 127	99.37 127	98.17 261

HY-MVS	92.50 7	97.79 93	97.17 116	99.63 17	98.98 130	99.32 9	97.49 385	99.52 14	95.69 101	98.32 147	97.41 280	93.32 118	99.77 142	98.08 141	95.75 239	99.81 102

VNet		97.21 125	96.57 144	99.13 70	98.97 131	97.82 88	99.03 298	99.21 32	94.31 150	99.18 98	98.88 194	86.26 250	99.89 110	98.93 86	94.32 266	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	94.26 237	94.91 258	99.31 199

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	98.87 200	89.33 203	99.50 172	93.84 244	94.57 262	99.27 206

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	98.87 200	89.33 203	99.50 172	93.84 244	94.57 262	99.16 214

sasdasda		97.09 132	96.32 152	99.39 40	98.93 135	98.95 27	99.72 186	97.35 307	94.45 138	97.88 166	99.42 134	86.71 241	99.52 168	98.48 117	93.97 272	99.72 115

Anonymous20231211		89.86 345	88.44 353	94.13 318	98.93 135	90.68 330	98.54 349	98.26 200	76.28 424	86.73 364	95.54 344	70.60 387	97.56 309	90.82 299	80.27 383	94.15 339

canonicalmvs		97.09 132	96.32 152	99.39 40	98.93 135	98.95 27	99.72 186	97.35 307	94.45 138	97.88 166	99.42 134	86.71 241	99.52 168	98.48 117	93.97 272	99.72 115

SDMVSNet		94.80 219	93.96 233	97.33 211	98.92 138	95.42 198	99.59 214	98.99 40	92.41 235	92.55 273	97.85 271	75.81 356	98.93 213	97.90 152	91.62 287	97.64 277

sd_testset		93.55 262	92.83 266	95.74 261	98.92 138	90.89 326	98.24 365	98.85 59	92.41 235	92.55 273	97.85 271	71.07 386	98.68 234	93.93 241	91.62 287	97.64 277

EPNet_dtu		95.71 193	95.39 188	96.66 231	98.92 138	93.41 264	99.57 219	98.90 50	96.19 89	97.52 176	98.56 232	92.65 140	97.36 314	77.89 411	98.33 167	99.20 212
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	95.58 247	99.78 108

CHOSEN 1792x2688		96.81 146	96.53 145	97.64 186	98.91 142	93.07 269	99.65 201	99.80 3	95.64 102	95.39 235	98.86 202	84.35 274	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	98.86 202	89.25 205	99.50 172	93.84 244	94.57 262	99.27 206

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	98.86 202	89.25 205	99.50 172	93.44 257	94.50 265	99.16 214

MSDG		94.37 239	93.36 256	97.40 205	98.88 145	93.95 249	99.37 254	97.38 303	85.75 379	90.80 292	99.17 163	84.11 277	99.88 116	86.35 356	98.43 165	98.36 258

MGCFI-Net		97.00 137	96.22 156	99.34 45	98.86 146	98.80 39	99.67 199	97.30 314	94.31 150	97.77 172	99.41 138	86.36 248	99.50 172	98.38 122	93.90 274	99.72 115

h-mvs33		94.92 216	94.36 221	96.59 233	98.85 147	91.29 318	98.93 313	98.94 44	95.90 94	98.77 119	98.42 245	90.89 181	99.77 142	97.80 156	70.76 422	98.72 247

Anonymous20240529		92.10 296	90.65 308	96.47 235	98.82 148	90.61 332	98.72 336	98.67 80	75.54 428	93.90 257	98.58 230	66.23 404	99.90 105	94.70 227	90.67 290	98.90 237

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	98.49 238	89.05 209	99.88 116	97.10 176	98.34 166	99.43 179

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	98.69 248

CANet_DTU		96.76 150	96.15 159	98.60 111	98.78 151	97.53 100	99.84 141	97.63 272	97.25 47	99.20 95	99.64 111	81.36 299	99.98 47	92.77 268	98.89 148	98.28 260

mvsany_test1		97.82 89	97.90 76	97.55 193	98.77 152	93.04 272	99.80 157	97.93 240	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	94.26 268	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	95.63 245	99.45 175

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	98.69 248

miper_enhance_ethall		94.36 241	93.98 232	95.49 264	98.68 157	95.24 210	99.73 182	97.29 317	93.28 194	89.86 304	95.97 330	94.37 85	97.05 337	92.20 272	84.45 345	94.19 332

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	91.10 281	98.17 155	98.59 227	93.86 105	98.19 279	95.64 205	95.24 255	99.28 205

test2506		97.53 108	97.19 114	98.58 115	98.66 160	96.90 132	98.81 328	99.77 5	94.93 118	97.95 161	98.96 182	92.51 147	99.20 193	94.93 217	98.15 175	99.64 129

ECVR-MVS		95.66 196	95.05 203	97.51 197	98.66 160	93.71 254	98.85 325	98.45 136	94.93 118	96.86 197	98.96 182	75.22 362	99.20 193	95.34 207	98.15 175	99.64 129

mamv4		95.24 208	96.90 125	90.25 389	98.65 162	72.11 436	98.28 363	97.64 271	89.99 312	95.93 223	98.25 255	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	97.79 255	94.56 134	99.74 40	98.35 247	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	94.26 240	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	99.08 223

MVSMamba_PlusPlus		97.83 86	97.45 100	98.99 83	98.60 165	98.15 66	99.58 216	97.74 262	90.34 304	99.26 94	98.32 250	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	98.84 206	93.36 115	98.30 269	95.84 201	94.30 267	99.05 227

test1111		95.57 199	94.98 206	97.37 207	98.56 166	93.37 266	98.86 323	98.45 136	94.95 117	96.63 203	98.95 187	75.21 363	99.11 199	95.02 214	98.14 177	99.64 129

MVSTER		95.53 200	95.22 195	96.45 238	98.56 166	97.72 91	99.91 100	97.67 267	92.38 237	91.39 283	97.14 287	97.24 18	97.30 321	94.80 223	87.85 319	94.34 321

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	98.73 213	95.50 49	99.69 156	98.53 115	94.63 260	98.99 231

VDD-MVS		93.77 255	92.94 264	96.27 245	98.55 169	90.22 341	98.77 333	97.79 255	90.85 287	96.82 199	99.42 134	61.18 424	99.77 142	98.95 84	94.13 269	98.82 240

tpmvs		94.28 243	93.57 243	96.40 240	98.55 169	91.50 316	95.70 422	98.55 112	87.47 354	92.15 276	94.26 395	91.42 166	98.95 212	88.15 335	95.85 235	98.76 243

UGNet		95.33 206	94.57 217	97.62 189	98.55 169	94.85 222	98.67 342	99.32 26	95.75 99	96.80 200	96.27 319	72.18 378	99.96 70	94.58 230	99.05 144	98.04 266
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	95.18 210	94.10 228	98.43 132	98.55 169	95.99 174	97.91 378	97.31 313	90.35 303	89.48 317	99.22 159	85.19 263	99.89 110	90.40 309	98.47 164	99.41 181
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	94.34 312	98.51 174	89.99 346	99.39 250	98.57 100	93.14 199	97.33 183	98.31 252	93.44 113	94.68 412	93.69 254	95.98 229	98.34 259

UWE-MVS		96.79 147	96.72 137	97.00 218	98.51 174	93.70 255	99.71 189	98.60 94	92.96 204	97.09 190	98.34 249	96.67 31	98.85 216	92.11 277	96.50 216	98.44 254

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	98.75 211	95.20 54	99.48 178	98.93 86	96.40 219	99.29 203

test_vis1_n_1920		95.44 202	95.31 191	95.82 258	98.50 176	88.74 362	99.98 18	97.30 314	97.84 25	99.85 15	99.19 161	66.82 402	99.97 58	98.82 95	99.46 119	98.76 243

BH-w/o		95.71 193	95.38 189	96.68 230	98.49 178	92.28 290	99.84 141	97.50 292	92.12 246	92.06 279	98.79 209	84.69 270	98.67 235	95.29 209	99.66 91	99.09 221

baseline1		95.78 189	94.86 209	98.54 121	98.47 179	98.07 74	99.06 291	97.99 233	92.68 220	94.13 254	98.62 226	93.28 121	98.69 233	93.79 249	85.76 332	98.84 239

fmvsm_s_conf0.5_n_7		97.70 102	97.74 83	97.59 192	98.44 180	95.16 216	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	97.66 276

EPMVS		96.53 163	96.01 162	98.09 154	98.43 181	96.12 172	96.36 409	99.43 20	93.53 184	97.64 174	95.04 372	94.41 80	98.38 261	91.13 290	98.11 178	99.75 111

kuosan		93.17 270	92.60 272	94.86 288	98.40 182	89.54 354	98.44 354	98.53 119	84.46 392	88.49 338	97.92 268	90.57 185	97.05 337	83.10 381	93.49 277	97.99 267

WBMVS		94.52 232	94.03 230	95.98 251	98.38 183	96.68 141	99.92 92	97.63 272	90.75 296	89.64 312	95.25 365	96.77 25	96.90 349	94.35 235	83.57 352	94.35 319

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	98.42 245	96.77 25	99.17 196	98.54 113	96.20 223	99.11 220

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	97.01 208	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	98.70 216	95.48 50	99.22 189	97.85 154	95.05 257	99.07 224

BH-untuned		95.18 210	94.83 210	96.22 246	98.36 186	91.22 319	99.80 157	97.32 312	90.91 285	91.08 286	98.67 218	83.51 279	98.54 243	94.23 238	99.61 99	98.92 234

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	98.72 214	94.81 68	99.21 190	96.98 180	94.63 260	99.03 228

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	98.72 214	94.83 67	99.21 190	97.00 178	94.64 259	98.95 232

ET-MVSNet_ETH3D		94.37 239	93.28 258	97.64 186	98.30 190	97.99 79	99.99 5	97.61 278	94.35 147	71.57 434	99.45 133	96.23 35	95.34 402	96.91 185	85.14 339	99.59 143

AUN-MVS		93.28 267	92.60 272	95.34 271	98.29 191	90.09 344	99.31 262	98.56 106	91.80 259	96.35 213	98.00 263	89.38 202	98.28 272	92.46 269	69.22 427	97.64 277

FMVSNet3		92.69 283	91.58 293	95.99 250	98.29 191	97.42 108	99.26 271	97.62 275	89.80 315	89.68 308	95.32 359	81.62 297	96.27 379	87.01 352	85.65 333	94.29 323

PMMVS		96.76 150	96.76 134	96.76 227	98.28 193	92.10 294	99.91 100	97.98 235	94.12 158	99.53 69	99.39 141	86.93 240	98.73 227	96.95 183	97.73 186	99.45 175

hse-mvs2		94.38 238	94.08 229	95.31 273	98.27 194	90.02 345	99.29 267	98.56 106	95.90 94	98.77 119	98.00 263	90.89 181	98.26 276	97.80 156	69.20 428	97.64 277

PVSNet_0	88.03 19	91.80 303	90.27 317	96.38 242	98.27 194	90.46 336	99.94 82	99.61 13	93.99 166	86.26 374	97.39 282	71.13 385	99.89 110	98.77 99	67.05 433	98.79 242

UA-Net		96.54 162	95.96 169	98.27 142	98.23 196	95.71 185	98.00 376	98.45 136	93.72 181	98.41 142	99.27 153	88.71 215	99.66 163	91.19 289	97.69 187	99.44 178

test_cas_vis1_n_1920		96.59 160	96.23 155	97.65 185	98.22 197	94.23 241	99.99 5	97.25 321	97.77 26	99.58 65	99.08 167	77.10 338	99.97 58	97.64 164	99.45 120	98.74 245

FE-MVS		95.70 195	95.01 205	97.79 175	98.21 198	94.57 229	95.03 423	98.69 75	88.90 331	97.50 178	96.19 321	92.60 143	99.49 177	89.99 314	97.94 184	99.31 199

GG-mvs-BLEND				98.54 121	98.21 198	98.01 78	93.87 428	98.52 121		97.92 162	97.92 268	99.02 3	97.94 296	98.17 134	99.58 103	99.67 123

mvs_anonymous		95.65 197	95.03 204	97.53 195	98.19 200	95.74 183	99.33 259	97.49 293	90.87 286	90.47 295	97.10 289	88.23 218	97.16 328	95.92 199	97.66 190	99.68 121

MVS_Test		96.46 165	95.74 178	98.61 110	98.18 201	97.23 115	99.31 262	97.15 333	91.07 282	98.84 114	97.05 293	88.17 219	98.97 209	94.39 232	97.50 192	99.61 140

BH-RMVSNet		95.18 210	94.31 224	97.80 173	98.17 202	95.23 211	99.76 168	97.53 288	92.52 231	94.27 252	99.25 157	76.84 343	98.80 219	90.89 298	99.54 105	99.35 191

dongtai		91.55 309	91.13 302	92.82 357	98.16 203	86.35 385	99.47 238	98.51 124	83.24 400	85.07 384	97.56 276	90.33 190	94.94 408	76.09 419	91.73 285	97.18 288

RPSCF		91.80 303	92.79 268	88.83 400	98.15 204	69.87 438	98.11 372	96.60 384	83.93 395	94.33 250	99.27 153	79.60 321	99.46 181	91.99 278	93.16 282	97.18 288

ETV-MVS		97.92 78	97.80 82	98.25 143	98.14 205	96.48 150	99.98 18	97.63 272	95.61 103	99.29 92	99.46 132	92.55 145	98.82 217	99.02 82	98.54 162	99.46 173

IS-MVSNet		96.29 175	95.90 174	97.45 200	98.13 206	94.80 225	99.08 286	97.61 278	92.02 251	95.54 233	98.96 182	90.64 184	98.08 285	93.73 252	97.41 196	99.47 172

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	97.76 261	98.05 17	99.65 50	99.58 120	80.88 306	99.93 96	99.59 50	98.17 173	97.29 286

ab-mvs		94.69 224	93.42 249	98.51 126	98.07 209	96.26 160	96.49 407	98.68 77	90.31 305	94.54 244	97.00 295	76.30 351	99.71 152	95.98 198	93.38 280	99.56 152

XVG-OURS-SEG-HR		94.79 220	94.70 216	95.08 278	98.05 210	89.19 356	99.08 286	97.54 286	93.66 182	94.87 242	99.58 120	78.78 329	99.79 137	97.31 170	93.40 279	96.25 295

EIA-MVS		97.53 108	97.46 98	97.76 180	98.04 211	94.84 223	99.98 18	97.61 278	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		94.82 217	94.74 215	95.06 279	98.00 212	89.19 356	99.08 286	97.55 284	94.10 159	94.71 243	99.62 115	80.51 312	99.74 148	96.04 197	93.06 284	96.25 295

mvsmamba		96.94 140	96.73 136	97.55 193	97.99 213	94.37 237	99.62 208	97.70 264	93.13 200	98.42 141	97.92 268	88.02 220	98.75 226	98.78 98	99.01 145	99.52 163

dp		95.05 213	94.43 219	96.91 221	97.99 213	92.73 279	96.29 412	97.98 235	89.70 316	95.93 223	94.67 385	93.83 107	98.45 249	86.91 355	96.53 215	99.54 157

tpmrst		96.27 177	95.98 165	97.13 215	97.96 215	93.15 268	96.34 410	98.17 212	92.07 247	98.71 125	95.12 369	93.91 102	98.73 227	94.91 220	96.62 213	99.50 169

TR-MVS		94.54 229	93.56 244	97.49 199	97.96 215	94.34 238	98.71 337	97.51 291	90.30 306	94.51 246	98.69 217	75.56 357	98.77 223	92.82 267	95.99 228	99.35 191

Vis-MVSNet (Re-imp)		96.32 172	95.98 165	97.35 210	97.93 217	94.82 224	99.47 238	98.15 220	91.83 256	95.09 240	99.11 165	91.37 168	97.47 312	93.47 256	97.43 193	99.74 112

MDTV_nov1_ep13			95.69 180		97.90 218	94.15 243	95.98 418	98.44 141	93.12 201	97.98 160	95.74 334	95.10 57	98.58 239	90.02 313	96.92 210

Fast-Effi-MVS+		95.02 214	94.19 226	97.52 196	97.88 219	94.55 230	99.97 36	97.08 345	88.85 333	94.47 247	97.96 267	84.59 271	98.41 253	89.84 316	97.10 203	99.59 143

ADS-MVSNet2		93.80 254	93.88 236	93.55 340	97.87 220	85.94 389	94.24 424	96.84 370	90.07 309	96.43 209	94.48 390	90.29 192	95.37 401	87.44 342	97.23 199	99.36 187

ADS-MVSNet		94.79 220	94.02 231	97.11 217	97.87 220	93.79 251	94.24 424	98.16 217	90.07 309	96.43 209	94.48 390	90.29 192	98.19 279	87.44 342	97.23 199	99.36 187

Effi-MVS+		96.30 174	95.69 180	98.16 147	97.85 222	96.26 160	97.41 387	97.21 325	90.37 302	98.65 128	98.58 230	86.61 245	98.70 232	97.11 175	97.37 197	99.52 163

PatchmatchNet		95.94 185	95.45 186	97.39 206	97.83 223	94.41 234	96.05 416	98.40 170	92.86 208	97.09 190	95.28 364	94.21 94	98.07 287	89.26 322	98.11 178	99.70 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 227	93.61 239	97.74 182	97.82 224	96.26 160	99.96 46	97.78 257	85.76 377	94.00 255	97.54 277	76.95 342	99.21 190	97.23 172	95.43 250	97.76 275

1112_ss		96.01 183	95.20 196	98.42 134	97.80 225	96.41 153	99.65 201	96.66 381	92.71 217	92.88 269	99.40 139	92.16 156	99.30 185	91.92 280	93.66 275	99.55 153

Test_1112_low_res		95.72 191	94.83 210	98.42 134	97.79 226	96.41 153	99.65 201	96.65 382	92.70 218	92.86 270	96.13 325	92.15 157	99.30 185	91.88 281	93.64 276	99.55 153

Effi-MVS+-dtu		94.53 231	95.30 192	92.22 365	97.77 227	82.54 411	99.59 214	97.06 347	94.92 120	95.29 237	95.37 357	85.81 254	97.89 297	94.80 223	97.07 204	96.23 297

tpm cat1		93.51 263	92.52 278	96.47 235	97.77 227	91.47 317	96.13 414	98.06 227	80.98 413	92.91 268	93.78 399	89.66 197	98.87 214	87.03 351	96.39 220	99.09 221

FA-MVS(test-final)		95.86 186	95.09 201	98.15 150	97.74 229	95.62 191	96.31 411	98.17 212	91.42 272	96.26 214	96.13 325	90.56 186	99.47 180	92.18 273	97.07 204	99.35 191

xiu_mvs_v1_base_debu		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 262	97.86 249	96.43 78	99.62 57	99.69 98	85.56 258	99.68 157	99.05 75	98.31 168	97.83 271

xiu_mvs_v1_base		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 262	97.86 249	96.43 78	99.62 57	99.69 98	85.56 258	99.68 157	99.05 75	98.31 168	97.83 271

xiu_mvs_v1_base_debi		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 262	97.86 249	96.43 78	99.62 57	99.69 98	85.56 258	99.68 157	99.05 75	98.31 168	97.83 271

EPP-MVSNet		96.69 155	96.60 142	96.96 220	97.74 229	93.05 271	99.37 254	98.56 106	88.75 335	95.83 227	99.01 173	96.01 36	98.56 241	96.92 184	97.20 201	99.25 208

gg-mvs-nofinetune		93.51 263	91.86 290	98.47 128	97.72 234	97.96 83	92.62 432	98.51 124	74.70 431	97.33 183	69.59 448	98.91 4	97.79 300	97.77 161	99.56 104	99.67 123

IB-MVS	92.85 6	94.99 215	93.94 234	98.16 147	97.72 234	95.69 188	99.99 5	98.81 64	94.28 153	92.70 271	96.90 297	95.08 58	99.17 196	96.07 196	73.88 415	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	91.83 256	97.45 179	99.04 170	97.50 9	99.10 201	94.75 225	96.37 221	99.16 214

VortexMVS		94.11 245	93.50 246	95.94 253	97.70 237	96.61 145	99.35 257	97.18 328	93.52 186	89.57 315	95.74 334	87.55 227	96.97 345	95.76 204	85.13 340	94.23 328

Syy-MVS		90.00 343	90.63 309	88.11 407	97.68 238	74.66 434	99.71 189	98.35 183	90.79 293	92.10 277	98.67 218	79.10 327	93.09 427	63.35 441	95.95 232	96.59 293

myMVS_eth3d		94.46 236	94.76 214	93.55 340	97.68 238	90.97 321	99.71 189	98.35 183	90.79 293	92.10 277	98.67 218	92.46 150	93.09 427	87.13 348	95.95 232	96.59 293

test_fmvs1_n		94.25 244	94.36 221	93.92 327	97.68 238	83.70 402	99.90 106	96.57 385	97.40 37	99.67 48	98.88 194	61.82 421	99.92 102	98.23 132	99.13 139	98.14 264

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	94.92 221	99.27 270	97.10 341	92.79 214	97.43 180	97.99 265	81.85 292	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	97.19 326	94.67 132	98.95 109	99.28 150	86.43 246	98.76 224	98.37 124	97.42 195	99.33 194
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		95.72 191	95.15 199	97.45 200	97.62 244	94.28 239	99.28 268	98.24 203	94.27 155	96.84 198	98.94 189	79.39 322	98.76 224	93.25 258	98.49 163	99.30 201
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	91.11 280	97.07 192	98.97 180	97.47 12	99.03 204	93.73 252	96.09 226	98.92 234

GDP-MVS		97.88 80	97.59 94	98.75 99	97.59 246	97.81 89	99.95 65	97.37 306	94.44 141	99.08 103	99.58 120	97.13 23	99.08 202	94.99 215	98.17 173	99.37 185

miper_ehance_all_eth		93.16 271	92.60 272	94.82 289	97.57 247	93.56 259	99.50 232	97.07 346	88.75 335	88.85 332	95.52 346	90.97 177	96.74 359	90.77 300	84.45 345	94.17 333

guyue		97.15 128	96.82 131	98.15 150	97.56 248	96.25 164	99.71 189	97.84 252	95.75 99	98.13 157	98.65 221	87.58 226	98.82 217	98.29 129	97.91 185	99.36 187

testing3		93.92 248	94.23 225	92.99 354	97.54 249	90.23 340	99.99 5	99.16 33	90.57 298	91.33 285	98.63 225	92.99 129	92.52 431	82.46 385	95.39 251	96.22 298

mamba_0404		95.75 190	95.16 198	97.50 198	97.53 250	95.39 201	99.11 282	97.25 321	90.81 289	95.27 238	98.83 207	84.74 267	98.67 235	95.24 210	97.69 187	98.45 253

LCM-MVSNet-Re		92.31 292	92.60 272	91.43 374	97.53 250	79.27 428	99.02 300	91.83 444	92.07 247	80.31 408	94.38 393	83.50 280	95.48 398	97.22 173	97.58 191	99.54 157

GBi-Net		90.88 320	89.82 326	94.08 319	97.53 250	91.97 295	98.43 355	96.95 359	87.05 360	89.68 308	94.72 381	71.34 382	96.11 384	87.01 352	85.65 333	94.17 333

test1		90.88 320	89.82 326	94.08 319	97.53 250	91.97 295	98.43 355	96.95 359	87.05 360	89.68 308	94.72 381	71.34 382	96.11 384	87.01 352	85.65 333	94.17 333

FMVSNet2		91.02 317	89.56 331	95.41 269	97.53 250	95.74 183	98.98 303	97.41 301	87.05 360	88.43 342	95.00 375	71.34 382	96.24 381	85.12 367	85.21 338	94.25 326

tttt0517		96.85 144	96.49 146	97.92 165	97.48 255	95.89 177	99.85 136	98.54 116	90.72 297	96.63 203	98.93 192	97.47 12	99.02 205	93.03 265	95.76 238	98.85 238

BP-MVS1		98.33 55	98.18 52	98.81 94	97.44 256	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	99.36 187

casdiffmvs_mvg		96.43 166	95.94 171	97.89 169	97.44 256	95.47 195	99.86 133	97.29 317	93.35 190	96.03 220	99.19 161	85.39 261	98.72 229	97.89 153	97.04 206	99.49 171
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	97.41 258	95.64 190	99.99 5	97.06 347	94.59 133	99.63 54	99.32 146	89.20 208	98.14 281	98.76 100	99.23 135	99.62 136

c3_l		92.53 287	91.87 289	94.52 301	97.40 259	92.99 273	99.40 246	96.93 364	87.86 350	88.69 335	95.44 351	89.95 195	96.44 371	90.45 306	80.69 379	94.14 342

fmvsm_s_conf0.1_n		97.30 119	97.21 113	97.60 191	97.38 260	94.40 236	99.90 106	98.64 84	96.47 77	99.51 73	99.65 110	84.99 266	99.93 96	99.22 69	99.09 142	98.46 252

CDS-MVSNet		96.34 171	96.07 160	97.13 215	97.37 261	94.96 219	99.53 227	97.91 244	91.55 264	95.37 236	98.32 250	95.05 60	97.13 331	93.80 248	95.75 239	99.30 201
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	97.36 262	96.48 150	99.96 46	98.29 196	91.93 252	95.77 228	98.07 261	95.54 46	98.29 270	90.55 304	98.89 148	99.70 118

miper_lstm_enhance		91.81 300	91.39 299	93.06 353	97.34 263	89.18 358	99.38 252	96.79 375	86.70 367	87.47 356	95.22 366	90.00 194	95.86 393	88.26 333	81.37 368	94.15 339

baseline		96.43 166	95.98 165	97.76 180	97.34 263	95.17 215	99.51 230	97.17 330	93.92 171	96.90 196	99.28 150	85.37 262	98.64 237	97.50 167	96.86 212	99.46 173

cl____		92.31 292	91.58 293	94.52 301	97.33 265	92.77 275	99.57 219	96.78 376	86.97 364	87.56 354	95.51 347	89.43 201	96.62 364	88.60 327	82.44 360	94.16 338

SD_0403		92.63 286	93.38 253	90.40 388	97.32 266	77.91 430	97.75 383	98.03 231	91.89 253	90.83 291	98.29 254	82.00 289	93.79 421	88.51 331	95.75 239	99.52 163

DIV-MVS_self_test		92.32 291	91.60 292	94.47 305	97.31 267	92.74 277	99.58 216	96.75 377	86.99 363	87.64 352	95.54 344	89.55 200	96.50 368	88.58 328	82.44 360	94.17 333

casdiffmvs		96.42 168	95.97 168	97.77 178	97.30 268	94.98 218	99.84 141	97.09 344	93.75 180	96.58 205	99.26 156	85.07 264	98.78 222	97.77 161	97.04 206	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		94.36 241	93.48 247	96.99 219	97.29 269	93.54 260	99.96 46	96.72 379	88.35 344	93.43 259	98.94 189	82.05 288	98.05 288	88.12 337	96.48 218	99.37 185

eth_miper_zixun_eth		92.41 290	91.93 287	93.84 331	97.28 270	90.68 330	98.83 326	96.97 358	88.57 340	89.19 327	95.73 337	89.24 207	96.69 362	89.97 315	81.55 366	94.15 339

MVSFormer		96.94 140	96.60 142	97.95 161	97.28 270	97.70 94	99.55 224	97.27 319	91.17 277	99.43 79	99.54 126	90.92 178	96.89 350	94.67 228	99.62 95	99.25 208

lupinMVS		97.85 84	97.60 92	98.62 109	97.28 270	97.70 94	99.99 5	97.55 284	95.50 108	99.43 79	99.67 106	90.92 178	98.71 230	98.40 121	99.62 95	99.45 175

mamba_test_0407		95.62 198	94.95 207	97.61 190	97.14 273	95.31 206	99.00 301	97.25 321	90.81 289	94.40 248	98.83 207	84.74 267	98.58 239	95.24 210	97.18 202	98.93 233

SCA		94.69 224	93.81 238	97.33 211	97.10 274	94.44 231	98.86 323	98.32 190	93.30 193	96.17 219	95.59 342	76.48 349	97.95 294	91.06 292	97.43 193	99.59 143

KinetiMVS		96.10 179	95.29 193	98.53 123	97.08 275	97.12 121	99.56 221	98.12 223	94.78 125	98.44 139	98.94 189	80.30 316	99.39 183	91.56 285	98.79 154	99.06 225

TAMVS		95.85 187	95.58 184	96.65 232	97.07 276	93.50 261	99.17 278	97.82 254	91.39 274	95.02 241	98.01 262	92.20 155	97.30 321	93.75 251	95.83 236	99.14 217

Fast-Effi-MVS+-dtu		93.72 258	93.86 237	93.29 345	97.06 277	86.16 386	99.80 157	96.83 371	92.66 221	92.58 272	97.83 273	81.39 298	97.67 305	89.75 317	96.87 211	96.05 300

CostFormer		96.10 179	95.88 175	96.78 226	97.03 278	92.55 285	97.08 396	97.83 253	90.04 311	98.72 124	94.89 379	95.01 62	98.29 270	96.54 191	95.77 237	99.50 169

test_fmvsmvis_n_1920		97.67 103	97.59 94	97.91 167	97.02 279	95.34 204	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	98.42 255

test-LLR		96.47 164	96.04 161	97.78 176	97.02 279	95.44 196	99.96 46	98.21 207	94.07 161	95.55 231	96.38 314	93.90 103	98.27 274	90.42 307	98.83 152	99.64 129

test-mter		96.39 169	95.93 172	97.78 176	97.02 279	95.44 196	99.96 46	98.21 207	91.81 258	95.55 231	96.38 314	95.17 55	98.27 274	90.42 307	98.83 152	99.64 129

icg_test_0407		95.21 209	94.80 213	96.46 237	96.97 282	91.64 310	98.81 328	97.12 336	92.33 239	95.60 230	98.88 194	85.65 256	98.42 251	92.12 274	95.70 242	99.32 196

ICG_test_0404		93.83 250	93.17 260	95.80 259	96.97 282	91.64 310	97.78 382	97.12 336	92.33 239	90.87 290	98.88 194	76.78 344	96.43 372	92.12 274	95.70 242	99.32 196

icg_test_0403		95.25 207	94.81 212	96.58 234	96.97 282	91.64 310	98.97 308	97.12 336	92.33 239	95.43 234	98.88 194	85.78 255	98.79 220	92.12 274	95.70 242	99.32 196

gm-plane-assit					96.97 282	93.76 253			91.47 268		98.96 182		98.79 220	94.92 218

WB-MVSnew		92.90 277	92.77 269	93.26 347	96.95 286	93.63 257	99.71 189	98.16 217	91.49 265	94.28 251	98.14 258	81.33 300	96.48 369	79.47 402	95.46 248	89.68 428

QAPM		95.40 203	94.17 227	99.10 72	96.92 287	97.71 92	99.40 246	98.68 77	89.31 319	88.94 331	98.89 193	82.48 286	99.96 70	93.12 264	99.83 77	99.62 136

KD-MVS_2432*1600		88.00 365	86.10 369	93.70 336	96.91 288	94.04 245	97.17 393	97.12 336	84.93 387	81.96 398	92.41 412	92.48 148	94.51 414	79.23 403	52.68 447	92.56 398

miper_refine_blended		88.00 365	86.10 369	93.70 336	96.91 288	94.04 245	97.17 393	97.12 336	84.93 387	81.96 398	92.41 412	92.48 148	94.51 414	79.23 403	52.68 447	92.56 398

tpm2		95.47 201	95.18 197	96.35 243	96.91 288	91.70 308	96.96 399	97.93 240	88.04 348	98.44 139	95.40 353	93.32 118	97.97 291	94.00 240	95.61 246	99.38 183

FMVSNet5		88.32 361	87.47 363	90.88 377	96.90 291	88.39 370	97.28 390	95.68 406	82.60 407	84.67 386	92.40 414	79.83 319	91.16 436	76.39 418	81.51 367	93.09 389

3Dnovator+	91.53 11	96.31 173	95.24 194	99.52 28	96.88 292	98.64 54	99.72 186	98.24 203	95.27 113	88.42 344	98.98 178	82.76 285	99.94 87	97.10 176	99.83 77	99.96 69

Patchmatch-test		92.65 285	91.50 296	96.10 249	96.85 293	90.49 335	91.50 437	97.19 326	82.76 406	90.23 296	95.59 342	95.02 61	98.00 290	77.41 413	96.98 209	99.82 100

MVS		96.60 159	95.56 185	99.72 13	96.85 293	99.22 20	98.31 361	98.94 44	91.57 263	90.90 289	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	96.82 295	97.47 106	99.45 243	98.81 64	95.52 107	89.39 318	99.00 175	81.97 290	99.95 79	97.27 171	99.83 77	99.84 97

EI-MVSNet		93.73 257	93.40 252	94.74 290	96.80 296	92.69 280	99.06 291	97.67 267	88.96 328	91.39 283	99.02 171	88.75 214	97.30 321	91.07 291	87.85 319	94.22 329

CVMVSNet		94.68 226	94.94 208	93.89 330	96.80 296	86.92 383	99.06 291	98.98 41	94.45 138	94.23 253	99.02 171	85.60 257	95.31 403	90.91 297	95.39 251	99.43 179

IterMVS-LS		92.69 283	92.11 283	94.43 309	96.80 296	92.74 277	99.45 243	96.89 367	88.98 326	89.65 311	95.38 356	88.77 213	96.34 376	90.98 295	82.04 363	94.22 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 161	96.46 149	96.91 221	96.79 299	92.50 286	99.90 106	97.38 303	96.02 93	97.79 171	99.32 146	86.36 248	98.99 206	98.26 130	96.33 222	99.23 211

IterMVS		90.91 319	90.17 321	93.12 350	96.78 300	90.42 338	98.89 317	97.05 350	89.03 323	86.49 369	95.42 352	76.59 347	95.02 405	87.22 347	84.09 348	93.93 360
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	96.74 301	98.52 58	98.31 361	98.86 56	95.82 96	89.91 302	98.98 178	87.49 229	99.96 70	97.80 156	99.73 87	99.96 69

IterMVS-SCA-FT		90.85 322	90.16 322	92.93 355	96.72 302	89.96 347	98.89 317	96.99 354	88.95 329	86.63 366	95.67 338	76.48 349	95.00 406	87.04 350	84.04 351	93.84 367

MVS-HIRNet		86.22 372	83.19 385	95.31 273	96.71 303	90.29 339	92.12 434	97.33 311	62.85 442	86.82 363	70.37 447	69.37 390	97.49 311	75.12 421	97.99 183	98.15 262

VDDNet		93.12 272	91.91 288	96.76 227	96.67 304	92.65 283	98.69 340	98.21 207	82.81 405	97.75 173	99.28 150	61.57 422	99.48 178	98.09 140	94.09 270	98.15 262

dmvs_re		93.20 269	93.15 261	93.34 343	96.54 305	83.81 401	98.71 337	98.51 124	91.39 274	92.37 275	98.56 232	78.66 331	97.83 299	93.89 242	89.74 291	98.38 257

Elysia		94.50 233	93.38 253	97.85 171	96.49 306	96.70 138	98.98 303	97.78 257	90.81 289	96.19 217	98.55 234	73.63 373	98.98 207	89.41 318	98.56 160	97.88 269

StellarMVS		94.50 233	93.38 253	97.85 171	96.49 306	96.70 138	98.98 303	97.78 257	90.81 289	96.19 217	98.55 234	73.63 373	98.98 207	89.41 318	98.56 160	97.88 269

MIMVSNet		90.30 335	88.67 349	95.17 277	96.45 308	91.64 310	92.39 433	97.15 333	85.99 374	90.50 294	93.19 407	66.95 401	94.86 410	82.01 389	93.43 278	99.01 230

CR-MVSNet		93.45 266	92.62 271	95.94 253	96.29 309	92.66 281	92.01 435	96.23 393	92.62 223	96.94 194	93.31 405	91.04 175	96.03 389	79.23 403	95.96 230	99.13 218

RPMNet		89.76 347	87.28 364	97.19 214	96.29 309	92.66 281	92.01 435	98.31 192	70.19 438	96.94 194	85.87 440	87.25 234	99.78 139	62.69 442	95.96 230	99.13 218

tt0805		91.28 312	90.18 320	94.60 296	96.26 311	87.55 376	98.39 359	98.72 72	89.00 325	89.22 324	98.47 242	62.98 417	98.96 211	90.57 303	88.00 318	97.28 287

Patchmtry		89.70 348	88.49 352	93.33 344	96.24 312	89.94 350	91.37 438	96.23 393	78.22 421	87.69 351	93.31 405	91.04 175	96.03 389	80.18 401	82.10 362	94.02 350

test_vis1_rt		86.87 370	86.05 372	89.34 396	96.12 313	78.07 429	99.87 122	83.54 455	92.03 250	78.21 419	89.51 426	45.80 441	99.91 103	96.25 194	93.11 283	90.03 425

JIA-IIPM		91.76 306	90.70 307	94.94 283	96.11 314	87.51 377	93.16 431	98.13 222	75.79 427	97.58 175	77.68 445	92.84 134	97.97 291	88.47 332	96.54 214	99.33 194

OpenMVS	90.15 15	94.77 222	93.59 242	98.33 138	96.07 315	97.48 105	99.56 221	98.57 100	90.46 300	86.51 368	98.95 187	78.57 332	99.94 87	93.86 243	99.74 86	97.57 282

PAPM		98.60 34	98.42 35	99.14 66	96.05 316	98.96 26	99.90 106	99.35 24	96.68 68	98.35 146	99.66 108	96.45 33	98.51 244	99.45 59	99.89 70	99.96 69

CLD-MVS		94.06 247	93.90 235	94.55 300	96.02 317	90.69 329	99.98 18	97.72 263	96.62 72	91.05 288	98.85 205	77.21 337	98.47 245	98.11 138	89.51 297	94.48 307
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 332	88.75 348	95.25 275	95.99 318	90.16 342	91.22 439	97.54 286	76.80 423	97.26 186	86.01 439	91.88 162	96.07 388	66.16 438	95.91 234	99.51 167

ACMH+	89.98 16	90.35 333	89.54 332	92.78 359	95.99 318	86.12 387	98.81 328	97.18 328	89.38 318	83.14 394	97.76 274	68.42 395	98.43 250	89.11 323	86.05 331	93.78 370

DeepMVS_CX				82.92 417	95.98 320	58.66 448		96.01 398	92.72 216	78.34 418	95.51 347	58.29 427	98.08 285	82.57 384	85.29 336	92.03 406

ACMP	92.05 9	92.74 281	92.42 280	93.73 332	95.91 321	88.72 363	99.81 153	97.53 288	94.13 157	87.00 362	98.23 256	74.07 370	98.47 245	96.22 195	88.86 304	93.99 355
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 261	93.03 263	95.35 270	95.86 322	86.94 382	99.87 122	96.36 391	96.85 59	99.54 68	98.79 209	52.41 435	99.83 132	98.64 108	98.97 146	99.29 203

HQP-NCC					95.78 323		99.87 122		96.82 61	93.37 260

ACMP_Plane					95.78 323		99.87 122		96.82 61	93.37 260

HQP-MVS		94.61 228	94.50 218	94.92 284	95.78 323	91.85 300	99.87 122	97.89 245	96.82 61	93.37 260	98.65 221	80.65 310	98.39 257	97.92 150	89.60 292	94.53 303

NP-MVS					95.77 326	91.79 302					98.65 221

test_fmvsmconf0.1_n		97.74 97	97.44 101	98.64 108	95.76 327	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					95.76 327	91.72 307						80.47 314

ACMM	91.95 10	92.88 278	92.52 278	93.98 326	95.75 329	89.08 360	99.77 163	97.52 290	93.00 203	89.95 301	97.99 265	76.17 353	98.46 248	93.63 255	88.87 303	94.39 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 250	92.84 265	96.80 225	95.73 330	93.57 258	99.88 119	97.24 324	92.57 228	92.92 267	96.66 306	78.73 330	97.67 305	87.75 340	94.06 271	99.17 213

plane_prior1					95.73 330

jason		97.24 123	96.86 128	98.38 137	95.73 330	97.32 110	99.97 36	97.40 302	95.34 111	98.60 133	99.54 126	87.70 223	98.56 241	97.94 149	99.47 117	99.25 208
jason: jason.
mmtdpeth		88.52 359	87.75 361	90.85 379	95.71 333	83.47 406	98.94 311	94.85 421	88.78 334	97.19 188	89.58 425	63.29 415	98.97 209	98.54 113	62.86 441	90.10 424

HQP_MVS		94.49 235	94.36 221	94.87 285	95.71 333	91.74 304	99.84 141	97.87 247	96.38 81	93.01 265	98.59 227	80.47 314	98.37 263	97.79 159	89.55 295	94.52 305

plane_prior7					95.71 333	91.59 315

ITE_SJBPF				92.38 362	95.69 336	85.14 393		95.71 405	92.81 211	89.33 321	98.11 259	70.23 388	98.42 251	85.91 362	88.16 316	93.59 378

fmvsm_s_conf0.1_n_a		97.09 132	96.90 125	97.63 188	95.65 337	94.21 242	99.83 148	98.50 130	96.27 86	99.65 50	99.64 111	84.72 269	99.93 96	99.04 78	98.84 151	98.74 245

ACMH	89.72 17	90.64 326	89.63 329	93.66 338	95.64 338	88.64 366	98.55 347	97.45 295	89.03 323	81.62 401	97.61 275	69.75 389	98.41 253	89.37 320	87.62 323	93.92 361
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 154	96.49 146	97.37 207	95.63 339	95.96 175	99.74 175	98.88 54	92.94 205	91.61 281	98.97 180	97.72 6	98.62 238	94.83 222	98.08 181	97.53 284

FMVSNet1		88.50 360	86.64 367	94.08 319	95.62 340	91.97 295	98.43 355	96.95 359	83.00 403	86.08 376	94.72 381	59.09 426	96.11 384	81.82 391	84.07 349	94.17 333

LuminaMVS		96.63 158	96.21 157	97.87 170	95.58 341	96.82 134	99.12 280	97.67 267	94.47 137	97.88 166	98.31 252	87.50 228	98.71 230	98.07 142	97.29 198	98.10 265

LPG-MVS_test		92.96 275	92.71 270	93.71 334	95.43 342	88.67 364	99.75 172	97.62 275	92.81 211	90.05 297	98.49 238	75.24 360	98.40 255	95.84 201	89.12 299	94.07 347

LGP-MVS_train				93.71 334	95.43 342	88.67 364		97.62 275	92.81 211	90.05 297	98.49 238	75.24 360	98.40 255	95.84 201	89.12 299	94.07 347

tpm		93.70 259	93.41 251	94.58 298	95.36 344	87.41 378	97.01 397	96.90 366	90.85 287	96.72 202	94.14 396	90.40 189	96.84 354	90.75 301	88.54 311	99.51 167

D2MVS		92.76 280	92.59 276	93.27 346	95.13 345	89.54 354	99.69 195	99.38 22	92.26 243	87.59 353	94.61 387	85.05 265	97.79 300	91.59 284	88.01 317	92.47 401

VPA-MVSNet		92.70 282	91.55 295	96.16 247	95.09 346	96.20 166	98.88 319	99.00 39	91.02 284	91.82 280	95.29 363	76.05 355	97.96 293	95.62 206	81.19 369	94.30 322

LTVRE_ROB	88.28 18	90.29 336	89.05 343	94.02 322	95.08 347	90.15 343	97.19 392	97.43 297	84.91 389	83.99 390	97.06 292	74.00 371	98.28 272	84.08 373	87.71 321	93.62 377
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		87.87 367	86.51 368	91.94 368	95.05 348	85.57 391	97.65 384	94.08 431	84.40 393	81.82 400	96.85 301	62.14 420	98.33 266	80.25 400	86.37 330	91.91 408

test0.0.03 1		93.86 249	93.61 239	94.64 294	95.02 349	92.18 293	99.93 89	98.58 98	94.07 161	87.96 348	98.50 237	93.90 103	94.96 407	81.33 392	93.17 281	96.78 290

UniMVSNet (Re)		93.07 274	92.13 282	95.88 255	94.84 350	96.24 165	99.88 119	98.98 41	92.49 233	89.25 322	95.40 353	87.09 236	97.14 330	93.13 263	78.16 393	94.26 324

USDC		90.00 343	88.96 344	93.10 352	94.81 351	88.16 372	98.71 337	95.54 410	93.66 182	83.75 392	97.20 286	65.58 406	98.31 268	83.96 376	87.49 325	92.85 395

VPNet		91.81 300	90.46 311	95.85 257	94.74 352	95.54 194	98.98 303	98.59 96	92.14 245	90.77 293	97.44 279	68.73 393	97.54 310	94.89 221	77.89 395	94.46 308

FIs		94.10 246	93.43 248	96.11 248	94.70 353	96.82 134	99.58 216	98.93 48	92.54 229	89.34 320	97.31 283	87.62 225	97.10 334	94.22 239	86.58 328	94.40 314

UniMVSNet_ETH3D		90.06 342	88.58 351	94.49 304	94.67 354	88.09 373	97.81 381	97.57 283	83.91 396	88.44 340	97.41 280	57.44 428	97.62 307	91.41 286	88.59 310	97.77 274

UniMVSNet_NR-MVSNet		92.95 276	92.11 283	95.49 264	94.61 355	95.28 208	99.83 148	99.08 36	91.49 265	89.21 325	96.86 300	87.14 235	96.73 360	93.20 259	77.52 398	94.46 308

test_fmvs2		89.47 352	89.70 328	88.77 403	94.54 356	75.74 431	99.83 148	94.70 427	94.71 129	91.08 286	96.82 305	54.46 431	97.78 302	92.87 266	88.27 314	92.80 396

MonoMVSNet		94.82 217	94.43 219	95.98 251	94.54 356	90.73 328	99.03 298	97.06 347	93.16 198	93.15 264	95.47 350	88.29 217	97.57 308	97.85 154	91.33 289	99.62 136

WR-MVS		92.31 292	91.25 300	95.48 267	94.45 358	95.29 207	99.60 213	98.68 77	90.10 308	88.07 347	96.89 298	80.68 309	96.80 358	93.14 262	79.67 386	94.36 316

nrg030		93.51 263	92.53 277	96.45 238	94.36 359	97.20 116	99.81 153	97.16 332	91.60 262	89.86 304	97.46 278	86.37 247	97.68 304	95.88 200	80.31 382	94.46 308

tfpnnormal		89.29 355	87.61 362	94.34 312	94.35 360	94.13 244	98.95 310	98.94 44	83.94 394	84.47 387	95.51 347	74.84 365	97.39 313	77.05 416	80.41 380	91.48 411

FC-MVSNet-test		93.81 253	93.15 261	95.80 259	94.30 361	96.20 166	99.42 245	98.89 52	92.33 239	89.03 330	97.27 285	87.39 231	96.83 356	93.20 259	86.48 329	94.36 316

SSC-MVS3.2		89.59 350	88.66 350	92.38 362	94.29 362	86.12 387	99.49 234	97.66 270	90.28 307	88.63 337	95.18 367	64.46 411	96.88 352	85.30 366	82.66 357	94.14 342

MS-PatchMatch		90.65 325	90.30 316	91.71 373	94.22 363	85.50 392	98.24 365	97.70 264	88.67 337	86.42 371	96.37 316	67.82 398	98.03 289	83.62 378	99.62 95	91.60 409

WR-MVS_H		91.30 310	90.35 314	94.15 316	94.17 364	92.62 284	99.17 278	98.94 44	88.87 332	86.48 370	94.46 392	84.36 273	96.61 365	88.19 334	78.51 391	93.21 387

DU-MVS		92.46 289	91.45 298	95.49 264	94.05 365	95.28 208	99.81 153	98.74 71	92.25 244	89.21 325	96.64 308	81.66 295	96.73 360	93.20 259	77.52 398	94.46 308

NR-MVSNet		91.56 308	90.22 318	95.60 262	94.05 365	95.76 182	98.25 364	98.70 74	91.16 279	80.78 407	96.64 308	83.23 283	96.57 366	91.41 286	77.73 397	94.46 308

CP-MVSNet		91.23 314	90.22 318	94.26 314	93.96 367	92.39 289	99.09 284	98.57 100	88.95 329	86.42 371	96.57 311	79.19 325	96.37 374	90.29 310	78.95 388	94.02 350

XXY-MVS		91.82 299	90.46 311	95.88 255	93.91 368	95.40 200	98.87 322	97.69 266	88.63 339	87.87 349	97.08 290	74.38 369	97.89 297	91.66 283	84.07 349	94.35 319

PS-CasMVS		90.63 327	89.51 334	93.99 325	93.83 369	91.70 308	98.98 303	98.52 121	88.48 341	86.15 375	96.53 313	75.46 358	96.31 378	88.83 325	78.86 390	93.95 358

test_0402		85.58 374	83.94 379	90.50 385	93.81 370	85.04 394	98.55 347	95.20 418	76.01 425	79.72 413	95.13 368	64.15 413	96.26 380	66.04 439	86.88 327	90.21 422

XVG-ACMP-BASELINE		91.22 315	90.75 306	92.63 361	93.73 371	85.61 390	98.52 351	97.44 296	92.77 215	89.90 303	96.85 301	66.64 403	98.39 257	92.29 271	88.61 308	93.89 363

TranMVSNet+NR-MVSNet		91.68 307	90.61 310	94.87 285	93.69 372	93.98 248	99.69 195	98.65 81	91.03 283	88.44 340	96.83 304	80.05 318	96.18 382	90.26 311	76.89 406	94.45 313

TransMVSNet (Re)		87.25 368	85.28 375	93.16 349	93.56 373	91.03 320	98.54 349	94.05 433	83.69 398	81.09 405	96.16 322	75.32 359	96.40 373	76.69 417	68.41 429	92.06 405

v10		90.25 337	88.82 346	94.57 299	93.53 374	93.43 263	99.08 286	96.87 369	85.00 386	87.34 360	94.51 388	80.93 305	97.02 344	82.85 383	79.23 387	93.26 385

testgi		89.01 357	88.04 358	91.90 369	93.49 375	84.89 396	99.73 182	95.66 407	93.89 175	85.14 382	98.17 257	59.68 425	94.66 413	77.73 412	88.88 302	96.16 299

v8		90.54 329	89.17 339	94.66 293	93.43 376	93.40 265	99.20 275	96.94 363	85.76 377	87.56 354	94.51 388	81.96 291	97.19 327	84.94 369	78.25 392	93.38 383

V42		91.28 312	90.12 323	94.74 290	93.42 377	93.46 262	99.68 197	97.02 351	87.36 356	89.85 306	95.05 371	81.31 301	97.34 316	87.34 345	80.07 384	93.40 381

pm-mvs1		89.36 354	87.81 360	94.01 323	93.40 378	91.93 298	98.62 345	96.48 389	86.25 372	83.86 391	96.14 324	73.68 372	97.04 340	86.16 359	75.73 411	93.04 391

v1144		91.09 316	89.83 325	94.87 285	93.25 379	93.69 256	99.62 208	96.98 356	86.83 366	89.64 312	94.99 376	80.94 304	97.05 337	85.08 368	81.16 370	93.87 365

v1192		90.62 328	89.25 338	94.72 292	93.13 380	93.07 269	99.50 232	97.02 351	86.33 371	89.56 316	95.01 373	79.22 324	97.09 336	82.34 387	81.16 370	94.01 352

v2v482		91.30 310	90.07 324	95.01 280	93.13 380	93.79 251	99.77 163	97.02 351	88.05 347	89.25 322	95.37 357	80.73 308	97.15 329	87.28 346	80.04 385	94.09 346

OPM-MVS		93.21 268	92.80 267	94.44 307	93.12 382	90.85 327	99.77 163	97.61 278	96.19 89	91.56 282	98.65 221	75.16 364	98.47 245	93.78 250	89.39 298	93.99 355
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 323	89.52 333	94.59 297	93.11 383	92.77 275	99.56 221	96.99 354	86.38 370	89.82 307	94.95 378	80.50 313	97.10 334	83.98 375	80.41 380	93.90 362

PEN-MVS		90.19 339	89.06 342	93.57 339	93.06 384	90.90 325	99.06 291	98.47 133	88.11 346	85.91 377	96.30 318	76.67 345	95.94 392	87.07 349	76.91 405	93.89 363

v1240		90.20 338	88.79 347	94.44 307	93.05 385	92.27 291	99.38 252	96.92 365	85.89 375	89.36 319	94.87 380	77.89 336	97.03 342	80.66 396	81.08 373	94.01 352

v148		90.70 324	89.63 329	93.92 327	92.97 386	90.97 321	99.75 172	96.89 367	87.51 353	88.27 345	95.01 373	81.67 294	97.04 340	87.40 344	77.17 403	93.75 371

v1921920		90.46 330	89.12 340	94.50 303	92.96 387	92.46 287	99.49 234	96.98 356	86.10 373	89.61 314	95.30 360	78.55 333	97.03 342	82.17 388	80.89 378	94.01 352

MVStest1		85.03 380	82.76 389	91.83 370	92.95 388	89.16 359	98.57 346	94.82 422	71.68 436	68.54 439	95.11 370	83.17 284	95.66 396	74.69 422	65.32 436	90.65 418

tt0320-xc		82.94 394	80.35 401	90.72 383	92.90 389	83.54 404	96.85 402	94.73 425	63.12 441	79.85 412	93.77 400	49.43 439	95.46 399	80.98 395	71.54 420	93.16 388

Baseline_NR-MVSNet		90.33 334	89.51 334	92.81 358	92.84 390	89.95 348	99.77 163	93.94 434	84.69 391	89.04 329	95.66 339	81.66 295	96.52 367	90.99 294	76.98 404	91.97 407

test_method		80.79 399	79.70 403	84.08 414	92.83 391	67.06 440	99.51 230	95.42 412	54.34 446	81.07 406	93.53 402	44.48 442	92.22 433	78.90 407	77.23 402	92.94 393

pmmvs4		92.10 296	91.07 304	95.18 276	92.82 392	94.96 219	99.48 237	96.83 371	87.45 355	88.66 336	96.56 312	83.78 278	96.83 356	89.29 321	84.77 343	93.75 371

LF4IMVS		89.25 356	88.85 345	90.45 387	92.81 393	81.19 421	98.12 371	94.79 423	91.44 269	86.29 373	97.11 288	65.30 409	98.11 283	88.53 330	85.25 337	92.07 404

tt0320		83.56 393	81.15 396	90.77 381	92.77 394	83.58 403	96.83 403	95.52 411	63.26 440	81.36 403	92.54 410	53.26 433	95.77 394	80.45 397	74.38 414	92.96 392

DTE-MVSNet		89.40 353	88.24 356	92.88 356	92.66 395	89.95 348	99.10 283	98.22 206	87.29 357	85.12 383	96.22 320	76.27 352	95.30 404	83.56 379	75.74 410	93.41 380

EU-MVSNet		90.14 341	90.34 315	89.54 395	92.55 396	81.06 422	98.69 340	98.04 230	91.41 273	86.59 367	96.84 303	80.83 307	93.31 426	86.20 358	81.91 364	94.26 324

APD_test1		81.15 398	80.92 398	81.86 418	92.45 397	59.76 447	96.04 417	93.61 437	73.29 434	77.06 422	96.64 308	44.28 443	96.16 383	72.35 426	82.52 358	89.67 429

sc_t1		85.01 381	82.46 391	92.67 360	92.44 398	83.09 407	97.39 388	95.72 404	65.06 439	85.64 380	96.16 322	49.50 438	97.34 316	84.86 370	75.39 412	97.57 282

our_test_3		90.39 331	89.48 336	93.12 350	92.40 399	89.57 353	99.33 259	96.35 392	87.84 351	85.30 381	94.99 376	84.14 276	96.09 387	80.38 398	84.56 344	93.71 376

ppachtmachnet_test		89.58 351	88.35 354	93.25 348	92.40 399	90.44 337	99.33 259	96.73 378	85.49 382	85.90 378	95.77 333	81.09 303	96.00 391	76.00 420	82.49 359	93.30 384

v7n		89.65 349	88.29 355	93.72 333	92.22 401	90.56 334	99.07 290	97.10 341	85.42 384	86.73 364	94.72 381	80.06 317	97.13 331	81.14 393	78.12 394	93.49 379

dmvs_testset		83.79 390	86.07 371	76.94 422	92.14 402	48.60 457	96.75 404	90.27 447	89.48 317	78.65 416	98.55 234	79.25 323	86.65 445	66.85 436	82.69 356	95.57 301

PS-MVSNAJss		93.64 260	93.31 257	94.61 295	92.11 403	92.19 292	99.12 280	97.38 303	92.51 232	88.45 339	96.99 296	91.20 170	97.29 324	94.36 233	87.71 321	94.36 316

pmmvs5		90.17 340	89.09 341	93.40 342	92.10 404	89.77 351	99.74 175	95.58 409	85.88 376	87.24 361	95.74 334	73.41 375	96.48 369	88.54 329	83.56 353	93.95 358

N_pmnet		80.06 402	80.78 399	77.89 421	91.94 405	45.28 459	98.80 331	56.82 461	78.10 422	80.08 410	93.33 403	77.03 339	95.76 395	68.14 434	82.81 355	92.64 397

test_djsdf		92.83 279	92.29 281	94.47 305	91.90 406	92.46 287	99.55 224	97.27 319	91.17 277	89.96 300	96.07 328	81.10 302	96.89 350	94.67 228	88.91 301	94.05 349

SixPastTwentyTwo		88.73 358	88.01 359	90.88 377	91.85 407	82.24 413	98.22 368	95.18 419	88.97 327	82.26 397	96.89 298	71.75 380	96.67 363	84.00 374	82.98 354	93.72 375

K. test v3		88.05 364	87.24 365	90.47 386	91.82 408	82.23 414	98.96 309	97.42 299	89.05 322	76.93 424	95.60 341	68.49 394	95.42 400	85.87 363	81.01 376	93.75 371

OurMVSNet-221017-0		89.81 346	89.48 336	90.83 380	91.64 409	81.21 420	98.17 370	95.38 414	91.48 267	85.65 379	97.31 283	72.66 376	97.29 324	88.15 335	84.83 342	93.97 357

mvs_tets		91.81 300	91.08 303	94.00 324	91.63 410	90.58 333	98.67 342	97.43 297	92.43 234	87.37 359	97.05 293	71.76 379	97.32 319	94.75 225	88.68 307	94.11 345

Gipuma		66.95 415	65.00 415	72.79 427	91.52 411	67.96 439	66.16 450	95.15 420	47.89 448	58.54 445	67.99 450	29.74 447	87.54 444	50.20 449	77.83 396	62.87 450
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	91.47 412	95.56 193	99.84 141	97.30 314	97.74 27	97.89 165	99.35 145	79.62 320	99.85 122	99.25 68	99.24 134	99.55 153

jajsoiax		91.92 298	91.18 301	94.15 316	91.35 413	90.95 324	99.00 301	97.42 299	92.61 224	87.38 358	97.08 290	72.46 377	97.36 314	94.53 231	88.77 305	94.13 344

MDA-MVSNet-bldmvs		84.09 388	81.52 395	91.81 371	91.32 414	88.00 375	98.67 342	95.92 400	80.22 416	55.60 448	93.32 404	68.29 396	93.60 424	73.76 423	76.61 407	93.82 369

MVP-Stereo		90.93 318	90.45 313	92.37 364	91.25 415	88.76 361	98.05 375	96.17 395	87.27 358	84.04 388	95.30 360	78.46 334	97.27 326	83.78 377	99.70 89	91.09 412
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 376	83.32 384	92.10 366	90.96 416	88.58 367	99.20 275	96.52 387	79.70 418	57.12 447	92.69 409	79.11 326	93.86 420	77.10 415	77.46 400	93.86 366

YYNet1		85.50 377	83.33 383	92.00 367	90.89 417	88.38 371	99.22 274	96.55 386	79.60 419	57.26 446	92.72 408	79.09 328	93.78 422	77.25 414	77.37 401	93.84 367

anonymousdsp		91.79 305	90.92 305	94.41 310	90.76 418	92.93 274	98.93 313	97.17 330	89.08 321	87.46 357	95.30 360	78.43 335	96.92 348	92.38 270	88.73 306	93.39 382

lessismore_v0				90.53 384	90.58 419	80.90 423		95.80 401		77.01 423	95.84 331	66.15 405	96.95 346	83.03 382	75.05 413	93.74 374

EG-PatchMatch MVS		85.35 378	83.81 381	89.99 393	90.39 420	81.89 416	98.21 369	96.09 397	81.78 410	74.73 430	93.72 401	51.56 437	97.12 333	79.16 406	88.61 308	90.96 415

EGC-MVSNET		69.38 408	63.76 418	86.26 411	90.32 421	81.66 419	96.24 413	93.85 435	0.99 458	3.22 459	92.33 415	52.44 434	92.92 429	59.53 445	84.90 341	84.21 439

CMPMVS	61.59 21	84.75 384	85.14 376	83.57 415	90.32 421	62.54 443	96.98 398	97.59 282	74.33 432	69.95 436	96.66 306	64.17 412	98.32 267	87.88 339	88.41 313	89.84 427
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 387	82.92 387	89.21 397	90.03 423	82.60 410	96.89 401	95.62 408	80.59 414	75.77 429	89.17 427	65.04 410	94.79 411	72.12 427	81.02 375	90.23 421

pmmvs6		85.69 373	83.84 380	91.26 376	90.00 424	84.41 399	97.82 380	96.15 396	75.86 426	81.29 404	95.39 355	61.21 423	96.87 353	83.52 380	73.29 416	92.50 400

ttmdpeth		88.23 363	87.06 366	91.75 372	89.91 425	87.35 379	98.92 316	95.73 403	87.92 349	84.02 389	96.31 317	68.23 397	96.84 354	86.33 357	76.12 408	91.06 413

DSMNet-mixed		88.28 362	88.24 356	88.42 405	89.64 426	75.38 433	98.06 374	89.86 448	85.59 381	88.20 346	92.14 416	76.15 354	91.95 434	78.46 409	96.05 227	97.92 268

UnsupCasMVSNet_eth		85.52 375	83.99 377	90.10 391	89.36 427	83.51 405	96.65 405	97.99 233	89.14 320	75.89 428	93.83 398	63.25 416	93.92 418	81.92 390	67.90 432	92.88 394

Anonymous20231206		86.32 371	85.42 374	89.02 399	89.11 428	80.53 426	99.05 295	95.28 415	85.43 383	82.82 395	93.92 397	74.40 368	93.44 425	66.99 435	81.83 365	93.08 390

Anonymous20240521		85.15 379	83.81 381	89.16 398	88.32 429	82.69 409	98.80 331	95.74 402	79.72 417	81.53 402	90.99 419	65.38 408	94.16 416	72.69 425	81.11 372	90.63 419

OpenMVS_ROB	79.82 20	83.77 391	81.68 394	90.03 392	88.30 430	82.82 408	98.46 352	95.22 417	73.92 433	76.00 427	91.29 418	55.00 430	96.94 347	68.40 433	88.51 312	90.34 420

test20.03		84.72 385	83.99 377	86.91 409	88.19 431	80.62 425	98.88 319	95.94 399	88.36 343	78.87 414	94.62 386	68.75 392	89.11 440	66.52 437	75.82 409	91.00 414

KD-MVS_self_test		83.59 392	82.06 392	88.20 406	86.93 432	80.70 424	97.21 391	96.38 390	82.87 404	82.49 396	88.97 428	67.63 399	92.32 432	73.75 424	62.30 443	91.58 410

MIMVSNet1		82.58 395	80.51 400	88.78 401	86.68 433	84.20 400	96.65 405	95.41 413	78.75 420	78.59 417	92.44 411	51.88 436	89.76 439	65.26 440	78.95 388	92.38 403

CL-MVSNet_self_test		84.50 386	83.15 386	88.53 404	86.00 434	81.79 417	98.82 327	97.35 307	85.12 385	83.62 393	90.91 421	76.66 346	91.40 435	69.53 431	60.36 444	92.40 402

UnsupCasMVSNet_bld		79.97 404	77.03 409	88.78 401	85.62 435	81.98 415	93.66 429	97.35 307	75.51 429	70.79 435	83.05 442	48.70 440	94.91 409	78.31 410	60.29 445	89.46 432

mvs5depth		84.87 382	82.90 388	90.77 381	85.59 436	84.84 397	91.10 440	93.29 439	83.14 401	85.07 384	94.33 394	62.17 419	97.32 319	78.83 408	72.59 419	90.14 423

Patchmatch-RL test		86.90 369	85.98 373	89.67 394	84.45 437	75.59 432	89.71 443	92.43 441	86.89 365	77.83 421	90.94 420	94.22 92	93.63 423	87.75 340	69.61 424	99.79 105

pmmvs-eth3d		84.03 389	81.97 393	90.20 390	84.15 438	87.09 381	98.10 373	94.73 425	83.05 402	74.10 432	87.77 434	65.56 407	94.01 417	81.08 394	69.24 426	89.49 431

test_fmvs3		79.99 403	80.17 402	79.45 420	84.02 439	62.83 441	99.05 295	93.49 438	88.29 345	80.06 411	86.65 437	28.09 449	88.00 441	88.63 326	73.27 417	87.54 437

PM-MVS		80.47 400	78.88 405	85.26 412	83.79 440	72.22 435	95.89 420	91.08 445	85.71 380	76.56 426	88.30 430	36.64 445	93.90 419	82.39 386	69.57 425	89.66 430

new-patchmatchnet		81.19 397	79.34 404	86.76 410	82.86 441	80.36 427	97.92 377	95.27 416	82.09 409	72.02 433	86.87 436	62.81 418	90.74 438	71.10 428	63.08 440	89.19 434

mvsany_test3		82.12 396	81.14 397	85.06 413	81.87 442	70.41 437	97.09 395	92.14 442	91.27 276	77.84 420	88.73 429	39.31 444	95.49 397	90.75 301	71.24 421	89.29 433

WB-MVS		76.28 406	77.28 408	73.29 426	81.18 443	54.68 451	97.87 379	94.19 430	81.30 411	69.43 437	90.70 422	77.02 340	82.06 449	35.71 454	68.11 431	83.13 440

test_f		78.40 405	77.59 407	80.81 419	80.82 444	62.48 444	96.96 399	93.08 440	83.44 399	74.57 431	84.57 441	27.95 450	92.63 430	84.15 372	72.79 418	87.32 438

SSC-MVS		75.42 407	76.40 410	72.49 430	80.68 445	53.62 452	97.42 386	94.06 432	80.42 415	68.75 438	90.14 424	76.54 348	81.66 450	33.25 455	66.34 435	82.19 441

pmmvs3		80.27 401	77.77 406	87.76 408	80.32 446	82.43 412	98.23 367	91.97 443	72.74 435	78.75 415	87.97 433	57.30 429	90.99 437	70.31 429	62.37 442	89.87 426

testf1		68.38 411	66.92 412	72.78 428	78.80 447	50.36 454	90.95 441	87.35 453	55.47 444	58.95 443	88.14 431	20.64 454	87.60 442	57.28 446	64.69 437	80.39 443

APD_test2		68.38 411	66.92 412	72.78 428	78.80 447	50.36 454	90.95 441	87.35 453	55.47 444	58.95 443	88.14 431	20.64 454	87.60 442	57.28 446	64.69 437	80.39 443

ambc				83.23 416	77.17 449	62.61 442	87.38 445	94.55 429		76.72 425	86.65 437	30.16 446	96.36 375	84.85 371	69.86 423	90.73 417

test_vis3_rt		68.82 409	66.69 414	75.21 425	76.24 450	60.41 446	96.44 408	68.71 460	75.13 430	50.54 451	69.52 449	16.42 459	96.32 377	80.27 399	66.92 434	68.89 447

TDRefinement		84.76 383	82.56 390	91.38 375	74.58 451	84.80 398	97.36 389	94.56 428	84.73 390	80.21 409	96.12 327	63.56 414	98.39 257	87.92 338	63.97 439	90.95 416

E-PMN		52.30 419	52.18 421	52.67 436	71.51 452	45.40 458	93.62 430	76.60 458	36.01 452	43.50 453	64.13 452	27.11 451	67.31 455	31.06 456	26.06 451	45.30 454

EMVS		51.44 421	51.22 423	52.11 437	70.71 453	44.97 460	94.04 426	75.66 459	35.34 454	42.40 454	61.56 455	28.93 448	65.87 456	27.64 457	24.73 452	45.49 453

PMMVS2		67.15 414	64.15 417	76.14 424	70.56 454	62.07 445	93.89 427	87.52 452	58.09 443	60.02 442	78.32 444	22.38 453	84.54 447	59.56 444	47.03 449	81.80 442

FPMVS		68.72 410	68.72 411	68.71 432	65.95 455	44.27 461	95.97 419	94.74 424	51.13 447	53.26 449	90.50 423	25.11 452	83.00 448	60.80 443	80.97 377	78.87 445

wuyk23d		20.37 425	20.84 428	18.99 440	65.34 456	27.73 463	50.43 451	7.67 464	9.50 457	8.01 458	6.34 458	6.13 462	26.24 457	23.40 458	10.69 456	2.99 455

LCM-MVSNet		67.77 413	64.73 416	76.87 423	62.95 457	56.25 450	89.37 444	93.74 436	44.53 449	61.99 441	80.74 443	20.42 456	86.53 446	69.37 432	59.50 446	87.84 435

MVE	53.74 22	51.54 420	47.86 424	62.60 434	59.56 458	50.93 453	79.41 448	77.69 457	35.69 453	36.27 455	61.76 454	5.79 463	69.63 453	37.97 453	36.61 450	67.24 448
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 417	52.24 420	67.66 433	49.27 459	56.82 449	83.94 446	82.02 456	70.47 437	33.28 456	64.54 451	17.23 458	69.16 454	45.59 451	23.85 453	77.02 446

tmp_tt		65.23 416	62.94 419	72.13 431	44.90 460	50.03 456	81.05 447	89.42 451	38.45 450	48.51 452	99.90 18	54.09 432	78.70 452	91.84 282	18.26 454	87.64 436

PMVS	49.05 23	53.75 418	51.34 422	60.97 435	40.80 461	34.68 462	74.82 449	89.62 450	37.55 451	28.67 457	72.12 446	7.09 461	81.63 451	43.17 452	68.21 430	66.59 449
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 423	39.14 426	33.31 438	19.94 462	24.83 464	98.36 360	9.75 463	15.53 456	51.31 450	87.14 435	19.62 457	17.74 458	47.10 450	3.47 457	57.36 451

testmvs		40.60 422	44.45 425	29.05 439	19.49 463	14.11 465	99.68 197	18.47 462	20.74 455	64.59 440	98.48 241	10.95 460	17.09 459	56.66 448	11.01 455	55.94 452

mmdepth		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

monomultidepth		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

test_blank		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.02 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

eth-test2					0.00 464

eth-test					0.00 464

uanet_test		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

DCPMVS		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

cdsmvs_eth3d_5k		23.43 424	31.24 427	0.00 441	0.00 464	0.00 466	0.00 452	98.09 224	0.00 459	0.00 460	99.67 106	83.37 281	0.00 460	0.00 459	0.00 458	0.00 456

pcd_1.5k_mvsjas		7.60 427	10.13 430	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	91.20 170	0.00 460	0.00 459	0.00 458	0.00 456

sosnet-low-res		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

sosnet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

uncertanet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

Regformer		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

ab-mvs-re		8.28 426	11.04 429	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	99.40 139	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

uanet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 460	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

WAC-MVS						90.97 321								86.10 361

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							95.78 421				59.23 456	93.20 125	97.74 303	91.06 292

test_post											63.35 453	94.43 79	98.13 282

patchmatchnet-post											91.70 417	95.12 56	97.95 294

MTMP							99.87 122	96.49 388

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	94.36 233		99.99 23

原ACMM2							99.90 106

testdata2													99.99 36	90.54 305

segment_acmp												96.68 29

testdata1							99.28 268		96.35 85

plane_prior5								97.87 247					98.37 263	97.79 159	89.55 295	94.52 305

plane_prior4											98.59 227

plane_prior3						91.64 310			96.63 70	93.01 265

plane_prior2							99.84 141		96.38 81

plane_prior						91.74 304	99.86 133		96.76 65						89.59 294

n2								0.00 465

nn								0.00 465

door-mid								89.69 449

test11								98.44 141

door								90.31 446

HQP5-MVS						91.85 300

BP-MVS														97.92 150

HQP4-MVS										93.37 260			98.39 257			94.53 303

HQP3-MVS								97.89 245							89.60 292

HQP2-MVS												80.65 310

MDTV_nov1_ep13_2view						96.26 160	96.11 415		91.89 253	98.06 158		94.40 81		94.30 236		99.67 123

ACMMP++_ref															87.04 326

ACMMP++															88.23 315

Test By Simon												92.82 136

High-resolution multi-view benchmark