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	99.09 286	98.84 62	93.32 193	96.74 202	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	89.90 318	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	92.06 251	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		93.83 255	92.06 290	99.15 64	99.94 13	97.50 103	99.94 82	98.42 161	96.22 87	99.41 81	41.37 462	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	97.00 179	99.98 32	99.99 23

MG-MVS		98.91 19	98.65 24	99.68 16	99.94 13	99.07 24	99.64 207	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	92.34 239	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	99.75 173	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	99.18 279	99.45 18	94.84 124	96.41 213	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	99.24 274	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	97.27 215	99.89 45	91.92 303	99.90 106	99.07 37	88.67 342	95.26 242	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	93.22 196	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	93.19 197	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	99.61 212	97.78 257	96.52 73	98.61 130	99.31 149	92.73 138	99.67 160	96.77 189	99.48 114	99.06 227

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	90.78 298	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	99.39 252	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		95.84 190	95.11 202	98.02 159	99.85 56	95.10 219	98.74 336	98.50 130	87.22 364	93.66 263	99.86 29	87.45 230	99.95 79	90.94 301	99.81 83	99.02 231

HPM-MVS		97.96 74	97.72 84	98.68 103	99.84 58	96.39 156	99.90 106	98.17 212	92.61 225	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	99.40 248	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	94.77 229	99.92 92	98.46 135	93.93 170	97.20 187	99.27 154	95.44 51	99.97 58	97.41 168	99.51 110	99.41 182
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	99.36 258	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	99.76 169	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	99.76 169	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	91.75 262	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	93.53 185	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	97.41 206	99.76 68	93.36 271	99.65 203	97.95 238	96.03 92	97.41 181	99.70 94	89.61 199	99.51 170	96.73 191	98.25 172	99.38 184

新几何1				99.42 37	99.75 71	98.27 65		98.63 90	92.69 220	99.55 66	99.82 49	94.40 81	100.00 1	91.21 293	99.94 55	99.99 23

MP-MVS-pluss		98.07 73	97.64 90	99.38 43	99.74 72	98.41 63	99.74 176	98.18 211	93.35 191	96.45 210	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	99.77 164	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	95.11 214	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	99.73 183	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	96.87 227	99.71 78	91.74 308	99.85 136	97.95 238	93.11 203	95.72 231	99.16 166	92.35 152	99.94 87	95.32 210	99.35 129	98.92 239

reproduce-ours		98.78 24	98.67 21	99.09 73	99.70 80	97.30 111	99.74 176	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	99.74 176	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	95.59 268	99.67 83	86.91 389	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	93.92 332	99.63 85	81.76 423	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		96.05 182	95.82 178	96.72 233	99.59 87	96.99 128	99.95 65	99.10 34	94.06 163	98.27 149	95.80 337	89.00 210	99.95 79	99.12 72	87.53 329	93.24 391

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	99.08 169	89.00 210	99.95 79	99.12 72	99.25 133	99.57 151

PatchMatch-RL		96.04 183	95.40 189	97.95 161	99.59 87	95.22 214	99.52 230	99.07 37	93.96 168	96.49 209	98.35 252	82.28 292	99.82 134	90.15 317	99.22 136	98.81 246

dcpmvs_2		97.42 115	98.09 59	95.42 273	99.58 91	87.24 385	99.23 275	96.95 364	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	99.34 260	98.55 112	91.86 257	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	96.55 208	99.69 98	92.28 154	99.98 47	97.13 175	99.44 121	99.93 81

API-MVS		97.86 82	97.66 88	98.47 128	99.52 94	95.41 199	99.47 240	98.87 55	91.68 263	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	99.21 162	84.48 277	99.95 79	94.92 220	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	88.48 346	99.06 105	99.66 108	90.30 191	99.64 165	96.32 195	99.97 42	99.96 69

cl22		93.77 260	93.25 264	95.33 277	99.49 97	94.43 235	99.61 212	98.09 224	90.38 306	89.16 333	95.61 345	90.56 186	97.34 319	91.93 284	84.45 350	94.21 336

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	94.97 218	99.94 55	100.00 1

MAR-MVS		97.43 111	97.19 114	98.15 150	99.47 98	94.79 228	99.05 297	98.76 69	92.65 223	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		94.54 234	93.42 254	97.91 167	99.46 100	94.04 248	98.93 315	97.48 294	81.15 417	90.04 304	99.55 124	87.02 238	99.95 79	88.97 329	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	98.46 355	99.42 21	97.03 54	99.02 107	99.09 168	99.35 2	98.21 281	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	93.81 251	99.94 55	99.98 51

TAPA-MVS	92.12 8	94.42 242	93.60 246	96.90 226	99.33 104	91.78 307	99.78 160	98.00 232	89.89 319	94.52 248	99.47 130	91.97 161	99.18 195	69.90 435	99.52 107	99.73 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 206	95.07 204	96.32 248	99.32 106	96.60 146	99.76 169	98.85 59	96.65 69	87.83 355	96.05 334	99.52 1	98.11 286	96.58 192	81.07 379	94.25 331

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	95.20 215	99.98 18	97.15 334	95.53 106	99.62 57	99.79 58	92.08 159	98.38 264	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	95.18 216	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	98.59 256

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	99.64 207	99.27 27	91.43 272	97.88 166	98.99 178	95.84 42	99.84 130	98.82 95	95.32 258	99.79 105

DCV-MVSNet		97.83 86	97.37 105	99.21 53	99.18 112	97.98 80	99.64 207	99.27 27	91.43 272	97.88 166	98.99 178	95.84 42	99.84 130	98.82 95	95.32 258	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	99.66 202	98.06 227	96.37 84	94.37 254	99.49 129	83.29 287	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	99.24 160	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	97.43 204	99.10 118	94.42 236	99.99 5	97.10 343	95.07 115	99.68 47	99.75 75	92.95 131	98.34 268	98.38 122	99.14 138	99.54 157

Anonymous202405211		93.10 278	91.99 291	96.40 244	99.10 118	89.65 357	98.88 321	97.93 240	83.71 402	94.00 260	98.75 214	68.79 396	99.88 116	95.08 215	91.71 291	99.68 121

fmvsm_s_conf0.5_n		97.80 91	97.85 79	97.67 184	99.06 120	94.41 237	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	98.62 255

HyFIR lowres test		96.66 157	96.43 150	97.36 211	99.05 121	93.91 253	99.70 195	99.80 3	90.54 302	96.26 216	98.08 265	92.15 157	98.23 280	96.84 188	95.46 253	99.93 81

LFMVS		94.75 228	93.56 249	98.30 140	99.03 122	95.70 186	98.74 336	97.98 235	87.81 357	98.47 138	99.39 141	67.43 405	99.53 167	98.01 144	95.20 261	99.67 123

fmvsm_s_conf0.5_n_4		97.75 96	97.86 78	97.42 205	99.01 123	94.69 230	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	97.64 282

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	81.83 298	99.94 87	99.78 30	98.79 154	97.51 290

AllTest		92.48 293	91.64 296	95.00 286	99.01 123	88.43 373	98.94 313	96.82 378	86.50 373	88.71 338	98.47 247	74.73 371	99.88 116	85.39 369	96.18 228	96.71 296

TestCases				95.00 286	99.01 123	88.43 373		96.82 378	86.50 373	88.71 338	98.47 247	74.73 371	99.88 116	85.39 369	96.18 228	96.71 296

COLMAP_ROB	90.47 14	92.18 300	91.49 302	94.25 320	99.00 127	88.04 379	98.42 361	96.70 385	82.30 413	88.43 347	99.01 175	76.97 346	99.85 122	86.11 365	96.50 220	94.86 307
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	84.15 280	99.97 58	99.76 35	99.50 112	98.39 261

test_fmvs1		95.35 207	95.68 183	94.36 316	98.99 128	84.98 400	99.96 46	96.65 387	97.60 31	99.73 42	98.96 184	71.58 386	99.93 96	98.31 127	99.37 127	98.17 266

HY-MVS	92.50 7	97.79 93	97.17 116	99.63 17	98.98 130	99.32 9	97.49 388	99.52 14	95.69 101	98.32 147	97.41 285	93.32 118	99.77 142	98.08 141	95.75 243	99.81 102

VNet		97.21 125	96.57 144	99.13 70	98.97 131	97.82 88	99.03 300	99.21 32	94.31 150	99.18 98	98.88 196	86.26 250	99.89 110	98.93 86	94.32 271	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	93.17 198	96.26 216	98.88 196	89.87 196	99.51 170	94.26 239	94.91 263	99.31 201

tfpn200view9		96.79 147	95.99 164	99.19 55	98.94 133	98.82 37	99.78 160	99.71 7	92.86 209	96.02 223	98.87 203	89.33 203	99.50 172	93.84 248	94.57 267	99.27 208

thres400		96.78 149	95.99 164	99.16 62	98.94 133	98.82 37	99.78 160	99.71 7	92.86 209	96.02 223	98.87 203	89.33 203	99.50 172	93.84 248	94.57 267	99.16 216

sasdasda		97.09 132	96.32 152	99.39 40	98.93 135	98.95 27	99.72 187	97.35 307	94.45 138	97.88 166	99.42 134	86.71 241	99.52 168	98.48 117	93.97 277	99.72 115

Anonymous20231211		89.86 350	88.44 358	94.13 323	98.93 135	90.68 335	98.54 352	98.26 200	76.28 429	86.73 369	95.54 349	70.60 392	97.56 312	90.82 304	80.27 388	94.15 344

canonicalmvs		97.09 132	96.32 152	99.39 40	98.93 135	98.95 27	99.72 187	97.35 307	94.45 138	97.88 166	99.42 134	86.71 241	99.52 168	98.48 117	93.97 277	99.72 115

SDMVSNet		94.80 223	93.96 238	97.33 213	98.92 138	95.42 198	99.59 216	98.99 40	92.41 236	92.55 278	97.85 276	75.81 361	98.93 213	97.90 152	91.62 292	97.64 282

sd_testset		93.55 267	92.83 271	95.74 266	98.92 138	90.89 331	98.24 368	98.85 59	92.41 236	92.55 278	97.85 276	71.07 391	98.68 236	93.93 245	91.62 292	97.64 282

EPNet_dtu		95.71 195	95.39 190	96.66 235	98.92 138	93.41 267	99.57 221	98.90 50	96.19 89	97.52 176	98.56 237	92.65 140	97.36 317	77.89 416	98.33 167	99.20 214
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	95.58 252	99.78 108

CHOSEN 1792x2688		96.81 146	96.53 145	97.64 186	98.91 142	93.07 273	99.65 203	99.80 3	95.64 102	95.39 238	98.86 205	84.35 279	99.90 105	96.98 181	99.16 137	99.95 76

thres100view900		96.74 152	95.92 174	99.18 56	98.90 143	98.77 42	99.74 176	99.71 7	92.59 227	95.84 227	98.86 205	89.25 205	99.50 172	93.84 248	94.57 267	99.27 208

thres600view7		96.69 155	95.87 177	99.14 66	98.90 143	98.78 41	99.74 176	99.71 7	92.59 227	95.84 227	98.86 205	89.25 205	99.50 172	93.44 261	94.50 270	99.16 216

MSDG		94.37 244	93.36 261	97.40 207	98.88 145	93.95 252	99.37 256	97.38 303	85.75 384	90.80 297	99.17 165	84.11 282	99.88 116	86.35 361	98.43 165	98.36 263

MGCFI-Net		97.00 137	96.22 156	99.34 45	98.86 146	98.80 39	99.67 201	97.30 315	94.31 150	97.77 172	99.41 138	86.36 248	99.50 172	98.38 122	93.90 279	99.72 115

h-mvs33		94.92 220	94.36 224	96.59 237	98.85 147	91.29 323	98.93 315	98.94 44	95.90 94	98.77 119	98.42 250	90.89 181	99.77 142	97.80 156	70.76 427	98.72 252

Anonymous20240529		92.10 301	90.65 313	96.47 239	98.82 148	90.61 337	98.72 338	98.67 80	75.54 433	93.90 262	98.58 235	66.23 409	99.90 105	94.70 229	90.67 295	98.90 242

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	96.38 214	98.49 243	89.05 209	99.88 116	97.10 177	98.34 166	99.43 180

PS-MVSNAJ		98.44 45	98.20 50	99.16 62	98.80 150	98.92 29	99.54 228	98.17 212	97.34 39	99.85 15	99.85 33	91.20 170	99.89 110	99.41 62	99.67 90	98.69 253

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	81.36 304	99.98 47	92.77 272	98.89 148	98.28 265

mvsany_test1		97.82 89	97.90 76	97.55 195	98.77 152	93.04 276	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	94.26 273	99.67 123

SymmetryMVS		97.64 104	97.46 98	98.17 146	98.74 154	95.39 201	99.61 212	99.26 29	96.52 73	98.61 130	99.31 149	92.73 138	99.67 160	96.77 189	95.63 250	99.45 176

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	99.52 230	98.08 226	97.05 53	99.86 12	99.86 29	90.65 183	99.71 152	99.39 64	98.63 158	98.69 253

miper_enhance_ethall		94.36 246	93.98 237	95.49 269	98.68 157	95.24 212	99.73 183	97.29 318	93.28 195	89.86 309	95.97 335	94.37 85	97.05 340	92.20 276	84.45 350	94.19 337

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	91.10 284	98.17 155	98.59 232	93.86 105	98.19 282	95.64 207	95.24 260	99.28 207

test2506		97.53 108	97.19 114	98.58 115	98.66 160	96.90 132	98.81 330	99.77 5	94.93 118	97.95 161	98.96 184	92.51 147	99.20 193	94.93 219	98.15 175	99.64 129

ECVR-MVS		95.66 198	95.05 205	97.51 199	98.66 160	93.71 257	98.85 327	98.45 136	94.93 118	96.86 198	98.96 184	75.22 367	99.20 193	95.34 209	98.15 175	99.64 129

mamv4		95.24 210	96.90 125	90.25 394	98.65 162	72.11 441	98.28 366	97.64 271	89.99 317	95.93 225	98.25 260	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	99.47 240	97.79 255	94.56 134	99.74 40	98.35 252	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	94.26 243	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	99.08 225

MVSMamba_PlusPlus		97.83 86	97.45 100	98.99 83	98.60 165	98.15 66	99.58 218	97.74 262	90.34 309	99.26 94	98.32 255	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	92.53 231	98.84 114	98.84 209	93.36 115	98.30 272	95.84 203	94.30 272	99.05 229

test1111		95.57 201	94.98 208	97.37 209	98.56 166	93.37 270	98.86 325	98.45 136	94.95 117	96.63 204	98.95 189	75.21 368	99.11 199	95.02 216	98.14 177	99.64 129

MVSTER		95.53 202	95.22 197	96.45 242	98.56 166	97.72 91	99.91 100	97.67 267	92.38 238	91.39 288	97.14 292	97.24 18	97.30 324	94.80 225	87.85 324	94.34 326

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	98.73 216	95.50 49	99.69 156	98.53 115	94.63 265	98.99 233

VDD-MVS		93.77 260	92.94 269	96.27 249	98.55 169	90.22 346	98.77 335	97.79 255	90.85 290	96.82 200	99.42 134	61.18 429	99.77 142	98.95 84	94.13 274	98.82 245

tpmvs		94.28 248	93.57 248	96.40 244	98.55 169	91.50 321	95.70 425	98.55 112	87.47 359	92.15 281	94.26 400	91.42 166	98.95 212	88.15 340	95.85 239	98.76 248

UGNet		95.33 208	94.57 220	97.62 190	98.55 169	94.85 224	98.67 344	99.32 26	95.75 99	96.80 201	96.27 324	72.18 383	99.96 70	94.58 232	99.05 144	98.04 271
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	95.18 212	94.10 231	98.43 132	98.55 169	95.99 174	97.91 381	97.31 314	90.35 308	89.48 322	99.22 161	85.19 265	99.89 110	90.40 314	98.47 164	99.41 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 185	96.49 146	94.34 317	98.51 174	89.99 351	99.39 252	98.57 100	93.14 200	97.33 183	98.31 257	93.44 113	94.68 417	93.69 258	95.98 233	98.34 264

UWE-MVS		96.79 147	96.72 137	97.00 221	98.51 174	93.70 258	99.71 190	98.60 94	92.96 205	97.09 190	98.34 254	96.67 31	98.85 216	92.11 282	96.50 220	98.44 259

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	98.75 214	95.20 54	99.48 178	98.93 86	96.40 223	99.29 205

test_vis1_n_1920		95.44 204	95.31 193	95.82 263	98.50 176	88.74 367	99.98 18	97.30 315	97.84 25	99.85 15	99.19 163	66.82 407	99.97 58	98.82 95	99.46 119	98.76 248

BH-w/o		95.71 195	95.38 191	96.68 234	98.49 178	92.28 294	99.84 141	97.50 292	92.12 248	92.06 284	98.79 212	84.69 273	98.67 237	95.29 211	99.66 91	99.09 223

baseline1		95.78 191	94.86 211	98.54 121	98.47 179	98.07 74	99.06 293	97.99 233	92.68 221	94.13 259	98.62 229	93.28 121	98.69 235	93.79 253	85.76 337	98.84 244

fmvsm_s_conf0.5_n_7		97.70 102	97.74 83	97.59 193	98.44 180	95.16 218	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	97.66 281

EPMVS		96.53 163	96.01 163	98.09 154	98.43 181	96.12 172	96.36 412	99.43 20	93.53 185	97.64 174	95.04 377	94.41 80	98.38 264	91.13 295	98.11 178	99.75 111

kuosan		93.17 275	92.60 277	94.86 293	98.40 182	89.54 359	98.44 357	98.53 119	84.46 397	88.49 343	97.92 273	90.57 185	97.05 340	83.10 386	93.49 282	97.99 272

WBMVS		94.52 237	94.03 235	95.98 255	98.38 183	96.68 141	99.92 92	97.63 272	90.75 299	89.64 317	95.25 370	96.77 25	96.90 352	94.35 237	83.57 357	94.35 324

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	98.42 250	96.77 25	99.17 196	98.54 113	96.20 227	99.11 222

sss		97.57 107	97.03 121	99.18 56	98.37 185	98.04 77	99.73 183	99.38 22	93.46 188	98.76 122	99.06 171	91.21 169	99.89 110	96.33 194	97.01 212	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	93.45 190	98.15 156	98.70 219	95.48 50	99.22 189	97.85 154	95.05 262	99.07 226

BH-untuned		95.18 212	94.83 212	96.22 250	98.36 186	91.22 324	99.80 157	97.32 313	90.91 288	91.08 291	98.67 221	83.51 284	98.54 246	94.23 240	99.61 99	98.92 239

testing91		97.16 127	96.90 125	97.97 160	98.35 188	95.67 189	99.91 100	98.42 161	92.91 208	97.33 183	98.72 217	94.81 68	99.21 190	96.98 181	94.63 265	99.03 230

testing99		97.17 126	96.91 124	97.95 161	98.35 188	95.70 186	99.91 100	98.43 149	92.94 206	97.36 182	98.72 217	94.83 67	99.21 190	97.00 179	94.64 264	98.95 235

ET-MVSNet_ETH3D		94.37 244	93.28 263	97.64 186	98.30 190	97.99 79	99.99 5	97.61 278	94.35 147	71.57 439	99.45 133	96.23 35	95.34 407	96.91 186	85.14 344	99.59 143

AUN-MVS		93.28 272	92.60 277	95.34 276	98.29 191	90.09 349	99.31 264	98.56 106	91.80 261	96.35 215	98.00 268	89.38 202	98.28 275	92.46 273	69.22 432	97.64 282

FMVSNet3		92.69 288	91.58 298	95.99 254	98.29 191	97.42 108	99.26 273	97.62 275	89.80 320	89.68 313	95.32 364	81.62 302	96.27 383	87.01 357	85.65 338	94.29 328

PMMVS		96.76 150	96.76 134	96.76 231	98.28 193	92.10 298	99.91 100	97.98 235	94.12 158	99.53 69	99.39 141	86.93 240	98.73 228	96.95 184	97.73 186	99.45 176

hse-mvs2		94.38 243	94.08 234	95.31 278	98.27 194	90.02 350	99.29 269	98.56 106	95.90 94	98.77 119	98.00 268	90.89 181	98.26 279	97.80 156	69.20 433	97.64 282

PVSNet_0	88.03 19	91.80 308	90.27 322	96.38 246	98.27 194	90.46 341	99.94 82	99.61 13	93.99 166	86.26 379	97.39 287	71.13 390	99.89 110	98.77 99	67.05 438	98.79 247

UA-Net		96.54 162	95.96 170	98.27 142	98.23 196	95.71 185	98.00 379	98.45 136	93.72 181	98.41 142	99.27 154	88.71 215	99.66 163	91.19 294	97.69 187	99.44 179

test_cas_vis1_n_1920		96.59 160	96.23 155	97.65 185	98.22 197	94.23 244	99.99 5	97.25 322	97.77 26	99.58 65	99.08 169	77.10 343	99.97 58	97.64 164	99.45 120	98.74 250

FE-MVS		95.70 197	95.01 207	97.79 175	98.21 198	94.57 232	95.03 426	98.69 75	88.90 336	97.50 178	96.19 326	92.60 143	99.49 177	89.99 319	97.94 184	99.31 201

GG-mvs-BLEND				98.54 121	98.21 198	98.01 78	93.87 431	98.52 121		97.92 162	97.92 273	99.02 3	97.94 299	98.17 134	99.58 103	99.67 123

mvs_anonymous		95.65 199	95.03 206	97.53 197	98.19 200	95.74 183	99.33 261	97.49 293	90.87 289	90.47 300	97.10 294	88.23 218	97.16 331	95.92 201	97.66 190	99.68 121

MVS_Test		96.46 165	95.74 179	98.61 110	98.18 201	97.23 115	99.31 264	97.15 334	91.07 285	98.84 114	97.05 298	88.17 219	98.97 209	94.39 234	97.50 192	99.61 140

BH-RMVSNet		95.18 212	94.31 227	97.80 173	98.17 202	95.23 213	99.76 169	97.53 288	92.52 232	94.27 257	99.25 159	76.84 348	98.80 219	90.89 303	99.54 105	99.35 192

dongtai		91.55 314	91.13 307	92.82 362	98.16 203	86.35 390	99.47 240	98.51 124	83.24 405	85.07 389	97.56 281	90.33 190	94.94 413	76.09 424	91.73 290	97.18 293

RPSCF		91.80 308	92.79 273	88.83 405	98.15 204	69.87 443	98.11 375	96.60 389	83.93 400	94.33 255	99.27 154	79.60 326	99.46 181	91.99 283	93.16 287	97.18 293

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	99.46 174

IS-MVSNet		96.29 176	95.90 175	97.45 202	98.13 206	94.80 227	99.08 288	97.61 278	92.02 253	95.54 236	98.96 184	90.64 184	98.08 288	93.73 256	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	80.88 311	99.93 96	99.59 50	98.17 173	97.29 291

ab-mvs		94.69 229	93.42 254	98.51 126	98.07 209	96.26 160	96.49 410	98.68 77	90.31 310	94.54 247	97.00 300	76.30 356	99.71 152	95.98 200	93.38 285	99.56 152

XVG-OURS-SEG-HR		94.79 224	94.70 219	95.08 283	98.05 210	89.19 361	99.08 288	97.54 286	93.66 182	94.87 245	99.58 120	78.78 334	99.79 137	97.31 170	93.40 284	96.25 300

EIA-MVS		97.53 108	97.46 98	97.76 180	98.04 211	94.84 225	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		94.82 221	94.74 218	95.06 284	98.00 212	89.19 361	99.08 288	97.55 284	94.10 159	94.71 246	99.62 115	80.51 317	99.74 148	96.04 199	93.06 289	96.25 300

mvsmamba		96.94 140	96.73 136	97.55 195	97.99 213	94.37 240	99.62 210	97.70 264	93.13 201	98.42 141	97.92 273	88.02 220	98.75 226	98.78 98	99.01 145	99.52 163

dp		95.05 215	94.43 222	96.91 224	97.99 213	92.73 283	96.29 415	97.98 235	89.70 321	95.93 225	94.67 390	93.83 107	98.45 252	86.91 360	96.53 219	99.54 157

tpmrst		96.27 178	95.98 166	97.13 217	97.96 215	93.15 272	96.34 413	98.17 212	92.07 249	98.71 125	95.12 374	93.91 102	98.73 228	94.91 222	96.62 217	99.50 169

TR-MVS		94.54 234	93.56 249	97.49 201	97.96 215	94.34 241	98.71 339	97.51 291	90.30 311	94.51 249	98.69 220	75.56 362	98.77 223	92.82 271	95.99 232	99.35 192

Vis-MVSNet (Re-imp)		96.32 173	95.98 166	97.35 212	97.93 217	94.82 226	99.47 240	98.15 220	91.83 258	95.09 243	99.11 167	91.37 168	97.47 315	93.47 260	97.43 193	99.74 112

MDTV_nov1_ep13			95.69 181		97.90 218	94.15 246	95.98 421	98.44 141	93.12 202	97.98 160	95.74 339	95.10 57	98.58 242	90.02 318	96.92 214

Fast-Effi-MVS+		95.02 217	94.19 229	97.52 198	97.88 219	94.55 233	99.97 36	97.08 347	88.85 338	94.47 250	97.96 272	84.59 274	98.41 256	89.84 321	97.10 207	99.59 143

ADS-MVSNet2		93.80 259	93.88 241	93.55 345	97.87 220	85.94 394	94.24 427	96.84 375	90.07 314	96.43 211	94.48 395	90.29 192	95.37 406	87.44 347	97.23 200	99.36 188

ADS-MVSNet		94.79 224	94.02 236	97.11 219	97.87 220	93.79 254	94.24 427	98.16 217	90.07 314	96.43 211	94.48 395	90.29 192	98.19 282	87.44 347	97.23 200	99.36 188

Effi-MVS+		96.30 175	95.69 181	98.16 147	97.85 222	96.26 160	97.41 390	97.21 326	90.37 307	98.65 128	98.58 235	86.61 245	98.70 234	97.11 176	97.37 197	99.52 163

PatchmatchNet		95.94 186	95.45 188	97.39 208	97.83 223	94.41 237	96.05 419	98.40 170	92.86 209	97.09 190	95.28 369	94.21 94	98.07 290	89.26 327	98.11 178	99.70 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 232	93.61 244	97.74 182	97.82 224	96.26 160	99.96 46	97.78 257	85.76 382	94.00 260	97.54 282	76.95 347	99.21 190	97.23 173	95.43 255	97.76 280

1112_ss		96.01 184	95.20 198	98.42 134	97.80 225	96.41 153	99.65 203	96.66 386	92.71 218	92.88 274	99.40 139	92.16 156	99.30 185	91.92 285	93.66 280	99.55 153

Test_1112_low_res		95.72 193	94.83 212	98.42 134	97.79 226	96.41 153	99.65 203	96.65 387	92.70 219	92.86 275	96.13 330	92.15 157	99.30 185	91.88 286	93.64 281	99.55 153

Effi-MVS+-dtu		94.53 236	95.30 194	92.22 370	97.77 227	82.54 416	99.59 216	97.06 351	94.92 120	95.29 240	95.37 362	85.81 254	97.89 300	94.80 225	97.07 208	96.23 302

tpm cat1		93.51 268	92.52 283	96.47 239	97.77 227	91.47 322	96.13 417	98.06 227	80.98 418	92.91 273	93.78 404	89.66 197	98.87 214	87.03 356	96.39 224	99.09 223

FA-MVS(test-final)		95.86 188	95.09 203	98.15 150	97.74 229	95.62 191	96.31 414	98.17 212	91.42 274	96.26 216	96.13 330	90.56 186	99.47 180	92.18 277	97.07 208	99.35 192

xiu_mvs_v1_base_debu		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 264	97.86 249	96.43 78	99.62 57	99.69 98	85.56 260	99.68 157	99.05 75	98.31 168	97.83 276

xiu_mvs_v1_base		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 264	97.86 249	96.43 78	99.62 57	99.69 98	85.56 260	99.68 157	99.05 75	98.31 168	97.83 276

xiu_mvs_v1_base_debi		97.43 111	97.06 117	98.55 117	97.74 229	98.14 68	99.31 264	97.86 249	96.43 78	99.62 57	99.69 98	85.56 260	99.68 157	99.05 75	98.31 168	97.83 276

EPP-MVSNet		96.69 155	96.60 142	96.96 223	97.74 229	93.05 275	99.37 256	98.56 106	88.75 340	95.83 229	99.01 175	96.01 36	98.56 244	96.92 185	97.20 202	99.25 210

gg-mvs-nofinetune		93.51 268	91.86 295	98.47 128	97.72 234	97.96 83	92.62 437	98.51 124	74.70 436	97.33 183	69.59 453	98.91 4	97.79 303	97.77 161	99.56 104	99.67 123

IB-MVS	92.85 6	94.99 218	93.94 239	98.16 147	97.72 234	95.69 188	99.99 5	98.81 64	94.28 153	92.70 276	96.90 302	95.08 58	99.17 196	96.07 198	73.88 420	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	91.83 258	97.45 179	99.04 172	97.50 9	99.10 201	94.75 227	96.37 225	99.16 216

VortexMVS		94.11 250	93.50 251	95.94 257	97.70 237	96.61 145	99.35 259	97.18 329	93.52 187	89.57 320	95.74 339	87.55 227	96.97 348	95.76 206	85.13 345	94.23 333

Syy-MVS		90.00 348	90.63 314	88.11 412	97.68 238	74.66 439	99.71 190	98.35 183	90.79 296	92.10 282	98.67 221	79.10 332	93.09 432	63.35 446	95.95 236	96.59 298

myMVS_eth3d		94.46 241	94.76 217	93.55 345	97.68 238	90.97 326	99.71 190	98.35 183	90.79 296	92.10 282	98.67 221	92.46 150	93.09 432	87.13 353	95.95 236	96.59 298

test_fmvs1_n		94.25 249	94.36 224	93.92 332	97.68 238	83.70 407	99.90 106	96.57 390	97.40 37	99.67 48	98.88 196	61.82 426	99.92 102	98.23 132	99.13 139	98.14 269

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		96.24 179	95.68 183	97.94 164	97.65 242	94.92 223	99.27 272	97.10 343	92.79 215	97.43 180	97.99 270	81.85 297	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	97.19 327	94.67 132	98.95 109	99.28 151	86.43 246	98.76 224	98.37 124	97.42 195	99.33 195
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		95.72 193	95.15 201	97.45 202	97.62 244	94.28 242	99.28 270	98.24 203	94.27 155	96.84 199	98.94 191	79.39 327	98.76 224	93.25 262	98.49 163	99.30 203
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	91.11 283	97.07 192	98.97 182	97.47 12	99.03 204	93.73 256	96.09 230	98.92 239

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	94.99 217	98.17 173	99.37 186

miper_ehance_all_eth		93.16 276	92.60 277	94.82 294	97.57 247	93.56 262	99.50 234	97.07 350	88.75 340	88.85 337	95.52 351	90.97 177	96.74 362	90.77 305	84.45 350	94.17 338

guyue		97.15 128	96.82 131	98.15 150	97.56 248	96.25 164	99.71 190	97.84 252	95.75 99	98.13 157	98.65 224	87.58 226	98.82 217	98.29 129	97.91 185	99.36 188

viewmanbaseed2359cas		96.45 166	96.07 160	97.59 193	97.55 249	94.59 231	99.70 195	97.33 311	93.62 184	97.00 194	99.32 146	85.57 259	98.71 231	97.26 172	97.33 198	99.47 172

testing3		93.92 253	94.23 228	92.99 359	97.54 250	90.23 345	99.99 5	99.16 33	90.57 301	91.33 290	98.63 228	92.99 129	92.52 436	82.46 390	95.39 256	96.22 303

mamba_0404		95.75 192	95.16 200	97.50 200	97.53 251	95.39 201	99.11 284	97.25 322	90.81 292	95.27 241	98.83 210	84.74 270	98.67 237	95.24 212	97.69 187	98.45 258

LCM-MVSNet-Re		92.31 297	92.60 277	91.43 379	97.53 251	79.27 433	99.02 302	91.83 449	92.07 249	80.31 413	94.38 398	83.50 285	95.48 403	97.22 174	97.58 191	99.54 157

GBi-Net		90.88 325	89.82 331	94.08 324	97.53 251	91.97 299	98.43 358	96.95 364	87.05 365	89.68 313	94.72 386	71.34 387	96.11 389	87.01 357	85.65 338	94.17 338

test1		90.88 325	89.82 331	94.08 324	97.53 251	91.97 299	98.43 358	96.95 364	87.05 365	89.68 313	94.72 386	71.34 387	96.11 389	87.01 357	85.65 338	94.17 338

FMVSNet2		91.02 322	89.56 336	95.41 274	97.53 251	95.74 183	98.98 305	97.41 301	87.05 365	88.43 347	95.00 380	71.34 387	96.24 385	85.12 372	85.21 343	94.25 331

tttt0517		96.85 144	96.49 146	97.92 165	97.48 256	95.89 177	99.85 136	98.54 116	90.72 300	96.63 204	98.93 194	97.47 12	99.02 205	93.03 269	95.76 242	98.85 243

BP-MVS1		98.33 55	98.18 52	98.81 94	97.44 257	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	99.36 188

casdiffmvs_mvg		96.43 167	95.94 172	97.89 169	97.44 257	95.47 195	99.86 133	97.29 318	93.35 191	96.03 222	99.19 163	85.39 263	98.72 230	97.89 153	97.04 210	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	97.41 259	95.64 190	99.99 5	97.06 351	94.59 133	99.63 54	99.32 146	89.20 208	98.14 284	98.76 100	99.23 135	99.62 136

c3_l		92.53 292	91.87 294	94.52 306	97.40 260	92.99 277	99.40 248	96.93 369	87.86 355	88.69 340	95.44 356	89.95 195	96.44 375	90.45 311	80.69 384	94.14 347

viewmambaseed2359dif		95.92 187	95.55 187	97.04 220	97.38 261	93.41 267	99.78 160	96.97 362	91.14 282	96.58 206	99.27 154	84.85 269	98.75 226	96.87 187	97.12 206	98.97 234

fmvsm_s_conf0.1_n		97.30 119	97.21 113	97.60 192	97.38 261	94.40 239	99.90 106	98.64 84	96.47 77	99.51 73	99.65 110	84.99 268	99.93 96	99.22 69	99.09 142	98.46 257

CDS-MVSNet		96.34 172	96.07 160	97.13 217	97.37 263	94.96 221	99.53 229	97.91 244	91.55 266	95.37 239	98.32 255	95.05 60	97.13 334	93.80 252	95.75 243	99.30 203
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	97.36 264	96.48 150	99.96 46	98.29 196	91.93 254	95.77 230	98.07 266	95.54 46	98.29 273	90.55 309	98.89 148	99.70 118

miper_lstm_enhance		91.81 305	91.39 304	93.06 358	97.34 265	89.18 363	99.38 254	96.79 380	86.70 372	87.47 361	95.22 371	90.00 194	95.86 398	88.26 338	81.37 373	94.15 344

baseline		96.43 167	95.98 166	97.76 180	97.34 265	95.17 217	99.51 232	97.17 331	93.92 171	96.90 197	99.28 151	85.37 264	98.64 240	97.50 167	96.86 216	99.46 174

cl____		92.31 297	91.58 298	94.52 306	97.33 267	92.77 279	99.57 221	96.78 381	86.97 369	87.56 359	95.51 352	89.43 201	96.62 367	88.60 332	82.44 365	94.16 343

SD_0403		92.63 291	93.38 258	90.40 393	97.32 268	77.91 435	97.75 386	98.03 231	91.89 255	90.83 296	98.29 259	82.00 294	93.79 426	88.51 336	95.75 243	99.52 163

DIV-MVS_self_test		92.32 296	91.60 297	94.47 310	97.31 269	92.74 281	99.58 218	96.75 382	86.99 368	87.64 357	95.54 349	89.55 200	96.50 372	88.58 333	82.44 365	94.17 338

casdiffmvs		96.42 169	95.97 169	97.77 178	97.30 270	94.98 220	99.84 141	97.09 346	93.75 180	96.58 206	99.26 158	85.07 266	98.78 222	97.77 161	97.04 210	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		94.36 246	93.48 252	96.99 222	97.29 271	93.54 263	99.96 46	96.72 384	88.35 349	93.43 264	98.94 191	82.05 293	98.05 291	88.12 342	96.48 222	99.37 186

eth_miper_zixun_eth		92.41 295	91.93 292	93.84 336	97.28 272	90.68 335	98.83 328	96.97 362	88.57 345	89.19 332	95.73 342	89.24 207	96.69 365	89.97 320	81.55 371	94.15 344

MVSFormer		96.94 140	96.60 142	97.95 161	97.28 272	97.70 94	99.55 226	97.27 320	91.17 279	99.43 79	99.54 126	90.92 178	96.89 353	94.67 230	99.62 95	99.25 210

lupinMVS		97.85 84	97.60 92	98.62 109	97.28 272	97.70 94	99.99 5	97.55 284	95.50 108	99.43 79	99.67 106	90.92 178	98.71 231	98.40 121	99.62 95	99.45 176

mamba_0408		94.98 219	94.09 232	97.64 186	97.14 275	95.31 206	93.48 434	97.08 347	90.48 303	94.40 251	98.62 229	84.49 275	98.67 237	93.99 243	97.18 203	98.93 236

mamba_test_0407_2		94.77 226	94.09 232	96.82 228	97.14 275	95.31 206	93.48 434	97.08 347	90.48 303	94.40 251	98.62 229	84.49 275	96.21 386	93.99 243	97.18 203	98.93 236

mamba_test_0407		95.62 200	94.95 209	97.61 191	97.14 275	95.31 206	99.00 303	97.25 322	90.81 292	94.40 251	98.83 210	84.74 270	98.58 242	95.24 212	97.18 203	98.93 236

SCA		94.69 229	93.81 243	97.33 213	97.10 278	94.44 234	98.86 325	98.32 190	93.30 194	96.17 221	95.59 347	76.48 354	97.95 297	91.06 297	97.43 193	99.59 143

KinetiMVS		96.10 180	95.29 195	98.53 123	97.08 279	97.12 121	99.56 223	98.12 223	94.78 125	98.44 139	98.94 191	80.30 321	99.39 183	91.56 290	98.79 154	99.06 227

TAMVS		95.85 189	95.58 185	96.65 236	97.07 280	93.50 264	99.17 280	97.82 254	91.39 276	95.02 244	98.01 267	92.20 155	97.30 324	93.75 255	95.83 240	99.14 219

Fast-Effi-MVS+-dtu		93.72 263	93.86 242	93.29 350	97.06 281	86.16 391	99.80 157	96.83 376	92.66 222	92.58 277	97.83 278	81.39 303	97.67 308	89.75 322	96.87 215	96.05 305

CostFormer		96.10 180	95.88 176	96.78 230	97.03 282	92.55 289	97.08 399	97.83 253	90.04 316	98.72 124	94.89 384	95.01 62	98.29 273	96.54 193	95.77 241	99.50 169

test_fmvsmvis_n_1920		97.67 103	97.59 94	97.91 167	97.02 283	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	98.42 260

test-LLR		96.47 164	96.04 162	97.78 176	97.02 283	95.44 196	99.96 46	98.21 207	94.07 161	95.55 234	96.38 319	93.90 103	98.27 277	90.42 312	98.83 152	99.64 129

test-mter		96.39 170	95.93 173	97.78 176	97.02 283	95.44 196	99.96 46	98.21 207	91.81 260	95.55 234	96.38 319	95.17 55	98.27 277	90.42 312	98.83 152	99.64 129

icg_test_0407_2		95.04 216	94.78 216	95.84 262	96.97 286	91.64 314	98.63 347	97.12 337	92.33 240	95.60 232	98.88 196	85.65 256	96.56 370	92.12 278	95.70 246	99.32 197

icg_test_0407		95.21 211	94.80 215	96.46 241	96.97 286	91.64 314	98.81 330	97.12 337	92.33 240	95.60 232	98.88 196	85.65 256	98.42 254	92.12 278	95.70 246	99.32 197

ICG_test_0404		93.83 255	93.17 265	95.80 264	96.97 286	91.64 314	97.78 385	97.12 337	92.33 240	90.87 295	98.88 196	76.78 349	96.43 376	92.12 278	95.70 246	99.32 197

icg_test_0403		95.25 209	94.81 214	96.58 238	96.97 286	91.64 314	98.97 310	97.12 337	92.33 240	95.43 237	98.88 196	85.78 255	98.79 220	92.12 278	95.70 246	99.32 197

gm-plane-assit					96.97 286	93.76 256			91.47 270		98.96 184		98.79 220	94.92 220

WB-MVSnew		92.90 282	92.77 274	93.26 352	96.95 291	93.63 260	99.71 190	98.16 217	91.49 267	94.28 256	98.14 263	81.33 305	96.48 373	79.47 407	95.46 253	89.68 433

QAPM		95.40 205	94.17 230	99.10 72	96.92 292	97.71 92	99.40 248	98.68 77	89.31 324	88.94 336	98.89 195	82.48 291	99.96 70	93.12 268	99.83 77	99.62 136

KD-MVS_2432*1600		88.00 370	86.10 374	93.70 341	96.91 293	94.04 248	97.17 396	97.12 337	84.93 392	81.96 403	92.41 417	92.48 148	94.51 419	79.23 408	52.68 452	92.56 403

miper_refine_blended		88.00 370	86.10 374	93.70 341	96.91 293	94.04 248	97.17 396	97.12 337	84.93 392	81.96 403	92.41 417	92.48 148	94.51 419	79.23 408	52.68 452	92.56 403

tpm2		95.47 203	95.18 199	96.35 247	96.91 293	91.70 312	96.96 402	97.93 240	88.04 353	98.44 139	95.40 358	93.32 118	97.97 294	94.00 242	95.61 251	99.38 184

FMVSNet5		88.32 366	87.47 368	90.88 382	96.90 296	88.39 375	97.28 393	95.68 411	82.60 412	84.67 391	92.40 419	79.83 324	91.16 441	76.39 423	81.51 372	93.09 394

3Dnovator+	91.53 11	96.31 174	95.24 196	99.52 28	96.88 297	98.64 54	99.72 187	98.24 203	95.27 113	88.42 349	98.98 180	82.76 290	99.94 87	97.10 177	99.83 77	99.96 69

Patchmatch-test		92.65 290	91.50 301	96.10 253	96.85 298	90.49 340	91.50 442	97.19 327	82.76 411	90.23 301	95.59 347	95.02 61	98.00 293	77.41 418	96.98 213	99.82 100

MVS		96.60 159	95.56 186	99.72 13	96.85 298	99.22 20	98.31 364	98.94 44	91.57 265	90.90 294	99.61 116	86.66 244	99.96 70	97.36 169	99.88 73	99.99 23

3Dnovator	91.47 12	96.28 177	95.34 192	99.08 75	96.82 300	97.47 106	99.45 245	98.81 64	95.52 107	89.39 323	99.00 177	81.97 295	99.95 79	97.27 171	99.83 77	99.84 97

EI-MVSNet		93.73 262	93.40 257	94.74 295	96.80 301	92.69 284	99.06 293	97.67 267	88.96 333	91.39 288	99.02 173	88.75 214	97.30 324	91.07 296	87.85 324	94.22 334

CVMVSNet		94.68 231	94.94 210	93.89 335	96.80 301	86.92 388	99.06 293	98.98 41	94.45 138	94.23 258	99.02 173	85.60 258	95.31 408	90.91 302	95.39 256	99.43 180

IterMVS-LS		92.69 288	92.11 288	94.43 314	96.80 301	92.74 281	99.45 245	96.89 372	88.98 331	89.65 316	95.38 361	88.77 213	96.34 380	90.98 300	82.04 368	94.22 334
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 161	96.46 149	96.91 224	96.79 304	92.50 290	99.90 106	97.38 303	96.02 93	97.79 171	99.32 146	86.36 248	98.99 206	98.26 130	96.33 226	99.23 213

IterMVS		90.91 324	90.17 326	93.12 355	96.78 305	90.42 343	98.89 319	97.05 354	89.03 328	86.49 374	95.42 357	76.59 352	95.02 410	87.22 352	84.09 353	93.93 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 145	95.96 170	99.48 34	96.74 306	98.52 58	98.31 364	98.86 56	95.82 96	89.91 307	98.98 180	87.49 229	99.96 70	97.80 156	99.73 87	99.96 69

IterMVS-SCA-FT		90.85 327	90.16 327	92.93 360	96.72 307	89.96 352	98.89 319	96.99 358	88.95 334	86.63 371	95.67 343	76.48 354	95.00 411	87.04 355	84.04 356	93.84 372

MVS-HIRNet		86.22 377	83.19 390	95.31 278	96.71 308	90.29 344	92.12 439	97.33 311	62.85 447	86.82 368	70.37 452	69.37 395	97.49 314	75.12 426	97.99 183	98.15 267

VDDNet		93.12 277	91.91 293	96.76 231	96.67 309	92.65 287	98.69 342	98.21 207	82.81 410	97.75 173	99.28 151	61.57 427	99.48 178	98.09 140	94.09 275	98.15 267

dmvs_re		93.20 274	93.15 266	93.34 348	96.54 310	83.81 406	98.71 339	98.51 124	91.39 276	92.37 280	98.56 237	78.66 336	97.83 302	93.89 246	89.74 296	98.38 262

Elysia		94.50 238	93.38 258	97.85 171	96.49 311	96.70 138	98.98 305	97.78 257	90.81 292	96.19 219	98.55 239	73.63 378	98.98 207	89.41 323	98.56 160	97.88 274

StellarMVS		94.50 238	93.38 258	97.85 171	96.49 311	96.70 138	98.98 305	97.78 257	90.81 292	96.19 219	98.55 239	73.63 378	98.98 207	89.41 323	98.56 160	97.88 274

MIMVSNet		90.30 340	88.67 354	95.17 282	96.45 313	91.64 314	92.39 438	97.15 334	85.99 379	90.50 299	93.19 412	66.95 406	94.86 415	82.01 394	93.43 283	99.01 232

CR-MVSNet		93.45 271	92.62 276	95.94 257	96.29 314	92.66 285	92.01 440	96.23 398	92.62 224	96.94 195	93.31 410	91.04 175	96.03 394	79.23 408	95.96 234	99.13 220

RPMNet		89.76 352	87.28 369	97.19 216	96.29 314	92.66 285	92.01 440	98.31 192	70.19 443	96.94 195	85.87 445	87.25 234	99.78 139	62.69 447	95.96 234	99.13 220

tt0805		91.28 317	90.18 325	94.60 301	96.26 316	87.55 381	98.39 362	98.72 72	89.00 330	89.22 329	98.47 247	62.98 422	98.96 211	90.57 308	88.00 323	97.28 292

Patchmtry		89.70 353	88.49 357	93.33 349	96.24 317	89.94 355	91.37 443	96.23 398	78.22 426	87.69 356	93.31 410	91.04 175	96.03 394	80.18 406	82.10 367	94.02 355

test_vis1_rt		86.87 375	86.05 377	89.34 401	96.12 318	78.07 434	99.87 122	83.54 460	92.03 252	78.21 424	89.51 431	45.80 446	99.91 103	96.25 196	93.11 288	90.03 430

JIA-IIPM		91.76 311	90.70 312	94.94 288	96.11 319	87.51 382	93.16 436	98.13 222	75.79 432	97.58 175	77.68 450	92.84 134	97.97 294	88.47 337	96.54 218	99.33 195

OpenMVS	90.15 15	94.77 226	93.59 247	98.33 138	96.07 320	97.48 105	99.56 223	98.57 100	90.46 305	86.51 373	98.95 189	78.57 337	99.94 87	93.86 247	99.74 86	97.57 287

PAPM		98.60 34	98.42 35	99.14 66	96.05 321	98.96 26	99.90 106	99.35 24	96.68 68	98.35 146	99.66 108	96.45 33	98.51 247	99.45 59	99.89 70	99.96 69

CLD-MVS		94.06 252	93.90 240	94.55 305	96.02 322	90.69 334	99.98 18	97.72 263	96.62 72	91.05 293	98.85 208	77.21 342	98.47 248	98.11 138	89.51 302	94.48 312
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 337	88.75 353	95.25 280	95.99 323	90.16 347	91.22 444	97.54 286	76.80 428	97.26 186	86.01 444	91.88 162	96.07 393	66.16 443	95.91 238	99.51 167

ACMH+	89.98 16	90.35 338	89.54 337	92.78 364	95.99 323	86.12 392	98.81 330	97.18 329	89.38 323	83.14 399	97.76 279	68.42 400	98.43 253	89.11 328	86.05 336	93.78 375

DeepMVS_CX				82.92 422	95.98 325	58.66 453		96.01 403	92.72 217	78.34 423	95.51 352	58.29 432	98.08 288	82.57 389	85.29 341	92.03 411

ACMP	92.05 9	92.74 286	92.42 285	93.73 337	95.91 326	88.72 368	99.81 153	97.53 288	94.13 157	87.00 367	98.23 261	74.07 375	98.47 248	96.22 197	88.86 309	93.99 360
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 266	93.03 268	95.35 275	95.86 327	86.94 387	99.87 122	96.36 396	96.85 59	99.54 68	98.79 212	52.41 440	99.83 132	98.64 108	98.97 146	99.29 205

HQP-NCC					95.78 328		99.87 122		96.82 61	93.37 265

ACMP_Plane					95.78 328		99.87 122		96.82 61	93.37 265

HQP-MVS		94.61 233	94.50 221	94.92 289	95.78 328	91.85 304	99.87 122	97.89 245	96.82 61	93.37 265	98.65 224	80.65 315	98.39 260	97.92 150	89.60 297	94.53 308

NP-MVS					95.77 331	91.79 306					98.65 224

test_fmvsmconf0.1_n		97.74 97	97.44 101	98.64 108	95.76 332	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					95.76 332	91.72 311						80.47 319

ACMM	91.95 10	92.88 283	92.52 283	93.98 331	95.75 334	89.08 365	99.77 164	97.52 290	93.00 204	89.95 306	97.99 270	76.17 358	98.46 251	93.63 259	88.87 308	94.39 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 255	92.84 270	96.80 229	95.73 335	93.57 261	99.88 119	97.24 325	92.57 229	92.92 272	96.66 311	78.73 335	97.67 308	87.75 345	94.06 276	99.17 215

plane_prior1					95.73 335

jason		97.24 123	96.86 128	98.38 137	95.73 335	97.32 110	99.97 36	97.40 302	95.34 111	98.60 133	99.54 126	87.70 223	98.56 244	97.94 149	99.47 117	99.25 210
jason: jason.
mmtdpeth		88.52 364	87.75 366	90.85 384	95.71 338	83.47 411	98.94 313	94.85 426	88.78 339	97.19 188	89.58 430	63.29 420	98.97 209	98.54 113	62.86 446	90.10 429

HQP_MVS		94.49 240	94.36 224	94.87 290	95.71 338	91.74 308	99.84 141	97.87 247	96.38 81	93.01 270	98.59 232	80.47 319	98.37 266	97.79 159	89.55 300	94.52 310

plane_prior7					95.71 338	91.59 320

ITE_SJBPF				92.38 367	95.69 341	85.14 398		95.71 410	92.81 212	89.33 326	98.11 264	70.23 393	98.42 254	85.91 367	88.16 321	93.59 383

fmvsm_s_conf0.1_n_a		97.09 132	96.90 125	97.63 189	95.65 342	94.21 245	99.83 148	98.50 130	96.27 86	99.65 50	99.64 111	84.72 272	99.93 96	99.04 78	98.84 151	98.74 250

ACMH	89.72 17	90.64 331	89.63 334	93.66 343	95.64 343	88.64 371	98.55 350	97.45 295	89.03 328	81.62 406	97.61 280	69.75 394	98.41 256	89.37 325	87.62 328	93.92 366
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 154	96.49 146	97.37 209	95.63 344	95.96 175	99.74 176	98.88 54	92.94 206	91.61 286	98.97 182	97.72 6	98.62 241	94.83 224	98.08 181	97.53 289

FMVSNet1		88.50 365	86.64 372	94.08 324	95.62 345	91.97 299	98.43 358	96.95 364	83.00 408	86.08 381	94.72 386	59.09 431	96.11 389	81.82 396	84.07 354	94.17 338

LuminaMVS		96.63 158	96.21 157	97.87 170	95.58 346	96.82 134	99.12 282	97.67 267	94.47 137	97.88 166	98.31 257	87.50 228	98.71 231	98.07 142	97.29 199	98.10 270

LPG-MVS_test		92.96 280	92.71 275	93.71 339	95.43 347	88.67 369	99.75 173	97.62 275	92.81 212	90.05 302	98.49 243	75.24 365	98.40 258	95.84 203	89.12 304	94.07 352

LGP-MVS_train				93.71 339	95.43 347	88.67 369		97.62 275	92.81 212	90.05 302	98.49 243	75.24 365	98.40 258	95.84 203	89.12 304	94.07 352

tpm		93.70 264	93.41 256	94.58 303	95.36 349	87.41 383	97.01 400	96.90 371	90.85 290	96.72 203	94.14 401	90.40 189	96.84 357	90.75 306	88.54 316	99.51 167

D2MVS		92.76 285	92.59 281	93.27 351	95.13 350	89.54 359	99.69 197	99.38 22	92.26 245	87.59 358	94.61 392	85.05 267	97.79 303	91.59 289	88.01 322	92.47 406

VPA-MVSNet		92.70 287	91.55 300	96.16 251	95.09 351	96.20 166	98.88 321	99.00 39	91.02 287	91.82 285	95.29 368	76.05 360	97.96 296	95.62 208	81.19 374	94.30 327

LTVRE_ROB	88.28 18	90.29 341	89.05 348	94.02 327	95.08 352	90.15 348	97.19 395	97.43 297	84.91 394	83.99 395	97.06 297	74.00 376	98.28 275	84.08 378	87.71 326	93.62 382
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		87.87 372	86.51 373	91.94 373	95.05 353	85.57 396	97.65 387	94.08 436	84.40 398	81.82 405	96.85 306	62.14 425	98.33 269	80.25 405	86.37 335	91.91 413

test0.0.03 1		93.86 254	93.61 244	94.64 299	95.02 354	92.18 297	99.93 89	98.58 98	94.07 161	87.96 353	98.50 242	93.90 103	94.96 412	81.33 397	93.17 286	96.78 295

UniMVSNet (Re)		93.07 279	92.13 287	95.88 259	94.84 355	96.24 165	99.88 119	98.98 41	92.49 234	89.25 327	95.40 358	87.09 236	97.14 333	93.13 267	78.16 398	94.26 329

USDC		90.00 348	88.96 349	93.10 357	94.81 356	88.16 377	98.71 339	95.54 415	93.66 182	83.75 397	97.20 291	65.58 411	98.31 271	83.96 381	87.49 330	92.85 400

VPNet		91.81 305	90.46 316	95.85 261	94.74 357	95.54 194	98.98 305	98.59 96	92.14 247	90.77 298	97.44 284	68.73 398	97.54 313	94.89 223	77.89 400	94.46 313

FIs		94.10 251	93.43 253	96.11 252	94.70 358	96.82 134	99.58 218	98.93 48	92.54 230	89.34 325	97.31 288	87.62 225	97.10 337	94.22 241	86.58 333	94.40 319

UniMVSNet_ETH3D		90.06 347	88.58 356	94.49 309	94.67 359	88.09 378	97.81 384	97.57 283	83.91 401	88.44 345	97.41 285	57.44 433	97.62 310	91.41 291	88.59 315	97.77 279

UniMVSNet_NR-MVSNet		92.95 281	92.11 288	95.49 269	94.61 360	95.28 210	99.83 148	99.08 36	91.49 267	89.21 330	96.86 305	87.14 235	96.73 363	93.20 263	77.52 403	94.46 313

test_fmvs2		89.47 357	89.70 333	88.77 408	94.54 361	75.74 436	99.83 148	94.70 432	94.71 129	91.08 291	96.82 310	54.46 436	97.78 305	92.87 270	88.27 319	92.80 401

MonoMVSNet		94.82 221	94.43 222	95.98 255	94.54 361	90.73 333	99.03 300	97.06 351	93.16 199	93.15 269	95.47 355	88.29 217	97.57 311	97.85 154	91.33 294	99.62 136

WR-MVS		92.31 297	91.25 305	95.48 272	94.45 363	95.29 209	99.60 215	98.68 77	90.10 313	88.07 352	96.89 303	80.68 314	96.80 361	93.14 266	79.67 391	94.36 321

nrg030		93.51 268	92.53 282	96.45 242	94.36 364	97.20 116	99.81 153	97.16 333	91.60 264	89.86 309	97.46 283	86.37 247	97.68 307	95.88 202	80.31 387	94.46 313

tfpnnormal		89.29 360	87.61 367	94.34 317	94.35 365	94.13 247	98.95 312	98.94 44	83.94 399	84.47 392	95.51 352	74.84 370	97.39 316	77.05 421	80.41 385	91.48 416

FC-MVSNet-test		93.81 258	93.15 266	95.80 264	94.30 366	96.20 166	99.42 247	98.89 52	92.33 240	89.03 335	97.27 290	87.39 231	96.83 359	93.20 263	86.48 334	94.36 321

SSC-MVS3.2		89.59 355	88.66 355	92.38 367	94.29 367	86.12 392	99.49 236	97.66 270	90.28 312	88.63 342	95.18 372	64.46 416	96.88 355	85.30 371	82.66 362	94.14 347

MS-PatchMatch		90.65 330	90.30 321	91.71 378	94.22 368	85.50 397	98.24 368	97.70 264	88.67 342	86.42 376	96.37 321	67.82 403	98.03 292	83.62 383	99.62 95	91.60 414

WR-MVS_H		91.30 315	90.35 319	94.15 321	94.17 369	92.62 288	99.17 280	98.94 44	88.87 337	86.48 375	94.46 397	84.36 278	96.61 368	88.19 339	78.51 396	93.21 392

DU-MVS		92.46 294	91.45 303	95.49 269	94.05 370	95.28 210	99.81 153	98.74 71	92.25 246	89.21 330	96.64 313	81.66 300	96.73 363	93.20 263	77.52 403	94.46 313

NR-MVSNet		91.56 313	90.22 323	95.60 267	94.05 370	95.76 182	98.25 367	98.70 74	91.16 281	80.78 412	96.64 313	83.23 288	96.57 369	91.41 291	77.73 402	94.46 313

CP-MVSNet		91.23 319	90.22 323	94.26 319	93.96 372	92.39 293	99.09 286	98.57 100	88.95 334	86.42 376	96.57 316	79.19 330	96.37 378	90.29 315	78.95 393	94.02 355

XXY-MVS		91.82 304	90.46 316	95.88 259	93.91 373	95.40 200	98.87 324	97.69 266	88.63 344	87.87 354	97.08 295	74.38 374	97.89 300	91.66 288	84.07 354	94.35 324

PS-CasMVS		90.63 332	89.51 339	93.99 330	93.83 374	91.70 312	98.98 305	98.52 121	88.48 346	86.15 380	96.53 318	75.46 363	96.31 382	88.83 330	78.86 395	93.95 363

test_0402		85.58 379	83.94 384	90.50 390	93.81 375	85.04 399	98.55 350	95.20 423	76.01 430	79.72 418	95.13 373	64.15 418	96.26 384	66.04 444	86.88 332	90.21 427

XVG-ACMP-BASELINE		91.22 320	90.75 311	92.63 366	93.73 376	85.61 395	98.52 354	97.44 296	92.77 216	89.90 308	96.85 306	66.64 408	98.39 260	92.29 275	88.61 313	93.89 368

TranMVSNet+NR-MVSNet		91.68 312	90.61 315	94.87 290	93.69 377	93.98 251	99.69 197	98.65 81	91.03 286	88.44 345	96.83 309	80.05 323	96.18 387	90.26 316	76.89 411	94.45 318

TransMVSNet (Re)		87.25 373	85.28 380	93.16 354	93.56 378	91.03 325	98.54 352	94.05 438	83.69 403	81.09 410	96.16 327	75.32 364	96.40 377	76.69 422	68.41 434	92.06 410

v10		90.25 342	88.82 351	94.57 304	93.53 379	93.43 266	99.08 288	96.87 374	85.00 391	87.34 365	94.51 393	80.93 310	97.02 347	82.85 388	79.23 392	93.26 390

testgi		89.01 362	88.04 363	91.90 374	93.49 380	84.89 401	99.73 183	95.66 412	93.89 175	85.14 387	98.17 262	59.68 430	94.66 418	77.73 417	88.88 307	96.16 304

v8		90.54 334	89.17 344	94.66 298	93.43 381	93.40 269	99.20 277	96.94 368	85.76 382	87.56 359	94.51 393	81.96 296	97.19 330	84.94 374	78.25 397	93.38 388

V42		91.28 317	90.12 328	94.74 295	93.42 382	93.46 265	99.68 199	97.02 355	87.36 361	89.85 311	95.05 376	81.31 306	97.34 319	87.34 350	80.07 389	93.40 386

pm-mvs1		89.36 359	87.81 365	94.01 328	93.40 383	91.93 302	98.62 348	96.48 394	86.25 377	83.86 396	96.14 329	73.68 377	97.04 343	86.16 364	75.73 416	93.04 396

v1144		91.09 321	89.83 330	94.87 290	93.25 384	93.69 259	99.62 210	96.98 360	86.83 371	89.64 317	94.99 381	80.94 309	97.05 340	85.08 373	81.16 375	93.87 370

v1192		90.62 333	89.25 343	94.72 297	93.13 385	93.07 273	99.50 234	97.02 355	86.33 376	89.56 321	95.01 378	79.22 329	97.09 339	82.34 392	81.16 375	94.01 357

v2v482		91.30 315	90.07 329	95.01 285	93.13 385	93.79 254	99.77 164	97.02 355	88.05 352	89.25 327	95.37 362	80.73 313	97.15 332	87.28 351	80.04 390	94.09 351

OPM-MVS		93.21 273	92.80 272	94.44 312	93.12 387	90.85 332	99.77 164	97.61 278	96.19 89	91.56 287	98.65 224	75.16 369	98.47 248	93.78 254	89.39 303	93.99 360
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 328	89.52 338	94.59 302	93.11 388	92.77 279	99.56 223	96.99 358	86.38 375	89.82 312	94.95 383	80.50 318	97.10 337	83.98 380	80.41 385	93.90 367

PEN-MVS		90.19 344	89.06 347	93.57 344	93.06 389	90.90 330	99.06 293	98.47 133	88.11 351	85.91 382	96.30 323	76.67 350	95.94 397	87.07 354	76.91 410	93.89 368

v1240		90.20 343	88.79 352	94.44 312	93.05 390	92.27 295	99.38 254	96.92 370	85.89 380	89.36 324	94.87 385	77.89 341	97.03 345	80.66 401	81.08 378	94.01 357

v148		90.70 329	89.63 334	93.92 332	92.97 391	90.97 326	99.75 173	96.89 372	87.51 358	88.27 350	95.01 378	81.67 299	97.04 343	87.40 349	77.17 408	93.75 376

v1921920		90.46 335	89.12 345	94.50 308	92.96 392	92.46 291	99.49 236	96.98 360	86.10 378	89.61 319	95.30 365	78.55 338	97.03 345	82.17 393	80.89 383	94.01 357

MVStest1		85.03 385	82.76 394	91.83 375	92.95 393	89.16 364	98.57 349	94.82 427	71.68 441	68.54 444	95.11 375	83.17 289	95.66 401	74.69 427	65.32 441	90.65 423

tt0320-xc		82.94 399	80.35 406	90.72 388	92.90 394	83.54 409	96.85 405	94.73 430	63.12 446	79.85 417	93.77 405	49.43 444	95.46 404	80.98 400	71.54 425	93.16 393

Baseline_NR-MVSNet		90.33 339	89.51 339	92.81 363	92.84 395	89.95 353	99.77 164	93.94 439	84.69 396	89.04 334	95.66 344	81.66 300	96.52 371	90.99 299	76.98 409	91.97 412

test_method		80.79 404	79.70 408	84.08 419	92.83 396	67.06 445	99.51 232	95.42 417	54.34 451	81.07 411	93.53 407	44.48 447	92.22 438	78.90 412	77.23 407	92.94 398

pmmvs4		92.10 301	91.07 309	95.18 281	92.82 397	94.96 221	99.48 239	96.83 376	87.45 360	88.66 341	96.56 317	83.78 283	96.83 359	89.29 326	84.77 348	93.75 376

LF4IMVS		89.25 361	88.85 350	90.45 392	92.81 398	81.19 426	98.12 374	94.79 428	91.44 271	86.29 378	97.11 293	65.30 414	98.11 286	88.53 335	85.25 342	92.07 409

tt0320		83.56 398	81.15 401	90.77 386	92.77 399	83.58 408	96.83 406	95.52 416	63.26 445	81.36 408	92.54 415	53.26 438	95.77 399	80.45 402	74.38 419	92.96 397

DTE-MVSNet		89.40 358	88.24 361	92.88 361	92.66 400	89.95 353	99.10 285	98.22 206	87.29 362	85.12 388	96.22 325	76.27 357	95.30 409	83.56 384	75.74 415	93.41 385

EU-MVSNet		90.14 346	90.34 320	89.54 400	92.55 401	81.06 427	98.69 342	98.04 230	91.41 275	86.59 372	96.84 308	80.83 312	93.31 431	86.20 363	81.91 369	94.26 329

APD_test1		81.15 403	80.92 403	81.86 423	92.45 402	59.76 452	96.04 420	93.61 442	73.29 439	77.06 427	96.64 313	44.28 448	96.16 388	72.35 431	82.52 363	89.67 434

sc_t1		85.01 386	82.46 396	92.67 365	92.44 403	83.09 412	97.39 391	95.72 409	65.06 444	85.64 385	96.16 327	49.50 443	97.34 319	84.86 375	75.39 417	97.57 287

our_test_3		90.39 336	89.48 341	93.12 355	92.40 404	89.57 358	99.33 261	96.35 397	87.84 356	85.30 386	94.99 381	84.14 281	96.09 392	80.38 403	84.56 349	93.71 381

ppachtmachnet_test		89.58 356	88.35 359	93.25 353	92.40 404	90.44 342	99.33 261	96.73 383	85.49 387	85.90 383	95.77 338	81.09 308	96.00 396	76.00 425	82.49 364	93.30 389

v7n		89.65 354	88.29 360	93.72 338	92.22 406	90.56 339	99.07 292	97.10 343	85.42 389	86.73 369	94.72 386	80.06 322	97.13 334	81.14 398	78.12 399	93.49 384

dmvs_testset		83.79 395	86.07 376	76.94 427	92.14 407	48.60 462	96.75 407	90.27 452	89.48 322	78.65 421	98.55 239	79.25 328	86.65 450	66.85 441	82.69 361	95.57 306

PS-MVSNAJss		93.64 265	93.31 262	94.61 300	92.11 408	92.19 296	99.12 282	97.38 303	92.51 233	88.45 344	96.99 301	91.20 170	97.29 327	94.36 235	87.71 326	94.36 321

pmmvs5		90.17 345	89.09 346	93.40 347	92.10 409	89.77 356	99.74 176	95.58 414	85.88 381	87.24 366	95.74 339	73.41 380	96.48 373	88.54 334	83.56 358	93.95 363

N_pmnet		80.06 407	80.78 404	77.89 426	91.94 410	45.28 464	98.80 333	56.82 466	78.10 427	80.08 415	93.33 408	77.03 344	95.76 400	68.14 439	82.81 360	92.64 402

test_djsdf		92.83 284	92.29 286	94.47 310	91.90 411	92.46 291	99.55 226	97.27 320	91.17 279	89.96 305	96.07 333	81.10 307	96.89 353	94.67 230	88.91 306	94.05 354

SixPastTwentyTwo		88.73 363	88.01 364	90.88 382	91.85 412	82.24 418	98.22 371	95.18 424	88.97 332	82.26 402	96.89 303	71.75 385	96.67 366	84.00 379	82.98 359	93.72 380

K. test v3		88.05 369	87.24 370	90.47 391	91.82 413	82.23 419	98.96 311	97.42 299	89.05 327	76.93 429	95.60 346	68.49 399	95.42 405	85.87 368	81.01 381	93.75 376

OurMVSNet-221017-0		89.81 351	89.48 341	90.83 385	91.64 414	81.21 425	98.17 373	95.38 419	91.48 269	85.65 384	97.31 288	72.66 381	97.29 327	88.15 340	84.83 347	93.97 362

mvs_tets		91.81 305	91.08 308	94.00 329	91.63 415	90.58 338	98.67 344	97.43 297	92.43 235	87.37 364	97.05 298	71.76 384	97.32 322	94.75 227	88.68 312	94.11 350

Gipuma		66.95 420	65.00 420	72.79 432	91.52 416	67.96 444	66.16 455	95.15 425	47.89 453	58.54 450	67.99 455	29.74 452	87.54 449	50.20 454	77.83 401	62.87 455
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 170	95.74 179	98.32 139	91.47 417	95.56 193	99.84 141	97.30 315	97.74 27	97.89 165	99.35 145	79.62 325	99.85 122	99.25 68	99.24 134	99.55 153

jajsoiax		91.92 303	91.18 306	94.15 321	91.35 418	90.95 329	99.00 303	97.42 299	92.61 225	87.38 363	97.08 295	72.46 382	97.36 317	94.53 233	88.77 310	94.13 349

MDA-MVSNet-bldmvs		84.09 393	81.52 400	91.81 376	91.32 419	88.00 380	98.67 344	95.92 405	80.22 421	55.60 453	93.32 409	68.29 401	93.60 429	73.76 428	76.61 412	93.82 374

MVP-Stereo		90.93 323	90.45 318	92.37 369	91.25 420	88.76 366	98.05 378	96.17 400	87.27 363	84.04 393	95.30 365	78.46 339	97.27 329	83.78 382	99.70 89	91.09 417
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 381	83.32 389	92.10 371	90.96 421	88.58 372	99.20 277	96.52 392	79.70 423	57.12 452	92.69 414	79.11 331	93.86 425	77.10 420	77.46 405	93.86 371

YYNet1		85.50 382	83.33 388	92.00 372	90.89 422	88.38 376	99.22 276	96.55 391	79.60 424	57.26 451	92.72 413	79.09 333	93.78 427	77.25 419	77.37 406	93.84 372

anonymousdsp		91.79 310	90.92 310	94.41 315	90.76 423	92.93 278	98.93 315	97.17 331	89.08 326	87.46 362	95.30 365	78.43 340	96.92 351	92.38 274	88.73 311	93.39 387

lessismore_v0				90.53 389	90.58 424	80.90 428		95.80 406		77.01 428	95.84 336	66.15 410	96.95 349	83.03 387	75.05 418	93.74 379

EG-PatchMatch MVS		85.35 383	83.81 386	89.99 398	90.39 425	81.89 421	98.21 372	96.09 402	81.78 415	74.73 435	93.72 406	51.56 442	97.12 336	79.16 411	88.61 313	90.96 420

EGC-MVSNET		69.38 413	63.76 423	86.26 416	90.32 426	81.66 424	96.24 416	93.85 440	0.99 463	3.22 464	92.33 420	52.44 439	92.92 434	59.53 450	84.90 346	84.21 444

CMPMVS	61.59 21	84.75 389	85.14 381	83.57 420	90.32 426	62.54 448	96.98 401	97.59 282	74.33 437	69.95 441	96.66 311	64.17 417	98.32 270	87.88 344	88.41 318	89.84 432
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 392	82.92 392	89.21 402	90.03 428	82.60 415	96.89 404	95.62 413	80.59 419	75.77 434	89.17 432	65.04 415	94.79 416	72.12 432	81.02 380	90.23 426

pmmvs6		85.69 378	83.84 385	91.26 381	90.00 429	84.41 404	97.82 383	96.15 401	75.86 431	81.29 409	95.39 360	61.21 428	96.87 356	83.52 385	73.29 421	92.50 405

ttmdpeth		88.23 368	87.06 371	91.75 377	89.91 430	87.35 384	98.92 318	95.73 408	87.92 354	84.02 394	96.31 322	68.23 402	96.84 357	86.33 362	76.12 413	91.06 418

DSMNet-mixed		88.28 367	88.24 361	88.42 410	89.64 431	75.38 438	98.06 377	89.86 453	85.59 386	88.20 351	92.14 421	76.15 359	91.95 439	78.46 414	96.05 231	97.92 273

UnsupCasMVSNet_eth		85.52 380	83.99 382	90.10 396	89.36 432	83.51 410	96.65 408	97.99 233	89.14 325	75.89 433	93.83 403	63.25 421	93.92 423	81.92 395	67.90 437	92.88 399

Anonymous20231206		86.32 376	85.42 379	89.02 404	89.11 433	80.53 431	99.05 297	95.28 420	85.43 388	82.82 400	93.92 402	74.40 373	93.44 430	66.99 440	81.83 370	93.08 395

Anonymous20240521		85.15 384	83.81 386	89.16 403	88.32 434	82.69 414	98.80 333	95.74 407	79.72 422	81.53 407	90.99 424	65.38 413	94.16 421	72.69 430	81.11 377	90.63 424

OpenMVS_ROB	79.82 20	83.77 396	81.68 399	90.03 397	88.30 435	82.82 413	98.46 355	95.22 422	73.92 438	76.00 432	91.29 423	55.00 435	96.94 350	68.40 438	88.51 317	90.34 425

test20.03		84.72 390	83.99 382	86.91 414	88.19 436	80.62 430	98.88 321	95.94 404	88.36 348	78.87 419	94.62 391	68.75 397	89.11 445	66.52 442	75.82 414	91.00 419

KD-MVS_self_test		83.59 397	82.06 397	88.20 411	86.93 437	80.70 429	97.21 394	96.38 395	82.87 409	82.49 401	88.97 433	67.63 404	92.32 437	73.75 429	62.30 448	91.58 415

MIMVSNet1		82.58 400	80.51 405	88.78 406	86.68 438	84.20 405	96.65 408	95.41 418	78.75 425	78.59 422	92.44 416	51.88 441	89.76 444	65.26 445	78.95 393	92.38 408

CL-MVSNet_self_test		84.50 391	83.15 391	88.53 409	86.00 439	81.79 422	98.82 329	97.35 307	85.12 390	83.62 398	90.91 426	76.66 351	91.40 440	69.53 436	60.36 449	92.40 407

UnsupCasMVSNet_bld		79.97 409	77.03 414	88.78 406	85.62 440	81.98 420	93.66 432	97.35 307	75.51 434	70.79 440	83.05 447	48.70 445	94.91 414	78.31 415	60.29 450	89.46 437

mvs5depth		84.87 387	82.90 393	90.77 386	85.59 441	84.84 402	91.10 445	93.29 444	83.14 406	85.07 389	94.33 399	62.17 424	97.32 322	78.83 413	72.59 424	90.14 428

Patchmatch-RL test		86.90 374	85.98 378	89.67 399	84.45 442	75.59 437	89.71 448	92.43 446	86.89 370	77.83 426	90.94 425	94.22 92	93.63 428	87.75 345	69.61 429	99.79 105

pmmvs-eth3d		84.03 394	81.97 398	90.20 395	84.15 443	87.09 386	98.10 376	94.73 430	83.05 407	74.10 437	87.77 439	65.56 412	94.01 422	81.08 399	69.24 431	89.49 436

test_fmvs3		79.99 408	80.17 407	79.45 425	84.02 444	62.83 446	99.05 297	93.49 443	88.29 350	80.06 416	86.65 442	28.09 454	88.00 446	88.63 331	73.27 422	87.54 442

PM-MVS		80.47 405	78.88 410	85.26 417	83.79 445	72.22 440	95.89 423	91.08 450	85.71 385	76.56 431	88.30 435	36.64 450	93.90 424	82.39 391	69.57 430	89.66 435

new-patchmatchnet		81.19 402	79.34 409	86.76 415	82.86 446	80.36 432	97.92 380	95.27 421	82.09 414	72.02 438	86.87 441	62.81 423	90.74 443	71.10 433	63.08 445	89.19 439

mvsany_test3		82.12 401	81.14 402	85.06 418	81.87 447	70.41 442	97.09 398	92.14 447	91.27 278	77.84 425	88.73 434	39.31 449	95.49 402	90.75 306	71.24 426	89.29 438

WB-MVS		76.28 411	77.28 413	73.29 431	81.18 448	54.68 456	97.87 382	94.19 435	81.30 416	69.43 442	90.70 427	77.02 345	82.06 454	35.71 459	68.11 436	83.13 445

test_f		78.40 410	77.59 412	80.81 424	80.82 449	62.48 449	96.96 402	93.08 445	83.44 404	74.57 436	84.57 446	27.95 455	92.63 435	84.15 377	72.79 423	87.32 443

SSC-MVS		75.42 412	76.40 415	72.49 435	80.68 450	53.62 457	97.42 389	94.06 437	80.42 420	68.75 443	90.14 429	76.54 353	81.66 455	33.25 460	66.34 440	82.19 446

pmmvs3		80.27 406	77.77 411	87.76 413	80.32 451	82.43 417	98.23 370	91.97 448	72.74 440	78.75 420	87.97 438	57.30 434	90.99 442	70.31 434	62.37 447	89.87 431

testf1		68.38 416	66.92 417	72.78 433	78.80 452	50.36 459	90.95 446	87.35 458	55.47 449	58.95 448	88.14 436	20.64 459	87.60 447	57.28 451	64.69 442	80.39 448

APD_test2		68.38 416	66.92 417	72.78 433	78.80 452	50.36 459	90.95 446	87.35 458	55.47 449	58.95 448	88.14 436	20.64 459	87.60 447	57.28 451	64.69 442	80.39 448

ambc				83.23 421	77.17 454	62.61 447	87.38 450	94.55 434		76.72 430	86.65 442	30.16 451	96.36 379	84.85 376	69.86 428	90.73 422

test_vis3_rt		68.82 414	66.69 419	75.21 430	76.24 455	60.41 451	96.44 411	68.71 465	75.13 435	50.54 456	69.52 454	16.42 464	96.32 381	80.27 404	66.92 439	68.89 452

TDRefinement		84.76 388	82.56 395	91.38 380	74.58 456	84.80 403	97.36 392	94.56 433	84.73 395	80.21 414	96.12 332	63.56 419	98.39 260	87.92 343	63.97 444	90.95 421

E-PMN		52.30 424	52.18 426	52.67 441	71.51 457	45.40 463	93.62 433	76.60 463	36.01 457	43.50 458	64.13 457	27.11 456	67.31 460	31.06 461	26.06 456	45.30 459

EMVS		51.44 426	51.22 428	52.11 442	70.71 458	44.97 465	94.04 429	75.66 464	35.34 459	42.40 459	61.56 460	28.93 453	65.87 461	27.64 462	24.73 457	45.49 458

PMMVS2		67.15 419	64.15 422	76.14 429	70.56 459	62.07 450	93.89 430	87.52 457	58.09 448	60.02 447	78.32 449	22.38 458	84.54 452	59.56 449	47.03 454	81.80 447

FPMVS		68.72 415	68.72 416	68.71 437	65.95 460	44.27 466	95.97 422	94.74 429	51.13 452	53.26 454	90.50 428	25.11 457	83.00 453	60.80 448	80.97 382	78.87 450

wuyk23d		20.37 430	20.84 433	18.99 445	65.34 461	27.73 468	50.43 456	7.67 469	9.50 462	8.01 463	6.34 463	6.13 467	26.24 462	23.40 463	10.69 461	2.99 460

LCM-MVSNet		67.77 418	64.73 421	76.87 428	62.95 462	56.25 455	89.37 449	93.74 441	44.53 454	61.99 446	80.74 448	20.42 461	86.53 451	69.37 437	59.50 451	87.84 440

MVE	53.74 22	51.54 425	47.86 429	62.60 439	59.56 463	50.93 458	79.41 453	77.69 462	35.69 458	36.27 460	61.76 459	5.79 468	69.63 458	37.97 458	36.61 455	67.24 453
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 422	52.24 425	67.66 438	49.27 464	56.82 454	83.94 451	82.02 461	70.47 442	33.28 461	64.54 456	17.23 463	69.16 459	45.59 456	23.85 458	77.02 451

tmp_tt		65.23 421	62.94 424	72.13 436	44.90 465	50.03 461	81.05 452	89.42 456	38.45 455	48.51 457	99.90 18	54.09 437	78.70 457	91.84 287	18.26 459	87.64 441

PMVS	49.05 23	53.75 423	51.34 427	60.97 440	40.80 466	34.68 467	74.82 454	89.62 455	37.55 456	28.67 462	72.12 451	7.09 466	81.63 456	43.17 457	68.21 435	66.59 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 428	39.14 431	33.31 443	19.94 467	24.83 469	98.36 363	9.75 468	15.53 461	51.31 455	87.14 440	19.62 462	17.74 463	47.10 455	3.47 462	57.36 456

testmvs		40.60 427	44.45 430	29.05 444	19.49 468	14.11 470	99.68 199	18.47 467	20.74 460	64.59 445	98.48 246	10.95 465	17.09 464	56.66 453	11.01 460	55.94 457

mmdepth		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

monomultidepth		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

test_blank		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.02 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

eth-test2					0.00 469

eth-test					0.00 469

uanet_test		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

DCPMVS		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

cdsmvs_eth3d_5k		23.43 429	31.24 432	0.00 446	0.00 469	0.00 471	0.00 457	98.09 224	0.00 464	0.00 465	99.67 106	83.37 286	0.00 465	0.00 464	0.00 463	0.00 461

pcd_1.5k_mvsjas		7.60 432	10.13 435	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	91.20 170	0.00 465	0.00 464	0.00 463	0.00 461

sosnet-low-res		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

sosnet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

uncertanet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

Regformer		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

ab-mvs-re		8.28 431	11.04 434	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	99.40 139	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

uanet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 465	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

WAC-MVS						90.97 326								86.10 366

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							95.78 424				59.23 461	93.20 125	97.74 306	91.06 297

test_post											63.35 458	94.43 79	98.13 285

patchmatchnet-post											91.70 422	95.12 56	97.95 297

MTMP							99.87 122	96.49 393

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							99.46 244		94.21 156	99.85 15			99.95 79	96.96 183

新几何2							99.40 248

无先验							99.49 236	98.71 73	93.46 188				100.00 1	94.36 235		99.99 23

原ACMM2							99.90 106

testdata2													99.99 36	90.54 310

segment_acmp												96.68 29

testdata1							99.28 270		96.35 85

plane_prior5								97.87 247					98.37 266	97.79 159	89.55 300	94.52 310

plane_prior4											98.59 232

plane_prior3						91.64 314			96.63 70	93.01 270

plane_prior2							99.84 141		96.38 81

plane_prior						91.74 308	99.86 133		96.76 65						89.59 299

n2								0.00 470

nn								0.00 470

door-mid								89.69 454

test11								98.44 141

door								90.31 451

HQP5-MVS						91.85 304

BP-MVS														97.92 150

HQP4-MVS										93.37 265			98.39 260			94.53 308

HQP3-MVS								97.89 245							89.60 297

HQP2-MVS												80.65 315

MDTV_nov1_ep13_2view						96.26 160	96.11 418		91.89 255	98.06 158		94.40 81		94.30 238		99.67 123

ACMMP++_ref															87.04 331

ACMMP++															88.23 320

Test By Simon												92.82 136

High-resolution multi-view benchmark