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		97.23 125	96.80 134	98.51 127	99.99 1	95.60 193	99.09 287	98.84 62	93.32 194	96.74 203	99.72 88	86.04 253	100.00 1	98.01 145	99.43 123	99.94 81

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 19	98.69 76	98.20 9	99.93 2	99.98 2	96.82 24	100.00 1	99.75 37	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 37	98.64 85	98.47 3	99.13 101	99.92 13	96.38 34	100.00 1	99.74 39	100.00 1	100.00 1

mPP-MVS		98.39 53	98.20 51	98.97 87	99.97 3	96.92 132	99.95 66	98.38 178	95.04 117	98.61 131	99.80 54	93.39 114	100.00 1	98.64 109	100.00 1	99.98 52

CPTT-MVS		97.64 105	97.32 109	98.58 116	99.97 3	95.77 182	99.96 47	98.35 184	89.90 319	98.36 146	99.79 58	91.18 173	99.99 36	98.37 125	99.99 21	99.99 23

DP-MVS Recon		98.41 50	98.02 65	99.56 25	99.97 3	98.70 48	99.92 93	98.44 142	92.06 252	98.40 145	99.84 44	95.68 44	100.00 1	98.19 134	99.71 88	99.97 62

PAPR		98.52 40	98.16 55	99.58 24	99.97 3	98.77 42	99.95 66	98.43 150	95.35 111	98.03 160	99.75 75	94.03 99	99.98 47	98.11 139	99.83 77	99.99 23

HFP-MVS		98.56 36	98.37 40	99.14 67	99.96 8	97.43 108	99.95 66	98.61 93	94.77 127	99.31 90	99.85 33	94.22 92	100.00 1	98.70 104	99.98 32	99.98 52

region2R		98.54 38	98.37 40	99.05 77	99.96 8	97.18 118	99.96 47	98.55 113	94.87 124	99.45 77	99.85 33	94.07 98	100.00 1	98.67 106	100.00 1	99.98 52

ACMMPR		98.50 41	98.32 44	99.05 77	99.96 8	97.18 118	99.95 66	98.60 95	94.77 127	99.31 90	99.84 44	93.73 108	100.00 1	98.70 104	99.98 32	99.98 52

NCCC		99.37 2	99.25 2	99.71 15	99.96 8	99.15 22	99.97 37	98.62 92	98.02 20	99.90 5	99.95 3	97.33 17	100.00 1	99.54 53	100.00 1	100.00 1

CP-MVS		98.45 45	98.32 44	98.87 92	99.96 8	96.62 145	99.97 37	98.39 174	94.43 143	98.90 113	99.87 27	94.30 89	100.00 1	99.04 79	99.99 21	99.99 23

test_one_0601					99.94 13	99.30 12		98.41 167	96.63 71	99.75 38	99.93 11	97.49 10

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 66	98.43 150					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 29	98.55 28	99.15 65	99.94 13	97.50 104	99.94 83	98.42 162	96.22 88	99.41 82	99.78 62	94.34 86	99.96 71	98.92 89	99.95 50	99.99 23

X-MVStestdata		93.83 256	92.06 291	99.15 65	99.94 13	97.50 104	99.94 83	98.42 162	96.22 88	99.41 82	41.37 463	94.34 86	99.96 71	98.92 89	99.95 50	99.99 23

test_prior				99.43 36	99.94 13	98.49 61		98.65 82					99.80 136			99.99 23

MSLP-MVS++		99.13 8	99.01 11	99.49 32	99.94 13	98.46 62	99.98 19	98.86 56	97.10 51	99.80 24	99.94 4	95.92 40	100.00 1	99.51 54	100.00 1	100.00 1

APDe-MVS		99.06 11	98.91 14	99.51 29	99.94 13	98.76 45	99.91 101	98.39 174	97.20 49	99.46 76	99.85 33	95.53 48	99.79 138	99.86 23	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 67	97.97 69	99.03 79	99.94 13	97.17 121	99.95 66	98.39 174	94.70 131	98.26 152	99.81 53	91.84 164	100.00 1	98.85 95	99.97 42	99.93 82
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	99.87 123	98.33 189	93.97 168	99.76 37	99.87 27	94.99 64	99.75 147	98.55 113	100.00 1	99.98 52

PAPM_NR		98.12 71	97.93 75	98.70 103	99.94 13	96.13 171	99.82 152	98.43 150	94.56 135	97.52 177	99.70 94	94.40 81	99.98 47	97.00 180	99.98 32	99.99 23

MG-MVS		98.91 19	98.65 24	99.68 16	99.94 13	99.07 24	99.64 208	99.44 19	97.33 42	99.00 109	99.72 88	94.03 99	99.98 47	98.73 103	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 47	98.43 150	97.27 45	99.80 24	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		98.41 167	97.71 29	99.84 19				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 12		98.43 150	97.26 47	99.80 24	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 66	98.32 191	97.28 43	99.83 20	99.91 14	97.22 19	100.00 1	99.99 5	100.00 1	99.89 91
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 47	98.42 162	97.28 43	99.86 13	99.94 4	97.22 19

MSP-MVS		99.09 9	99.12 5	98.98 86	99.93 24	97.24 115	99.95 66	98.42 162	97.50 36	99.52 72	99.88 24	97.43 16	99.71 153	99.50 56	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		98.43 150		99.63 55			99.85 123

FOURS1					99.92 31	97.66 98	99.95 66	98.36 182	95.58 105	99.52 72

ZD-MVS					99.92 31	98.57 56		98.52 122	92.34 240	99.31 90	99.83 46	95.06 59	99.80 136	99.70 44	99.97 42

GST-MVS		98.27 60	97.97 69	99.17 60	99.92 31	97.57 100	99.93 90	98.39 174	94.04 166	98.80 118	99.74 82	92.98 130	100.00 1	98.16 136	99.76 85	99.93 82

TEST9					99.92 31	98.92 29	99.96 47	98.43 150	93.90 174	99.71 45	99.86 29	95.88 41	99.85 123

train_agg		98.88 20	98.65 24	99.59 23	99.92 31	98.92 29	99.96 47	98.43 150	94.35 148	99.71 45	99.86 29	95.94 38	99.85 123	99.69 45	99.98 32	99.99 23

test_8					99.92 31	98.88 32	99.96 47	98.43 150	94.35 148	99.69 47	99.85 33	95.94 38	99.85 123

PGM-MVS		98.34 55	98.13 57	98.99 84	99.92 31	97.00 128	99.75 174	99.50 17	93.90 174	99.37 87	99.76 67	93.24 123	100.00 1	97.75 164	99.96 46	99.98 52

ACMMP		97.74 98	97.44 102	98.66 107	99.92 31	96.13 171	99.18 280	99.45 18	94.84 125	96.41 214	99.71 91	91.40 167	99.99 36	97.99 147	98.03 183	99.87 94
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 66	98.43 150	96.48 76	99.80 24	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		98.41 167					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 167					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 66	98.56 107	97.56 35	99.44 78	99.85 33	95.38 52	100.00 1	99.31 66	99.99 21	99.87 94

APD-MVS		98.62 33	98.35 43	99.41 39	99.90 42	98.51 59	99.87 123	98.36 182	94.08 161	99.74 41	99.73 85	94.08 97	99.74 149	99.42 62	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	99.24 275	98.47 134	98.14 14	99.08 104	99.91 14	93.09 127	100.00 1	99.04 79	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 47				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	99.87 123	98.44 142	97.48 37	99.64 54	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 75

CSCG		97.10 131	97.04 121	97.27 216	99.89 45	91.92 304	99.90 107	99.07 37	88.67 343	95.26 243	99.82 49	93.17 126	99.98 47	98.15 137	99.47 118	99.90 90

ZNCC-MVS		98.31 57	98.03 64	99.17 60	99.88 49	97.59 99	99.94 83	98.44 142	94.31 151	98.50 138	99.82 49	93.06 128	99.99 36	98.30 129	99.99 21	99.93 82

SR-MVS		98.46 44	98.30 47	98.93 90	99.88 49	97.04 127	99.84 142	98.35 184	94.92 121	99.32 89	99.80 54	93.35 116	99.78 140	99.30 67	99.95 50	99.96 70

9.14			98.38 38		99.87 51		99.91 101	98.33 189	93.22 197	99.78 35	99.89 22	94.57 77	99.85 123	99.84 25	99.97 42

SMA-MVS		98.76 26	98.48 32	99.62 20	99.87 51	98.87 33	99.86 134	98.38 178	93.19 198	99.77 36	99.94 4	95.54 46	100.00 1	99.74 39	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 80	97.85 80	98.04 159	99.86 53	95.39 202	99.61 213	97.78 258	96.52 74	98.61 131	99.31 150	92.73 138	99.67 161	96.77 190	99.48 115	99.06 228

lecture		98.67 30	98.46 33	99.28 48	99.86 53	97.88 87	99.97 37	99.25 30	96.07 92	99.79 33	99.70 94	92.53 146	99.98 47	99.51 54	99.48 115	99.97 62

PHI-MVS		98.41 50	98.21 50	99.03 79	99.86 53	97.10 125	99.98 19	98.80 68	90.78 299	99.62 58	99.78 62	95.30 53	100.00 1	99.80 28	99.93 61	99.99 23

MTAPA		98.29 59	97.96 72	99.30 47	99.85 56	97.93 85	99.39 253	98.28 198	95.76 99	97.18 190	99.88 24	92.74 137	100.00 1	98.67 106	99.88 73	99.99 23

LS3D		95.84 191	95.11 203	98.02 160	99.85 56	95.10 220	98.74 337	98.50 131	87.22 365	93.66 264	99.86 29	87.45 231	99.95 80	90.94 302	99.81 83	99.02 232

HPM-MVS		97.96 75	97.72 85	98.68 104	99.84 58	96.39 157	99.90 107	98.17 213	92.61 226	98.62 130	99.57 124	91.87 163	99.67 161	98.87 94	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 60	98.11 59	98.75 100	99.83 59	96.59 149	99.40 249	98.51 125	95.29 113	98.51 137	99.76 67	93.60 112	99.71 153	98.53 116	99.52 108	99.95 77

save fliter					99.82 60	98.79 40	99.96 47	98.40 171	97.66 31

PLC	95.54 3	97.93 78	97.89 78	98.05 158	99.82 60	94.77 230	99.92 93	98.46 136	93.93 171	97.20 188	99.27 155	95.44 51	99.97 59	97.41 169	99.51 111	99.41 183
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 65	98.08 61	98.78 97	99.81 62	96.60 147	99.82 152	98.30 196	93.95 170	99.37 87	99.77 65	92.84 134	99.76 146	98.95 85	99.92 64	99.97 62

EI-MVSNet-UG-set		98.14 70	97.99 67	98.60 112	99.80 63	96.27 160	99.36 259	98.50 131	95.21 115	98.30 149	99.75 75	93.29 120	99.73 152	98.37 125	99.30 132	99.81 103

SR-MVS-dyc-post		98.31 57	98.17 54	98.71 102	99.79 64	96.37 158	99.76 170	98.31 193	94.43 143	99.40 84	99.75 75	93.28 121	99.78 140	98.90 92	99.92 64	99.97 62

RE-MVS-def			98.13 57		99.79 64	96.37 158	99.76 170	98.31 193	94.43 143	99.40 84	99.75 75	92.95 131		98.90 92	99.92 64	99.97 62

HPM-MVS_fast		97.80 92	97.50 98	98.68 104	99.79 64	96.42 153	99.88 120	98.16 218	91.75 263	98.94 111	99.54 127	91.82 165	99.65 165	97.62 167	99.99 21	99.99 23

SF-MVS		98.67 30	98.40 36	99.50 30	99.77 67	98.67 49	99.90 107	98.21 208	93.53 186	99.81 22	99.89 22	94.70 73	99.86 122	99.84 25	99.93 61	99.96 70

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 78	99.75 75	93.24 123	99.99 36	99.94 11	99.41 125	99.95 77

旧先验1					99.76 68	97.52 102		98.64 85			99.85 33	95.63 45			99.94 55	99.99 23

OMC-MVS		97.28 121	97.23 113	97.41 207	99.76 68	93.36 272	99.65 204	97.95 239	96.03 93	97.41 182	99.70 94	89.61 199	99.51 171	96.73 192	98.25 173	99.38 185

新几何1				99.42 38	99.75 71	98.27 66		98.63 91	92.69 221	99.55 67	99.82 49	94.40 81	100.00 1	91.21 294	99.94 55	99.99 23

MP-MVS-pluss		98.07 74	97.64 91	99.38 44	99.74 72	98.41 64	99.74 177	98.18 212	93.35 192	96.45 211	99.85 33	92.64 141	99.97 59	98.91 91	99.89 70	99.77 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 17	98.77 19	99.41 39	99.74 72	98.67 49	99.77 165	98.38 178	96.73 67	99.88 10	99.74 82	94.89 66	99.59 167	99.80 28	99.98 32	99.97 62
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 36	99.74 72	98.56 57		98.40 171		99.65 51		94.76 69	99.75 147		99.98 32	99.99 23

原ACMM1				98.96 88	99.73 75	96.99 129		98.51 125	94.06 164	99.62 58	99.85 33	94.97 65	99.96 71	95.11 215	99.95 50	99.92 87

TSAR-MVS + GP.		98.60 34	98.51 31	98.86 93	99.73 75	96.63 144	99.97 37	97.92 244	98.07 17	98.76 123	99.55 125	95.00 63	99.94 88	99.91 16	97.68 190	99.99 23

CANet		98.27 60	97.82 82	99.63 17	99.72 77	99.10 23	99.98 19	98.51 125	97.00 57	98.52 135	99.71 91	87.80 223	99.95 80	99.75 37	99.38 127	99.83 99

reproduce_model		98.75 27	98.66 23	99.03 79	99.71 78	97.10 125	99.73 184	98.23 206	97.02 56	99.18 99	99.90 18	94.54 78	99.99 36	99.77 33	99.90 69	99.99 23

F-COLMAP		96.93 143	96.95 124	96.87 228	99.71 78	91.74 309	99.85 137	97.95 239	93.11 204	95.72 232	99.16 167	92.35 152	99.94 88	95.32 211	99.35 130	98.92 240

reproduce-ours		98.78 24	98.67 21	99.09 74	99.70 80	97.30 112	99.74 177	98.25 202	97.10 51	99.10 102	99.90 18	94.59 74	99.99 36	99.77 33	99.91 67	99.99 23

our_new_method		98.78 24	98.67 21	99.09 74	99.70 80	97.30 112	99.74 177	98.25 202	97.10 51	99.10 102	99.90 18	94.59 74	99.99 36	99.77 33	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 83	98.34 188	96.38 82	99.81 22	99.76 67	94.59 74	99.98 47	99.84 25	99.96 46	99.97 62
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 66	99.12 5	95.59 269	99.67 83	86.91 390	99.95 66	98.89 52	97.60 32	99.90 5	99.76 67	96.54 32	99.98 47	99.94 11	99.82 81	99.88 92

ACMMP_NAP		98.49 42	98.14 56	99.54 27	99.66 84	98.62 55	99.85 137	98.37 181	94.68 132	99.53 70	99.83 46	92.87 133	100.00 1	98.66 108	99.84 76	99.99 23

DeepPCF-MVS	95.94 2	97.71 102	98.98 12	93.92 333	99.63 85	81.76 424	99.96 47	98.56 107	99.47 1	99.19 98	99.99 1	94.16 96	100.00 1	99.92 13	99.93 61	100.00 1

EPNet		98.49 42	98.40 36	98.77 99	99.62 86	96.80 138	99.90 107	99.51 16	97.60 32	99.20 96	99.36 145	93.71 109	99.91 104	97.99 147	98.71 158	99.61 141
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 86	99.80 54	90.49 188	99.96 71	99.89 18	99.43 123	99.98 52

PVSNet_BlendedMVS		96.05 183	95.82 179	96.72 234	99.59 87	96.99 129	99.95 66	99.10 34	94.06 164	98.27 150	95.80 338	89.00 211	99.95 80	99.12 73	87.53 330	93.24 392

PVSNet_Blended		97.94 77	97.64 91	98.83 94	99.59 87	96.99 129	100.00 1	99.10 34	95.38 110	98.27 150	99.08 170	89.00 211	99.95 80	99.12 73	99.25 134	99.57 152

PatchMatch-RL		96.04 184	95.40 190	97.95 162	99.59 87	95.22 215	99.52 231	99.07 37	93.96 169	96.49 210	98.35 253	82.28 293	99.82 135	90.15 318	99.22 137	98.81 247

dcpmvs_2		97.42 116	98.09 60	95.42 274	99.58 91	87.24 386	99.23 276	96.95 365	94.28 154	98.93 112	99.73 85	94.39 84	99.16 199	99.89 18	99.82 81	99.86 96

test222					99.55 92	97.41 110	99.34 261	98.55 113	91.86 258	99.27 94	99.83 46	93.84 106			99.95 50	99.99 23

CNLPA		97.76 96	97.38 105	98.92 91	99.53 93	96.84 134	99.87 123	98.14 222	93.78 178	96.55 209	99.69 98	92.28 154	99.98 47	97.13 176	99.44 122	99.93 82

API-MVS		97.86 83	97.66 89	98.47 129	99.52 94	95.41 200	99.47 241	98.87 55	91.68 264	98.84 115	99.85 33	92.34 153	99.99 36	98.44 121	99.96 46	100.00 1

PVSNet	91.05 13	97.13 130	96.69 140	98.45 131	99.52 94	95.81 180	99.95 66	99.65 12	94.73 129	99.04 107	99.21 163	84.48 278	99.95 80	94.92 221	98.74 157	99.58 150

114514_t		97.41 117	96.83 131	99.14 67	99.51 96	97.83 88	99.89 117	98.27 200	88.48 347	99.06 106	99.66 109	90.30 191	99.64 166	96.32 196	99.97 42	99.96 70

cl22		93.77 261	93.25 265	95.33 278	99.49 97	94.43 236	99.61 213	98.09 225	90.38 307	89.16 334	95.61 346	90.56 186	97.34 320	91.93 285	84.45 351	94.21 337

testdata				98.42 135	99.47 98	95.33 206		98.56 107	93.78 178	99.79 33	99.85 33	93.64 111	99.94 88	94.97 219	99.94 55	100.00 1

MAR-MVS		97.43 112	97.19 115	98.15 151	99.47 98	94.79 229	99.05 298	98.76 69	92.65 224	98.66 128	99.82 49	88.52 217	99.98 47	98.12 138	99.63 94	99.67 124
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		94.54 235	93.42 255	97.91 168	99.46 100	94.04 249	98.93 316	97.48 295	81.15 418	90.04 305	99.55 125	87.02 239	99.95 80	88.97 330	98.11 179	99.73 114

MVS_111021_LR		98.42 49	98.38 38	98.53 124	99.39 101	95.79 181	99.87 123	99.86 2	96.70 68	98.78 119	99.79 58	92.03 160	99.90 106	99.17 72	99.86 75	99.88 92

CHOSEN 280x420		99.01 14	99.03 10	98.95 89	99.38 102	98.87 33	98.46 356	99.42 21	97.03 55	99.02 108	99.09 169	99.35 2	98.21 282	99.73 41	99.78 84	99.77 110

MVS_111021_HR		98.72 28	98.62 26	99.01 83	99.36 103	97.18 118	99.93 90	99.90 1	96.81 65	98.67 127	99.77 65	93.92 101	99.89 111	99.27 68	99.94 55	99.96 70

DPM-MVS		98.83 21	98.46 33	99.97 1	99.33 104	99.92 1	99.96 47	98.44 142	97.96 21	99.55 67	99.94 4	97.18 21	100.00 1	93.81 252	99.94 55	99.98 52

TAPA-MVS	92.12 8	94.42 243	93.60 247	96.90 227	99.33 104	91.78 308	99.78 161	98.00 233	89.89 320	94.52 249	99.47 131	91.97 161	99.18 196	69.90 436	99.52 108	99.73 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 207	95.07 205	96.32 249	99.32 106	96.60 147	99.76 170	98.85 59	96.65 70	87.83 356	96.05 335	99.52 1	98.11 287	96.58 193	81.07 380	94.25 332

fmvsm_s_conf0.5_n_9		98.15 69	98.02 65	98.55 118	99.28 107	95.84 179	99.99 5	98.57 101	98.17 11	99.93 2	99.74 82	87.04 238	99.97 59	99.86 23	99.59 103	99.83 99

SPE-MVS-test		97.88 81	97.94 74	97.70 184	99.28 107	95.20 216	99.98 19	97.15 335	95.53 107	99.62 58	99.79 58	92.08 159	98.38 265	98.75 102	99.28 133	99.52 164

test_fmvsm_n_1920		98.44 46	98.61 27	97.92 166	99.27 109	95.18 217	100.00 1	98.90 50	98.05 18	99.80 24	99.73 85	92.64 141	99.99 36	99.58 52	99.51 111	98.59 257

fmvsm_l_conf0.5_n_a		99.00 15	98.91 14	99.28 48	99.21 110	97.91 86	99.98 19	98.85 59	98.25 5	99.92 4	99.75 75	94.72 71	99.97 59	99.87 21	99.64 92	99.95 77

fmvsm_s_conf0.5_n_8		98.38 54	98.05 63	99.35 45	99.20 111	98.12 72	99.98 19	98.81 64	98.22 7	99.80 24	99.71 91	87.37 233	99.97 59	99.91 16	99.48 115	99.97 62

test_yl		97.83 87	97.37 106	99.21 54	99.18 112	97.98 81	99.64 208	99.27 27	91.43 273	97.88 167	98.99 179	95.84 42	99.84 131	98.82 96	95.32 259	99.79 106

DCV-MVSNet		97.83 87	97.37 106	99.21 54	99.18 112	97.98 81	99.64 208	99.27 27	91.43 273	97.88 167	98.99 179	95.84 42	99.84 131	98.82 96	95.32 259	99.79 106

fmvsm_l_conf0.5_n		98.94 16	98.84 17	99.25 50	99.17 114	97.81 90	99.98 19	98.86 56	98.25 5	99.90 5	99.76 67	94.21 94	99.97 59	99.87 21	99.52 108	99.98 52

DeepC-MVS	94.51 4	96.92 144	96.40 152	98.45 131	99.16 115	95.90 177	99.66 203	98.06 228	96.37 85	94.37 255	99.49 130	83.29 288	99.90 106	97.63 166	99.61 99	99.55 154
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 38	98.22 49	99.50 30	99.15 116	98.65 53	100.00 1	98.58 99	97.70 30	98.21 155	99.24 161	92.58 144	99.94 88	98.63 111	99.94 55	99.92 87
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 50	98.08 61	99.39 41	99.12 117	98.29 65	99.98 19	98.64 85	98.14 14	99.86 13	99.76 67	87.99 222	99.97 59	99.72 42	99.54 106	99.91 89

fmvsm_l_conf0.5_n_9		98.55 37	98.23 48	99.49 32	99.10 118	98.50 60	99.99 5	98.70 74	98.14 14	99.94 1	99.68 105	89.02 210	99.98 47	99.89 18	99.61 99	99.99 23

CS-MVS		97.79 94	97.91 76	97.43 205	99.10 118	94.42 237	99.99 5	97.10 344	95.07 116	99.68 48	99.75 75	92.95 131	98.34 269	98.38 123	99.14 139	99.54 158

Anonymous202405211		93.10 279	91.99 292	96.40 245	99.10 118	89.65 358	98.88 322	97.93 241	83.71 403	94.00 261	98.75 215	68.79 397	99.88 117	95.08 216	91.71 292	99.68 122

fmvsm_s_conf0.5_n		97.80 92	97.85 80	97.67 185	99.06 121	94.41 238	99.98 19	98.97 43	97.34 40	99.63 55	99.69 98	87.27 234	99.97 59	99.62 50	99.06 144	98.62 256

HyFIR lowres test		96.66 158	96.43 151	97.36 212	99.05 122	93.91 254	99.70 196	99.80 3	90.54 303	96.26 217	98.08 266	92.15 157	98.23 281	96.84 189	95.46 254	99.93 82

LFMVS		94.75 229	93.56 250	98.30 141	99.03 123	95.70 187	98.74 337	97.98 236	87.81 358	98.47 139	99.39 142	67.43 406	99.53 168	98.01 145	95.20 262	99.67 124

fmvsm_s_conf0.5_n_4		97.75 97	97.86 79	97.42 206	99.01 124	94.69 231	99.97 37	98.76 69	97.91 23	99.87 11	99.76 67	86.70 244	99.93 97	99.67 47	99.12 142	97.64 283

fmvsm_s_conf0.5_n_2		97.59 107	97.28 110	98.53 124	99.01 124	98.15 67	99.98 19	98.59 97	98.17 11	99.75 38	99.63 115	81.83 299	99.94 88	99.78 31	98.79 155	97.51 291

AllTest		92.48 294	91.64 297	95.00 287	99.01 124	88.43 374	98.94 314	96.82 379	86.50 374	88.71 339	98.47 248	74.73 372	99.88 117	85.39 370	96.18 229	96.71 297

TestCases				95.00 287	99.01 124	88.43 374		96.82 379	86.50 374	88.71 339	98.47 248	74.73 372	99.88 117	85.39 370	96.18 229	96.71 297

COLMAP_ROB	90.47 14	92.18 301	91.49 303	94.25 321	99.00 128	88.04 380	98.42 362	96.70 386	82.30 414	88.43 348	99.01 176	76.97 347	99.85 123	86.11 366	96.50 221	94.86 308
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 76	97.66 89	98.81 95	98.99 129	98.07 75	99.98 19	98.81 64	98.18 10	99.89 8	99.70 94	84.15 281	99.97 59	99.76 36	99.50 113	98.39 262

test_fmvs1		95.35 208	95.68 184	94.36 317	98.99 129	84.98 401	99.96 47	96.65 388	97.60 32	99.73 43	98.96 185	71.58 387	99.93 97	98.31 128	99.37 128	98.17 267

HY-MVS	92.50 7	97.79 94	97.17 117	99.63 17	98.98 131	99.32 9	97.49 389	99.52 14	95.69 102	98.32 148	97.41 286	93.32 118	99.77 143	98.08 142	95.75 244	99.81 103

VNet		97.21 126	96.57 145	99.13 71	98.97 132	97.82 89	99.03 301	99.21 32	94.31 151	99.18 99	98.88 197	86.26 251	99.89 111	98.93 87	94.32 272	99.69 121

thres200		96.96 140	96.21 158	99.22 53	98.97 132	98.84 36	99.85 137	99.71 7	93.17 199	96.26 217	98.88 197	89.87 196	99.51 171	94.26 240	94.91 264	99.31 202

tfpn200view9		96.79 148	95.99 165	99.19 56	98.94 134	98.82 37	99.78 161	99.71 7	92.86 210	96.02 224	98.87 204	89.33 203	99.50 173	93.84 249	94.57 268	99.27 209

thres400		96.78 150	95.99 165	99.16 63	98.94 134	98.82 37	99.78 161	99.71 7	92.86 210	96.02 224	98.87 204	89.33 203	99.50 173	93.84 249	94.57 268	99.16 217

sasdasda		97.09 133	96.32 153	99.39 41	98.93 136	98.95 27	99.72 188	97.35 308	94.45 139	97.88 167	99.42 135	86.71 242	99.52 169	98.48 118	93.97 278	99.72 116

Anonymous20231211		89.86 351	88.44 359	94.13 324	98.93 136	90.68 336	98.54 353	98.26 201	76.28 430	86.73 370	95.54 350	70.60 393	97.56 313	90.82 305	80.27 389	94.15 345

canonicalmvs		97.09 133	96.32 153	99.39 41	98.93 136	98.95 27	99.72 188	97.35 308	94.45 139	97.88 167	99.42 135	86.71 242	99.52 169	98.48 118	93.97 278	99.72 116

SDMVSNet		94.80 224	93.96 239	97.33 214	98.92 139	95.42 199	99.59 217	98.99 40	92.41 237	92.55 279	97.85 277	75.81 362	98.93 214	97.90 153	91.62 293	97.64 283

sd_testset		93.55 268	92.83 272	95.74 267	98.92 139	90.89 332	98.24 369	98.85 59	92.41 237	92.55 279	97.85 277	71.07 392	98.68 237	93.93 246	91.62 293	97.64 283

EPNet_dtu		95.71 196	95.39 191	96.66 236	98.92 139	93.41 268	99.57 222	98.90 50	96.19 90	97.52 177	98.56 238	92.65 140	97.36 318	77.89 417	98.33 168	99.20 215
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 72	97.60 93	99.60 22	98.92 139	99.28 17	99.89 117	99.52 14	95.58 105	98.24 154	99.39 142	93.33 117	99.74 149	97.98 149	95.58 253	99.78 109

CHOSEN 1792x2688		96.81 147	96.53 146	97.64 187	98.91 143	93.07 274	99.65 204	99.80 3	95.64 103	95.39 239	98.86 206	84.35 280	99.90 106	96.98 182	99.16 138	99.95 77

thres100view900		96.74 153	95.92 175	99.18 57	98.90 144	98.77 42	99.74 177	99.71 7	92.59 228	95.84 228	98.86 206	89.25 205	99.50 173	93.84 249	94.57 268	99.27 209

thres600view7		96.69 156	95.87 178	99.14 67	98.90 144	98.78 41	99.74 177	99.71 7	92.59 228	95.84 228	98.86 206	89.25 205	99.50 173	93.44 262	94.50 271	99.16 217

MSDG		94.37 245	93.36 262	97.40 208	98.88 146	93.95 253	99.37 257	97.38 304	85.75 385	90.80 298	99.17 166	84.11 283	99.88 117	86.35 362	98.43 166	98.36 264

MGCFI-Net		97.00 138	96.22 157	99.34 46	98.86 147	98.80 39	99.67 202	97.30 316	94.31 151	97.77 173	99.41 139	86.36 249	99.50 173	98.38 123	93.90 280	99.72 116

h-mvs33		94.92 221	94.36 225	96.59 238	98.85 148	91.29 324	98.93 316	98.94 44	95.90 95	98.77 120	98.42 251	90.89 181	99.77 143	97.80 157	70.76 428	98.72 253

Anonymous20240529		92.10 302	90.65 314	96.47 240	98.82 149	90.61 338	98.72 339	98.67 81	75.54 434	93.90 263	98.58 236	66.23 410	99.90 106	94.70 230	90.67 296	98.90 243

PVSNet_Blended_VisFu		97.27 122	96.81 133	98.66 107	98.81 150	96.67 143	99.92 93	98.64 85	94.51 137	96.38 215	98.49 244	89.05 209	99.88 117	97.10 178	98.34 167	99.43 181

PS-MVSNAJ		98.44 46	98.20 51	99.16 63	98.80 151	98.92 29	99.54 229	98.17 213	97.34 40	99.85 16	99.85 33	91.20 170	99.89 111	99.41 63	99.67 90	98.69 254

CANet_DTU		96.76 151	96.15 160	98.60 112	98.78 152	97.53 101	99.84 142	97.63 273	97.25 48	99.20 96	99.64 112	81.36 305	99.98 47	92.77 273	98.89 149	98.28 266

mvsany_test1		97.82 90	97.90 77	97.55 196	98.77 153	93.04 277	99.80 158	97.93 241	96.95 59	99.61 65	99.68 105	90.92 178	99.83 133	99.18 71	98.29 172	99.80 105

alignmvs		97.81 91	97.33 108	99.25 50	98.77 153	98.66 51	99.99 5	98.44 142	94.40 147	98.41 143	99.47 131	93.65 110	99.42 183	98.57 112	94.26 274	99.67 124

SymmetryMVS		97.64 105	97.46 99	98.17 147	98.74 155	95.39 202	99.61 213	99.26 29	96.52 74	98.61 131	99.31 150	92.73 138	99.67 161	96.77 190	95.63 251	99.45 177

SteuartSystems-ACMMP		99.02 13	98.97 13	99.18 57	98.72 156	97.71 93	99.98 19	98.44 142	96.85 60	99.80 24	99.91 14	97.57 8	99.85 123	99.44 61	99.99 21	99.99 23
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 67	97.97 69	99.02 82	98.69 157	98.66 51	99.52 231	98.08 227	97.05 54	99.86 13	99.86 29	90.65 183	99.71 153	99.39 65	98.63 159	98.69 254

miper_enhance_ethall		94.36 247	93.98 238	95.49 270	98.68 158	95.24 213	99.73 184	97.29 319	93.28 196	89.86 310	95.97 336	94.37 85	97.05 341	92.20 277	84.45 351	94.19 338

fmvsm_s_conf0.5_n_5		98.08 73	97.71 87	99.17 60	98.67 159	97.69 97	99.99 5	98.57 101	97.40 38	99.89 8	99.69 98	85.99 254	99.96 71	99.80 28	99.40 126	99.85 97

ETVMVS		97.03 137	96.64 141	98.20 146	98.67 159	97.12 122	99.89 117	98.57 101	91.10 285	98.17 156	98.59 233	93.86 105	98.19 283	95.64 208	95.24 261	99.28 208

test2506		97.53 109	97.19 115	98.58 116	98.66 161	96.90 133	98.81 331	99.77 5	94.93 119	97.95 162	98.96 185	92.51 147	99.20 194	94.93 220	98.15 176	99.64 130

ECVR-MVS		95.66 199	95.05 206	97.51 200	98.66 161	93.71 258	98.85 328	98.45 137	94.93 119	96.86 199	98.96 185	75.22 368	99.20 194	95.34 210	98.15 176	99.64 130

mamv4		95.24 211	96.90 126	90.25 395	98.65 163	72.11 442	98.28 367	97.64 272	89.99 318	95.93 226	98.25 261	94.74 70	99.11 200	99.01 84	99.64 92	99.53 162

balanced_conf03		98.27 60	97.99 67	99.11 72	98.64 164	98.43 63	99.47 241	97.79 256	94.56 135	99.74 41	98.35 253	94.33 88	99.25 188	99.12 73	99.96 46	99.64 130

fmvsm_s_conf0.5_n_a		97.73 100	97.72 85	97.77 179	98.63 165	94.26 244	99.96 47	98.92 49	97.18 50	99.75 38	99.69 98	87.00 240	99.97 59	99.46 59	98.89 149	99.08 226

MVSMamba_PlusPlus		97.83 87	97.45 101	98.99 84	98.60 166	98.15 67	99.58 219	97.74 263	90.34 310	99.26 95	98.32 256	94.29 90	99.23 189	99.03 82	99.89 70	99.58 150

testing222		97.08 136	96.75 136	98.06 157	98.56 167	96.82 135	99.85 137	98.61 93	92.53 232	98.84 115	98.84 210	93.36 115	98.30 273	95.84 204	94.30 273	99.05 230

test1111		95.57 202	94.98 209	97.37 210	98.56 167	93.37 271	98.86 326	98.45 137	94.95 118	96.63 205	98.95 190	75.21 369	99.11 200	95.02 217	98.14 178	99.64 130

MVSTER		95.53 203	95.22 198	96.45 243	98.56 167	97.72 92	99.91 101	97.67 268	92.38 239	91.39 289	97.14 293	97.24 18	97.30 325	94.80 226	87.85 325	94.34 327

testing3-2		97.72 101	97.43 104	98.60 112	98.55 170	97.11 124	100.00 1	99.23 31	93.78 178	97.90 164	98.73 217	95.50 49	99.69 157	98.53 116	94.63 266	98.99 234

VDD-MVS		93.77 261	92.94 270	96.27 250	98.55 170	90.22 347	98.77 336	97.79 256	90.85 291	96.82 201	99.42 135	61.18 430	99.77 143	98.95 85	94.13 275	98.82 246

tpmvs		94.28 249	93.57 249	96.40 245	98.55 170	91.50 322	95.70 426	98.55 113	87.47 360	92.15 282	94.26 401	91.42 166	98.95 213	88.15 341	95.85 240	98.76 249

UGNet		95.33 209	94.57 221	97.62 191	98.55 170	94.85 225	98.67 345	99.32 26	95.75 100	96.80 202	96.27 325	72.18 384	99.96 71	94.58 233	99.05 145	98.04 272
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	95.18 213	94.10 232	98.43 133	98.55 170	95.99 175	97.91 382	97.31 315	90.35 309	89.48 323	99.22 162	85.19 266	99.89 111	90.40 315	98.47 165	99.41 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 186	96.49 147	94.34 318	98.51 175	89.99 352	99.39 253	98.57 101	93.14 201	97.33 184	98.31 258	93.44 113	94.68 418	93.69 259	95.98 234	98.34 265

UWE-MVS		96.79 148	96.72 138	97.00 222	98.51 175	93.70 259	99.71 191	98.60 95	92.96 206	97.09 191	98.34 255	96.67 31	98.85 217	92.11 283	96.50 221	98.44 260

myMVS_eth3d28		97.86 83	97.59 95	98.68 104	98.50 177	97.26 114	99.92 93	98.55 113	93.79 177	98.26 152	98.75 215	95.20 54	99.48 179	98.93 87	96.40 224	99.29 206

test_vis1_n_1920		95.44 205	95.31 194	95.82 264	98.50 177	88.74 368	99.98 19	97.30 316	97.84 26	99.85 16	99.19 164	66.82 408	99.97 59	98.82 96	99.46 120	98.76 249

BH-w/o		95.71 196	95.38 192	96.68 235	98.49 179	92.28 295	99.84 142	97.50 293	92.12 249	92.06 285	98.79 213	84.69 274	98.67 238	95.29 212	99.66 91	99.09 224

baseline1		95.78 192	94.86 212	98.54 122	98.47 180	98.07 75	99.06 294	97.99 234	92.68 222	94.13 260	98.62 230	93.28 121	98.69 236	93.79 254	85.76 338	98.84 245

fmvsm_s_conf0.5_n_7		97.70 103	97.74 84	97.59 194	98.44 181	95.16 219	99.97 37	98.65 82	97.95 22	99.62 58	99.78 62	86.09 252	99.94 88	99.69 45	99.50 113	97.66 282

EPMVS		96.53 164	96.01 164	98.09 155	98.43 182	96.12 173	96.36 413	99.43 20	93.53 186	97.64 175	95.04 378	94.41 80	98.38 265	91.13 296	98.11 179	99.75 112

kuosan		93.17 276	92.60 278	94.86 294	98.40 183	89.54 360	98.44 358	98.53 120	84.46 398	88.49 344	97.92 274	90.57 185	97.05 341	83.10 387	93.49 283	97.99 273

WBMVS		94.52 238	94.03 236	95.98 256	98.38 184	96.68 142	99.92 93	97.63 273	90.75 300	89.64 318	95.25 371	96.77 25	96.90 353	94.35 238	83.57 358	94.35 325

UBG		97.84 86	97.69 88	98.29 142	98.38 184	96.59 149	99.90 107	98.53 120	93.91 173	98.52 135	98.42 251	96.77 25	99.17 197	98.54 114	96.20 228	99.11 223

sss		97.57 108	97.03 122	99.18 57	98.37 186	98.04 78	99.73 184	99.38 22	93.46 189	98.76 123	99.06 172	91.21 169	99.89 111	96.33 195	97.01 213	99.62 137

testing11		97.48 111	97.27 111	98.10 154	98.36 187	96.02 174	99.92 93	98.45 137	93.45 191	98.15 157	98.70 220	95.48 50	99.22 190	97.85 155	95.05 263	99.07 227

BH-untuned		95.18 213	94.83 213	96.22 251	98.36 187	91.22 325	99.80 158	97.32 314	90.91 289	91.08 292	98.67 222	83.51 285	98.54 247	94.23 241	99.61 99	98.92 240

testing91		97.16 128	96.90 126	97.97 161	98.35 189	95.67 190	99.91 101	98.42 162	92.91 209	97.33 184	98.72 218	94.81 68	99.21 191	96.98 182	94.63 266	99.03 231

testing99		97.17 127	96.91 125	97.95 162	98.35 189	95.70 187	99.91 101	98.43 150	92.94 207	97.36 183	98.72 218	94.83 67	99.21 191	97.00 180	94.64 265	98.95 236

ET-MVSNet_ETH3D		94.37 245	93.28 264	97.64 187	98.30 191	97.99 80	99.99 5	97.61 279	94.35 148	71.57 440	99.45 134	96.23 35	95.34 408	96.91 187	85.14 345	99.59 144

AUN-MVS		93.28 273	92.60 278	95.34 277	98.29 192	90.09 350	99.31 265	98.56 107	91.80 262	96.35 216	98.00 269	89.38 202	98.28 276	92.46 274	69.22 433	97.64 283

FMVSNet3		92.69 289	91.58 299	95.99 255	98.29 192	97.42 109	99.26 274	97.62 276	89.80 321	89.68 314	95.32 365	81.62 303	96.27 384	87.01 358	85.65 339	94.29 329

PMMVS		96.76 151	96.76 135	96.76 232	98.28 194	92.10 299	99.91 101	97.98 236	94.12 159	99.53 70	99.39 142	86.93 241	98.73 229	96.95 185	97.73 187	99.45 177

hse-mvs2		94.38 244	94.08 235	95.31 279	98.27 195	90.02 351	99.29 270	98.56 107	95.90 95	98.77 120	98.00 269	90.89 181	98.26 280	97.80 157	69.20 434	97.64 283

PVSNet_0	88.03 19	91.80 309	90.27 323	96.38 247	98.27 195	90.46 342	99.94 83	99.61 13	93.99 167	86.26 380	97.39 288	71.13 391	99.89 111	98.77 100	67.05 439	98.79 248

UA-Net		96.54 163	95.96 171	98.27 143	98.23 197	95.71 186	98.00 380	98.45 137	93.72 182	98.41 143	99.27 155	88.71 216	99.66 164	91.19 295	97.69 188	99.44 180

test_cas_vis1_n_1920		96.59 161	96.23 156	97.65 186	98.22 198	94.23 245	99.99 5	97.25 323	97.77 27	99.58 66	99.08 170	77.10 344	99.97 59	97.64 165	99.45 121	98.74 251

FE-MVS		95.70 198	95.01 208	97.79 176	98.21 199	94.57 233	95.03 427	98.69 76	88.90 337	97.50 179	96.19 327	92.60 143	99.49 178	89.99 320	97.94 185	99.31 202

GG-mvs-BLEND				98.54 122	98.21 199	98.01 79	93.87 432	98.52 122		97.92 163	97.92 274	99.02 3	97.94 300	98.17 135	99.58 104	99.67 124

mvs_anonymous		95.65 200	95.03 207	97.53 198	98.19 201	95.74 184	99.33 262	97.49 294	90.87 290	90.47 301	97.10 295	88.23 219	97.16 332	95.92 202	97.66 191	99.68 122

MVS_Test		96.46 166	95.74 180	98.61 111	98.18 202	97.23 116	99.31 265	97.15 335	91.07 286	98.84 115	97.05 299	88.17 220	98.97 210	94.39 235	97.50 193	99.61 141

BH-RMVSNet		95.18 213	94.31 228	97.80 174	98.17 203	95.23 214	99.76 170	97.53 289	92.52 233	94.27 258	99.25 160	76.84 349	98.80 220	90.89 304	99.54 106	99.35 193

dongtai		91.55 315	91.13 308	92.82 363	98.16 204	86.35 391	99.47 241	98.51 125	83.24 406	85.07 390	97.56 282	90.33 190	94.94 414	76.09 425	91.73 291	97.18 294

RPSCF		91.80 309	92.79 274	88.83 406	98.15 205	69.87 444	98.11 376	96.60 390	83.93 401	94.33 256	99.27 155	79.60 327	99.46 182	91.99 284	93.16 288	97.18 294

ETV-MVS		97.92 79	97.80 83	98.25 144	98.14 206	96.48 151	99.98 19	97.63 273	95.61 104	99.29 93	99.46 133	92.55 145	98.82 218	99.02 83	98.54 163	99.46 175

IS-MVSNet		96.29 177	95.90 176	97.45 203	98.13 207	94.80 228	99.08 289	97.61 279	92.02 254	95.54 237	98.96 185	90.64 184	98.08 289	93.73 257	97.41 197	99.47 173

test_fmvsmconf_n		98.43 48	98.32 44	98.78 97	98.12 208	96.41 154	99.99 5	98.83 63	98.22 7	99.67 49	99.64 112	91.11 174	99.94 88	99.67 47	99.62 95	99.98 52

fmvsm_s_conf0.1_n_2		97.25 123	96.85 130	98.43 133	98.08 209	98.08 74	99.92 93	97.76 262	98.05 18	99.65 51	99.58 121	80.88 312	99.93 97	99.59 51	98.17 174	97.29 292

ab-mvs		94.69 230	93.42 255	98.51 127	98.07 210	96.26 161	96.49 411	98.68 78	90.31 311	94.54 248	97.00 301	76.30 357	99.71 153	95.98 201	93.38 286	99.56 153

XVG-OURS-SEG-HR		94.79 225	94.70 220	95.08 284	98.05 211	89.19 362	99.08 289	97.54 287	93.66 183	94.87 246	99.58 121	78.78 335	99.79 138	97.31 171	93.40 285	96.25 301

EIA-MVS		97.53 109	97.46 99	97.76 181	98.04 212	94.84 226	99.98 19	97.61 279	94.41 146	97.90 164	99.59 118	92.40 151	98.87 215	98.04 144	99.13 140	99.59 144

XVG-OURS		94.82 222	94.74 219	95.06 285	98.00 213	89.19 362	99.08 289	97.55 285	94.10 160	94.71 247	99.62 116	80.51 318	99.74 149	96.04 200	93.06 290	96.25 301

mvsmamba		96.94 141	96.73 137	97.55 196	97.99 214	94.37 241	99.62 211	97.70 265	93.13 202	98.42 142	97.92 274	88.02 221	98.75 227	98.78 99	99.01 146	99.52 164

dp		95.05 216	94.43 223	96.91 225	97.99 214	92.73 284	96.29 416	97.98 236	89.70 322	95.93 226	94.67 391	93.83 107	98.45 253	86.91 361	96.53 220	99.54 158

tpmrst		96.27 179	95.98 167	97.13 218	97.96 216	93.15 273	96.34 414	98.17 213	92.07 250	98.71 126	95.12 375	93.91 102	98.73 229	94.91 223	96.62 218	99.50 170

TR-MVS		94.54 235	93.56 250	97.49 202	97.96 216	94.34 242	98.71 340	97.51 292	90.30 312	94.51 250	98.69 221	75.56 363	98.77 224	92.82 272	95.99 233	99.35 193

Vis-MVSNet (Re-imp)		96.32 174	95.98 167	97.35 213	97.93 218	94.82 227	99.47 241	98.15 221	91.83 259	95.09 244	99.11 168	91.37 168	97.47 316	93.47 261	97.43 194	99.74 113

MDTV_nov1_ep13			95.69 182		97.90 219	94.15 247	95.98 422	98.44 142	93.12 203	97.98 161	95.74 340	95.10 57	98.58 243	90.02 319	96.92 215

Fast-Effi-MVS+		95.02 218	94.19 230	97.52 199	97.88 220	94.55 234	99.97 37	97.08 348	88.85 339	94.47 251	97.96 273	84.59 275	98.41 257	89.84 322	97.10 208	99.59 144

ADS-MVSNet2		93.80 260	93.88 242	93.55 346	97.87 221	85.94 395	94.24 428	96.84 376	90.07 315	96.43 212	94.48 396	90.29 192	95.37 407	87.44 348	97.23 201	99.36 189

ADS-MVSNet		94.79 225	94.02 237	97.11 220	97.87 221	93.79 255	94.24 428	98.16 218	90.07 315	96.43 212	94.48 396	90.29 192	98.19 283	87.44 348	97.23 201	99.36 189

Effi-MVS+		96.30 176	95.69 182	98.16 148	97.85 223	96.26 161	97.41 391	97.21 327	90.37 308	98.65 129	98.58 236	86.61 246	98.70 235	97.11 177	97.37 198	99.52 164

PatchmatchNet		95.94 187	95.45 189	97.39 209	97.83 224	94.41 238	96.05 420	98.40 171	92.86 210	97.09 191	95.28 370	94.21 94	98.07 291	89.26 328	98.11 179	99.70 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 233	93.61 245	97.74 183	97.82 225	96.26 161	99.96 47	97.78 258	85.76 383	94.00 261	97.54 283	76.95 348	99.21 191	97.23 174	95.43 256	97.76 281

1112_ss		96.01 185	95.20 199	98.42 135	97.80 226	96.41 154	99.65 204	96.66 387	92.71 219	92.88 275	99.40 140	92.16 156	99.30 186	91.92 286	93.66 281	99.55 154

Test_1112_low_res		95.72 194	94.83 213	98.42 135	97.79 227	96.41 154	99.65 204	96.65 388	92.70 220	92.86 276	96.13 331	92.15 157	99.30 186	91.88 287	93.64 282	99.55 154

Effi-MVS+-dtu		94.53 237	95.30 195	92.22 371	97.77 228	82.54 417	99.59 217	97.06 352	94.92 121	95.29 241	95.37 363	85.81 255	97.89 301	94.80 226	97.07 209	96.23 303

tpm cat1		93.51 269	92.52 284	96.47 240	97.77 228	91.47 323	96.13 418	98.06 228	80.98 419	92.91 274	93.78 405	89.66 197	98.87 215	87.03 357	96.39 225	99.09 224

FA-MVS(test-final)		95.86 189	95.09 204	98.15 151	97.74 230	95.62 192	96.31 415	98.17 213	91.42 275	96.26 217	96.13 331	90.56 186	99.47 181	92.18 278	97.07 209	99.35 193

xiu_mvs_v1_base_debu		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 265	97.86 250	96.43 79	99.62 58	99.69 98	85.56 261	99.68 158	99.05 76	98.31 169	97.83 277

xiu_mvs_v1_base		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 265	97.86 250	96.43 79	99.62 58	99.69 98	85.56 261	99.68 158	99.05 76	98.31 169	97.83 277

xiu_mvs_v1_base_debi		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 265	97.86 250	96.43 79	99.62 58	99.69 98	85.56 261	99.68 158	99.05 76	98.31 169	97.83 277

EPP-MVSNet		96.69 156	96.60 143	96.96 224	97.74 230	93.05 276	99.37 257	98.56 107	88.75 341	95.83 230	99.01 176	96.01 36	98.56 245	96.92 186	97.20 203	99.25 211

gg-mvs-nofinetune		93.51 269	91.86 296	98.47 129	97.72 235	97.96 84	92.62 438	98.51 125	74.70 437	97.33 184	69.59 454	98.91 4	97.79 304	97.77 162	99.56 105	99.67 124

IB-MVS	92.85 6	94.99 219	93.94 240	98.16 148	97.72 235	95.69 189	99.99 5	98.81 64	94.28 154	92.70 277	96.90 303	95.08 58	99.17 197	96.07 199	73.88 421	99.60 143
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		97.41 117	97.02 123	98.59 115	97.71 237	97.52 102	99.97 37	98.54 117	91.83 259	97.45 180	99.04 173	97.50 9	99.10 202	94.75 228	96.37 226	99.16 217

VortexMVS		94.11 251	93.50 252	95.94 258	97.70 238	96.61 146	99.35 260	97.18 330	93.52 188	89.57 321	95.74 340	87.55 228	96.97 349	95.76 207	85.13 346	94.23 334

Syy-MVS		90.00 349	90.63 315	88.11 413	97.68 239	74.66 440	99.71 191	98.35 184	90.79 297	92.10 283	98.67 222	79.10 333	93.09 433	63.35 447	95.95 237	96.59 299

myMVS_eth3d		94.46 242	94.76 218	93.55 346	97.68 239	90.97 327	99.71 191	98.35 184	90.79 297	92.10 283	98.67 222	92.46 150	93.09 433	87.13 354	95.95 237	96.59 299

test_fmvs1_n		94.25 250	94.36 225	93.92 333	97.68 239	83.70 408	99.90 107	96.57 391	97.40 38	99.67 49	98.88 197	61.82 427	99.92 103	98.23 133	99.13 140	98.14 270

fmvsm_s_conf0.5_n_6		98.27 60	97.96 72	99.23 52	97.66 242	98.11 73	99.98 19	98.64 85	97.85 25	99.87 11	99.72 88	88.86 213	99.93 97	99.64 49	99.36 129	99.63 136

RRT-MVS		96.24 180	95.68 184	97.94 165	97.65 243	94.92 224	99.27 273	97.10 344	92.79 216	97.43 181	97.99 271	81.85 298	99.37 185	98.46 120	98.57 160	99.53 162

diffmvs		97.00 138	96.64 141	98.09 155	97.64 244	96.17 170	99.81 154	97.19 328	94.67 133	98.95 110	99.28 152	86.43 247	98.76 225	98.37 125	97.42 196	99.33 196
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		95.72 194	95.15 202	97.45 203	97.62 245	94.28 243	99.28 271	98.24 204	94.27 156	96.84 200	98.94 192	79.39 328	98.76 225	93.25 263	98.49 164	99.30 204
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 131	96.72 138	98.22 145	97.60 246	96.70 139	99.92 93	98.54 117	91.11 284	97.07 193	98.97 183	97.47 12	99.03 205	93.73 257	96.09 231	98.92 240

GDP-MVS		97.88 81	97.59 95	98.75 100	97.59 247	97.81 90	99.95 66	97.37 307	94.44 142	99.08 104	99.58 121	97.13 23	99.08 203	94.99 218	98.17 174	99.37 187

miper_ehance_all_eth		93.16 277	92.60 278	94.82 295	97.57 248	93.56 263	99.50 235	97.07 351	88.75 341	88.85 338	95.52 352	90.97 177	96.74 363	90.77 306	84.45 351	94.17 339

guyue		97.15 129	96.82 132	98.15 151	97.56 249	96.25 165	99.71 191	97.84 253	95.75 100	98.13 158	98.65 225	87.58 227	98.82 218	98.29 130	97.91 186	99.36 189

viewmanbaseed2359cas		96.45 167	96.07 161	97.59 194	97.55 250	94.59 232	99.70 196	97.33 312	93.62 185	97.00 195	99.32 147	85.57 260	98.71 232	97.26 173	97.33 199	99.47 173

testing3		93.92 254	94.23 229	92.99 360	97.54 251	90.23 346	99.99 5	99.16 33	90.57 302	91.33 291	98.63 229	92.99 129	92.52 437	82.46 391	95.39 257	96.22 304

mamba_0404		95.75 193	95.16 201	97.50 201	97.53 252	95.39 202	99.11 285	97.25 323	90.81 293	95.27 242	98.83 211	84.74 271	98.67 238	95.24 213	97.69 188	98.45 259

LCM-MVSNet-Re		92.31 298	92.60 278	91.43 380	97.53 252	79.27 434	99.02 303	91.83 450	92.07 250	80.31 414	94.38 399	83.50 286	95.48 404	97.22 175	97.58 192	99.54 158

GBi-Net		90.88 326	89.82 332	94.08 325	97.53 252	91.97 300	98.43 359	96.95 365	87.05 366	89.68 314	94.72 387	71.34 388	96.11 390	87.01 358	85.65 339	94.17 339

test1		90.88 326	89.82 332	94.08 325	97.53 252	91.97 300	98.43 359	96.95 365	87.05 366	89.68 314	94.72 387	71.34 388	96.11 390	87.01 358	85.65 339	94.17 339

FMVSNet2		91.02 323	89.56 337	95.41 275	97.53 252	95.74 184	98.98 306	97.41 302	87.05 366	88.43 348	95.00 381	71.34 388	96.24 386	85.12 373	85.21 344	94.25 332

tttt0517		96.85 145	96.49 147	97.92 166	97.48 257	95.89 178	99.85 137	98.54 117	90.72 301	96.63 205	98.93 195	97.47 12	99.02 206	93.03 270	95.76 243	98.85 244

BP-MVS1		98.33 56	98.18 53	98.81 95	97.44 258	97.98 81	99.96 47	98.17 213	94.88 123	98.77 120	99.59 118	97.59 7	99.08 203	98.24 132	98.93 148	99.36 189

casdiffmvs_mvg		96.43 168	95.94 173	97.89 170	97.44 258	95.47 196	99.86 134	97.29 319	93.35 192	96.03 223	99.19 164	85.39 264	98.72 231	97.89 154	97.04 211	99.49 172
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		97.38 119	97.24 112	97.80 174	97.41 260	95.64 191	99.99 5	97.06 352	94.59 134	99.63 55	99.32 147	89.20 208	98.14 285	98.76 101	99.23 136	99.62 137

c3_l		92.53 293	91.87 295	94.52 307	97.40 261	92.99 278	99.40 249	96.93 370	87.86 356	88.69 341	95.44 357	89.95 195	96.44 376	90.45 312	80.69 385	94.14 348

viewmambaseed2359dif		95.92 188	95.55 188	97.04 221	97.38 262	93.41 268	99.78 161	96.97 363	91.14 283	96.58 207	99.27 155	84.85 270	98.75 227	96.87 188	97.12 207	98.97 235

fmvsm_s_conf0.1_n		97.30 120	97.21 114	97.60 193	97.38 262	94.40 240	99.90 107	98.64 85	96.47 78	99.51 74	99.65 111	84.99 269	99.93 97	99.22 70	99.09 143	98.46 258

CDS-MVSNet		96.34 173	96.07 161	97.13 218	97.37 264	94.96 222	99.53 230	97.91 245	91.55 267	95.37 240	98.32 256	95.05 60	97.13 335	93.80 253	95.75 244	99.30 204
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 153	96.26 155	98.16 148	97.36 265	96.48 151	99.96 47	98.29 197	91.93 255	95.77 231	98.07 267	95.54 46	98.29 274	90.55 310	98.89 149	99.70 119

miper_lstm_enhance		91.81 306	91.39 305	93.06 359	97.34 266	89.18 364	99.38 255	96.79 381	86.70 373	87.47 362	95.22 372	90.00 194	95.86 399	88.26 339	81.37 374	94.15 345

baseline		96.43 168	95.98 167	97.76 181	97.34 266	95.17 218	99.51 233	97.17 332	93.92 172	96.90 198	99.28 152	85.37 265	98.64 241	97.50 168	96.86 217	99.46 175

cl____		92.31 298	91.58 299	94.52 307	97.33 268	92.77 280	99.57 222	96.78 382	86.97 370	87.56 360	95.51 353	89.43 201	96.62 368	88.60 333	82.44 366	94.16 344

SD_0403		92.63 292	93.38 259	90.40 394	97.32 269	77.91 436	97.75 387	98.03 232	91.89 256	90.83 297	98.29 260	82.00 295	93.79 427	88.51 337	95.75 244	99.52 164

DIV-MVS_self_test		92.32 297	91.60 298	94.47 311	97.31 270	92.74 282	99.58 219	96.75 383	86.99 369	87.64 358	95.54 350	89.55 200	96.50 373	88.58 334	82.44 366	94.17 339

casdiffmvs		96.42 170	95.97 170	97.77 179	97.30 271	94.98 221	99.84 142	97.09 347	93.75 181	96.58 207	99.26 159	85.07 267	98.78 223	97.77 162	97.04 211	99.54 158
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		94.36 247	93.48 253	96.99 223	97.29 272	93.54 264	99.96 47	96.72 385	88.35 350	93.43 265	98.94 192	82.05 294	98.05 292	88.12 343	96.48 223	99.37 187

eth_miper_zixun_eth		92.41 296	91.93 293	93.84 337	97.28 273	90.68 336	98.83 329	96.97 363	88.57 346	89.19 333	95.73 343	89.24 207	96.69 366	89.97 321	81.55 372	94.15 345

MVSFormer		96.94 141	96.60 143	97.95 162	97.28 273	97.70 95	99.55 227	97.27 321	91.17 280	99.43 80	99.54 127	90.92 178	96.89 354	94.67 231	99.62 95	99.25 211

lupinMVS		97.85 85	97.60 93	98.62 110	97.28 273	97.70 95	99.99 5	97.55 285	95.50 109	99.43 80	99.67 107	90.92 178	98.71 232	98.40 122	99.62 95	99.45 177

mamba_0408		94.98 220	94.09 233	97.64 187	97.14 276	95.31 207	93.48 435	97.08 348	90.48 304	94.40 252	98.62 230	84.49 276	98.67 238	93.99 244	97.18 204	98.93 237

mamba_test_0407_2		94.77 227	94.09 233	96.82 229	97.14 276	95.31 207	93.48 435	97.08 348	90.48 304	94.40 252	98.62 230	84.49 276	96.21 387	93.99 244	97.18 204	98.93 237

mamba_test_0407		95.62 201	94.95 210	97.61 192	97.14 276	95.31 207	99.00 304	97.25 323	90.81 293	94.40 252	98.83 211	84.74 271	98.58 243	95.24 213	97.18 204	98.93 237

SCA		94.69 230	93.81 244	97.33 214	97.10 279	94.44 235	98.86 326	98.32 191	93.30 195	96.17 222	95.59 348	76.48 355	97.95 298	91.06 298	97.43 194	99.59 144

KinetiMVS		96.10 181	95.29 196	98.53 124	97.08 280	97.12 122	99.56 224	98.12 224	94.78 126	98.44 140	98.94 192	80.30 322	99.39 184	91.56 291	98.79 155	99.06 228

TAMVS		95.85 190	95.58 186	96.65 237	97.07 281	93.50 265	99.17 281	97.82 255	91.39 277	95.02 245	98.01 268	92.20 155	97.30 325	93.75 256	95.83 241	99.14 220

Fast-Effi-MVS+-dtu		93.72 264	93.86 243	93.29 351	97.06 282	86.16 392	99.80 158	96.83 377	92.66 223	92.58 278	97.83 279	81.39 304	97.67 309	89.75 323	96.87 216	96.05 306

CostFormer		96.10 181	95.88 177	96.78 231	97.03 283	92.55 290	97.08 400	97.83 254	90.04 317	98.72 125	94.89 385	95.01 62	98.29 274	96.54 194	95.77 242	99.50 170

test_fmvsmvis_n_1920		97.67 104	97.59 95	97.91 168	97.02 284	95.34 205	99.95 66	98.45 137	97.87 24	97.02 194	99.59 118	89.64 198	99.98 47	99.41 63	99.34 131	98.42 261

test-LLR		96.47 165	96.04 163	97.78 177	97.02 284	95.44 197	99.96 47	98.21 208	94.07 162	95.55 235	96.38 320	93.90 103	98.27 278	90.42 313	98.83 153	99.64 130

test-mter		96.39 171	95.93 174	97.78 177	97.02 284	95.44 197	99.96 47	98.21 208	91.81 261	95.55 235	96.38 320	95.17 55	98.27 278	90.42 313	98.83 153	99.64 130

icg_test_0407_2		95.04 217	94.78 217	95.84 263	96.97 287	91.64 315	98.63 348	97.12 338	92.33 241	95.60 233	98.88 197	85.65 257	96.56 371	92.12 279	95.70 247	99.32 198

icg_test_0407		95.21 212	94.80 216	96.46 242	96.97 287	91.64 315	98.81 331	97.12 338	92.33 241	95.60 233	98.88 197	85.65 257	98.42 255	92.12 279	95.70 247	99.32 198

ICG_test_0404		93.83 256	93.17 266	95.80 265	96.97 287	91.64 315	97.78 386	97.12 338	92.33 241	90.87 296	98.88 197	76.78 350	96.43 377	92.12 279	95.70 247	99.32 198

icg_test_0403		95.25 210	94.81 215	96.58 239	96.97 287	91.64 315	98.97 311	97.12 338	92.33 241	95.43 238	98.88 197	85.78 256	98.79 221	92.12 279	95.70 247	99.32 198

gm-plane-assit					96.97 287	93.76 257			91.47 271		98.96 185		98.79 221	94.92 221

WB-MVSnew		92.90 283	92.77 275	93.26 353	96.95 292	93.63 261	99.71 191	98.16 218	91.49 268	94.28 257	98.14 264	81.33 306	96.48 374	79.47 408	95.46 254	89.68 434

QAPM		95.40 206	94.17 231	99.10 73	96.92 293	97.71 93	99.40 249	98.68 78	89.31 325	88.94 337	98.89 196	82.48 292	99.96 71	93.12 269	99.83 77	99.62 137

KD-MVS_2432*1600		88.00 371	86.10 375	93.70 342	96.91 294	94.04 249	97.17 397	97.12 338	84.93 393	81.96 404	92.41 418	92.48 148	94.51 420	79.23 409	52.68 453	92.56 404

miper_refine_blended		88.00 371	86.10 375	93.70 342	96.91 294	94.04 249	97.17 397	97.12 338	84.93 393	81.96 404	92.41 418	92.48 148	94.51 420	79.23 409	52.68 453	92.56 404

tpm2		95.47 204	95.18 200	96.35 248	96.91 294	91.70 313	96.96 403	97.93 241	88.04 354	98.44 140	95.40 359	93.32 118	97.97 295	94.00 243	95.61 252	99.38 185

FMVSNet5		88.32 367	87.47 369	90.88 383	96.90 297	88.39 376	97.28 394	95.68 412	82.60 413	84.67 392	92.40 420	79.83 325	91.16 442	76.39 424	81.51 373	93.09 395

3Dnovator+	91.53 11	96.31 175	95.24 197	99.52 28	96.88 298	98.64 54	99.72 188	98.24 204	95.27 114	88.42 350	98.98 181	82.76 291	99.94 88	97.10 178	99.83 77	99.96 70

Patchmatch-test		92.65 291	91.50 302	96.10 254	96.85 299	90.49 341	91.50 443	97.19 328	82.76 412	90.23 302	95.59 348	95.02 61	98.00 294	77.41 419	96.98 214	99.82 101

MVS		96.60 160	95.56 187	99.72 13	96.85 299	99.22 20	98.31 365	98.94 44	91.57 266	90.90 295	99.61 117	86.66 245	99.96 71	97.36 170	99.88 73	99.99 23

3Dnovator	91.47 12	96.28 178	95.34 193	99.08 76	96.82 301	97.47 107	99.45 246	98.81 64	95.52 108	89.39 324	99.00 178	81.97 296	99.95 80	97.27 172	99.83 77	99.84 98

EI-MVSNet		93.73 263	93.40 258	94.74 296	96.80 302	92.69 285	99.06 294	97.67 268	88.96 334	91.39 289	99.02 174	88.75 215	97.30 325	91.07 297	87.85 325	94.22 335

CVMVSNet		94.68 232	94.94 211	93.89 336	96.80 302	86.92 389	99.06 294	98.98 41	94.45 139	94.23 259	99.02 174	85.60 259	95.31 409	90.91 303	95.39 257	99.43 181

IterMVS-LS		92.69 289	92.11 289	94.43 315	96.80 302	92.74 282	99.45 246	96.89 373	88.98 332	89.65 317	95.38 362	88.77 214	96.34 381	90.98 301	82.04 369	94.22 335
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 162	96.46 150	96.91 225	96.79 305	92.50 291	99.90 107	97.38 304	96.02 94	97.79 172	99.32 147	86.36 249	98.99 207	98.26 131	96.33 227	99.23 214

IterMVS		90.91 325	90.17 327	93.12 356	96.78 306	90.42 344	98.89 320	97.05 355	89.03 329	86.49 375	95.42 358	76.59 353	95.02 411	87.22 353	84.09 354	93.93 366
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 146	95.96 171	99.48 35	96.74 307	98.52 58	98.31 365	98.86 56	95.82 97	89.91 308	98.98 181	87.49 230	99.96 71	97.80 157	99.73 87	99.96 70

IterMVS-SCA-FT		90.85 328	90.16 328	92.93 361	96.72 308	89.96 353	98.89 320	96.99 359	88.95 335	86.63 372	95.67 344	76.48 355	95.00 412	87.04 356	84.04 357	93.84 373

MVS-HIRNet		86.22 378	83.19 391	95.31 279	96.71 309	90.29 345	92.12 440	97.33 312	62.85 448	86.82 369	70.37 453	69.37 396	97.49 315	75.12 427	97.99 184	98.15 268

VDDNet		93.12 278	91.91 294	96.76 232	96.67 310	92.65 288	98.69 343	98.21 208	82.81 411	97.75 174	99.28 152	61.57 428	99.48 179	98.09 141	94.09 276	98.15 268

dmvs_re		93.20 275	93.15 267	93.34 349	96.54 311	83.81 407	98.71 340	98.51 125	91.39 277	92.37 281	98.56 238	78.66 337	97.83 303	93.89 247	89.74 297	98.38 263

Elysia		94.50 239	93.38 259	97.85 172	96.49 312	96.70 139	98.98 306	97.78 258	90.81 293	96.19 220	98.55 240	73.63 379	98.98 208	89.41 324	98.56 161	97.88 275

StellarMVS		94.50 239	93.38 259	97.85 172	96.49 312	96.70 139	98.98 306	97.78 258	90.81 293	96.19 220	98.55 240	73.63 379	98.98 208	89.41 324	98.56 161	97.88 275

MIMVSNet		90.30 341	88.67 355	95.17 283	96.45 314	91.64 315	92.39 439	97.15 335	85.99 380	90.50 300	93.19 413	66.95 407	94.86 416	82.01 395	93.43 284	99.01 233

CR-MVSNet		93.45 272	92.62 277	95.94 258	96.29 315	92.66 286	92.01 441	96.23 399	92.62 225	96.94 196	93.31 411	91.04 175	96.03 395	79.23 409	95.96 235	99.13 221

RPMNet		89.76 353	87.28 370	97.19 217	96.29 315	92.66 286	92.01 441	98.31 193	70.19 444	96.94 196	85.87 446	87.25 235	99.78 140	62.69 448	95.96 235	99.13 221

tt0805		91.28 318	90.18 326	94.60 302	96.26 317	87.55 382	98.39 363	98.72 72	89.00 331	89.22 330	98.47 248	62.98 423	98.96 212	90.57 309	88.00 324	97.28 293

Patchmtry		89.70 354	88.49 358	93.33 350	96.24 318	89.94 356	91.37 444	96.23 399	78.22 427	87.69 357	93.31 411	91.04 175	96.03 395	80.18 407	82.10 368	94.02 356

test_vis1_rt		86.87 376	86.05 378	89.34 402	96.12 319	78.07 435	99.87 123	83.54 461	92.03 253	78.21 425	89.51 432	45.80 447	99.91 104	96.25 197	93.11 289	90.03 431

JIA-IIPM		91.76 312	90.70 313	94.94 289	96.11 320	87.51 383	93.16 437	98.13 223	75.79 433	97.58 176	77.68 451	92.84 134	97.97 295	88.47 338	96.54 219	99.33 196

OpenMVS	90.15 15	94.77 227	93.59 248	98.33 139	96.07 321	97.48 106	99.56 224	98.57 101	90.46 306	86.51 374	98.95 190	78.57 338	99.94 88	93.86 248	99.74 86	97.57 288

PAPM		98.60 34	98.42 35	99.14 67	96.05 322	98.96 26	99.90 107	99.35 24	96.68 69	98.35 147	99.66 109	96.45 33	98.51 248	99.45 60	99.89 70	99.96 70

CLD-MVS		94.06 253	93.90 241	94.55 306	96.02 323	90.69 335	99.98 19	97.72 264	96.62 73	91.05 294	98.85 209	77.21 343	98.47 249	98.11 139	89.51 303	94.48 313
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 338	88.75 354	95.25 281	95.99 324	90.16 348	91.22 445	97.54 287	76.80 429	97.26 187	86.01 445	91.88 162	96.07 394	66.16 444	95.91 239	99.51 168

ACMH+	89.98 16	90.35 339	89.54 338	92.78 365	95.99 324	86.12 393	98.81 331	97.18 330	89.38 324	83.14 400	97.76 280	68.42 401	98.43 254	89.11 329	86.05 337	93.78 376

DeepMVS_CX				82.92 423	95.98 326	58.66 454		96.01 404	92.72 218	78.34 424	95.51 353	58.29 433	98.08 289	82.57 390	85.29 342	92.03 412

ACMP	92.05 9	92.74 287	92.42 286	93.73 338	95.91 327	88.72 369	99.81 154	97.53 289	94.13 158	87.00 368	98.23 262	74.07 376	98.47 249	96.22 198	88.86 310	93.99 361
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 267	93.03 269	95.35 276	95.86 328	86.94 388	99.87 123	96.36 397	96.85 60	99.54 69	98.79 213	52.41 441	99.83 133	98.64 109	98.97 147	99.29 206

HQP-NCC					95.78 329		99.87 123		96.82 62	93.37 266

ACMP_Plane					95.78 329		99.87 123		96.82 62	93.37 266

HQP-MVS		94.61 234	94.50 222	94.92 290	95.78 329	91.85 305	99.87 123	97.89 246	96.82 62	93.37 266	98.65 225	80.65 316	98.39 261	97.92 151	89.60 298	94.53 309

NP-MVS					95.77 332	91.79 307					98.65 225

test_fmvsmconf0.1_n		97.74 98	97.44 102	98.64 109	95.76 333	96.20 167	99.94 83	98.05 230	98.17 11	98.89 114	99.42 135	87.65 225	99.90 106	99.50 56	99.60 102	99.82 101

plane_prior6					95.76 333	91.72 312						80.47 320

ACMM	91.95 10	92.88 284	92.52 284	93.98 332	95.75 335	89.08 366	99.77 165	97.52 291	93.00 205	89.95 307	97.99 271	76.17 359	98.46 252	93.63 260	88.87 309	94.39 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 256	92.84 271	96.80 230	95.73 336	93.57 262	99.88 120	97.24 326	92.57 230	92.92 273	96.66 312	78.73 336	97.67 309	87.75 346	94.06 277	99.17 216

plane_prior1					95.73 336

jason		97.24 124	96.86 129	98.38 138	95.73 336	97.32 111	99.97 37	97.40 303	95.34 112	98.60 134	99.54 127	87.70 224	98.56 245	97.94 150	99.47 118	99.25 211
jason: jason.
mmtdpeth		88.52 365	87.75 367	90.85 385	95.71 339	83.47 412	98.94 314	94.85 427	88.78 340	97.19 189	89.58 431	63.29 421	98.97 210	98.54 114	62.86 447	90.10 430

HQP_MVS		94.49 241	94.36 225	94.87 291	95.71 339	91.74 309	99.84 142	97.87 248	96.38 82	93.01 271	98.59 233	80.47 320	98.37 267	97.79 160	89.55 301	94.52 311

plane_prior7					95.71 339	91.59 321

ITE_SJBPF				92.38 368	95.69 342	85.14 399		95.71 411	92.81 213	89.33 327	98.11 265	70.23 394	98.42 255	85.91 368	88.16 322	93.59 384

fmvsm_s_conf0.1_n_a		97.09 133	96.90 126	97.63 190	95.65 343	94.21 246	99.83 149	98.50 131	96.27 87	99.65 51	99.64 112	84.72 273	99.93 97	99.04 79	98.84 152	98.74 251

ACMH	89.72 17	90.64 332	89.63 335	93.66 344	95.64 344	88.64 372	98.55 351	97.45 296	89.03 329	81.62 407	97.61 281	69.75 395	98.41 257	89.37 326	87.62 329	93.92 367
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 155	96.49 147	97.37 210	95.63 345	95.96 176	99.74 177	98.88 54	92.94 207	91.61 287	98.97 183	97.72 6	98.62 242	94.83 225	98.08 182	97.53 290

FMVSNet1		88.50 366	86.64 373	94.08 325	95.62 346	91.97 300	98.43 359	96.95 365	83.00 409	86.08 382	94.72 387	59.09 432	96.11 390	81.82 397	84.07 355	94.17 339

LuminaMVS		96.63 159	96.21 158	97.87 171	95.58 347	96.82 135	99.12 283	97.67 268	94.47 138	97.88 167	98.31 258	87.50 229	98.71 232	98.07 143	97.29 200	98.10 271

LPG-MVS_test		92.96 281	92.71 276	93.71 340	95.43 348	88.67 370	99.75 174	97.62 276	92.81 213	90.05 303	98.49 244	75.24 366	98.40 259	95.84 204	89.12 305	94.07 353

LGP-MVS_train				93.71 340	95.43 348	88.67 370		97.62 276	92.81 213	90.05 303	98.49 244	75.24 366	98.40 259	95.84 204	89.12 305	94.07 353

tpm		93.70 265	93.41 257	94.58 304	95.36 350	87.41 384	97.01 401	96.90 372	90.85 291	96.72 204	94.14 402	90.40 189	96.84 358	90.75 307	88.54 317	99.51 168

D2MVS		92.76 286	92.59 282	93.27 352	95.13 351	89.54 360	99.69 198	99.38 22	92.26 246	87.59 359	94.61 393	85.05 268	97.79 304	91.59 290	88.01 323	92.47 407

VPA-MVSNet		92.70 288	91.55 301	96.16 252	95.09 352	96.20 167	98.88 322	99.00 39	91.02 288	91.82 286	95.29 369	76.05 361	97.96 297	95.62 209	81.19 375	94.30 328

LTVRE_ROB	88.28 18	90.29 342	89.05 349	94.02 328	95.08 353	90.15 349	97.19 396	97.43 298	84.91 395	83.99 396	97.06 298	74.00 377	98.28 276	84.08 379	87.71 327	93.62 383
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		87.87 373	86.51 374	91.94 374	95.05 354	85.57 397	97.65 388	94.08 437	84.40 399	81.82 406	96.85 307	62.14 426	98.33 270	80.25 406	86.37 336	91.91 414

test0.0.03 1		93.86 255	93.61 245	94.64 300	95.02 355	92.18 298	99.93 90	98.58 99	94.07 162	87.96 354	98.50 243	93.90 103	94.96 413	81.33 398	93.17 287	96.78 296

UniMVSNet (Re)		93.07 280	92.13 288	95.88 260	94.84 356	96.24 166	99.88 120	98.98 41	92.49 235	89.25 328	95.40 359	87.09 237	97.14 334	93.13 268	78.16 399	94.26 330

USDC		90.00 349	88.96 350	93.10 358	94.81 357	88.16 378	98.71 340	95.54 416	93.66 183	83.75 398	97.20 292	65.58 412	98.31 272	83.96 382	87.49 331	92.85 401

VPNet		91.81 306	90.46 317	95.85 262	94.74 358	95.54 195	98.98 306	98.59 97	92.14 248	90.77 299	97.44 285	68.73 399	97.54 314	94.89 224	77.89 401	94.46 314

FIs		94.10 252	93.43 254	96.11 253	94.70 359	96.82 135	99.58 219	98.93 48	92.54 231	89.34 326	97.31 289	87.62 226	97.10 338	94.22 242	86.58 334	94.40 320

UniMVSNet_ETH3D		90.06 348	88.58 357	94.49 310	94.67 360	88.09 379	97.81 385	97.57 284	83.91 402	88.44 346	97.41 286	57.44 434	97.62 311	91.41 292	88.59 316	97.77 280

UniMVSNet_NR-MVSNet		92.95 282	92.11 289	95.49 270	94.61 361	95.28 211	99.83 149	99.08 36	91.49 268	89.21 331	96.86 306	87.14 236	96.73 364	93.20 264	77.52 404	94.46 314

test_fmvs2		89.47 358	89.70 334	88.77 409	94.54 362	75.74 437	99.83 149	94.70 433	94.71 130	91.08 292	96.82 311	54.46 437	97.78 306	92.87 271	88.27 320	92.80 402

MonoMVSNet		94.82 222	94.43 223	95.98 256	94.54 362	90.73 334	99.03 301	97.06 352	93.16 200	93.15 270	95.47 356	88.29 218	97.57 312	97.85 155	91.33 295	99.62 137

WR-MVS		92.31 298	91.25 306	95.48 273	94.45 364	95.29 210	99.60 216	98.68 78	90.10 314	88.07 353	96.89 304	80.68 315	96.80 362	93.14 267	79.67 392	94.36 322

nrg030		93.51 269	92.53 283	96.45 243	94.36 365	97.20 117	99.81 154	97.16 334	91.60 265	89.86 310	97.46 284	86.37 248	97.68 308	95.88 203	80.31 388	94.46 314

tfpnnormal		89.29 361	87.61 368	94.34 318	94.35 366	94.13 248	98.95 313	98.94 44	83.94 400	84.47 393	95.51 353	74.84 371	97.39 317	77.05 422	80.41 386	91.48 417

FC-MVSNet-test		93.81 259	93.15 267	95.80 265	94.30 367	96.20 167	99.42 248	98.89 52	92.33 241	89.03 336	97.27 291	87.39 232	96.83 360	93.20 264	86.48 335	94.36 322

SSC-MVS3.2		89.59 356	88.66 356	92.38 368	94.29 368	86.12 393	99.49 237	97.66 271	90.28 313	88.63 343	95.18 373	64.46 417	96.88 356	85.30 372	82.66 363	94.14 348

MS-PatchMatch		90.65 331	90.30 322	91.71 379	94.22 369	85.50 398	98.24 369	97.70 265	88.67 343	86.42 377	96.37 322	67.82 404	98.03 293	83.62 384	99.62 95	91.60 415

WR-MVS_H		91.30 316	90.35 320	94.15 322	94.17 370	92.62 289	99.17 281	98.94 44	88.87 338	86.48 376	94.46 398	84.36 279	96.61 369	88.19 340	78.51 397	93.21 393

DU-MVS		92.46 295	91.45 304	95.49 270	94.05 371	95.28 211	99.81 154	98.74 71	92.25 247	89.21 331	96.64 314	81.66 301	96.73 364	93.20 264	77.52 404	94.46 314

NR-MVSNet		91.56 314	90.22 324	95.60 268	94.05 371	95.76 183	98.25 368	98.70 74	91.16 282	80.78 413	96.64 314	83.23 289	96.57 370	91.41 292	77.73 403	94.46 314

CP-MVSNet		91.23 320	90.22 324	94.26 320	93.96 373	92.39 294	99.09 287	98.57 101	88.95 335	86.42 377	96.57 317	79.19 331	96.37 379	90.29 316	78.95 394	94.02 356

XXY-MVS		91.82 305	90.46 317	95.88 260	93.91 374	95.40 201	98.87 325	97.69 267	88.63 345	87.87 355	97.08 296	74.38 375	97.89 301	91.66 289	84.07 355	94.35 325

PS-CasMVS		90.63 333	89.51 340	93.99 331	93.83 375	91.70 313	98.98 306	98.52 122	88.48 347	86.15 381	96.53 319	75.46 364	96.31 383	88.83 331	78.86 396	93.95 364

test_0402		85.58 380	83.94 385	90.50 391	93.81 376	85.04 400	98.55 351	95.20 424	76.01 431	79.72 419	95.13 374	64.15 419	96.26 385	66.04 445	86.88 333	90.21 428

XVG-ACMP-BASELINE		91.22 321	90.75 312	92.63 367	93.73 377	85.61 396	98.52 355	97.44 297	92.77 217	89.90 309	96.85 307	66.64 409	98.39 261	92.29 276	88.61 314	93.89 369

TranMVSNet+NR-MVSNet		91.68 313	90.61 316	94.87 291	93.69 378	93.98 252	99.69 198	98.65 82	91.03 287	88.44 346	96.83 310	80.05 324	96.18 388	90.26 317	76.89 412	94.45 319

TransMVSNet (Re)		87.25 374	85.28 381	93.16 355	93.56 379	91.03 326	98.54 353	94.05 439	83.69 404	81.09 411	96.16 328	75.32 365	96.40 378	76.69 423	68.41 435	92.06 411

v10		90.25 343	88.82 352	94.57 305	93.53 380	93.43 267	99.08 289	96.87 375	85.00 392	87.34 366	94.51 394	80.93 311	97.02 348	82.85 389	79.23 393	93.26 391

testgi		89.01 363	88.04 364	91.90 375	93.49 381	84.89 402	99.73 184	95.66 413	93.89 176	85.14 388	98.17 263	59.68 431	94.66 419	77.73 418	88.88 308	96.16 305

v8		90.54 335	89.17 345	94.66 299	93.43 382	93.40 270	99.20 278	96.94 369	85.76 383	87.56 360	94.51 394	81.96 297	97.19 331	84.94 375	78.25 398	93.38 389

V42		91.28 318	90.12 329	94.74 296	93.42 383	93.46 266	99.68 200	97.02 356	87.36 362	89.85 312	95.05 377	81.31 307	97.34 320	87.34 351	80.07 390	93.40 387

pm-mvs1		89.36 360	87.81 366	94.01 329	93.40 384	91.93 303	98.62 349	96.48 395	86.25 378	83.86 397	96.14 330	73.68 378	97.04 344	86.16 365	75.73 417	93.04 397

v1144		91.09 322	89.83 331	94.87 291	93.25 385	93.69 260	99.62 211	96.98 361	86.83 372	89.64 318	94.99 382	80.94 310	97.05 341	85.08 374	81.16 376	93.87 371

v1192		90.62 334	89.25 344	94.72 298	93.13 386	93.07 274	99.50 235	97.02 356	86.33 377	89.56 322	95.01 379	79.22 330	97.09 340	82.34 393	81.16 376	94.01 358

v2v482		91.30 316	90.07 330	95.01 286	93.13 386	93.79 255	99.77 165	97.02 356	88.05 353	89.25 328	95.37 363	80.73 314	97.15 333	87.28 352	80.04 391	94.09 352

OPM-MVS		93.21 274	92.80 273	94.44 313	93.12 388	90.85 333	99.77 165	97.61 279	96.19 90	91.56 288	98.65 225	75.16 370	98.47 249	93.78 255	89.39 304	93.99 361
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 329	89.52 339	94.59 303	93.11 389	92.77 280	99.56 224	96.99 359	86.38 376	89.82 313	94.95 384	80.50 319	97.10 338	83.98 381	80.41 386	93.90 368

PEN-MVS		90.19 345	89.06 348	93.57 345	93.06 390	90.90 331	99.06 294	98.47 134	88.11 352	85.91 383	96.30 324	76.67 351	95.94 398	87.07 355	76.91 411	93.89 369

v1240		90.20 344	88.79 353	94.44 313	93.05 391	92.27 296	99.38 255	96.92 371	85.89 381	89.36 325	94.87 386	77.89 342	97.03 346	80.66 402	81.08 379	94.01 358

v148		90.70 330	89.63 335	93.92 333	92.97 392	90.97 327	99.75 174	96.89 373	87.51 359	88.27 351	95.01 379	81.67 300	97.04 344	87.40 350	77.17 409	93.75 377

v1921920		90.46 336	89.12 346	94.50 309	92.96 393	92.46 292	99.49 237	96.98 361	86.10 379	89.61 320	95.30 366	78.55 339	97.03 346	82.17 394	80.89 384	94.01 358

MVStest1		85.03 386	82.76 395	91.83 376	92.95 394	89.16 365	98.57 350	94.82 428	71.68 442	68.54 445	95.11 376	83.17 290	95.66 402	74.69 428	65.32 442	90.65 424

tt0320-xc		82.94 400	80.35 407	90.72 389	92.90 395	83.54 410	96.85 406	94.73 431	63.12 447	79.85 418	93.77 406	49.43 445	95.46 405	80.98 401	71.54 426	93.16 394

Baseline_NR-MVSNet		90.33 340	89.51 340	92.81 364	92.84 396	89.95 354	99.77 165	93.94 440	84.69 397	89.04 335	95.66 345	81.66 301	96.52 372	90.99 300	76.98 410	91.97 413

test_method		80.79 405	79.70 409	84.08 420	92.83 397	67.06 446	99.51 233	95.42 418	54.34 452	81.07 412	93.53 408	44.48 448	92.22 439	78.90 413	77.23 408	92.94 399

pmmvs4		92.10 302	91.07 310	95.18 282	92.82 398	94.96 222	99.48 240	96.83 377	87.45 361	88.66 342	96.56 318	83.78 284	96.83 360	89.29 327	84.77 349	93.75 377

LF4IMVS		89.25 362	88.85 351	90.45 393	92.81 399	81.19 427	98.12 375	94.79 429	91.44 272	86.29 379	97.11 294	65.30 415	98.11 287	88.53 336	85.25 343	92.07 410

tt0320		83.56 399	81.15 402	90.77 387	92.77 400	83.58 409	96.83 407	95.52 417	63.26 446	81.36 409	92.54 416	53.26 439	95.77 400	80.45 403	74.38 420	92.96 398

DTE-MVSNet		89.40 359	88.24 362	92.88 362	92.66 401	89.95 354	99.10 286	98.22 207	87.29 363	85.12 389	96.22 326	76.27 358	95.30 410	83.56 385	75.74 416	93.41 386

EU-MVSNet		90.14 347	90.34 321	89.54 401	92.55 402	81.06 428	98.69 343	98.04 231	91.41 276	86.59 373	96.84 309	80.83 313	93.31 432	86.20 364	81.91 370	94.26 330

APD_test1		81.15 404	80.92 404	81.86 424	92.45 403	59.76 453	96.04 421	93.61 443	73.29 440	77.06 428	96.64 314	44.28 449	96.16 389	72.35 432	82.52 364	89.67 435

sc_t1		85.01 387	82.46 397	92.67 366	92.44 404	83.09 413	97.39 392	95.72 410	65.06 445	85.64 386	96.16 328	49.50 444	97.34 320	84.86 376	75.39 418	97.57 288

our_test_3		90.39 337	89.48 342	93.12 356	92.40 405	89.57 359	99.33 262	96.35 398	87.84 357	85.30 387	94.99 382	84.14 282	96.09 393	80.38 404	84.56 350	93.71 382

ppachtmachnet_test		89.58 357	88.35 360	93.25 354	92.40 405	90.44 343	99.33 262	96.73 384	85.49 388	85.90 384	95.77 339	81.09 309	96.00 397	76.00 426	82.49 365	93.30 390

v7n		89.65 355	88.29 361	93.72 339	92.22 407	90.56 340	99.07 293	97.10 344	85.42 390	86.73 370	94.72 387	80.06 323	97.13 335	81.14 399	78.12 400	93.49 385

dmvs_testset		83.79 396	86.07 377	76.94 428	92.14 408	48.60 463	96.75 408	90.27 453	89.48 323	78.65 422	98.55 240	79.25 329	86.65 451	66.85 442	82.69 362	95.57 307

PS-MVSNAJss		93.64 266	93.31 263	94.61 301	92.11 409	92.19 297	99.12 283	97.38 304	92.51 234	88.45 345	96.99 302	91.20 170	97.29 328	94.36 236	87.71 327	94.36 322

pmmvs5		90.17 346	89.09 347	93.40 348	92.10 410	89.77 357	99.74 177	95.58 415	85.88 382	87.24 367	95.74 340	73.41 381	96.48 374	88.54 335	83.56 359	93.95 364

N_pmnet		80.06 408	80.78 405	77.89 427	91.94 411	45.28 465	98.80 334	56.82 467	78.10 428	80.08 416	93.33 409	77.03 345	95.76 401	68.14 440	82.81 361	92.64 403

test_djsdf		92.83 285	92.29 287	94.47 311	91.90 412	92.46 292	99.55 227	97.27 321	91.17 280	89.96 306	96.07 334	81.10 308	96.89 354	94.67 231	88.91 307	94.05 355

SixPastTwentyTwo		88.73 364	88.01 365	90.88 383	91.85 413	82.24 419	98.22 372	95.18 425	88.97 333	82.26 403	96.89 304	71.75 386	96.67 367	84.00 380	82.98 360	93.72 381

K. test v3		88.05 370	87.24 371	90.47 392	91.82 414	82.23 420	98.96 312	97.42 300	89.05 328	76.93 430	95.60 347	68.49 400	95.42 406	85.87 369	81.01 382	93.75 377

OurMVSNet-221017-0		89.81 352	89.48 342	90.83 386	91.64 415	81.21 426	98.17 374	95.38 420	91.48 270	85.65 385	97.31 289	72.66 382	97.29 328	88.15 341	84.83 348	93.97 363

mvs_tets		91.81 306	91.08 309	94.00 330	91.63 416	90.58 339	98.67 345	97.43 298	92.43 236	87.37 365	97.05 299	71.76 385	97.32 323	94.75 228	88.68 313	94.11 351

Gipuma		66.95 421	65.00 421	72.79 433	91.52 417	67.96 445	66.16 456	95.15 426	47.89 454	58.54 451	67.99 456	29.74 453	87.54 450	50.20 455	77.83 402	62.87 456
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 171	95.74 180	98.32 140	91.47 418	95.56 194	99.84 142	97.30 316	97.74 28	97.89 166	99.35 146	79.62 326	99.85 123	99.25 69	99.24 135	99.55 154

jajsoiax		91.92 304	91.18 307	94.15 322	91.35 419	90.95 330	99.00 304	97.42 300	92.61 226	87.38 364	97.08 296	72.46 383	97.36 318	94.53 234	88.77 311	94.13 350

MDA-MVSNet-bldmvs		84.09 394	81.52 401	91.81 377	91.32 420	88.00 381	98.67 345	95.92 406	80.22 422	55.60 454	93.32 410	68.29 402	93.60 430	73.76 429	76.61 413	93.82 375

MVP-Stereo		90.93 324	90.45 319	92.37 370	91.25 421	88.76 367	98.05 379	96.17 401	87.27 364	84.04 394	95.30 366	78.46 340	97.27 330	83.78 383	99.70 89	91.09 418
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 382	83.32 390	92.10 372	90.96 422	88.58 373	99.20 278	96.52 393	79.70 424	57.12 453	92.69 415	79.11 332	93.86 426	77.10 421	77.46 406	93.86 372

YYNet1		85.50 383	83.33 389	92.00 373	90.89 423	88.38 377	99.22 277	96.55 392	79.60 425	57.26 452	92.72 414	79.09 334	93.78 428	77.25 420	77.37 407	93.84 373

anonymousdsp		91.79 311	90.92 311	94.41 316	90.76 424	92.93 279	98.93 316	97.17 332	89.08 327	87.46 363	95.30 366	78.43 341	96.92 352	92.38 275	88.73 312	93.39 388

lessismore_v0				90.53 390	90.58 425	80.90 429		95.80 407		77.01 429	95.84 337	66.15 411	96.95 350	83.03 388	75.05 419	93.74 380

EG-PatchMatch MVS		85.35 384	83.81 387	89.99 399	90.39 426	81.89 422	98.21 373	96.09 403	81.78 416	74.73 436	93.72 407	51.56 443	97.12 337	79.16 412	88.61 314	90.96 421

EGC-MVSNET		69.38 414	63.76 424	86.26 417	90.32 427	81.66 425	96.24 417	93.85 441	0.99 464	3.22 465	92.33 421	52.44 440	92.92 435	59.53 451	84.90 347	84.21 445

CMPMVS	61.59 21	84.75 390	85.14 382	83.57 421	90.32 427	62.54 449	96.98 402	97.59 283	74.33 438	69.95 442	96.66 312	64.17 418	98.32 271	87.88 345	88.41 319	89.84 433
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 393	82.92 393	89.21 403	90.03 429	82.60 416	96.89 405	95.62 414	80.59 420	75.77 435	89.17 433	65.04 416	94.79 417	72.12 433	81.02 381	90.23 427

pmmvs6		85.69 379	83.84 386	91.26 382	90.00 430	84.41 405	97.82 384	96.15 402	75.86 432	81.29 410	95.39 361	61.21 429	96.87 357	83.52 386	73.29 422	92.50 406

ttmdpeth		88.23 369	87.06 372	91.75 378	89.91 431	87.35 385	98.92 319	95.73 409	87.92 355	84.02 395	96.31 323	68.23 403	96.84 358	86.33 363	76.12 414	91.06 419

DSMNet-mixed		88.28 368	88.24 362	88.42 411	89.64 432	75.38 439	98.06 378	89.86 454	85.59 387	88.20 352	92.14 422	76.15 360	91.95 440	78.46 415	96.05 232	97.92 274

UnsupCasMVSNet_eth		85.52 381	83.99 383	90.10 397	89.36 433	83.51 411	96.65 409	97.99 234	89.14 326	75.89 434	93.83 404	63.25 422	93.92 424	81.92 396	67.90 438	92.88 400

Anonymous20231206		86.32 377	85.42 380	89.02 405	89.11 434	80.53 432	99.05 298	95.28 421	85.43 389	82.82 401	93.92 403	74.40 374	93.44 431	66.99 441	81.83 371	93.08 396

Anonymous20240521		85.15 385	83.81 387	89.16 404	88.32 435	82.69 415	98.80 334	95.74 408	79.72 423	81.53 408	90.99 425	65.38 414	94.16 422	72.69 431	81.11 378	90.63 425

OpenMVS_ROB	79.82 20	83.77 397	81.68 400	90.03 398	88.30 436	82.82 414	98.46 356	95.22 423	73.92 439	76.00 433	91.29 424	55.00 436	96.94 351	68.40 439	88.51 318	90.34 426

test20.03		84.72 391	83.99 383	86.91 415	88.19 437	80.62 431	98.88 322	95.94 405	88.36 349	78.87 420	94.62 392	68.75 398	89.11 446	66.52 443	75.82 415	91.00 420

KD-MVS_self_test		83.59 398	82.06 398	88.20 412	86.93 438	80.70 430	97.21 395	96.38 396	82.87 410	82.49 402	88.97 434	67.63 405	92.32 438	73.75 430	62.30 449	91.58 416

MIMVSNet1		82.58 401	80.51 406	88.78 407	86.68 439	84.20 406	96.65 409	95.41 419	78.75 426	78.59 423	92.44 417	51.88 442	89.76 445	65.26 446	78.95 394	92.38 409

CL-MVSNet_self_test		84.50 392	83.15 392	88.53 410	86.00 440	81.79 423	98.82 330	97.35 308	85.12 391	83.62 399	90.91 427	76.66 352	91.40 441	69.53 437	60.36 450	92.40 408

UnsupCasMVSNet_bld		79.97 410	77.03 415	88.78 407	85.62 441	81.98 421	93.66 433	97.35 308	75.51 435	70.79 441	83.05 448	48.70 446	94.91 415	78.31 416	60.29 451	89.46 438

mvs5depth		84.87 388	82.90 394	90.77 387	85.59 442	84.84 403	91.10 446	93.29 445	83.14 407	85.07 390	94.33 400	62.17 425	97.32 323	78.83 414	72.59 425	90.14 429

Patchmatch-RL test		86.90 375	85.98 379	89.67 400	84.45 443	75.59 438	89.71 449	92.43 447	86.89 371	77.83 427	90.94 426	94.22 92	93.63 429	87.75 346	69.61 430	99.79 106

pmmvs-eth3d		84.03 395	81.97 399	90.20 396	84.15 444	87.09 387	98.10 377	94.73 431	83.05 408	74.10 438	87.77 440	65.56 413	94.01 423	81.08 400	69.24 432	89.49 437

test_fmvs3		79.99 409	80.17 408	79.45 426	84.02 445	62.83 447	99.05 298	93.49 444	88.29 351	80.06 417	86.65 443	28.09 455	88.00 447	88.63 332	73.27 423	87.54 443

PM-MVS		80.47 406	78.88 411	85.26 418	83.79 446	72.22 441	95.89 424	91.08 451	85.71 386	76.56 432	88.30 436	36.64 451	93.90 425	82.39 392	69.57 431	89.66 436

new-patchmatchnet		81.19 403	79.34 410	86.76 416	82.86 447	80.36 433	97.92 381	95.27 422	82.09 415	72.02 439	86.87 442	62.81 424	90.74 444	71.10 434	63.08 446	89.19 440

mvsany_test3		82.12 402	81.14 403	85.06 419	81.87 448	70.41 443	97.09 399	92.14 448	91.27 279	77.84 426	88.73 435	39.31 450	95.49 403	90.75 307	71.24 427	89.29 439

WB-MVS		76.28 412	77.28 414	73.29 432	81.18 449	54.68 457	97.87 383	94.19 436	81.30 417	69.43 443	90.70 428	77.02 346	82.06 455	35.71 460	68.11 437	83.13 446

test_f		78.40 411	77.59 413	80.81 425	80.82 450	62.48 450	96.96 403	93.08 446	83.44 405	74.57 437	84.57 447	27.95 456	92.63 436	84.15 378	72.79 424	87.32 444

SSC-MVS		75.42 413	76.40 416	72.49 436	80.68 451	53.62 458	97.42 390	94.06 438	80.42 421	68.75 444	90.14 430	76.54 354	81.66 456	33.25 461	66.34 441	82.19 447

pmmvs3		80.27 407	77.77 412	87.76 414	80.32 452	82.43 418	98.23 371	91.97 449	72.74 441	78.75 421	87.97 439	57.30 435	90.99 443	70.31 435	62.37 448	89.87 432

testf1		68.38 417	66.92 418	72.78 434	78.80 453	50.36 460	90.95 447	87.35 459	55.47 450	58.95 449	88.14 437	20.64 460	87.60 448	57.28 452	64.69 443	80.39 449

APD_test2		68.38 417	66.92 418	72.78 434	78.80 453	50.36 460	90.95 447	87.35 459	55.47 450	58.95 449	88.14 437	20.64 460	87.60 448	57.28 452	64.69 443	80.39 449

ambc				83.23 422	77.17 455	62.61 448	87.38 451	94.55 435		76.72 431	86.65 443	30.16 452	96.36 380	84.85 377	69.86 429	90.73 423

test_vis3_rt		68.82 415	66.69 420	75.21 431	76.24 456	60.41 452	96.44 412	68.71 466	75.13 436	50.54 457	69.52 455	16.42 465	96.32 382	80.27 405	66.92 440	68.89 453

TDRefinement		84.76 389	82.56 396	91.38 381	74.58 457	84.80 404	97.36 393	94.56 434	84.73 396	80.21 415	96.12 333	63.56 420	98.39 261	87.92 344	63.97 445	90.95 422

E-PMN		52.30 425	52.18 427	52.67 442	71.51 458	45.40 464	93.62 434	76.60 464	36.01 458	43.50 459	64.13 458	27.11 457	67.31 461	31.06 462	26.06 457	45.30 460

EMVS		51.44 427	51.22 429	52.11 443	70.71 459	44.97 466	94.04 430	75.66 465	35.34 460	42.40 460	61.56 461	28.93 454	65.87 462	27.64 463	24.73 458	45.49 459

PMMVS2		67.15 420	64.15 423	76.14 430	70.56 460	62.07 451	93.89 431	87.52 458	58.09 449	60.02 448	78.32 450	22.38 459	84.54 453	59.56 450	47.03 455	81.80 448

FPMVS		68.72 416	68.72 417	68.71 438	65.95 461	44.27 467	95.97 423	94.74 430	51.13 453	53.26 455	90.50 429	25.11 458	83.00 454	60.80 449	80.97 383	78.87 451

wuyk23d		20.37 431	20.84 434	18.99 446	65.34 462	27.73 469	50.43 457	7.67 470	9.50 463	8.01 464	6.34 464	6.13 468	26.24 463	23.40 464	10.69 462	2.99 461

LCM-MVSNet		67.77 419	64.73 422	76.87 429	62.95 463	56.25 456	89.37 450	93.74 442	44.53 455	61.99 447	80.74 449	20.42 462	86.53 452	69.37 438	59.50 452	87.84 441

MVE	53.74 22	51.54 426	47.86 430	62.60 440	59.56 464	50.93 459	79.41 454	77.69 463	35.69 459	36.27 461	61.76 460	5.79 469	69.63 459	37.97 459	36.61 456	67.24 454
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 423	52.24 426	67.66 439	49.27 465	56.82 455	83.94 452	82.02 462	70.47 443	33.28 462	64.54 457	17.23 464	69.16 460	45.59 457	23.85 459	77.02 452

tmp_tt		65.23 422	62.94 425	72.13 437	44.90 466	50.03 462	81.05 453	89.42 457	38.45 456	48.51 458	99.90 18	54.09 438	78.70 458	91.84 288	18.26 460	87.64 442

PMVS	49.05 23	53.75 424	51.34 428	60.97 441	40.80 467	34.68 468	74.82 455	89.62 456	37.55 457	28.67 463	72.12 452	7.09 467	81.63 457	43.17 458	68.21 436	66.59 455
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 429	39.14 432	33.31 444	19.94 468	24.83 470	98.36 364	9.75 469	15.53 462	51.31 456	87.14 441	19.62 463	17.74 464	47.10 456	3.47 463	57.36 457

testmvs		40.60 428	44.45 431	29.05 445	19.49 469	14.11 471	99.68 200	18.47 468	20.74 461	64.59 446	98.48 247	10.95 466	17.09 465	56.66 454	11.01 461	55.94 458

mmdepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

monomultidepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

test_blank		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.02 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

eth-test2					0.00 470

eth-test					0.00 470

uanet_test		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

DCPMVS		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

cdsmvs_eth3d_5k		23.43 430	31.24 433	0.00 447	0.00 470	0.00 472	0.00 458	98.09 225	0.00 465	0.00 466	99.67 107	83.37 287	0.00 466	0.00 465	0.00 464	0.00 462

pcd_1.5k_mvsjas		7.60 433	10.13 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	91.20 170	0.00 466	0.00 465	0.00 464	0.00 462

sosnet-low-res		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

sosnet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uncertanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

Regformer		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

ab-mvs-re		8.28 432	11.04 435	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	99.40 140	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 466	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

WAC-MVS						90.97 327								86.10 367

PC_three_1452									96.96 58	99.80 24	99.79 58	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 150	97.27 45	99.80 24	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 76	99.83 20	99.91 14	97.87 5	100.00 1	99.92 13	100.00 1	100.00 1

GSMVS																99.59 144

sam_mvs1												94.72 71				99.59 144

sam_mvs												94.25 91

MTGPA								98.28 198

test_post1							95.78 425				59.23 462	93.20 125	97.74 307	91.06 298

test_post											63.35 459	94.43 79	98.13 286

patchmatchnet-post											91.70 423	95.12 56	97.95 298

MTMP							99.87 123	96.49 394

test9_res														99.71 43	99.99 21	100.00 1

agg_prior2														99.48 58	100.00 1	100.00 1

test_prior4						98.05 77	99.94 83

test_prior2							99.95 66		95.78 98	99.73 43	99.76 67	96.00 37		99.78 31	100.00 1

旧先验2							99.46 245		94.21 157	99.85 16			99.95 80	96.96 184

新几何2							99.40 249

无先验							99.49 237	98.71 73	93.46 189				100.00 1	94.36 236		99.99 23

原ACMM2							99.90 107

testdata2													99.99 36	90.54 311

segment_acmp												96.68 29

testdata1							99.28 271		96.35 86

plane_prior5								97.87 248					98.37 267	97.79 160	89.55 301	94.52 311

plane_prior4											98.59 233

plane_prior3						91.64 315			96.63 71	93.01 271

plane_prior2							99.84 142		96.38 82

plane_prior						91.74 309	99.86 134		96.76 66						89.59 300

n2								0.00 471

nn								0.00 471

door-mid								89.69 455

test11								98.44 142

door								90.31 452

HQP5-MVS						91.85 305

BP-MVS														97.92 151

HQP4-MVS										93.37 266			98.39 261			94.53 309

HQP3-MVS								97.89 246							89.60 298

HQP2-MVS												80.65 316

MDTV_nov1_ep13_2view						96.26 161	96.11 419		91.89 256	98.06 159		94.40 81		94.30 239		99.67 124

ACMMP++_ref															87.04 332

ACMMP++															88.23 321

Test By Simon												92.82 136

High-resolution multi-view benchmark