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	99.09 293	98.84 62	93.32 196	96.74 205	99.72 88	86.04 254	100.00 1	98.01 146	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	98.61 132	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	89.90 325	98.36 147	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	92.06 258	98.40 146	99.84 44	95.68 44	100.00 1	98.19 135	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	98.03 161	99.75 75	94.03 99	99.98 47	98.11 140	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	94.43 144	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		93.83 262	92.06 297	99.15 65	99.94 13	97.50 104	99.94 83	98.42 162	96.22 88	99.41 82	41.37 470	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	98.26 153	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	93.97 169	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	97.52 178	99.70 94	94.40 81	99.98 47	97.00 182	99.98 32	99.99 23

MG-MVS		98.91 19	98.65 24	99.68 16	99.94 13	99.07 24	99.64 211	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	92.34 246	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	94.04 167	98.80 118	99.74 82	92.98 130	100.00 1	98.16 137	99.76 85	99.93 82

TEST9					99.92 31	98.92 29	99.96 47	98.43 150	93.90 175	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	94.35 149	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	94.35 149	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	99.75 175	99.50 17	93.90 175	99.37 87	99.76 67	93.24 123	100.00 1	97.75 165	99.96 46	99.98 52

ACMMP		97.74 98	97.44 102	98.66 107	99.92 31	96.13 171	99.18 286	99.45 18	94.84 125	96.41 218	99.71 91	91.40 167	99.99 36	97.99 148	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	94.08 162	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	99.24 281	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	97.27 220	99.89 45	91.92 308	99.90 107	99.07 37	88.67 349	95.26 249	99.82 49	93.17 126	99.98 47	98.15 138	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	94.31 152	98.50 139	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	93.22 199	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	93.19 200	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	95.39 203	99.61 218	97.78 258	96.52 74	98.61 132	99.31 150	92.73 138	99.67 161	96.77 192	99.48 115	99.06 232

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	90.78 305	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	99.39 259	98.28 198	95.76 99	97.18 192	99.88 24	92.74 137	100.00 1	98.67 106	99.88 73	99.99 23

LS3D		95.84 195	95.11 207	98.02 160	99.85 56	95.10 221	98.74 343	98.50 131	87.22 371	93.66 270	99.86 29	87.45 231	99.95 80	90.94 308	99.81 83	99.02 236

HPM-MVS		97.96 75	97.72 85	98.68 104	99.84 58	96.39 157	99.90 107	98.17 213	92.61 232	98.62 131	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	99.40 255	98.51 125	95.29 113	98.51 138	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	94.77 231	99.92 93	98.46 136	93.93 172	97.20 190	99.27 155	95.44 51	99.97 59	97.41 170	99.51 111	99.41 184
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	93.95 171	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	99.36 265	98.50 131	95.21 115	98.30 150	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	99.76 171	98.31 193	94.43 144	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	99.76 171	98.31 193	94.43 144	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	91.75 269	98.94 111	99.54 127	91.82 165	99.65 165	97.62 168	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	93.53 187	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	97.41 210	99.76 68	93.36 276	99.65 207	97.95 239	96.03 93	97.41 183	99.70 94	89.61 199	99.51 171	96.73 194	98.25 173	99.38 186

新几何1				99.42 38	99.75 71	98.27 66		98.63 91	92.69 227	99.55 67	99.82 49	94.40 81	100.00 1	91.21 300	99.94 55	99.99 23

MP-MVS-pluss		98.07 74	97.64 91	99.38 44	99.74 72	98.41 64	99.74 178	98.18 212	93.35 194	96.45 215	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	94.06 165	99.62 58	99.85 33	94.97 65	99.96 71	95.11 221	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	98.76 124	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	98.52 136	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	99.73 185	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	96.87 232	99.71 78	91.74 313	99.85 137	97.95 239	93.11 206	95.72 238	99.16 172	92.35 152	99.94 88	95.32 217	99.35 130	98.92 244

reproduce-ours		98.78 24	98.67 21	99.09 74	99.70 80	97.30 112	99.74 178	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	99.74 178	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	95.59 273	99.67 83	86.91 396	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	93.92 339	99.63 85	81.76 431	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	97.99 148	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		96.05 186	95.82 181	96.72 238	99.59 87	96.99 129	99.95 66	99.10 34	94.06 165	98.27 151	95.80 344	89.00 211	99.95 80	99.12 73	87.53 336	93.24 398

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	98.27 151	99.08 175	89.00 211	99.95 80	99.12 73	99.25 134	99.57 152

PatchMatch-RL		96.04 187	95.40 194	97.95 162	99.59 87	95.22 216	99.52 237	99.07 37	93.96 170	96.49 214	98.35 259	82.28 297	99.82 135	90.15 324	99.22 137	98.81 251

dcpmvs_2		97.42 116	98.09 60	95.42 280	99.58 91	87.24 392	99.23 282	96.95 371	94.28 155	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	99.34 267	98.55 113	91.86 264	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	93.78 179	96.55 212	99.69 98	92.28 154	99.98 47	97.13 178	99.44 122	99.93 82

API-MVS		97.86 83	97.66 89	98.47 129	99.52 94	95.41 201	99.47 247	98.87 55	91.68 270	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	99.21 165	84.48 281	99.95 80	94.92 227	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	88.48 353	99.06 106	99.66 109	90.30 191	99.64 166	96.32 202	99.97 42	99.96 70

cl22		93.77 267	93.25 271	95.33 284	99.49 97	94.43 238	99.61 218	98.09 225	90.38 313	89.16 340	95.61 352	90.56 186	97.34 326	91.93 291	84.45 357	94.21 343

testdata				98.42 135	99.47 98	95.33 207		98.56 107	93.78 179	99.79 33	99.85 33	93.64 111	99.94 88	94.97 225	99.94 55	100.00 1

MAR-MVS		97.43 112	97.19 115	98.15 151	99.47 98	94.79 230	99.05 304	98.76 69	92.65 230	98.66 129	99.82 49	88.52 217	99.98 47	98.12 139	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		94.54 239	93.42 261	97.91 168	99.46 100	94.04 253	98.93 322	97.48 295	81.15 425	90.04 311	99.55 125	87.02 239	99.95 80	88.97 336	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	98.46 362	99.42 21	97.03 55	99.02 108	99.09 174	99.35 2	98.21 288	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	98.67 128	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	93.81 258	99.94 55	99.98 52

TAPA-MVS	92.12 8	94.42 247	93.60 253	96.90 231	99.33 104	91.78 312	99.78 161	98.00 233	89.89 326	94.52 255	99.47 131	91.97 161	99.18 196	69.90 442	99.52 108	99.73 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 211	95.07 209	96.32 253	99.32 106	96.60 147	99.76 171	98.85 59	96.65 70	87.83 362	96.05 341	99.52 1	98.11 293	96.58 197	81.07 386	94.25 338

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	97.70 185	99.28 107	95.20 217	99.98 19	97.15 338	95.53 107	99.62 58	99.79 58	92.08 159	98.38 271	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	95.18 218	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	98.59 261

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	99.64 211	99.27 27	91.43 279	97.88 168	98.99 185	95.84 42	99.84 131	98.82 96	95.32 263	99.79 106

DCV-MVSNet		97.83 87	97.37 106	99.21 54	99.18 112	97.98 81	99.64 211	99.27 27	91.43 279	97.88 168	98.99 185	95.84 42	99.84 131	98.82 96	95.32 263	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	96.40 153	98.45 131	99.16 115	95.90 177	99.66 206	98.06 228	96.37 85	94.37 261	99.49 130	83.29 291	99.90 106	97.63 167	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	98.21 156	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	97.43 208	99.10 118	94.42 239	99.99 5	97.10 350	95.07 116	99.68 48	99.75 75	92.95 131	98.34 275	98.38 123	99.14 139	99.54 158

Anonymous202405211		93.10 285	91.99 298	96.40 249	99.10 118	89.65 362	98.88 328	97.93 241	83.71 409	94.00 267	98.75 221	68.79 403	99.88 117	95.08 222	91.71 296	99.68 122

fmvsm_s_conf0.5_n		97.80 92	97.85 80	97.67 186	99.06 121	94.41 240	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	98.62 260

HyFIR lowres test		96.66 159	96.43 151	97.36 215	99.05 122	93.91 258	99.70 197	99.80 3	90.54 309	96.26 221	98.08 272	92.15 157	98.23 287	96.84 191	95.46 258	99.93 82

LFMVS		94.75 233	93.56 256	98.30 141	99.03 123	95.70 187	98.74 343	97.98 236	87.81 364	98.47 140	99.39 142	67.43 412	99.53 168	98.01 146	95.20 266	99.67 124

fmvsm_s_conf0.5_n_4		97.75 97	97.86 79	97.42 209	99.01 124	94.69 233	99.97 37	98.76 69	97.91 23	99.87 11	99.76 67	86.70 245	99.93 97	99.67 47	99.12 142	97.64 289

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	81.83 303	99.94 88	99.78 31	98.79 155	97.51 297

AllTest		92.48 300	91.64 303	95.00 293	99.01 124	88.43 380	98.94 320	96.82 385	86.50 380	88.71 345	98.47 254	74.73 378	99.88 117	85.39 376	96.18 233	96.71 303

TestCases				95.00 293	99.01 124	88.43 380		96.82 385	86.50 380	88.71 345	98.47 254	74.73 378	99.88 117	85.39 376	96.18 233	96.71 303

COLMAP_ROB	90.47 14	92.18 307	91.49 309	94.25 327	99.00 128	88.04 386	98.42 368	96.70 392	82.30 420	88.43 354	99.01 182	76.97 353	99.85 123	86.11 372	96.50 225	94.86 314
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	84.15 284	99.97 59	99.76 36	99.50 113	98.39 268

test_fmvs1		95.35 212	95.68 187	94.36 323	98.99 129	84.98 407	99.96 47	96.65 394	97.60 32	99.73 43	98.96 191	71.58 393	99.93 97	98.31 128	99.37 128	98.17 273

HY-MVS	92.50 7	97.79 94	97.17 117	99.63 17	98.98 131	99.32 9	97.49 396	99.52 14	95.69 102	98.32 149	97.41 292	93.32 118	99.77 143	98.08 143	95.75 248	99.81 103

VNet		97.21 126	96.57 145	99.13 71	98.97 132	97.82 89	99.03 307	99.21 32	94.31 152	99.18 99	98.88 203	86.26 252	99.89 111	98.93 87	94.32 276	99.69 121

thres200		96.96 140	96.21 160	99.22 53	98.97 132	98.84 36	99.85 137	99.71 7	93.17 201	96.26 221	98.88 203	89.87 196	99.51 171	94.26 246	94.91 268	99.31 203

tfpn200view9		96.79 148	95.99 167	99.19 56	98.94 134	98.82 37	99.78 161	99.71 7	92.86 215	96.02 228	98.87 210	89.33 203	99.50 173	93.84 255	94.57 272	99.27 212

thres400		96.78 150	95.99 167	99.16 63	98.94 134	98.82 37	99.78 161	99.71 7	92.86 215	96.02 228	98.87 210	89.33 203	99.50 173	93.84 255	94.57 272	99.16 220

sasdasda		97.09 133	96.32 154	99.39 41	98.93 136	98.95 27	99.72 189	97.35 308	94.45 140	97.88 168	99.42 135	86.71 243	99.52 169	98.48 118	93.97 282	99.72 116

Anonymous20231211		89.86 357	88.44 365	94.13 330	98.93 136	90.68 340	98.54 359	98.26 201	76.28 437	86.73 376	95.54 356	70.60 399	97.56 319	90.82 311	80.27 395	94.15 351

canonicalmvs		97.09 133	96.32 154	99.39 41	98.93 136	98.95 27	99.72 189	97.35 308	94.45 140	97.88 168	99.42 135	86.71 243	99.52 169	98.48 118	93.97 282	99.72 116

SDMVSNet		94.80 228	93.96 243	97.33 218	98.92 139	95.42 200	99.59 222	98.99 40	92.41 243	92.55 285	97.85 283	75.81 368	98.93 214	97.90 154	91.62 297	97.64 289

sd_testset		93.55 274	92.83 278	95.74 271	98.92 139	90.89 336	98.24 375	98.85 59	92.41 243	92.55 285	97.85 283	71.07 398	98.68 241	93.93 252	91.62 297	97.64 289

EPNet_dtu		95.71 200	95.39 195	96.66 240	98.92 139	93.41 272	99.57 227	98.90 50	96.19 90	97.52 178	98.56 244	92.65 140	97.36 324	77.89 423	98.33 168	99.20 218
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	98.24 155	99.39 142	93.33 117	99.74 149	97.98 150	95.58 257	99.78 109

CHOSEN 1792x2688		96.81 147	96.53 146	97.64 189	98.91 143	93.07 278	99.65 207	99.80 3	95.64 103	95.39 245	98.86 212	84.35 283	99.90 106	96.98 184	99.16 138	99.95 77

thres100view900		96.74 154	95.92 177	99.18 57	98.90 144	98.77 42	99.74 178	99.71 7	92.59 234	95.84 232	98.86 212	89.25 205	99.50 173	93.84 255	94.57 272	99.27 212

thres600view7		96.69 157	95.87 180	99.14 67	98.90 144	98.78 41	99.74 178	99.71 7	92.59 234	95.84 232	98.86 212	89.25 205	99.50 173	93.44 268	94.50 275	99.16 220

MSDG		94.37 249	93.36 268	97.40 211	98.88 146	93.95 257	99.37 263	97.38 304	85.75 391	90.80 304	99.17 169	84.11 286	99.88 117	86.35 368	98.43 166	98.36 270

MGCFI-Net		97.00 138	96.22 159	99.34 46	98.86 147	98.80 39	99.67 205	97.30 317	94.31 152	97.77 174	99.41 139	86.36 250	99.50 173	98.38 123	93.90 284	99.72 116

h-mvs33		94.92 225	94.36 229	96.59 242	98.85 148	91.29 328	98.93 322	98.94 44	95.90 95	98.77 121	98.42 257	90.89 181	99.77 143	97.80 158	70.76 434	98.72 257

Anonymous20240529		92.10 308	90.65 320	96.47 244	98.82 149	90.61 342	98.72 345	98.67 81	75.54 441	93.90 269	98.58 242	66.23 416	99.90 106	94.70 236	90.67 300	98.90 247

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	96.38 219	98.49 250	89.05 209	99.88 117	97.10 180	98.34 167	99.43 182

PS-MVSNAJ		98.44 46	98.20 51	99.16 63	98.80 151	98.92 29	99.54 235	98.17 213	97.34 40	99.85 16	99.85 33	91.20 170	99.89 111	99.41 63	99.67 90	98.69 258

CANet_DTU		96.76 151	96.15 162	98.60 112	98.78 152	97.53 101	99.84 142	97.63 273	97.25 48	99.20 96	99.64 112	81.36 309	99.98 47	92.77 279	98.89 149	98.28 272

mvsany_test1		97.82 90	97.90 77	97.55 198	98.77 153	93.04 281	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	94.40 148	98.41 144	99.47 131	93.65 110	99.42 183	98.57 112	94.26 278	99.67 124

SymmetryMVS		97.64 105	97.46 99	98.17 147	98.74 155	95.39 203	99.61 218	99.26 29	96.52 74	98.61 132	99.31 150	92.73 138	99.67 161	96.77 192	95.63 255	99.45 178

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	99.52 237	98.08 227	97.05 54	99.86 13	99.86 29	90.65 183	99.71 153	99.39 65	98.63 159	98.69 258

miper_enhance_ethall		94.36 251	93.98 242	95.49 274	98.68 158	95.24 214	99.73 185	97.29 320	93.28 198	89.86 316	95.97 342	94.37 85	97.05 347	92.20 283	84.45 357	94.19 344

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	85.99 255	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	91.10 291	98.17 157	98.59 239	93.86 105	98.19 289	95.64 214	95.24 265	99.28 210

test2506		97.53 109	97.19 115	98.58 116	98.66 161	96.90 133	98.81 337	99.77 5	94.93 119	97.95 163	98.96 191	92.51 147	99.20 194	94.93 226	98.15 176	99.64 130

ECVR-MVS		95.66 203	95.05 210	97.51 202	98.66 161	93.71 262	98.85 334	98.45 137	94.93 119	96.86 201	98.96 191	75.22 374	99.20 194	95.34 216	98.15 176	99.64 130

mamv4		95.24 215	96.90 126	90.25 401	98.65 163	72.11 449	98.28 373	97.64 272	89.99 324	95.93 230	98.25 267	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	99.47 247	97.79 256	94.56 135	99.74 41	98.35 259	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	97.77 180	98.63 165	94.26 247	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	99.08 230

MVSMamba_PlusPlus		97.83 87	97.45 101	98.99 84	98.60 166	98.15 67	99.58 224	97.74 263	90.34 316	99.26 95	98.32 262	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	92.53 238	98.84 115	98.84 216	93.36 115	98.30 279	95.84 210	94.30 277	99.05 234

test1111		95.57 206	94.98 213	97.37 213	98.56 167	93.37 275	98.86 332	98.45 137	94.95 118	96.63 207	98.95 196	75.21 375	99.11 200	95.02 223	98.14 178	99.64 130

MVSTER		95.53 207	95.22 202	96.45 247	98.56 167	97.72 92	99.91 101	97.67 268	92.38 245	91.39 295	97.14 299	97.24 18	97.30 331	94.80 232	87.85 329	94.34 333

testing3-2		97.72 101	97.43 104	98.60 112	98.55 170	97.11 124	100.00 1	99.23 31	93.78 179	97.90 165	98.73 223	95.50 49	99.69 157	98.53 116	94.63 270	98.99 238

VDD-MVS		93.77 267	92.94 276	96.27 254	98.55 170	90.22 351	98.77 342	97.79 256	90.85 297	96.82 203	99.42 135	61.18 436	99.77 143	98.95 85	94.13 279	98.82 250

tpmvs		94.28 253	93.57 255	96.40 249	98.55 170	91.50 326	95.70 433	98.55 113	87.47 366	92.15 288	94.26 407	91.42 166	98.95 213	88.15 347	95.85 244	98.76 253

UGNet		95.33 213	94.57 225	97.62 193	98.55 170	94.85 226	98.67 351	99.32 26	95.75 100	96.80 204	96.27 331	72.18 390	99.96 71	94.58 239	99.05 145	98.04 278
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	95.18 217	94.10 236	98.43 133	98.55 170	95.99 175	97.91 389	97.31 316	90.35 315	89.48 329	99.22 162	85.19 268	99.89 111	90.40 321	98.47 165	99.41 184
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 189	96.49 147	94.34 324	98.51 175	89.99 356	99.39 259	98.57 101	93.14 203	97.33 186	98.31 264	93.44 113	94.68 424	93.69 265	95.98 238	98.34 271

UWE-MVS		96.79 148	96.72 138	97.00 226	98.51 175	93.70 263	99.71 192	98.60 95	92.96 211	97.09 193	98.34 261	96.67 31	98.85 219	92.11 289	96.50 225	98.44 266

myMVS_eth3d28		97.86 83	97.59 95	98.68 104	98.50 177	97.26 114	99.92 93	98.55 113	93.79 178	98.26 153	98.75 221	95.20 54	99.48 179	98.93 87	96.40 228	99.29 208

test_vis1_n_1920		95.44 209	95.31 198	95.82 268	98.50 177	88.74 374	99.98 19	97.30 317	97.84 26	99.85 16	99.19 167	66.82 414	99.97 59	98.82 96	99.46 120	98.76 253

BH-w/o		95.71 200	95.38 196	96.68 239	98.49 179	92.28 299	99.84 142	97.50 293	92.12 255	92.06 291	98.79 219	84.69 277	98.67 243	95.29 218	99.66 91	99.09 228

baseline1		95.78 196	94.86 216	98.54 122	98.47 180	98.07 75	99.06 300	97.99 234	92.68 228	94.13 266	98.62 236	93.28 121	98.69 240	93.79 260	85.76 344	98.84 249

fmvsm_s_conf0.5_n_7		97.70 103	97.74 84	97.59 196	98.44 181	95.16 220	99.97 37	98.65 82	97.95 22	99.62 58	99.78 62	86.09 253	99.94 88	99.69 45	99.50 113	97.66 288

EPMVS		96.53 166	96.01 166	98.09 155	98.43 182	96.12 173	96.36 420	99.43 20	93.53 187	97.64 176	95.04 384	94.41 80	98.38 271	91.13 302	98.11 179	99.75 112

kuosan		93.17 282	92.60 284	94.86 300	98.40 183	89.54 364	98.44 364	98.53 120	84.46 404	88.49 350	97.92 280	90.57 185	97.05 347	83.10 393	93.49 287	97.99 279

WBMVS		94.52 242	94.03 240	95.98 260	98.38 184	96.68 142	99.92 93	97.63 273	90.75 306	89.64 324	95.25 377	96.77 25	96.90 359	94.35 244	83.57 364	94.35 331

UBG		97.84 86	97.69 88	98.29 142	98.38 184	96.59 149	99.90 107	98.53 120	93.91 174	98.52 136	98.42 257	96.77 25	99.17 197	98.54 114	96.20 232	99.11 227

sss		97.57 108	97.03 122	99.18 57	98.37 186	98.04 78	99.73 185	99.38 22	93.46 190	98.76 124	99.06 177	91.21 169	99.89 111	96.33 201	97.01 217	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	93.45 192	98.15 158	98.70 226	95.48 50	99.22 190	97.85 156	95.05 267	99.07 231

BH-untuned		95.18 217	94.83 217	96.22 255	98.36 187	91.22 329	99.80 158	97.32 315	90.91 295	91.08 298	98.67 228	83.51 288	98.54 253	94.23 247	99.61 99	98.92 244

testing91		97.16 128	96.90 126	97.97 161	98.35 189	95.67 190	99.91 101	98.42 162	92.91 214	97.33 186	98.72 224	94.81 68	99.21 191	96.98 184	94.63 270	99.03 235

testing99		97.17 127	96.91 125	97.95 162	98.35 189	95.70 187	99.91 101	98.43 150	92.94 212	97.36 184	98.72 224	94.83 67	99.21 191	97.00 182	94.64 269	98.95 240

ET-MVSNet_ETH3D		94.37 249	93.28 270	97.64 189	98.30 191	97.99 80	99.99 5	97.61 279	94.35 149	71.57 447	99.45 134	96.23 35	95.34 414	96.91 189	85.14 351	99.59 144

AUN-MVS		93.28 279	92.60 284	95.34 283	98.29 192	90.09 354	99.31 271	98.56 107	91.80 268	96.35 220	98.00 275	89.38 202	98.28 282	92.46 280	69.22 439	97.64 289

FMVSNet3		92.69 295	91.58 305	95.99 259	98.29 192	97.42 109	99.26 280	97.62 276	89.80 327	89.68 320	95.32 371	81.62 307	96.27 390	87.01 364	85.65 345	94.29 335

PMMVS		96.76 151	96.76 135	96.76 236	98.28 194	92.10 303	99.91 101	97.98 236	94.12 160	99.53 70	99.39 142	86.93 241	98.73 233	96.95 187	97.73 187	99.45 178

hse-mvs2		94.38 248	94.08 239	95.31 285	98.27 195	90.02 355	99.29 276	98.56 107	95.90 95	98.77 121	98.00 275	90.89 181	98.26 286	97.80 158	69.20 440	97.64 289

PVSNet_0	88.03 19	91.80 315	90.27 329	96.38 251	98.27 195	90.46 346	99.94 83	99.61 13	93.99 168	86.26 386	97.39 294	71.13 397	99.89 111	98.77 100	67.05 446	98.79 252

UA-Net		96.54 165	95.96 173	98.27 143	98.23 197	95.71 186	98.00 387	98.45 137	93.72 183	98.41 144	99.27 155	88.71 216	99.66 164	91.19 301	97.69 188	99.44 181

test_cas_vis1_n_1920		96.59 162	96.23 157	97.65 188	98.22 198	94.23 248	99.99 5	97.25 325	97.77 27	99.58 66	99.08 175	77.10 348	99.97 59	97.64 166	99.45 121	98.74 255

FE-MVS		95.70 202	95.01 212	97.79 176	98.21 199	94.57 235	95.03 434	98.69 76	88.90 343	97.50 180	96.19 333	92.60 143	99.49 178	89.99 326	97.94 185	99.31 203

GG-mvs-BLEND				98.54 122	98.21 199	98.01 79	93.87 439	98.52 122		97.92 164	97.92 280	99.02 3	97.94 306	98.17 136	99.58 104	99.67 124

mvs_anonymous		95.65 204	95.03 211	97.53 200	98.19 201	95.74 184	99.33 268	97.49 294	90.87 296	90.47 307	97.10 301	88.23 219	97.16 338	95.92 208	97.66 191	99.68 122

MVS_Test		96.46 168	95.74 183	98.61 111	98.18 202	97.23 116	99.31 271	97.15 338	91.07 292	98.84 115	97.05 305	88.17 220	98.97 210	94.39 241	97.50 193	99.61 141

BH-RMVSNet		95.18 217	94.31 232	97.80 174	98.17 203	95.23 215	99.76 171	97.53 289	92.52 239	94.27 264	99.25 160	76.84 355	98.80 223	90.89 310	99.54 106	99.35 194

dongtai		91.55 321	91.13 314	92.82 369	98.16 204	86.35 397	99.47 247	98.51 125	83.24 412	85.07 396	97.56 288	90.33 190	94.94 420	76.09 431	91.73 295	97.18 300

RPSCF		91.80 315	92.79 280	88.83 412	98.15 205	69.87 451	98.11 383	96.60 396	83.93 407	94.33 262	99.27 155	79.60 331	99.46 182	91.99 290	93.16 292	97.18 300

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	98.82 221	99.02 83	98.54 163	99.46 175

IS-MVSNet		96.29 179	95.90 178	97.45 205	98.13 207	94.80 229	99.08 295	97.61 279	92.02 260	95.54 243	98.96 191	90.64 184	98.08 295	93.73 263	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	80.88 316	99.93 97	99.59 51	98.17 174	97.29 298

ab-mvs		94.69 234	93.42 261	98.51 127	98.07 210	96.26 161	96.49 418	98.68 78	90.31 317	94.54 254	97.00 307	76.30 363	99.71 153	95.98 207	93.38 290	99.56 153

XVG-OURS-SEG-HR		94.79 229	94.70 224	95.08 290	98.05 211	89.19 366	99.08 295	97.54 287	93.66 184	94.87 252	99.58 121	78.78 339	99.79 138	97.31 173	93.40 289	96.25 307

EIA-MVS		97.53 109	97.46 99	97.76 182	98.04 212	94.84 227	99.98 19	97.61 279	94.41 147	97.90 165	99.59 118	92.40 151	98.87 217	98.04 145	99.13 140	99.59 144

XVG-OURS		94.82 226	94.74 223	95.06 291	98.00 213	89.19 366	99.08 295	97.55 285	94.10 161	94.71 253	99.62 116	80.51 322	99.74 149	96.04 206	93.06 294	96.25 307

mvsmamba		96.94 141	96.73 137	97.55 198	97.99 214	94.37 244	99.62 214	97.70 265	93.13 204	98.42 143	97.92 280	88.02 221	98.75 231	98.78 99	99.01 146	99.52 164

dp		95.05 220	94.43 227	96.91 229	97.99 214	92.73 288	96.29 423	97.98 236	89.70 328	95.93 230	94.67 397	93.83 107	98.45 259	86.91 367	96.53 224	99.54 158

tpmrst		96.27 181	95.98 169	97.13 222	97.96 216	93.15 277	96.34 421	98.17 213	92.07 256	98.71 127	95.12 381	93.91 102	98.73 233	94.91 229	96.62 222	99.50 170

TR-MVS		94.54 239	93.56 256	97.49 204	97.96 216	94.34 245	98.71 346	97.51 292	90.30 318	94.51 256	98.69 227	75.56 369	98.77 227	92.82 278	95.99 237	99.35 194

Vis-MVSNet (Re-imp)		96.32 176	95.98 169	97.35 217	97.93 218	94.82 228	99.47 247	98.15 221	91.83 265	95.09 250	99.11 173	91.37 168	97.47 322	93.47 267	97.43 194	99.74 113

MDTV_nov1_ep13			95.69 185		97.90 219	94.15 250	95.98 429	98.44 142	93.12 205	97.98 162	95.74 346	95.10 57	98.58 249	90.02 325	96.92 219

Fast-Effi-MVS+		95.02 222	94.19 234	97.52 201	97.88 220	94.55 236	99.97 37	97.08 354	88.85 345	94.47 257	97.96 279	84.59 278	98.41 263	89.84 328	97.10 211	99.59 144

ADS-MVSNet2		93.80 266	93.88 246	93.55 352	97.87 221	85.94 401	94.24 435	96.84 382	90.07 321	96.43 216	94.48 402	90.29 192	95.37 413	87.44 354	97.23 204	99.36 190

ADS-MVSNet		94.79 229	94.02 241	97.11 224	97.87 221	93.79 259	94.24 435	98.16 218	90.07 321	96.43 216	94.48 402	90.29 192	98.19 289	87.44 354	97.23 204	99.36 190

Effi-MVS+		96.30 178	95.69 185	98.16 148	97.85 223	96.26 161	97.41 398	97.21 330	90.37 314	98.65 130	98.58 242	86.61 247	98.70 239	97.11 179	97.37 198	99.52 164

PatchmatchNet		95.94 190	95.45 192	97.39 212	97.83 224	94.41 240	96.05 427	98.40 171	92.86 215	97.09 193	95.28 376	94.21 94	98.07 297	89.26 334	98.11 179	99.70 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 237	93.61 251	97.74 184	97.82 225	96.26 161	99.96 47	97.78 258	85.76 389	94.00 267	97.54 289	76.95 354	99.21 191	97.23 176	95.43 260	97.76 287

1112_ss		96.01 188	95.20 203	98.42 135	97.80 226	96.41 154	99.65 207	96.66 393	92.71 225	92.88 281	99.40 140	92.16 156	99.30 186	91.92 292	93.66 285	99.55 154

Test_1112_low_res		95.72 198	94.83 217	98.42 135	97.79 227	96.41 154	99.65 207	96.65 394	92.70 226	92.86 282	96.13 337	92.15 157	99.30 186	91.88 293	93.64 286	99.55 154

Effi-MVS+-dtu		94.53 241	95.30 199	92.22 377	97.77 228	82.54 424	99.59 222	97.06 358	94.92 121	95.29 247	95.37 369	85.81 256	97.89 307	94.80 232	97.07 212	96.23 309

tpm cat1		93.51 275	92.52 290	96.47 244	97.77 228	91.47 327	96.13 425	98.06 228	80.98 426	92.91 280	93.78 411	89.66 197	98.87 217	87.03 363	96.39 229	99.09 228

FA-MVS(test-final)		95.86 193	95.09 208	98.15 151	97.74 230	95.62 192	96.31 422	98.17 213	91.42 281	96.26 221	96.13 337	90.56 186	99.47 181	92.18 284	97.07 212	99.35 194

xiu_mvs_v1_base_debu		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 271	97.86 250	96.43 79	99.62 58	99.69 98	85.56 263	99.68 158	99.05 76	98.31 169	97.83 283

xiu_mvs_v1_base		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 271	97.86 250	96.43 79	99.62 58	99.69 98	85.56 263	99.68 158	99.05 76	98.31 169	97.83 283

xiu_mvs_v1_base_debi		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 271	97.86 250	96.43 79	99.62 58	99.69 98	85.56 263	99.68 158	99.05 76	98.31 169	97.83 283

EPP-MVSNet		96.69 157	96.60 143	96.96 228	97.74 230	93.05 280	99.37 263	98.56 107	88.75 347	95.83 234	99.01 182	96.01 36	98.56 251	96.92 188	97.20 206	99.25 214

gg-mvs-nofinetune		93.51 275	91.86 302	98.47 129	97.72 235	97.96 84	92.62 445	98.51 125	74.70 444	97.33 186	69.59 461	98.91 4	97.79 310	97.77 163	99.56 105	99.67 124

IB-MVS	92.85 6	94.99 223	93.94 244	98.16 148	97.72 235	95.69 189	99.99 5	98.81 64	94.28 155	92.70 283	96.90 309	95.08 58	99.17 197	96.07 205	73.88 427	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	91.83 265	97.45 181	99.04 179	97.50 9	99.10 202	94.75 234	96.37 230	99.16 220

VortexMVS		94.11 255	93.50 258	95.94 262	97.70 238	96.61 146	99.35 266	97.18 333	93.52 189	89.57 327	95.74 346	87.55 228	96.97 355	95.76 213	85.13 352	94.23 340

Syy-MVS		90.00 355	90.63 321	88.11 419	97.68 239	74.66 447	99.71 192	98.35 184	90.79 303	92.10 289	98.67 228	79.10 337	93.09 439	63.35 454	95.95 241	96.59 305

myMVS_eth3d		94.46 246	94.76 222	93.55 352	97.68 239	90.97 331	99.71 192	98.35 184	90.79 303	92.10 289	98.67 228	92.46 150	93.09 439	87.13 360	95.95 241	96.59 305

test_fmvs1_n		94.25 254	94.36 229	93.92 339	97.68 239	83.70 414	99.90 107	96.57 397	97.40 38	99.67 49	98.88 203	61.82 433	99.92 103	98.23 134	99.13 140	98.14 276

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		96.24 182	95.68 187	97.94 165	97.65 243	94.92 225	99.27 279	97.10 350	92.79 221	97.43 182	97.99 277	81.85 302	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	97.19 331	94.67 133	98.95 110	99.28 152	86.43 248	98.76 229	98.37 125	97.42 196	99.33 197
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		96.59 162	96.23 157	97.66 187	97.63 245	94.70 232	99.77 165	97.33 312	93.41 193	97.34 185	99.17 169	86.72 242	98.83 220	97.40 171	97.32 201	99.46 175

viewdifsd2359ckpt13		96.19 183	95.77 182	97.45 205	97.62 246	94.40 242	99.70 197	97.23 329	92.76 223	96.63 207	99.05 178	84.96 272	98.64 246	96.65 195	97.35 199	99.31 203

Vis-MVSNet		95.72 198	95.15 206	97.45 205	97.62 246	94.28 246	99.28 277	98.24 204	94.27 157	96.84 202	98.94 198	79.39 332	98.76 229	93.25 269	98.49 164	99.30 206
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	97.60 248	96.70 139	99.92 93	98.54 117	91.11 290	97.07 195	98.97 189	97.47 12	99.03 205	93.73 263	96.09 235	98.92 244

GDP-MVS		97.88 81	97.59 95	98.75 100	97.59 249	97.81 90	99.95 66	97.37 307	94.44 143	99.08 104	99.58 121	97.13 23	99.08 203	94.99 224	98.17 174	99.37 188

miper_ehance_all_eth		93.16 283	92.60 284	94.82 301	97.57 250	93.56 267	99.50 241	97.07 357	88.75 347	88.85 344	95.52 358	90.97 177	96.74 369	90.77 312	84.45 357	94.17 345

guyue		97.15 129	96.82 132	98.15 151	97.56 251	96.25 165	99.71 192	97.84 253	95.75 100	98.13 159	98.65 231	87.58 227	98.82 221	98.29 130	97.91 186	99.36 190

viewmanbaseed2359cas		96.45 169	96.07 163	97.59 196	97.55 252	94.59 234	99.70 197	97.33 312	93.62 186	97.00 197	99.32 147	85.57 262	98.71 236	97.26 175	97.33 200	99.47 173

testing3		93.92 260	94.23 233	92.99 366	97.54 253	90.23 350	99.99 5	99.16 33	90.57 308	91.33 297	98.63 235	92.99 129	92.52 443	82.46 397	95.39 261	96.22 310

SSM_0404		95.75 197	95.16 205	97.50 203	97.53 254	95.39 203	99.11 291	97.25 325	90.81 299	95.27 248	98.83 217	84.74 274	98.67 243	95.24 219	97.69 188	98.45 265

LCM-MVSNet-Re		92.31 304	92.60 284	91.43 386	97.53 254	79.27 441	99.02 309	91.83 456	92.07 256	80.31 420	94.38 405	83.50 289	95.48 410	97.22 177	97.58 192	99.54 158

GBi-Net		90.88 332	89.82 338	94.08 331	97.53 254	91.97 304	98.43 365	96.95 371	87.05 372	89.68 320	94.72 393	71.34 394	96.11 396	87.01 364	85.65 345	94.17 345

test1		90.88 332	89.82 338	94.08 331	97.53 254	91.97 304	98.43 365	96.95 371	87.05 372	89.68 320	94.72 393	71.34 394	96.11 396	87.01 364	85.65 345	94.17 345

FMVSNet2		91.02 329	89.56 343	95.41 281	97.53 254	95.74 184	98.98 312	97.41 302	87.05 372	88.43 354	95.00 387	71.34 394	96.24 392	85.12 379	85.21 350	94.25 338

tttt0517		96.85 145	96.49 147	97.92 166	97.48 259	95.89 178	99.85 137	98.54 117	90.72 307	96.63 207	98.93 201	97.47 12	99.02 206	93.03 276	95.76 247	98.85 248

BP-MVS1		98.33 56	98.18 53	98.81 95	97.44 260	97.98 81	99.96 47	98.17 213	94.88 123	98.77 121	99.59 118	97.59 7	99.08 203	98.24 133	98.93 148	99.36 190

casdiffmvs_mvg		96.43 170	95.94 175	97.89 170	97.44 260	95.47 196	99.86 134	97.29 320	93.35 194	96.03 227	99.19 167	85.39 266	98.72 235	97.89 155	97.04 214	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	97.41 262	95.64 191	99.99 5	97.06 358	94.59 134	99.63 55	99.32 147	89.20 208	98.14 291	98.76 101	99.23 136	99.62 137

c3_l		92.53 299	91.87 301	94.52 313	97.40 263	92.99 282	99.40 255	96.93 376	87.86 362	88.69 347	95.44 363	89.95 195	96.44 382	90.45 318	80.69 391	94.14 354

viewmambaseed2359dif		95.92 192	95.55 191	97.04 225	97.38 264	93.41 272	99.78 161	96.97 369	91.14 289	96.58 210	99.27 155	84.85 273	98.75 231	96.87 190	97.12 210	98.97 239

fmvsm_s_conf0.1_n		97.30 120	97.21 114	97.60 195	97.38 264	94.40 242	99.90 107	98.64 85	96.47 78	99.51 74	99.65 111	84.99 271	99.93 97	99.22 70	99.09 143	98.46 264

CDS-MVSNet		96.34 175	96.07 163	97.13 222	97.37 266	94.96 223	99.53 236	97.91 245	91.55 273	95.37 246	98.32 262	95.05 60	97.13 341	93.80 259	95.75 248	99.30 206
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 154	96.26 156	98.16 148	97.36 267	96.48 151	99.96 47	98.29 197	91.93 261	95.77 235	98.07 273	95.54 46	98.29 280	90.55 316	98.89 149	99.70 119

miper_lstm_enhance		91.81 312	91.39 311	93.06 365	97.34 268	89.18 368	99.38 261	96.79 387	86.70 379	87.47 368	95.22 378	90.00 194	95.86 405	88.26 345	81.37 380	94.15 351

baseline		96.43 170	95.98 169	97.76 182	97.34 268	95.17 219	99.51 239	97.17 335	93.92 173	96.90 200	99.28 152	85.37 267	98.64 246	97.50 169	96.86 221	99.46 175

cl____		92.31 304	91.58 305	94.52 313	97.33 270	92.77 284	99.57 227	96.78 388	86.97 376	87.56 366	95.51 359	89.43 201	96.62 374	88.60 339	82.44 372	94.16 350

SD_0403		92.63 298	93.38 265	90.40 400	97.32 271	77.91 443	97.75 394	98.03 232	91.89 262	90.83 303	98.29 266	82.00 299	93.79 433	88.51 343	95.75 248	99.52 164

DIV-MVS_self_test		92.32 303	91.60 304	94.47 317	97.31 272	92.74 286	99.58 224	96.75 389	86.99 375	87.64 364	95.54 356	89.55 200	96.50 379	88.58 340	82.44 372	94.17 345

casdiffmvs		96.42 172	95.97 172	97.77 180	97.30 273	94.98 222	99.84 142	97.09 353	93.75 182	96.58 210	99.26 159	85.07 269	98.78 226	97.77 163	97.04 214	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		94.36 251	93.48 259	96.99 227	97.29 274	93.54 268	99.96 47	96.72 391	88.35 356	93.43 271	98.94 198	82.05 298	98.05 298	88.12 349	96.48 227	99.37 188

eth_miper_zixun_eth		92.41 302	91.93 299	93.84 343	97.28 275	90.68 340	98.83 335	96.97 369	88.57 352	89.19 339	95.73 349	89.24 207	96.69 372	89.97 327	81.55 378	94.15 351

MVSFormer		96.94 141	96.60 143	97.95 162	97.28 275	97.70 95	99.55 233	97.27 322	91.17 286	99.43 80	99.54 127	90.92 178	96.89 360	94.67 237	99.62 95	99.25 214

lupinMVS		97.85 85	97.60 93	98.62 110	97.28 275	97.70 95	99.99 5	97.55 285	95.50 109	99.43 80	99.67 107	90.92 178	98.71 236	98.40 122	99.62 95	99.45 178

diffmvs_AUTHOR		96.75 153	96.41 152	97.79 176	97.20 278	95.46 197	99.69 200	97.15 338	94.46 139	98.78 119	99.21 165	85.64 260	98.77 227	98.27 131	97.31 202	99.13 224

mamba_0408		94.98 224	94.09 237	97.64 189	97.14 279	95.31 208	93.48 442	97.08 354	90.48 310	94.40 258	98.62 236	84.49 279	98.67 243	93.99 250	97.18 207	98.93 241

SSM_04072		94.77 231	94.09 237	96.82 233	97.14 279	95.31 208	93.48 442	97.08 354	90.48 310	94.40 258	98.62 236	84.49 279	96.21 393	93.99 250	97.18 207	98.93 241

SSM_0407		95.62 205	94.95 214	97.61 194	97.14 279	95.31 208	99.00 310	97.25 325	90.81 299	94.40 258	98.83 217	84.74 274	98.58 249	95.24 219	97.18 207	98.93 241

SCA		94.69 234	93.81 248	97.33 218	97.10 282	94.44 237	98.86 332	98.32 191	93.30 197	96.17 226	95.59 354	76.48 361	97.95 304	91.06 304	97.43 194	99.59 144

viewmacassd2359aftdt		95.93 191	95.45 192	97.36 215	97.09 283	94.12 252	99.57 227	97.26 324	93.05 209	96.50 213	99.17 169	82.76 294	98.68 241	96.61 196	97.04 214	99.28 210

KinetiMVS		96.10 184	95.29 200	98.53 124	97.08 284	97.12 122	99.56 230	98.12 224	94.78 126	98.44 141	98.94 198	80.30 326	99.39 184	91.56 297	98.79 155	99.06 232

TAMVS		95.85 194	95.58 189	96.65 241	97.07 285	93.50 269	99.17 287	97.82 255	91.39 283	95.02 251	98.01 274	92.20 155	97.30 331	93.75 262	95.83 245	99.14 223

Fast-Effi-MVS+-dtu		93.72 270	93.86 247	93.29 357	97.06 286	86.16 398	99.80 158	96.83 383	92.66 229	92.58 284	97.83 285	81.39 308	97.67 315	89.75 329	96.87 220	96.05 312

CostFormer		96.10 184	95.88 179	96.78 235	97.03 287	92.55 294	97.08 407	97.83 254	90.04 323	98.72 126	94.89 391	95.01 62	98.29 280	96.54 198	95.77 246	99.50 170

test_fmvsmvis_n_1920		97.67 104	97.59 95	97.91 168	97.02 288	95.34 206	99.95 66	98.45 137	97.87 24	97.02 196	99.59 118	89.64 198	99.98 47	99.41 63	99.34 131	98.42 267

test-LLR		96.47 167	96.04 165	97.78 178	97.02 288	95.44 198	99.96 47	98.21 208	94.07 163	95.55 241	96.38 326	93.90 103	98.27 284	90.42 319	98.83 153	99.64 130

test-mter		96.39 173	95.93 176	97.78 178	97.02 288	95.44 198	99.96 47	98.21 208	91.81 267	95.55 241	96.38 326	95.17 55	98.27 284	90.42 319	98.83 153	99.64 130

icg_test_0407_2		95.04 221	94.78 221	95.84 267	96.97 291	91.64 319	98.63 354	97.12 343	92.33 247	95.60 239	98.88 203	85.65 258	96.56 377	92.12 285	95.70 251	99.32 199

IMVS_0407		95.21 216	94.80 220	96.46 246	96.97 291	91.64 319	98.81 337	97.12 343	92.33 247	95.60 239	98.88 203	85.65 258	98.42 261	92.12 285	95.70 251	99.32 199

IMVS_0404		93.83 262	93.17 272	95.80 269	96.97 291	91.64 319	97.78 393	97.12 343	92.33 247	90.87 302	98.88 203	76.78 356	96.43 383	92.12 285	95.70 251	99.32 199

IMVS_0403		95.25 214	94.81 219	96.58 243	96.97 291	91.64 319	98.97 317	97.12 343	92.33 247	95.43 244	98.88 203	85.78 257	98.79 224	92.12 285	95.70 251	99.32 199

gm-plane-assit					96.97 291	93.76 261			91.47 277		98.96 191		98.79 224	94.92 227

WB-MVSnew		92.90 289	92.77 281	93.26 359	96.95 296	93.63 265	99.71 192	98.16 218	91.49 274	94.28 263	98.14 270	81.33 310	96.48 380	79.47 414	95.46 258	89.68 440

QAPM		95.40 210	94.17 235	99.10 73	96.92 297	97.71 93	99.40 255	98.68 78	89.31 331	88.94 343	98.89 202	82.48 296	99.96 71	93.12 275	99.83 77	99.62 137

KD-MVS_2432*1600		88.00 377	86.10 381	93.70 348	96.91 298	94.04 253	97.17 404	97.12 343	84.93 399	81.96 410	92.41 424	92.48 148	94.51 426	79.23 415	52.68 460	92.56 410

miper_refine_blended		88.00 377	86.10 381	93.70 348	96.91 298	94.04 253	97.17 404	97.12 343	84.93 399	81.96 410	92.41 424	92.48 148	94.51 426	79.23 415	52.68 460	92.56 410

tpm2		95.47 208	95.18 204	96.35 252	96.91 298	91.70 317	96.96 410	97.93 241	88.04 360	98.44 141	95.40 365	93.32 118	97.97 301	94.00 249	95.61 256	99.38 186

FMVSNet5		88.32 373	87.47 375	90.88 389	96.90 301	88.39 382	97.28 401	95.68 418	82.60 419	84.67 398	92.40 426	79.83 329	91.16 448	76.39 430	81.51 379	93.09 401

3Dnovator+	91.53 11	96.31 177	95.24 201	99.52 28	96.88 302	98.64 54	99.72 189	98.24 204	95.27 114	88.42 356	98.98 187	82.76 294	99.94 88	97.10 180	99.83 77	99.96 70

Patchmatch-test		92.65 297	91.50 308	96.10 258	96.85 303	90.49 345	91.50 450	97.19 331	82.76 418	90.23 308	95.59 354	95.02 61	98.00 300	77.41 425	96.98 218	99.82 101

MVS		96.60 161	95.56 190	99.72 13	96.85 303	99.22 20	98.31 371	98.94 44	91.57 272	90.90 301	99.61 117	86.66 246	99.96 71	97.36 172	99.88 73	99.99 23

3Dnovator	91.47 12	96.28 180	95.34 197	99.08 76	96.82 305	97.47 107	99.45 252	98.81 64	95.52 108	89.39 330	99.00 184	81.97 300	99.95 80	97.27 174	99.83 77	99.84 98

EI-MVSNet		93.73 269	93.40 264	94.74 302	96.80 306	92.69 289	99.06 300	97.67 268	88.96 340	91.39 295	99.02 180	88.75 215	97.30 331	91.07 303	87.85 329	94.22 341

CVMVSNet		94.68 236	94.94 215	93.89 342	96.80 306	86.92 395	99.06 300	98.98 41	94.45 140	94.23 265	99.02 180	85.60 261	95.31 415	90.91 309	95.39 261	99.43 182

IterMVS-LS		92.69 295	92.11 295	94.43 321	96.80 306	92.74 286	99.45 252	96.89 379	88.98 338	89.65 323	95.38 368	88.77 214	96.34 387	90.98 307	82.04 375	94.22 341
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 164	96.46 150	96.91 229	96.79 309	92.50 295	99.90 107	97.38 304	96.02 94	97.79 173	99.32 147	86.36 250	98.99 207	98.26 132	96.33 231	99.23 217

IterMVS		90.91 331	90.17 333	93.12 362	96.78 310	90.42 348	98.89 326	97.05 361	89.03 335	86.49 381	95.42 364	76.59 359	95.02 417	87.22 359	84.09 360	93.93 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 146	95.96 173	99.48 35	96.74 311	98.52 58	98.31 371	98.86 56	95.82 97	89.91 314	98.98 187	87.49 230	99.96 71	97.80 158	99.73 87	99.96 70

IterMVS-SCA-FT		90.85 334	90.16 334	92.93 367	96.72 312	89.96 357	98.89 326	96.99 365	88.95 341	86.63 378	95.67 350	76.48 361	95.00 418	87.04 362	84.04 363	93.84 379

MVS-HIRNet		86.22 384	83.19 397	95.31 285	96.71 313	90.29 349	92.12 447	97.33 312	62.85 455	86.82 375	70.37 460	69.37 402	97.49 321	75.12 433	97.99 184	98.15 274

viewdifsd2359ckpt11		94.09 257	93.63 250	95.46 278	96.68 314	88.92 371	99.62 214	97.12 343	93.07 207	95.73 236	99.22 162	77.05 349	98.88 216	96.52 199	87.69 334	98.58 262

viewmsd2359difaftdt		94.09 257	93.64 249	95.46 278	96.68 314	88.92 371	99.62 214	97.13 342	93.07 207	95.73 236	99.22 162	77.05 349	98.89 215	96.52 199	87.70 333	98.58 262

VDDNet		93.12 284	91.91 300	96.76 236	96.67 316	92.65 292	98.69 349	98.21 208	82.81 417	97.75 175	99.28 152	61.57 434	99.48 179	98.09 142	94.09 280	98.15 274

dmvs_re		93.20 281	93.15 273	93.34 355	96.54 317	83.81 413	98.71 346	98.51 125	91.39 283	92.37 287	98.56 244	78.66 341	97.83 309	93.89 253	89.74 301	98.38 269

Elysia		94.50 243	93.38 265	97.85 172	96.49 318	96.70 139	98.98 312	97.78 258	90.81 299	96.19 224	98.55 246	73.63 385	98.98 208	89.41 330	98.56 161	97.88 281

StellarMVS		94.50 243	93.38 265	97.85 172	96.49 318	96.70 139	98.98 312	97.78 258	90.81 299	96.19 224	98.55 246	73.63 385	98.98 208	89.41 330	98.56 161	97.88 281

MIMVSNet		90.30 347	88.67 361	95.17 289	96.45 320	91.64 319	92.39 446	97.15 338	85.99 386	90.50 306	93.19 419	66.95 413	94.86 422	82.01 401	93.43 288	99.01 237

CR-MVSNet		93.45 278	92.62 283	95.94 262	96.29 321	92.66 290	92.01 448	96.23 405	92.62 231	96.94 198	93.31 417	91.04 175	96.03 401	79.23 415	95.96 239	99.13 224

RPMNet		89.76 359	87.28 376	97.19 221	96.29 321	92.66 290	92.01 448	98.31 193	70.19 451	96.94 198	85.87 453	87.25 235	99.78 140	62.69 455	95.96 239	99.13 224

tt0805		91.28 324	90.18 332	94.60 308	96.26 323	87.55 388	98.39 369	98.72 72	89.00 337	89.22 336	98.47 254	62.98 429	98.96 212	90.57 315	88.00 328	97.28 299

Patchmtry		89.70 360	88.49 364	93.33 356	96.24 324	89.94 360	91.37 451	96.23 405	78.22 434	87.69 363	93.31 417	91.04 175	96.03 401	80.18 413	82.10 374	94.02 362

test_vis1_rt		86.87 382	86.05 384	89.34 408	96.12 325	78.07 442	99.87 123	83.54 468	92.03 259	78.21 431	89.51 438	45.80 454	99.91 104	96.25 203	93.11 293	90.03 437

JIA-IIPM		91.76 318	90.70 319	94.94 295	96.11 326	87.51 389	93.16 444	98.13 223	75.79 440	97.58 177	77.68 458	92.84 134	97.97 301	88.47 344	96.54 223	99.33 197

OpenMVS	90.15 15	94.77 231	93.59 254	98.33 139	96.07 327	97.48 106	99.56 230	98.57 101	90.46 312	86.51 380	98.95 196	78.57 342	99.94 88	93.86 254	99.74 86	97.57 294

PAPM		98.60 34	98.42 35	99.14 67	96.05 328	98.96 26	99.90 107	99.35 24	96.68 69	98.35 148	99.66 109	96.45 33	98.51 254	99.45 60	99.89 70	99.96 70

CLD-MVS		94.06 259	93.90 245	94.55 312	96.02 329	90.69 339	99.98 19	97.72 264	96.62 73	91.05 300	98.85 215	77.21 347	98.47 255	98.11 140	89.51 307	94.48 319
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 344	88.75 360	95.25 287	95.99 330	90.16 352	91.22 452	97.54 287	76.80 436	97.26 189	86.01 452	91.88 162	96.07 400	66.16 450	95.91 243	99.51 168

ACMH+	89.98 16	90.35 345	89.54 344	92.78 371	95.99 330	86.12 399	98.81 337	97.18 333	89.38 330	83.14 406	97.76 286	68.42 407	98.43 260	89.11 335	86.05 343	93.78 382

DeepMVS_CX				82.92 430	95.98 332	58.66 461		96.01 410	92.72 224	78.34 430	95.51 359	58.29 440	98.08 295	82.57 396	85.29 348	92.03 418

ACMP	92.05 9	92.74 293	92.42 292	93.73 344	95.91 333	88.72 375	99.81 154	97.53 289	94.13 159	87.00 374	98.23 268	74.07 382	98.47 255	96.22 204	88.86 314	93.99 367
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 273	93.03 275	95.35 282	95.86 334	86.94 394	99.87 123	96.36 403	96.85 60	99.54 69	98.79 219	52.41 448	99.83 133	98.64 109	98.97 147	99.29 208

HQP-NCC					95.78 335		99.87 123		96.82 62	93.37 272

ACMP_Plane					95.78 335		99.87 123		96.82 62	93.37 272

HQP-MVS		94.61 238	94.50 226	94.92 296	95.78 335	91.85 309	99.87 123	97.89 246	96.82 62	93.37 272	98.65 231	80.65 320	98.39 267	97.92 152	89.60 302	94.53 315

NP-MVS					95.77 338	91.79 311					98.65 231

test_fmvsmconf0.1_n		97.74 98	97.44 102	98.64 109	95.76 339	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					95.76 339	91.72 316						80.47 324

ACMM	91.95 10	92.88 290	92.52 290	93.98 338	95.75 341	89.08 370	99.77 165	97.52 291	93.00 210	89.95 313	97.99 277	76.17 365	98.46 258	93.63 266	88.87 313	94.39 327
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 262	92.84 277	96.80 234	95.73 342	93.57 266	99.88 120	97.24 328	92.57 236	92.92 279	96.66 318	78.73 340	97.67 315	87.75 352	94.06 281	99.17 219

plane_prior1					95.73 342

jason		97.24 124	96.86 129	98.38 138	95.73 342	97.32 111	99.97 37	97.40 303	95.34 112	98.60 135	99.54 127	87.70 224	98.56 251	97.94 151	99.47 118	99.25 214
jason: jason.
mmtdpeth		88.52 371	87.75 373	90.85 391	95.71 345	83.47 419	98.94 320	94.85 433	88.78 346	97.19 191	89.58 437	63.29 427	98.97 210	98.54 114	62.86 454	90.10 436

HQP_MVS		94.49 245	94.36 229	94.87 297	95.71 345	91.74 313	99.84 142	97.87 248	96.38 82	93.01 277	98.59 239	80.47 324	98.37 273	97.79 161	89.55 305	94.52 317

plane_prior7					95.71 345	91.59 325

ITE_SJBPF				92.38 374	95.69 348	85.14 405		95.71 417	92.81 218	89.33 333	98.11 271	70.23 400	98.42 261	85.91 374	88.16 326	93.59 390

fmvsm_s_conf0.1_n_a		97.09 133	96.90 126	97.63 192	95.65 349	94.21 249	99.83 149	98.50 131	96.27 87	99.65 51	99.64 112	84.72 276	99.93 97	99.04 79	98.84 152	98.74 255

ACMH	89.72 17	90.64 338	89.63 341	93.66 350	95.64 350	88.64 378	98.55 357	97.45 296	89.03 335	81.62 413	97.61 287	69.75 401	98.41 263	89.37 332	87.62 335	93.92 373
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 156	96.49 147	97.37 213	95.63 351	95.96 176	99.74 178	98.88 54	92.94 212	91.61 293	98.97 189	97.72 6	98.62 248	94.83 231	98.08 182	97.53 296

FMVSNet1		88.50 372	86.64 379	94.08 331	95.62 352	91.97 304	98.43 365	96.95 371	83.00 415	86.08 388	94.72 393	59.09 439	96.11 396	81.82 403	84.07 361	94.17 345

LuminaMVS		96.63 160	96.21 160	97.87 171	95.58 353	96.82 135	99.12 289	97.67 268	94.47 138	97.88 168	98.31 264	87.50 229	98.71 236	98.07 144	97.29 203	98.10 277

LPG-MVS_test		92.96 287	92.71 282	93.71 346	95.43 354	88.67 376	99.75 175	97.62 276	92.81 218	90.05 309	98.49 250	75.24 372	98.40 265	95.84 210	89.12 309	94.07 359

LGP-MVS_train				93.71 346	95.43 354	88.67 376		97.62 276	92.81 218	90.05 309	98.49 250	75.24 372	98.40 265	95.84 210	89.12 309	94.07 359

tpm		93.70 271	93.41 263	94.58 310	95.36 356	87.41 390	97.01 408	96.90 378	90.85 297	96.72 206	94.14 408	90.40 189	96.84 364	90.75 313	88.54 321	99.51 168

D2MVS		92.76 292	92.59 288	93.27 358	95.13 357	89.54 364	99.69 200	99.38 22	92.26 252	87.59 365	94.61 399	85.05 270	97.79 310	91.59 296	88.01 327	92.47 413

VPA-MVSNet		92.70 294	91.55 307	96.16 256	95.09 358	96.20 167	98.88 328	99.00 39	91.02 294	91.82 292	95.29 375	76.05 367	97.96 303	95.62 215	81.19 381	94.30 334

LTVRE_ROB	88.28 18	90.29 348	89.05 355	94.02 334	95.08 359	90.15 353	97.19 403	97.43 298	84.91 401	83.99 402	97.06 304	74.00 383	98.28 282	84.08 385	87.71 331	93.62 389
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		87.87 379	86.51 380	91.94 380	95.05 360	85.57 403	97.65 395	94.08 443	84.40 405	81.82 412	96.85 313	62.14 432	98.33 276	80.25 412	86.37 342	91.91 420

test0.0.03 1		93.86 261	93.61 251	94.64 306	95.02 361	92.18 302	99.93 90	98.58 99	94.07 163	87.96 360	98.50 249	93.90 103	94.96 419	81.33 404	93.17 291	96.78 302

UniMVSNet (Re)		93.07 286	92.13 294	95.88 264	94.84 362	96.24 166	99.88 120	98.98 41	92.49 241	89.25 334	95.40 365	87.09 237	97.14 340	93.13 274	78.16 405	94.26 336

USDC		90.00 355	88.96 356	93.10 364	94.81 363	88.16 384	98.71 346	95.54 422	93.66 184	83.75 404	97.20 298	65.58 418	98.31 278	83.96 388	87.49 337	92.85 407

VPNet		91.81 312	90.46 323	95.85 266	94.74 364	95.54 195	98.98 312	98.59 97	92.14 254	90.77 305	97.44 291	68.73 405	97.54 320	94.89 230	77.89 407	94.46 320

FIs		94.10 256	93.43 260	96.11 257	94.70 365	96.82 135	99.58 224	98.93 48	92.54 237	89.34 332	97.31 295	87.62 226	97.10 344	94.22 248	86.58 340	94.40 326

UniMVSNet_ETH3D		90.06 354	88.58 363	94.49 316	94.67 366	88.09 385	97.81 392	97.57 284	83.91 408	88.44 352	97.41 292	57.44 441	97.62 317	91.41 298	88.59 320	97.77 286

UniMVSNet_NR-MVSNet		92.95 288	92.11 295	95.49 274	94.61 367	95.28 212	99.83 149	99.08 36	91.49 274	89.21 337	96.86 312	87.14 236	96.73 370	93.20 270	77.52 410	94.46 320

test_fmvs2		89.47 364	89.70 340	88.77 415	94.54 368	75.74 444	99.83 149	94.70 439	94.71 130	91.08 298	96.82 317	54.46 444	97.78 312	92.87 277	88.27 324	92.80 408

MonoMVSNet		94.82 226	94.43 227	95.98 260	94.54 368	90.73 338	99.03 307	97.06 358	93.16 202	93.15 276	95.47 362	88.29 218	97.57 318	97.85 156	91.33 299	99.62 137

WR-MVS		92.31 304	91.25 312	95.48 277	94.45 370	95.29 211	99.60 221	98.68 78	90.10 320	88.07 359	96.89 310	80.68 319	96.80 368	93.14 273	79.67 398	94.36 328

nrg030		93.51 275	92.53 289	96.45 247	94.36 371	97.20 117	99.81 154	97.16 337	91.60 271	89.86 316	97.46 290	86.37 249	97.68 314	95.88 209	80.31 394	94.46 320

tfpnnormal		89.29 367	87.61 374	94.34 324	94.35 372	94.13 251	98.95 319	98.94 44	83.94 406	84.47 399	95.51 359	74.84 377	97.39 323	77.05 428	80.41 392	91.48 423

FC-MVSNet-test		93.81 265	93.15 273	95.80 269	94.30 373	96.20 167	99.42 254	98.89 52	92.33 247	89.03 342	97.27 297	87.39 232	96.83 366	93.20 270	86.48 341	94.36 328

SSC-MVS3.2		89.59 362	88.66 362	92.38 374	94.29 374	86.12 399	99.49 243	97.66 271	90.28 319	88.63 349	95.18 379	64.46 423	96.88 362	85.30 378	82.66 369	94.14 354

MS-PatchMatch		90.65 337	90.30 328	91.71 385	94.22 375	85.50 404	98.24 375	97.70 265	88.67 349	86.42 383	96.37 328	67.82 410	98.03 299	83.62 390	99.62 95	91.60 421

WR-MVS_H		91.30 322	90.35 326	94.15 328	94.17 376	92.62 293	99.17 287	98.94 44	88.87 344	86.48 382	94.46 404	84.36 282	96.61 375	88.19 346	78.51 403	93.21 399

DU-MVS		92.46 301	91.45 310	95.49 274	94.05 377	95.28 212	99.81 154	98.74 71	92.25 253	89.21 337	96.64 320	81.66 305	96.73 370	93.20 270	77.52 410	94.46 320

NR-MVSNet		91.56 320	90.22 330	95.60 272	94.05 377	95.76 183	98.25 374	98.70 74	91.16 288	80.78 419	96.64 320	83.23 292	96.57 376	91.41 298	77.73 409	94.46 320

CP-MVSNet		91.23 326	90.22 330	94.26 326	93.96 379	92.39 298	99.09 293	98.57 101	88.95 341	86.42 383	96.57 323	79.19 335	96.37 385	90.29 322	78.95 400	94.02 362

XXY-MVS		91.82 311	90.46 323	95.88 264	93.91 380	95.40 202	98.87 331	97.69 267	88.63 351	87.87 361	97.08 302	74.38 381	97.89 307	91.66 295	84.07 361	94.35 331

PS-CasMVS		90.63 339	89.51 346	93.99 337	93.83 381	91.70 317	98.98 312	98.52 122	88.48 353	86.15 387	96.53 325	75.46 370	96.31 389	88.83 337	78.86 402	93.95 370

test_0402		85.58 386	83.94 391	90.50 397	93.81 382	85.04 406	98.55 357	95.20 430	76.01 438	79.72 425	95.13 380	64.15 425	96.26 391	66.04 451	86.88 339	90.21 434

XVG-ACMP-BASELINE		91.22 327	90.75 318	92.63 373	93.73 383	85.61 402	98.52 361	97.44 297	92.77 222	89.90 315	96.85 313	66.64 415	98.39 267	92.29 282	88.61 318	93.89 375

TranMVSNet+NR-MVSNet		91.68 319	90.61 322	94.87 297	93.69 384	93.98 256	99.69 200	98.65 82	91.03 293	88.44 352	96.83 316	80.05 328	96.18 394	90.26 323	76.89 418	94.45 325

TransMVSNet (Re)		87.25 380	85.28 387	93.16 361	93.56 385	91.03 330	98.54 359	94.05 445	83.69 410	81.09 417	96.16 334	75.32 371	96.40 384	76.69 429	68.41 442	92.06 417

v10		90.25 349	88.82 358	94.57 311	93.53 386	93.43 271	99.08 295	96.87 381	85.00 398	87.34 372	94.51 400	80.93 315	97.02 354	82.85 395	79.23 399	93.26 397

testgi		89.01 369	88.04 370	91.90 381	93.49 387	84.89 408	99.73 185	95.66 419	93.89 177	85.14 394	98.17 269	59.68 438	94.66 425	77.73 424	88.88 312	96.16 311

v8		90.54 341	89.17 351	94.66 305	93.43 388	93.40 274	99.20 284	96.94 375	85.76 389	87.56 366	94.51 400	81.96 301	97.19 337	84.94 381	78.25 404	93.38 395

V42		91.28 324	90.12 335	94.74 302	93.42 389	93.46 270	99.68 203	97.02 362	87.36 368	89.85 318	95.05 383	81.31 311	97.34 326	87.34 357	80.07 396	93.40 393

pm-mvs1		89.36 366	87.81 372	94.01 335	93.40 390	91.93 307	98.62 355	96.48 401	86.25 384	83.86 403	96.14 336	73.68 384	97.04 350	86.16 371	75.73 423	93.04 403

v1144		91.09 328	89.83 337	94.87 297	93.25 391	93.69 264	99.62 214	96.98 367	86.83 378	89.64 324	94.99 388	80.94 314	97.05 347	85.08 380	81.16 382	93.87 377

v1192		90.62 340	89.25 350	94.72 304	93.13 392	93.07 278	99.50 241	97.02 362	86.33 383	89.56 328	95.01 385	79.22 334	97.09 346	82.34 399	81.16 382	94.01 364

v2v482		91.30 322	90.07 336	95.01 292	93.13 392	93.79 259	99.77 165	97.02 362	88.05 359	89.25 334	95.37 369	80.73 318	97.15 339	87.28 358	80.04 397	94.09 358

OPM-MVS		93.21 280	92.80 279	94.44 319	93.12 394	90.85 337	99.77 165	97.61 279	96.19 90	91.56 294	98.65 231	75.16 376	98.47 255	93.78 261	89.39 308	93.99 367
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 335	89.52 345	94.59 309	93.11 395	92.77 284	99.56 230	96.99 365	86.38 382	89.82 319	94.95 390	80.50 323	97.10 344	83.98 387	80.41 392	93.90 374

PEN-MVS		90.19 351	89.06 354	93.57 351	93.06 396	90.90 335	99.06 300	98.47 134	88.11 358	85.91 389	96.30 330	76.67 357	95.94 404	87.07 361	76.91 417	93.89 375

v1240		90.20 350	88.79 359	94.44 319	93.05 397	92.27 300	99.38 261	96.92 377	85.89 387	89.36 331	94.87 392	77.89 346	97.03 352	80.66 408	81.08 385	94.01 364

v148		90.70 336	89.63 341	93.92 339	92.97 398	90.97 331	99.75 175	96.89 379	87.51 365	88.27 357	95.01 385	81.67 304	97.04 350	87.40 356	77.17 415	93.75 383

v1921920		90.46 342	89.12 352	94.50 315	92.96 399	92.46 296	99.49 243	96.98 367	86.10 385	89.61 326	95.30 372	78.55 343	97.03 352	82.17 400	80.89 390	94.01 364

MVStest1		85.03 392	82.76 401	91.83 382	92.95 400	89.16 369	98.57 356	94.82 434	71.68 449	68.54 452	95.11 382	83.17 293	95.66 408	74.69 434	65.32 449	90.65 430

tt0320-xc		82.94 406	80.35 413	90.72 395	92.90 401	83.54 417	96.85 413	94.73 437	63.12 454	79.85 424	93.77 412	49.43 452	95.46 411	80.98 407	71.54 432	93.16 400

Baseline_NR-MVSNet		90.33 346	89.51 346	92.81 370	92.84 402	89.95 358	99.77 165	93.94 446	84.69 403	89.04 341	95.66 351	81.66 305	96.52 378	90.99 306	76.98 416	91.97 419

test_method		80.79 412	79.70 415	84.08 427	92.83 403	67.06 453	99.51 239	95.42 424	54.34 459	81.07 418	93.53 414	44.48 455	92.22 445	78.90 419	77.23 414	92.94 405

pmmvs4		92.10 308	91.07 316	95.18 288	92.82 404	94.96 223	99.48 246	96.83 383	87.45 367	88.66 348	96.56 324	83.78 287	96.83 366	89.29 333	84.77 355	93.75 383

LF4IMVS		89.25 368	88.85 357	90.45 399	92.81 405	81.19 434	98.12 382	94.79 435	91.44 278	86.29 385	97.11 300	65.30 421	98.11 293	88.53 342	85.25 349	92.07 416

tt0320		83.56 405	81.15 408	90.77 393	92.77 406	83.58 416	96.83 414	95.52 423	63.26 453	81.36 415	92.54 422	53.26 446	95.77 406	80.45 409	74.38 426	92.96 404

DTE-MVSNet		89.40 365	88.24 368	92.88 368	92.66 407	89.95 358	99.10 292	98.22 207	87.29 369	85.12 395	96.22 332	76.27 364	95.30 416	83.56 391	75.74 422	93.41 392

EU-MVSNet		90.14 353	90.34 327	89.54 407	92.55 408	81.06 435	98.69 349	98.04 231	91.41 282	86.59 379	96.84 315	80.83 317	93.31 438	86.20 370	81.91 376	94.26 336

APD_test1		81.15 410	80.92 410	81.86 431	92.45 409	59.76 460	96.04 428	93.61 449	73.29 447	77.06 434	96.64 320	44.28 456	96.16 395	72.35 438	82.52 370	89.67 441

sc_t1		85.01 393	82.46 403	92.67 372	92.44 410	83.09 420	97.39 399	95.72 416	65.06 452	85.64 392	96.16 334	49.50 451	97.34 326	84.86 382	75.39 424	97.57 294

our_test_3		90.39 343	89.48 348	93.12 362	92.40 411	89.57 363	99.33 268	96.35 404	87.84 363	85.30 393	94.99 388	84.14 285	96.09 399	80.38 410	84.56 356	93.71 388

ppachtmachnet_test		89.58 363	88.35 366	93.25 360	92.40 411	90.44 347	99.33 268	96.73 390	85.49 394	85.90 390	95.77 345	81.09 313	96.00 403	76.00 432	82.49 371	93.30 396

v7n		89.65 361	88.29 367	93.72 345	92.22 413	90.56 344	99.07 299	97.10 350	85.42 396	86.73 376	94.72 393	80.06 327	97.13 341	81.14 405	78.12 406	93.49 391

dmvs_testset		83.79 402	86.07 383	76.94 435	92.14 414	48.60 470	96.75 415	90.27 460	89.48 329	78.65 428	98.55 246	79.25 333	86.65 458	66.85 448	82.69 368	95.57 313

PS-MVSNAJss		93.64 272	93.31 269	94.61 307	92.11 415	92.19 301	99.12 289	97.38 304	92.51 240	88.45 351	96.99 308	91.20 170	97.29 334	94.36 242	87.71 331	94.36 328

pmmvs5		90.17 352	89.09 353	93.40 354	92.10 416	89.77 361	99.74 178	95.58 421	85.88 388	87.24 373	95.74 346	73.41 387	96.48 380	88.54 341	83.56 365	93.95 370

N_pmnet		80.06 415	80.78 411	77.89 434	91.94 417	45.28 472	98.80 340	56.82 474	78.10 435	80.08 422	93.33 415	77.03 351	95.76 407	68.14 446	82.81 367	92.64 409

test_djsdf		92.83 291	92.29 293	94.47 317	91.90 418	92.46 296	99.55 233	97.27 322	91.17 286	89.96 312	96.07 340	81.10 312	96.89 360	94.67 237	88.91 311	94.05 361

SixPastTwentyTwo		88.73 370	88.01 371	90.88 389	91.85 419	82.24 426	98.22 379	95.18 431	88.97 339	82.26 409	96.89 310	71.75 392	96.67 373	84.00 386	82.98 366	93.72 387

K. test v3		88.05 376	87.24 377	90.47 398	91.82 420	82.23 427	98.96 318	97.42 300	89.05 334	76.93 436	95.60 353	68.49 406	95.42 412	85.87 375	81.01 388	93.75 383

OurMVSNet-221017-0		89.81 358	89.48 348	90.83 392	91.64 421	81.21 433	98.17 381	95.38 426	91.48 276	85.65 391	97.31 295	72.66 388	97.29 334	88.15 347	84.83 354	93.97 369

mvs_tets		91.81 312	91.08 315	94.00 336	91.63 422	90.58 343	98.67 351	97.43 298	92.43 242	87.37 371	97.05 305	71.76 391	97.32 329	94.75 234	88.68 317	94.11 357

Gipuma		66.95 428	65.00 428	72.79 440	91.52 423	67.96 452	66.16 463	95.15 432	47.89 461	58.54 458	67.99 463	29.74 460	87.54 457	50.20 462	77.83 408	62.87 463
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 173	95.74 183	98.32 140	91.47 424	95.56 194	99.84 142	97.30 317	97.74 28	97.89 167	99.35 146	79.62 330	99.85 123	99.25 69	99.24 135	99.55 154

jajsoiax		91.92 310	91.18 313	94.15 328	91.35 425	90.95 334	99.00 310	97.42 300	92.61 232	87.38 370	97.08 302	72.46 389	97.36 324	94.53 240	88.77 315	94.13 356

MDA-MVSNet-bldmvs		84.09 400	81.52 407	91.81 383	91.32 426	88.00 387	98.67 351	95.92 412	80.22 429	55.60 461	93.32 416	68.29 408	93.60 436	73.76 435	76.61 419	93.82 381

MVP-Stereo		90.93 330	90.45 325	92.37 376	91.25 427	88.76 373	98.05 386	96.17 407	87.27 370	84.04 400	95.30 372	78.46 344	97.27 336	83.78 389	99.70 89	91.09 424
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 388	83.32 396	92.10 378	90.96 428	88.58 379	99.20 284	96.52 399	79.70 431	57.12 460	92.69 421	79.11 336	93.86 432	77.10 427	77.46 412	93.86 378

YYNet1		85.50 389	83.33 395	92.00 379	90.89 429	88.38 383	99.22 283	96.55 398	79.60 432	57.26 459	92.72 420	79.09 338	93.78 434	77.25 426	77.37 413	93.84 379

anonymousdsp		91.79 317	90.92 317	94.41 322	90.76 430	92.93 283	98.93 322	97.17 335	89.08 333	87.46 369	95.30 372	78.43 345	96.92 358	92.38 281	88.73 316	93.39 394

lessismore_v0				90.53 396	90.58 431	80.90 436		95.80 413		77.01 435	95.84 343	66.15 417	96.95 356	83.03 394	75.05 425	93.74 386

EG-PatchMatch MVS		85.35 390	83.81 393	89.99 405	90.39 432	81.89 429	98.21 380	96.09 409	81.78 422	74.73 442	93.72 413	51.56 450	97.12 343	79.16 418	88.61 318	90.96 427

EGC-MVSNET		69.38 421	63.76 431	86.26 424	90.32 433	81.66 432	96.24 424	93.85 447	0.99 471	3.22 472	92.33 427	52.44 447	92.92 441	59.53 458	84.90 353	84.21 452

CMPMVS	61.59 21	84.75 396	85.14 388	83.57 428	90.32 433	62.54 456	96.98 409	97.59 283	74.33 445	69.95 449	96.66 318	64.17 424	98.32 277	87.88 351	88.41 323	89.84 439
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 399	82.92 399	89.21 409	90.03 435	82.60 423	96.89 412	95.62 420	80.59 427	75.77 441	89.17 439	65.04 422	94.79 423	72.12 439	81.02 387	90.23 433

pmmvs6		85.69 385	83.84 392	91.26 388	90.00 436	84.41 411	97.82 391	96.15 408	75.86 439	81.29 416	95.39 367	61.21 435	96.87 363	83.52 392	73.29 428	92.50 412

ttmdpeth		88.23 375	87.06 378	91.75 384	89.91 437	87.35 391	98.92 325	95.73 415	87.92 361	84.02 401	96.31 329	68.23 409	96.84 364	86.33 369	76.12 420	91.06 425

DSMNet-mixed		88.28 374	88.24 368	88.42 417	89.64 438	75.38 446	98.06 385	89.86 461	85.59 393	88.20 358	92.14 428	76.15 366	91.95 446	78.46 421	96.05 236	97.92 280

UnsupCasMVSNet_eth		85.52 387	83.99 389	90.10 403	89.36 439	83.51 418	96.65 416	97.99 234	89.14 332	75.89 440	93.83 410	63.25 428	93.92 430	81.92 402	67.90 445	92.88 406

Anonymous20231206		86.32 383	85.42 386	89.02 411	89.11 440	80.53 439	99.05 304	95.28 427	85.43 395	82.82 407	93.92 409	74.40 380	93.44 437	66.99 447	81.83 377	93.08 402

Anonymous20240521		85.15 391	83.81 393	89.16 410	88.32 441	82.69 422	98.80 340	95.74 414	79.72 430	81.53 414	90.99 431	65.38 420	94.16 428	72.69 437	81.11 384	90.63 431

OpenMVS_ROB	79.82 20	83.77 403	81.68 406	90.03 404	88.30 442	82.82 421	98.46 362	95.22 429	73.92 446	76.00 439	91.29 430	55.00 443	96.94 357	68.40 445	88.51 322	90.34 432

test20.03		84.72 397	83.99 389	86.91 422	88.19 443	80.62 438	98.88 328	95.94 411	88.36 355	78.87 426	94.62 398	68.75 404	89.11 453	66.52 449	75.82 421	91.00 426

KD-MVS_self_test		83.59 404	82.06 404	88.20 418	86.93 444	80.70 437	97.21 402	96.38 402	82.87 416	82.49 408	88.97 440	67.63 411	92.32 444	73.75 436	62.30 456	91.58 422

MIMVSNet1		82.58 407	80.51 412	88.78 413	86.68 445	84.20 412	96.65 416	95.41 425	78.75 433	78.59 429	92.44 423	51.88 449	89.76 452	65.26 452	78.95 400	92.38 415

CL-MVSNet_self_test		84.50 398	83.15 398	88.53 416	86.00 446	81.79 430	98.82 336	97.35 308	85.12 397	83.62 405	90.91 433	76.66 358	91.40 447	69.53 443	60.36 457	92.40 414

UnsupCasMVSNet_bld		79.97 417	77.03 422	88.78 413	85.62 447	81.98 428	93.66 440	97.35 308	75.51 442	70.79 448	83.05 455	48.70 453	94.91 421	78.31 422	60.29 458	89.46 444

mvs5depth		84.87 394	82.90 400	90.77 393	85.59 448	84.84 409	91.10 453	93.29 451	83.14 413	85.07 396	94.33 406	62.17 431	97.32 329	78.83 420	72.59 431	90.14 435

Patchmatch-RL test		86.90 381	85.98 385	89.67 406	84.45 449	75.59 445	89.71 456	92.43 453	86.89 377	77.83 433	90.94 432	94.22 92	93.63 435	87.75 352	69.61 436	99.79 106

pmmvs-eth3d		84.03 401	81.97 405	90.20 402	84.15 450	87.09 393	98.10 384	94.73 437	83.05 414	74.10 445	87.77 446	65.56 419	94.01 429	81.08 406	69.24 438	89.49 443

test_fmvs3		79.99 416	80.17 414	79.45 433	84.02 451	62.83 454	99.05 304	93.49 450	88.29 357	80.06 423	86.65 450	28.09 462	88.00 454	88.63 338	73.27 429	87.54 450

PM-MVS		80.47 413	78.88 417	85.26 425	83.79 452	72.22 448	95.89 431	91.08 458	85.71 392	76.56 438	88.30 442	36.64 458	93.90 431	82.39 398	69.57 437	89.66 442

new-patchmatchnet		81.19 409	79.34 416	86.76 423	82.86 453	80.36 440	97.92 388	95.27 428	82.09 421	72.02 446	86.87 449	62.81 430	90.74 450	71.10 440	63.08 453	89.19 446

FE-MVSNET		81.05 411	78.81 418	87.79 420	81.98 454	83.70 414	98.23 377	91.78 457	81.27 424	74.29 444	87.44 447	60.92 437	90.67 451	64.92 453	68.43 441	89.01 447

mvsany_test3		82.12 408	81.14 409	85.06 426	81.87 455	70.41 450	97.09 406	92.14 454	91.27 285	77.84 432	88.73 441	39.31 457	95.49 409	90.75 313	71.24 433	89.29 445

WB-MVS		76.28 419	77.28 421	73.29 439	81.18 456	54.68 464	97.87 390	94.19 442	81.30 423	69.43 450	90.70 434	77.02 352	82.06 462	35.71 467	68.11 444	83.13 453

test_f		78.40 418	77.59 420	80.81 432	80.82 457	62.48 457	96.96 410	93.08 452	83.44 411	74.57 443	84.57 454	27.95 463	92.63 442	84.15 384	72.79 430	87.32 451

SSC-MVS		75.42 420	76.40 423	72.49 443	80.68 458	53.62 465	97.42 397	94.06 444	80.42 428	68.75 451	90.14 436	76.54 360	81.66 463	33.25 468	66.34 448	82.19 454

pmmvs3		80.27 414	77.77 419	87.76 421	80.32 459	82.43 425	98.23 377	91.97 455	72.74 448	78.75 427	87.97 445	57.30 442	90.99 449	70.31 441	62.37 455	89.87 438

testf1		68.38 424	66.92 425	72.78 441	78.80 460	50.36 467	90.95 454	87.35 466	55.47 457	58.95 456	88.14 443	20.64 467	87.60 455	57.28 459	64.69 450	80.39 456

APD_test2		68.38 424	66.92 425	72.78 441	78.80 460	50.36 467	90.95 454	87.35 466	55.47 457	58.95 456	88.14 443	20.64 467	87.60 455	57.28 459	64.69 450	80.39 456

ambc				83.23 429	77.17 462	62.61 455	87.38 458	94.55 441		76.72 437	86.65 450	30.16 459	96.36 386	84.85 383	69.86 435	90.73 429

test_vis3_rt		68.82 422	66.69 427	75.21 438	76.24 463	60.41 459	96.44 419	68.71 473	75.13 443	50.54 464	69.52 462	16.42 472	96.32 388	80.27 411	66.92 447	68.89 460

TDRefinement		84.76 395	82.56 402	91.38 387	74.58 464	84.80 410	97.36 400	94.56 440	84.73 402	80.21 421	96.12 339	63.56 426	98.39 267	87.92 350	63.97 452	90.95 428

E-PMN		52.30 432	52.18 434	52.67 449	71.51 465	45.40 471	93.62 441	76.60 471	36.01 465	43.50 466	64.13 465	27.11 464	67.31 468	31.06 469	26.06 464	45.30 467

EMVS		51.44 434	51.22 436	52.11 450	70.71 466	44.97 473	94.04 437	75.66 472	35.34 467	42.40 467	61.56 468	28.93 461	65.87 469	27.64 470	24.73 465	45.49 466

PMMVS2		67.15 427	64.15 430	76.14 437	70.56 467	62.07 458	93.89 438	87.52 465	58.09 456	60.02 455	78.32 457	22.38 466	84.54 460	59.56 457	47.03 462	81.80 455

FPMVS		68.72 423	68.72 424	68.71 445	65.95 468	44.27 474	95.97 430	94.74 436	51.13 460	53.26 462	90.50 435	25.11 465	83.00 461	60.80 456	80.97 389	78.87 458

wuyk23d		20.37 438	20.84 441	18.99 453	65.34 469	27.73 476	50.43 464	7.67 477	9.50 470	8.01 471	6.34 471	6.13 475	26.24 470	23.40 471	10.69 469	2.99 468

LCM-MVSNet		67.77 426	64.73 429	76.87 436	62.95 470	56.25 463	89.37 457	93.74 448	44.53 462	61.99 454	80.74 456	20.42 469	86.53 459	69.37 444	59.50 459	87.84 448

MVE	53.74 22	51.54 433	47.86 437	62.60 447	59.56 471	50.93 466	79.41 461	77.69 470	35.69 466	36.27 468	61.76 467	5.79 476	69.63 466	37.97 466	36.61 463	67.24 461
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 430	52.24 433	67.66 446	49.27 472	56.82 462	83.94 459	82.02 469	70.47 450	33.28 469	64.54 464	17.23 471	69.16 467	45.59 464	23.85 466	77.02 459

tmp_tt		65.23 429	62.94 432	72.13 444	44.90 473	50.03 469	81.05 460	89.42 464	38.45 463	48.51 465	99.90 18	54.09 445	78.70 465	91.84 294	18.26 467	87.64 449

PMVS	49.05 23	53.75 431	51.34 435	60.97 448	40.80 474	34.68 475	74.82 462	89.62 463	37.55 464	28.67 470	72.12 459	7.09 474	81.63 464	43.17 465	68.21 443	66.59 462
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 436	39.14 439	33.31 451	19.94 475	24.83 477	98.36 370	9.75 476	15.53 469	51.31 463	87.14 448	19.62 470	17.74 471	47.10 463	3.47 470	57.36 464

testmvs		40.60 435	44.45 438	29.05 452	19.49 476	14.11 478	99.68 203	18.47 475	20.74 468	64.59 453	98.48 253	10.95 473	17.09 472	56.66 461	11.01 468	55.94 465

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.02 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

eth-test2					0.00 477

eth-test					0.00 477

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

cdsmvs_eth3d_5k		23.43 437	31.24 440	0.00 454	0.00 477	0.00 479	0.00 465	98.09 225	0.00 472	0.00 473	99.67 107	83.37 290	0.00 473	0.00 472	0.00 471	0.00 469

pcd_1.5k_mvsjas		7.60 440	10.13 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	91.20 170	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

ab-mvs-re		8.28 439	11.04 442	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	99.40 140	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 473	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						90.97 331								86.10 373

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							95.78 432				59.23 469	93.20 125	97.74 313	91.06 304

test_post											63.35 466	94.43 79	98.13 292

patchmatchnet-post											91.70 429	95.12 56	97.95 304

MTMP							99.87 123	96.49 400

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							99.46 251		94.21 158	99.85 16			99.95 80	96.96 186

新几何2							99.40 255

无先验							99.49 243	98.71 73	93.46 190				100.00 1	94.36 242		99.99 23

原ACMM2							99.90 107

testdata2													99.99 36	90.54 317

segment_acmp												96.68 29

testdata1							99.28 277		96.35 86

plane_prior5								97.87 248					98.37 273	97.79 161	89.55 305	94.52 317

plane_prior4											98.59 239

plane_prior3						91.64 319			96.63 71	93.01 277

plane_prior2							99.84 142		96.38 82

plane_prior						91.74 313	99.86 134		96.76 66						89.59 304

n2								0.00 478

nn								0.00 478

door-mid								89.69 462

test11								98.44 142

door								90.31 459

HQP5-MVS						91.85 309

BP-MVS														97.92 152

HQP4-MVS										93.37 272			98.39 267			94.53 315

HQP3-MVS								97.89 246							89.60 302

HQP2-MVS												80.65 320

MDTV_nov1_ep13_2view						96.26 161	96.11 426		91.89 262	98.06 160		94.40 81		94.30 245		99.67 124

ACMMP++_ref															87.04 338

ACMMP++															88.23 325

Test By Simon												92.82 136

High-resolution multi-view benchmark