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	99.09 292	98.84 62	93.32 195	96.74 204	99.72 88	86.04 253	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	89.90 324	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	92.06 257	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		93.83 261	92.06 296	99.15 65	99.94 13	97.50 104	99.94 83	98.42 162	96.22 88	99.41 82	41.37 469	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	97.00 181	99.98 32	99.99 23

MG-MVS		98.91 19	98.65 24	99.68 16	99.94 13	99.07 24	99.64 210	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	92.34 245	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	99.75 174	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	99.18 285	99.45 18	94.84 125	96.41 217	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	99.24 280	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	97.27 219	99.89 45	91.92 307	99.90 107	99.07 37	88.67 348	95.26 248	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	93.22 198	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	93.19 199	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	99.61 217	97.78 258	96.52 74	98.61 132	99.31 150	92.73 138	99.67 161	96.77 191	99.48 115	99.06 231

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	90.78 304	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	99.39 258	98.28 198	95.76 99	97.18 191	99.88 24	92.74 137	100.00 1	98.67 106	99.88 73	99.99 23

LS3D		95.84 194	95.11 206	98.02 160	99.85 56	95.10 221	98.74 342	98.50 131	87.22 370	93.66 269	99.86 29	87.45 231	99.95 80	90.94 307	99.81 83	99.02 235

HPM-MVS		97.96 75	97.72 85	98.68 104	99.84 58	96.39 157	99.90 107	98.17 213	92.61 231	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	99.40 254	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	97.20 189	99.27 155	95.44 51	99.97 59	97.41 170	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	93.95 171	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	99.36 264	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	99.76 170	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	99.76 170	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	91.75 268	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	97.41 209	99.76 68	93.36 275	99.65 206	97.95 239	96.03 93	97.41 183	99.70 94	89.61 199	99.51 171	96.73 193	98.25 173	99.38 185

新几何1				99.42 38	99.75 71	98.27 66		98.63 91	92.69 226	99.55 67	99.82 49	94.40 81	100.00 1	91.21 299	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	93.35 193	96.45 214	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	95.11 220	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	99.73 184	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	96.87 231	99.71 78	91.74 312	99.85 137	97.95 239	93.11 205	95.72 237	99.16 171	92.35 152	99.94 88	95.32 216	99.35 130	98.92 243

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	95.59 272	99.67 83	86.91 395	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	93.92 338	99.63 85	81.76 430	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		96.05 185	95.82 180	96.72 237	99.59 87	96.99 129	99.95 66	99.10 34	94.06 165	98.27 151	95.80 343	89.00 211	99.95 80	99.12 73	87.53 335	93.24 397

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	99.08 174	89.00 211	99.95 80	99.12 73	99.25 134	99.57 152

PatchMatch-RL		96.04 186	95.40 193	97.95 162	99.59 87	95.22 216	99.52 236	99.07 37	93.96 170	96.49 213	98.35 258	82.28 296	99.82 135	90.15 323	99.22 137	98.81 250

dcpmvs_2		97.42 116	98.09 60	95.42 279	99.58 91	87.24 391	99.23 281	96.95 370	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	99.34 266	98.55 113	91.86 263	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	96.55 211	99.69 98	92.28 154	99.98 47	97.13 177	99.44 122	99.93 82

API-MVS		97.86 83	97.66 89	98.47 129	99.52 94	95.41 201	99.47 246	98.87 55	91.68 269	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	84.48 280	99.95 80	94.92 226	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	88.48 352	99.06 106	99.66 109	90.30 191	99.64 166	96.32 201	99.97 42	99.96 70

cl22		93.77 266	93.25 270	95.33 283	99.49 97	94.43 237	99.61 217	98.09 225	90.38 312	89.16 339	95.61 351	90.56 186	97.34 325	91.93 290	84.45 356	94.21 342

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	94.97 224	99.94 55	100.00 1

MAR-MVS		97.43 112	97.19 115	98.15 151	99.47 98	94.79 230	99.05 303	98.76 69	92.65 229	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		94.54 238	93.42 260	97.91 168	99.46 100	94.04 252	98.93 321	97.48 295	81.15 424	90.04 310	99.55 125	87.02 239	99.95 80	88.97 335	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	98.46 361	99.42 21	97.03 55	99.02 108	99.09 173	99.35 2	98.21 287	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	93.81 257	99.94 55	99.98 52

TAPA-MVS	92.12 8	94.42 246	93.60 252	96.90 230	99.33 104	91.78 311	99.78 161	98.00 233	89.89 325	94.52 254	99.47 131	91.97 161	99.18 196	69.90 441	99.52 108	99.73 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 210	95.07 208	96.32 252	99.32 106	96.60 147	99.76 170	98.85 59	96.65 70	87.83 361	96.05 340	99.52 1	98.11 292	96.58 196	81.07 385	94.25 337

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	97.15 337	95.53 107	99.62 58	99.79 58	92.08 159	98.38 270	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	98.59 260

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	99.64 210	99.27 27	91.43 278	97.88 168	98.99 184	95.84 42	99.84 131	98.82 96	95.32 262	99.79 106

DCV-MVSNet		97.83 87	97.37 106	99.21 54	99.18 112	97.98 81	99.64 210	99.27 27	91.43 278	97.88 168	98.99 184	95.84 42	99.84 131	98.82 96	95.32 262	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	99.66 205	98.06 228	96.37 85	94.37 260	99.49 130	83.29 290	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	97.43 207	99.10 118	94.42 238	99.99 5	97.10 349	95.07 116	99.68 48	99.75 75	92.95 131	98.34 274	98.38 123	99.14 139	99.54 158

Anonymous202405211		93.10 284	91.99 297	96.40 248	99.10 118	89.65 361	98.88 327	97.93 241	83.71 408	94.00 266	98.75 220	68.79 402	99.88 117	95.08 221	91.71 295	99.68 122

fmvsm_s_conf0.5_n		97.80 92	97.85 80	97.67 186	99.06 121	94.41 239	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	98.62 259

HyFIR lowres test		96.66 159	96.43 151	97.36 214	99.05 122	93.91 257	99.70 196	99.80 3	90.54 308	96.26 220	98.08 271	92.15 157	98.23 286	96.84 190	95.46 257	99.93 82

LFMVS		94.75 232	93.56 255	98.30 141	99.03 123	95.70 187	98.74 342	97.98 236	87.81 363	98.47 140	99.39 142	67.43 411	99.53 168	98.01 146	95.20 265	99.67 124

fmvsm_s_conf0.5_n_4		97.75 97	97.86 79	97.42 208	99.01 124	94.69 232	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	97.64 288

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	81.83 302	99.94 88	99.78 31	98.79 155	97.51 296

AllTest		92.48 299	91.64 302	95.00 292	99.01 124	88.43 379	98.94 319	96.82 384	86.50 379	88.71 344	98.47 253	74.73 377	99.88 117	85.39 375	96.18 232	96.71 302

TestCases				95.00 292	99.01 124	88.43 379		96.82 384	86.50 379	88.71 344	98.47 253	74.73 377	99.88 117	85.39 375	96.18 232	96.71 302

COLMAP_ROB	90.47 14	92.18 306	91.49 308	94.25 326	99.00 128	88.04 385	98.42 367	96.70 391	82.30 419	88.43 353	99.01 181	76.97 352	99.85 123	86.11 371	96.50 224	94.86 313
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	84.15 283	99.97 59	99.76 36	99.50 113	98.39 267

test_fmvs1		95.35 211	95.68 186	94.36 322	98.99 129	84.98 406	99.96 47	96.65 393	97.60 32	99.73 43	98.96 190	71.58 392	99.93 97	98.31 128	99.37 128	98.17 272

HY-MVS	92.50 7	97.79 94	97.17 117	99.63 17	98.98 131	99.32 9	97.49 395	99.52 14	95.69 102	98.32 149	97.41 291	93.32 118	99.77 143	98.08 143	95.75 247	99.81 103

VNet		97.21 126	96.57 145	99.13 71	98.97 132	97.82 89	99.03 306	99.21 32	94.31 152	99.18 99	98.88 202	86.26 251	99.89 111	98.93 87	94.32 275	99.69 121

thres200		96.96 140	96.21 159	99.22 53	98.97 132	98.84 36	99.85 137	99.71 7	93.17 200	96.26 220	98.88 202	89.87 196	99.51 171	94.26 245	94.91 267	99.31 202

tfpn200view9		96.79 148	95.99 166	99.19 56	98.94 134	98.82 37	99.78 161	99.71 7	92.86 214	96.02 227	98.87 209	89.33 203	99.50 173	93.84 254	94.57 271	99.27 211

thres400		96.78 150	95.99 166	99.16 63	98.94 134	98.82 37	99.78 161	99.71 7	92.86 214	96.02 227	98.87 209	89.33 203	99.50 173	93.84 254	94.57 271	99.16 219

sasdasda		97.09 133	96.32 154	99.39 41	98.93 136	98.95 27	99.72 188	97.35 308	94.45 140	97.88 168	99.42 135	86.71 242	99.52 169	98.48 118	93.97 281	99.72 116

Anonymous20231211		89.86 356	88.44 364	94.13 329	98.93 136	90.68 339	98.54 358	98.26 201	76.28 436	86.73 375	95.54 355	70.60 398	97.56 318	90.82 310	80.27 394	94.15 350

canonicalmvs		97.09 133	96.32 154	99.39 41	98.93 136	98.95 27	99.72 188	97.35 308	94.45 140	97.88 168	99.42 135	86.71 242	99.52 169	98.48 118	93.97 281	99.72 116

SDMVSNet		94.80 227	93.96 242	97.33 217	98.92 139	95.42 200	99.59 221	98.99 40	92.41 242	92.55 284	97.85 282	75.81 367	98.93 214	97.90 154	91.62 296	97.64 288

sd_testset		93.55 273	92.83 277	95.74 270	98.92 139	90.89 335	98.24 374	98.85 59	92.41 242	92.55 284	97.85 282	71.07 397	98.68 240	93.93 251	91.62 296	97.64 288

EPNet_dtu		95.71 199	95.39 194	96.66 239	98.92 139	93.41 271	99.57 226	98.90 50	96.19 90	97.52 178	98.56 243	92.65 140	97.36 323	77.89 422	98.33 168	99.20 217
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	95.58 256	99.78 109

CHOSEN 1792x2688		96.81 147	96.53 146	97.64 188	98.91 143	93.07 277	99.65 206	99.80 3	95.64 103	95.39 244	98.86 211	84.35 282	99.90 106	96.98 183	99.16 138	99.95 77

thres100view900		96.74 154	95.92 176	99.18 57	98.90 144	98.77 42	99.74 177	99.71 7	92.59 233	95.84 231	98.86 211	89.25 205	99.50 173	93.84 254	94.57 271	99.27 211

thres600view7		96.69 157	95.87 179	99.14 67	98.90 144	98.78 41	99.74 177	99.71 7	92.59 233	95.84 231	98.86 211	89.25 205	99.50 173	93.44 267	94.50 274	99.16 219

MSDG		94.37 248	93.36 267	97.40 210	98.88 146	93.95 256	99.37 262	97.38 304	85.75 390	90.80 303	99.17 169	84.11 285	99.88 117	86.35 367	98.43 166	98.36 269

MGCFI-Net		97.00 138	96.22 158	99.34 46	98.86 147	98.80 39	99.67 204	97.30 316	94.31 152	97.77 174	99.41 139	86.36 249	99.50 173	98.38 123	93.90 283	99.72 116

h-mvs33		94.92 224	94.36 228	96.59 241	98.85 148	91.29 327	98.93 321	98.94 44	95.90 95	98.77 121	98.42 256	90.89 181	99.77 143	97.80 158	70.76 433	98.72 256

Anonymous20240529		92.10 307	90.65 319	96.47 243	98.82 149	90.61 341	98.72 344	98.67 81	75.54 440	93.90 268	98.58 241	66.23 415	99.90 106	94.70 235	90.67 299	98.90 246

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	96.38 218	98.49 249	89.05 209	99.88 117	97.10 179	98.34 167	99.43 181

PS-MVSNAJ		98.44 46	98.20 51	99.16 63	98.80 151	98.92 29	99.54 234	98.17 213	97.34 40	99.85 16	99.85 33	91.20 170	99.89 111	99.41 63	99.67 90	98.69 257

CANet_DTU		96.76 151	96.15 161	98.60 112	98.78 152	97.53 101	99.84 142	97.63 273	97.25 48	99.20 96	99.64 112	81.36 308	99.98 47	92.77 278	98.89 149	98.28 271

mvsany_test1		97.82 90	97.90 77	97.55 197	98.77 153	93.04 280	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	94.26 277	99.67 124

SymmetryMVS		97.64 105	97.46 99	98.17 147	98.74 155	95.39 203	99.61 217	99.26 29	96.52 74	98.61 132	99.31 150	92.73 138	99.67 161	96.77 191	95.63 254	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	99.52 236	98.08 227	97.05 54	99.86 13	99.86 29	90.65 183	99.71 153	99.39 65	98.63 159	98.69 257

miper_enhance_ethall		94.36 250	93.98 241	95.49 273	98.68 158	95.24 214	99.73 184	97.29 319	93.28 197	89.86 315	95.97 341	94.37 85	97.05 346	92.20 282	84.45 356	94.19 343

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	91.10 290	98.17 157	98.59 238	93.86 105	98.19 288	95.64 213	95.24 264	99.28 209

test2506		97.53 109	97.19 115	98.58 116	98.66 161	96.90 133	98.81 336	99.77 5	94.93 119	97.95 163	98.96 190	92.51 147	99.20 194	94.93 225	98.15 176	99.64 130

ECVR-MVS		95.66 202	95.05 209	97.51 201	98.66 161	93.71 261	98.85 333	98.45 137	94.93 119	96.86 200	98.96 190	75.22 373	99.20 194	95.34 215	98.15 176	99.64 130

mamv4		95.24 214	96.90 126	90.25 400	98.65 163	72.11 448	98.28 372	97.64 272	89.99 323	95.93 229	98.25 266	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	99.47 246	97.79 256	94.56 135	99.74 41	98.35 258	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	94.26 246	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	99.08 229

MVSMamba_PlusPlus		97.83 87	97.45 101	98.99 84	98.60 166	98.15 67	99.58 223	97.74 263	90.34 315	99.26 95	98.32 261	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	92.53 237	98.84 115	98.84 215	93.36 115	98.30 278	95.84 209	94.30 276	99.05 233

test1111		95.57 205	94.98 212	97.37 212	98.56 167	93.37 274	98.86 331	98.45 137	94.95 118	96.63 206	98.95 195	75.21 374	99.11 200	95.02 222	98.14 178	99.64 130

MVSTER		95.53 206	95.22 201	96.45 246	98.56 167	97.72 92	99.91 101	97.67 268	92.38 244	91.39 294	97.14 298	97.24 18	97.30 330	94.80 231	87.85 328	94.34 332

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	98.73 222	95.50 49	99.69 157	98.53 116	94.63 269	98.99 237

VDD-MVS		93.77 266	92.94 275	96.27 253	98.55 170	90.22 350	98.77 341	97.79 256	90.85 296	96.82 202	99.42 135	61.18 435	99.77 143	98.95 85	94.13 278	98.82 249

tpmvs		94.28 252	93.57 254	96.40 248	98.55 170	91.50 325	95.70 432	98.55 113	87.47 365	92.15 287	94.26 406	91.42 166	98.95 213	88.15 346	95.85 243	98.76 252

UGNet		95.33 212	94.57 224	97.62 192	98.55 170	94.85 226	98.67 350	99.32 26	95.75 100	96.80 203	96.27 330	72.18 389	99.96 71	94.58 238	99.05 145	98.04 277
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	95.18 216	94.10 235	98.43 133	98.55 170	95.99 175	97.91 388	97.31 315	90.35 314	89.48 328	99.22 162	85.19 267	99.89 111	90.40 320	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		95.95 188	96.49 147	94.34 323	98.51 175	89.99 355	99.39 258	98.57 101	93.14 202	97.33 185	98.31 263	93.44 113	94.68 423	93.69 264	95.98 237	98.34 270

UWE-MVS		96.79 148	96.72 138	97.00 225	98.51 175	93.70 262	99.71 191	98.60 95	92.96 210	97.09 192	98.34 260	96.67 31	98.85 219	92.11 288	96.50 224	98.44 265

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	98.75 220	95.20 54	99.48 179	98.93 87	96.40 227	99.29 207

test_vis1_n_1920		95.44 208	95.31 197	95.82 267	98.50 177	88.74 373	99.98 19	97.30 316	97.84 26	99.85 16	99.19 167	66.82 413	99.97 59	98.82 96	99.46 120	98.76 252

BH-w/o		95.71 199	95.38 195	96.68 238	98.49 179	92.28 298	99.84 142	97.50 293	92.12 254	92.06 290	98.79 218	84.69 276	98.67 242	95.29 217	99.66 91	99.09 227

baseline1		95.78 195	94.86 215	98.54 122	98.47 180	98.07 75	99.06 299	97.99 234	92.68 227	94.13 265	98.62 235	93.28 121	98.69 239	93.79 259	85.76 343	98.84 248

fmvsm_s_conf0.5_n_7		97.70 103	97.74 84	97.59 195	98.44 181	95.16 220	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	97.66 287

EPMVS		96.53 165	96.01 165	98.09 155	98.43 182	96.12 173	96.36 419	99.43 20	93.53 187	97.64 176	95.04 383	94.41 80	98.38 270	91.13 301	98.11 179	99.75 112

kuosan		93.17 281	92.60 283	94.86 299	98.40 183	89.54 363	98.44 363	98.53 120	84.46 403	88.49 349	97.92 279	90.57 185	97.05 346	83.10 392	93.49 286	97.99 278

WBMVS		94.52 241	94.03 239	95.98 259	98.38 184	96.68 142	99.92 93	97.63 273	90.75 305	89.64 323	95.25 376	96.77 25	96.90 358	94.35 243	83.57 363	94.35 330

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	98.42 256	96.77 25	99.17 197	98.54 114	96.20 231	99.11 226

sss		97.57 108	97.03 122	99.18 57	98.37 186	98.04 78	99.73 184	99.38 22	93.46 190	98.76 124	99.06 176	91.21 169	99.89 111	96.33 200	97.01 216	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	98.70 225	95.48 50	99.22 190	97.85 156	95.05 266	99.07 230

BH-untuned		95.18 216	94.83 216	96.22 254	98.36 187	91.22 328	99.80 158	97.32 314	90.91 294	91.08 297	98.67 227	83.51 287	98.54 252	94.23 246	99.61 99	98.92 243

testing91		97.16 128	96.90 126	97.97 161	98.35 189	95.67 190	99.91 101	98.42 162	92.91 213	97.33 185	98.72 223	94.81 68	99.21 191	96.98 183	94.63 269	99.03 234

testing99		97.17 127	96.91 125	97.95 162	98.35 189	95.70 187	99.91 101	98.43 150	92.94 211	97.36 184	98.72 223	94.83 67	99.21 191	97.00 181	94.64 268	98.95 239

ET-MVSNet_ETH3D		94.37 248	93.28 269	97.64 188	98.30 191	97.99 80	99.99 5	97.61 279	94.35 149	71.57 446	99.45 134	96.23 35	95.34 413	96.91 188	85.14 350	99.59 144

AUN-MVS		93.28 278	92.60 283	95.34 282	98.29 192	90.09 353	99.31 270	98.56 107	91.80 267	96.35 219	98.00 274	89.38 202	98.28 281	92.46 279	69.22 438	97.64 288

FMVSNet3		92.69 294	91.58 304	95.99 258	98.29 192	97.42 109	99.26 279	97.62 276	89.80 326	89.68 319	95.32 370	81.62 306	96.27 389	87.01 363	85.65 344	94.29 334

PMMVS		96.76 151	96.76 135	96.76 235	98.28 194	92.10 302	99.91 101	97.98 236	94.12 160	99.53 70	99.39 142	86.93 241	98.73 232	96.95 186	97.73 187	99.45 177

hse-mvs2		94.38 247	94.08 238	95.31 284	98.27 195	90.02 354	99.29 275	98.56 107	95.90 95	98.77 121	98.00 274	90.89 181	98.26 285	97.80 158	69.20 439	97.64 288

PVSNet_0	88.03 19	91.80 314	90.27 328	96.38 250	98.27 195	90.46 345	99.94 83	99.61 13	93.99 168	86.26 385	97.39 293	71.13 396	99.89 111	98.77 100	67.05 445	98.79 251

UA-Net		96.54 164	95.96 172	98.27 143	98.23 197	95.71 186	98.00 386	98.45 137	93.72 183	98.41 144	99.27 155	88.71 216	99.66 164	91.19 300	97.69 188	99.44 180

test_cas_vis1_n_1920		96.59 162	96.23 157	97.65 187	98.22 198	94.23 247	99.99 5	97.25 324	97.77 27	99.58 66	99.08 174	77.10 347	99.97 59	97.64 166	99.45 121	98.74 254

FE-MVS		95.70 201	95.01 211	97.79 176	98.21 199	94.57 234	95.03 433	98.69 76	88.90 342	97.50 180	96.19 332	92.60 143	99.49 178	89.99 325	97.94 185	99.31 202

GG-mvs-BLEND				98.54 122	98.21 199	98.01 79	93.87 438	98.52 122		97.92 164	97.92 279	99.02 3	97.94 305	98.17 136	99.58 104	99.67 124

mvs_anonymous		95.65 203	95.03 210	97.53 199	98.19 201	95.74 184	99.33 267	97.49 294	90.87 295	90.47 306	97.10 300	88.23 219	97.16 337	95.92 207	97.66 191	99.68 122

MVS_Test		96.46 167	95.74 182	98.61 111	98.18 202	97.23 116	99.31 270	97.15 337	91.07 291	98.84 115	97.05 304	88.17 220	98.97 210	94.39 240	97.50 193	99.61 141

BH-RMVSNet		95.18 216	94.31 231	97.80 174	98.17 203	95.23 215	99.76 170	97.53 289	92.52 238	94.27 263	99.25 160	76.84 354	98.80 222	90.89 309	99.54 106	99.35 193

dongtai		91.55 320	91.13 313	92.82 368	98.16 204	86.35 396	99.47 246	98.51 125	83.24 411	85.07 395	97.56 287	90.33 190	94.94 419	76.09 430	91.73 294	97.18 299

RPSCF		91.80 314	92.79 279	88.83 411	98.15 205	69.87 450	98.11 382	96.60 395	83.93 406	94.33 261	99.27 155	79.60 330	99.46 182	91.99 289	93.16 291	97.18 299

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	98.82 220	99.02 83	98.54 163	99.46 175

IS-MVSNet		96.29 178	95.90 177	97.45 204	98.13 207	94.80 229	99.08 294	97.61 279	92.02 259	95.54 242	98.96 190	90.64 184	98.08 294	93.73 262	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	80.88 315	99.93 97	99.59 51	98.17 174	97.29 297

ab-mvs		94.69 233	93.42 260	98.51 127	98.07 210	96.26 161	96.49 417	98.68 78	90.31 316	94.54 253	97.00 306	76.30 362	99.71 153	95.98 206	93.38 289	99.56 153

XVG-OURS-SEG-HR		94.79 228	94.70 223	95.08 289	98.05 211	89.19 365	99.08 294	97.54 287	93.66 184	94.87 251	99.58 121	78.78 338	99.79 138	97.31 172	93.40 288	96.25 306

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		94.82 225	94.74 222	95.06 290	98.00 213	89.19 365	99.08 294	97.55 285	94.10 161	94.71 252	99.62 116	80.51 321	99.74 149	96.04 205	93.06 293	96.25 306

mvsmamba		96.94 141	96.73 137	97.55 197	97.99 214	94.37 243	99.62 213	97.70 265	93.13 203	98.42 143	97.92 279	88.02 221	98.75 230	98.78 99	99.01 146	99.52 164

dp		95.05 219	94.43 226	96.91 228	97.99 214	92.73 287	96.29 422	97.98 236	89.70 327	95.93 229	94.67 396	93.83 107	98.45 258	86.91 366	96.53 223	99.54 158

tpmrst		96.27 180	95.98 168	97.13 221	97.96 216	93.15 276	96.34 420	98.17 213	92.07 255	98.71 127	95.12 380	93.91 102	98.73 232	94.91 228	96.62 221	99.50 170

TR-MVS		94.54 238	93.56 255	97.49 203	97.96 216	94.34 244	98.71 345	97.51 292	90.30 317	94.51 255	98.69 226	75.56 368	98.77 226	92.82 277	95.99 236	99.35 193

Vis-MVSNet (Re-imp)		96.32 175	95.98 168	97.35 216	97.93 218	94.82 228	99.47 246	98.15 221	91.83 264	95.09 249	99.11 172	91.37 168	97.47 321	93.47 266	97.43 194	99.74 113

MDTV_nov1_ep13			95.69 184		97.90 219	94.15 249	95.98 428	98.44 142	93.12 204	97.98 162	95.74 345	95.10 57	98.58 248	90.02 324	96.92 218

Fast-Effi-MVS+		95.02 221	94.19 233	97.52 200	97.88 220	94.55 235	99.97 37	97.08 353	88.85 344	94.47 256	97.96 278	84.59 277	98.41 262	89.84 327	97.10 210	99.59 144

ADS-MVSNet2		93.80 265	93.88 245	93.55 351	97.87 221	85.94 400	94.24 434	96.84 381	90.07 320	96.43 215	94.48 401	90.29 192	95.37 412	87.44 353	97.23 203	99.36 189

ADS-MVSNet		94.79 228	94.02 240	97.11 223	97.87 221	93.79 258	94.24 434	98.16 218	90.07 320	96.43 215	94.48 401	90.29 192	98.19 288	87.44 353	97.23 203	99.36 189

Effi-MVS+		96.30 177	95.69 184	98.16 148	97.85 223	96.26 161	97.41 397	97.21 329	90.37 313	98.65 130	98.58 241	86.61 246	98.70 238	97.11 178	97.37 198	99.52 164

PatchmatchNet		95.94 189	95.45 191	97.39 211	97.83 224	94.41 239	96.05 426	98.40 171	92.86 214	97.09 192	95.28 375	94.21 94	98.07 296	89.26 333	98.11 179	99.70 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 236	93.61 250	97.74 184	97.82 225	96.26 161	99.96 47	97.78 258	85.76 388	94.00 266	97.54 288	76.95 353	99.21 191	97.23 175	95.43 259	97.76 286

1112_ss		96.01 187	95.20 202	98.42 135	97.80 226	96.41 154	99.65 206	96.66 392	92.71 224	92.88 280	99.40 140	92.16 156	99.30 186	91.92 291	93.66 284	99.55 154

Test_1112_low_res		95.72 197	94.83 216	98.42 135	97.79 227	96.41 154	99.65 206	96.65 393	92.70 225	92.86 281	96.13 336	92.15 157	99.30 186	91.88 292	93.64 285	99.55 154

Effi-MVS+-dtu		94.53 240	95.30 198	92.22 376	97.77 228	82.54 423	99.59 221	97.06 357	94.92 121	95.29 246	95.37 368	85.81 255	97.89 306	94.80 231	97.07 211	96.23 308

tpm cat1		93.51 274	92.52 289	96.47 243	97.77 228	91.47 326	96.13 424	98.06 228	80.98 425	92.91 279	93.78 410	89.66 197	98.87 217	87.03 362	96.39 228	99.09 227

FA-MVS(test-final)		95.86 192	95.09 207	98.15 151	97.74 230	95.62 192	96.31 421	98.17 213	91.42 280	96.26 220	96.13 336	90.56 186	99.47 181	92.18 283	97.07 211	99.35 193

xiu_mvs_v1_base_debu		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 270	97.86 250	96.43 79	99.62 58	99.69 98	85.56 262	99.68 158	99.05 76	98.31 169	97.83 282

xiu_mvs_v1_base		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 270	97.86 250	96.43 79	99.62 58	99.69 98	85.56 262	99.68 158	99.05 76	98.31 169	97.83 282

xiu_mvs_v1_base_debi		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 270	97.86 250	96.43 79	99.62 58	99.69 98	85.56 262	99.68 158	99.05 76	98.31 169	97.83 282

EPP-MVSNet		96.69 157	96.60 143	96.96 227	97.74 230	93.05 279	99.37 262	98.56 107	88.75 346	95.83 233	99.01 181	96.01 36	98.56 250	96.92 187	97.20 205	99.25 213

gg-mvs-nofinetune		93.51 274	91.86 301	98.47 129	97.72 235	97.96 84	92.62 444	98.51 125	74.70 443	97.33 185	69.59 460	98.91 4	97.79 309	97.77 163	99.56 105	99.67 124

IB-MVS	92.85 6	94.99 222	93.94 243	98.16 148	97.72 235	95.69 189	99.99 5	98.81 64	94.28 155	92.70 282	96.90 308	95.08 58	99.17 197	96.07 204	73.88 426	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	91.83 264	97.45 181	99.04 178	97.50 9	99.10 202	94.75 233	96.37 229	99.16 219

VortexMVS		94.11 254	93.50 257	95.94 261	97.70 238	96.61 146	99.35 265	97.18 332	93.52 189	89.57 326	95.74 345	87.55 228	96.97 354	95.76 212	85.13 351	94.23 339

Syy-MVS		90.00 354	90.63 320	88.11 418	97.68 239	74.66 446	99.71 191	98.35 184	90.79 302	92.10 288	98.67 227	79.10 336	93.09 438	63.35 453	95.95 240	96.59 304

myMVS_eth3d		94.46 245	94.76 221	93.55 351	97.68 239	90.97 330	99.71 191	98.35 184	90.79 302	92.10 288	98.67 227	92.46 150	93.09 438	87.13 359	95.95 240	96.59 304

test_fmvs1_n		94.25 253	94.36 228	93.92 338	97.68 239	83.70 413	99.90 107	96.57 396	97.40 38	99.67 49	98.88 202	61.82 432	99.92 103	98.23 134	99.13 140	98.14 275

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		96.24 181	95.68 186	97.94 165	97.65 243	94.92 225	99.27 278	97.10 349	92.79 220	97.43 182	97.99 276	81.85 301	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	97.19 330	94.67 133	98.95 110	99.28 152	86.43 247	98.76 228	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
viewdifsd2359ckpt13		96.19 182	95.77 181	97.45 204	97.62 245	94.40 241	99.70 196	97.23 328	92.76 222	96.63 206	99.05 177	84.96 271	98.64 245	96.65 194	97.35 199	99.31 202

Vis-MVSNet		95.72 197	95.15 205	97.45 204	97.62 245	94.28 245	99.28 276	98.24 204	94.27 157	96.84 201	98.94 197	79.39 331	98.76 228	93.25 268	98.49 164	99.30 205
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	97.60 247	96.70 139	99.92 93	98.54 117	91.11 289	97.07 194	98.97 188	97.47 12	99.03 205	93.73 262	96.09 234	98.92 243

GDP-MVS		97.88 81	97.59 95	98.75 100	97.59 248	97.81 90	99.95 66	97.37 307	94.44 143	99.08 104	99.58 121	97.13 23	99.08 203	94.99 223	98.17 174	99.37 187

miper_ehance_all_eth		93.16 282	92.60 283	94.82 300	97.57 249	93.56 266	99.50 240	97.07 356	88.75 346	88.85 343	95.52 357	90.97 177	96.74 368	90.77 311	84.45 356	94.17 344

guyue		97.15 129	96.82 132	98.15 151	97.56 250	96.25 165	99.71 191	97.84 253	95.75 100	98.13 159	98.65 230	87.58 227	98.82 220	98.29 130	97.91 186	99.36 189

viewmanbaseed2359cas		96.45 168	96.07 162	97.59 195	97.55 251	94.59 233	99.70 196	97.33 312	93.62 186	97.00 196	99.32 147	85.57 261	98.71 235	97.26 174	97.33 200	99.47 173

testing3		93.92 259	94.23 232	92.99 365	97.54 252	90.23 349	99.99 5	99.16 33	90.57 307	91.33 296	98.63 234	92.99 129	92.52 442	82.46 396	95.39 260	96.22 309

SSM_0404		95.75 196	95.16 204	97.50 202	97.53 253	95.39 203	99.11 290	97.25 324	90.81 298	95.27 247	98.83 216	84.74 273	98.67 242	95.24 218	97.69 188	98.45 264

LCM-MVSNet-Re		92.31 303	92.60 283	91.43 385	97.53 253	79.27 440	99.02 308	91.83 455	92.07 255	80.31 419	94.38 404	83.50 288	95.48 409	97.22 176	97.58 192	99.54 158

GBi-Net		90.88 331	89.82 337	94.08 330	97.53 253	91.97 303	98.43 364	96.95 370	87.05 371	89.68 319	94.72 392	71.34 393	96.11 395	87.01 363	85.65 344	94.17 344

test1		90.88 331	89.82 337	94.08 330	97.53 253	91.97 303	98.43 364	96.95 370	87.05 371	89.68 319	94.72 392	71.34 393	96.11 395	87.01 363	85.65 344	94.17 344

FMVSNet2		91.02 328	89.56 342	95.41 280	97.53 253	95.74 184	98.98 311	97.41 302	87.05 371	88.43 353	95.00 386	71.34 393	96.24 391	85.12 378	85.21 349	94.25 337

tttt0517		96.85 145	96.49 147	97.92 166	97.48 258	95.89 178	99.85 137	98.54 117	90.72 306	96.63 206	98.93 200	97.47 12	99.02 206	93.03 275	95.76 246	98.85 247

BP-MVS1		98.33 56	98.18 53	98.81 95	97.44 259	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	99.36 189

casdiffmvs_mvg		96.43 169	95.94 174	97.89 170	97.44 259	95.47 196	99.86 134	97.29 319	93.35 193	96.03 226	99.19 167	85.39 265	98.72 234	97.89 155	97.04 213	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	97.41 261	95.64 191	99.99 5	97.06 357	94.59 134	99.63 55	99.32 147	89.20 208	98.14 290	98.76 101	99.23 136	99.62 137

c3_l		92.53 298	91.87 300	94.52 312	97.40 262	92.99 281	99.40 254	96.93 375	87.86 361	88.69 346	95.44 362	89.95 195	96.44 381	90.45 317	80.69 390	94.14 353

viewmambaseed2359dif		95.92 191	95.55 190	97.04 224	97.38 263	93.41 271	99.78 161	96.97 368	91.14 288	96.58 209	99.27 155	84.85 272	98.75 230	96.87 189	97.12 209	98.97 238

fmvsm_s_conf0.1_n		97.30 120	97.21 114	97.60 194	97.38 263	94.40 241	99.90 107	98.64 85	96.47 78	99.51 74	99.65 111	84.99 270	99.93 97	99.22 70	99.09 143	98.46 263

CDS-MVSNet		96.34 174	96.07 162	97.13 221	97.37 265	94.96 223	99.53 235	97.91 245	91.55 272	95.37 245	98.32 261	95.05 60	97.13 340	93.80 258	95.75 247	99.30 205
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	97.36 266	96.48 151	99.96 47	98.29 197	91.93 260	95.77 234	98.07 272	95.54 46	98.29 279	90.55 315	98.89 149	99.70 119

miper_lstm_enhance		91.81 311	91.39 310	93.06 364	97.34 267	89.18 367	99.38 260	96.79 386	86.70 378	87.47 367	95.22 377	90.00 194	95.86 404	88.26 344	81.37 379	94.15 350

baseline		96.43 169	95.98 168	97.76 182	97.34 267	95.17 219	99.51 238	97.17 334	93.92 173	96.90 199	99.28 152	85.37 266	98.64 245	97.50 169	96.86 220	99.46 175

cl____		92.31 303	91.58 304	94.52 312	97.33 269	92.77 283	99.57 226	96.78 387	86.97 375	87.56 365	95.51 358	89.43 201	96.62 373	88.60 338	82.44 371	94.16 349

SD_0403		92.63 297	93.38 264	90.40 399	97.32 270	77.91 442	97.75 393	98.03 232	91.89 261	90.83 302	98.29 265	82.00 298	93.79 432	88.51 342	95.75 247	99.52 164

DIV-MVS_self_test		92.32 302	91.60 303	94.47 316	97.31 271	92.74 285	99.58 223	96.75 388	86.99 374	87.64 363	95.54 355	89.55 200	96.50 378	88.58 339	82.44 371	94.17 344

casdiffmvs		96.42 171	95.97 171	97.77 180	97.30 272	94.98 222	99.84 142	97.09 352	93.75 182	96.58 209	99.26 159	85.07 268	98.78 225	97.77 163	97.04 213	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		94.36 250	93.48 258	96.99 226	97.29 273	93.54 267	99.96 47	96.72 390	88.35 355	93.43 270	98.94 197	82.05 297	98.05 297	88.12 348	96.48 226	99.37 187

eth_miper_zixun_eth		92.41 301	91.93 298	93.84 342	97.28 274	90.68 339	98.83 334	96.97 368	88.57 351	89.19 338	95.73 348	89.24 207	96.69 371	89.97 326	81.55 377	94.15 350

MVSFormer		96.94 141	96.60 143	97.95 162	97.28 274	97.70 95	99.55 232	97.27 321	91.17 285	99.43 80	99.54 127	90.92 178	96.89 359	94.67 236	99.62 95	99.25 213

lupinMVS		97.85 85	97.60 93	98.62 110	97.28 274	97.70 95	99.99 5	97.55 285	95.50 109	99.43 80	99.67 107	90.92 178	98.71 235	98.40 122	99.62 95	99.45 177

diffmvs_AUTHOR		96.75 153	96.41 152	97.79 176	97.20 277	95.46 197	99.69 199	97.15 337	94.46 139	98.78 119	99.21 165	85.64 259	98.77 226	98.27 131	97.31 201	99.13 223

mamba_0408		94.98 223	94.09 236	97.64 188	97.14 278	95.31 208	93.48 441	97.08 353	90.48 309	94.40 257	98.62 235	84.49 278	98.67 242	93.99 249	97.18 206	98.93 240

SSM_04072		94.77 230	94.09 236	96.82 232	97.14 278	95.31 208	93.48 441	97.08 353	90.48 309	94.40 257	98.62 235	84.49 278	96.21 392	93.99 249	97.18 206	98.93 240

SSM_0407		95.62 204	94.95 213	97.61 193	97.14 278	95.31 208	99.00 309	97.25 324	90.81 298	94.40 257	98.83 216	84.74 273	98.58 248	95.24 218	97.18 206	98.93 240

SCA		94.69 233	93.81 247	97.33 217	97.10 281	94.44 236	98.86 331	98.32 191	93.30 196	96.17 225	95.59 353	76.48 360	97.95 303	91.06 303	97.43 194	99.59 144

viewmacassd2359aftdt		95.93 190	95.45 191	97.36 214	97.09 282	94.12 251	99.57 226	97.26 323	93.05 208	96.50 212	99.17 169	82.76 293	98.68 240	96.61 195	97.04 213	99.28 209

KinetiMVS		96.10 183	95.29 199	98.53 124	97.08 283	97.12 122	99.56 229	98.12 224	94.78 126	98.44 141	98.94 197	80.30 325	99.39 184	91.56 296	98.79 155	99.06 231

TAMVS		95.85 193	95.58 188	96.65 240	97.07 284	93.50 268	99.17 286	97.82 255	91.39 282	95.02 250	98.01 273	92.20 155	97.30 330	93.75 261	95.83 244	99.14 222

Fast-Effi-MVS+-dtu		93.72 269	93.86 246	93.29 356	97.06 285	86.16 397	99.80 158	96.83 382	92.66 228	92.58 283	97.83 284	81.39 307	97.67 314	89.75 328	96.87 219	96.05 311

CostFormer		96.10 183	95.88 178	96.78 234	97.03 286	92.55 293	97.08 406	97.83 254	90.04 322	98.72 126	94.89 390	95.01 62	98.29 279	96.54 197	95.77 245	99.50 170

test_fmvsmvis_n_1920		97.67 104	97.59 95	97.91 168	97.02 287	95.34 206	99.95 66	98.45 137	97.87 24	97.02 195	99.59 118	89.64 198	99.98 47	99.41 63	99.34 131	98.42 266

test-LLR		96.47 166	96.04 164	97.78 178	97.02 287	95.44 198	99.96 47	98.21 208	94.07 163	95.55 240	96.38 325	93.90 103	98.27 283	90.42 318	98.83 153	99.64 130

test-mter		96.39 172	95.93 175	97.78 178	97.02 287	95.44 198	99.96 47	98.21 208	91.81 266	95.55 240	96.38 325	95.17 55	98.27 283	90.42 318	98.83 153	99.64 130

icg_test_0407_2		95.04 220	94.78 220	95.84 266	96.97 290	91.64 318	98.63 353	97.12 342	92.33 246	95.60 238	98.88 202	85.65 257	96.56 376	92.12 284	95.70 250	99.32 198

IMVS_0407		95.21 215	94.80 219	96.46 245	96.97 290	91.64 318	98.81 336	97.12 342	92.33 246	95.60 238	98.88 202	85.65 257	98.42 260	92.12 284	95.70 250	99.32 198

IMVS_0404		93.83 261	93.17 271	95.80 268	96.97 290	91.64 318	97.78 392	97.12 342	92.33 246	90.87 301	98.88 202	76.78 355	96.43 382	92.12 284	95.70 250	99.32 198

IMVS_0403		95.25 213	94.81 218	96.58 242	96.97 290	91.64 318	98.97 316	97.12 342	92.33 246	95.43 243	98.88 202	85.78 256	98.79 223	92.12 284	95.70 250	99.32 198

gm-plane-assit					96.97 290	93.76 260			91.47 276		98.96 190		98.79 223	94.92 226

WB-MVSnew		92.90 288	92.77 280	93.26 358	96.95 295	93.63 264	99.71 191	98.16 218	91.49 273	94.28 262	98.14 269	81.33 309	96.48 379	79.47 413	95.46 257	89.68 439

QAPM		95.40 209	94.17 234	99.10 73	96.92 296	97.71 93	99.40 254	98.68 78	89.31 330	88.94 342	98.89 201	82.48 295	99.96 71	93.12 274	99.83 77	99.62 137

KD-MVS_2432*1600		88.00 376	86.10 380	93.70 347	96.91 297	94.04 252	97.17 403	97.12 342	84.93 398	81.96 409	92.41 423	92.48 148	94.51 425	79.23 414	52.68 459	92.56 409

miper_refine_blended		88.00 376	86.10 380	93.70 347	96.91 297	94.04 252	97.17 403	97.12 342	84.93 398	81.96 409	92.41 423	92.48 148	94.51 425	79.23 414	52.68 459	92.56 409

tpm2		95.47 207	95.18 203	96.35 251	96.91 297	91.70 316	96.96 409	97.93 241	88.04 359	98.44 141	95.40 364	93.32 118	97.97 300	94.00 248	95.61 255	99.38 185

FMVSNet5		88.32 372	87.47 374	90.88 388	96.90 300	88.39 381	97.28 400	95.68 417	82.60 418	84.67 397	92.40 425	79.83 328	91.16 447	76.39 429	81.51 378	93.09 400

3Dnovator+	91.53 11	96.31 176	95.24 200	99.52 28	96.88 301	98.64 54	99.72 188	98.24 204	95.27 114	88.42 355	98.98 186	82.76 293	99.94 88	97.10 179	99.83 77	99.96 70

Patchmatch-test		92.65 296	91.50 307	96.10 257	96.85 302	90.49 344	91.50 449	97.19 330	82.76 417	90.23 307	95.59 353	95.02 61	98.00 299	77.41 424	96.98 217	99.82 101

MVS		96.60 161	95.56 189	99.72 13	96.85 302	99.22 20	98.31 370	98.94 44	91.57 271	90.90 300	99.61 117	86.66 245	99.96 71	97.36 171	99.88 73	99.99 23

3Dnovator	91.47 12	96.28 179	95.34 196	99.08 76	96.82 304	97.47 107	99.45 251	98.81 64	95.52 108	89.39 329	99.00 183	81.97 299	99.95 80	97.27 173	99.83 77	99.84 98

EI-MVSNet		93.73 268	93.40 263	94.74 301	96.80 305	92.69 288	99.06 299	97.67 268	88.96 339	91.39 294	99.02 179	88.75 215	97.30 330	91.07 302	87.85 328	94.22 340

CVMVSNet		94.68 235	94.94 214	93.89 341	96.80 305	86.92 394	99.06 299	98.98 41	94.45 140	94.23 264	99.02 179	85.60 260	95.31 414	90.91 308	95.39 260	99.43 181

IterMVS-LS		92.69 294	92.11 294	94.43 320	96.80 305	92.74 285	99.45 251	96.89 378	88.98 337	89.65 322	95.38 367	88.77 214	96.34 386	90.98 306	82.04 374	94.22 340
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 163	96.46 150	96.91 228	96.79 308	92.50 294	99.90 107	97.38 304	96.02 94	97.79 173	99.32 147	86.36 249	98.99 207	98.26 132	96.33 230	99.23 216

IterMVS		90.91 330	90.17 332	93.12 361	96.78 309	90.42 347	98.89 325	97.05 360	89.03 334	86.49 380	95.42 363	76.59 358	95.02 416	87.22 358	84.09 359	93.93 371
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 146	95.96 172	99.48 35	96.74 310	98.52 58	98.31 370	98.86 56	95.82 97	89.91 313	98.98 186	87.49 230	99.96 71	97.80 158	99.73 87	99.96 70

IterMVS-SCA-FT		90.85 333	90.16 333	92.93 366	96.72 311	89.96 356	98.89 325	96.99 364	88.95 340	86.63 377	95.67 349	76.48 360	95.00 417	87.04 361	84.04 362	93.84 378

MVS-HIRNet		86.22 383	83.19 396	95.31 284	96.71 312	90.29 348	92.12 446	97.33 312	62.85 454	86.82 374	70.37 459	69.37 401	97.49 320	75.12 432	97.99 184	98.15 273

viewdifsd2359ckpt11		94.09 256	93.63 249	95.46 277	96.68 313	88.92 370	99.62 213	97.12 342	93.07 206	95.73 235	99.22 162	77.05 348	98.88 216	96.52 198	87.69 333	98.58 261

viewmsd2359difaftdt		94.09 256	93.64 248	95.46 277	96.68 313	88.92 370	99.62 213	97.13 341	93.07 206	95.73 235	99.22 162	77.05 348	98.89 215	96.52 198	87.70 332	98.58 261

VDDNet		93.12 283	91.91 299	96.76 235	96.67 315	92.65 291	98.69 348	98.21 208	82.81 416	97.75 175	99.28 152	61.57 433	99.48 179	98.09 142	94.09 279	98.15 273

dmvs_re		93.20 280	93.15 272	93.34 354	96.54 316	83.81 412	98.71 345	98.51 125	91.39 282	92.37 286	98.56 243	78.66 340	97.83 308	93.89 252	89.74 300	98.38 268

Elysia		94.50 242	93.38 264	97.85 172	96.49 317	96.70 139	98.98 311	97.78 258	90.81 298	96.19 223	98.55 245	73.63 384	98.98 208	89.41 329	98.56 161	97.88 280

StellarMVS		94.50 242	93.38 264	97.85 172	96.49 317	96.70 139	98.98 311	97.78 258	90.81 298	96.19 223	98.55 245	73.63 384	98.98 208	89.41 329	98.56 161	97.88 280

MIMVSNet		90.30 346	88.67 360	95.17 288	96.45 319	91.64 318	92.39 445	97.15 337	85.99 385	90.50 305	93.19 418	66.95 412	94.86 421	82.01 400	93.43 287	99.01 236

CR-MVSNet		93.45 277	92.62 282	95.94 261	96.29 320	92.66 289	92.01 447	96.23 404	92.62 230	96.94 197	93.31 416	91.04 175	96.03 400	79.23 414	95.96 238	99.13 223

RPMNet		89.76 358	87.28 375	97.19 220	96.29 320	92.66 289	92.01 447	98.31 193	70.19 450	96.94 197	85.87 452	87.25 235	99.78 140	62.69 454	95.96 238	99.13 223

tt0805		91.28 323	90.18 331	94.60 307	96.26 322	87.55 387	98.39 368	98.72 72	89.00 336	89.22 335	98.47 253	62.98 428	98.96 212	90.57 314	88.00 327	97.28 298

Patchmtry		89.70 359	88.49 363	93.33 355	96.24 323	89.94 359	91.37 450	96.23 404	78.22 433	87.69 362	93.31 416	91.04 175	96.03 400	80.18 412	82.10 373	94.02 361

test_vis1_rt		86.87 381	86.05 383	89.34 407	96.12 324	78.07 441	99.87 123	83.54 467	92.03 258	78.21 430	89.51 437	45.80 453	99.91 104	96.25 202	93.11 292	90.03 436

JIA-IIPM		91.76 317	90.70 318	94.94 294	96.11 325	87.51 388	93.16 443	98.13 223	75.79 439	97.58 177	77.68 457	92.84 134	97.97 300	88.47 343	96.54 222	99.33 196

OpenMVS	90.15 15	94.77 230	93.59 253	98.33 139	96.07 326	97.48 106	99.56 229	98.57 101	90.46 311	86.51 379	98.95 195	78.57 341	99.94 88	93.86 253	99.74 86	97.57 293

PAPM		98.60 34	98.42 35	99.14 67	96.05 327	98.96 26	99.90 107	99.35 24	96.68 69	98.35 148	99.66 109	96.45 33	98.51 253	99.45 60	99.89 70	99.96 70

CLD-MVS		94.06 258	93.90 244	94.55 311	96.02 328	90.69 338	99.98 19	97.72 264	96.62 73	91.05 299	98.85 214	77.21 346	98.47 254	98.11 140	89.51 306	94.48 318
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 343	88.75 359	95.25 286	95.99 329	90.16 351	91.22 451	97.54 287	76.80 435	97.26 188	86.01 451	91.88 162	96.07 399	66.16 449	95.91 242	99.51 168

ACMH+	89.98 16	90.35 344	89.54 343	92.78 370	95.99 329	86.12 398	98.81 336	97.18 332	89.38 329	83.14 405	97.76 285	68.42 406	98.43 259	89.11 334	86.05 342	93.78 381

DeepMVS_CX				82.92 429	95.98 331	58.66 460		96.01 409	92.72 223	78.34 429	95.51 358	58.29 439	98.08 294	82.57 395	85.29 347	92.03 417

ACMP	92.05 9	92.74 292	92.42 291	93.73 343	95.91 332	88.72 374	99.81 154	97.53 289	94.13 159	87.00 373	98.23 267	74.07 381	98.47 254	96.22 203	88.86 313	93.99 366
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 272	93.03 274	95.35 281	95.86 333	86.94 393	99.87 123	96.36 402	96.85 60	99.54 69	98.79 218	52.41 447	99.83 133	98.64 109	98.97 147	99.29 207

HQP-NCC					95.78 334		99.87 123		96.82 62	93.37 271

ACMP_Plane					95.78 334		99.87 123		96.82 62	93.37 271

HQP-MVS		94.61 237	94.50 225	94.92 295	95.78 334	91.85 308	99.87 123	97.89 246	96.82 62	93.37 271	98.65 230	80.65 319	98.39 266	97.92 152	89.60 301	94.53 314

NP-MVS					95.77 337	91.79 310					98.65 230

test_fmvsmconf0.1_n		97.74 98	97.44 102	98.64 109	95.76 338	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					95.76 338	91.72 315						80.47 323

ACMM	91.95 10	92.88 289	92.52 289	93.98 337	95.75 340	89.08 369	99.77 165	97.52 291	93.00 209	89.95 312	97.99 276	76.17 364	98.46 257	93.63 265	88.87 312	94.39 326
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 261	92.84 276	96.80 233	95.73 341	93.57 265	99.88 120	97.24 327	92.57 235	92.92 278	96.66 317	78.73 339	97.67 314	87.75 351	94.06 280	99.17 218

plane_prior1					95.73 341

jason		97.24 124	96.86 129	98.38 138	95.73 341	97.32 111	99.97 37	97.40 303	95.34 112	98.60 135	99.54 127	87.70 224	98.56 250	97.94 151	99.47 118	99.25 213
jason: jason.
mmtdpeth		88.52 370	87.75 372	90.85 390	95.71 344	83.47 418	98.94 319	94.85 432	88.78 345	97.19 190	89.58 436	63.29 426	98.97 210	98.54 114	62.86 453	90.10 435

HQP_MVS		94.49 244	94.36 228	94.87 296	95.71 344	91.74 312	99.84 142	97.87 248	96.38 82	93.01 276	98.59 238	80.47 323	98.37 272	97.79 161	89.55 304	94.52 316

plane_prior7					95.71 344	91.59 324

ITE_SJBPF				92.38 373	95.69 347	85.14 404		95.71 416	92.81 217	89.33 332	98.11 270	70.23 399	98.42 260	85.91 373	88.16 325	93.59 389

fmvsm_s_conf0.1_n_a		97.09 133	96.90 126	97.63 191	95.65 348	94.21 248	99.83 149	98.50 131	96.27 87	99.65 51	99.64 112	84.72 275	99.93 97	99.04 79	98.84 152	98.74 254

ACMH	89.72 17	90.64 337	89.63 340	93.66 349	95.64 349	88.64 377	98.55 356	97.45 296	89.03 334	81.62 412	97.61 286	69.75 400	98.41 262	89.37 331	87.62 334	93.92 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 156	96.49 147	97.37 212	95.63 350	95.96 176	99.74 177	98.88 54	92.94 211	91.61 292	98.97 188	97.72 6	98.62 247	94.83 230	98.08 182	97.53 295

FMVSNet1		88.50 371	86.64 378	94.08 330	95.62 351	91.97 303	98.43 364	96.95 370	83.00 414	86.08 387	94.72 392	59.09 438	96.11 395	81.82 402	84.07 360	94.17 344

LuminaMVS		96.63 160	96.21 159	97.87 171	95.58 352	96.82 135	99.12 288	97.67 268	94.47 138	97.88 168	98.31 263	87.50 229	98.71 235	98.07 144	97.29 202	98.10 276

LPG-MVS_test		92.96 286	92.71 281	93.71 345	95.43 353	88.67 375	99.75 174	97.62 276	92.81 217	90.05 308	98.49 249	75.24 371	98.40 264	95.84 209	89.12 308	94.07 358

LGP-MVS_train				93.71 345	95.43 353	88.67 375		97.62 276	92.81 217	90.05 308	98.49 249	75.24 371	98.40 264	95.84 209	89.12 308	94.07 358

tpm		93.70 270	93.41 262	94.58 309	95.36 355	87.41 389	97.01 407	96.90 377	90.85 296	96.72 205	94.14 407	90.40 189	96.84 363	90.75 312	88.54 320	99.51 168

D2MVS		92.76 291	92.59 287	93.27 357	95.13 356	89.54 363	99.69 199	99.38 22	92.26 251	87.59 364	94.61 398	85.05 269	97.79 309	91.59 295	88.01 326	92.47 412

VPA-MVSNet		92.70 293	91.55 306	96.16 255	95.09 357	96.20 167	98.88 327	99.00 39	91.02 293	91.82 291	95.29 374	76.05 366	97.96 302	95.62 214	81.19 380	94.30 333

LTVRE_ROB	88.28 18	90.29 347	89.05 354	94.02 333	95.08 358	90.15 352	97.19 402	97.43 298	84.91 400	83.99 401	97.06 303	74.00 382	98.28 281	84.08 384	87.71 330	93.62 388
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		87.87 378	86.51 379	91.94 379	95.05 359	85.57 402	97.65 394	94.08 442	84.40 404	81.82 411	96.85 312	62.14 431	98.33 275	80.25 411	86.37 341	91.91 419

test0.0.03 1		93.86 260	93.61 250	94.64 305	95.02 360	92.18 301	99.93 90	98.58 99	94.07 163	87.96 359	98.50 248	93.90 103	94.96 418	81.33 403	93.17 290	96.78 301

UniMVSNet (Re)		93.07 285	92.13 293	95.88 263	94.84 361	96.24 166	99.88 120	98.98 41	92.49 240	89.25 333	95.40 364	87.09 237	97.14 339	93.13 273	78.16 404	94.26 335

USDC		90.00 354	88.96 355	93.10 363	94.81 362	88.16 383	98.71 345	95.54 421	93.66 184	83.75 403	97.20 297	65.58 417	98.31 277	83.96 387	87.49 336	92.85 406

VPNet		91.81 311	90.46 322	95.85 265	94.74 363	95.54 195	98.98 311	98.59 97	92.14 253	90.77 304	97.44 290	68.73 404	97.54 319	94.89 229	77.89 406	94.46 319

FIs		94.10 255	93.43 259	96.11 256	94.70 364	96.82 135	99.58 223	98.93 48	92.54 236	89.34 331	97.31 294	87.62 226	97.10 343	94.22 247	86.58 339	94.40 325

UniMVSNet_ETH3D		90.06 353	88.58 362	94.49 315	94.67 365	88.09 384	97.81 391	97.57 284	83.91 407	88.44 351	97.41 291	57.44 440	97.62 316	91.41 297	88.59 319	97.77 285

UniMVSNet_NR-MVSNet		92.95 287	92.11 294	95.49 273	94.61 366	95.28 212	99.83 149	99.08 36	91.49 273	89.21 336	96.86 311	87.14 236	96.73 369	93.20 269	77.52 409	94.46 319

test_fmvs2		89.47 363	89.70 339	88.77 414	94.54 367	75.74 443	99.83 149	94.70 438	94.71 130	91.08 297	96.82 316	54.46 443	97.78 311	92.87 276	88.27 323	92.80 407

MonoMVSNet		94.82 225	94.43 226	95.98 259	94.54 367	90.73 337	99.03 306	97.06 357	93.16 201	93.15 275	95.47 361	88.29 218	97.57 317	97.85 156	91.33 298	99.62 137

WR-MVS		92.31 303	91.25 311	95.48 276	94.45 369	95.29 211	99.60 220	98.68 78	90.10 319	88.07 358	96.89 309	80.68 318	96.80 367	93.14 272	79.67 397	94.36 327

nrg030		93.51 274	92.53 288	96.45 246	94.36 370	97.20 117	99.81 154	97.16 336	91.60 270	89.86 315	97.46 289	86.37 248	97.68 313	95.88 208	80.31 393	94.46 319

tfpnnormal		89.29 366	87.61 373	94.34 323	94.35 371	94.13 250	98.95 318	98.94 44	83.94 405	84.47 398	95.51 358	74.84 376	97.39 322	77.05 427	80.41 391	91.48 422

FC-MVSNet-test		93.81 264	93.15 272	95.80 268	94.30 372	96.20 167	99.42 253	98.89 52	92.33 246	89.03 341	97.27 296	87.39 232	96.83 365	93.20 269	86.48 340	94.36 327

SSC-MVS3.2		89.59 361	88.66 361	92.38 373	94.29 373	86.12 398	99.49 242	97.66 271	90.28 318	88.63 348	95.18 378	64.46 422	96.88 361	85.30 377	82.66 368	94.14 353

MS-PatchMatch		90.65 336	90.30 327	91.71 384	94.22 374	85.50 403	98.24 374	97.70 265	88.67 348	86.42 382	96.37 327	67.82 409	98.03 298	83.62 389	99.62 95	91.60 420

WR-MVS_H		91.30 321	90.35 325	94.15 327	94.17 375	92.62 292	99.17 286	98.94 44	88.87 343	86.48 381	94.46 403	84.36 281	96.61 374	88.19 345	78.51 402	93.21 398

DU-MVS		92.46 300	91.45 309	95.49 273	94.05 376	95.28 212	99.81 154	98.74 71	92.25 252	89.21 336	96.64 319	81.66 304	96.73 369	93.20 269	77.52 409	94.46 319

NR-MVSNet		91.56 319	90.22 329	95.60 271	94.05 376	95.76 183	98.25 373	98.70 74	91.16 287	80.78 418	96.64 319	83.23 291	96.57 375	91.41 297	77.73 408	94.46 319

CP-MVSNet		91.23 325	90.22 329	94.26 325	93.96 378	92.39 297	99.09 292	98.57 101	88.95 340	86.42 382	96.57 322	79.19 334	96.37 384	90.29 321	78.95 399	94.02 361

XXY-MVS		91.82 310	90.46 322	95.88 263	93.91 379	95.40 202	98.87 330	97.69 267	88.63 350	87.87 360	97.08 301	74.38 380	97.89 306	91.66 294	84.07 360	94.35 330

PS-CasMVS		90.63 338	89.51 345	93.99 336	93.83 380	91.70 316	98.98 311	98.52 122	88.48 352	86.15 386	96.53 324	75.46 369	96.31 388	88.83 336	78.86 401	93.95 369

test_0402		85.58 385	83.94 390	90.50 396	93.81 381	85.04 405	98.55 356	95.20 429	76.01 437	79.72 424	95.13 379	64.15 424	96.26 390	66.04 450	86.88 338	90.21 433

XVG-ACMP-BASELINE		91.22 326	90.75 317	92.63 372	93.73 382	85.61 401	98.52 360	97.44 297	92.77 221	89.90 314	96.85 312	66.64 414	98.39 266	92.29 281	88.61 317	93.89 374

TranMVSNet+NR-MVSNet		91.68 318	90.61 321	94.87 296	93.69 383	93.98 255	99.69 199	98.65 82	91.03 292	88.44 351	96.83 315	80.05 327	96.18 393	90.26 322	76.89 417	94.45 324

TransMVSNet (Re)		87.25 379	85.28 386	93.16 360	93.56 384	91.03 329	98.54 358	94.05 444	83.69 409	81.09 416	96.16 333	75.32 370	96.40 383	76.69 428	68.41 441	92.06 416

v10		90.25 348	88.82 357	94.57 310	93.53 385	93.43 270	99.08 294	96.87 380	85.00 397	87.34 371	94.51 399	80.93 314	97.02 353	82.85 394	79.23 398	93.26 396

testgi		89.01 368	88.04 369	91.90 380	93.49 386	84.89 407	99.73 184	95.66 418	93.89 177	85.14 393	98.17 268	59.68 437	94.66 424	77.73 423	88.88 311	96.16 310

v8		90.54 340	89.17 350	94.66 304	93.43 387	93.40 273	99.20 283	96.94 374	85.76 388	87.56 365	94.51 399	81.96 300	97.19 336	84.94 380	78.25 403	93.38 394

V42		91.28 323	90.12 334	94.74 301	93.42 388	93.46 269	99.68 202	97.02 361	87.36 367	89.85 317	95.05 382	81.31 310	97.34 325	87.34 356	80.07 395	93.40 392

pm-mvs1		89.36 365	87.81 371	94.01 334	93.40 389	91.93 306	98.62 354	96.48 400	86.25 383	83.86 402	96.14 335	73.68 383	97.04 349	86.16 370	75.73 422	93.04 402

v1144		91.09 327	89.83 336	94.87 296	93.25 390	93.69 263	99.62 213	96.98 366	86.83 377	89.64 323	94.99 387	80.94 313	97.05 346	85.08 379	81.16 381	93.87 376

v1192		90.62 339	89.25 349	94.72 303	93.13 391	93.07 277	99.50 240	97.02 361	86.33 382	89.56 327	95.01 384	79.22 333	97.09 345	82.34 398	81.16 381	94.01 363

v2v482		91.30 321	90.07 335	95.01 291	93.13 391	93.79 258	99.77 165	97.02 361	88.05 358	89.25 333	95.37 368	80.73 317	97.15 338	87.28 357	80.04 396	94.09 357

OPM-MVS		93.21 279	92.80 278	94.44 318	93.12 393	90.85 336	99.77 165	97.61 279	96.19 90	91.56 293	98.65 230	75.16 375	98.47 254	93.78 260	89.39 307	93.99 366
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 334	89.52 344	94.59 308	93.11 394	92.77 283	99.56 229	96.99 364	86.38 381	89.82 318	94.95 389	80.50 322	97.10 343	83.98 386	80.41 391	93.90 373

PEN-MVS		90.19 350	89.06 353	93.57 350	93.06 395	90.90 334	99.06 299	98.47 134	88.11 357	85.91 388	96.30 329	76.67 356	95.94 403	87.07 360	76.91 416	93.89 374

v1240		90.20 349	88.79 358	94.44 318	93.05 396	92.27 299	99.38 260	96.92 376	85.89 386	89.36 330	94.87 391	77.89 345	97.03 351	80.66 407	81.08 384	94.01 363

v148		90.70 335	89.63 340	93.92 338	92.97 397	90.97 330	99.75 174	96.89 378	87.51 364	88.27 356	95.01 384	81.67 303	97.04 349	87.40 355	77.17 414	93.75 382

v1921920		90.46 341	89.12 351	94.50 314	92.96 398	92.46 295	99.49 242	96.98 366	86.10 384	89.61 325	95.30 371	78.55 342	97.03 351	82.17 399	80.89 389	94.01 363

MVStest1		85.03 391	82.76 400	91.83 381	92.95 399	89.16 368	98.57 355	94.82 433	71.68 448	68.54 451	95.11 381	83.17 292	95.66 407	74.69 433	65.32 448	90.65 429

tt0320-xc		82.94 405	80.35 412	90.72 394	92.90 400	83.54 416	96.85 412	94.73 436	63.12 453	79.85 423	93.77 411	49.43 451	95.46 410	80.98 406	71.54 431	93.16 399

Baseline_NR-MVSNet		90.33 345	89.51 345	92.81 369	92.84 401	89.95 357	99.77 165	93.94 445	84.69 402	89.04 340	95.66 350	81.66 304	96.52 377	90.99 305	76.98 415	91.97 418

test_method		80.79 411	79.70 414	84.08 426	92.83 402	67.06 452	99.51 238	95.42 423	54.34 458	81.07 417	93.53 413	44.48 454	92.22 444	78.90 418	77.23 413	92.94 404

pmmvs4		92.10 307	91.07 315	95.18 287	92.82 403	94.96 223	99.48 245	96.83 382	87.45 366	88.66 347	96.56 323	83.78 286	96.83 365	89.29 332	84.77 354	93.75 382

LF4IMVS		89.25 367	88.85 356	90.45 398	92.81 404	81.19 433	98.12 381	94.79 434	91.44 277	86.29 384	97.11 299	65.30 420	98.11 292	88.53 341	85.25 348	92.07 415

tt0320		83.56 404	81.15 407	90.77 392	92.77 405	83.58 415	96.83 413	95.52 422	63.26 452	81.36 414	92.54 421	53.26 445	95.77 405	80.45 408	74.38 425	92.96 403

DTE-MVSNet		89.40 364	88.24 367	92.88 367	92.66 406	89.95 357	99.10 291	98.22 207	87.29 368	85.12 394	96.22 331	76.27 363	95.30 415	83.56 390	75.74 421	93.41 391

EU-MVSNet		90.14 352	90.34 326	89.54 406	92.55 407	81.06 434	98.69 348	98.04 231	91.41 281	86.59 378	96.84 314	80.83 316	93.31 437	86.20 369	81.91 375	94.26 335

APD_test1		81.15 409	80.92 409	81.86 430	92.45 408	59.76 459	96.04 427	93.61 448	73.29 446	77.06 433	96.64 319	44.28 455	96.16 394	72.35 437	82.52 369	89.67 440

sc_t1		85.01 392	82.46 402	92.67 371	92.44 409	83.09 419	97.39 398	95.72 415	65.06 451	85.64 391	96.16 333	49.50 450	97.34 325	84.86 381	75.39 423	97.57 293

our_test_3		90.39 342	89.48 347	93.12 361	92.40 410	89.57 362	99.33 267	96.35 403	87.84 362	85.30 392	94.99 387	84.14 284	96.09 398	80.38 409	84.56 355	93.71 387

ppachtmachnet_test		89.58 362	88.35 365	93.25 359	92.40 410	90.44 346	99.33 267	96.73 389	85.49 393	85.90 389	95.77 344	81.09 312	96.00 402	76.00 431	82.49 370	93.30 395

v7n		89.65 360	88.29 366	93.72 344	92.22 412	90.56 343	99.07 298	97.10 349	85.42 395	86.73 375	94.72 392	80.06 326	97.13 340	81.14 404	78.12 405	93.49 390

dmvs_testset		83.79 401	86.07 382	76.94 434	92.14 413	48.60 469	96.75 414	90.27 459	89.48 328	78.65 427	98.55 245	79.25 332	86.65 457	66.85 447	82.69 367	95.57 312

PS-MVSNAJss		93.64 271	93.31 268	94.61 306	92.11 414	92.19 300	99.12 288	97.38 304	92.51 239	88.45 350	96.99 307	91.20 170	97.29 333	94.36 241	87.71 330	94.36 327

pmmvs5		90.17 351	89.09 352	93.40 353	92.10 415	89.77 360	99.74 177	95.58 420	85.88 387	87.24 372	95.74 345	73.41 386	96.48 379	88.54 340	83.56 364	93.95 369

N_pmnet		80.06 414	80.78 410	77.89 433	91.94 416	45.28 471	98.80 339	56.82 473	78.10 434	80.08 421	93.33 414	77.03 350	95.76 406	68.14 445	82.81 366	92.64 408

test_djsdf		92.83 290	92.29 292	94.47 316	91.90 417	92.46 295	99.55 232	97.27 321	91.17 285	89.96 311	96.07 339	81.10 311	96.89 359	94.67 236	88.91 310	94.05 360

SixPastTwentyTwo		88.73 369	88.01 370	90.88 388	91.85 418	82.24 425	98.22 378	95.18 430	88.97 338	82.26 408	96.89 309	71.75 391	96.67 372	84.00 385	82.98 365	93.72 386

K. test v3		88.05 375	87.24 376	90.47 397	91.82 419	82.23 426	98.96 317	97.42 300	89.05 333	76.93 435	95.60 352	68.49 405	95.42 411	85.87 374	81.01 387	93.75 382

OurMVSNet-221017-0		89.81 357	89.48 347	90.83 391	91.64 420	81.21 432	98.17 380	95.38 425	91.48 275	85.65 390	97.31 294	72.66 387	97.29 333	88.15 346	84.83 353	93.97 368

mvs_tets		91.81 311	91.08 314	94.00 335	91.63 421	90.58 342	98.67 350	97.43 298	92.43 241	87.37 370	97.05 304	71.76 390	97.32 328	94.75 233	88.68 316	94.11 356

Gipuma		66.95 427	65.00 427	72.79 439	91.52 422	67.96 451	66.16 462	95.15 431	47.89 460	58.54 457	67.99 462	29.74 459	87.54 456	50.20 461	77.83 407	62.87 462
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 172	95.74 182	98.32 140	91.47 423	95.56 194	99.84 142	97.30 316	97.74 28	97.89 167	99.35 146	79.62 329	99.85 123	99.25 69	99.24 135	99.55 154

jajsoiax		91.92 309	91.18 312	94.15 327	91.35 424	90.95 333	99.00 309	97.42 300	92.61 231	87.38 369	97.08 301	72.46 388	97.36 323	94.53 239	88.77 314	94.13 355

MDA-MVSNet-bldmvs		84.09 399	81.52 406	91.81 382	91.32 425	88.00 386	98.67 350	95.92 411	80.22 428	55.60 460	93.32 415	68.29 407	93.60 435	73.76 434	76.61 418	93.82 380

MVP-Stereo		90.93 329	90.45 324	92.37 375	91.25 426	88.76 372	98.05 385	96.17 406	87.27 369	84.04 399	95.30 371	78.46 343	97.27 335	83.78 388	99.70 89	91.09 423
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 387	83.32 395	92.10 377	90.96 427	88.58 378	99.20 283	96.52 398	79.70 430	57.12 459	92.69 420	79.11 335	93.86 431	77.10 426	77.46 411	93.86 377

YYNet1		85.50 388	83.33 394	92.00 378	90.89 428	88.38 382	99.22 282	96.55 397	79.60 431	57.26 458	92.72 419	79.09 337	93.78 433	77.25 425	77.37 412	93.84 378

anonymousdsp		91.79 316	90.92 316	94.41 321	90.76 429	92.93 282	98.93 321	97.17 334	89.08 332	87.46 368	95.30 371	78.43 344	96.92 357	92.38 280	88.73 315	93.39 393

lessismore_v0				90.53 395	90.58 430	80.90 435		95.80 412		77.01 434	95.84 342	66.15 416	96.95 355	83.03 393	75.05 424	93.74 385

EG-PatchMatch MVS		85.35 389	83.81 392	89.99 404	90.39 431	81.89 428	98.21 379	96.09 408	81.78 421	74.73 441	93.72 412	51.56 449	97.12 342	79.16 417	88.61 317	90.96 426

EGC-MVSNET		69.38 420	63.76 430	86.26 423	90.32 432	81.66 431	96.24 423	93.85 446	0.99 470	3.22 471	92.33 426	52.44 446	92.92 440	59.53 457	84.90 352	84.21 451

CMPMVS	61.59 21	84.75 395	85.14 387	83.57 427	90.32 432	62.54 455	96.98 408	97.59 283	74.33 444	69.95 448	96.66 317	64.17 423	98.32 276	87.88 350	88.41 322	89.84 438
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 398	82.92 398	89.21 408	90.03 434	82.60 422	96.89 411	95.62 419	80.59 426	75.77 440	89.17 438	65.04 421	94.79 422	72.12 438	81.02 386	90.23 432

pmmvs6		85.69 384	83.84 391	91.26 387	90.00 435	84.41 410	97.82 390	96.15 407	75.86 438	81.29 415	95.39 366	61.21 434	96.87 362	83.52 391	73.29 427	92.50 411

ttmdpeth		88.23 374	87.06 377	91.75 383	89.91 436	87.35 390	98.92 324	95.73 414	87.92 360	84.02 400	96.31 328	68.23 408	96.84 363	86.33 368	76.12 419	91.06 424

DSMNet-mixed		88.28 373	88.24 367	88.42 416	89.64 437	75.38 445	98.06 384	89.86 460	85.59 392	88.20 357	92.14 427	76.15 365	91.95 445	78.46 420	96.05 235	97.92 279

UnsupCasMVSNet_eth		85.52 386	83.99 388	90.10 402	89.36 438	83.51 417	96.65 415	97.99 234	89.14 331	75.89 439	93.83 409	63.25 427	93.92 429	81.92 401	67.90 444	92.88 405

Anonymous20231206		86.32 382	85.42 385	89.02 410	89.11 439	80.53 438	99.05 303	95.28 426	85.43 394	82.82 406	93.92 408	74.40 379	93.44 436	66.99 446	81.83 376	93.08 401

Anonymous20240521		85.15 390	83.81 392	89.16 409	88.32 440	82.69 421	98.80 339	95.74 413	79.72 429	81.53 413	90.99 430	65.38 419	94.16 427	72.69 436	81.11 383	90.63 430

OpenMVS_ROB	79.82 20	83.77 402	81.68 405	90.03 403	88.30 441	82.82 420	98.46 361	95.22 428	73.92 445	76.00 438	91.29 429	55.00 442	96.94 356	68.40 444	88.51 321	90.34 431

test20.03		84.72 396	83.99 388	86.91 421	88.19 442	80.62 437	98.88 327	95.94 410	88.36 354	78.87 425	94.62 397	68.75 403	89.11 452	66.52 448	75.82 420	91.00 425

KD-MVS_self_test		83.59 403	82.06 403	88.20 417	86.93 443	80.70 436	97.21 401	96.38 401	82.87 415	82.49 407	88.97 439	67.63 410	92.32 443	73.75 435	62.30 455	91.58 421

MIMVSNet1		82.58 406	80.51 411	88.78 412	86.68 444	84.20 411	96.65 415	95.41 424	78.75 432	78.59 428	92.44 422	51.88 448	89.76 451	65.26 451	78.95 399	92.38 414

CL-MVSNet_self_test		84.50 397	83.15 397	88.53 415	86.00 445	81.79 429	98.82 335	97.35 308	85.12 396	83.62 404	90.91 432	76.66 357	91.40 446	69.53 442	60.36 456	92.40 413

UnsupCasMVSNet_bld		79.97 416	77.03 421	88.78 412	85.62 446	81.98 427	93.66 439	97.35 308	75.51 441	70.79 447	83.05 454	48.70 452	94.91 420	78.31 421	60.29 457	89.46 443

mvs5depth		84.87 393	82.90 399	90.77 392	85.59 447	84.84 408	91.10 452	93.29 450	83.14 412	85.07 395	94.33 405	62.17 430	97.32 328	78.83 419	72.59 430	90.14 434

Patchmatch-RL test		86.90 380	85.98 384	89.67 405	84.45 448	75.59 444	89.71 455	92.43 452	86.89 376	77.83 432	90.94 431	94.22 92	93.63 434	87.75 351	69.61 435	99.79 106

pmmvs-eth3d		84.03 400	81.97 404	90.20 401	84.15 449	87.09 392	98.10 383	94.73 436	83.05 413	74.10 444	87.77 445	65.56 418	94.01 428	81.08 405	69.24 437	89.49 442

test_fmvs3		79.99 415	80.17 413	79.45 432	84.02 450	62.83 453	99.05 303	93.49 449	88.29 356	80.06 422	86.65 449	28.09 461	88.00 453	88.63 337	73.27 428	87.54 449

PM-MVS		80.47 412	78.88 416	85.26 424	83.79 451	72.22 447	95.89 430	91.08 457	85.71 391	76.56 437	88.30 441	36.64 457	93.90 430	82.39 397	69.57 436	89.66 441

new-patchmatchnet		81.19 408	79.34 415	86.76 422	82.86 452	80.36 439	97.92 387	95.27 427	82.09 420	72.02 445	86.87 448	62.81 429	90.74 449	71.10 439	63.08 452	89.19 445

FE-MVSNET		81.05 410	78.81 417	87.79 419	81.98 453	83.70 413	98.23 376	91.78 456	81.27 423	74.29 443	87.44 446	60.92 436	90.67 450	64.92 452	68.43 440	89.01 446

mvsany_test3		82.12 407	81.14 408	85.06 425	81.87 454	70.41 449	97.09 405	92.14 453	91.27 284	77.84 431	88.73 440	39.31 456	95.49 408	90.75 312	71.24 432	89.29 444

WB-MVS		76.28 418	77.28 420	73.29 438	81.18 455	54.68 463	97.87 389	94.19 441	81.30 422	69.43 449	90.70 433	77.02 351	82.06 461	35.71 466	68.11 443	83.13 452

test_f		78.40 417	77.59 419	80.81 431	80.82 456	62.48 456	96.96 409	93.08 451	83.44 410	74.57 442	84.57 453	27.95 462	92.63 441	84.15 383	72.79 429	87.32 450

SSC-MVS		75.42 419	76.40 422	72.49 442	80.68 457	53.62 464	97.42 396	94.06 443	80.42 427	68.75 450	90.14 435	76.54 359	81.66 462	33.25 467	66.34 447	82.19 453

pmmvs3		80.27 413	77.77 418	87.76 420	80.32 458	82.43 424	98.23 376	91.97 454	72.74 447	78.75 426	87.97 444	57.30 441	90.99 448	70.31 440	62.37 454	89.87 437

testf1		68.38 423	66.92 424	72.78 440	78.80 459	50.36 466	90.95 453	87.35 465	55.47 456	58.95 455	88.14 442	20.64 466	87.60 454	57.28 458	64.69 449	80.39 455

APD_test2		68.38 423	66.92 424	72.78 440	78.80 459	50.36 466	90.95 453	87.35 465	55.47 456	58.95 455	88.14 442	20.64 466	87.60 454	57.28 458	64.69 449	80.39 455

ambc				83.23 428	77.17 461	62.61 454	87.38 457	94.55 440		76.72 436	86.65 449	30.16 458	96.36 385	84.85 382	69.86 434	90.73 428

test_vis3_rt		68.82 421	66.69 426	75.21 437	76.24 462	60.41 458	96.44 418	68.71 472	75.13 442	50.54 463	69.52 461	16.42 471	96.32 387	80.27 410	66.92 446	68.89 459

TDRefinement		84.76 394	82.56 401	91.38 386	74.58 463	84.80 409	97.36 399	94.56 439	84.73 401	80.21 420	96.12 338	63.56 425	98.39 266	87.92 349	63.97 451	90.95 427

E-PMN		52.30 431	52.18 433	52.67 448	71.51 464	45.40 470	93.62 440	76.60 470	36.01 464	43.50 465	64.13 464	27.11 463	67.31 467	31.06 468	26.06 463	45.30 466

EMVS		51.44 433	51.22 435	52.11 449	70.71 465	44.97 472	94.04 436	75.66 471	35.34 466	42.40 466	61.56 467	28.93 460	65.87 468	27.64 469	24.73 464	45.49 465

PMMVS2		67.15 426	64.15 429	76.14 436	70.56 466	62.07 457	93.89 437	87.52 464	58.09 455	60.02 454	78.32 456	22.38 465	84.54 459	59.56 456	47.03 461	81.80 454

FPMVS		68.72 422	68.72 423	68.71 444	65.95 467	44.27 473	95.97 429	94.74 435	51.13 459	53.26 461	90.50 434	25.11 464	83.00 460	60.80 455	80.97 388	78.87 457

wuyk23d		20.37 437	20.84 440	18.99 452	65.34 468	27.73 475	50.43 463	7.67 476	9.50 469	8.01 470	6.34 470	6.13 474	26.24 469	23.40 470	10.69 468	2.99 467

LCM-MVSNet		67.77 425	64.73 428	76.87 435	62.95 469	56.25 462	89.37 456	93.74 447	44.53 461	61.99 453	80.74 455	20.42 468	86.53 458	69.37 443	59.50 458	87.84 447

MVE	53.74 22	51.54 432	47.86 436	62.60 446	59.56 470	50.93 465	79.41 460	77.69 469	35.69 465	36.27 467	61.76 466	5.79 475	69.63 465	37.97 465	36.61 462	67.24 460
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 429	52.24 432	67.66 445	49.27 471	56.82 461	83.94 458	82.02 468	70.47 449	33.28 468	64.54 463	17.23 470	69.16 466	45.59 463	23.85 465	77.02 458

tmp_tt		65.23 428	62.94 431	72.13 443	44.90 472	50.03 468	81.05 459	89.42 463	38.45 462	48.51 464	99.90 18	54.09 444	78.70 464	91.84 293	18.26 466	87.64 448

PMVS	49.05 23	53.75 430	51.34 434	60.97 447	40.80 473	34.68 474	74.82 461	89.62 462	37.55 463	28.67 469	72.12 458	7.09 473	81.63 463	43.17 464	68.21 442	66.59 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 435	39.14 438	33.31 450	19.94 474	24.83 476	98.36 369	9.75 475	15.53 468	51.31 462	87.14 447	19.62 469	17.74 470	47.10 462	3.47 469	57.36 463

testmvs		40.60 434	44.45 437	29.05 451	19.49 475	14.11 477	99.68 202	18.47 474	20.74 467	64.59 452	98.48 252	10.95 472	17.09 471	56.66 460	11.01 467	55.94 464

mmdepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

monomultidepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

test_blank		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.02 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

eth-test2					0.00 476

eth-test					0.00 476

uanet_test		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

DCPMVS		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

cdsmvs_eth3d_5k		23.43 436	31.24 439	0.00 453	0.00 476	0.00 478	0.00 464	98.09 225	0.00 471	0.00 472	99.67 107	83.37 289	0.00 472	0.00 471	0.00 470	0.00 468

pcd_1.5k_mvsjas		7.60 439	10.13 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	91.20 170	0.00 472	0.00 471	0.00 470	0.00 468

sosnet-low-res		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

sosnet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uncertanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

Regformer		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

ab-mvs-re		8.28 438	11.04 441	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	99.40 140	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 472	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

WAC-MVS						90.97 330								86.10 372

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							95.78 431				59.23 468	93.20 125	97.74 312	91.06 303

test_post											63.35 465	94.43 79	98.13 291

patchmatchnet-post											91.70 428	95.12 56	97.95 303

MTMP							99.87 123	96.49 399

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							99.46 250		94.21 158	99.85 16			99.95 80	96.96 185

新几何2							99.40 254

无先验							99.49 242	98.71 73	93.46 190				100.00 1	94.36 241		99.99 23

原ACMM2							99.90 107

testdata2													99.99 36	90.54 316

segment_acmp												96.68 29

testdata1							99.28 276		96.35 86

plane_prior5								97.87 248					98.37 272	97.79 161	89.55 304	94.52 316

plane_prior4											98.59 238

plane_prior3						91.64 318			96.63 71	93.01 276

plane_prior2							99.84 142		96.38 82

plane_prior						91.74 312	99.86 134		96.76 66						89.59 303

n2								0.00 477

nn								0.00 477

door-mid								89.69 461

test11								98.44 142

door								90.31 458

HQP5-MVS						91.85 308

BP-MVS														97.92 152

HQP4-MVS										93.37 271			98.39 266			94.53 314

HQP3-MVS								97.89 246							89.60 301

HQP2-MVS												80.65 319

MDTV_nov1_ep13_2view						96.26 161	96.11 425		91.89 261	98.06 160		94.40 81		94.30 244		99.67 124

ACMMP++_ref															87.04 337

ACMMP++															88.23 324

Test By Simon												92.82 136

High-resolution multi-view benchmark