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.

test2506		99.48 83	99.38 81	99.75 116	99.89 121	99.51 129	99.45 302	100.00 1	98.38 70	99.83 160	100.00 1	98.86 119	99.81 178	99.25 178	98.78 164	99.94 128

test1111		98.42 174	98.12 184	99.29 168	99.88 123	98.15 224	99.46 300	100.00 1	98.36 74	99.42 191	100.00 1	87.91 302	99.79 180	99.31 175	98.78 164	99.94 128

ECVR-MVS		98.43 172	98.14 183	99.32 166	99.89 121	98.21 222	99.46 300	100.00 1	98.38 70	99.47 190	100.00 1	87.91 302	99.80 179	99.35 170	98.78 164	99.94 128

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 130	98.79 47	100.00 1	100.00 1	99.54 23	100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 45	100.00 1	100.00 1	99.54 23	100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 130	98.72 51	100.00 1	100.00 1	99.60 14

GeoE		98.06 190	97.65 204	99.29 168	99.47 221	98.41 203	100.00 1	99.19 278	94.85 272	98.88 223	100.00 1	91.21 263	99.59 199	97.02 267	98.19 196	99.88 167

ZD-MVS					100.00 1	99.98 17		99.80 42	97.31 171	100.00 1	100.00 1	99.32 61	99.99 93	100.00 1	100.00 1

test1172		99.64 59	99.53 67	99.96 49	100.00 1	99.90 65	100.00 1	99.51 76	97.59 142	100.00 1	100.00 1	98.65 131	99.99 93	99.99 62	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 63	99.52 70	99.97 34	99.99 59	99.91 57	100.00 1	99.42 130	97.62 136	100.00 1	100.00 1	98.65 131	99.99 93	99.99 62	100.00 1	100.00 1

RE-MVS-def			99.55 65		99.99 59	99.91 57	100.00 1	99.42 130	97.62 136	100.00 1	100.00 1	98.94 111		99.99 62	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 130	99.03 19	100.00 1	100.00 1	99.50 36	100.00 1	100.00 1	100.00 1	100.00 1

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 23	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 130	99.03 19	100.00 1	100.00 1	99.56 20	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 130	99.03 19	100.00 1	100.00 1	99.50 36	100.00 1

xxxxxxxxxxxxxcwj		99.77 19	99.70 21	99.97 34	99.99 59	99.93 47	100.00 1	99.31 230	98.93 32	100.00 1	100.00 1	99.05 93	99.99 93	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 55	99.57 58	99.95 61	99.99 59	99.85 92	100.00 1	99.42 130	97.67 130	100.00 1	100.00 1	99.05 93	99.99 93	100.00 1	100.00 1	100.00 1

ETH3D cwj APD-0.16		99.63 63	99.53 67	99.94 76	99.97 101	99.82 97	100.00 1	99.42 130	97.39 162	100.00 1	100.00 1	98.98 103	99.99 93	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 36	99.62 47	99.97 34	99.99 59	99.90 65	100.00 1	99.79 44	97.97 103	99.97 112	100.00 1	98.97 105	100.00 1	99.94 96	100.00 1	100.00 1

ETH3 D test6400		99.68 48	99.59 51	99.97 34	99.99 59	99.93 47	100.00 1	99.42 130	97.97 103	100.00 1	100.00 1	98.90 116	99.99 93	100.00 1	100.00 1	100.00 1

9.14			99.57 58		99.99 59		100.00 1	99.42 130	97.54 148	100.00 1	100.00 1	99.15 87	99.99 93	100.00 1	100.00 1

testtj		99.70 40	99.61 48	99.99 12	100.00 1	99.98 17	100.00 1	99.53 71	97.71 126	100.00 1	100.00 1	99.23 77	100.00 1	100.00 1	100.00 1	100.00 1

ETH3D-3000-0.1		99.66 55	99.57 58	99.95 61	99.99 59	99.85 92	100.00 1	99.42 130	97.58 144	100.00 1	100.00 1	99.12 89	99.99 93	100.00 1	100.00 1	100.00 1

ET-MVSNet_ETH3D		96.41 259	95.48 288	99.20 178	99.81 130	99.75 106	100.00 1	99.02 334	97.30 173	78.33 365	100.00 1	97.73 157	97.94 337	99.70 131	87.41 337	99.92 140

EIA-MVS		99.26 107	99.19 105	99.45 149	99.63 175	98.75 188	100.00 1	99.27 251	96.93 193	99.95 139	100.00 1	97.47 169	99.79 180	99.74 124	99.72 142	99.82 187

ETV-MVS		99.34 96	99.24 96	99.64 126	99.58 190	99.33 148	100.00 1	99.25 256	97.57 146	99.96 117	100.00 1	97.44 172	99.79 180	99.70 131	99.65 145	99.81 191

CS-MVS		99.28 103	99.18 107	99.57 140	99.45 224	99.21 162	100.00 1	99.22 267	97.14 180	99.96 117	100.00 1	97.49 166	99.66 195	99.64 145	99.76 140	99.83 182

DVP-MVS		99.83 7	99.78 6	100.00 1	100.00 1	99.99 5	100.00 1	99.42 130	99.04 14	100.00 1	100.00 1	99.53 26	100.00 1	100.00 1	100.00 1	100.00 1
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
test_0728_THIRD									98.79 47	100.00 1	100.00 1	99.61 13	100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 5	100.00 1	99.42 130	99.04 14	100.00 1	100.00 1	99.53 26

SR-MVS		99.68 48	99.58 55	99.98 25	100.00 1	99.95 34	100.00 1	99.64 66	97.59 142	100.00 1	100.00 1	98.99 102	99.99 93	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 63	99.51 71	100.00 1	99.90 119	100.00 1	100.00 1	99.43 122	99.00 25	100.00 1	100.00 1	99.58 19	100.00 1	97.64 250	100.00 1	100.00 1

GST-MVS		99.64 59	99.53 67	99.95 61	100.00 1	99.86 89	100.00 1	99.79 44	97.72 123	99.95 139	100.00 1	98.39 142	100.00 1	99.96 88	99.99 111	100.00 1

test_yl		99.51 76	99.37 84	99.95 61	99.82 126	99.90 65	100.00 1	99.47 80	97.48 155	100.00 1	100.00 1	99.80 4	100.00 1	99.98 79	97.75 216	99.94 128

thisisatest0530		99.37 92	99.27 92	99.69 121	99.59 185	99.41 141	100.00 1	99.46 95	96.46 224	99.90 150	100.00 1	99.44 46	99.85 168	98.97 190	99.58 149	99.80 201

Anonymous20240529		96.93 238	96.22 253	99.05 183	99.79 143	97.30 266	99.16 336	99.47 80	88.51 347	98.69 237	100.00 1	83.50 337	100.00 1	99.83 110	97.02 227	99.83 182

DCV-MVSNet		99.51 76	99.37 84	99.95 61	99.82 126	99.90 65	100.00 1	99.47 80	97.48 155	100.00 1	100.00 1	99.80 4	100.00 1	99.98 79	97.75 216	99.94 128

tttt0517		99.34 96	99.23 99	99.67 123	99.57 193	99.38 143	100.00 1	99.46 95	96.33 232	99.89 153	100.00 1	99.44 46	99.84 170	98.93 192	99.46 153	99.78 203

thisisatest0515		99.42 87	99.31 91	99.74 117	99.59 185	99.55 126	100.00 1	99.46 95	96.65 214	99.92 147	100.00 1	99.44 46	99.85 168	99.09 187	99.63 147	99.81 191

SMA-MVS		99.69 44	99.59 51	99.98 25	99.99 59	99.93 47	100.00 1	99.43 122	97.50 154	100.00 1	100.00 1	99.43 50	100.00 1	100.00 1	100.00 1	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
DPE-MVS		99.79 15	99.73 17	99.99 12	99.99 59	99.98 17	100.00 1	99.42 130	98.91 35	100.00 1	100.00 1	99.22 79	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CHOSEN 280x420		99.85 3	99.87 1	99.80 107	99.99 59	99.97 22	99.97 220	99.98 16	98.96 28	100.00 1	100.00 1	99.96 3	99.42 236	100.00 1	100.00 1	100.00 1

CANet		99.40 88	99.24 96	99.89 88	99.99 59	99.76 104	100.00 1	99.73 57	98.40 67	99.78 171	100.00 1	95.28 216	99.96 136	100.00 1	99.99 111	99.96 120

CANet_DTU		99.02 129	98.90 133	99.41 154	99.88 123	98.71 192	100.00 1	99.29 238	98.84 40	100.00 1	100.00 1	94.02 233	100.00 1	98.08 234	99.96 124	99.52 223

MP-MVS-pluss		99.61 69	99.50 73	99.97 34	99.98 97	99.92 54	100.00 1	99.42 130	97.53 150	99.77 172	100.00 1	98.77 127	100.00 1	99.99 62	100.00 1	99.99 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.81 11	99.77 9	99.94 76	100.00 1	99.86 89	100.00 1	99.42 130	98.87 39	100.00 1	100.00 1	99.65 12	99.96 136	100.00 1	100.00 1	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
TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 25	100.00 1	99.43 122	99.05 13	100.00 1	100.00 1	99.45 45	99.99 93	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xiu_mvs_v1_base_debu		99.35 93	99.21 101	99.79 109	99.67 163	99.71 112	99.78 256	99.36 208	98.13 88	100.00 1	100.00 1	97.00 185	100.00 1	99.83 110	99.07 157	99.66 217

ACMMP_NAP		99.67 53	99.57 58	99.97 34	99.98 97	99.92 54	100.00 1	99.42 130	97.83 116	100.00 1	100.00 1	98.89 118	100.00 1	99.98 79	100.00 1	100.00 1

zzz-MVS		99.68 48	99.59 51	99.97 34	99.99 59	99.91 57	100.00 1	99.42 130	98.32 78	99.94 141	100.00 1	98.65 131	100.00 1	99.96 88	100.00 1	100.00 1

CS-MVS-test		99.18 116	99.11 112	99.39 159	99.40 235	98.63 198	100.00 1	99.20 277	97.06 188	99.97 112	100.00 1	97.11 177	99.68 194	99.66 142	99.57 150	99.85 177

xiu_mvs_v2_base		99.51 76	99.41 77	99.82 100	99.70 156	99.73 110	99.92 235	99.40 184	98.15 86	100.00 1	100.00 1	98.50 139	100.00 1	99.85 107	99.13 155	99.74 208

xiu_mvs_v1_base		99.35 93	99.21 101	99.79 109	99.67 163	99.71 112	99.78 256	99.36 208	98.13 88	100.00 1	100.00 1	97.00 185	100.00 1	99.83 110	99.07 157	99.66 217

xiu_mvs_v1_base_debi		99.35 93	99.21 101	99.79 109	99.67 163	99.71 112	99.78 256	99.36 208	98.13 88	100.00 1	100.00 1	97.00 185	100.00 1	99.83 110	99.07 157	99.66 217

MTAPA		99.68 48	99.59 51	99.97 34	99.99 59	99.91 57	100.00 1	99.42 130	98.32 78	99.94 141	100.00 1	98.65 131	100.00 1	99.96 88	100.00 1	100.00 1

gm-plane-assit					99.52 207	97.26 268			95.86 244		100.00 1		99.43 234	98.76 202

TEST9					100.00 1	99.95 34	100.00 1	99.42 130	97.65 132	100.00 1	100.00 1	99.53 26	99.97 122

train_agg		99.71 36	99.63 43	99.97 34	100.00 1	99.95 34	100.00 1	99.42 130	97.70 127	100.00 1	100.00 1	99.51 31	99.97 122	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 57	100.00 1	99.42 130	97.70 127	100.00 1	100.00 1	99.51 31	99.98 117

cdsmvs_eth3d_5k		24.41 343	32.55 345	0.00 359	0.00 382	0.00 383	0.00 370	99.39 197	0.00 377	0.00 378	100.00 1	93.55 238	0.00 378	0.00 376	0.00 376	0.00 374

agg_prior1		99.71 36	99.63 43	99.95 61	100.00 1	99.88 83	100.00 1	99.42 130	97.72 123	100.00 1	100.00 1	99.51 31	99.97 122	100.00 1	100.00 1	100.00 1

tmp_tt		75.80 336	74.26 338	80.43 349	52.91 381	53.67 379	87.42 368	97.98 360	61.80 368	67.04 371	100.00 1	76.43 357	96.40 350	96.47 281	28.26 373	91.23 364

canonicalmvs		99.03 125	98.73 145	99.94 76	99.75 152	99.95 34	100.00 1	99.30 233	97.64 134	100.00 1	100.00 1	95.22 218	99.97 122	99.76 122	96.90 230	99.91 142

alignmvs		99.38 90	99.21 101	99.91 82	99.73 153	99.92 54	100.00 1	99.51 76	97.61 138	100.00 1	100.00 1	99.06 92	99.93 155	99.83 110	97.12 224	99.90 151

UA-Net		99.06 122	98.83 136	99.74 117	99.52 207	99.40 142	99.08 346	99.45 102	97.64 134	99.83 160	100.00 1	95.80 209	99.94 153	98.35 223	99.80 138	99.88 167

HFP-MVS		99.74 27	99.67 31	99.96 49	100.00 1	99.89 73	100.00 1	99.76 48	97.95 108	100.00 1	100.00 1	99.31 63	100.00 1	99.99 62	100.00 1	100.00 1

region2R		99.72 33	99.64 39	99.97 34	100.00 1	99.90 65	100.00 1	99.74 55	97.86 115	100.00 1	100.00 1	99.19 83	100.00 1	99.99 62	100.00 1	100.00 1

PS-MVSNAJ		99.64 59	99.57 58	99.85 96	99.78 145	99.81 98	99.95 229	99.42 130	98.38 70	100.00 1	100.00 1	98.75 128	100.00 1	99.88 103	99.99 111	99.74 208

#test#		99.75 22	99.68 26	99.96 49	100.00 1	99.89 73	100.00 1	99.76 48	98.07 94	100.00 1	100.00 1	99.31 63	100.00 1	99.99 62	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 44	99.63 43	99.87 93	99.99 59	99.64 122	99.95 229	99.44 112	98.35 76	100.00 1	100.00 1	98.98 103	99.97 122	99.98 79	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 40	99.64 39	99.87 93	100.00 1	99.64 122	99.98 214	99.44 112	98.35 76	99.99 103	100.00 1	99.04 96	99.96 136	99.98 79	100.00 1	100.00 1

Regformer-3		99.73 30	99.65 35	99.96 49	100.00 1	99.89 73	100.00 1	99.44 112	98.40 67	100.00 1	100.00 1	99.03 100	99.97 122	99.99 62	100.00 1	100.00 1

Regformer-4		99.73 30	99.65 35	99.95 61	100.00 1	99.89 73	100.00 1	99.44 112	98.40 67	100.00 1	100.00 1	99.03 100	99.97 122	99.99 62	100.00 1	100.00 1

Regformer-1		99.75 22	99.68 26	99.98 25	100.00 1	99.94 42	100.00 1	99.44 112	98.43 64	100.00 1	100.00 1	99.23 77	99.99 93	99.99 62	100.00 1	100.00 1

Regformer-2		99.75 22	99.68 26	99.97 34	100.00 1	99.94 42	100.00 1	99.44 112	98.42 66	100.00 1	100.00 1	99.22 79	99.99 93	99.99 62	100.00 1	100.00 1

HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 59	99.98 17	100.00 1	99.83 38	98.88 37	99.96 117	100.00 1	99.21 81	100.00 1	100.00 1	100.00 1	99.99 110

XVS		99.79 15	99.73 17	99.98 25	100.00 1	99.94 42	100.00 1	99.75 52	98.67 54	100.00 1	100.00 1	99.16 85	100.00 1	100.00 1	100.00 1	100.00 1

test_prior3		99.81 11	99.78 6	99.90 85	100.00 1	99.75 106	100.00 1	99.73 57	98.82 43	100.00 1	100.00 1	99.47 42	99.97 122	100.00 1	100.00 1	100.00 1

test_prior2							100.00 1		98.82 43	100.00 1	100.00 1	99.47 42		100.00 1	100.00 1

新几何1				99.99 12	100.00 1	99.96 25		99.81 41	97.89 111	100.00 1	100.00 1	99.20 82	100.00 1	97.91 243	100.00 1	100.00 1

旧先验1					99.99 59	99.88 83		99.82 39			100.00 1	99.27 73			100.00 1	100.00 1

原ACMM1				99.93 79	100.00 1	99.80 100		99.66 65	98.18 83	100.00 1	100.00 1	99.43 50	100.00 1	99.50 162	100.00 1	100.00 1

test222					99.99 59	99.90 65	100.00 1	99.69 64	97.66 131	100.00 1	100.00 1	99.30 69			100.00 1	100.00 1

testdata				99.66 125	99.99 59	98.97 182		99.73 57	97.96 107	100.00 1	100.00 1	99.42 52	100.00 1	99.28 177	100.00 1	100.00 1

1314		99.38 90	99.19 105	99.96 49	98.88 279	99.89 73	99.24 321	99.93 29	98.88 37	98.79 233	100.00 1	97.02 181	100.00 1	100.00 1	100.00 1	100.00 1

1121		99.63 63	99.51 71	99.99 12	99.99 59	99.96 25	99.24 321	99.74 55	97.89 111	100.00 1	100.00 1	99.39 56	100.00 1	99.33 172	100.00 1	100.00 1

LFMVS		97.42 215	96.62 235	99.81 103	99.80 140	99.50 130	99.16 336	99.56 69	94.48 286	100.00 1	100.00 1	79.35 351	100.00 1	99.89 102	97.37 222	99.94 128

VDD-MVS		96.58 252	95.99 262	98.34 224	99.52 207	95.33 297	99.18 330	99.38 200	96.64 215	99.77 172	100.00 1	72.51 362	100.00 1	100.00 1	96.94 229	99.70 213

VDDNet		96.39 263	95.55 283	98.90 194	99.27 246	97.45 258	99.15 338	99.92 33	91.28 333	99.98 108	100.00 1	73.55 359	100.00 1	99.85 107	96.98 228	99.24 227

MVS		99.22 113	98.96 122	99.98 25	99.00 268	99.95 34	99.24 321	99.94 21	98.14 87	98.88 223	100.00 1	95.63 213	100.00 1	99.85 107	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 59	99.96 25	100.00 1	99.42 130	99.01 24	100.00 1	100.00 1	99.33 58	100.00 1	100.00 1	100.00 1	100.00 1
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
MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 25	100.00 1	99.95 18	99.11 7	100.00 1	100.00 1	99.60 14	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 6	99.99 12	100.00 1	99.98 17	100.00 1	99.44 112	99.06 11	100.00 1	100.00 1	99.56 20	99.99 93	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 57	99.55 65	99.97 34	99.99 59	99.91 57	100.00 1	99.48 79	97.54 148	100.00 1	100.00 1	98.97 105	99.99 93	99.98 79	100.00 1	100.00 1

EI-MVSNet		97.98 194	97.93 194	98.16 238	99.11 252	97.84 246	99.74 265	99.29 238	94.39 289	98.65 239	100.00 1	97.21 175	98.88 270	97.62 252	95.31 244	97.75 252

CVMVSNet		98.56 164	98.47 165	98.82 199	99.11 252	97.67 252	99.74 265	99.47 80	97.57 146	99.06 214	100.00 1	95.72 211	98.97 260	98.21 230	97.33 223	99.83 182

TESTMET0.1,1		99.08 120	98.96 122	99.44 150	99.63 175	99.38 143	100.00 1	99.45 102	95.53 256	99.48 187	100.00 1	99.71 10	99.02 253	96.84 273	99.99 111	99.91 142

ACMMPR		99.74 27	99.67 31	99.96 49	100.00 1	99.89 73	100.00 1	99.76 48	97.95 108	100.00 1	100.00 1	99.29 70	100.00 1	99.99 62	100.00 1	100.00 1

MP-MVS		99.61 69	99.49 75	99.98 25	99.99 59	99.94 42	100.00 1	99.42 130	97.82 117	99.99 103	100.00 1	98.20 145	100.00 1	99.99 62	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		80.17 330	81.95 333	74.80 352	58.54 379	59.58 378	100.00 1	87.14 379	76.09 363	99.61 183	100.00 1	67.06 368	74.19 375	98.84 197	50.30 369	90.64 365

PGM-MVS		99.69 44	99.61 48	99.95 61	99.99 59	99.85 92	100.00 1	99.58 68	97.69 129	100.00 1	100.00 1	99.44 46	100.00 1	99.79 116	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 57	99.19 5	100.00 1	100.00 1	99.31 63	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 30	99.64 39	99.99 12	100.00 1	99.97 22	100.00 1	99.42 130	98.02 97	100.00 1	100.00 1	99.32 61	99.99 93	100.00 1	100.00 1	100.00 1

casdiffmvs		98.65 158	98.38 170	99.46 148	99.52 207	98.74 191	100.00 1	99.15 291	96.91 196	99.05 215	100.00 1	92.75 250	99.83 171	99.70 131	98.38 183	99.81 191

diffmvs		98.96 137	98.73 145	99.63 127	99.54 197	99.16 165	100.00 1	99.18 284	97.33 168	99.96 117	100.00 1	94.60 227	99.91 159	99.66 142	98.33 189	99.82 187

baseline2		98.99 133	98.93 128	99.18 179	99.26 248	99.15 166	100.00 1	99.46 95	96.71 208	96.79 317	100.00 1	99.42 52	99.25 245	98.75 203	99.94 127	99.15 229

TSAR-MVS + GP.		99.61 69	99.69 23	99.35 162	99.99 59	98.06 231	100.00 1	99.36 208	99.83 2	100.00 1	100.00 1	98.95 109	99.99 93	100.00 1	99.11 156	100.00 1

abl_6		99.53 75	99.40 78	99.92 81	100.00 1	99.76 104	100.00 1	99.42 130	97.21 177	100.00 1	100.00 1	98.12 148	100.00 1	99.82 115	100.00 1	100.00 1

mPP-MVS		99.69 44	99.60 50	99.97 34	100.00 1	99.91 57	100.00 1	99.42 130	97.91 110	100.00 1	100.00 1	99.04 96	100.00 1	100.00 1	100.00 1	100.00 1

XVG-OURS-SEG-HR		98.27 185	98.31 174	98.14 239	99.59 185	95.92 290	100.00 1	99.36 208	98.48 62	99.21 202	100.00 1	89.27 288	99.94 153	99.76 122	99.17 154	98.56 237

DWT-MVSNet_test		99.22 113	99.22 100	99.22 177	99.56 196	97.98 236	99.89 241	99.39 197	97.20 178	99.96 117	100.00 1	99.52 29	99.82 174	99.11 186	98.34 185	99.87 175

MVSFormer		98.94 140	98.82 137	99.28 171	99.45 224	99.49 133	100.00 1	99.13 300	95.46 263	99.97 112	100.00 1	96.76 193	98.59 295	98.63 210	100.00 1	99.74 208

jason		99.11 118	98.96 122	99.59 133	99.17 250	99.31 150	100.00 1	99.13 300	97.38 163	99.83 160	100.00 1	95.54 214	99.72 191	99.57 154	99.97 122	99.74 208
jason: jason.
lupinMVS		99.29 102	99.16 109	99.69 121	99.45 224	99.49 133	100.00 1	99.15 291	97.45 158	99.97 112	100.00 1	96.76 193	99.76 185	99.67 140	100.00 1	99.81 191

HPM-MVS_fast		99.60 72	99.49 75	99.91 82	99.99 59	99.78 101	100.00 1	99.42 130	97.09 184	100.00 1	100.00 1	98.95 109	99.96 136	99.98 79	100.00 1	100.00 1

HPM-MVS		99.59 73	99.50 73	99.89 88	100.00 1	99.70 116	100.00 1	99.42 130	97.46 157	100.00 1	100.00 1	98.60 135	99.96 136	99.99 62	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.30 181	98.36 173	98.13 242	99.58 190	95.91 291	100.00 1	99.36 208	98.69 52	99.23 201	100.00 1	91.20 264	99.92 158	99.34 171	97.82 212	98.56 237

baseline		98.69 156	98.45 166	99.41 154	99.52 207	98.67 195	100.00 1	99.17 289	97.03 190	99.13 208	100.00 1	93.17 243	99.74 188	99.70 131	98.34 185	99.81 191

CHOSEN 1792x2688		99.00 131	98.91 130	99.25 174	99.90 119	97.79 249	100.00 1	99.99 13	98.79 47	98.28 264	100.00 1	93.63 237	99.95 142	99.66 142	99.95 126	100.00 1

EPNet		99.62 67	99.69 23	99.42 153	99.99 59	98.37 209	100.00 1	99.89 35	98.83 41	100.00 1	100.00 1	98.97 105	100.00 1	99.90 100	99.61 148	99.89 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
APD-MVS		99.68 48	99.58 55	99.97 34	99.99 59	99.96 25	100.00 1	99.42 130	97.53 150	100.00 1	100.00 1	99.27 73	99.97 122	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 47	99.07 9	100.00 1	100.00 1	99.39 56	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 5	100.00 1	99.71 63	99.07 9	100.00 1	100.00 1	99.59 17	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 89	99.25 94	99.81 103	99.97 101	99.48 137	100.00 1	99.42 130	95.53 256	100.00 1	100.00 1	98.37 143	99.95 142	99.97 86	100.00 1	100.00 1

CP-MVS		99.67 53	99.58 55	99.95 61	100.00 1	99.84 95	100.00 1	99.42 130	97.77 120	100.00 1	100.00 1	99.07 91	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 17	99.71 20	99.98 25	99.76 150	99.95 34	100.00 1	99.42 130	98.69 52	100.00 1	100.00 1	99.52 29	99.99 93	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
BH-w/o		98.82 149	98.81 139	98.88 196	99.62 178	96.71 281	100.00 1	99.28 243	97.09 184	98.81 231	100.00 1	94.91 223	99.96 136	99.54 158	100.00 1	99.96 120

DELS-MVS		99.62 67	99.56 64	99.82 100	99.92 116	99.45 138	100.00 1	99.78 46	98.92 34	99.73 176	100.00 1	97.70 159	100.00 1	99.93 97	100.00 1	100.00 1
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
BH-untuned		98.64 159	98.65 151	98.60 209	99.59 185	96.17 287	100.00 1	99.28 243	96.67 213	98.41 256	100.00 1	94.52 228	99.83 171	99.41 166	100.00 1	99.81 191

CPTT-MVS		99.49 81	99.38 81	99.85 96	100.00 1	99.54 127	100.00 1	99.42 130	97.58 144	99.98 108	100.00 1	97.43 173	100.00 1	99.99 62	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 98	99.18 107	99.78 113	99.82 126	99.49 133	100.00 1	99.95 18	97.36 164	99.63 181	100.00 1	96.45 201	99.95 142	99.79 116	99.65 145	99.89 156

PVSNet_Blended		99.48 83	99.36 86	99.83 99	99.98 97	99.60 124	100.00 1	100.00 1	97.79 119	100.00 1	100.00 1	96.57 197	99.99 93	100.00 1	99.88 132	99.90 151

BH-RMVSNet		98.46 171	98.08 187	99.59 133	99.61 180	99.19 164	100.00 1	99.28 243	97.06 188	98.95 219	100.00 1	88.99 291	99.82 174	98.83 199	100.00 1	99.77 204

WTY-MVS		99.54 74	99.40 78	99.95 61	99.81 130	99.93 47	100.00 1	100.00 1	97.98 101	99.84 158	100.00 1	98.94 111	99.98 117	99.86 105	98.21 194	99.94 128

DROMVSNet		99.19 115	99.09 113	99.48 147	99.42 228	99.07 169	100.00 1	99.21 273	96.95 192	99.96 117	100.00 1	96.88 191	99.48 226	99.64 145	99.79 139	99.88 167

1112_ss		98.91 142	98.71 148	99.51 143	99.69 157	98.75 188	99.99 196	99.15 291	96.82 199	98.84 228	100.00 1	97.45 170	99.89 160	98.66 206	97.75 216	99.89 156

ab-mvs-re		8.33 344	11.11 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

DP-MVS Recon		99.76 20	99.69 23	99.98 25	100.00 1	99.95 34	100.00 1	99.52 72	97.99 99	99.99 103	100.00 1	99.72 9	100.00 1	99.96 88	100.00 1	100.00 1

MVS_111021_LR		99.70 40	99.65 35	99.88 92	99.96 107	99.70 116	100.00 1	99.97 17	98.96 28	100.00 1	100.00 1	97.93 153	99.95 142	99.99 62	100.00 1	100.00 1

DP-MVS		98.86 146	98.54 161	99.81 103	99.97 101	99.45 138	99.52 296	99.40 184	94.35 290	98.36 257	100.00 1	96.13 204	99.97 122	99.12 185	100.00 1	100.00 1

QAPM		98.99 133	98.66 150	99.96 49	99.01 264	99.87 86	99.88 244	99.93 29	97.99 99	98.68 238	100.00 1	93.17 243	100.00 1	99.32 174	100.00 1	100.00 1

Vis-MVSNet		98.52 168	98.25 176	99.34 163	99.68 160	98.55 200	99.68 278	99.41 182	97.34 167	99.94 141	100.00 1	90.38 276	99.70 193	99.03 189	98.84 162	99.76 206
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		99.08 120	98.91 130	99.59 133	99.65 168	99.38 143	99.78 256	99.24 261	96.70 209	99.51 185	100.00 1	98.44 141	99.52 221	98.47 218	98.39 182	99.88 167

PAPM_NR		99.74 27	99.66 34	99.99 12	100.00 1	99.96 25	100.00 1	99.47 80	97.87 114	100.00 1	100.00 1	99.60 14	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 20	99.68 26	99.99 12	100.00 1	99.96 25	100.00 1	99.47 80	98.16 84	100.00 1	100.00 1	99.51 31	100.00 1	100.00 1	100.00 1	100.00 1

RPSCF		97.37 217	98.24 177	94.76 327	99.80 140	84.57 362	99.99 196	99.05 329	94.95 270	99.82 168	100.00 1	94.03 232	100.00 1	98.15 233	98.38 183	99.70 213

Vis-MVSNet (Re-imp)		98.99 133	98.89 134	99.29 168	99.64 173	98.89 184	99.98 214	99.31 230	96.74 204	99.48 187	100.00 1	98.11 149	99.10 249	98.39 221	98.34 185	99.89 156

MVS_111021_HR		99.71 36	99.63 43	99.93 79	99.95 108	99.83 96	100.00 1	100.00 1	98.89 36	100.00 1	100.00 1	97.85 155	99.95 142	100.00 1	100.00 1	100.00 1

CSCG		99.28 103	99.35 88	99.05 183	99.99 59	97.15 271	100.00 1	99.47 80	97.44 159	99.42 191	100.00 1	97.83 156	100.00 1	99.99 62	100.00 1	100.00 1

API-MVS		99.72 33	99.70 21	99.79 109	99.97 101	99.37 146	99.96 224	99.94 21	98.48 62	100.00 1	100.00 1	98.92 114	100.00 1	100.00 1	100.00 1	100.00 1

EPP-MVSNet		99.10 119	99.00 118	99.40 156	99.51 212	98.68 194	99.92 235	99.43 122	95.47 262	99.65 180	100.00 1	99.51 31	99.76 185	99.53 160	98.00 202	99.75 207

PMMVS		99.12 117	98.97 121	99.58 137	99.57 193	98.98 180	100.00 1	99.30 233	97.14 180	99.96 117	100.00 1	96.53 200	99.82 174	99.70 131	98.49 174	99.94 128

PAPM		99.78 17	99.76 12	99.85 96	99.01 264	99.95 34	100.00 1	99.75 52	99.37 3	99.99 103	100.00 1	99.76 8	99.60 197	100.00 1	100.00 1	100.00 1

ACMMP		99.65 57	99.57 58	99.89 88	99.99 59	99.66 120	99.75 264	99.73 57	98.16 84	99.75 175	100.00 1	98.90 116	100.00 1	99.96 88	99.88 132	100.00 1
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
CNLPA		99.72 33	99.65 35	99.91 82	99.97 101	99.72 111	100.00 1	99.47 80	98.43 64	99.88 155	100.00 1	99.14 88	100.00 1	99.97 86	100.00 1	100.00 1

PHI-MVS		99.50 79	99.39 80	99.82 100	100.00 1	99.45 138	100.00 1	99.94 21	96.38 229	100.00 1	100.00 1	98.18 146	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 101	99.25 94	99.44 150	100.00 1	98.32 215	100.00 1	99.86 37	98.04 96	100.00 1	100.00 1	96.10 205	100.00 1	99.55 155	99.73 141	100.00 1

F-COLMAP		99.64 59	99.64 39	99.67 123	99.99 59	99.07 169	100.00 1	99.44 112	98.30 80	99.90 150	100.00 1	99.18 84	99.99 93	99.91 99	100.00 1	99.94 128

DeepPCF-MVS	98.03 4	98.54 166	99.72 19	94.98 324	99.99 59	84.94 361	100.00 1	99.42 130	99.98 1	100.00 1	100.00 1	98.11 149	100.00 1	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 105	99.38 81	98.96 191	99.95 108	97.06 275	100.00 1	99.40 184	98.83 41	99.88 155	100.00 1	97.01 182	99.86 164	99.47 163	99.84 135	99.97 117

DeepC-MVS	97.84 5	99.00 131	98.80 140	99.60 131	99.93 113	99.03 174	100.00 1	99.40 184	98.61 58	99.33 197	100.00 1	92.23 255	99.95 142	99.74 124	99.96 124	99.83 182
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	98.92 1	99.75 22	99.67 31	99.99 12	99.99 59	99.96 25	99.73 269	99.52 72	99.06 11	100.00 1	100.00 1	98.80 126	100.00 1	99.95 94	100.00 1	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
MG-MVS		99.75 22	99.68 26	99.97 34	100.00 1	99.91 57	99.98 214	99.47 80	99.09 8	100.00 1	100.00 1	98.59 136	100.00 1	99.95 94	100.00 1	100.00 1

AdaColmap		99.44 86	99.26 93	99.95 61	100.00 1	99.86 89	99.70 274	99.99 13	98.53 60	99.90 150	100.00 1	95.34 215	100.00 1	99.92 98	100.00 1	100.00 1

PLC	98.56 2	99.70 40	99.74 16	99.58 137	100.00 1	98.79 187	100.00 1	99.54 70	98.58 59	99.96 117	100.00 1	99.59 17	100.00 1	100.00 1	100.00 1	99.94 128
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	98.23 3	98.69 156	98.37 172	99.62 128	99.78 145	99.02 175	99.23 327	99.06 327	96.43 225	98.08 273	100.00 1	94.72 225	99.95 142	98.16 232	99.91 130	99.90 151
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		99.49 81	99.36 86	99.89 88	99.97 101	99.66 120	99.74 265	99.95 18	97.89 111	100.00 1	100.00 1	96.71 195	100.00 1	100.00 1	100.00 1	100.00 1
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
3Dnovator+	95.58 15	99.03 125	98.71 148	99.96 49	98.99 271	99.89 73	100.00 1	99.51 76	98.96 28	98.32 261	100.00 1	92.78 249	100.00 1	99.87 104	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 122	98.76 143	99.96 49	98.86 282	99.90 65	99.98 214	99.93 29	98.95 31	98.49 252	100.00 1	92.91 248	100.00 1	99.71 129	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 204	97.37 213	98.45 217	99.94 111	95.70 294	100.00 1	99.40 184	97.65 132	99.53 184	100.00 1	99.31 63	99.66 195	80.48 362	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	95.20 17	98.76 151	98.41 167	99.78 113	98.89 278	99.81 98	99.99 196	99.76 48	98.02 97	98.02 278	100.00 1	91.44 261	100.00 1	99.63 148	99.97 122	99.55 221

MSDG		98.90 144	98.63 153	99.70 120	99.92 116	99.25 156	100.00 1	99.37 202	95.71 249	99.40 196	100.00 1	96.58 196	99.95 142	96.80 276	99.94 127	99.91 142

LS3D		99.31 100	99.13 110	99.87 93	99.99 59	99.71 112	99.55 292	99.46 95	97.32 169	99.82 168	100.00 1	96.85 192	99.97 122	99.14 183	100.00 1	99.92 140

COLMAP_ROB	97.10 7	98.29 183	98.17 182	98.65 207	99.94 111	97.39 260	99.30 317	99.40 184	95.64 251	97.75 289	100.00 1	92.69 252	99.95 142	98.89 195	99.92 129	98.62 236
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous202405211		97.87 196	97.53 207	98.90 194	99.81 130	96.70 282	99.35 313	99.46 95	92.98 320	98.83 230	99.99 175	90.63 271	100.00 1	99.70 131	97.03 226	100.00 1

VNet		99.04 124	98.75 144	99.90 85	99.81 130	99.75 106	99.50 298	99.47 80	98.36 74	100.00 1	99.99 175	94.66 226	100.00 1	99.90 100	97.09 225	99.96 120

baseline1		98.91 142	98.61 155	99.81 103	99.71 154	99.77 102	99.78 256	99.44 112	97.51 153	98.81 231	99.99 175	98.25 144	99.76 185	98.60 213	95.41 240	99.89 156

OPM-MVS		97.21 222	97.18 222	97.32 283	98.08 313	94.66 315	100.00 1	99.28 243	98.65 56	98.92 220	99.98 178	86.03 322	99.56 207	98.28 228	95.41 240	97.72 288
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
AllTest		98.55 165	98.40 169	98.99 188	99.93 113	97.35 262	100.00 1	99.40 184	97.08 186	99.09 210	99.98 178	93.37 239	99.95 142	96.94 269	99.84 135	99.68 215

TestCases				98.99 188	99.93 113	97.35 262		99.40 184	97.08 186	99.09 210	99.98 178	93.37 239	99.95 142	96.94 269	99.84 135	99.68 215

LPG-MVS_test		97.31 219	97.32 215	97.28 285	98.85 283	94.60 318	100.00 1	99.37 202	97.35 165	98.85 226	99.98 178	86.66 315	99.56 207	99.55 155	95.26 246	97.70 296

LGP-MVS_train				97.28 285	98.85 283	94.60 318		99.37 202	97.35 165	98.85 226	99.98 178	86.66 315	99.56 207	99.55 155	95.26 246	97.70 296

cascas		98.43 172	98.07 188	99.50 145	99.65 168	99.02 175	100.00 1	99.22 267	94.21 293	99.72 177	99.98 178	92.03 258	99.93 155	99.68 138	98.12 199	99.54 222

ACMP	97.00 8	97.19 223	97.16 223	97.27 287	98.97 273	94.58 321	100.00 1	99.32 225	97.97 103	97.45 300	99.98 178	85.79 324	99.56 207	99.70 131	95.24 249	97.67 307
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	97.17 6	97.37 217	97.40 211	97.29 284	99.01 264	94.64 317	100.00 1	99.25 256	98.07 94	98.44 255	99.98 178	87.38 309	99.55 212	99.25 178	95.19 251	97.69 300
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP_MVS		97.71 202	97.82 197	97.37 279	99.00 268	94.80 309	100.00 1	99.40 184	99.00 25	99.08 212	99.97 186	88.58 299	99.55 212	99.79 116	95.57 238	97.76 241

plane_prior4											99.97 186

SixPastTwentyTwo		95.71 289	95.49 286	96.38 311	97.42 339	93.01 334	99.84 247	98.23 351	94.75 274	95.98 330	99.97 186	85.35 326	98.43 308	94.71 303	93.17 278	97.69 300

NP-MVS					99.07 256	94.81 308					99.97 186

HQP-MVS		97.73 200	97.85 195	97.39 278	99.07 256	94.82 306	100.00 1	99.40 184	99.04 14	99.17 203	99.97 186	88.61 297	99.57 203	99.79 116	95.58 234	97.77 239

ITE_SJBPF				96.84 303	98.96 274	93.49 330		98.12 354	98.12 91	98.35 258	99.97 186	84.45 328	99.56 207	95.63 291	95.25 248	97.49 329

ACMH+	96.20 13	96.49 257	96.33 250	97.00 294	99.06 260	93.80 328	99.81 251	99.31 230	97.32 169	95.89 332	99.97 186	82.62 341	99.54 215	98.34 224	94.63 264	97.65 313

CLD-MVS		97.64 204	97.74 199	97.36 280	99.01 264	94.76 314	100.00 1	99.34 221	99.30 4	99.00 217	99.97 186	87.49 308	99.57 203	99.96 88	95.58 234	97.75 252
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMH	96.25 11	96.77 242	96.62 235	97.21 288	98.96 274	94.43 323	99.64 281	99.33 223	97.43 160	96.55 322	99.97 186	83.52 336	99.54 215	99.07 188	95.13 255	97.66 308
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		99.25 110	99.01 117	99.95 61	99.81 130	99.87 86	100.00 1	99.94 21	97.13 182	99.83 160	99.96 195	97.01 182	100.00 1	99.59 150	97.85 209	99.98 112

tfpn200view9		99.26 107	99.03 115	99.96 49	99.81 130	99.89 73	100.00 1	99.94 21	97.23 174	99.83 160	99.96 195	97.04 178	100.00 1	99.59 150	97.85 209	99.98 112

RRT_MVS		98.48 170	98.41 167	98.70 205	99.64 173	99.67 119	100.00 1	99.10 308	96.26 235	98.75 234	99.96 195	99.04 96	98.75 282	98.42 219	94.51 267	97.68 302

thres600view7		99.24 112	99.00 118	99.95 61	99.81 130	99.87 86	100.00 1	99.94 21	97.13 182	99.83 160	99.96 195	97.01 182	100.00 1	99.54 158	97.77 215	99.97 117

thres400		99.26 107	99.03 115	99.95 61	99.81 130	99.89 73	100.00 1	99.94 21	97.23 174	99.83 160	99.96 195	97.04 178	100.00 1	99.59 150	97.85 209	99.97 117

thres200		99.27 105	99.04 114	99.96 49	99.81 130	99.90 65	100.00 1	99.94 21	97.31 171	99.83 160	99.96 195	97.04 178	100.00 1	99.62 149	97.88 207	99.98 112

EPNet_dtu		98.53 167	98.23 179	99.43 152	99.92 116	99.01 177	99.96 224	99.47 80	98.80 45	99.96 117	99.96 195	98.56 137	99.30 243	87.78 349	99.68 143	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Fast-Effi-MVS+		98.40 177	98.02 191	99.55 142	99.63 175	99.06 171	100.00 1	99.15 291	95.07 267	99.42 191	99.95 202	93.26 242	99.73 190	97.44 256	98.24 192	99.87 175

nrg030		97.64 204	97.27 219	98.75 203	98.34 298	99.53 128	100.00 1	99.22 267	96.21 237	98.27 266	99.95 202	94.40 229	98.98 258	99.23 180	89.78 319	97.75 252

test0.0.03 1		98.12 189	98.03 190	98.39 220	99.11 252	98.07 230	100.00 1	99.93 29	96.70 209	96.91 313	99.95 202	99.31 63	98.19 317	91.93 327	98.44 177	98.91 232

OurMVSNet-221017-0		96.14 278	95.98 263	96.62 307	97.49 336	93.44 331	99.92 235	98.16 352	95.86 244	97.65 291	99.95 202	85.71 325	98.78 277	94.93 302	94.18 269	97.64 316

sss		99.45 85	99.34 90	99.80 107	99.76 150	99.50 130	100.00 1	99.91 34	97.72 123	99.98 108	99.94 206	98.45 140	100.00 1	99.53 160	98.75 167	99.89 156

TR-MVS		98.14 188	97.74 199	99.33 164	99.59 185	98.28 217	99.27 318	99.21 273	96.42 226	99.15 207	99.94 206	88.87 294	99.79 180	98.88 196	98.29 190	99.93 138

USDC		95.90 286	95.70 276	96.50 309	98.60 290	92.56 341	100.00 1	98.30 350	97.77 120	96.92 311	99.94 206	81.25 347	99.45 233	93.54 317	94.96 261	97.49 329

RRT_test8_iter05		98.32 180	98.23 179	98.60 209	99.65 168	99.23 161	100.00 1	99.09 314	95.65 250	98.07 274	99.93 209	99.25 75	98.82 273	98.90 194	93.50 274	97.75 252

mvs-test1		98.89 145	98.99 120	98.60 209	99.77 148	95.96 288	100.00 1	98.94 339	97.61 138	99.93 146	99.92 210	95.89 206	99.93 155	99.36 168	99.50 151	99.90 151

lessismore_v0				96.05 315	97.55 332	91.80 345		99.22 267		91.87 352	99.91 211	83.50 337	98.68 286	92.48 324	90.42 316	97.68 302

HyFIR lowres test		99.32 99	99.24 96	99.58 137	99.95 108	99.26 154	100.00 1	99.99 13	96.72 207	99.29 199	99.91 211	99.49 38	99.47 228	99.74 124	98.08 201	100.00 1

Effi-MVS+		98.58 162	98.24 177	99.61 129	99.60 182	99.26 154	97.85 361	99.10 308	96.22 236	99.97 112	99.89 213	93.75 235	99.77 184	99.43 164	98.34 185	99.81 191

VPNet		96.41 259	95.76 273	98.33 225	98.61 289	98.30 216	99.48 299	99.45 102	96.98 191	98.87 225	99.88 214	81.57 344	98.93 262	99.22 182	87.82 335	97.76 241

TinyColmap		95.50 292	95.12 295	96.64 306	98.69 286	93.00 335	99.40 308	97.75 364	96.40 228	96.14 328	99.87 215	79.47 350	99.50 224	93.62 316	94.72 263	97.40 334

LF4IMVS		96.19 272	96.18 254	96.23 314	98.26 305	92.09 343	100.00 1	97.89 362	97.82 117	97.94 281	99.87 215	82.71 340	99.38 238	97.41 258	93.71 270	97.20 339

testgi		96.18 273	95.93 265	96.93 299	98.98 272	94.20 326	100.00 1	99.07 320	97.16 179	96.06 329	99.86 217	84.08 334	97.79 340	90.38 339	97.80 214	98.81 233

MVS_Test		98.93 141	98.65 151	99.77 115	99.62 178	99.50 130	99.99 196	99.19 278	95.52 258	99.96 117	99.86 217	96.54 199	99.98 117	98.65 208	98.48 175	99.82 187

test_djsdf		97.55 210	97.38 212	98.07 245	97.50 334	97.99 235	100.00 1	99.13 300	95.46 263	98.47 253	99.85 219	92.01 259	98.59 295	98.63 210	95.36 242	97.62 319

HY-MVS	96.53 9	99.50 79	99.35 88	99.96 49	99.81 130	99.93 47	99.64 281	100.00 1	97.97 103	99.84 158	99.85 219	98.94 111	99.99 93	99.86 105	98.23 193	99.95 125

TDRefinement		91.93 316	90.48 324	96.27 313	81.60 372	92.65 340	99.10 343	97.61 365	93.96 299	93.77 346	99.85 219	80.03 348	99.53 220	97.82 246	70.59 362	96.63 350

XXY-MVS		97.14 227	96.63 234	98.67 206	98.65 287	98.92 183	99.54 294	99.29 238	95.57 255	97.63 292	99.83 222	87.79 306	99.35 241	98.39 221	92.95 281	97.75 252

CDS-MVSNet		98.96 137	98.95 126	99.01 187	99.48 218	98.36 211	99.93 234	99.37 202	96.79 201	99.31 198	99.83 222	99.77 7	98.91 264	98.07 235	97.98 203	99.77 204
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DeepMVS_CX				89.98 342	98.90 277	71.46 369		99.18 284	97.61 138	96.92 311	99.83 222	86.07 320	99.83 171	96.02 286	97.65 220	98.65 235

FIs		97.95 195	97.73 201	98.62 208	98.53 293	99.24 158	100.00 1	99.43 122	96.74 204	97.87 284	99.82 225	95.27 217	98.89 267	98.78 200	93.07 279	97.74 275

FC-MVSNet-test		97.84 197	97.63 205	98.45 217	98.30 303	99.05 172	100.00 1	99.43 122	96.63 217	97.61 295	99.82 225	95.19 219	98.57 298	98.64 209	93.05 280	97.73 282

mvs_anonymous		98.80 150	98.60 157	99.38 160	99.57 193	99.24 158	100.00 1	99.21 273	95.87 242	98.92 220	99.82 225	96.39 202	99.03 252	99.13 184	98.50 173	99.88 167

ab-mvs		98.42 174	98.02 191	99.61 129	99.71 154	99.00 179	99.10 343	99.64 66	96.70 209	99.04 216	99.81 228	90.64 270	99.98 117	99.64 145	97.93 205	99.84 179

TAMVS		98.76 151	98.73 145	98.86 197	99.44 227	97.69 251	99.57 290	99.34 221	96.57 218	99.12 209	99.81 228	98.83 123	99.16 247	97.97 242	97.91 206	99.73 212

PatchMatch-RL		99.02 129	98.78 141	99.74 117	99.99 59	99.29 151	100.00 1	100.00 1	98.38 70	99.89 153	99.81 228	93.14 245	99.99 93	97.85 245	99.98 119	99.95 125

hse-mvs2		96.79 241	96.38 245	98.04 255	99.68 160	95.54 296	99.81 251	99.42 130	98.21 81	100.00 1	99.80 231	97.49 166	99.46 232	99.72 127	73.27 361	99.12 230

AUN-MVS		96.26 270	95.67 280	98.06 249	99.68 160	95.60 295	99.82 250	99.42 130	96.78 202	99.88 155	99.80 231	94.84 224	99.47 228	97.48 255	73.29 360	99.12 230

MVSTER		98.58 162	98.52 162	98.77 202	99.65 168	99.68 118	100.00 1	99.29 238	95.63 252	98.65 239	99.80 231	99.78 6	98.88 270	98.59 214	95.31 244	97.73 282

h-mvs33		97.03 233	96.53 237	98.51 214	99.79 143	95.90 292	99.45 302	99.45 102	98.21 81	100.00 1	99.78 234	97.49 166	99.99 93	99.72 127	74.92 358	99.65 220

EU-MVSNet		96.63 249	96.53 237	96.94 298	97.59 330	96.87 278	99.76 263	99.47 80	96.35 230	96.85 315	99.78 234	92.57 253	96.27 353	95.33 295	91.08 308	97.68 302

PVSNet_0	93.57 19	96.41 259	95.74 274	98.41 219	99.84 125	95.22 299	100.00 1	100.00 1	98.08 93	97.55 298	99.78 234	84.40 329	100.00 1	100.00 1	81.99 350	100.00 1

bset_n11_16_dypcd		97.66 203	97.54 206	98.04 255	96.88 345	98.04 233	100.00 1	99.25 256	95.72 248	98.47 253	99.73 237	96.23 203	98.34 312	98.18 231	93.37 277	97.74 275

K. test v3		95.46 293	95.14 294	96.40 310	97.53 333	93.40 332	99.99 196	99.23 265	95.49 261	92.70 351	99.73 237	84.26 330	98.12 322	93.94 314	93.38 276	97.68 302

IterMVS-SCA-FT		96.72 246	96.42 244	97.62 273	99.40 235	96.83 279	99.99 196	99.14 296	94.65 279	97.55 298	99.72 239	89.65 283	98.31 313	95.62 292	92.05 293	97.73 282

pm-mvs1		95.76 288	95.01 296	98.00 258	98.23 307	97.45 258	99.24 321	99.04 332	93.13 319	95.93 331	99.72 239	86.28 318	98.84 272	95.62 292	87.92 334	97.72 288

tfpnnormal		96.36 264	95.69 279	98.37 222	98.55 291	98.71 192	99.69 276	99.45 102	93.16 318	96.69 321	99.71 241	88.44 301	98.99 257	94.17 309	91.38 305	97.41 333

pmmvs4		97.17 224	96.80 228	98.27 228	97.68 325	98.64 197	100.00 1	99.18 284	94.22 292	98.55 245	99.71 241	93.67 236	98.47 306	95.66 290	92.57 287	97.71 295

TransMVSNet (Re)		94.78 299	93.72 303	97.93 264	98.34 298	97.88 243	99.23 327	97.98 360	91.60 331	94.55 341	99.71 241	87.89 304	98.36 310	89.30 346	84.92 346	97.56 325

ADS-MVSNet2		98.28 184	98.51 163	97.62 273	99.51 212	95.03 302	99.24 321	99.41 182	95.52 258	99.96 117	99.70 244	97.57 163	97.94 337	97.11 265	98.54 171	99.88 167

ADS-MVSNet		98.70 155	98.51 163	99.28 171	99.51 212	98.39 206	99.24 321	99.44 112	95.52 258	99.96 117	99.70 244	97.57 163	99.58 202	97.11 265	98.54 171	99.88 167

jajsoiax		97.07 230	96.79 230	97.89 266	97.28 342	97.12 272	99.95 229	99.19 278	96.55 219	97.31 303	99.69 246	87.35 311	98.91 264	98.70 205	95.12 256	97.66 308

mvs_tets		97.00 235	96.69 232	97.94 262	97.41 341	97.27 267	99.60 288	99.18 284	96.51 223	97.35 302	99.69 246	86.53 317	98.91 264	98.84 197	95.09 257	97.65 313

test123		79.44 332	79.23 335	80.05 350	80.03 373	71.72 368	100.00 1	77.93 381	62.52 367	94.81 338	99.69 246	78.21 354	74.53 374	92.57 322	27.33 374	93.90 360

IB-MVS	96.24 12	97.54 211	96.95 224	99.33 164	99.67 163	98.10 229	100.00 1	99.47 80	97.42 161	99.26 200	99.69 246	98.83 123	99.89 160	99.43 164	78.77 356	100.00 1
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
GG-mvs-BLEND				99.59 133	99.54 197	99.49 133	99.17 335	99.52 72		99.96 117	99.68 250	100.00 1	99.33 242	99.71 129	99.99 111	99.96 120

WR-MVS		97.09 228	96.64 233	98.46 216	98.43 295	99.09 168	99.97 220	99.33 223	95.62 253	97.76 286	99.67 251	91.17 265	98.56 300	98.49 217	89.28 324	97.74 275

tpm2		98.64 159	98.58 159	98.81 201	99.42 228	97.12 272	99.69 276	99.37 202	93.63 306	99.94 141	99.67 251	98.96 108	99.47 228	98.62 212	97.95 204	99.83 182

UniMVSNet_ETH3D		95.28 295	94.41 300	97.89 266	98.91 276	95.14 300	99.13 340	99.35 216	92.11 328	97.17 307	99.66 253	70.28 365	99.36 239	97.88 244	95.18 252	99.16 228

IterMVS		96.76 243	96.46 242	97.63 271	99.41 230	96.89 277	99.99 196	99.13 300	94.74 276	97.59 297	99.66 253	89.63 285	98.28 315	95.71 288	92.31 290	97.72 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-CasMVS		96.34 266	95.78 272	98.03 257	98.18 310	98.27 218	99.71 272	99.32 225	94.75 274	96.82 316	99.65 255	86.98 314	98.15 319	97.74 247	88.85 328	97.66 308

DU-MVS		96.93 238	96.49 240	98.22 232	98.31 301	98.41 203	100.00 1	99.37 202	96.41 227	97.76 286	99.65 255	92.14 256	98.50 303	97.98 239	86.84 340	97.75 252

CP-MVSNet		96.73 244	96.25 252	98.18 235	98.21 308	98.67 195	99.77 261	99.32 225	95.06 268	97.20 306	99.65 255	90.10 277	98.19 317	98.06 236	88.90 327	97.66 308

NR-MVSNet		96.63 249	96.04 260	98.38 221	98.31 301	98.98 180	99.22 329	99.35 216	95.87 242	94.43 344	99.65 255	92.73 251	98.40 309	96.78 277	88.05 333	97.75 252

GA-MVS		97.72 201	97.27 219	99.06 181	99.24 249	97.93 240	100.00 1	99.24 261	95.80 247	98.99 218	99.64 259	89.77 281	99.36 239	95.12 300	97.62 221	99.89 156

UniMVSNet_NR-MVSNet		97.16 225	96.80 228	98.22 232	98.38 297	98.41 203	100.00 1	99.45 102	96.14 238	97.76 286	99.64 259	95.05 220	98.50 303	97.98 239	86.84 340	97.75 252

TranMVSNet+NR-MVSNet		96.45 258	96.01 261	97.79 269	98.00 316	97.62 254	100.00 1	99.35 216	95.98 240	97.31 303	99.64 259	90.09 278	98.00 334	96.89 272	86.80 343	97.75 252

tpmrst		98.98 136	98.93 128	99.14 180	99.61 180	97.74 250	99.52 296	99.36 208	96.05 239	99.98 108	99.64 259	99.04 96	99.86 164	98.94 191	98.19 196	99.82 187

cl____		97.54 211	97.32 215	98.18 235	99.47 221	98.14 226	100.00 1	99.10 308	94.16 296	97.60 296	99.63 263	97.52 165	98.65 289	96.47 281	91.97 296	97.76 241

DIV-MVS_self_test		97.52 213	97.35 214	98.05 253	99.46 223	98.11 227	100.00 1	99.10 308	94.21 293	97.62 294	99.63 263	97.65 160	98.29 314	96.47 281	91.98 295	97.76 241

Fast-Effi-MVS+-dtu		98.38 178	98.56 160	97.82 268	99.58 190	94.44 322	100.00 1	99.16 290	96.75 203	99.51 185	99.63 263	95.03 221	99.60 197	97.71 248	99.67 144	99.42 225

MDTV_nov1_ep13			98.94 127		99.53 200	98.36 211	99.39 309	99.46 95	96.54 220	99.99 103	99.63 263	98.92 114	99.86 164	98.30 227	98.71 168

ppachtmachnet_test		96.17 275	95.89 266	97.02 293	97.61 328	95.24 298	99.99 196	99.24 261	93.31 314	96.71 320	99.62 267	94.34 230	98.07 329	89.87 341	92.30 291	97.75 252

anonymousdsp		97.16 225	96.88 226	98.00 258	97.08 344	98.06 231	99.81 251	99.15 291	94.58 280	97.84 285	99.62 267	90.49 273	98.60 293	97.98 239	95.32 243	97.33 337

miper_lstm_enhance		97.40 216	97.28 217	97.75 270	99.48 218	97.52 256	100.00 1	99.07 320	94.08 297	98.01 279	99.61 269	97.38 174	97.98 335	96.44 284	91.47 304	97.76 241

pmmvs6		93.64 307	92.87 312	95.94 317	97.47 338	91.41 348	98.92 350	99.02 334	87.84 351	95.01 337	99.61 269	77.24 356	98.77 280	94.33 307	86.41 344	97.63 317

PS-MVSNAJss		98.03 192	98.06 189	97.94 262	97.63 326	97.33 265	99.89 241	99.23 265	96.27 234	98.03 276	99.59 271	98.75 128	98.78 277	98.52 216	94.61 265	97.70 296

SCA		98.30 181	97.98 193	99.23 175	99.41 230	98.25 219	99.99 196	99.45 102	96.91 196	99.76 174	99.58 272	89.65 283	99.54 215	98.31 225	98.79 163	99.91 142

Patchmatch-test		97.83 198	97.42 209	99.06 181	99.08 255	97.66 253	98.66 354	99.21 273	93.65 305	98.25 268	99.58 272	99.47 42	99.57 203	90.25 340	98.59 170	99.95 125

PatchmatchNet		99.03 125	98.96 122	99.26 173	99.49 216	98.33 213	99.38 310	99.45 102	96.64 215	99.96 117	99.58 272	99.49 38	99.50 224	97.63 251	99.00 160	99.93 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet		95.52 291	94.99 297	97.08 290	97.49 336	96.45 286	100.00 1	99.25 256	93.82 300	96.17 327	99.57 275	87.81 305	97.18 343	94.57 304	86.26 345	97.62 319

eth_miper_zixun_eth		97.47 214	97.28 217	98.06 249	99.41 230	97.94 239	99.62 286	99.08 316	94.46 287	98.19 271	99.56 276	96.91 190	98.50 303	96.78 277	91.49 302	97.74 275

PEN-MVS		96.01 283	95.48 288	97.58 275	97.74 323	97.26 268	99.90 238	99.29 238	94.55 281	96.79 317	99.55 277	87.38 309	97.84 339	96.92 271	87.24 338	97.65 313

CMPMVS	66.12 22	90.65 324	92.04 317	86.46 346	96.18 350	66.87 372	98.03 360	99.38 200	83.38 359	85.49 362	99.55 277	77.59 355	98.80 276	94.44 306	94.31 268	93.72 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IterMVS-LS		97.56 209	97.44 208	97.92 265	99.38 240	97.90 241	99.89 241	99.10 308	94.41 288	98.32 261	99.54 279	97.21 175	98.11 324	97.50 254	91.62 300	97.75 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_part1		96.98 236	96.36 247	98.85 198	99.78 145	99.77 102	99.63 283	99.30 233	92.74 325	97.05 308	99.52 280	93.10 246	99.02 253	98.02 237	88.10 332	97.76 241

dp		98.72 153	98.61 155	99.03 186	99.53 200	97.39 260	99.45 302	99.39 197	95.62 253	99.94 141	99.52 280	98.83 123	99.82 174	96.77 279	98.42 179	99.89 156

LTVRE_ROB	95.29 16	96.32 267	96.10 257	96.99 295	98.55 291	93.88 327	99.45 302	99.28 243	94.50 285	96.46 323	99.52 280	84.86 327	99.48 226	97.26 263	95.03 258	97.59 323
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
v7n		96.06 282	95.42 291	97.99 260	97.58 331	97.35 262	99.86 245	99.11 306	92.81 324	97.91 283	99.49 283	90.99 268	98.92 263	92.51 323	88.49 330	97.70 296

test_0402		94.35 301	93.70 304	96.32 312	97.92 318	93.60 329	99.61 287	98.85 345	88.19 350	94.68 340	99.48 284	80.01 349	98.58 297	89.39 345	95.15 254	96.77 347

Baseline_NR-MVSNet		96.16 276	95.70 276	97.56 276	98.28 304	96.79 280	100.00 1	97.86 363	91.93 330	97.63 292	99.47 285	92.14 256	98.35 311	97.13 264	86.83 342	97.54 326

Anonymous20231211		96.29 268	95.70 276	98.07 245	99.80 140	97.49 257	99.15 338	99.40 184	89.11 344	97.75 289	99.45 286	88.93 293	98.98 258	98.26 229	89.47 322	97.73 282

pmmvs5		95.94 285	95.61 281	96.95 297	97.42 339	94.66 315	100.00 1	98.08 356	93.60 307	97.05 308	99.43 287	87.02 312	98.46 307	95.76 287	92.12 292	97.72 288

v148		96.29 268	95.84 268	97.63 271	97.74 323	96.53 285	100.00 1	99.07 320	93.52 308	98.01 279	99.42 288	91.22 262	98.60 293	96.37 285	87.22 339	97.75 252

miper_enhance_ethall		98.33 179	98.27 175	98.51 214	99.66 167	99.04 173	100.00 1	99.22 267	97.53 150	98.51 250	99.38 289	99.49 38	98.75 282	98.02 237	92.61 284	97.76 241

CostFormer		98.84 147	98.77 142	99.04 185	99.41 230	97.58 255	99.67 279	99.35 216	94.66 278	99.96 117	99.36 290	99.28 72	99.74 188	99.41 166	97.81 213	99.81 191

tpm		98.24 186	98.22 181	98.32 226	99.13 251	95.79 293	99.53 295	99.12 305	95.20 266	99.96 117	99.36 290	97.58 162	99.28 244	97.41 258	96.67 231	99.88 167

EPMVS		99.25 110	99.13 110	99.60 131	99.60 182	99.20 163	99.60 288	100.00 1	96.93 193	99.92 147	99.36 290	99.05 93	99.71 192	98.77 201	98.94 161	99.90 151

XVG-ACMP-BASELINE		96.60 251	96.52 239	96.84 303	98.41 296	93.29 333	99.99 196	99.32 225	97.76 122	98.51 250	99.29 293	81.95 343	99.54 215	98.40 220	95.03 258	97.68 302

tpmvs		98.59 161	98.38 170	99.23 175	99.69 157	97.90 241	99.31 316	99.47 80	94.52 283	99.68 179	99.28 294	97.64 161	99.89 160	97.71 248	98.17 198	99.89 156

v1921920		96.16 276	95.50 284	98.14 239	97.88 321	97.96 237	99.99 196	99.07 320	93.33 313	98.60 243	99.24 295	89.37 287	98.71 285	91.28 330	90.74 312	97.75 252

cl22		98.23 187	98.11 185	98.58 213	99.82 126	99.01 177	100.00 1	99.28 243	96.92 195	98.33 260	99.21 296	98.09 151	98.97 260	98.72 204	92.61 284	97.76 241

miper_ehance_all_eth		97.81 199	97.66 203	98.23 231	99.49 216	98.37 209	99.99 196	99.11 306	94.78 273	98.25 268	99.21 296	98.18 146	98.57 298	97.35 261	92.61 284	97.76 241

c3_l		97.58 208	97.42 209	98.06 249	99.48 218	98.16 223	99.96 224	99.10 308	94.54 282	98.13 272	99.20 298	97.87 154	98.25 316	97.28 262	91.20 307	97.75 252

test-LLR		99.03 125	98.91 130	99.40 156	99.40 235	99.28 152	100.00 1	99.45 102	96.70 209	99.42 191	99.12 299	99.31 63	99.01 255	96.82 274	99.99 111	99.91 142

test-mter		98.96 137	98.82 137	99.40 156	99.40 235	99.28 152	100.00 1	99.45 102	95.44 265	99.42 191	99.12 299	99.70 11	99.01 255	96.82 274	99.99 111	99.91 142

v144192		96.40 262	95.81 269	98.17 237	97.89 320	98.11 227	99.99 196	99.06 327	93.39 311	98.75 234	99.09 301	90.43 275	98.66 288	93.10 319	90.55 314	97.75 252

v2v482		96.70 247	96.18 254	98.27 228	98.04 314	98.39 206	100.00 1	99.13 300	94.19 295	98.58 244	99.08 302	90.48 274	98.67 287	95.69 289	90.44 315	97.75 252

our_test_3		96.51 254	96.35 248	96.98 296	97.61 328	95.05 301	99.98 214	99.01 336	94.68 277	96.77 319	99.06 303	95.87 208	98.14 320	91.81 328	92.37 289	97.75 252

Test_1112_low_res		98.83 148	98.60 157	99.51 143	99.69 157	98.75 188	99.99 196	99.14 296	96.81 200	98.84 228	99.06 303	97.45 170	99.89 160	98.66 206	97.75 216	99.89 156

V42		96.65 248	96.16 256	98.11 244	98.17 311	98.23 220	99.99 196	99.09 314	93.97 298	98.74 236	99.05 305	91.09 266	98.82 273	95.46 294	89.90 317	97.27 338

MVP-Stereo		96.51 254	96.48 241	96.60 308	95.65 354	94.25 324	98.84 353	98.16 352	95.85 246	95.23 335	99.04 306	92.54 254	99.13 248	92.98 320	99.98 119	96.43 352
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		98.41 176	98.11 185	99.31 167	99.54 197	98.55 200	99.18 330	100.00 1	98.64 57	99.79 170	99.04 306	87.61 307	100.00 1	99.30 176	99.89 131	99.40 226
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
D2MVS		97.63 207	97.83 196	97.05 291	98.83 285	94.60 318	100.00 1	99.82 39	96.89 198	98.28 264	99.03 308	94.05 231	99.47 228	98.58 215	94.97 260	97.09 341

PVSNet_BlendedMVS		98.71 154	98.62 154	98.98 190	99.98 97	99.60 124	100.00 1	100.00 1	97.23 174	100.00 1	99.03 308	96.57 197	99.99 93	100.00 1	94.75 262	97.35 336

MS-PatchMatch		95.66 290	95.87 267	95.05 322	97.80 322	89.25 354	98.88 352	99.30 233	96.35 230	96.86 314	99.01 310	81.35 346	99.43 234	93.30 318	99.98 119	96.46 351

v8		96.35 265	95.73 275	98.21 234	98.11 312	98.23 220	99.94 233	99.07 320	92.66 326	98.29 263	99.00 311	91.46 260	98.77 280	94.17 309	88.83 329	97.62 319

v1144		96.51 254	95.97 264	98.13 242	97.98 317	98.04 233	99.99 196	99.08 316	93.51 309	98.62 242	98.98 312	90.98 269	98.62 290	93.79 315	90.79 311	97.74 275

CR-MVSNet		98.02 193	97.71 202	98.93 192	99.31 243	98.86 185	99.13 340	99.00 337	96.53 221	99.96 117	98.98 312	96.94 188	98.10 327	91.18 331	98.40 180	99.84 179

Patchmtry		96.81 240	96.37 246	98.14 239	99.31 243	98.55 200	98.91 351	99.00 337	90.45 338	97.92 282	98.98 312	96.94 188	98.12 322	94.27 308	91.53 301	97.75 252

v1192		96.18 273	95.49 286	98.26 230	98.01 315	98.15 224	99.99 196	99.08 316	93.36 312	98.54 246	98.97 315	89.47 286	98.89 267	91.15 332	90.82 310	97.75 252

v1240		95.96 284	95.25 292	98.07 245	97.91 319	97.87 245	99.96 224	99.07 320	93.24 316	98.64 241	98.96 316	88.98 292	98.61 291	89.58 344	90.92 309	97.75 252

v10		96.14 278	95.50 284	98.07 245	98.19 309	97.96 237	99.83 248	99.07 320	92.10 329	98.07 274	98.94 317	91.07 267	98.61 291	92.41 326	89.82 318	97.63 317

VPA-MVSNet		97.03 233	96.43 243	98.82 199	98.64 288	99.32 149	99.38 310	99.47 80	96.73 206	98.91 222	98.94 317	87.00 313	99.40 237	99.23 180	89.59 320	97.76 241

FMVSNet3		97.30 220	96.95 224	98.37 222	99.65 168	99.25 156	99.71 272	99.28 243	94.23 291	98.53 247	98.91 319	93.30 241	98.11 324	95.31 296	93.60 271	97.73 282

UniMVSNet (Re)		97.29 221	96.85 227	98.59 212	98.49 294	99.13 167	100.00 1	99.42 130	96.52 222	98.24 270	98.90 320	94.93 222	98.89 267	97.54 253	87.61 336	97.75 252

test20.03		93.11 311	92.85 313	93.88 336	95.19 358	91.83 344	100.00 1	98.87 344	93.68 304	92.76 349	98.88 321	89.20 289	92.71 367	77.88 366	89.19 325	97.09 341

FMVSNet2		96.22 271	95.60 282	98.06 249	99.53 200	98.33 213	99.45 302	99.27 251	93.71 301	98.03 276	98.84 322	84.23 331	98.10 327	93.97 313	93.40 275	97.73 282

GBi-Net		96.07 280	95.80 270	96.89 300	99.53 200	94.87 303	99.18 330	99.27 251	93.71 301	98.53 247	98.81 323	84.23 331	98.07 329	95.31 296	93.60 271	97.72 288

test1		96.07 280	95.80 270	96.89 300	99.53 200	94.87 303	99.18 330	99.27 251	93.71 301	98.53 247	98.81 323	84.23 331	98.07 329	95.31 296	93.60 271	97.72 288

FMVSNet1		94.45 300	93.63 305	96.89 300	98.87 281	94.87 303	99.18 330	99.27 251	90.95 336	97.31 303	98.81 323	72.89 361	98.07 329	92.61 321	92.81 282	97.72 288

Effi-MVS+-dtu		98.51 169	98.86 135	97.47 277	99.77 148	94.21 325	100.00 1	98.94 339	97.61 138	99.91 149	98.75 326	95.89 206	99.51 223	99.36 168	99.48 152	98.68 234

EGC-MVSNET		79.46 331	74.04 339	95.72 319	96.00 352	92.73 338	99.09 345	99.04 332	5.08 376	16.72 376	98.71 327	73.03 360	98.74 284	82.05 359	96.64 232	95.69 358

tpm cat1		98.05 191	97.76 198	98.92 193	99.50 215	97.10 274	99.77 261	99.30 233	90.20 341	99.72 177	98.71 327	97.71 158	99.86 164	96.75 280	98.20 195	99.81 191

WR-MVS_H		96.73 244	96.32 251	97.95 261	98.26 305	97.88 243	99.72 271	99.43 122	95.06 268	96.99 310	98.68 329	93.02 247	98.53 301	97.43 257	88.33 331	97.43 332

EG-PatchMatch MVS		92.94 312	92.49 316	94.29 332	95.87 353	87.07 359	99.07 348	98.11 355	93.19 317	88.98 357	98.66 330	70.89 363	99.08 250	92.43 325	95.21 250	96.72 348

UnsupCasMVSNet_eth		94.25 302	93.89 301	95.34 320	97.63 326	92.13 342	99.73 269	99.36 208	94.88 271	92.78 348	98.63 331	82.72 339	96.53 349	94.57 304	84.73 347	97.36 335

Anonymous20231206		93.45 309	93.17 309	94.30 331	95.00 359	89.69 353	99.98 214	98.43 349	93.30 315	94.50 343	98.59 332	90.52 272	95.73 358	77.46 368	90.73 313	97.48 331

N_pmnet		91.88 318	93.37 308	87.40 345	97.24 343	66.33 373	99.90 238	91.05 376	89.77 343	95.65 333	98.58 333	90.05 279	98.11 324	85.39 352	92.72 283	97.75 252

MIMVSNet		97.06 231	96.73 231	98.05 253	99.38 240	96.64 284	98.47 357	99.35 216	93.41 310	99.48 187	98.53 334	89.66 282	97.70 341	94.16 311	98.11 200	99.80 201

test_method		91.04 323	91.10 320	90.85 341	98.34 298	77.63 366	100.00 1	98.93 342	76.69 362	96.25 326	98.52 335	70.44 364	97.98 335	89.02 348	91.74 298	96.92 345

LCM-MVSNet-Re		96.52 253	97.21 221	94.44 328	99.27 246	85.80 360	99.85 246	96.61 370	95.98 240	92.75 350	98.48 336	93.97 234	97.55 342	99.58 153	98.43 178	99.98 112

FMVSNet5		95.32 294	95.43 290	94.99 323	99.39 239	92.99 336	99.25 320	99.24 261	90.45 338	97.44 301	98.45 337	95.78 210	94.39 362	87.02 350	91.88 297	97.59 323

MIMVSNet1		91.96 315	91.20 318	94.23 333	94.94 360	91.69 346	99.34 314	99.22 267	88.23 348	94.18 345	98.45 337	75.52 358	93.41 366	79.37 364	91.49 302	97.60 322

YYNet1		92.44 314	90.92 322	97.03 292	96.20 349	97.06 275	99.99 196	99.14 296	88.21 349	67.93 369	98.43 339	88.63 296	96.28 352	90.64 334	89.08 326	97.74 275

MDA-MVSNet-bldmvs		91.65 320	89.94 327	96.79 305	96.72 346	96.70 282	99.42 307	98.94 339	88.89 345	66.97 372	98.37 340	81.43 345	95.91 356	89.24 347	89.46 323	97.75 252

FPMVS		77.92 335	79.45 334	73.34 354	76.87 375	46.81 380	98.24 358	99.05 329	59.89 369	73.55 366	98.34 341	36.81 375	86.55 369	80.96 360	91.35 306	86.65 367

MDA-MVSNet_test_wron		92.61 313	91.09 321	97.19 289	96.71 347	97.26 268	100.00 1	99.14 296	88.61 346	67.90 370	98.32 342	89.03 290	96.57 348	90.47 338	89.59 320	97.74 275

MVS_0304		95.11 298	94.84 299	95.91 318	99.60 182	91.24 351	98.64 355	99.88 36	94.51 284	99.62 182	98.31 343	69.19 367	98.82 273	95.22 299	98.60 169	97.66 308

Anonymous20240521		93.29 310	92.76 314	94.90 326	95.64 355	91.27 349	99.97 220	98.82 346	87.04 352	94.71 339	98.19 344	83.86 335	96.80 345	84.04 355	92.56 288	96.64 349

new_pmnet		94.11 306	93.47 307	96.04 316	96.60 348	92.82 337	99.97 220	98.91 343	90.21 340	95.26 334	98.05 345	85.89 323	98.14 320	84.28 354	92.01 294	97.16 340

patchmatchnet-post											97.79 346	99.41 54	99.54 215

KD-MVS_2432*1600		94.15 303	93.08 310	97.35 281	99.53 200	97.83 247	99.63 283	99.19 278	92.88 322	96.29 324	97.68 347	98.84 121	96.70 346	89.73 342	63.92 365	97.53 327

miper_refine_blended		94.15 303	93.08 310	97.35 281	99.53 200	97.83 247	99.63 283	99.19 278	92.88 322	96.29 324	97.68 347	98.84 121	96.70 346	89.73 342	63.92 365	97.53 327

Patchmatch-RL test		93.49 308	93.63 305	93.05 338	91.78 363	83.41 363	98.21 359	96.95 367	91.58 332	91.05 353	97.64 349	99.40 55	95.83 357	94.11 312	81.95 351	99.91 142

DSMNet-mixed		95.18 297	95.21 293	95.08 321	96.03 351	90.21 352	99.65 280	93.64 374	92.91 321	98.34 259	97.40 350	90.05 279	95.51 359	91.02 333	97.86 208	99.51 224

CL-MVSNet_self_test		91.07 322	90.35 325	93.24 337	93.27 362	89.16 355	99.55 292	99.25 256	92.34 327	95.23 335	97.05 351	88.86 295	93.59 365	80.67 361	66.95 364	96.96 344

OpenMVS_ROB	88.34 20	91.89 317	91.12 319	94.19 334	95.55 356	87.63 358	99.26 319	98.03 357	86.61 353	90.65 356	96.82 352	70.14 366	98.78 277	86.54 351	96.50 233	96.15 353

pmmvs3		90.62 325	89.36 329	94.40 329	90.53 367	91.49 347	100.00 1	96.73 368	84.21 357	93.65 347	96.65 353	82.56 342	94.83 360	82.28 358	77.62 357	96.89 346

PM-MVS		88.39 328	87.41 331	91.31 340	91.73 364	82.02 365	99.79 255	96.62 369	91.06 335	90.71 355	95.73 354	48.60 372	95.96 355	90.56 336	81.91 352	95.97 356

pmmvs-eth3d		91.73 319	90.67 323	94.92 325	91.63 365	92.71 339	99.90 238	98.54 348	91.19 334	88.08 358	95.50 355	79.31 352	96.13 354	90.55 337	81.32 353	95.91 357

ambc				88.45 343	86.84 368	70.76 370	97.79 362	98.02 359		90.91 354	95.14 356	38.69 374	98.51 302	94.97 301	84.23 348	96.09 355

RPMNet		95.26 296	93.82 302	99.56 141	99.31 243	98.86 185	99.13 340	99.42 130	79.82 361	99.96 117	95.13 357	95.69 212	99.98 117	77.54 367	98.40 180	99.84 179

new-patchmatchnet		90.30 326	89.46 328	92.84 339	90.77 366	88.55 357	99.83 248	98.80 347	90.07 342	87.86 359	95.00 358	78.77 353	94.30 363	84.86 353	79.15 355	95.68 359

PatchT		95.90 286	94.95 298	98.75 203	99.03 262	98.39 206	99.08 346	99.32 225	85.52 354	99.96 117	94.99 359	97.94 152	98.05 333	80.20 363	98.47 176	99.81 191

KD-MVS_self_test		91.16 321	90.09 326	94.35 330	94.44 361	91.27 349	99.74 265	99.08 316	90.82 337	94.53 342	94.91 360	86.11 319	94.78 361	82.67 357	68.52 363	96.99 343

UnsupCasMVSNet_bld		89.50 327	88.00 330	93.99 335	95.30 357	88.86 356	98.52 356	99.28 243	85.50 355	87.80 360	94.11 361	61.63 369	96.96 344	90.63 335	79.26 354	96.15 353

LCM-MVSNet		79.01 334	76.93 337	85.27 347	78.28 374	68.01 371	96.57 363	98.03 357	55.10 370	82.03 364	93.27 362	31.99 377	93.95 364	82.72 356	74.37 359	93.84 361

PMMVS2		79.15 333	77.28 336	84.76 348	82.34 371	72.66 367	99.70 274	95.11 373	71.68 365	84.78 363	90.87 363	32.05 376	89.99 368	75.53 369	63.45 367	91.64 363

Gipuma		84.73 329	83.50 332	88.40 344	97.50 334	82.21 364	88.87 366	99.05 329	65.81 366	85.71 361	90.49 364	53.70 370	96.31 351	78.64 365	91.74 298	86.67 366
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
JIA-IIPM		97.09 228	96.34 249	99.36 161	98.88 279	98.59 199	99.81 251	99.43 122	84.81 356	99.96 117	90.34 365	98.55 138	99.52 221	97.00 268	98.28 191	99.98 112

test_post											89.05 366	99.49 38	99.59 199

PMVS	60.66 23	65.98 341	65.05 342	68.75 357	55.06 380	38.40 381	88.19 367	96.98 366	48.30 374	44.82 375	88.52 367	12.22 380	86.49 370	67.58 370	83.79 349	81.35 370
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_post1							99.32 315				88.24 368	99.33 58	99.59 199	98.31 225

MVS-HIRNet		94.12 305	92.73 315	98.29 227	99.33 242	95.95 289	99.38 310	99.19 278	74.54 364	98.26 267	86.34 369	86.07 320	99.06 251	91.60 329	99.87 134	99.85 177

E-PMN		70.72 337	70.06 340	72.69 355	83.92 370	65.48 375	99.95 229	92.72 375	49.88 372	72.30 367	86.26 370	47.17 373	77.43 372	53.83 373	44.49 370	75.17 372

EMVS		69.88 338	69.09 341	72.24 356	84.70 369	65.82 374	99.96 224	87.08 380	49.82 373	71.51 368	84.74 371	49.30 371	75.32 373	50.97 374	43.71 371	75.59 371

gg-mvs-nofinetune		96.95 237	96.10 257	99.50 145	99.41 230	99.36 147	99.07 348	99.52 72	83.69 358	99.96 117	83.60 372	100.00 1	99.20 246	99.68 138	99.99 111	99.96 120

MVE	68.59 21	67.22 339	64.68 343	74.84 351	74.67 377	62.32 377	95.84 364	90.87 377	50.98 371	58.72 373	81.05 373	12.20 381	78.95 371	61.06 372	56.75 368	83.24 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 340	63.44 344	73.88 353	61.14 378	63.45 376	95.68 365	87.18 378	79.93 360	47.35 374	80.68 374	22.35 378	72.33 376	61.24 371	35.42 372	85.88 368

X-MVStestdata		97.04 232	96.06 259	99.98 25	100.00 1	99.94 42	100.00 1	99.75 52	98.67 54	100.00 1	66.97 375	99.16 85	100.00 1	100.00 1	100.00 1	100.00 1

wuyk23d		28.28 342	29.73 346	23.92 358	75.89 376	32.61 382	66.50 369	12.88 382	16.09 375	14.59 377	16.59 376	12.35 379	32.36 377	39.36 375	13.36 375	6.79 373

test_blank		0.07 346	0.09 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.79 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet_test		0.01 347	0.02 350	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.14 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		8.24 345	10.99 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.14 378	98.75 128	0.00 378	0.00 376	0.00 376	0.00 374

sosnet-low-res		0.01 347	0.02 350	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.14 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet		0.01 347	0.02 350	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.14 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uncertanet		0.01 347	0.02 350	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.14 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

Regformer		0.01 347	0.02 350	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.14 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet		0.01 347	0.02 350	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.14 378	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

FOURS1					100.00 1	99.97 22	100.00 1	99.42 130	98.52 61	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 130					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 130					100.00 1	100.00 1	100.00 1	100.00 1

eth-test2					0.00 382

eth-test					0.00 382

IU-MVS					100.00 1	99.99 5		99.42 130	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

save fliter					99.99 59	99.93 47	100.00 1	99.42 130	98.93 32

test_0728_SECOND				100.00 1	99.99 59	99.99 5	100.00 1	99.42 130					100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 142

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 70				99.91 142

sam_mvs												99.33 58

MTGPA								99.42 130

MTMP							100.00 1	99.18 284

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 83		99.42 130		100.00 1			99.97 122

test_prior4						99.93 47	100.00 1

test_prior				99.90 85	100.00 1	99.75 106		99.73 57					99.97 122			100.00 1

旧先验2							100.00 1		98.11 92	100.00 1			100.00 1	99.67 140

新几何2							100.00 1

无先验							100.00 1	99.80 42	97.98 101				100.00 1	99.33 172		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 260

segment_acmp												99.55 22

testdata1							100.00 1		98.77 50

test12				99.95 61	99.99 59	99.89 73		99.42 130		100.00 1		99.24 76	99.97 122		100.00 1	100.00 1

plane_prior7					99.00 268	94.78 313

plane_prior6					99.06 260	94.80 309						88.58 299

plane_prior5								99.40 184					99.55 212	99.79 116	95.57 238	97.76 241

plane_prior3						94.79 312			99.03 19	99.08 212

plane_prior2							100.00 1		99.00 25

plane_prior1					99.02 263

plane_prior						94.80 309	100.00 1		99.03 19						95.58 234

n2								0.00 383

nn								0.00 383

door-mid								96.32 371

test11								99.42 130

door								96.13 372

HQP5-MVS						94.82 306

HQP-NCC					99.07 256		100.00 1		99.04 14	99.17 203

ACMP_Plane					99.07 256		100.00 1		99.04 14	99.17 203

BP-MVS														99.79 116

HQP4-MVS										99.17 203			99.57 203			97.77 239

HQP3-MVS								99.40 184							95.58 234

HQP2-MVS												88.61 297

MDTV_nov1_ep13_2view						99.24 158	99.56 291		96.31 233	99.96 117		98.86 119		98.92 193		99.89 156

ACMMP++_ref															94.58 266

ACMMP++															95.17 253

Test By Simon												99.10 90

High-resolution multi-view benchmark