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.

test_vis1_n		97.92 231	97.44 266	99.34 136	99.53 163	98.08 236	99.74 44	99.49 143	99.15 20	100.00 1	99.94 6	79.51 391	99.98 13	99.88 14	99.76 110	99.97 4

fmvsm_s_conf0.1_n_a		99.26 69	99.06 82	99.85 28	99.52 167	99.62 65	99.54 139	99.62 41	98.69 79	99.99 2	99.96 1	94.47 237	99.94 69	99.88 14	99.92 24	99.98 2

fmvsm_s_conf0.1_n		99.29 63	99.10 76	99.86 21	99.70 101	99.65 57	99.53 147	99.62 41	98.74 75	99.99 2	99.95 3	94.53 235	99.94 69	99.89 13	99.96 12	99.97 4

test_vis1_n_1920		98.63 160	98.40 167	99.31 143	99.86 20	97.94 248	99.67 64	99.62 41	99.43 7	99.99 2	99.91 20	87.29 365	100.00 1	99.92 12	99.92 24	99.98 2

test_fmvs1_n		98.41 172	98.14 183	99.21 163	99.82 42	97.71 260	99.74 44	99.49 143	99.32 14	99.99 2	99.95 3	85.32 374	99.97 21	99.82 16	99.84 77	99.96 7

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 28	99.86 20	99.61 67	99.56 122	99.63 39	99.48 3	99.98 6	99.83 68	98.75 55	99.99 4	99.97 1	99.96 12	99.94 11

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 28	99.84 32	99.63 64	99.56 122	99.63 39	99.47 4	99.98 6	99.82 76	98.75 55	99.99 4	99.97 1	99.97 7	99.94 11

fmvsm_s_conf0.5_n_a		99.56 13	99.47 17	99.85 28	99.83 39	99.64 63	99.52 148	99.65 33	99.10 27	99.98 6	99.92 14	97.35 120	99.96 30	99.94 10	99.92 24	99.95 9

fmvsm_s_conf0.5_n		99.51 18	99.40 25	99.85 28	99.84 32	99.65 57	99.51 156	99.67 23	99.13 22	99.98 6	99.92 14	96.60 146	99.96 30	99.95 8	99.96 12	99.95 9

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 52	99.84 32	99.44 94	99.58 109	99.69 18	99.43 7	99.98 6	99.91 20	98.62 70	100.00 1	99.97 1	99.95 16	99.90 17

test_fmvs1		98.88 124	98.79 126	99.16 168	99.69 106	97.61 263	99.55 134	99.49 143	99.32 14	99.98 6	99.91 20	91.41 320	99.96 30	99.82 16	99.92 24	99.90 17

dcpmvs_2		99.23 75	99.58 7	98.16 296	99.83 39	94.68 357	99.76 37	99.52 101	99.07 35	99.98 6	99.88 36	98.56 74	99.93 84	99.67 21	99.98 4	99.87 31

test_cas_vis1_n_1920		99.16 83	99.01 95	99.61 84	99.81 46	98.86 175	99.65 75	99.64 36	99.39 10	99.97 13	99.94 6	93.20 273	99.98 13	99.55 29	99.91 31	99.99 1

mvsany_test1		99.50 20	99.46 20	99.62 83	99.61 141	99.09 136	98.94 338	99.48 155	99.10 27	99.96 14	99.91 20	98.85 39	99.96 30	99.72 18	99.58 137	99.82 54

test_fmvsmconf_n		99.70 3	99.64 4	99.87 11	99.80 52	99.66 53	99.48 178	99.64 36	99.45 5	99.92 15	99.92 14	98.62 70	99.99 4	99.96 7	99.99 1	99.96 7

SED-MVS		99.61 7	99.52 11	99.88 5	99.84 32	99.90 2	99.60 95	99.48 155	99.08 33	99.91 16	99.81 90	99.20 7	99.96 30	98.91 99	99.85 69	99.79 74

test_241102_ONE					99.84 32	99.90 2		99.48 155	99.07 35	99.91 16	99.74 143	99.20 7	99.76 198

EI-MVSNet-UG-set		99.58 9	99.57 8	99.64 78	99.78 56	99.14 131	99.60 95	99.45 193	99.01 40	99.90 18	99.83 68	98.98 23	99.93 84	99.59 25	99.95 16	99.86 33

EI-MVSNet-Vis-set		99.58 9	99.56 10	99.64 78	99.78 56	99.15 130	99.61 94	99.45 193	99.01 40	99.89 19	99.82 76	99.01 18	99.92 95	99.56 28	99.95 16	99.85 36

DVP-MVS++		99.59 8	99.50 13	99.88 5	99.51 170	99.88 8	99.87 9	99.51 115	98.99 45	99.88 20	99.81 90	99.27 5	99.96 30	98.85 112	99.80 97	99.81 61

test_241102_TWO								99.48 155	99.08 33	99.88 20	99.81 90	98.94 29	99.96 30	98.91 99	99.84 77	99.88 26

DPE-MVS		99.46 31	99.32 40	99.91 2	99.78 56	99.88 8	99.36 230	99.51 115	98.73 76	99.88 20	99.84 64	98.72 61	99.96 30	98.16 193	99.87 54	99.88 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SD-MVS		99.41 47	99.52 11	99.05 180	99.74 80	99.68 48	99.46 187	99.52 101	99.11 26	99.88 20	99.91 20	99.43 1	97.70 385	98.72 130	99.93 22	99.77 82
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
APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 62	99.89 4	99.75 41	99.56 69	99.02 38	99.88 20	99.85 54	99.18 10	99.96 30	99.22 69	99.92 24	99.90 17
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_fmvsmconf0.1_n		99.55 14	99.45 21	99.86 21	99.44 197	99.65 57	99.50 163	99.61 48	99.45 5	99.87 25	99.92 14	97.31 121	99.97 21	99.95 8	99.99 1	99.97 4

test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 55	99.38 212	99.37 100	99.58 109	99.62 41	99.41 9	99.87 25	99.92 14	98.81 44	100.00 1	99.97 1	99.93 22	99.94 11

test0726					99.85 26	99.89 4	99.62 88	99.50 135	99.10 27	99.86 27	99.82 76	98.94 29

Vis-MVSNet		99.12 95	98.97 101	99.56 94	99.78 56	99.10 135	99.68 61	99.66 28	98.49 93	99.86 27	99.87 44	94.77 218	99.84 153	99.19 71	99.41 148	99.74 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PC_three_1452									98.18 131	99.84 29	99.70 158	99.31 3	98.52 368	98.30 184	99.80 97	99.81 61

IU-MVS					99.84 32	99.88 8		99.32 267	98.30 112	99.84 29				98.86 110	99.85 69	99.89 20

xiu_mvs_v1_base_debu		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 295	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 227

xiu_mvs_v1_base		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 295	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 227

xiu_mvs_v1_base_debi		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 295	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 227

DeepPCF-MVS	98.18 3	98.81 139	99.37 30	97.12 343	99.60 146	91.75 383	98.61 368	99.44 201	99.35 12	99.83 34	99.85 54	98.70 63	99.81 178	99.02 87	99.91 31	99.81 61

MVS_0304		99.42 42	99.32 40	99.72 65	99.70 101	99.27 113	99.52 148	97.57 387	99.51 2	99.82 35	99.78 121	98.09 100	99.96 30	99.97 1	99.97 7	99.94 11

TSAR-MVS + GP.		99.36 55	99.36 32	99.36 135	99.67 111	98.61 199	99.07 305	99.33 257	99.00 43	99.82 35	99.81 90	99.06 16	99.84 153	99.09 80	99.42 147	99.65 129

FOURS1					99.91 1	99.93 1	99.87 9	99.56 69	99.10 27	99.81 37

DVP-MVS		99.57 12	99.47 17	99.88 5	99.85 26	99.89 4	99.57 116	99.37 239	99.10 27	99.81 37	99.80 103	98.94 29	99.96 30	98.93 96	99.86 62	99.81 61
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									98.99 45	99.81 37	99.80 103	99.09 14	99.96 30	98.85 112	99.90 39	99.88 26

MVSFormer		99.17 81	99.12 74	99.29 151	99.51 170	98.94 165	99.88 4	99.46 182	97.55 207	99.80 40	99.65 186	97.39 116	99.28 298	99.03 85	99.85 69	99.65 129

lupinMVS		99.13 89	99.01 95	99.46 122	99.51 170	98.94 165	99.05 310	99.16 301	97.86 168	99.80 40	99.56 223	97.39 116	99.86 140	98.94 94	99.85 69	99.58 154

tttt0517		98.42 170	98.14 183	99.28 155	99.66 120	98.38 223	99.74 44	96.85 391	97.68 194	99.79 42	99.74 143	91.39 321	99.89 127	98.83 118	99.56 138	99.57 156

APD-MVS_3200maxsize		99.48 26	99.35 34	99.85 28	99.76 65	99.83 16	99.63 82	99.54 85	98.36 106	99.79 42	99.82 76	98.86 38	99.95 59	98.62 143	99.81 93	99.78 80

jason		99.13 89	99.03 87	99.45 123	99.46 191	98.87 172	99.12 295	99.26 285	98.03 156	99.79 42	99.65 186	97.02 132	99.85 146	99.02 87	99.90 39	99.65 129
jason: jason.
SteuartSystems-ACMMP		99.54 15	99.42 22	99.87 11	99.82 42	99.81 25	99.59 101	99.51 115	98.62 83	99.79 42	99.83 68	99.28 4	99.97 21	98.48 165	99.90 39	99.84 40
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	98.69 1	99.49 22	99.39 27	99.77 55	99.63 131	99.59 70	99.36 230	99.46 182	99.07 35	99.79 42	99.82 76	98.85 39	99.92 95	98.68 137	99.87 54	99.82 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf0.01_n		99.22 76	99.03 87	99.79 49	98.42 363	99.48 89	99.55 134	99.51 115	99.39 10	99.78 47	99.93 9	94.80 213	99.95 59	99.93 11	99.95 16	99.94 11

CS-MVS		99.50 20	99.48 15	99.54 97	99.76 65	99.42 96	99.90 1	99.55 77	98.56 87	99.78 47	99.70 158	98.65 68	99.79 187	99.65 23	99.78 104	99.41 195

SMA-MVS		99.44 37	99.30 49	99.85 28	99.73 87	99.83 16	99.56 122	99.47 173	97.45 220	99.78 47	99.82 76	99.18 10	99.91 105	98.79 123	99.89 48	99.81 61
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
TSAR-MVS + MP.		99.58 9	99.50 13	99.81 44	99.91 1	99.66 53	99.63 82	99.39 223	98.91 58	99.78 47	99.85 54	99.36 2	99.94 69	98.84 115	99.88 51	99.82 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test2506		96.81 314	96.65 310	97.29 339	99.74 80	92.21 382	99.60 95	85.06 411	99.13 22	99.77 51	99.93 9	87.82 363	99.85 146	99.38 48	99.38 149	99.80 70

test_part2					99.81 46	99.83 16				99.77 51

MSP-MVS		99.42 42	99.27 58	99.88 5	99.89 8	99.80 27	99.67 64	99.50 135	98.70 78	99.77 51	99.49 247	98.21 94	99.95 59	98.46 169	99.77 107	99.88 26
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
UA-Net		99.42 42	99.29 53	99.80 46	99.62 137	99.55 77	99.50 163	99.70 15	98.79 70	99.77 51	99.96 1	97.45 115	99.96 30	98.92 98	99.90 39	99.89 20

APD-MVS		99.27 67	99.08 80	99.84 39	99.75 73	99.79 30	99.50 163	99.50 135	97.16 247	99.77 51	99.82 76	98.78 48	99.94 69	97.56 250	99.86 62	99.80 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS-dyc-post		99.45 33	99.31 47	99.85 28	99.76 65	99.82 22	99.63 82	99.52 101	98.38 102	99.76 56	99.82 76	98.53 76	99.95 59	98.61 146	99.81 93	99.77 82

RE-MVS-def			99.34 36		99.76 65	99.82 22	99.63 82	99.52 101	98.38 102	99.76 56	99.82 76	98.75 55		98.61 146	99.81 93	99.77 82

ACMMP_NAP		99.47 29	99.34 36	99.88 5	99.87 15	99.86 13	99.47 184	99.48 155	98.05 153	99.76 56	99.86 49	98.82 43	99.93 84	98.82 122	99.91 31	99.84 40

HPM-MVS_fast		99.51 18	99.40 25	99.85 28	99.91 1	99.79 30	99.76 37	99.56 69	97.72 188	99.76 56	99.75 138	99.13 12	99.92 95	99.07 83	99.92 24	99.85 36

MM		99.40 50	99.28 55	99.74 61	99.67 111	99.31 107	99.52 148	98.87 341	99.55 1	99.74 60	99.80 103	96.47 151	99.98 13	99.97 1	99.97 7	99.94 11

VNet		99.11 99	98.90 110	99.73 64	99.52 167	99.56 75	99.41 207	99.39 223	99.01 40	99.74 60	99.78 121	95.56 185	99.92 95	99.52 34	98.18 233	99.72 103

patch_mono-2		99.26 69	99.62 5	98.16 296	99.81 46	94.59 359	99.52 148	99.64 36	99.33 13	99.73 62	99.90 26	99.00 22	99.99 4	99.69 19	99.98 4	99.89 20

SR-MVS		99.43 40	99.29 53	99.86 21	99.75 73	99.83 16	99.59 101	99.62 41	98.21 124	99.73 62	99.79 115	98.68 64	99.96 30	98.44 171	99.77 107	99.79 74

thisisatest0530		98.35 178	98.03 198	99.31 143	99.63 131	98.56 202	99.54 139	96.75 393	97.53 211	99.73 62	99.65 186	91.25 324	99.89 127	98.62 143	99.56 138	99.48 178

CS-MVS-test		99.49 22	99.48 15	99.54 97	99.78 56	99.30 109	99.89 2	99.58 61	98.56 87	99.73 62	99.69 168	98.55 75	99.82 173	99.69 19	99.85 69	99.48 178

EC-MVSNet		99.44 37	99.39 27	99.58 90	99.56 156	99.49 87	99.88 4	99.58 61	98.38 102	99.73 62	99.69 168	98.20 95	99.70 223	99.64 24	99.82 90	99.54 161

diffmvs		99.14 87	99.02 91	99.51 113	99.61 141	98.96 157	99.28 256	99.49 143	98.46 95	99.72 67	99.71 154	96.50 150	99.88 132	99.31 58	99.11 171	99.67 122
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SF-MVS		99.38 53	99.24 63	99.79 49	99.79 54	99.68 48	99.57 116	99.54 85	97.82 178	99.71 68	99.80 103	98.95 27	99.93 84	98.19 189	99.84 77	99.74 92

xiu_mvs_v2_base		99.26 69	99.25 62	99.29 151	99.53 163	98.91 169	99.02 318	99.45 193	98.80 69	99.71 68	99.26 309	98.94 29	99.98 13	99.34 55	99.23 161	98.98 241

PS-MVSNAJ		99.32 59	99.32 40	99.30 148	99.57 152	98.94 165	98.97 332	99.46 182	98.92 57	99.71 68	99.24 311	99.01 18	99.98 13	99.35 51	99.66 128	98.97 242

PGM-MVS		99.45 33	99.31 47	99.86 21	99.87 15	99.78 36	99.58 109	99.65 33	97.84 173	99.71 68	99.80 103	99.12 13	99.97 21	98.33 180	99.87 54	99.83 49

114514_t		98.93 120	98.67 136	99.72 65	99.85 26	99.53 82	99.62 88	99.59 57	92.65 377	99.71 68	99.78 121	98.06 102	99.90 116	98.84 115	99.91 31	99.74 92

PVSNet_Blended_VisFu		99.36 55	99.28 55	99.61 84	99.86 20	99.07 141	99.47 184	99.93 2	97.66 197	99.71 68	99.86 49	97.73 110	99.96 30	99.47 43	99.82 90	99.79 74

MTAPA		99.52 17	99.39 27	99.89 4	99.90 4	99.86 13	99.66 69	99.47 173	98.79 70	99.68 74	99.81 90	98.43 83	99.97 21	98.88 102	99.90 39	99.83 49

HFP-MVS		99.49 22	99.37 30	99.86 21	99.87 15	99.80 27	99.66 69	99.67 23	98.15 133	99.68 74	99.69 168	99.06 16	99.96 30	98.69 135	99.87 54	99.84 40

VDDNet		97.55 282	97.02 301	99.16 168	99.49 181	98.12 235	99.38 224	99.30 275	95.35 343	99.68 74	99.90 26	82.62 386	99.93 84	99.31 58	98.13 237	99.42 193

HPM-MVS		99.42 42	99.28 55	99.83 40	99.90 4	99.72 42	99.81 20	99.54 85	97.59 201	99.68 74	99.63 198	98.91 34	99.94 69	98.58 152	99.91 31	99.84 40
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDD-MVS		97.73 262	97.35 278	98.88 211	99.47 190	97.12 279	99.34 238	98.85 343	98.19 127	99.67 78	99.85 54	82.98 384	99.92 95	99.49 40	98.32 224	99.60 146

ACMMPR		99.49 22	99.36 32	99.86 21	99.87 15	99.79 30	99.66 69	99.67 23	98.15 133	99.67 78	99.69 168	98.95 27	99.96 30	98.69 135	99.87 54	99.84 40

PVSNet_BlendedMVS		98.86 128	98.80 123	99.03 182	99.76 65	98.79 184	99.28 256	99.91 3	97.42 225	99.67 78	99.37 280	97.53 113	99.88 132	98.98 90	97.29 283	98.42 342

PVSNet_Blended		99.08 105	98.97 101	99.42 128	99.76 65	98.79 184	98.78 354	99.91 3	96.74 280	99.67 78	99.49 247	97.53 113	99.88 132	98.98 90	99.85 69	99.60 146

sss		99.17 81	99.05 83	99.53 105	99.62 137	98.97 153	99.36 230	99.62 41	97.83 174	99.67 78	99.65 186	97.37 119	99.95 59	99.19 71	99.19 164	99.68 119

ECVR-MVS		98.04 211	98.05 196	98.00 308	99.74 80	94.37 362	99.59 101	94.98 401	99.13 22	99.66 83	99.93 9	90.67 330	99.84 153	99.40 47	99.38 149	99.80 70

h-mvs33		97.70 268	97.28 289	98.97 192	99.70 101	97.27 271	99.36 230	99.45 193	98.94 54	99.66 83	99.64 192	94.93 204	99.99 4	99.48 41	84.36 390	99.65 129

hse-mvs2		97.50 287	97.14 295	98.59 247	99.49 181	97.05 286	99.28 256	99.22 292	98.94 54	99.66 83	99.42 265	94.93 204	99.65 239	99.48 41	83.80 392	99.08 227

region2R		99.48 26	99.35 34	99.87 11	99.88 11	99.80 27	99.65 75	99.66 28	98.13 137	99.66 83	99.68 174	98.96 24	99.96 30	98.62 143	99.87 54	99.84 40

RPSCF		98.22 186	98.62 147	96.99 345	99.82 42	91.58 384	99.72 49	99.44 201	96.61 292	99.66 83	99.89 30	95.92 171	99.82 173	97.46 260	99.10 174	99.57 156

OMC-MVS		99.08 105	99.04 85	99.20 164	99.67 111	98.22 229	99.28 256	99.52 101	98.07 148	99.66 83	99.81 90	97.79 108	99.78 192	97.79 224	99.81 93	99.60 146

test1111		98.04 211	98.11 187	97.83 319	99.74 80	93.82 367	99.58 109	95.40 400	99.12 25	99.65 89	99.93 9	90.73 329	99.84 153	99.43 46	99.38 149	99.82 54

test_one_0601					99.81 46	99.88 8		99.49 143	98.97 51	99.65 89	99.81 90	99.09 14

LFMVS		97.90 234	97.35 278	99.54 97	99.52 167	99.01 148	99.39 219	98.24 375	97.10 255	99.65 89	99.79 115	84.79 377	99.91 105	99.28 63	98.38 217	99.69 115

MVS_111021_LR		99.41 47	99.33 38	99.65 73	99.77 62	99.51 86	98.94 338	99.85 6	98.82 65	99.65 89	99.74 143	98.51 78	99.80 184	98.83 118	99.89 48	99.64 136

SDMVSNet		99.11 99	98.90 110	99.75 58	99.81 46	99.59 70	99.81 20	99.65 33	98.78 73	99.64 93	99.88 36	94.56 231	99.93 84	99.67 21	98.26 226	99.72 103

sd_testset		98.75 146	98.57 156	99.29 151	99.81 46	98.26 227	99.56 122	99.62 41	98.78 73	99.64 93	99.88 36	92.02 304	99.88 132	99.54 30	98.26 226	99.72 103

9.14			99.10 76		99.72 91		99.40 215	99.51 115	97.53 211	99.64 93	99.78 121	98.84 41	99.91 105	97.63 241	99.82 90

GST-MVS		99.40 50	99.24 63	99.85 28	99.86 20	99.79 30	99.60 95	99.67 23	97.97 159	99.63 96	99.68 174	98.52 77	99.95 59	98.38 174	99.86 62	99.81 61

CPTT-MVS		99.11 99	98.90 110	99.74 61	99.80 52	99.46 92	99.59 101	99.49 143	97.03 263	99.63 96	99.69 168	97.27 124	99.96 30	97.82 222	99.84 77	99.81 61

ACMMP		99.45 33	99.32 40	99.82 41	99.89 8	99.67 51	99.62 88	99.69 18	98.12 138	99.63 96	99.84 64	98.73 60	99.96 30	98.55 161	99.83 86	99.81 61
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
DeepC-MVS	98.35 2	99.30 61	99.19 68	99.64 78	99.82 42	99.23 118	99.62 88	99.55 77	98.94 54	99.63 96	99.95 3	95.82 176	99.94 69	99.37 50	99.97 7	99.73 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FE-MVS		98.48 165	98.17 179	99.40 130	99.54 162	98.96 157	99.68 61	98.81 348	95.54 341	99.62 100	99.70 158	93.82 260	99.93 84	97.35 268	99.46 144	99.32 209

CHOSEN 280x420		99.12 95	99.13 73	99.08 175	99.66 120	97.89 249	98.43 378	99.71 13	98.88 59	99.62 100	99.76 135	96.63 145	99.70 223	99.46 44	99.99 1	99.66 125

PHI-MVS		99.30 61	99.17 70	99.70 67	99.56 156	99.52 85	99.58 109	99.80 8	97.12 251	99.62 100	99.73 149	98.58 72	99.90 116	98.61 146	99.91 31	99.68 119

test_yl		98.86 128	98.63 142	99.54 97	99.49 181	99.18 122	99.50 163	99.07 313	98.22 122	99.61 103	99.51 241	95.37 191	99.84 153	98.60 149	98.33 220	99.59 150

DCV-MVSNet		98.86 128	98.63 142	99.54 97	99.49 181	99.18 122	99.50 163	99.07 313	98.22 122	99.61 103	99.51 241	95.37 191	99.84 153	98.60 149	98.33 220	99.59 150

MG-MVS		99.13 89	99.02 91	99.45 123	99.57 152	98.63 196	99.07 305	99.34 250	98.99 45	99.61 103	99.82 76	97.98 104	99.87 137	97.00 287	99.80 97	99.85 36

MP-MVS-pluss		99.37 54	99.20 67	99.88 5	99.90 4	99.87 12	99.30 246	99.52 101	97.18 245	99.60 106	99.79 115	98.79 47	99.95 59	98.83 118	99.91 31	99.83 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CDPH-MVS		99.13 89	98.91 109	99.80 46	99.75 73	99.71 44	99.15 289	99.41 212	96.60 294	99.60 106	99.55 226	98.83 42	99.90 116	97.48 257	99.83 86	99.78 80

EPP-MVSNet		99.13 89	98.99 97	99.53 105	99.65 126	99.06 142	99.81 20	99.33 257	97.43 223	99.60 106	99.88 36	97.14 126	99.84 153	99.13 76	98.94 185	99.69 115

HyFIR lowres test		99.11 99	98.92 107	99.65 73	99.90 4	99.37 100	99.02 318	99.91 3	97.67 196	99.59 109	99.75 138	95.90 173	99.73 207	99.53 32	99.02 182	99.86 33

FA-MVS(test-final)		98.75 146	98.53 160	99.41 129	99.55 160	99.05 144	99.80 25	99.01 319	96.59 296	99.58 110	99.59 212	95.39 190	99.90 116	97.78 225	99.49 143	99.28 212

MVS_Test		99.10 103	98.97 101	99.48 117	99.49 181	99.14 131	99.67 64	99.34 250	97.31 234	99.58 110	99.76 135	97.65 112	99.82 173	98.87 105	99.07 177	99.46 186

MDTV_nov1_ep13_2view						95.18 350	99.35 235		96.84 276	99.58 110		95.19 200		97.82 222		99.46 186

DELS-MVS		99.48 26	99.42 22	99.65 73	99.72 91	99.40 99	99.05 310	99.66 28	99.14 21	99.57 113	99.80 103	98.46 81	99.94 69	99.57 27	99.84 77	99.60 146
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
ZD-MVS					99.71 96	99.79 30		99.61 48	96.84 276	99.56 114	99.54 231	98.58 72	99.96 30	96.93 294	99.75 112

CR-MVSNet		98.17 193	97.93 210	98.87 215	99.18 263	98.49 213	99.22 279	99.33 257	96.96 267	99.56 114	99.38 277	94.33 241	99.00 343	94.83 346	98.58 207	99.14 219

RPMNet		96.72 315	95.90 327	99.19 165	99.18 263	98.49 213	99.22 279	99.52 101	88.72 390	99.56 114	97.38 384	94.08 251	99.95 59	86.87 394	98.58 207	99.14 219

IS-MVSNet		99.05 108	98.87 115	99.57 92	99.73 87	99.32 104	99.75 41	99.20 296	98.02 157	99.56 114	99.86 49	96.54 149	99.67 231	98.09 196	99.13 170	99.73 97

ZNCC-MVS		99.47 29	99.33 38	99.87 11	99.87 15	99.81 25	99.64 78	99.67 23	98.08 147	99.55 118	99.64 192	98.91 34	99.96 30	98.72 130	99.90 39	99.82 54

thisisatest0515		98.14 196	97.79 221	99.19 165	99.50 179	98.50 212	98.61 368	96.82 392	96.95 269	99.54 119	99.43 263	91.66 316	99.86 140	98.08 200	99.51 142	99.22 216

MVS_111021_HR		99.41 47	99.32 40	99.66 69	99.72 91	99.47 91	98.95 336	99.85 6	98.82 65	99.54 119	99.73 149	98.51 78	99.74 201	98.91 99	99.88 51	99.77 82

CP-MVS		99.45 33	99.32 40	99.85 28	99.83 39	99.75 39	99.69 55	99.52 101	98.07 148	99.53 121	99.63 198	98.93 33	99.97 21	98.74 127	99.91 31	99.83 49

WTY-MVS		99.06 107	98.88 114	99.61 84	99.62 137	99.16 125	99.37 226	99.56 69	98.04 154	99.53 121	99.62 203	96.84 138	99.94 69	98.85 112	98.49 215	99.72 103

MCST-MVS		99.43 40	99.30 49	99.82 41	99.79 54	99.74 41	99.29 251	99.40 220	98.79 70	99.52 123	99.62 203	98.91 34	99.90 116	98.64 141	99.75 112	99.82 54

PatchT		97.03 310	96.44 315	98.79 232	98.99 305	98.34 224	99.16 286	99.07 313	92.13 378	99.52 123	97.31 387	94.54 234	98.98 345	88.54 387	98.73 201	99.03 235

CANet		99.25 73	99.14 72	99.59 87	99.41 203	99.16 125	99.35 235	99.57 64	98.82 65	99.51 125	99.61 207	96.46 152	99.95 59	99.59 25	99.98 4	99.65 129

mPP-MVS		99.44 37	99.30 49	99.86 21	99.88 11	99.79 30	99.69 55	99.48 155	98.12 138	99.50 126	99.75 138	98.78 48	99.97 21	98.57 155	99.89 48	99.83 49

PatchMatch-RL		98.84 138	98.62 147	99.52 111	99.71 96	99.28 111	99.06 308	99.77 9	97.74 187	99.50 126	99.53 235	95.41 189	99.84 153	97.17 281	99.64 131	99.44 191

PVSNet	96.02 17	98.85 135	98.84 120	98.89 209	99.73 87	97.28 270	98.32 384	99.60 54	97.86 168	99.50 126	99.57 220	96.75 142	99.86 140	98.56 158	99.70 122	99.54 161

LS3D		99.27 67	99.12 74	99.74 61	99.18 263	99.75 39	99.56 122	99.57 64	98.45 96	99.49 129	99.85 54	97.77 109	99.94 69	98.33 180	99.84 77	99.52 167

MP-MVS		99.33 58	99.15 71	99.87 11	99.88 11	99.82 22	99.66 69	99.46 182	98.09 143	99.48 130	99.74 143	98.29 91	99.96 30	97.93 211	99.87 54	99.82 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
旧先验2							98.96 333		96.70 283	99.47 131			99.94 69	98.19 189

MSDG		98.98 116	98.80 123	99.53 105	99.76 65	99.19 120	98.75 357	99.55 77	97.25 239	99.47 131	99.77 129	97.82 107	99.87 137	96.93 294	99.90 39	99.54 161

CDS-MVSNet		99.09 104	99.03 87	99.25 158	99.42 200	98.73 188	99.45 188	99.46 182	98.11 140	99.46 133	99.77 129	98.01 103	99.37 279	98.70 132	98.92 188	99.66 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSLP-MVS++		99.46 31	99.47 17	99.44 127	99.60 146	99.16 125	99.41 207	99.71 13	98.98 48	99.45 134	99.78 121	99.19 9	99.54 256	99.28 63	99.84 77	99.63 140

XVG-OURS		98.73 149	98.68 135	98.88 211	99.70 101	97.73 256	98.92 340	99.55 77	98.52 91	99.45 134	99.84 64	95.27 195	99.91 105	98.08 200	98.84 194	99.00 238

casdiffmvs_mvg		99.15 85	99.02 91	99.55 96	99.66 120	99.09 136	99.64 78	99.56 69	98.26 116	99.45 134	99.87 44	96.03 165	99.81 178	99.54 30	99.15 168	99.73 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpmrst		98.33 179	98.48 162	97.90 314	99.16 273	94.78 355	99.31 244	99.11 306	97.27 237	99.45 134	99.59 212	95.33 193	99.84 153	98.48 165	98.61 204	99.09 226

TAMVS		99.12 95	99.08 80	99.24 160	99.46 191	98.55 203	99.51 156	99.46 182	98.09 143	99.45 134	99.82 76	98.34 89	99.51 257	98.70 132	98.93 186	99.67 122

ETV-MVS		99.26 69	99.21 66	99.40 130	99.46 191	99.30 109	99.56 122	99.52 101	98.52 91	99.44 139	99.27 307	98.41 86	99.86 140	99.10 79	99.59 136	99.04 234

CANet_DTU		98.97 118	98.87 115	99.25 158	99.33 225	98.42 222	99.08 304	99.30 275	99.16 19	99.43 140	99.75 138	95.27 195	99.97 21	98.56 158	99.95 16	99.36 203

SCA		98.19 190	98.16 180	98.27 291	99.30 233	95.55 338	99.07 305	98.97 323	97.57 204	99.43 140	99.57 220	92.72 284	99.74 201	97.58 245	99.20 163	99.52 167

testdata				99.54 97	99.75 73	98.95 162		99.51 115	97.07 257	99.43 140	99.70 158	98.87 37	99.94 69	97.76 229	99.64 131	99.72 103

DPM-MVS		98.95 119	98.71 132	99.66 69	99.63 131	99.55 77	98.64 367	99.10 307	97.93 162	99.42 143	99.55 226	98.67 66	99.80 184	95.80 325	99.68 126	99.61 144

XVG-OURS-SEG-HR		98.69 153	98.62 147	98.89 209	99.71 96	97.74 255	99.12 295	99.54 85	98.44 99	99.42 143	99.71 154	94.20 245	99.92 95	98.54 162	98.90 190	99.00 238

baseline		99.15 85	99.02 91	99.53 105	99.66 120	99.14 131	99.72 49	99.48 155	98.35 107	99.42 143	99.84 64	96.07 163	99.79 187	99.51 35	99.14 169	99.67 122

DP-MVS Recon		99.12 95	98.95 105	99.65 73	99.74 80	99.70 46	99.27 261	99.57 64	96.40 310	99.42 143	99.68 174	98.75 55	99.80 184	97.98 208	99.72 118	99.44 191

Effi-MVS+-dtu		98.78 143	98.89 113	98.47 267	99.33 225	96.91 299	99.57 116	99.30 275	98.47 94	99.41 147	98.99 338	96.78 140	99.74 201	98.73 129	99.38 149	98.74 264

casdiffmvs		99.13 89	98.98 100	99.56 94	99.65 126	99.16 125	99.56 122	99.50 135	98.33 110	99.41 147	99.86 49	95.92 171	99.83 166	99.45 45	99.16 165	99.70 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MIMVSNet		97.73 262	97.45 261	98.57 251	99.45 196	97.50 265	99.02 318	98.98 322	96.11 330	99.41 147	99.14 322	90.28 332	98.74 363	95.74 326	98.93 186	99.47 184

CSCG		99.32 59	99.32 40	99.32 142	99.85 26	98.29 225	99.71 51	99.66 28	98.11 140	99.41 147	99.80 103	98.37 88	99.96 30	98.99 89	99.96 12	99.72 103

F-COLMAP		99.19 77	99.04 85	99.64 78	99.78 56	99.27 113	99.42 205	99.54 85	97.29 236	99.41 147	99.59 212	98.42 85	99.93 84	98.19 189	99.69 123	99.73 97

EIA-MVS		99.18 79	99.09 79	99.45 123	99.49 181	99.18 122	99.67 64	99.53 96	97.66 197	99.40 152	99.44 261	98.10 99	99.81 178	98.94 94	99.62 134	99.35 204

mvsmamba		98.92 121	98.87 115	99.08 175	99.07 291	99.16 125	99.88 4	99.51 115	98.15 133	99.40 152	99.89 30	97.12 127	99.33 289	99.38 48	97.40 279	98.73 266

MDTV_nov1_ep13			98.32 172		99.11 281	94.44 361	99.27 261	98.74 355	97.51 214	99.40 152	99.62 203	94.78 215	99.76 198	97.59 244	98.81 198

iter_conf_final		98.71 150	98.61 153	98.99 188	99.49 181	98.96 157	99.63 82	99.41 212	98.19 127	99.39 155	99.77 129	94.82 210	99.38 274	99.30 61	97.52 263	98.64 302

CVMVSNet		98.57 162	98.67 136	98.30 286	99.35 219	95.59 337	99.50 163	99.55 77	98.60 85	99.39 155	99.83 68	94.48 236	99.45 261	98.75 126	98.56 210	99.85 36

CNVR-MVS		99.42 42	99.30 49	99.78 52	99.62 137	99.71 44	99.26 270	99.52 101	98.82 65	99.39 155	99.71 154	98.96 24	99.85 146	98.59 151	99.80 97	99.77 82

Effi-MVS+		98.81 139	98.59 154	99.48 117	99.46 191	99.12 134	98.08 390	99.50 135	97.50 215	99.38 158	99.41 269	96.37 156	99.81 178	99.11 78	98.54 212	99.51 173

mvs_anonymous		99.03 111	98.99 97	99.16 168	99.38 212	98.52 209	99.51 156	99.38 231	97.79 179	99.38 158	99.81 90	97.30 122	99.45 261	99.35 51	98.99 183	99.51 173

iter_conf05		98.55 163	98.44 163	98.87 215	99.34 223	98.60 200	99.55 134	99.42 209	98.21 124	99.37 160	99.77 129	93.55 266	99.38 274	99.30 61	97.48 271	98.63 310

XVS		99.53 16	99.42 22	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 160	99.74 143	98.81 44	99.94 69	98.79 123	99.86 62	99.84 40

X-MVStestdata		96.55 317	95.45 335	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 160	64.01 407	98.81 44	99.94 69	98.79 123	99.86 62	99.84 40

PatchmatchNet		98.31 180	98.36 168	98.19 294	99.16 273	95.32 346	99.27 261	98.92 330	97.37 229	99.37 160	99.58 216	94.90 207	99.70 223	97.43 263	99.21 162	99.54 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AllTest		98.87 125	98.72 130	99.31 143	99.86 20	98.48 215	99.56 122	99.61 48	97.85 171	99.36 164	99.85 54	95.95 168	99.85 146	96.66 307	99.83 86	99.59 150

TestCases				99.31 143	99.86 20	98.48 215		99.61 48	97.85 171	99.36 164	99.85 54	95.95 168	99.85 146	96.66 307	99.83 86	99.59 150

Vis-MVSNet (Re-imp)		98.87 125	98.72 130	99.31 143	99.71 96	98.88 171	99.80 25	99.44 201	97.91 164	99.36 164	99.78 121	95.49 188	99.43 270	97.91 212	99.11 171	99.62 142

alignmvs		98.81 139	98.56 158	99.58 90	99.43 198	99.42 96	99.51 156	98.96 325	98.61 84	99.35 167	98.92 348	94.78 215	99.77 194	99.35 51	98.11 238	99.54 161

VPA-MVSNet		98.29 183	97.95 207	99.30 148	99.16 273	99.54 79	99.50 163	99.58 61	98.27 115	99.35 167	99.37 280	92.53 293	99.65 239	99.35 51	94.46 344	98.72 267

AdaColmap		99.01 115	98.80 123	99.66 69	99.56 156	99.54 79	99.18 284	99.70 15	98.18 131	99.35 167	99.63 198	96.32 157	99.90 116	97.48 257	99.77 107	99.55 159

test222					99.75 73	99.49 87	98.91 342	99.49 143	96.42 308	99.34 170	99.65 186	98.28 92			99.69 123	99.72 103

API-MVS		99.04 109	99.03 87	99.06 178	99.40 208	99.31 107	99.55 134	99.56 69	98.54 89	99.33 171	99.39 276	98.76 52	99.78 192	96.98 289	99.78 104	98.07 361

bld_raw_dy_0_64		98.69 153	98.58 155	98.99 188	98.88 318	98.96 157	99.80 25	99.41 212	97.91 164	99.32 172	99.87 44	95.70 181	99.31 295	99.09 80	97.27 284	98.71 269

v144192		97.92 231	97.60 246	98.87 215	98.83 328	98.65 194	99.55 134	99.34 250	96.20 321	99.32 172	99.40 272	94.36 240	99.26 302	96.37 315	95.03 335	98.70 274

GeoE		98.85 135	98.62 147	99.53 105	99.61 141	99.08 139	99.80 25	99.51 115	97.10 255	99.31 174	99.78 121	95.23 199	99.77 194	98.21 187	99.03 180	99.75 88

canonicalmvs		99.02 112	98.86 118	99.51 113	99.42 200	99.32 104	99.80 25	99.48 155	98.63 82	99.31 174	98.81 353	97.09 129	99.75 200	99.27 66	97.90 244	99.47 184

V42		98.06 205	97.79 221	98.86 219	98.98 308	98.84 177	99.69 55	99.34 250	96.53 298	99.30 176	99.37 280	94.67 226	99.32 292	97.57 249	94.66 341	98.42 342

ab-mvs		98.86 128	98.63 142	99.54 97	99.64 128	99.19 120	99.44 194	99.54 85	97.77 182	99.30 176	99.81 90	94.20 245	99.93 84	99.17 74	98.82 196	99.49 177

TAPA-MVS	97.07 15	97.74 261	97.34 281	98.94 196	99.70 101	97.53 264	99.25 272	99.51 115	91.90 379	99.30 176	99.63 198	98.78 48	99.64 242	88.09 389	99.87 54	99.65 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
新几何1				99.75 58	99.75 73	99.59 70		99.54 85	96.76 279	99.29 179	99.64 192	98.43 83	99.94 69	96.92 296	99.66 128	99.72 103

test_fmvs2		97.25 302	97.30 286	97.09 344	99.43 198	93.31 375	99.73 47	98.87 341	98.83 64	99.28 180	99.80 103	84.45 379	99.66 234	97.88 214	97.45 273	98.30 350

VPNet		97.84 243	97.44 266	99.01 184	99.21 255	98.94 165	99.48 178	99.57 64	98.38 102	99.28 180	99.73 149	88.89 347	99.39 273	99.19 71	93.27 362	98.71 269

HY-MVS	97.30 7	98.85 135	98.64 141	99.47 120	99.42 200	99.08 139	99.62 88	99.36 240	97.39 228	99.28 180	99.68 174	96.44 154	99.92 95	98.37 176	98.22 228	99.40 197

PAPM_NR		99.04 109	98.84 120	99.66 69	99.74 80	99.44 94	99.39 219	99.38 231	97.70 192	99.28 180	99.28 304	98.34 89	99.85 146	96.96 291	99.45 145	99.69 115

HPM-MVS++		99.39 52	99.23 65	99.87 11	99.75 73	99.84 15	99.43 198	99.51 115	98.68 81	99.27 184	99.53 235	98.64 69	99.96 30	98.44 171	99.80 97	99.79 74

v1240		97.69 269	97.32 284	98.79 232	98.85 326	98.43 220	99.48 178	99.36 240	96.11 330	99.27 184	99.36 283	93.76 263	99.24 306	94.46 349	95.23 330	98.70 274

thres600view7		97.86 239	97.51 254	98.92 200	99.72 91	97.95 246	99.59 101	98.74 355	97.94 161	99.27 184	98.62 359	91.75 310	99.86 140	93.73 358	98.19 232	98.96 244

PLC	97.94 4	99.02 112	98.85 119	99.53 105	99.66 120	99.01 148	99.24 274	99.52 101	96.85 275	99.27 184	99.48 252	98.25 93	99.91 105	97.76 229	99.62 134	99.65 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
thres100view900		97.76 255	97.45 261	98.69 241	99.72 91	97.86 252	99.59 101	98.74 355	97.93 162	99.26 188	98.62 359	91.75 310	99.83 166	93.22 363	98.18 233	98.37 348

EPMVS		97.82 248	97.65 240	98.35 281	98.88 318	95.98 330	99.49 174	94.71 403	97.57 204	99.26 188	99.48 252	92.46 298	99.71 217	97.87 216	99.08 176	99.35 204

Fast-Effi-MVS+-dtu		98.77 145	98.83 122	98.60 246	99.41 203	96.99 293	99.52 148	99.49 143	98.11 140	99.24 190	99.34 290	96.96 136	99.79 187	97.95 210	99.45 145	99.02 237

v1921920		97.80 252	97.45 261	98.84 223	98.80 329	98.53 205	99.52 148	99.34 250	96.15 327	99.24 190	99.47 255	93.98 254	99.29 297	95.40 336	95.13 333	98.69 278

LPG-MVS_test		98.22 186	98.13 185	98.49 260	99.33 225	97.05 286	99.58 109	99.55 77	97.46 217	99.24 190	99.83 68	92.58 291	99.72 211	98.09 196	97.51 265	98.68 283

LGP-MVS_train				98.49 260	99.33 225	97.05 286		99.55 77	97.46 217	99.24 190	99.83 68	92.58 291	99.72 211	98.09 196	97.51 265	98.68 283

v1144		97.98 222	97.69 236	98.85 222	98.87 322	98.66 193	99.54 139	99.35 246	96.27 316	99.23 194	99.35 286	94.67 226	99.23 307	96.73 302	95.16 332	98.68 283

Anonymous20240529		98.09 201	97.68 237	99.34 136	99.66 120	98.44 219	99.40 215	99.43 207	93.67 367	99.22 195	99.89 30	90.23 336	99.93 84	99.26 67	98.33 220	99.66 125

OPM-MVS		98.19 190	98.10 188	98.45 269	98.88 318	97.07 284	99.28 256	99.38 231	98.57 86	99.22 195	99.81 90	92.12 302	99.66 234	98.08 200	97.54 262	98.61 322
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_djsdf		98.67 156	98.57 156	98.98 190	98.70 345	98.91 169	99.88 4	99.46 182	97.55 207	99.22 195	99.88 36	95.73 179	99.28 298	99.03 85	97.62 255	98.75 261

test12				99.75 58	99.64 128	99.61 67		99.29 279		99.21 198		98.38 87	99.89 127		99.74 115	99.74 92

NCCC		99.34 57	99.19 68	99.79 49	99.61 141	99.65 57	99.30 246	99.48 155	98.86 60	99.21 198	99.63 198	98.72 61	99.90 116	98.25 185	99.63 133	99.80 70

PMMVS		98.80 142	98.62 147	99.34 136	99.27 242	98.70 190	98.76 356	99.31 271	97.34 231	99.21 198	99.07 328	97.20 125	99.82 173	98.56 158	98.87 191	99.52 167

v1192		97.81 250	97.44 266	98.91 204	98.88 318	98.68 191	99.51 156	99.34 250	96.18 323	99.20 201	99.34 290	94.03 252	99.36 283	95.32 338	95.18 331	98.69 278

EI-MVSNet		98.67 156	98.67 136	98.68 242	99.35 219	97.97 242	99.50 163	99.38 231	96.93 272	99.20 201	99.83 68	97.87 105	99.36 283	98.38 174	97.56 260	98.71 269

MVSTER		98.49 164	98.32 172	99.00 186	99.35 219	99.02 146	99.54 139	99.38 231	97.41 226	99.20 201	99.73 149	93.86 259	99.36 283	98.87 105	97.56 260	98.62 313

UWE-MVS		97.58 281	97.29 288	98.48 262	99.09 287	96.25 325	99.01 323	96.61 396	97.86 168	99.19 204	99.01 336	88.72 348	99.90 116	97.38 266	98.69 202	99.28 212

Anonymous202405211		98.30 182	97.98 203	99.26 157	99.57 152	98.16 231	99.41 207	98.55 368	96.03 335	99.19 204	99.74 143	91.87 307	99.92 95	99.16 75	98.29 225	99.70 113

v2v482		98.06 205	97.77 226	98.92 200	98.90 315	98.82 181	99.57 116	99.36 240	96.65 287	99.19 204	99.35 286	94.20 245	99.25 303	97.72 235	94.97 336	98.69 278

CNLPA		99.14 87	98.99 97	99.59 87	99.58 150	99.41 98	99.16 286	99.44 201	98.45 96	99.19 204	99.49 247	98.08 101	99.89 127	97.73 233	99.75 112	99.48 178

UGNet		98.87 125	98.69 134	99.40 130	99.22 254	98.72 189	99.44 194	99.68 20	99.24 17	99.18 208	99.42 265	92.74 283	99.96 30	99.34 55	99.94 21	99.53 166
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
tfpn200view9		97.72 264	97.38 274	98.72 237	99.69 106	97.96 244	99.50 163	98.73 360	97.83 174	99.17 209	98.45 364	91.67 314	99.83 166	93.22 363	98.18 233	98.37 348

thres400		97.77 254	97.38 274	98.92 200	99.69 106	97.96 244	99.50 163	98.73 360	97.83 174	99.17 209	98.45 364	91.67 314	99.83 166	93.22 363	98.18 233	98.96 244

Test_1112_low_res		98.89 123	98.66 139	99.57 92	99.69 106	98.95 162	99.03 315	99.47 173	96.98 265	99.15 211	99.23 312	96.77 141	99.89 127	98.83 118	98.78 199	99.86 33

baseline1		98.31 180	97.95 207	99.38 134	99.50 179	98.74 187	99.59 101	98.93 327	98.41 100	99.14 212	99.60 210	94.59 229	99.79 187	98.48 165	93.29 361	99.61 144

1112_ss		98.98 116	98.77 127	99.59 87	99.68 110	99.02 146	99.25 272	99.48 155	97.23 242	99.13 213	99.58 216	96.93 137	99.90 116	98.87 105	98.78 199	99.84 40

CLD-MVS		98.16 194	98.10 188	98.33 282	99.29 237	96.82 304	98.75 357	99.44 201	97.83 174	99.13 213	99.55 226	92.92 277	99.67 231	98.32 182	97.69 251	98.48 334
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
原ACMM1				99.65 73	99.73 87	99.33 103		99.47 173	97.46 217	99.12 215	99.66 185	98.67 66	99.91 105	97.70 238	99.69 123	99.71 112

tpm		97.67 274	97.55 248	98.03 303	99.02 300	95.01 352	99.43 198	98.54 369	96.44 306	99.12 215	99.34 290	91.83 309	99.60 250	97.75 231	96.46 300	99.48 178

HQP_MVS		98.27 185	98.22 178	98.44 272	99.29 237	96.97 295	99.39 219	99.47 173	98.97 51	99.11 217	99.61 207	92.71 286	99.69 228	97.78 225	97.63 253	98.67 290

plane_prior3						97.00 292			98.69 79	99.11 217

CHOSEN 1792x2688		99.19 77	99.10 76	99.45 123	99.89 8	98.52 209	99.39 219	99.94 1	98.73 76	99.11 217	99.89 30	95.50 187	99.94 69	99.50 36	99.97 7	99.89 20

v8		97.95 227	97.63 244	98.93 198	98.95 312	98.81 183	99.80 25	99.41 212	96.03 335	99.10 220	99.42 265	94.92 206	99.30 296	96.94 293	94.08 352	98.66 298

ADS-MVSNet2		98.02 215	98.07 195	97.87 315	99.33 225	95.19 349	99.23 275	99.08 310	96.24 318	99.10 220	99.67 180	94.11 249	98.93 355	96.81 299	99.05 178	99.48 178

ADS-MVSNet		98.20 189	98.08 192	98.56 254	99.33 225	96.48 317	99.23 275	99.15 302	96.24 318	99.10 220	99.67 180	94.11 249	99.71 217	96.81 299	99.05 178	99.48 178

thres200		97.61 279	97.28 289	98.62 245	99.64 128	98.03 238	99.26 270	98.74 355	97.68 194	99.09 223	98.32 369	91.66 316	99.81 178	92.88 368	98.22 228	98.03 364

dp		97.75 259	97.80 220	97.59 331	99.10 284	93.71 370	99.32 241	98.88 339	96.48 303	99.08 224	99.55 226	92.67 289	99.82 173	96.52 310	98.58 207	99.24 215

WB-MVSnew		97.65 276	97.65 240	97.63 328	98.78 333	97.62 262	99.13 292	98.33 372	97.36 230	99.07 225	98.94 344	95.64 184	99.15 320	92.95 367	98.68 203	96.12 392

GBi-Net		97.68 271	97.48 256	98.29 287	99.51 170	97.26 273	99.43 198	99.48 155	96.49 300	99.07 225	99.32 297	90.26 333	98.98 345	97.10 282	96.65 295	98.62 313

test1		97.68 271	97.48 256	98.29 287	99.51 170	97.26 273	99.43 198	99.48 155	96.49 300	99.07 225	99.32 297	90.26 333	98.98 345	97.10 282	96.65 295	98.62 313

FMVSNet3		98.03 213	97.76 230	98.84 223	99.39 211	98.98 150	99.40 215	99.38 231	96.67 285	99.07 225	99.28 304	92.93 276	98.98 345	97.10 282	96.65 295	98.56 329

IterMVS-LS		98.46 167	98.42 165	98.58 250	99.59 148	98.00 240	99.37 226	99.43 207	96.94 271	99.07 225	99.59 212	97.87 105	99.03 338	98.32 182	95.62 322	98.71 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		98.08 203	98.16 180	97.85 316	99.55 160	94.67 358	99.70 52	98.92 330	98.15 133	99.06 230	99.35 286	93.67 265	99.25 303	97.77 228	97.25 285	99.64 136

pmmvs4		98.13 197	97.90 212	98.81 229	98.61 354	98.87 172	98.99 326	99.21 295	96.44 306	99.06 230	99.58 216	95.90 173	99.11 329	97.18 280	96.11 308	98.46 339

XVG-ACMP-BASELINE		97.83 245	97.71 235	98.20 293	99.11 281	96.33 322	99.41 207	99.52 101	98.06 152	99.05 232	99.50 244	89.64 342	99.73 207	97.73 233	97.38 281	98.53 330

CostFormer		97.72 264	97.73 233	97.71 326	99.15 277	94.02 366	99.54 139	99.02 318	94.67 358	99.04 233	99.35 286	92.35 301	99.77 194	98.50 164	97.94 243	99.34 207

DP-MVS		99.16 83	98.95 105	99.78 52	99.77 62	99.53 82	99.41 207	99.50 135	97.03 263	99.04 233	99.88 36	97.39 116	99.92 95	98.66 139	99.90 39	99.87 31

ACMM	97.58 5	98.37 177	98.34 170	98.48 262	99.41 203	97.10 280	99.56 122	99.45 193	98.53 90	99.04 233	99.85 54	93.00 275	99.71 217	98.74 127	97.45 273	98.64 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+		98.70 151	98.43 164	99.51 113	99.51 170	99.28 111	99.52 148	99.47 173	96.11 330	99.01 236	99.34 290	96.20 161	99.84 153	97.88 214	98.82 196	99.39 198

nrg030		98.64 159	98.42 165	99.28 155	99.05 297	99.69 47	99.81 20	99.46 182	98.04 154	99.01 236	99.82 76	96.69 144	99.38 274	99.34 55	94.59 343	98.78 254

test_prior2							98.96 333		98.34 108	99.01 236	99.52 238	98.68 64		97.96 209	99.74 115

MAR-MVS		98.86 128	98.63 142	99.54 97	99.37 215	99.66 53	99.45 188	99.54 85	96.61 292	99.01 236	99.40 272	97.09 129	99.86 140	97.68 240	99.53 141	99.10 222
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
PS-MVSNAJss		98.92 121	98.92 107	98.90 206	98.78 333	98.53 205	99.78 32	99.54 85	98.07 148	99.00 240	99.76 135	99.01 18	99.37 279	99.13 76	97.23 286	98.81 251

PAPR		98.63 160	98.34 170	99.51 113	99.40 208	99.03 145	98.80 352	99.36 240	96.33 311	99.00 240	99.12 326	98.46 81	99.84 153	95.23 340	99.37 156	99.66 125

D2MVS		98.41 172	98.50 161	98.15 299	99.26 244	96.62 312	99.40 215	99.61 48	97.71 189	98.98 242	99.36 283	96.04 164	99.67 231	98.70 132	97.41 278	98.15 358

v10		97.85 240	97.52 252	98.86 219	98.99 305	98.67 192	99.75 41	99.41 212	95.70 339	98.98 242	99.41 269	94.75 220	99.23 307	96.01 321	94.63 342	98.67 290

miper_enhance_ethall		98.16 194	98.08 192	98.41 275	98.96 311	97.72 257	98.45 377	99.32 267	96.95 269	98.97 244	99.17 318	97.06 131	99.22 310	97.86 217	95.99 311	98.29 351

UniMVSNet (Re)		98.29 183	98.00 201	99.13 173	99.00 302	99.36 102	99.49 174	99.51 115	97.95 160	98.97 244	99.13 323	96.30 158	99.38 274	98.36 178	93.34 360	98.66 298

TEST9					99.67 111	99.65 57	99.05 310	99.41 212	96.22 320	98.95 246	99.49 247	98.77 51	99.91 105

train_agg		99.02 112	98.77 127	99.77 55	99.67 111	99.65 57	99.05 310	99.41 212	96.28 314	98.95 246	99.49 247	98.76 52	99.91 105	97.63 241	99.72 118	99.75 88

RRT_MVS		98.70 151	98.66 139	98.83 225	98.90 315	98.45 218	99.89 2	99.28 281	97.76 183	98.94 248	99.92 14	96.98 134	99.25 303	99.28 63	97.00 292	98.80 252

BH-RMVSNet		98.41 172	98.08 192	99.40 130	99.41 203	98.83 180	99.30 246	98.77 351	97.70 192	98.94 248	99.65 186	92.91 279	99.74 201	96.52 310	99.55 140	99.64 136

test_8					99.67 111	99.61 67	99.03 315	99.41 212	96.28 314	98.93 250	99.48 252	98.76 52	99.91 105

3Dnovator	97.25 9	99.24 74	99.05 83	99.81 44	99.12 279	99.66 53	99.84 13	99.74 10	99.09 32	98.92 251	99.90 26	95.94 170	99.98 13	98.95 93	99.92 24	99.79 74

v7n		97.87 237	97.52 252	98.92 200	98.76 338	98.58 201	99.84 13	99.46 182	96.20 321	98.91 252	99.70 158	94.89 208	99.44 266	96.03 319	93.89 355	98.75 261

JIA-IIPM		97.50 287	97.02 301	98.93 198	98.73 340	97.80 254	99.30 246	98.97 323	91.73 380	98.91 252	94.86 394	95.10 201	99.71 217	97.58 245	97.98 241	99.28 212

v148		97.79 253	97.55 248	98.50 259	98.74 339	97.72 257	99.54 139	99.33 257	96.26 317	98.90 254	99.51 241	94.68 225	99.14 321	97.83 221	93.15 364	98.63 310

GA-MVS		97.85 240	97.47 258	99.00 186	99.38 212	97.99 241	98.57 371	99.15 302	97.04 262	98.90 254	99.30 300	89.83 339	99.38 274	96.70 304	98.33 220	99.62 142

tpm2		97.44 294	97.34 281	97.74 325	99.15 277	94.36 363	99.45 188	98.94 326	93.45 372	98.90 254	99.44 261	91.35 322	99.59 251	97.31 269	98.07 239	99.29 211

tt0805		97.97 225	97.77 226	98.57 251	99.59 148	96.61 313	99.45 188	99.08 310	98.21 124	98.88 257	99.80 103	88.66 351	99.70 223	98.58 152	97.72 250	99.39 198

miper_ehance_all_eth		98.18 192	98.10 188	98.41 275	99.23 250	97.72 257	98.72 360	99.31 271	96.60 294	98.88 257	99.29 302	97.29 123	99.13 324	97.60 243	95.99 311	98.38 347

eth_miper_zixun_eth		98.05 210	97.96 205	98.33 282	99.26 244	97.38 268	98.56 373	99.31 271	96.65 287	98.88 257	99.52 238	96.58 147	99.12 328	97.39 265	95.53 325	98.47 336

cl22		97.85 240	97.64 243	98.48 262	99.09 287	97.87 250	98.60 370	99.33 257	97.11 254	98.87 260	99.22 313	92.38 300	99.17 319	98.21 187	95.99 311	98.42 342

agg_prior					99.67 111	99.62 65		99.40 220		98.87 260			99.91 105

anonymousdsp		98.44 168	98.28 175	98.94 196	98.50 360	98.96 157	99.77 34	99.50 135	97.07 257	98.87 260	99.77 129	94.76 219	99.28 298	98.66 139	97.60 256	98.57 328

DSMNet-mixed		97.25 302	97.35 278	96.95 348	97.84 371	93.61 373	99.57 116	96.63 395	96.13 329	98.87 260	98.61 361	94.59 229	97.70 385	95.08 342	98.86 192	99.55 159

FMVSNet2		97.72 264	97.36 276	98.80 231	99.51 170	98.84 177	99.45 188	99.42 209	96.49 300	98.86 264	99.29 302	90.26 333	98.98 345	96.44 312	96.56 298	98.58 327

c3_l		98.12 199	98.04 197	98.38 279	99.30 233	97.69 261	98.81 351	99.33 257	96.67 285	98.83 265	99.34 290	97.11 128	98.99 344	97.58 245	95.34 328	98.48 334

ITE_SJBPF				98.08 301	99.29 237	96.37 320		98.92 330	98.34 108	98.83 265	99.75 138	91.09 325	99.62 248	95.82 323	97.40 279	98.25 354

Anonymous20231211		97.88 235	97.54 251	98.90 206	99.71 96	98.53 205	99.48 178	99.57 64	94.16 363	98.81 267	99.68 174	93.23 270	99.42 271	98.84 115	94.42 346	98.76 259

Patchmtry		97.75 259	97.40 273	98.81 229	99.10 284	98.87 172	99.11 301	99.33 257	94.83 355	98.81 267	99.38 277	94.33 241	99.02 340	96.10 317	95.57 323	98.53 330

miper_lstm_enhance		98.00 220	97.91 211	98.28 290	99.34 223	97.43 267	98.88 344	99.36 240	96.48 303	98.80 269	99.55 226	95.98 166	98.91 356	97.27 271	95.50 326	98.51 332

BH-untuned		98.42 170	98.36 168	98.59 247	99.49 181	96.70 307	99.27 261	99.13 305	97.24 241	98.80 269	99.38 277	95.75 178	99.74 201	97.07 285	99.16 165	99.33 208

FIs		98.78 143	98.63 142	99.23 162	99.18 263	99.54 79	99.83 16	99.59 57	98.28 113	98.79 271	99.81 90	96.75 142	99.37 279	99.08 82	96.38 302	98.78 254

OurMVSNet-221017-0		97.88 235	97.77 226	98.19 294	98.71 344	96.53 315	99.88 4	99.00 320	97.79 179	98.78 272	99.94 6	91.68 313	99.35 286	97.21 274	96.99 293	98.69 278

MVS-HIRNet		95.75 333	95.16 338	97.51 333	99.30 233	93.69 371	98.88 344	95.78 398	85.09 393	98.78 272	92.65 396	91.29 323	99.37 279	94.85 345	99.85 69	99.46 186

tpmvs		97.98 222	98.02 200	97.84 318	99.04 298	94.73 356	99.31 244	99.20 296	96.10 334	98.76 274	99.42 265	94.94 203	99.81 178	96.97 290	98.45 216	98.97 242

Patchmatch-test		97.93 228	97.65 240	98.77 234	99.18 263	97.07 284	99.03 315	99.14 304	96.16 325	98.74 275	99.57 220	94.56 231	99.72 211	93.36 362	99.11 171	99.52 167

QAPM		98.67 156	98.30 174	99.80 46	99.20 257	99.67 51	99.77 34	99.72 11	94.74 357	98.73 276	99.90 26	95.78 177	99.98 13	96.96 291	99.88 51	99.76 87

3Dnovator+	97.12 13	99.18 79	98.97 101	99.82 41	99.17 271	99.68 48	99.81 20	99.51 115	99.20 18	98.72 277	99.89 30	95.68 182	99.97 21	98.86 110	99.86 62	99.81 61

IterMVS-SCA-FT		97.82 248	97.75 231	98.06 302	99.57 152	96.36 321	99.02 318	99.49 143	97.18 245	98.71 278	99.72 153	92.72 284	99.14 321	97.44 262	95.86 316	98.67 290

UniMVSNet_NR-MVSNet		98.22 186	97.97 204	98.96 193	98.92 314	98.98 150	99.48 178	99.53 96	97.76 183	98.71 278	99.46 259	96.43 155	99.22 310	98.57 155	92.87 367	98.69 278

DU-MVS		98.08 203	97.79 221	98.96 193	98.87 322	98.98 150	99.41 207	99.45 193	97.87 167	98.71 278	99.50 244	94.82 210	99.22 310	98.57 155	92.87 367	98.68 283

tpm cat1		97.39 296	97.36 276	97.50 334	99.17 271	93.73 369	99.43 198	99.31 271	91.27 381	98.71 278	99.08 327	94.31 243	99.77 194	96.41 314	98.50 214	99.00 238

XXY-MVS		98.38 176	98.09 191	99.24 160	99.26 244	99.32 104	99.56 122	99.55 77	97.45 220	98.71 278	99.83 68	93.23 270	99.63 247	98.88 102	96.32 304	98.76 259

IterMVS		97.83 245	97.77 226	98.02 305	99.58 150	96.27 324	99.02 318	99.48 155	97.22 243	98.71 278	99.70 158	92.75 281	99.13 324	97.46 260	96.00 310	98.67 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		98.75 146	98.62 147	99.15 172	99.08 290	99.45 93	99.86 12	99.60 54	98.23 121	98.70 284	99.82 76	96.80 139	99.22 310	99.07 83	96.38 302	98.79 253

COLMAP_ROB	97.56 6	98.86 128	98.75 129	99.17 167	99.88 11	98.53 205	99.34 238	99.59 57	97.55 207	98.70 284	99.89 30	95.83 175	99.90 116	98.10 195	99.90 39	99.08 227
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS		97.76 255	97.41 272	98.82 226	99.06 294	97.87 250	98.87 346	98.56 367	96.63 291	98.68 286	99.22 313	92.49 294	99.65 239	95.40 336	97.79 248	98.95 246

WR-MVS		98.06 205	97.73 233	99.06 178	98.86 325	99.25 116	99.19 282	99.35 246	97.30 235	98.66 287	99.43 263	93.94 255	99.21 315	98.58 152	94.28 348	98.71 269

HQP-NCC					99.19 260		98.98 329		98.24 118	98.66 287

ACMP_Plane					99.19 260		98.98 329		98.24 118	98.66 287

HQP4-MVS										98.66 287			99.64 242			98.64 302

HQP-MVS		98.02 215	97.90 212	98.37 280	99.19 260	96.83 302	98.98 329	99.39 223	98.24 118	98.66 287	99.40 272	92.47 295	99.64 242	97.19 278	97.58 258	98.64 302

LF4IMVS		97.52 284	97.46 260	97.70 327	98.98 308	95.55 338	99.29 251	98.82 346	98.07 148	98.66 287	99.64 192	89.97 338	99.61 249	97.01 286	96.68 294	97.94 371

mvs_tets		98.40 175	98.23 177	98.91 204	98.67 348	98.51 211	99.66 69	99.53 96	98.19 127	98.65 293	99.81 90	92.75 281	99.44 266	99.31 58	97.48 271	98.77 257

TESTMET0.1,1		97.55 282	97.27 292	98.40 277	98.93 313	96.53 315	98.67 363	97.61 386	96.96 267	98.64 294	99.28 304	88.63 353	99.45 261	97.30 270	99.38 149	99.21 217

jajsoiax		98.43 169	98.28 175	98.88 211	98.60 355	98.43 220	99.82 17	99.53 96	98.19 127	98.63 295	99.80 103	93.22 272	99.44 266	99.22 69	97.50 267	98.77 257

Baseline_NR-MVSNet		97.76 255	97.45 261	98.68 242	99.09 287	98.29 225	99.41 207	98.85 343	95.65 340	98.63 295	99.67 180	94.82 210	99.10 331	98.07 203	92.89 366	98.64 302

EPNet		98.86 128	98.71 132	99.30 148	97.20 383	98.18 230	99.62 88	98.91 334	99.28 16	98.63 295	99.81 90	95.96 167	99.99 4	99.24 68	99.72 118	99.73 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test-LLR		98.06 205	97.90 212	98.55 256	98.79 330	97.10 280	98.67 363	97.75 383	97.34 231	98.61 298	98.85 350	94.45 238	99.45 261	97.25 272	99.38 149	99.10 222

test-mter		97.49 292	97.13 297	98.55 256	98.79 330	97.10 280	98.67 363	97.75 383	96.65 287	98.61 298	98.85 350	88.23 357	99.45 261	97.25 272	99.38 149	99.10 222

DIV-MVS_self_test		98.01 218	97.85 218	98.48 262	99.24 249	97.95 246	98.71 361	99.35 246	96.50 299	98.60 300	99.54 231	95.72 180	99.03 338	97.21 274	95.77 317	98.46 339

cl____		98.01 218	97.84 219	98.55 256	99.25 248	97.97 242	98.71 361	99.34 250	96.47 305	98.59 301	99.54 231	95.65 183	99.21 315	97.21 274	95.77 317	98.46 339

ETVMVS		97.50 287	96.90 305	99.29 151	99.23 250	98.78 186	99.32 241	98.90 336	97.52 213	98.56 302	98.09 378	84.72 378	99.69 228	97.86 217	97.88 245	99.39 198

FMVSNet1		96.84 313	96.36 317	98.29 287	99.32 231	97.26 273	99.43 198	99.48 155	95.11 347	98.55 303	99.32 297	83.95 381	98.98 345	95.81 324	96.26 305	98.62 313

UniMVSNet_ETH3D		97.32 299	96.81 307	98.87 215	99.40 208	97.46 266	99.51 156	99.53 96	95.86 338	98.54 304	99.77 129	82.44 387	99.66 234	98.68 137	97.52 263	99.50 176

AUN-MVS		96.88 312	96.31 318	98.59 247	99.48 189	97.04 289	99.27 261	99.22 292	97.44 222	98.51 305	99.41 269	91.97 305	99.66 234	97.71 236	83.83 391	99.07 232

PCF-MVS	97.08 14	97.66 275	97.06 300	99.47 120	99.61 141	99.09 136	98.04 391	99.25 287	91.24 382	98.51 305	99.70 158	94.55 233	99.91 105	92.76 371	99.85 69	99.42 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet		97.93 228	97.66 239	98.76 235	98.78 333	98.62 197	99.65 75	99.49 143	97.76 183	98.49 307	99.60 210	94.23 244	98.97 352	98.00 207	92.90 365	98.70 274

CP-MVSNet		98.09 201	97.78 224	99.01 184	98.97 310	99.24 117	99.67 64	99.46 182	97.25 239	98.48 308	99.64 192	93.79 261	99.06 334	98.63 142	94.10 351	98.74 264

ACMP	97.20 11	98.06 205	97.94 209	98.45 269	99.37 215	97.01 291	99.44 194	99.49 143	97.54 210	98.45 309	99.79 115	91.95 306	99.72 211	97.91 212	97.49 270	98.62 313
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		97.69 269	97.43 270	98.48 262	98.60 355	97.30 269	98.18 389	99.39 223	92.96 375	98.41 310	98.78 355	93.77 262	99.27 301	98.16 193	98.61 204	98.86 248

WR-MVS_H		98.13 197	97.87 217	98.90 206	99.02 300	98.84 177	99.70 52	99.59 57	97.27 237	98.40 311	99.19 317	95.53 186	99.23 307	98.34 179	93.78 357	98.61 322

BH-w/o		98.00 220	97.89 216	98.32 284	99.35 219	96.20 327	99.01 323	98.90 336	96.42 308	98.38 312	99.00 337	95.26 197	99.72 211	96.06 318	98.61 204	99.03 235

pmmvs5		97.52 284	97.30 286	98.16 296	98.57 357	96.73 306	99.27 261	98.90 336	96.14 328	98.37 313	99.53 235	91.54 319	99.14 321	97.51 254	95.87 315	98.63 310

EU-MVSNet		97.98 222	98.03 198	97.81 322	98.72 342	96.65 311	99.66 69	99.66 28	98.09 143	98.35 314	99.82 76	95.25 198	98.01 378	97.41 264	95.30 329	98.78 254

FMVSNet5		96.43 321	96.19 320	97.15 340	99.11 281	95.89 332	99.32 241	99.52 101	94.47 362	98.34 315	99.07 328	87.54 364	97.07 389	92.61 372	95.72 320	98.47 336

testing91		97.44 294	97.02 301	98.71 239	99.18 263	96.89 301	99.19 282	99.04 316	97.78 181	98.31 316	98.29 370	85.41 373	99.85 146	98.01 206	97.95 242	99.39 198

PS-CasMVS		97.93 228	97.59 247	98.95 195	98.99 305	99.06 142	99.68 61	99.52 101	97.13 249	98.31 316	99.68 174	92.44 299	99.05 335	98.51 163	94.08 352	98.75 261

USDC		97.34 298	97.20 293	97.75 324	99.07 291	95.20 348	98.51 375	99.04 316	97.99 158	98.31 316	99.86 49	89.02 345	99.55 255	95.67 330	97.36 282	98.49 333

PEN-MVS		97.76 255	97.44 266	98.72 237	98.77 337	98.54 204	99.78 32	99.51 115	97.06 259	98.29 319	99.64 192	92.63 290	98.89 358	98.09 196	93.16 363	98.72 267

tfpnnormal		97.84 243	97.47 258	98.98 190	99.20 257	99.22 119	99.64 78	99.61 48	96.32 312	98.27 320	99.70 158	93.35 269	99.44 266	95.69 328	95.40 327	98.27 352

testing99		97.36 297	96.94 304	98.63 244	99.18 263	96.70 307	99.30 246	98.93 327	97.71 189	98.23 321	98.26 371	84.92 376	99.84 153	98.04 205	97.85 247	99.35 204

testing222		97.16 305	96.50 313	99.16 168	99.16 273	98.47 217	99.27 261	98.66 364	97.71 189	98.23 321	98.15 373	82.28 388	99.84 153	97.36 267	97.66 252	99.18 218

ppachtmachnet_test		97.49 292	97.45 261	97.61 330	98.62 352	95.24 347	98.80 352	99.46 182	96.11 330	98.22 323	99.62 203	96.45 153	98.97 352	93.77 357	95.97 314	98.61 322

testing11		97.50 287	97.10 298	98.71 239	99.20 257	96.91 299	99.29 251	98.82 346	97.89 166	98.21 324	98.40 366	85.63 371	99.83 166	98.45 170	98.04 240	99.37 202

our_test_3		97.65 276	97.68 237	97.55 332	98.62 352	94.97 353	98.84 348	99.30 275	96.83 278	98.19 325	99.34 290	97.01 133	99.02 340	95.00 344	96.01 309	98.64 302

LTVRE_ROB	97.16 12	98.02 215	97.90 212	98.40 277	99.23 250	96.80 305	99.70 52	99.60 54	97.12 251	98.18 326	99.70 158	91.73 312	99.72 211	98.39 173	97.45 273	98.68 283
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
ACMH	97.28 8	98.10 200	97.99 202	98.44 272	99.41 203	96.96 297	99.60 95	99.56 69	98.09 143	98.15 327	99.91 20	90.87 328	99.70 223	98.88 102	97.45 273	98.67 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		97.24 304	97.32 284	96.99 345	98.45 362	93.51 374	98.82 350	99.32 267	97.41 226	98.13 328	99.30 300	88.99 346	99.56 253	95.68 329	99.80 97	97.90 374

MVS		97.28 300	96.55 312	99.48 117	98.78 333	98.95 162	99.27 261	99.39 223	83.53 394	98.08 329	99.54 231	96.97 135	99.87 137	94.23 353	99.16 165	99.63 140

PAPM		97.59 280	97.09 299	99.07 177	99.06 294	98.26 227	98.30 385	99.10 307	94.88 353	98.08 329	99.34 290	96.27 159	99.64 242	89.87 382	98.92 188	99.31 210

OpenMVS	96.50 16	98.47 166	98.12 186	99.52 111	99.04 298	99.53 82	99.82 17	99.72 11	94.56 360	98.08 329	99.88 36	94.73 221	99.98 13	97.47 259	99.76 110	99.06 233

gg-mvs-nofinetune		96.17 326	95.32 337	98.73 236	98.79 330	98.14 233	99.38 224	94.09 404	91.07 384	98.07 332	91.04 400	89.62 343	99.35 286	96.75 301	99.09 175	98.68 283

test0.0.03 1		97.71 267	97.42 271	98.56 254	98.41 364	97.82 253	98.78 354	98.63 365	97.34 231	98.05 333	98.98 340	94.45 238	98.98 345	95.04 343	97.15 290	98.89 247

APD_test1		95.87 330	96.49 314	94.00 364	99.53 163	84.01 392	99.54 139	99.32 267	95.91 337	97.99 334	99.85 54	85.49 372	99.88 132	91.96 374	98.84 194	98.12 359

1314		98.68 155	98.54 159	99.11 174	98.89 317	98.65 194	99.27 261	99.49 143	96.89 273	97.99 334	99.56 223	97.72 111	99.83 166	97.74 232	99.27 160	98.84 250

DTE-MVSNet		97.51 286	97.19 294	98.46 268	98.63 351	98.13 234	99.84 13	99.48 155	96.68 284	97.97 336	99.67 180	92.92 277	98.56 367	96.88 298	92.60 370	98.70 274

SixPastTwentyTwo		97.50 287	97.33 283	98.03 303	98.65 349	96.23 326	99.77 34	98.68 363	97.14 248	97.90 337	99.93 9	90.45 331	99.18 318	97.00 287	96.43 301	98.67 290

testing3		97.28 300	96.76 309	98.82 226	99.37 215	98.07 237	99.45 188	99.36 240	97.56 206	97.89 338	98.95 343	83.70 382	98.82 359	96.03 319	98.56 210	99.58 154

pm-mvs1		97.68 271	97.28 289	98.88 211	99.06 294	98.62 197	99.50 163	99.45 193	96.32 312	97.87 339	99.79 115	92.47 295	99.35 286	97.54 252	93.54 359	98.67 290

testgi		97.65 276	97.50 255	98.13 300	99.36 218	96.45 318	99.42 205	99.48 155	97.76 183	97.87 339	99.45 260	91.09 325	98.81 360	94.53 348	98.52 213	99.13 221

EPNet_dtu		98.03 213	97.96 205	98.23 292	98.27 365	95.54 340	99.23 275	98.75 352	99.02 38	97.82 341	99.71 154	96.11 162	99.48 258	93.04 366	99.65 130	99.69 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		97.12 307	96.89 306	97.83 319	99.07 291	95.52 341	98.57 371	98.74 355	97.58 203	97.81 342	99.79 115	88.16 358	99.56 253	95.10 341	97.21 287	98.39 346

ACMH+	97.24 10	97.92 231	97.78 224	98.32 284	99.46 191	96.68 310	99.56 122	99.54 85	98.41 100	97.79 343	99.87 44	90.18 337	99.66 234	98.05 204	97.18 289	98.62 313

N_pmnet		94.95 342	95.83 329	92.31 370	98.47 361	79.33 402	99.12 295	92.81 408	93.87 365	97.68 344	99.13 323	93.87 258	99.01 342	91.38 377	96.19 306	98.59 326

KD-MVS_2432*1600		94.62 343	93.72 351	97.31 337	97.19 384	95.82 333	98.34 381	99.20 296	95.00 351	97.57 345	98.35 367	87.95 360	98.10 375	92.87 369	77.00 398	98.01 365

miper_refine_blended		94.62 343	93.72 351	97.31 337	97.19 384	95.82 333	98.34 381	99.20 296	95.00 351	97.57 345	98.35 367	87.95 360	98.10 375	92.87 369	77.00 398	98.01 365

PVSNet_0	94.43 19	96.09 328	95.47 334	97.94 311	99.31 232	94.34 364	97.81 392	99.70 15	97.12 251	97.46 347	98.75 356	89.71 340	99.79 187	97.69 239	81.69 394	99.68 119

Syy-MVS		97.09 309	97.14 295	96.95 348	99.00 302	92.73 379	99.29 251	99.39 223	97.06 259	97.41 348	98.15 373	93.92 257	98.68 365	91.71 375	98.34 218	99.45 189

myMVS_eth3d		96.89 311	96.37 316	98.43 274	99.00 302	97.16 277	99.29 251	99.39 223	97.06 259	97.41 348	98.15 373	83.46 383	98.68 365	95.27 339	98.34 218	99.45 189

pmmvs6		96.53 318	96.09 323	97.82 321	98.69 346	95.47 342	99.37 226	99.47 173	93.46 371	97.41 348	99.78 121	87.06 366	99.33 289	96.92 296	92.70 369	98.65 300

new_pmnet		96.38 322	96.03 324	97.41 335	98.13 368	95.16 351	99.05 310	99.20 296	93.94 364	97.39 351	98.79 354	91.61 318	99.04 336	90.43 380	95.77 317	98.05 363

CL-MVSNet_self_test		94.49 345	93.97 349	96.08 359	96.16 388	93.67 372	98.33 383	99.38 231	95.13 345	97.33 352	98.15 373	92.69 288	96.57 392	88.67 386	79.87 396	97.99 368

IB-MVS	95.67 18	96.22 323	95.44 336	98.57 251	99.21 255	96.70 307	98.65 366	97.74 385	96.71 282	97.27 353	98.54 362	86.03 368	99.92 95	98.47 168	86.30 388	99.10 222
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				98.45 269	98.55 358	98.16 231	99.43 198	93.68 405		97.23 354	98.46 363	89.30 344	99.22 310	95.43 335	98.22 228	97.98 369

MVP-Stereo		97.81 250	97.75 231	97.99 309	97.53 376	96.60 314	98.96 333	98.85 343	97.22 243	97.23 354	99.36 283	95.28 194	99.46 260	95.51 332	99.78 104	97.92 373
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20240521		96.20 325	95.89 328	97.13 342	97.72 375	94.96 354	99.79 31	99.29 279	93.01 374	97.20 356	99.03 333	89.69 341	98.36 371	91.16 378	96.13 307	98.07 361

TransMVSNet (Re)		97.15 306	96.58 311	98.86 219	99.12 279	98.85 176	99.49 174	98.91 334	95.48 342	97.16 357	99.80 103	93.38 268	99.11 329	94.16 355	91.73 372	98.62 313

KD-MVS_self_test		95.00 340	94.34 345	96.96 347	97.07 386	95.39 345	99.56 122	99.44 201	95.11 347	97.13 358	97.32 386	91.86 308	97.27 388	90.35 381	81.23 395	98.23 356

NR-MVSNet		97.97 225	97.61 245	99.02 183	98.87 322	99.26 115	99.47 184	99.42 209	97.63 199	97.08 359	99.50 244	95.07 202	99.13 324	97.86 217	93.59 358	98.68 283

Anonymous20231206		96.22 323	96.03 324	96.79 353	97.31 381	94.14 365	99.63 82	99.08 310	96.17 324	97.04 360	99.06 330	93.94 255	97.76 384	86.96 393	95.06 334	98.47 336

test_0402		96.64 316	96.24 319	97.85 316	98.85 326	96.43 319	99.44 194	99.26 285	93.52 369	96.98 361	99.52 238	88.52 354	99.20 317	92.58 373	97.50 267	97.93 372

MIMVSNet1		95.51 334	95.04 339	96.92 350	97.38 378	95.60 336	99.52 148	99.50 135	93.65 368	96.97 362	99.17 318	85.28 375	96.56 393	88.36 388	95.55 324	98.60 325

TDRefinement		95.42 336	94.57 343	97.97 310	89.83 404	96.11 329	99.48 178	98.75 352	96.74 280	96.68 363	99.88 36	88.65 352	99.71 217	98.37 176	82.74 393	98.09 360

baseline2		97.87 237	97.55 248	98.82 226	99.18 263	98.02 239	99.41 207	96.58 397	96.97 266	96.51 364	99.17 318	93.43 267	99.57 252	97.71 236	99.03 180	98.86 248

pmmvs3		94.09 349	93.25 355	96.60 355	94.76 398	94.49 360	98.92 340	98.18 378	89.66 385	96.48 365	98.06 379	86.28 367	97.33 387	89.68 383	87.20 387	97.97 370

DeepMVS_CX				93.34 367	99.29 237	82.27 395		99.22 292	85.15 392	96.33 366	99.05 331	90.97 327	99.73 207	93.57 360	97.77 249	98.01 365

LCM-MVSNet-Re		97.83 245	98.15 182	96.87 351	99.30 233	92.25 381	99.59 101	98.26 373	97.43 223	96.20 367	99.13 323	96.27 159	98.73 364	98.17 192	98.99 183	99.64 136

test20.03		96.12 327	95.96 326	96.63 354	97.44 377	95.45 343	99.51 156	99.38 231	96.55 297	96.16 368	99.25 310	93.76 263	96.17 394	87.35 392	94.22 349	98.27 352

K. test v3		97.10 308	96.79 308	98.01 306	98.72 342	96.33 322	99.87 9	97.05 390	97.59 201	96.16 368	99.80 103	88.71 349	99.04 336	96.69 305	96.55 299	98.65 300

UnsupCasMVSNet_eth		96.44 320	96.12 321	97.40 336	98.65 349	95.65 335	99.36 230	99.51 115	97.13 249	96.04 370	98.99 338	88.40 355	98.17 374	96.71 303	90.27 380	98.40 345

test_method		91.10 357	91.36 359	90.31 376	95.85 389	73.72 409	94.89 397	99.25 287	68.39 400	95.82 371	99.02 335	80.50 390	98.95 354	93.64 359	94.89 340	98.25 354

lessismore_v0				97.79 323	98.69 346	95.44 344		94.75 402		95.71 372	99.87 44	88.69 350	99.32 292	95.89 322	94.93 338	98.62 313

test_vis1_rt		95.81 332	95.65 332	96.32 358	99.67 111	91.35 385	99.49 174	96.74 394	98.25 117	95.24 373	98.10 377	74.96 392	99.90 116	99.53 32	98.85 193	97.70 377

dmvs_testset		95.02 339	96.12 321	91.72 372	99.10 284	80.43 400	99.58 109	97.87 382	97.47 216	95.22 374	98.82 352	93.99 253	95.18 397	88.09 389	94.91 339	99.56 158

Patchmatch-RL test		95.84 331	95.81 330	95.95 360	95.61 391	90.57 386	98.24 386	98.39 371	95.10 349	95.20 375	98.67 358	94.78 215	97.77 383	96.28 316	90.02 381	99.51 173

test_fmvs3		92.10 355	91.77 358	93.08 368	96.19 387	86.25 390	99.82 17	98.62 366	96.65 287	95.19 376	96.90 388	55.05 403	95.93 396	96.63 309	90.92 378	97.06 384

ambc				93.06 369	92.68 400	82.36 394	98.47 376	98.73 360		95.09 377	97.41 383	55.55 401	99.10 331	96.42 313	91.32 373	97.71 375

PM-MVS		92.96 353	92.23 357	95.14 362	95.61 391	89.98 388	99.37 226	98.21 376	94.80 356	95.04 378	97.69 381	65.06 396	97.90 381	94.30 350	89.98 382	97.54 381

OpenMVS_ROB	92.34 20	94.38 347	93.70 353	96.41 357	97.38 378	93.17 376	99.06 308	98.75 352	86.58 391	94.84 379	98.26 371	81.53 389	99.32 292	89.01 385	97.87 246	96.76 385

mvsany_test3		93.77 350	93.45 354	94.74 363	95.78 390	88.01 389	99.64 78	98.25 374	98.28 113	94.31 380	97.97 380	68.89 395	98.51 369	97.50 255	90.37 379	97.71 375

EG-PatchMatch MVS		95.97 329	95.69 331	96.81 352	97.78 372	92.79 378	99.16 286	98.93 327	96.16 325	94.08 381	99.22 313	82.72 385	99.47 259	95.67 330	97.50 267	98.17 357

test_f		91.90 356	91.26 360	93.84 365	95.52 394	85.92 391	99.69 55	98.53 370	95.31 344	93.87 382	96.37 391	55.33 402	98.27 372	95.70 327	90.98 377	97.32 383

pmmvs-eth3d		95.34 338	94.73 341	97.15 340	95.53 393	95.94 331	99.35 235	99.10 307	95.13 345	93.55 383	97.54 382	88.15 359	97.91 380	94.58 347	89.69 383	97.61 378

new-patchmatchnet		94.48 346	94.08 347	95.67 361	95.08 396	92.41 380	99.18 284	99.28 281	94.55 361	93.49 384	97.37 385	87.86 362	97.01 390	91.57 376	88.36 384	97.61 378

UnsupCasMVSNet_bld		93.53 351	92.51 356	96.58 356	97.38 378	93.82 367	98.24 386	99.48 155	91.10 383	93.10 385	96.66 389	74.89 393	98.37 370	94.03 356	87.71 386	97.56 380

WB-MVS		93.10 352	94.10 346	90.12 377	95.51 395	81.88 397	99.73 47	99.27 284	95.05 350	93.09 386	98.91 349	94.70 224	91.89 401	76.62 400	94.02 354	96.58 387

SSC-MVS		92.73 354	93.73 350	89.72 378	95.02 397	81.38 398	99.76 37	99.23 290	94.87 354	92.80 387	98.93 345	94.71 223	91.37 402	74.49 402	93.80 356	96.42 388

Gipuma		90.99 358	90.15 363	93.51 366	98.73 340	90.12 387	93.98 398	99.45 193	79.32 396	92.28 388	94.91 393	69.61 394	97.98 379	87.42 391	95.67 321	92.45 396
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CMPMVS	69.68 23	94.13 348	94.90 340	91.84 371	97.24 382	80.01 401	98.52 374	99.48 155	89.01 388	91.99 389	99.67 180	85.67 370	99.13 324	95.44 334	97.03 291	96.39 389
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testf1		90.42 359	90.68 361	89.65 379	97.78 372	73.97 407	99.13 292	98.81 348	89.62 386	91.80 390	98.93 345	62.23 399	98.80 361	86.61 395	91.17 374	96.19 390

APD_test2		90.42 359	90.68 361	89.65 379	97.78 372	73.97 407	99.13 292	98.81 348	89.62 386	91.80 390	98.93 345	62.23 399	98.80 361	86.61 395	91.17 374	96.19 390

PMMVS2		86.87 362	85.37 366	91.35 374	90.21 403	83.80 393	98.89 343	97.45 389	83.13 395	91.67 392	95.03 392	48.49 405	94.70 398	85.86 397	77.62 397	95.54 393

LCM-MVSNet		86.80 363	85.22 367	91.53 373	87.81 405	80.96 399	98.23 388	98.99 321	71.05 398	90.13 393	96.51 390	48.45 406	96.88 391	90.51 379	85.30 389	96.76 385

ET-MVSNet_ETH3D		96.49 319	95.64 333	99.05 180	99.53 163	98.82 181	98.84 348	97.51 388	97.63 199	84.77 394	99.21 316	92.09 303	98.91 356	98.98 90	92.21 371	99.41 195

E-PMN		80.61 367	79.88 369	82.81 384	90.75 402	76.38 405	97.69 393	95.76 399	66.44 402	83.52 395	92.25 397	62.54 398	87.16 404	68.53 404	61.40 401	84.89 402

FPMVS		84.93 364	85.65 365	82.75 385	86.77 406	63.39 411	98.35 380	98.92 330	74.11 397	83.39 396	98.98 340	50.85 404	92.40 400	84.54 398	94.97 336	92.46 395

EMVS		80.02 368	79.22 370	82.43 386	91.19 401	76.40 404	97.55 395	92.49 409	66.36 403	83.01 397	91.27 399	64.63 397	85.79 405	65.82 405	60.65 402	85.08 401

test_vis3_rt		87.04 361	85.81 364	90.73 375	93.99 399	81.96 396	99.76 37	90.23 410	92.81 376	81.35 398	91.56 398	40.06 407	99.07 333	94.27 352	88.23 385	91.15 398

YYNet1		95.36 337	94.51 344	97.92 312	97.89 370	97.10 280	99.10 303	99.23 290	93.26 373	80.77 399	99.04 332	92.81 280	98.02 377	94.30 350	94.18 350	98.64 302

MDA-MVSNet_test_wron		95.45 335	94.60 342	98.01 306	98.16 367	97.21 276	99.11 301	99.24 289	93.49 370	80.73 400	98.98 340	93.02 274	98.18 373	94.22 354	94.45 345	98.64 302

MDA-MVSNet-bldmvs		94.96 341	93.98 348	97.92 312	98.24 366	97.27 271	99.15 289	99.33 257	93.80 366	80.09 401	99.03 333	88.31 356	97.86 382	93.49 361	94.36 347	98.62 313

tmp_tt		82.80 365	81.52 368	86.66 381	66.61 411	68.44 410	92.79 400	97.92 380	68.96 399	80.04 402	99.85 54	85.77 369	96.15 395	97.86 217	43.89 404	95.39 394

MVE	76.82 21	76.91 370	74.31 374	84.70 382	85.38 408	76.05 406	96.88 396	93.17 406	67.39 401	71.28 403	89.01 402	21.66 413	87.69 403	71.74 403	72.29 400	90.35 399
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 369	74.86 373	84.62 383	75.88 409	77.61 403	97.63 394	93.15 407	88.81 389	64.27 404	89.29 401	36.51 408	83.93 406	75.89 401	52.31 403	92.33 397

PMVS	70.75 22	75.98 371	74.97 372	79.01 387	70.98 410	55.18 412	93.37 399	98.21 376	65.08 404	61.78 405	93.83 395	21.74 412	92.53 399	78.59 399	91.12 376	89.34 400
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		39.01 374	42.50 376	28.53 389	39.17 412	20.91 414	98.75 357	19.17 414	19.83 407	38.57 406	66.67 404	33.16 409	15.42 408	37.50 408	29.66 406	49.26 403

testmvs		39.17 373	43.78 375	25.37 390	36.04 413	16.84 415	98.36 379	26.56 412	20.06 406	38.51 407	67.32 403	29.64 410	15.30 409	37.59 407	39.90 405	43.98 404

wuyk23d		40.18 372	41.29 377	36.84 388	86.18 407	49.12 413	79.73 401	22.81 413	27.64 405	25.46 408	28.45 408	21.98 411	48.89 407	55.80 406	23.56 407	12.51 405

EGC-MVSNET		82.80 365	77.86 371	97.62 329	97.91 369	96.12 328	99.33 240	99.28 281	8.40 408	25.05 409	99.27 307	84.11 380	99.33 289	89.20 384	98.22 228	97.42 382

test_blank		0.13 378	0.17 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	1.57 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.02 379	0.03 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.27 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.02 379	0.03 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.27 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		24.64 375	32.85 378	0.00 391	0.00 414	0.00 416	0.00 402	99.51 115	0.00 409	0.00 410	99.56 223	96.58 147	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		8.27 377	11.03 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.27 410	99.01 18	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.02 379	0.03 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.27 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.02 379	0.03 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.27 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.02 379	0.03 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.27 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.02 379	0.03 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.27 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		8.30 376	11.06 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	99.58 216	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.02 379	0.03 382	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.27 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						97.16 277								95.47 333

MSC_two_6792asdad				99.87 11	99.51 170	99.76 37		99.33 257					99.96 30	98.87 105	99.84 77	99.89 20

No_MVS				99.87 11	99.51 170	99.76 37		99.33 257					99.96 30	98.87 105	99.84 77	99.89 20

eth-test2					0.00 414

eth-test					0.00 414

OPU-MVS				99.64 78	99.56 156	99.72 42	99.60 95				99.70 158	99.27 5	99.42 271	98.24 186	99.80 97	99.79 74

save fliter					99.76 65	99.59 70	99.14 291	99.40 220	99.00 43

test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 116	99.51 115					99.96 30	98.93 96	99.86 62	99.88 26

GSMVS																99.52 167

sam_mvs1												94.86 209				99.52 167

sam_mvs												94.72 222

MTGPA								99.47 173

test_post1							99.23 275				65.14 406	94.18 248	99.71 217	97.58 245

test_post											65.99 405	94.65 228	99.73 207

patchmatchnet-post											98.70 357	94.79 214	99.74 201

MTMP							99.54 139	98.88 339

gm-plane-assit					98.54 359	92.96 377			94.65 359		99.15 321		99.64 242	97.56 250

test9_res														97.49 256	99.72 118	99.75 88

agg_prior2														97.21 274	99.73 117	99.75 88

test_prior4						99.56 75	98.99 326

test_prior				99.68 68	99.67 111	99.48 89		99.56 69					99.83 166			99.74 92

新几何2							99.01 323

旧先验1					99.74 80	99.59 70		99.54 85			99.69 168	98.47 80			99.68 126	99.73 97

无先验							98.99 326	99.51 115	96.89 273				99.93 84	97.53 253		99.72 103

原ACMM2							98.95 336

testdata2													99.95 59	96.67 306

segment_acmp												98.96 24

testdata1							98.85 347		98.32 111

plane_prior7					99.29 237	97.03 290

plane_prior6					99.27 242	96.98 294						92.71 286

plane_prior5								99.47 173					99.69 228	97.78 225	97.63 253	98.67 290

plane_prior4											99.61 207

plane_prior2							99.39 219		98.97 51

plane_prior1					99.26 244

plane_prior						96.97 295	99.21 281		98.45 96						97.60 256

n2								0.00 415

nn								0.00 415

door-mid								98.05 379

test11								99.35 246

door								97.92 380

HQP5-MVS						96.83 302

BP-MVS														97.19 278

HQP3-MVS								99.39 223							97.58 258

HQP2-MVS												92.47 295

NP-MVS					99.23 250	96.92 298					99.40 272

ACMMP++_ref															97.19 288

ACMMP++															97.43 277

Test By Simon												98.75 55

High-resolution multi-view benchmark