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_1920		96.71 136	96.84 116	96.31 268	99.11 104	89.74 339	99.05 65	98.58 150	98.08 12	99.87 1	99.37 38	78.48 346	99.93 25	99.29 14	99.69 61	99.27 129

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 47	99.41 56	97.54 76	98.89 104	99.31 12	98.49 8	99.86 2	99.42 29	96.45 24	99.96 4	99.86 1	99.74 50	99.90 3

test_fmvsm_n_1920		98.87 10	99.01 3	98.45 97	99.42 55	96.43 130	98.96 89	99.36 9	98.63 5	99.86 2	99.51 13	95.91 40	99.97 1	99.72 5	99.75 45	98.94 180

fmvsm_l_conf0.5_n_a		99.09 1	99.08 1	99.11 51	99.43 54	97.48 78	98.88 109	99.30 13	98.47 9	99.85 4	99.43 28	96.71 17	99.96 4	99.86 1	99.80 22	99.89 5

test_fmvsmconf_n		98.92 7	98.87 6	99.04 55	98.88 129	97.25 91	98.82 126	99.34 10	98.75 3	99.80 5	99.61 4	95.16 70	99.95 7	99.70 6	99.80 22	99.93 1

fmvsm_s_conf0.5_n_a		98.38 47	98.42 25	98.27 112	99.09 106	95.41 181	98.86 116	99.37 8	97.69 21	99.78 6	99.61 4	92.38 118	99.91 39	99.58 10	99.43 109	99.49 96

test_cas_vis1_n_1920		97.38 106	97.36 93	97.45 177	98.95 123	93.25 279	99.00 78	98.53 163	97.70 20	99.77 7	99.35 44	84.71 292	99.85 63	98.57 31	99.66 66	99.26 132

fmvsm_s_conf0.1_n_a		98.08 61	98.04 61	98.21 119	97.66 243	95.39 182	98.89 104	99.17 26	97.24 50	99.76 8	99.67 1	91.13 157	99.88 56	99.39 13	99.41 111	99.35 115

fmvsm_s_conf0.5_n		98.42 44	98.51 18	98.13 127	99.30 68	95.25 191	98.85 118	99.39 7	97.94 14	99.74 9	99.62 3	92.59 115	99.91 39	99.65 7	99.52 97	99.25 134

SED-MVS		99.09 1	98.91 4	99.63 4	99.71 19	99.24 5	99.02 74	98.87 69	97.65 22	99.73 10	99.48 18	97.53 7	99.94 8	98.43 46	99.81 15	99.70 53

test_241102_ONE					99.71 19	99.24 5		98.87 69	97.62 24	99.73 10	99.39 32	97.53 7	99.74 111

SD-MVS		98.64 16	98.68 11	98.53 89	99.33 59	98.36 41	98.90 99	98.85 78	97.28 45	99.72 12	99.39 32	96.63 20	97.60 353	98.17 59	99.85 5	99.64 71
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
fmvsm_s_conf0.1_n		98.18 59	98.21 51	98.11 131	98.54 164	95.24 192	98.87 113	99.24 17	97.50 31	99.70 13	99.67 1	91.33 152	99.89 47	99.47 12	99.54 94	99.21 139

test_fmvs1_n		95.90 172	95.99 154	95.63 295	98.67 151	88.32 366	99.26 28	98.22 226	96.40 94	99.67 14	99.26 57	73.91 377	99.70 119	99.02 20	99.50 99	98.87 184

test_vis1_n		95.47 192	95.13 192	96.49 252	97.77 232	90.41 330	99.27 27	98.11 249	96.58 85	99.66 15	99.18 73	67.00 390	99.62 137	99.21 16	99.40 114	99.44 107

mvsany_test1		97.69 82	97.70 73	97.66 168	98.24 190	94.18 244	97.53 301	97.53 302	95.52 131	99.66 15	99.51 13	94.30 91	99.56 146	98.38 49	98.62 150	99.23 136

test_fmvs1		96.42 148	96.67 128	95.66 294	98.82 136	88.53 362	98.80 135	98.20 229	96.39 95	99.64 17	99.20 67	80.35 335	99.67 126	99.04 19	99.57 85	98.78 193

IU-MVS					99.71 19	99.23 7		98.64 137	95.28 145	99.63 18				98.35 52	99.81 15	99.83 13

PC_three_1452									95.08 158	99.60 19	99.16 77	97.86 2	98.47 285	97.52 103	99.72 56	99.74 37

test0726					99.72 12	99.25 2	99.06 63	98.88 62	97.62 24	99.56 20	99.50 15	97.42 9

TSAR-MVS + MP.		98.78 11	98.62 13	99.24 36	99.69 24	98.28 46	99.14 51	98.66 132	96.84 71	99.56 20	99.31 51	96.34 25	99.70 119	98.32 53	99.73 53	99.73 42
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPE-MVS		98.92 7	98.67 12	99.65 2	99.58 32	99.20 9	98.42 205	98.91 56	97.58 27	99.54 22	99.46 24	97.10 12	99.94 8	97.64 92	99.84 12	99.83 13
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++		99.08 3	98.89 5	99.64 3	99.17 94	99.23 7	99.69 1	98.88 62	97.32 42	99.53 23	99.47 20	97.81 3	99.94 8	98.47 42	99.72 56	99.74 37

test_241102_TWO								98.87 69	97.65 22	99.53 23	99.48 18	97.34 11	99.94 8	98.43 46	99.80 22	99.83 13

DVP-MVS		99.03 5	98.83 9	99.63 4	99.72 12	99.25 2	98.97 84	98.58 150	97.62 24	99.45 25	99.46 24	97.42 9	99.94 8	98.47 42	99.81 15	99.69 56
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									97.32 42	99.45 25	99.46 24	97.88 1	99.94 8	98.47 42	99.86 1	99.85 10

test_fmvsmconf0.1_n		98.58 23	98.44 24	98.99 57	97.73 237	97.15 96	98.84 122	98.97 42	98.75 3	99.43 27	99.54 8	93.29 105	99.93 25	99.64 9	99.79 28	99.89 5

MSP-MVS		98.74 13	98.55 17	99.29 29	99.75 3	98.23 47	99.26 28	98.88 62	97.52 29	99.41 28	98.78 134	96.00 36	99.79 98	97.79 81	99.59 81	99.85 10
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
APDe-MVS		99.02 6	98.84 8	99.55 9	99.57 33	98.96 16	99.39 11	98.93 50	97.38 39	99.41 28	99.54 8	96.66 18	99.84 67	98.86 23	99.85 5	99.87 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
FOURS1					99.82 1	98.66 24	99.69 1	98.95 46	97.46 34	99.39 30

SMA-MVS		98.58 23	98.25 44	99.56 8	99.51 39	99.04 15	98.95 90	98.80 93	93.67 232	99.37 31	99.52 11	96.52 22	99.89 47	98.06 64	99.81 15	99.76 34
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
SteuartSystems-ACMMP		98.90 9	98.75 10	99.36 21	99.22 89	98.43 33	99.10 59	98.87 69	97.38 39	99.35 32	99.40 31	97.78 5	99.87 58	97.77 82	99.85 5	99.78 21
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		98.59 21	98.32 40	99.41 17	99.54 35	98.71 22	99.04 68	98.81 86	95.12 153	99.32 33	99.39 32	96.22 27	99.84 67	97.72 85	99.73 53	99.67 65

dcpmvs_2		98.08 61	98.59 14	96.56 244	99.57 33	90.34 332	99.15 49	98.38 199	96.82 73	99.29 34	99.49 17	95.78 44	99.57 143	98.94 21	99.86 1	99.77 27

test_part2					99.63 29	99.18 10				99.27 35

DeepPCF-MVS	96.37 2	97.93 70	98.48 23	96.30 269	99.00 114	89.54 344	97.43 307	98.87 69	98.16 11	99.26 36	99.38 37	96.12 32	99.64 131	98.30 54	99.77 34	99.72 45

APD-MVS		98.35 52	98.00 64	99.42 16	99.51 39	98.72 21	98.80 135	98.82 81	94.52 187	99.23 37	99.25 61	95.54 51	99.80 88	96.52 144	99.77 34	99.74 37
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_one_0601					99.66 26	99.25 2		98.86 75	97.55 28	99.20 38	99.47 20	97.57 6

APD-MVS_3200maxsize		98.53 32	98.33 39	99.15 46	99.50 41	97.92 63	99.15 49	98.81 86	96.24 99	99.20 38	99.37 38	95.30 61	99.80 88	97.73 84	99.67 64	99.72 45

patch_mono-2		98.36 50	98.87 6	96.82 220	99.53 36	90.68 325	98.64 171	99.29 14	97.88 15	99.19 40	99.52 11	96.80 15	99.97 1	99.11 18	99.86 1	99.82 16

MM		98.51 33	98.24 46	99.33 26	99.12 102	98.14 56	98.93 95	97.02 340	98.96 1	99.17 41	99.47 20	91.97 136	99.94 8	99.85 4	99.69 61	99.91 2

SR-MVS-dyc-post		98.54 31	98.35 32	99.13 48	99.49 45	97.86 64	99.11 56	98.80 93	96.49 88	99.17 41	99.35 44	95.34 59	99.82 76	97.72 85	99.65 69	99.71 49

RE-MVS-def			98.34 35		99.49 45	97.86 64	99.11 56	98.80 93	96.49 88	99.17 41	99.35 44	95.29 62		97.72 85	99.65 69	99.71 49

9.14			98.06 59		99.47 47		98.71 156	98.82 81	94.36 193	99.16 44	99.29 53	96.05 34	99.81 81	97.00 118	99.71 58

ACMMP_NAP		98.61 18	98.30 41	99.55 9	99.62 30	98.95 17	98.82 126	98.81 86	95.80 118	99.16 44	99.47 20	95.37 57	99.92 31	97.89 75	99.75 45	99.79 19

SR-MVS		98.57 27	98.35 32	99.24 36	99.53 36	98.18 51	99.09 60	98.82 81	96.58 85	99.10 46	99.32 49	95.39 55	99.82 76	97.70 89	99.63 74	99.72 45

PGM-MVS		98.49 35	98.23 48	99.27 34	99.72 12	98.08 58	98.99 81	99.49 5	95.43 135	99.03 47	99.32 49	95.56 49	99.94 8	96.80 137	99.77 34	99.78 21

VNet		97.79 76	97.40 91	98.96 62	98.88 129	97.55 75	98.63 174	98.93 50	96.74 78	99.02 48	98.84 125	90.33 174	99.83 69	98.53 34	96.66 213	99.50 91

xiu_mvs_v1_base_debu		97.60 89	97.56 79	97.72 158	98.35 176	95.98 150	97.86 275	98.51 169	97.13 59	99.01 49	98.40 173	91.56 144	99.80 88	98.53 34	98.68 145	97.37 257

xiu_mvs_v1_base		97.60 89	97.56 79	97.72 158	98.35 176	95.98 150	97.86 275	98.51 169	97.13 59	99.01 49	98.40 173	91.56 144	99.80 88	98.53 34	98.68 145	97.37 257

xiu_mvs_v1_base_debi		97.60 89	97.56 79	97.72 158	98.35 176	95.98 150	97.86 275	98.51 169	97.13 59	99.01 49	98.40 173	91.56 144	99.80 88	98.53 34	98.68 145	97.37 257

iter_conf05_11		98.04 64	97.94 66	98.34 107	98.60 159	96.38 134	99.24 31	98.57 152	95.90 113	98.99 52	98.79 133	92.97 109	99.47 169	98.58 30	99.85 5	99.17 150

TSAR-MVS + GP.		98.38 47	98.24 46	98.81 70	99.22 89	97.25 91	98.11 244	98.29 218	97.19 54	98.99 52	99.02 98	96.22 27	99.67 126	98.52 40	98.56 154	99.51 89

CS-MVS		98.44 41	98.49 21	98.31 110	99.08 107	96.73 113	99.67 3	98.47 180	97.17 55	98.94 54	99.10 86	95.73 45	99.13 205	98.71 27	99.49 101	99.09 160

HFP-MVS		98.63 17	98.40 26	99.32 28	99.72 12	98.29 45	99.23 33	98.96 45	96.10 107	98.94 54	99.17 74	96.06 33	99.92 31	97.62 93	99.78 32	99.75 35

region2R		98.61 18	98.38 28	99.29 29	99.74 7	98.16 53	99.23 33	98.93 50	96.15 104	98.94 54	99.17 74	95.91 40	99.94 8	97.55 100	99.79 28	99.78 21

HPM-MVS_fast		98.38 47	98.13 54	99.12 50	99.75 3	97.86 64	99.44 10	98.82 81	94.46 190	98.94 54	99.20 67	95.16 70	99.74 111	97.58 96	99.85 5	99.77 27

test_fmvsmconf0.01_n		97.86 72	97.54 82	98.83 69	95.48 359	96.83 108	98.95 90	98.60 142	98.58 6	98.93 58	99.55 6	88.57 212	99.91 39	99.54 11	99.61 77	99.77 27

ACMMPR		98.59 21	98.36 30	99.29 29	99.74 7	98.15 54	99.23 33	98.95 46	96.10 107	98.93 58	99.19 72	95.70 46	99.94 8	97.62 93	99.79 28	99.78 21

DeepC-MVS_fast	96.70 1	98.55 30	98.34 35	99.18 42	99.25 81	98.04 59	98.50 194	98.78 100	97.72 17	98.92 60	99.28 54	95.27 63	99.82 76	97.55 100	99.77 34	99.69 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS-test		98.49 35	98.50 20	98.46 96	99.20 92	97.05 99	99.64 4	98.50 174	97.45 35	98.88 61	99.14 81	95.25 65	99.15 202	98.83 25	99.56 91	99.20 140

EC-MVSNet		98.21 58	98.11 56	98.49 93	98.34 181	97.26 90	99.61 5	98.43 189	96.78 74	98.87 62	98.84 125	93.72 100	99.01 226	98.91 22	99.50 99	99.19 144

MVSMamba_pp		98.02 65	97.82 68	98.61 79	98.25 189	97.32 83	98.73 150	98.56 156	96.18 103	98.84 63	98.72 143	92.90 110	99.45 172	98.37 50	99.85 5	99.07 168

EI-MVSNet-Vis-set		98.47 38	98.39 27	98.69 74	99.46 49	96.49 127	98.30 217	98.69 121	97.21 52	98.84 63	99.36 42	95.41 54	99.78 101	98.62 29	99.65 69	99.80 18

MSLP-MVS++		98.56 29	98.57 15	98.55 85	99.26 80	96.80 109	98.71 156	99.05 36	97.28 45	98.84 63	99.28 54	96.47 23	99.40 176	98.52 40	99.70 59	99.47 100

PHI-MVS		98.34 53	98.06 59	99.18 42	99.15 100	98.12 57	99.04 68	99.09 31	93.32 247	98.83 66	99.10 86	96.54 21	99.83 69	97.70 89	99.76 40	99.59 79

MVSFormer		97.57 93	97.49 84	97.84 146	98.07 208	95.76 169	99.47 8	98.40 193	94.98 162	98.79 67	98.83 127	92.34 119	98.41 298	96.91 123	99.59 81	99.34 116

lupinMVS		97.44 101	97.22 100	98.12 130	98.07 208	95.76 169	97.68 290	97.76 282	94.50 188	98.79 67	98.61 152	92.34 119	99.30 185	97.58 96	99.59 81	99.31 122

CDPH-MVS		97.94 69	97.49 84	99.28 32	99.47 47	98.44 31	97.91 266	98.67 129	92.57 278	98.77 69	98.85 124	95.93 39	99.72 113	95.56 177	99.69 61	99.68 61

CNVR-MVS		98.78 11	98.56 16	99.45 15	99.32 62	98.87 19	98.47 197	98.81 86	97.72 17	98.76 70	99.16 77	97.05 13	99.78 101	98.06 64	99.66 66	99.69 56

EI-MVSNet-UG-set		98.41 45	98.34 35	98.61 79	99.45 52	96.32 139	98.28 220	98.68 124	97.17 55	98.74 71	99.37 38	95.25 65	99.79 98	98.57 31	99.54 94	99.73 42

diffmvs		97.58 92	97.40 91	98.13 127	98.32 186	95.81 168	98.06 250	98.37 201	96.20 101	98.74 71	98.89 120	91.31 154	99.25 189	98.16 60	98.52 155	99.34 116
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GST-MVS		98.43 43	98.12 55	99.34 23	99.72 12	98.38 35	99.09 60	98.82 81	95.71 123	98.73 73	99.06 96	95.27 63	99.93 25	97.07 117	99.63 74	99.72 45

UA-Net		97.96 67	97.62 75	98.98 59	98.86 132	97.47 80	98.89 104	99.08 32	96.67 82	98.72 74	99.54 8	93.15 107	99.81 81	94.87 196	98.83 141	99.65 69

h-mvs33		96.17 159	95.62 172	97.81 150	99.03 110	94.45 231	98.64 171	98.75 106	97.48 32	98.67 75	98.72 143	89.76 181	99.86 62	97.95 69	81.59 381	99.11 158

hse-mvs2		95.71 181	95.30 186	96.93 212	98.50 166	93.53 265	98.36 207	98.10 252	97.48 32	98.67 75	97.99 213	89.76 181	99.02 224	97.95 69	80.91 386	98.22 229

ZD-MVS					99.46 49	98.70 23		98.79 98	93.21 252	98.67 75	98.97 105	95.70 46	99.83 69	96.07 155	99.58 84

旧先验2							97.57 300		91.30 319	98.67 75			99.80 88	95.70 174

PS-MVSNAJ		97.73 78	97.77 70	97.62 170	98.68 150	95.58 173	97.34 316	98.51 169	97.29 44	98.66 79	97.88 223	94.51 83	99.90 45	97.87 76	99.17 125	97.39 255

xiu_mvs_v2_base		97.66 85	97.70 73	97.56 174	98.61 158	95.46 179	97.44 305	98.46 181	97.15 57	98.65 80	98.15 200	94.33 90	99.80 88	97.84 79	98.66 149	97.41 253

LFMVS		95.86 174	94.98 201	98.47 95	98.87 131	96.32 139	98.84 122	96.02 367	93.40 244	98.62 81	99.20 67	74.99 371	99.63 134	97.72 85	97.20 199	99.46 104

HPM-MVS		98.36 50	98.10 58	99.13 48	99.74 7	97.82 68	99.53 7	98.80 93	94.63 180	98.61 82	98.97 105	95.13 72	99.77 106	97.65 91	99.83 14	99.79 19
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				98.26 115	99.20 92	95.36 184		98.68 124	91.89 300	98.60 83	99.10 86	94.44 88	99.82 76	94.27 219	99.44 108	99.58 83

CP-MVS		98.57 27	98.36 30	99.19 40	99.66 26	97.86 64	99.34 17	98.87 69	95.96 110	98.60 83	99.13 82	96.05 34	99.94 8	97.77 82	99.86 1	99.77 27

jason		97.32 109	97.08 106	98.06 136	97.45 262	95.59 172	97.87 274	97.91 276	94.79 173	98.55 85	98.83 127	91.12 158	99.23 192	97.58 96	99.60 79	99.34 116
jason: jason.
MVS_0304		98.47 38	98.22 50	99.21 39	99.00 114	97.80 69	98.88 109	95.32 377	98.86 2	98.53 86	99.44 27	94.38 89	99.94 8	99.86 1	99.70 59	99.90 3

MCST-MVS		98.65 15	98.37 29	99.48 13	99.60 31	98.87 19	98.41 206	98.68 124	97.04 63	98.52 87	98.80 131	96.78 16	99.83 69	97.93 71	99.61 77	99.74 37

XVS		98.70 14	98.49 21	99.34 23	99.70 22	98.35 42	99.29 23	98.88 62	97.40 36	98.46 88	99.20 67	95.90 42	99.89 47	97.85 77	99.74 50	99.78 21

X-MVStestdata		94.06 292	92.30 315	99.34 23	99.70 22	98.35 42	99.29 23	98.88 62	97.40 36	98.46 88	43.50 408	95.90 42	99.89 47	97.85 77	99.74 50	99.78 21

MG-MVS		97.81 75	97.60 76	98.44 99	99.12 102	95.97 155	97.75 285	98.78 100	96.89 70	98.46 88	99.22 64	93.90 99	99.68 125	94.81 200	99.52 97	99.67 65

test_fmvsmvis_n_1920		98.44 41	98.51 18	98.23 118	98.33 183	96.15 146	98.97 84	99.15 28	98.55 7	98.45 91	99.55 6	94.26 93	99.97 1	99.65 7	99.66 66	98.57 214

NCCC		98.61 18	98.35 32	99.38 18	99.28 77	98.61 26	98.45 198	98.76 104	97.82 16	98.45 91	98.93 114	96.65 19	99.83 69	97.38 109	99.41 111	99.71 49

MVS_Test		97.28 110	97.00 109	98.13 127	98.33 183	95.97 155	98.74 146	98.07 259	94.27 195	98.44 93	98.07 205	92.48 116	99.26 188	96.43 147	98.19 171	99.16 151

MVS_111021_LR		98.34 53	98.23 48	98.67 76	99.27 78	96.90 105	97.95 261	99.58 3	97.14 58	98.44 93	99.01 102	95.03 75	99.62 137	97.91 73	99.75 45	99.50 91

ETV-MVS		97.96 67	97.81 69	98.40 104	98.42 170	97.27 86	98.73 150	98.55 159	96.84 71	98.38 95	97.44 262	95.39 55	99.35 181	97.62 93	98.89 136	98.58 213

test2506		94.44 264	93.91 261	96.04 277	99.02 111	88.99 354	99.06 63	79.47 413	96.96 67	98.36 96	99.26 57	77.21 358	99.52 158	96.78 138	99.04 128	99.59 79

VDDNet		95.36 203	94.53 220	97.86 145	98.10 207	95.13 198	98.85 118	97.75 283	90.46 335	98.36 96	99.39 32	73.27 379	99.64 131	97.98 67	96.58 216	98.81 189

mPP-MVS		98.51 33	98.26 43	99.25 35	99.75 3	98.04 59	99.28 25	98.81 86	96.24 99	98.35 98	99.23 62	95.46 52	99.94 8	97.42 107	99.81 15	99.77 27

DELS-MVS		98.40 46	98.20 52	98.99 57	99.00 114	97.66 70	97.75 285	98.89 59	97.71 19	98.33 99	98.97 105	94.97 76	99.88 56	98.42 48	99.76 40	99.42 111
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
MVS_111021_HR		98.47 38	98.34 35	98.88 68	99.22 89	97.32 83	97.91 266	99.58 3	97.20 53	98.33 99	99.00 103	95.99 37	99.64 131	98.05 66	99.76 40	99.69 56

ZNCC-MVS		98.49 35	98.20 52	99.35 22	99.73 11	98.39 34	99.19 44	98.86 75	95.77 119	98.31 101	99.10 86	95.46 52	99.93 25	97.57 99	99.81 15	99.74 37

HPM-MVS++		98.58 23	98.25 44	99.55 9	99.50 41	99.08 11	98.72 155	98.66 132	97.51 30	98.15 102	98.83 127	95.70 46	99.92 31	97.53 102	99.67 64	99.66 68

新几何1				99.16 45	99.34 57	98.01 61		98.69 121	90.06 343	98.13 103	98.95 112	94.60 81	99.89 47	91.97 288	99.47 104	99.59 79

API-MVS		97.41 104	97.25 97	97.91 143	98.70 146	96.80 109	98.82 126	98.69 121	94.53 185	98.11 104	98.28 188	94.50 86	99.57 143	94.12 224	99.49 101	97.37 257

ECVR-MVS		95.95 167	95.71 166	96.65 230	99.02 111	90.86 320	99.03 71	91.80 400	96.96 67	98.10 105	99.26 57	81.31 325	99.51 159	96.90 126	99.04 128	99.59 79

CPTT-MVS		97.72 79	97.32 95	98.92 64	99.64 28	97.10 97	99.12 55	98.81 86	92.34 286	98.09 106	99.08 94	93.01 108	99.92 31	96.06 158	99.77 34	99.75 35

test12				99.18 42	99.16 98	98.19 50		98.53 163		98.07 107		95.13 72	99.72 113		99.56 91	99.63 73

test222					99.23 88	97.17 95	97.40 308	98.66 132	88.68 363	98.05 108	98.96 110	94.14 95			99.53 96	99.61 75

DP-MVS Recon		97.86 72	97.46 87	99.06 54	99.53 36	98.35 42	98.33 210	98.89 59	92.62 275	98.05 108	98.94 113	95.34 59	99.65 129	96.04 159	99.42 110	99.19 144

Vis-MVSNet		97.42 103	97.11 104	98.34 107	98.66 152	96.23 142	99.22 37	99.00 39	96.63 84	98.04 110	99.21 65	88.05 228	99.35 181	96.01 161	99.21 122	99.45 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		95.94 169	95.78 160	96.41 261	98.99 118	90.12 334	99.04 68	92.45 399	96.99 66	98.03 111	99.27 56	81.40 324	99.48 166	96.87 132	99.04 128	99.63 73

baseline		97.64 86	97.44 89	98.25 116	98.35 176	96.20 143	99.00 78	98.32 208	96.33 98	98.03 111	99.17 74	91.35 151	99.16 199	98.10 62	98.29 170	99.39 112

test_yl		97.22 112	96.78 120	98.54 87	98.73 141	96.60 119	98.45 198	98.31 210	94.70 174	98.02 113	98.42 171	90.80 165	99.70 119	96.81 135	96.79 210	99.34 116

DCV-MVSNet		97.22 112	96.78 120	98.54 87	98.73 141	96.60 119	98.45 198	98.31 210	94.70 174	98.02 113	98.42 171	90.80 165	99.70 119	96.81 135	96.79 210	99.34 116

MTAPA		98.58 23	98.29 42	99.46 14	99.76 2	98.64 25	98.90 99	98.74 108	97.27 49	98.02 113	99.39 32	94.81 79	99.96 4	97.91 73	99.79 28	99.77 27

sss		97.39 105	96.98 111	98.61 79	98.60 159	96.61 118	98.22 225	98.93 50	93.97 207	98.01 116	98.48 166	91.98 134	99.85 63	96.45 146	98.15 172	99.39 112

alignmvs		97.56 94	97.07 107	99.01 56	98.66 152	98.37 40	98.83 124	98.06 264	96.74 78	98.00 117	97.65 245	90.80 165	99.48 166	98.37 50	96.56 217	99.19 144

OMC-MVS		97.55 95	97.34 94	98.20 121	99.33 59	95.92 162	98.28 220	98.59 145	95.52 131	97.97 118	99.10 86	93.28 106	99.49 161	95.09 191	98.88 137	99.19 144

VDD-MVS		95.82 177	95.23 188	97.61 171	98.84 135	93.98 248	98.68 163	97.40 316	95.02 160	97.95 119	99.34 48	74.37 376	99.78 101	98.64 28	96.80 209	99.08 164

casdiffmvs		97.63 87	97.41 90	98.28 111	98.33 183	96.14 147	98.82 126	98.32 208	96.38 96	97.95 119	99.21 65	91.23 156	99.23 192	98.12 61	98.37 164	99.48 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		96.73 135	96.60 130	97.12 199	99.25 81	95.35 186	98.26 223	99.26 15	94.28 194	97.94 121	97.46 259	92.74 113	99.81 81	96.88 129	93.32 283	96.20 346

PVSNet_Blended		97.38 106	97.12 103	98.14 124	99.25 81	95.35 186	97.28 321	99.26 15	93.13 257	97.94 121	98.21 196	92.74 113	99.81 81	96.88 129	99.40 114	99.27 129

DPM-MVS		97.55 95	96.99 110	99.23 38	99.04 109	98.55 27	97.17 331	98.35 204	94.85 171	97.93 123	98.58 157	95.07 74	99.71 118	92.60 267	99.34 118	99.43 109

MP-MVS		98.33 55	98.01 63	99.28 32	99.75 3	98.18 51	99.22 37	98.79 98	96.13 105	97.92 124	99.23 62	94.54 82	99.94 8	96.74 140	99.78 32	99.73 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDTV_nov1_ep13_2view						84.26 381	96.89 351		90.97 328	97.90 125		89.89 180		93.91 231		99.18 149

test_prior2							97.80 281		96.12 106	97.89 126	98.69 145	95.96 38		96.89 127	99.60 79

iter_conf05		98.16 60	98.02 62	98.59 82	98.96 122	97.07 98	98.90 99	98.57 152	94.81 172	97.84 127	98.90 118	95.22 68	99.59 140	99.15 17	99.84 12	99.12 156

mamv4		97.13 119	98.11 56	94.17 342	98.97 121	83.70 383	98.66 168	98.71 116	94.63 180	97.83 128	98.90 118	96.25 26	99.55 153	99.27 15	99.76 40	99.27 129

原ACMM1				98.65 77	99.32 62	96.62 116		98.67 129	93.27 251	97.81 129	98.97 105	95.18 69	99.83 69	93.84 233	99.46 107	99.50 91

114514_t		96.93 127	96.27 143	98.92 64	99.50 41	97.63 72	98.85 118	98.90 57	84.80 383	97.77 130	99.11 84	92.84 111	99.66 128	94.85 197	99.77 34	99.47 100

casdiffmvs_mvg		97.72 79	97.48 86	98.44 99	98.42 170	96.59 121	98.92 97	98.44 185	96.20 101	97.76 131	99.20 67	91.66 142	99.23 192	98.27 58	98.41 163	99.49 96
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PMMVS		96.60 139	96.33 140	97.41 181	97.90 225	93.93 249	97.35 315	98.41 191	92.84 269	97.76 131	97.45 261	91.10 160	99.20 196	96.26 151	97.91 179	99.11 158

PVSNet	91.96 18	96.35 152	96.15 147	96.96 210	99.17 94	92.05 299	96.08 369	98.68 124	93.69 228	97.75 133	97.80 233	88.86 207	99.69 124	94.26 220	99.01 131	99.15 152

TEST9					99.31 64	98.50 29	97.92 264	98.73 111	92.63 274	97.74 134	98.68 146	96.20 29	99.80 88

train_agg		97.97 66	97.52 83	99.33 26	99.31 64	98.50 29	97.92 264	98.73 111	92.98 263	97.74 134	98.68 146	96.20 29	99.80 88	96.59 141	99.57 85	99.68 61

FE-MVS		95.62 187	94.90 205	97.78 152	98.37 175	94.92 209	97.17 331	97.38 318	90.95 329	97.73 136	97.70 239	85.32 280	99.63 134	91.18 300	98.33 167	98.79 190

CANet		98.05 63	97.76 71	98.90 67	98.73 141	97.27 86	98.35 208	98.78 100	97.37 41	97.72 137	98.96 110	91.53 148	99.92 31	98.79 26	99.65 69	99.51 89

test_8					99.29 73	98.44 31	97.89 272	98.72 113	92.98 263	97.70 138	98.66 149	96.20 29	99.80 88

MP-MVS-pluss		98.31 56	97.92 67	99.49 12	99.72 12	98.88 18	98.43 203	98.78 100	94.10 199	97.69 139	99.42 29	95.25 65	99.92 31	98.09 63	99.80 22	99.67 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
sasdasda		97.67 83	97.23 98	98.98 59	98.70 146	98.38 35	99.34 17	98.39 195	96.76 76	97.67 140	97.40 266	92.26 122	99.49 161	98.28 55	96.28 231	99.08 164

canonicalmvs		97.67 83	97.23 98	98.98 59	98.70 146	98.38 35	99.34 17	98.39 195	96.76 76	97.67 140	97.40 266	92.26 122	99.49 161	98.28 55	96.28 231	99.08 164

PVSNet_Blended_VisFu		97.70 81	97.46 87	98.44 99	99.27 78	95.91 163	98.63 174	99.16 27	94.48 189	97.67 140	98.88 121	92.80 112	99.91 39	97.11 115	99.12 126	99.50 91

WTY-MVS		97.37 108	96.92 113	98.72 73	98.86 132	96.89 107	98.31 215	98.71 116	95.26 146	97.67 140	98.56 160	92.21 126	99.78 101	95.89 163	96.85 208	99.48 98

Effi-MVS+		97.12 120	96.69 126	98.39 105	98.19 198	96.72 114	97.37 312	98.43 189	93.71 225	97.65 144	98.02 209	92.20 127	99.25 189	96.87 132	97.79 184	99.19 144

thisisatest0530		96.01 164	95.36 180	97.97 140	98.38 173	95.52 177	98.88 109	94.19 390	94.04 201	97.64 145	98.31 186	83.82 314	99.46 171	95.29 186	97.70 189	98.93 181

tttt0517		96.07 162	95.51 174	97.78 152	98.41 172	94.84 212	99.28 25	94.33 388	94.26 196	97.64 145	98.64 150	84.05 307	99.47 169	95.34 182	97.60 192	99.03 170

HyFIR lowres test		96.90 129	96.49 135	98.14 124	99.33 59	95.56 174	97.38 310	99.65 2	92.34 286	97.61 147	98.20 197	89.29 192	99.10 213	96.97 120	97.60 192	99.77 27

ACMMP		98.23 57	97.95 65	99.09 52	99.74 7	97.62 73	99.03 71	99.41 6	95.98 109	97.60 148	99.36 42	94.45 87	99.93 25	97.14 114	98.85 140	99.70 53
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
MGCFI-Net		97.62 88	97.19 101	98.92 64	98.66 152	98.20 49	99.32 22	98.38 199	96.69 81	97.58 149	97.42 265	92.10 130	99.50 160	98.28 55	96.25 234	99.08 164

agg_prior					99.30 68	98.38 35		98.72 113		97.57 150			99.81 81

tpmrst		95.63 186	95.69 169	95.44 303	97.54 253	88.54 361	96.97 341	97.56 295	93.50 239	97.52 151	96.93 311	89.49 185	99.16 199	95.25 188	96.42 222	98.64 207

MDTV_nov1_ep13			95.40 175		97.48 257	88.34 365	96.85 354	97.29 321	93.74 221	97.48 152	97.26 273	89.18 195	99.05 217	91.92 289	97.43 196

FA-MVS(test-final)		96.41 151	95.94 155	97.82 149	98.21 194	95.20 194	97.80 281	97.58 292	93.21 252	97.36 153	97.70 239	89.47 187	99.56 146	94.12 224	97.99 176	98.71 199

EPMVS		94.99 225	94.48 223	96.52 250	97.22 277	91.75 304	97.23 323	91.66 401	94.11 198	97.28 154	96.81 318	85.70 271	98.84 251	93.04 256	97.28 198	98.97 176

EIA-MVS		97.75 77	97.58 77	98.27 112	98.38 173	96.44 129	99.01 76	98.60 142	95.88 115	97.26 155	97.53 256	94.97 76	99.33 183	97.38 109	99.20 123	99.05 169

IS-MVSNet		97.22 112	96.88 114	98.25 116	98.85 134	96.36 137	99.19 44	97.97 270	95.39 137	97.23 156	98.99 104	91.11 159	98.93 238	94.60 207	98.59 152	99.47 100

EPP-MVSNet		97.46 97	97.28 96	97.99 139	98.64 155	95.38 183	99.33 21	98.31 210	93.61 236	97.19 157	99.07 95	94.05 96	99.23 192	96.89 127	98.43 162	99.37 114

thisisatest0515		95.61 190	94.89 206	97.76 155	98.15 204	95.15 197	96.77 357	94.41 386	92.95 265	97.18 158	97.43 263	84.78 289	99.45 172	94.63 204	97.73 188	98.68 201

CANet_DTU		96.96 126	96.55 132	98.21 119	98.17 203	96.07 149	97.98 259	98.21 227	97.24 50	97.13 159	98.93 114	86.88 251	99.91 39	95.00 194	99.37 117	98.66 205

CHOSEN 1792x2688		97.12 120	96.80 117	98.08 133	99.30 68	94.56 229	98.05 251	99.71 1	93.57 237	97.09 160	98.91 117	88.17 222	99.89 47	96.87 132	99.56 91	99.81 17

PatchT		93.06 313	91.97 319	96.35 265	96.69 312	92.67 290	94.48 392	97.08 332	86.62 371	97.08 161	92.23 392	87.94 230	97.90 341	78.89 389	96.69 212	98.49 217

PatchmatchNet		95.71 181	95.52 173	96.29 270	97.58 248	90.72 324	96.84 355	97.52 303	94.06 200	97.08 161	96.96 307	89.24 194	98.90 244	92.03 285	98.37 164	99.26 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MAR-MVS		96.91 128	96.40 138	98.45 97	98.69 149	96.90 105	98.66 168	98.68 124	92.40 285	97.07 163	97.96 216	91.54 147	99.75 109	93.68 237	98.92 134	98.69 200
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
PAPM_NR		97.46 97	97.11 104	98.50 91	99.50 41	96.41 133	98.63 174	98.60 142	95.18 150	97.06 164	98.06 206	94.26 93	99.57 143	93.80 235	98.87 139	99.52 86

TAMVS		97.02 124	96.79 119	97.70 161	98.06 211	95.31 189	98.52 189	98.31 210	93.95 208	97.05 165	98.61 152	93.49 102	98.52 280	95.33 183	97.81 183	99.29 127

CSCG		97.85 74	97.74 72	98.20 121	99.67 25	95.16 195	99.22 37	99.32 11	93.04 261	97.02 166	98.92 116	95.36 58	99.91 39	97.43 106	99.64 73	99.52 86

CDS-MVSNet		96.99 125	96.69 126	97.90 144	98.05 212	95.98 150	98.20 228	98.33 207	93.67 232	96.95 167	98.49 165	93.54 101	98.42 291	95.24 189	97.74 187	99.31 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XVG-OURS-SEG-HR		96.51 145	96.34 139	97.02 205	98.77 139	93.76 254	97.79 283	98.50 174	95.45 134	96.94 168	99.09 92	87.87 233	99.55 153	96.76 139	95.83 245	97.74 243

CR-MVSNet		94.76 239	94.15 243	96.59 240	97.00 291	93.43 268	94.96 382	97.56 295	92.46 279	96.93 169	96.24 337	88.15 223	97.88 345	87.38 352	96.65 214	98.46 218

RPMNet		92.81 315	91.34 324	97.24 189	97.00 291	93.43 268	94.96 382	98.80 93	82.27 389	96.93 169	92.12 393	86.98 249	99.82 76	76.32 394	96.65 214	98.46 218

SCA		95.46 193	95.13 192	96.46 258	97.67 241	91.29 313	97.33 317	97.60 291	94.68 177	96.92 171	97.10 283	83.97 309	98.89 245	92.59 269	98.32 169	99.20 140

PatchMatch-RL		96.59 140	96.03 152	98.27 112	99.31 64	96.51 126	97.91 266	99.06 34	93.72 224	96.92 171	98.06 206	88.50 217	99.65 129	91.77 292	99.00 132	98.66 205

DeepC-MVS	95.98 3	97.88 71	97.58 77	98.77 71	99.25 81	96.93 103	98.83 124	98.75 106	96.96 67	96.89 173	99.50 15	90.46 171	99.87 58	97.84 79	99.76 40	99.52 86
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVG-OURS		96.55 144	96.41 137	96.99 206	98.75 140	93.76 254	97.50 304	98.52 166	95.67 125	96.83 174	99.30 52	88.95 206	99.53 155	95.88 164	96.26 233	97.69 246

AdaColmap		97.15 118	96.70 125	98.48 94	99.16 98	96.69 115	98.01 255	98.89 59	94.44 191	96.83 174	98.68 146	90.69 168	99.76 107	94.36 214	99.29 121	98.98 175

CostFormer		94.95 230	94.73 212	95.60 297	97.28 273	89.06 351	97.53 301	96.89 349	89.66 350	96.82 176	96.72 322	86.05 265	98.95 237	95.53 179	96.13 239	98.79 190

UGNet		96.78 134	96.30 142	98.19 123	98.24 190	95.89 165	98.88 109	98.93 50	97.39 38	96.81 177	97.84 227	82.60 319	99.90 45	96.53 143	99.49 101	98.79 190
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
CNLPA		97.45 100	97.03 108	98.73 72	99.05 108	97.44 82	98.07 249	98.53 163	95.32 143	96.80 178	98.53 161	93.32 103	99.72 113	94.31 218	99.31 120	99.02 171

CHOSEN 280x420		97.18 116	97.18 102	97.20 191	98.81 137	93.27 277	95.78 376	99.15 28	95.25 147	96.79 179	98.11 203	92.29 121	99.07 216	98.56 33	99.85 5	99.25 134

HY-MVS	93.96 8	96.82 133	96.23 146	98.57 83	98.46 169	97.00 100	98.14 239	98.21 227	93.95 208	96.72 180	97.99 213	91.58 143	99.76 107	94.51 211	96.54 218	98.95 179

PAPR		96.84 132	96.24 145	98.65 77	98.72 145	96.92 104	97.36 314	98.57 152	93.33 246	96.67 181	97.57 253	94.30 91	99.56 146	91.05 307	98.59 152	99.47 100

Anonymous20240529		95.10 218	94.22 237	97.75 156	99.01 113	94.26 241	98.87 113	98.83 80	85.79 379	96.64 182	98.97 105	78.73 343	99.85 63	96.27 150	94.89 251	99.12 156

UWE-MVS		94.30 271	93.89 264	95.53 298	97.83 228	88.95 355	97.52 303	93.25 394	94.44 191	96.63 183	97.07 290	78.70 344	99.28 187	91.99 286	97.56 194	98.36 223

thres600view7		95.49 191	94.77 209	97.67 165	98.98 119	95.02 201	98.85 118	96.90 347	95.38 138	96.63 183	96.90 312	84.29 299	99.59 140	88.65 343	96.33 224	98.40 220

thres100view900		95.38 200	94.70 213	97.41 181	98.98 119	94.92 209	98.87 113	96.90 347	95.38 138	96.61 185	96.88 313	84.29 299	99.56 146	88.11 346	96.29 228	97.76 241

Vis-MVSNet (Re-imp)		96.87 130	96.55 132	97.83 147	98.73 141	95.46 179	99.20 42	98.30 216	94.96 164	96.60 186	98.87 122	90.05 177	98.59 274	93.67 239	98.60 151	99.46 104

CVMVSNet		95.43 196	96.04 151	93.57 346	97.93 223	83.62 384	98.12 242	98.59 145	95.68 124	96.56 187	99.02 98	87.51 239	97.51 358	93.56 243	97.44 195	99.60 77

RPSCF		94.87 234	95.40 175	93.26 352	98.89 127	82.06 390	98.33 210	98.06 264	90.30 340	96.56 187	99.26 57	87.09 246	99.49 161	93.82 234	96.32 225	98.24 227

tfpn200view9		95.32 207	94.62 216	97.43 179	98.94 124	94.98 205	98.68 163	96.93 345	95.33 141	96.55 189	96.53 330	84.23 303	99.56 146	88.11 346	96.29 228	97.76 241

thres400		95.38 200	94.62 216	97.65 169	98.94 124	94.98 205	98.68 163	96.93 345	95.33 141	96.55 189	96.53 330	84.23 303	99.56 146	88.11 346	96.29 228	98.40 220

thres200		95.25 209	94.57 218	97.28 188	98.81 137	94.92 209	98.20 228	97.11 330	95.24 149	96.54 191	96.22 341	84.58 296	99.53 155	87.93 350	96.50 220	97.39 255

ab-mvs		96.42 148	95.71 166	98.55 85	98.63 156	96.75 112	97.88 273	98.74 108	93.84 214	96.54 191	98.18 199	85.34 278	99.75 109	95.93 162	96.35 223	99.15 152

Anonymous202405211		95.28 208	94.49 222	97.67 165	99.00 114	93.75 256	98.70 160	97.04 337	90.66 331	96.49 193	98.80 131	78.13 350	99.83 69	96.21 154	95.36 250	99.44 107

ADS-MVSNet2		94.58 251	94.40 231	95.11 313	98.00 214	88.74 358	96.04 370	97.30 320	90.15 341	96.47 194	96.64 327	87.89 231	97.56 356	90.08 319	97.06 201	99.02 171

ADS-MVSNet		95.00 223	94.45 227	96.63 234	98.00 214	91.91 301	96.04 370	97.74 284	90.15 341	96.47 194	96.64 327	87.89 231	98.96 232	90.08 319	97.06 201	99.02 171

Effi-MVS+-dtu		96.29 154	96.56 131	95.51 299	97.89 226	90.22 333	98.80 135	98.10 252	96.57 87	96.45 196	96.66 324	90.81 164	98.91 241	95.72 171	97.99 176	97.40 254

ETVMVS		94.50 258	93.44 291	97.68 164	98.18 200	95.35 186	98.19 231	97.11 330	93.73 222	96.40 197	95.39 363	74.53 373	98.84 251	91.10 302	96.31 226	98.84 187

PLC	95.07 4	97.20 115	96.78 120	98.44 99	99.29 73	96.31 141	98.14 239	98.76 104	92.41 284	96.39 198	98.31 186	94.92 78	99.78 101	94.06 227	98.77 144	99.23 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		94.13 284	93.80 270	95.12 312	96.50 322	87.91 371	97.44 305	95.89 373	92.62 275	96.37 199	96.30 336	84.13 306	98.30 311	93.24 249	91.66 303	99.14 154

TAPA-MVS	93.98 7	95.35 204	94.56 219	97.74 157	99.13 101	94.83 214	98.33 210	98.64 137	86.62 371	96.29 200	98.61 152	94.00 98	99.29 186	80.00 385	99.41 111	99.09 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		95.84 175	95.12 194	98.01 138	98.49 168	95.98 150	98.73 150	97.03 338	95.37 140	96.22 201	98.19 198	89.96 179	99.16 199	94.60 207	87.48 353	98.90 183

tpm2		94.19 279	93.76 275	95.46 302	97.23 276	89.04 352	97.31 319	96.85 353	87.08 370	96.21 202	96.79 319	83.75 315	98.74 261	92.43 277	96.23 236	98.59 211

F-COLMAP		97.09 122	96.80 117	97.97 140	99.45 52	94.95 208	98.55 187	98.62 141	93.02 262	96.17 203	98.58 157	94.01 97	99.81 81	93.95 229	98.90 135	99.14 154

GeoE		96.58 142	96.07 149	98.10 132	98.35 176	95.89 165	99.34 17	98.12 246	93.12 258	96.09 204	98.87 122	89.71 183	98.97 228	92.95 259	98.08 175	99.43 109

JIA-IIPM		93.35 303	92.49 311	95.92 283	96.48 324	90.65 326	95.01 381	96.96 343	85.93 377	96.08 205	87.33 398	87.70 237	98.78 259	91.35 298	95.58 248	98.34 224

BH-RMVSNet		95.92 171	95.32 184	97.69 162	98.32 186	94.64 221	98.19 231	97.45 312	94.56 183	96.03 206	98.61 152	85.02 283	99.12 207	90.68 312	99.06 127	99.30 125

dp		94.15 283	93.90 262	94.90 319	97.31 272	86.82 377	96.97 341	97.19 327	91.22 324	96.02 207	96.61 329	85.51 274	99.02 224	90.00 323	94.30 253	98.85 185

EPNet		97.28 110	96.87 115	98.51 90	94.98 368	96.14 147	98.90 99	97.02 340	98.28 10	95.99 208	99.11 84	91.36 150	99.89 47	96.98 119	99.19 124	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		97.16 117	96.66 129	98.68 75	98.53 165	97.19 94	98.93 95	98.90 57	92.83 270	95.99 208	99.37 38	92.12 129	99.87 58	93.67 239	99.57 85	98.97 176

SDMVSNet		96.85 131	96.42 136	98.14 124	99.30 68	96.38 134	99.21 40	99.23 20	95.92 111	95.96 210	98.76 140	85.88 268	99.44 174	97.93 71	95.59 246	98.60 209

sd_testset		96.17 159	95.76 161	97.42 180	99.30 68	94.34 238	98.82 126	99.08 32	95.92 111	95.96 210	98.76 140	82.83 318	99.32 184	95.56 177	95.59 246	98.60 209

AUN-MVS		94.53 255	93.73 277	96.92 215	98.50 166	93.52 266	98.34 209	98.10 252	93.83 216	95.94 212	97.98 215	85.59 273	99.03 221	94.35 215	80.94 385	98.22 229

testing222		94.12 286	93.03 300	97.37 186	98.02 213	94.66 219	97.94 263	96.65 360	94.63 180	95.78 213	95.76 353	71.49 381	98.92 239	91.17 301	95.88 243	98.52 215

TR-MVS		94.94 232	94.20 238	97.17 195	97.75 233	94.14 245	97.59 298	97.02 340	92.28 290	95.75 214	97.64 247	83.88 311	98.96 232	89.77 325	96.15 238	98.40 220

bld_raw_dy_0_64		97.09 122	96.76 124	98.08 133	98.89 127	96.54 125	98.17 237	98.52 166	88.80 362	95.67 215	98.83 127	93.32 103	99.48 166	98.86 23	99.75 45	98.21 231

WB-MVSnew		94.19 279	94.04 249	94.66 329	96.82 305	92.14 295	97.86 275	95.96 370	93.50 239	95.64 216	96.77 320	88.06 227	97.99 335	84.87 368	96.86 207	93.85 385

VPA-MVSNet		95.75 179	95.11 195	97.69 162	97.24 275	97.27 86	98.94 93	99.23 20	95.13 152	95.51 217	97.32 270	85.73 270	98.91 241	97.33 111	89.55 329	96.89 280

testing91		94.98 227	94.25 236	97.20 191	97.94 221	93.41 270	98.00 257	97.58 292	94.99 161	95.45 218	96.04 347	77.20 359	99.42 175	94.97 195	96.02 241	98.78 193

testing99		94.83 235	94.08 247	97.07 203	97.94 221	93.13 283	98.10 246	97.17 328	94.86 169	95.34 219	96.00 350	76.31 365	99.40 176	95.08 192	95.90 242	98.68 201

HQP_MVS		96.14 161	95.90 157	96.85 218	97.42 264	94.60 227	98.80 135	98.56 156	97.28 45	95.34 219	98.28 188	87.09 246	99.03 221	96.07 155	94.27 254	96.92 272

plane_prior3						94.61 225			97.02 64	95.34 219

testing11		95.00 223	94.28 234	97.16 196	97.96 220	93.36 275	98.09 247	97.06 336	94.94 167	95.33 222	96.15 343	76.89 362	99.40 176	95.77 170	96.30 227	98.72 196

Fast-Effi-MVS+		96.28 156	95.70 168	98.03 137	98.29 188	95.97 155	98.58 180	98.25 224	91.74 303	95.29 223	97.23 277	91.03 162	99.15 202	92.90 261	97.96 178	98.97 176

test_fmvs2		93.43 301	93.58 284	92.95 356	96.97 294	83.91 382	99.19 44	97.24 325	95.74 120	95.20 224	98.27 191	69.65 383	98.72 263	96.26 151	93.73 271	96.24 344

mvsmamba		96.57 143	96.32 141	97.32 187	96.60 316	96.43 130	99.54 6	97.98 269	96.49 88	95.20 224	98.64 150	90.82 163	98.55 276	97.97 68	93.65 274	96.98 267

EI-MVSNet		95.96 166	95.83 159	96.36 264	97.93 223	93.70 260	98.12 242	98.27 219	93.70 227	95.07 226	99.02 98	92.23 125	98.54 278	94.68 202	93.46 278	96.84 286

MVSTER		96.06 163	95.72 163	97.08 202	98.23 192	95.93 161	98.73 150	98.27 219	94.86 169	95.07 226	98.09 204	88.21 221	98.54 278	96.59 141	93.46 278	96.79 289

OPM-MVS		95.69 184	95.33 183	96.76 223	96.16 337	94.63 222	98.43 203	98.39 195	96.64 83	95.02 228	98.78 134	85.15 282	99.05 217	95.21 190	94.20 257	96.60 312
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Fast-Effi-MVS+-dtu		95.87 173	95.85 158	95.91 284	97.74 236	91.74 305	98.69 162	98.15 242	95.56 129	94.92 229	97.68 244	88.98 204	98.79 258	93.19 251	97.78 185	97.20 261

TESTMET0.1,1		94.18 282	93.69 280	95.63 295	96.92 297	89.12 350	96.91 346	94.78 383	93.17 254	94.88 230	96.45 333	78.52 345	98.92 239	93.09 253	98.50 157	98.85 185

VPNet		94.99 225	94.19 239	97.40 183	97.16 284	96.57 122	98.71 156	98.97 42	95.67 125	94.84 231	98.24 195	80.36 334	98.67 268	96.46 145	87.32 357	96.96 269

1112_ss		96.63 138	96.00 153	98.50 91	98.56 161	96.37 136	98.18 236	98.10 252	92.92 266	94.84 231	98.43 169	92.14 128	99.58 142	94.35 215	96.51 219	99.56 85

test-LLR		95.10 218	94.87 207	95.80 289	96.77 306	89.70 340	96.91 346	95.21 378	95.11 154	94.83 233	95.72 358	87.71 235	98.97 228	93.06 254	98.50 157	98.72 196

test-mter		94.08 290	93.51 288	95.80 289	96.77 306	89.70 340	96.91 346	95.21 378	92.89 267	94.83 233	95.72 358	77.69 353	98.97 228	93.06 254	98.50 157	98.72 196

Test_1112_low_res		96.34 153	95.66 171	98.36 106	98.56 161	95.94 158	97.71 288	98.07 259	92.10 295	94.79 235	97.29 272	91.75 139	99.56 146	94.17 222	96.50 220	99.58 83

GA-MVS		94.81 236	94.03 250	97.14 197	97.15 285	93.86 251	96.76 358	97.58 292	94.00 205	94.76 236	97.04 297	80.91 329	98.48 282	91.79 291	96.25 234	99.09 160

BH-untuned		95.95 167	95.72 163	96.65 230	98.55 163	92.26 294	98.23 224	97.79 281	93.73 222	94.62 237	98.01 211	88.97 205	99.00 227	93.04 256	98.51 156	98.68 201

test_djsdf		96.00 165	95.69 169	96.93 212	95.72 351	95.49 178	99.47 8	98.40 193	94.98 162	94.58 238	97.86 224	89.16 196	98.41 298	96.91 123	94.12 262	96.88 281

cascas		94.63 247	93.86 266	96.93 212	96.91 299	94.27 240	96.00 373	98.51 169	85.55 380	94.54 239	96.23 339	84.20 305	98.87 248	95.80 168	96.98 206	97.66 247

DP-MVS		96.59 140	95.93 156	98.57 83	99.34 57	96.19 145	98.70 160	98.39 195	89.45 354	94.52 240	99.35 44	91.85 137	99.85 63	92.89 263	98.88 137	99.68 61

gg-mvs-nofinetune		92.21 322	90.58 330	97.13 198	96.75 309	95.09 199	95.85 374	89.40 406	85.43 381	94.50 241	81.98 401	80.80 332	98.40 304	92.16 279	98.33 167	97.88 238

mvs_anonymous		96.70 137	96.53 134	97.18 194	98.19 198	93.78 253	98.31 215	98.19 231	94.01 204	94.47 242	98.27 191	92.08 132	98.46 286	97.39 108	97.91 179	99.31 122

HQP-NCC					97.20 279		98.05 251		96.43 91	94.45 243

ACMP_Plane					97.20 279		98.05 251		96.43 91	94.45 243

HQP4-MVS										94.45 243			98.96 232			96.87 283

HQP-MVS		95.72 180	95.40 175	96.69 228	97.20 279	94.25 242	98.05 251	98.46 181	96.43 91	94.45 243	97.73 236	86.75 252	98.96 232	95.30 184	94.18 258	96.86 285

MSDG		95.93 170	95.30 186	97.83 147	98.90 126	95.36 184	96.83 356	98.37 201	91.32 318	94.43 247	98.73 142	90.27 175	99.60 139	90.05 321	98.82 142	98.52 215

dmvs_re		94.48 261	94.18 241	95.37 305	97.68 240	90.11 335	98.54 188	97.08 332	94.56 183	94.42 248	97.24 276	84.25 301	97.76 349	91.02 308	92.83 290	98.24 227

nrg030		96.28 156	95.72 163	97.96 142	96.90 300	98.15 54	99.39 11	98.31 210	95.47 133	94.42 248	98.35 179	92.09 131	98.69 264	97.50 104	89.05 337	97.04 264

CLD-MVS		95.62 187	95.34 181	96.46 258	97.52 256	93.75 256	97.27 322	98.46 181	95.53 130	94.42 248	98.00 212	86.21 262	98.97 228	96.25 153	94.37 252	96.66 307
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		95.62 187	95.34 181	96.47 255	97.46 259	93.54 263	98.99 81	98.54 161	94.67 178	94.36 251	98.77 136	85.39 275	99.11 209	95.71 172	94.15 260	96.76 292

LGP-MVS_train				96.47 255	97.46 259	93.54 263		98.54 161	94.67 178	94.36 251	98.77 136	85.39 275	99.11 209	95.71 172	94.15 260	96.76 292

v144192		94.39 267	93.70 279	96.48 254	96.06 340	94.35 237	98.58 180	98.16 241	91.45 311	94.33 253	97.02 300	87.50 241	98.45 287	91.08 304	89.11 336	96.63 309

V42		94.78 238	94.14 244	96.70 227	96.33 330	95.22 193	98.97 84	98.09 256	92.32 288	94.31 254	97.06 294	88.39 218	98.55 276	92.90 261	88.87 341	96.34 340

ACMM	93.85 9	95.69 184	95.38 179	96.61 237	97.61 246	93.84 252	98.91 98	98.44 185	95.25 147	94.28 255	98.47 167	86.04 267	99.12 207	95.50 180	93.95 267	96.87 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		95.46 193	95.21 189	96.22 272	98.12 205	93.72 259	98.32 214	98.13 245	93.71 225	94.26 256	97.31 271	92.24 124	98.10 325	94.63 204	90.12 320	96.84 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		94.20 278	93.47 290	96.40 263	95.98 343	94.08 246	98.52 189	98.15 242	91.33 317	94.25 257	97.20 280	86.41 259	98.42 291	90.04 322	89.39 333	96.69 306

BH-w/o		95.38 200	95.08 196	96.26 271	98.34 181	91.79 302	97.70 289	97.43 314	92.87 268	94.24 258	97.22 278	88.66 210	98.84 251	91.55 296	97.70 189	98.16 233

XVG-ACMP-BASELINE		94.54 253	94.14 244	95.75 292	96.55 319	91.65 307	98.11 244	98.44 185	94.96 164	94.22 259	97.90 220	79.18 342	99.11 209	94.05 228	93.85 269	96.48 334

v1144		94.59 250	93.92 259	96.60 239	96.21 332	94.78 218	98.59 178	98.14 244	91.86 302	94.21 260	97.02 300	87.97 229	98.41 298	91.72 293	89.57 327	96.61 311

v1192		94.32 270	93.58 284	96.53 249	96.10 338	94.45 231	98.50 194	98.17 239	91.54 309	94.19 261	97.06 294	86.95 250	98.43 290	90.14 317	89.57 327	96.70 301

PAPM		94.95 230	94.00 254	97.78 152	97.04 290	95.65 171	96.03 372	98.25 224	91.23 323	94.19 261	97.80 233	91.27 155	98.86 250	82.61 379	97.61 191	98.84 187

Patchmatch-test		94.42 265	93.68 281	96.63 234	97.60 247	91.76 303	94.83 386	97.49 307	89.45 354	94.14 263	97.10 283	88.99 201	98.83 254	85.37 366	98.13 173	99.29 127

v1240		94.06 292	93.29 296	96.34 266	96.03 342	93.90 250	98.44 201	98.17 239	91.18 326	94.13 264	97.01 302	86.05 265	98.42 291	89.13 338	89.50 331	96.70 301

GBi-Net		94.49 259	93.80 270	96.56 244	98.21 194	95.00 202	98.82 126	98.18 234	92.46 279	94.09 265	97.07 290	81.16 326	97.95 337	92.08 281	92.14 295	96.72 297

test1		94.49 259	93.80 270	96.56 244	98.21 194	95.00 202	98.82 126	98.18 234	92.46 279	94.09 265	97.07 290	81.16 326	97.95 337	92.08 281	92.14 295	96.72 297

FMVSNet3		94.97 229	94.26 235	97.11 200	98.18 200	96.62 116	98.56 186	98.26 223	93.67 232	94.09 265	97.10 283	84.25 301	98.01 332	92.08 281	92.14 295	96.70 301

MIMVSNet		93.26 307	92.21 316	96.41 261	97.73 237	93.13 283	95.65 377	97.03 338	91.27 322	94.04 268	96.06 346	75.33 369	97.19 363	86.56 356	96.23 236	98.92 182

FIs		96.51 145	96.12 148	97.67 165	97.13 286	97.54 76	99.36 14	99.22 23	95.89 114	94.03 269	98.35 179	91.98 134	98.44 289	96.40 148	92.76 291	97.01 265

v2v482		94.69 240	94.03 250	96.65 230	96.17 335	94.79 217	98.67 166	98.08 257	92.72 272	94.00 270	97.16 281	87.69 238	98.45 287	92.91 260	88.87 341	96.72 297

testing3		93.19 310	92.48 312	95.30 308	98.07 208	92.27 293	98.64 171	97.17 328	93.94 210	93.98 271	97.04 297	67.97 387	96.01 382	88.40 344	97.14 200	97.63 248

FC-MVSNet-test		96.42 148	96.05 150	97.53 175	96.95 295	97.27 86	99.36 14	99.23 20	95.83 117	93.93 272	98.37 177	92.00 133	98.32 307	96.02 160	92.72 292	97.00 266

UniMVSNet (Re)		95.78 178	95.19 190	97.58 172	96.99 293	97.47 80	98.79 140	99.18 25	95.60 127	93.92 273	97.04 297	91.68 140	98.48 282	95.80 168	87.66 352	96.79 289

miper_enhance_ethall		95.10 218	94.75 211	96.12 276	97.53 255	93.73 258	96.61 363	98.08 257	92.20 294	93.89 274	96.65 326	92.44 117	98.30 311	94.21 221	91.16 309	96.34 340

UniMVSNet_NR-MVSNet		95.71 181	95.15 191	97.40 183	96.84 303	96.97 101	98.74 146	99.24 17	95.16 151	93.88 275	97.72 238	91.68 140	98.31 309	95.81 166	87.25 358	96.92 272

DU-MVS		95.42 197	94.76 210	97.40 183	96.53 320	96.97 101	98.66 168	98.99 41	95.43 135	93.88 275	97.69 241	88.57 212	98.31 309	95.81 166	87.25 358	96.92 272

Baseline_NR-MVSNet		94.35 268	93.81 269	95.96 282	96.20 333	94.05 247	98.61 177	96.67 358	91.44 312	93.85 277	97.60 250	88.57 212	98.14 322	94.39 213	86.93 361	95.68 358

PS-MVSNAJss		96.43 147	96.26 144	96.92 215	95.84 349	95.08 200	99.16 48	98.50 174	95.87 116	93.84 278	98.34 183	94.51 83	98.61 271	96.88 129	93.45 280	97.06 263

UniMVSNet_ETH3D		94.24 276	93.33 294	96.97 209	97.19 282	93.38 273	98.74 146	98.57 152	91.21 325	93.81 279	98.58 157	72.85 380	98.77 260	95.05 193	93.93 268	98.77 195

tt0805		94.54 253	93.85 267	96.63 234	97.98 218	93.06 287	98.77 142	97.84 279	93.67 232	93.80 280	98.04 208	76.88 363	98.96 232	94.79 201	92.86 289	97.86 240

tpmvs		94.60 248	94.36 232	95.33 307	97.46 259	88.60 360	96.88 352	97.68 285	91.29 320	93.80 280	96.42 334	88.58 211	99.24 191	91.06 305	96.04 240	98.17 232

3Dnovator	94.51 5	97.46 97	96.93 112	99.07 53	97.78 231	97.64 71	99.35 16	99.06 34	97.02 64	93.75 282	99.16 77	89.25 193	99.92 31	97.22 113	99.75 45	99.64 71

eth_miper_zixun_eth		94.68 242	94.41 230	95.47 301	97.64 244	91.71 306	96.73 360	98.07 259	92.71 273	93.64 283	97.21 279	90.54 170	98.17 320	93.38 245	89.76 324	96.54 321

ITE_SJBPF				95.44 303	97.42 264	91.32 312		97.50 305	95.09 157	93.59 284	98.35 179	81.70 322	98.88 247	89.71 327	93.39 282	96.12 348

TranMVSNet+NR-MVSNet		95.14 216	94.48 223	97.11 200	96.45 325	96.36 137	99.03 71	99.03 37	95.04 159	93.58 285	97.93 218	88.27 220	98.03 331	94.13 223	86.90 363	96.95 271

COLMAP_ROB	93.27 12	95.33 206	94.87 207	96.71 225	99.29 73	93.24 280	98.58 180	98.11 249	89.92 345	93.57 286	99.10 86	86.37 260	99.79 98	90.78 310	98.10 174	97.09 262
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.36 302	92.80 304	95.07 315	97.58 248	87.97 370	96.76 358	97.86 278	82.17 390	93.53 287	96.04 347	86.13 263	99.13 205	89.24 336	95.87 244	98.10 234

AllTest		95.24 210	94.65 215	96.99 206	99.25 81	93.21 281	98.59 178	98.18 234	91.36 314	93.52 288	98.77 136	84.67 293	99.72 113	89.70 328	97.87 181	98.02 236

TestCases				96.99 206	99.25 81	93.21 281		98.18 234	91.36 314	93.52 288	98.77 136	84.67 293	99.72 113	89.70 328	97.87 181	98.02 236

miper_ehance_all_eth		95.01 222	94.69 214	95.97 281	97.70 239	93.31 276	97.02 339	98.07 259	92.23 291	93.51 290	96.96 307	91.85 137	98.15 321	93.68 237	91.16 309	96.44 337

FMVSNet2		94.47 262	93.61 283	97.04 204	98.21 194	96.43 130	98.79 140	98.27 219	92.46 279	93.50 291	97.09 287	81.16 326	98.00 334	91.09 303	91.93 298	96.70 301

v148		94.29 273	93.76 275	95.91 284	96.10 338	92.93 288	98.58 180	97.97 270	92.59 277	93.47 292	96.95 309	88.53 216	98.32 307	92.56 271	87.06 360	96.49 332

c3_l		94.79 237	94.43 229	95.89 286	97.75 233	93.12 285	97.16 333	98.03 266	92.23 291	93.46 293	97.05 296	91.39 149	98.01 332	93.58 242	89.21 335	96.53 323

Syy-MVS		92.55 318	92.61 309	92.38 359	97.39 268	83.41 385	97.91 266	97.46 308	93.16 255	93.42 294	95.37 364	84.75 290	96.12 380	77.00 393	96.99 203	97.60 249

myMVS_eth3d		92.73 316	92.01 318	94.89 320	97.39 268	90.94 318	97.91 266	97.46 308	93.16 255	93.42 294	95.37 364	68.09 386	96.12 380	88.34 345	96.99 203	97.60 249

pmmvs4		94.69 240	93.99 256	96.81 221	95.74 350	95.94 158	97.40 308	97.67 286	90.42 337	93.37 296	97.59 251	89.08 199	98.20 318	92.97 258	91.67 302	96.30 343

PCF-MVS	93.45 11	94.68 242	93.43 292	98.42 103	98.62 157	96.77 111	95.48 380	98.20 229	84.63 384	93.34 297	98.32 185	88.55 215	99.81 81	84.80 371	98.96 133	98.68 201
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl22		94.68 242	94.19 239	96.13 275	98.11 206	93.60 261	96.94 343	98.31 210	92.43 283	93.32 298	96.87 315	86.51 255	98.28 315	94.10 226	91.16 309	96.51 329

XXY-MVS		95.20 213	94.45 227	97.46 176	96.75 309	96.56 123	98.86 116	98.65 136	93.30 249	93.27 299	98.27 191	84.85 287	98.87 248	94.82 199	91.26 308	96.96 269

jajsoiax		95.45 195	95.03 198	96.73 224	95.42 363	94.63 222	99.14 51	98.52 166	95.74 120	93.22 300	98.36 178	83.87 312	98.65 269	96.95 122	94.04 263	96.91 277

mvs_tets		95.41 199	95.00 199	96.65 230	95.58 355	94.42 233	99.00 78	98.55 159	95.73 122	93.21 301	98.38 176	83.45 316	98.63 270	97.09 116	94.00 265	96.91 277

anonymousdsp		95.42 197	94.91 204	96.94 211	95.10 367	95.90 164	99.14 51	98.41 191	93.75 219	93.16 302	97.46 259	87.50 241	98.41 298	95.63 176	94.03 264	96.50 331

v8		94.47 262	93.77 273	96.57 243	96.36 328	94.83 214	99.05 65	98.19 231	91.92 299	93.16 302	96.97 305	88.82 209	98.48 282	91.69 294	87.79 350	96.39 338

WR-MVS		95.15 215	94.46 225	97.22 190	96.67 314	96.45 128	98.21 226	98.81 86	94.15 197	93.16 302	97.69 241	87.51 239	98.30 311	95.29 186	88.62 343	96.90 279

EPNet_dtu		95.21 212	94.95 203	95.99 279	96.17 335	90.45 329	98.16 238	97.27 323	96.77 75	93.14 305	98.33 184	90.34 173	98.42 291	85.57 363	98.81 143	99.09 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		96.29 154	95.40 175	98.96 62	97.85 227	97.60 74	99.23 33	98.93 50	89.76 348	93.11 306	99.02 98	89.11 198	99.93 25	91.99 286	99.62 76	99.34 116

GG-mvs-BLEND				96.59 240	96.34 329	94.98 205	96.51 366	88.58 407		93.10 307	94.34 378	80.34 336	98.05 330	89.53 331	96.99 203	96.74 294

v10		94.29 273	93.55 286	96.51 251	96.39 327	94.80 216	98.99 81	98.19 231	91.35 316	93.02 308	96.99 303	88.09 225	98.41 298	90.50 314	88.41 345	96.33 342

3Dnovator+	94.38 6	97.43 102	96.78 120	99.38 18	97.83 228	98.52 28	99.37 13	98.71 116	97.09 62	92.99 309	99.13 82	89.36 190	99.89 47	96.97 120	99.57 85	99.71 49

D2MVS		95.18 214	95.08 196	95.48 300	97.10 288	92.07 298	98.30 217	99.13 30	94.02 203	92.90 310	96.73 321	89.48 186	98.73 262	94.48 212	93.60 277	95.65 359

Patchmtry		93.22 308	92.35 314	95.84 288	96.77 306	93.09 286	94.66 389	97.56 295	87.37 369	92.90 310	96.24 337	88.15 223	97.90 341	87.37 353	90.10 321	96.53 323

DIV-MVS_self_test		94.52 256	94.03 250	95.99 279	97.57 252	93.38 273	97.05 337	97.94 273	91.74 303	92.81 312	97.10 283	89.12 197	98.07 329	92.60 267	90.30 317	96.53 323

Anonymous20231211		94.10 288	93.26 297	96.61 237	99.11 104	94.28 239	99.01 76	98.88 62	86.43 373	92.81 312	97.57 253	81.66 323	98.68 267	94.83 198	89.02 339	96.88 281

cl____		94.51 257	94.01 253	96.02 278	97.58 248	93.40 272	97.05 337	97.96 272	91.73 305	92.76 314	97.08 289	89.06 200	98.13 323	92.61 266	90.29 318	96.52 326

miper_lstm_enhance		94.33 269	94.07 248	95.11 313	97.75 233	90.97 317	97.22 324	98.03 266	91.67 307	92.76 314	96.97 305	90.03 178	97.78 348	92.51 274	89.64 326	96.56 318

v7n		94.19 279	93.43 292	96.47 255	95.90 346	94.38 236	99.26 28	98.34 206	91.99 297	92.76 314	97.13 282	88.31 219	98.52 280	89.48 333	87.70 351	96.52 326

MVS		94.67 245	93.54 287	98.08 133	96.88 301	96.56 123	98.19 231	98.50 174	78.05 394	92.69 317	98.02 209	91.07 161	99.63 134	90.09 318	98.36 166	98.04 235

DSMNet-mixed		92.52 320	92.58 310	92.33 360	94.15 377	82.65 388	98.30 217	94.26 389	89.08 359	92.65 318	95.73 356	85.01 284	95.76 384	86.24 358	97.76 186	98.59 211

EU-MVSNet		93.66 297	94.14 244	92.25 362	95.96 345	83.38 386	98.52 189	98.12 246	94.69 176	92.61 319	98.13 202	87.36 244	96.39 378	91.82 290	90.00 322	96.98 267

IterMVS-SCA-FT		94.11 287	93.87 265	94.85 322	97.98 218	90.56 328	97.18 329	98.11 249	93.75 219	92.58 320	97.48 258	83.97 309	97.41 360	92.48 276	91.30 306	96.58 314

pmmvs5		93.65 299	92.97 302	95.68 293	95.49 358	92.37 292	98.20 228	97.28 322	89.66 350	92.58 320	97.26 273	82.14 320	98.09 327	93.18 252	90.95 312	96.58 314

WR-MVS_H		95.05 221	94.46 225	96.81 221	96.86 302	95.82 167	99.24 31	99.24 17	93.87 213	92.53 322	96.84 317	90.37 172	98.24 317	93.24 249	87.93 349	96.38 339

ACMP	93.49 10	95.34 205	94.98 201	96.43 260	97.67 241	93.48 267	98.73 150	98.44 185	94.94 167	92.53 322	98.53 161	84.50 298	99.14 204	95.48 181	94.00 265	96.66 307
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0.0.03 1		94.08 290	93.51 288	95.80 289	95.53 357	92.89 289	97.38 310	95.97 369	95.11 154	92.51 324	96.66 324	87.71 235	96.94 367	87.03 354	93.67 272	97.57 251

IB-MVS	91.98 17	93.27 306	91.97 319	97.19 193	97.47 258	93.41 270	97.09 336	95.99 368	93.32 247	92.47 325	95.73 356	78.06 351	99.53 155	94.59 209	82.98 376	98.62 208
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
IterMVS		94.09 289	93.85 267	94.80 325	97.99 216	90.35 331	97.18 329	98.12 246	93.68 230	92.46 326	97.34 268	84.05 307	97.41 360	92.51 274	91.33 305	96.62 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		94.94 232	94.30 233	96.83 219	96.72 311	95.56 174	99.11 56	98.95 46	93.89 211	92.42 327	97.90 220	87.19 245	98.12 324	94.32 217	88.21 346	96.82 288

PS-CasMVS		94.67 245	93.99 256	96.71 225	96.68 313	95.26 190	99.13 54	99.03 37	93.68 230	92.33 328	97.95 217	85.35 277	98.10 325	93.59 241	88.16 348	96.79 289

FMVSNet1		93.19 310	92.07 317	96.56 244	97.54 253	95.00 202	98.82 126	98.18 234	90.38 338	92.27 329	97.07 290	73.68 378	97.95 337	89.36 335	91.30 306	96.72 297

PEN-MVS		94.42 265	93.73 277	96.49 252	96.28 331	94.84 212	99.17 47	99.00 39	93.51 238	92.23 330	97.83 230	86.10 264	97.90 341	92.55 272	86.92 362	96.74 294

OurMVSNet-221017-0		94.21 277	94.00 254	94.85 322	95.60 354	89.22 349	98.89 104	97.43 314	95.29 144	92.18 331	98.52 164	82.86 317	98.59 274	93.46 244	91.76 300	96.74 294

MS-PatchMatch		93.84 296	93.63 282	94.46 337	96.18 334	89.45 345	97.76 284	98.27 219	92.23 291	92.13 332	97.49 257	79.50 339	98.69 264	89.75 326	99.38 116	95.25 363

ppachtmachnet_test		93.22 308	92.63 308	94.97 317	95.45 361	90.84 321	96.88 352	97.88 277	90.60 332	92.08 333	97.26 273	88.08 226	97.86 346	85.12 367	90.33 316	96.22 345

1314		96.25 158	95.73 162	97.79 151	97.13 286	95.55 176	98.19 231	98.59 145	93.47 241	92.03 334	97.82 231	91.33 152	99.49 161	94.62 206	98.44 160	98.32 226

baseline2		95.11 217	94.52 221	96.87 217	96.65 315	93.56 262	98.27 222	94.10 392	93.45 242	92.02 335	97.43 263	87.45 243	99.19 197	93.88 232	97.41 197	97.87 239

DTE-MVSNet		93.98 294	93.26 297	96.14 274	96.06 340	94.39 235	99.20 42	98.86 75	93.06 260	91.78 336	97.81 232	85.87 269	97.58 355	90.53 313	86.17 367	96.46 336

LF4IMVS		93.14 312	92.79 305	94.20 340	95.88 347	88.67 359	97.66 292	97.07 334	93.81 217	91.71 337	97.65 245	77.96 352	98.81 256	91.47 297	91.92 299	95.12 366

our_test_3		93.65 299	93.30 295	94.69 327	95.45 361	89.68 342	96.91 346	97.65 287	91.97 298	91.66 338	96.88 313	89.67 184	97.93 340	88.02 349	91.49 304	96.48 334

testgi		93.06 313	92.45 313	94.88 321	96.43 326	89.90 336	98.75 143	97.54 301	95.60 127	91.63 339	97.91 219	74.46 375	97.02 365	86.10 359	93.67 272	97.72 245

tfpnnormal		93.66 297	92.70 307	96.55 248	96.94 296	95.94 158	98.97 84	99.19 24	91.04 327	91.38 340	97.34 268	84.94 285	98.61 271	85.45 365	89.02 339	95.11 367

LTVRE_ROB	92.95 15	94.60 248	93.90 262	96.68 229	97.41 267	94.42 233	98.52 189	98.59 145	91.69 306	91.21 341	98.35 179	84.87 286	99.04 220	91.06 305	93.44 281	96.60 312
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
OpenMVS	93.04 13	95.83 176	95.00 199	98.32 109	97.18 283	97.32 83	99.21 40	98.97 42	89.96 344	91.14 342	99.05 97	86.64 254	99.92 31	93.38 245	99.47 104	97.73 244

pm-mvs1		93.94 295	93.06 299	96.59 240	96.49 323	95.16 195	98.95 90	98.03 266	92.32 288	91.08 343	97.84 227	84.54 297	98.41 298	92.16 279	86.13 369	96.19 347

MVS-HIRNet		89.46 346	88.40 346	92.64 357	97.58 248	82.15 389	94.16 395	93.05 398	75.73 397	90.90 344	82.52 400	79.42 340	98.33 306	83.53 376	98.68 145	97.43 252

FMVSNet5		91.81 323	90.92 326	94.49 334	97.21 278	92.09 297	98.00 257	97.55 300	89.31 357	90.86 345	95.61 361	74.48 374	95.32 388	85.57 363	89.70 325	96.07 350

USDC		93.33 305	92.71 306	95.21 309	96.83 304	90.83 322	96.91 346	97.50 305	93.84 214	90.72 346	98.14 201	77.69 353	98.82 255	89.51 332	93.21 286	95.97 352

MVP-Stereo		94.28 275	93.92 259	95.35 306	94.95 369	92.60 291	97.97 260	97.65 287	91.61 308	90.68 347	97.09 287	86.32 261	98.42 291	89.70 328	99.34 118	95.02 370
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	92.99 14	94.30 271	93.77 273	95.88 287	97.81 230	92.04 300	98.71 156	98.37 201	93.99 206	90.60 348	98.47 167	80.86 331	99.05 217	92.75 265	92.40 294	96.55 320

CL-MVSNet_self_test		90.11 339	89.14 342	93.02 355	91.86 390	88.23 368	96.51 366	98.07 259	90.49 333	90.49 349	94.41 374	84.75 290	95.34 387	80.79 383	74.95 398	95.50 360

KD-MVS_self_test		90.38 337	89.38 340	93.40 349	92.85 386	88.94 356	97.95 261	97.94 273	90.35 339	90.25 350	93.96 379	79.82 337	95.94 383	84.62 373	76.69 396	95.33 362

Anonymous20231206		91.66 325	91.10 325	93.33 350	94.02 381	87.35 374	98.58 180	97.26 324	90.48 334	90.16 351	96.31 335	83.83 313	96.53 376	79.36 387	89.90 323	96.12 348

SixPastTwentyTwo		93.34 304	92.86 303	94.75 326	95.67 352	89.41 347	98.75 143	96.67 358	93.89 211	90.15 352	98.25 194	80.87 330	98.27 316	90.90 309	90.64 314	96.57 316

PVSNet_0	88.72 19	91.28 329	90.03 335	95.00 316	97.99 216	87.29 375	94.84 385	98.50 174	92.06 296	89.86 353	95.19 366	79.81 338	99.39 179	92.27 278	69.79 401	98.33 225

ACMH	92.88 16	94.55 252	93.95 258	96.34 266	97.63 245	93.26 278	98.81 134	98.49 179	93.43 243	89.74 354	98.53 161	81.91 321	99.08 215	93.69 236	93.30 284	96.70 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		91.77 324	90.63 329	95.17 311	94.69 375	91.24 314	98.67 166	97.92 275	86.14 375	89.62 355	97.56 255	75.79 368	98.34 305	90.75 311	84.56 371	95.94 353

TinyColmap		92.31 321	91.53 322	94.65 330	96.92 297	89.75 338	96.92 344	96.68 357	90.45 336	89.62 355	97.85 226	76.06 367	98.81 256	86.74 355	92.51 293	95.41 361

Anonymous20240521		91.18 330	90.44 331	93.42 347	93.70 382	88.47 363	98.94 93	97.56 295	88.46 364	89.56 357	95.08 369	77.15 361	96.97 366	83.92 374	89.55 329	94.82 372

TransMVSNet (Re)		92.67 317	91.51 323	96.15 273	96.58 318	94.65 220	98.90 99	96.73 354	90.86 330	89.46 358	97.86 224	85.62 272	98.09 327	86.45 357	81.12 383	95.71 357

NR-MVSNet		94.98 227	94.16 242	97.44 178	96.53 320	97.22 93	98.74 146	98.95 46	94.96 164	89.25 359	97.69 241	89.32 191	98.18 319	94.59 209	87.40 355	96.92 272

LCM-MVSNet-Re		95.22 211	95.32 184	94.91 318	98.18 200	87.85 372	98.75 143	95.66 374	95.11 154	88.96 360	96.85 316	90.26 176	97.65 351	95.65 175	98.44 160	99.22 138

KD-MVS_2432*1600		89.61 344	87.96 351	94.54 332	94.06 379	91.59 308	95.59 378	97.63 289	89.87 346	88.95 361	94.38 376	78.28 348	96.82 368	84.83 369	68.05 402	95.21 364

miper_refine_blended		89.61 344	87.96 351	94.54 332	94.06 379	91.59 308	95.59 378	97.63 289	89.87 346	88.95 361	94.38 376	78.28 348	96.82 368	84.83 369	68.05 402	95.21 364

test_fmvs3		87.17 353	87.06 356	87.50 371	91.21 392	75.66 396	99.05 65	96.61 361	92.79 271	88.85 363	92.78 388	43.72 403	93.49 394	93.95 229	84.56 371	93.34 388

TDRefinement		91.06 332	89.68 337	95.21 309	85.35 406	91.49 310	98.51 193	97.07 334	91.47 310	88.83 364	97.84 227	77.31 357	99.09 214	92.79 264	77.98 394	95.04 369

N_pmnet		87.12 355	87.77 353	85.17 375	95.46 360	61.92 411	97.37 312	70.66 416	85.83 378	88.73 365	96.04 347	85.33 279	97.76 349	80.02 384	90.48 315	95.84 354

test_0402		91.32 327	90.27 333	94.48 335	96.60 316	91.12 315	98.50 194	97.22 326	86.10 376	88.30 366	96.98 304	77.65 355	97.99 335	78.13 391	92.94 288	94.34 374

test20.03		90.89 334	90.38 332	92.43 358	93.48 383	88.14 369	98.33 210	97.56 295	93.40 244	87.96 367	96.71 323	80.69 333	94.13 393	79.15 388	86.17 367	95.01 371

MIMVSNet1		89.67 343	88.28 348	93.82 344	92.81 387	91.08 316	98.01 255	97.45 312	87.95 366	87.90 368	95.87 352	67.63 389	94.56 392	78.73 390	88.18 347	95.83 355

mvsany_test3		88.80 348	88.04 349	91.09 366	89.78 396	81.57 391	97.83 280	95.49 375	93.81 217	87.53 369	93.95 380	56.14 399	97.43 359	94.68 202	83.13 375	94.26 375

Patchmatch-RL test		91.49 326	90.85 327	93.41 348	91.37 391	84.40 380	92.81 396	95.93 372	91.87 301	87.25 370	94.87 370	88.99 201	96.53 376	92.54 273	82.00 378	99.30 125

pmmvs3		86.67 356	84.86 361	92.11 363	88.16 400	87.19 376	96.63 362	94.75 384	79.88 392	87.22 371	92.75 390	66.56 391	95.20 389	81.24 382	76.56 397	93.96 383

dongtai		82.47 360	81.88 363	84.22 377	95.19 366	76.03 394	94.59 391	74.14 415	82.63 387	87.19 372	96.09 345	64.10 393	87.85 405	58.91 403	84.11 374	88.78 397

test_vis1_rt		91.29 328	90.65 328	93.19 354	97.45 262	86.25 378	98.57 185	90.90 404	93.30 249	86.94 373	93.59 382	62.07 396	99.11 209	97.48 105	95.58 248	94.22 377

K. test v3		92.55 318	91.91 321	94.48 335	95.64 353	89.24 348	99.07 62	94.88 382	94.04 201	86.78 374	97.59 251	77.64 356	97.64 352	92.08 281	89.43 332	96.57 316

lessismore_v0				94.45 338	94.93 370	88.44 364		91.03 403		86.77 375	97.64 247	76.23 366	98.42 291	90.31 316	85.64 370	96.51 329

APD_test1		88.22 350	88.01 350	88.86 369	95.98 343	74.66 401	97.21 325	96.44 363	83.96 386	86.66 376	97.90 220	60.95 397	97.84 347	82.73 377	90.23 319	94.09 380

ambc				89.49 368	86.66 403	75.78 395	92.66 397	96.72 355		86.55 377	92.50 391	46.01 401	97.90 341	90.32 315	82.09 377	94.80 373

PM-MVS		87.77 351	86.55 357	91.40 365	91.03 394	83.36 387	96.92 344	95.18 380	91.28 321	86.48 378	93.42 383	53.27 400	96.74 370	89.43 334	81.97 379	94.11 379

OpenMVS_ROB	86.42 20	89.00 347	87.43 355	93.69 345	93.08 385	89.42 346	97.91 266	96.89 349	78.58 393	85.86 379	94.69 371	69.48 384	98.29 314	77.13 392	93.29 285	93.36 387

UnsupCasMVSNet_eth		90.99 333	89.92 336	94.19 341	94.08 378	89.83 337	97.13 335	98.67 129	93.69 228	85.83 380	96.19 342	75.15 370	96.74 370	89.14 337	79.41 390	96.00 351

new_pmnet		90.06 340	89.00 344	93.22 353	94.18 376	88.32 366	96.42 368	96.89 349	86.19 374	85.67 381	93.62 381	77.18 360	97.10 364	81.61 381	89.29 334	94.23 376

dmvs_testset		87.64 352	88.93 345	83.79 378	95.25 364	63.36 410	97.20 326	91.17 402	93.07 259	85.64 382	95.98 351	85.30 281	91.52 400	69.42 399	87.33 356	96.49 332

test_f		86.07 357	85.39 358	88.10 370	89.28 398	75.57 397	97.73 287	96.33 365	89.41 356	85.35 383	91.56 394	43.31 405	95.53 385	91.32 299	84.23 373	93.21 389

EG-PatchMatch MVS		91.13 331	90.12 334	94.17 342	94.73 374	89.00 353	98.13 241	97.81 280	89.22 358	85.32 384	96.46 332	67.71 388	98.42 291	87.89 351	93.82 270	95.08 368

pmmvs-eth3d		90.36 338	89.05 343	94.32 339	91.10 393	92.12 296	97.63 297	96.95 344	88.86 361	84.91 385	93.13 387	78.32 347	96.74 370	88.70 341	81.81 380	94.09 380

DeepMVS_CX				86.78 372	97.09 289	72.30 402		95.17 381	75.92 396	84.34 386	95.19 366	70.58 382	95.35 386	79.98 386	89.04 338	92.68 390

new-patchmatchnet		88.50 349	87.45 354	91.67 364	90.31 395	85.89 379	97.16 333	97.33 319	89.47 353	83.63 387	92.77 389	76.38 364	95.06 390	82.70 378	77.29 395	94.06 382

UnsupCasMVSNet_bld		87.17 353	85.12 360	93.31 351	91.94 389	88.77 357	94.92 384	98.30 216	84.30 385	82.30 388	90.04 395	63.96 394	97.25 362	85.85 362	74.47 400	93.93 384

WB-MVS		84.86 358	85.33 359	83.46 379	89.48 397	69.56 405	98.19 231	96.42 364	89.55 352	81.79 389	94.67 372	84.80 288	90.12 401	52.44 405	80.64 387	90.69 392

CMPMVS	66.06 21	89.70 342	89.67 338	89.78 367	93.19 384	76.56 393	97.00 340	98.35 204	80.97 391	81.57 390	97.75 235	74.75 372	98.61 271	89.85 324	93.63 275	94.17 378
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SSC-MVS		84.27 359	84.71 362	82.96 383	89.19 399	68.83 406	98.08 248	96.30 366	89.04 360	81.37 391	94.47 373	84.60 295	89.89 402	49.80 407	79.52 389	90.15 393

test_method		79.03 362	78.17 364	81.63 384	86.06 405	54.40 416	82.75 404	96.89 349	39.54 408	80.98 392	95.57 362	58.37 398	94.73 391	84.74 372	78.61 391	95.75 356

kuosan		78.45 366	77.69 367	80.72 385	92.73 388	75.32 398	94.63 390	74.51 414	75.96 395	80.87 393	93.19 386	63.23 395	79.99 409	42.56 409	81.56 382	86.85 401

ET-MVSNet_ETH3D		94.13 284	92.98 301	97.58 172	98.22 193	96.20 143	97.31 319	95.37 376	94.53 185	79.56 394	97.63 249	86.51 255	97.53 357	96.91 123	90.74 313	99.02 171

LCM-MVSNet		78.70 365	76.24 371	86.08 373	77.26 412	71.99 403	94.34 393	96.72 355	61.62 403	76.53 395	89.33 396	33.91 411	92.78 398	81.85 380	74.60 399	93.46 386

PMMVS2		77.95 368	75.44 372	85.46 374	82.54 407	74.95 399	94.23 394	93.08 397	72.80 398	74.68 396	87.38 397	36.36 408	91.56 399	73.95 395	63.94 404	89.87 394

testf1		79.02 363	77.70 365	82.99 381	88.10 401	66.90 407	94.67 387	93.11 395	71.08 399	74.02 397	93.41 384	34.15 409	93.25 395	72.25 397	78.50 392	88.82 395

APD_test2		79.02 363	77.70 365	82.99 381	88.10 401	66.90 407	94.67 387	93.11 395	71.08 399	74.02 397	93.41 384	34.15 409	93.25 395	72.25 397	78.50 392	88.82 395

Gipuma		78.40 367	76.75 370	83.38 380	95.54 356	80.43 392	79.42 405	97.40 316	64.67 402	73.46 399	80.82 403	45.65 402	93.14 397	66.32 401	87.43 354	76.56 405
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		90.70 336	89.39 339	94.62 331	94.79 373	90.65 326	97.20 326	97.46 308	87.54 368	72.54 400	95.74 354	86.51 255	96.66 374	86.00 360	86.76 365	96.54 321

MDA-MVSNet_test_wron		90.71 335	89.38 340	94.68 328	94.83 371	90.78 323	97.19 328	97.46 308	87.60 367	72.41 401	95.72 358	86.51 255	96.71 373	85.92 361	86.80 364	96.56 318

MDA-MVSNet-bldmvs		89.97 341	88.35 347	94.83 324	95.21 365	91.34 311	97.64 294	97.51 304	88.36 365	71.17 402	96.13 344	79.22 341	96.63 375	83.65 375	86.27 366	96.52 326

FPMVS		77.62 369	77.14 369	79.05 387	79.25 410	60.97 412	95.79 375	95.94 371	65.96 401	67.93 403	94.40 375	37.73 407	88.88 404	68.83 400	88.46 344	87.29 398

test_vis3_rt		79.22 361	77.40 368	84.67 376	86.44 404	74.85 400	97.66 292	81.43 411	84.98 382	67.12 404	81.91 402	28.09 413	97.60 353	88.96 339	80.04 388	81.55 402

tmp_tt		68.90 372	66.97 374	74.68 389	50.78 416	59.95 413	87.13 401	83.47 410	38.80 409	62.21 405	96.23 339	64.70 392	76.91 411	88.91 340	30.49 409	87.19 399

E-PMN		64.94 374	64.25 376	67.02 391	82.28 408	59.36 414	91.83 399	85.63 408	52.69 405	60.22 406	77.28 405	41.06 406	80.12 408	46.15 408	41.14 406	61.57 407

EMVS		64.07 375	63.26 378	66.53 392	81.73 409	58.81 415	91.85 398	84.75 409	51.93 407	59.09 407	75.13 406	43.32 404	79.09 410	42.03 410	39.47 407	61.69 406

MVE	62.14 22	63.28 376	59.38 379	74.99 388	74.33 413	65.47 409	85.55 402	80.50 412	52.02 406	51.10 408	75.00 407	10.91 417	80.50 407	51.60 406	53.40 405	78.99 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		69.08 371	65.37 375	80.22 386	65.99 414	71.96 404	90.91 400	90.09 405	82.62 388	49.93 409	78.39 404	29.36 412	81.75 406	62.49 402	38.52 408	86.95 400

PMVS	61.03 23	65.95 373	63.57 377	73.09 390	57.90 415	51.22 417	85.05 403	93.93 393	54.45 404	44.32 410	83.57 399	13.22 414	89.15 403	58.68 404	81.00 384	78.91 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		21.48 379	24.95 382	11.09 395	14.89 417	6.47 420	96.56 364	9.87 418	7.55 411	17.93 411	39.02 409	9.43 418	5.90 414	16.56 413	12.72 411	20.91 409

test123		20.95 380	23.72 383	12.64 394	13.54 418	8.19 419	96.55 365	6.13 419	7.48 412	16.74 412	37.98 410	12.97 415	6.05 413	16.69 412	5.43 412	23.68 408

wuyk23d		30.17 377	30.18 381	30.16 393	78.61 411	43.29 418	66.79 406	14.21 417	17.31 410	14.82 413	11.93 413	11.55 416	41.43 412	37.08 411	19.30 410	5.76 410

EGC-MVSNET		75.22 370	69.54 373	92.28 361	94.81 372	89.58 343	97.64 294	96.50 362	1.82 413	5.57 414	95.74 354	68.21 385	96.26 379	73.80 396	91.71 301	90.99 391

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

cdsmvs_eth3d_5k		23.98 378	31.98 380	0.00 396	0.00 419	0.00 421	0.00 407	98.59 145	0.00 414	0.00 415	98.61 152	90.60 169	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		7.88 382	10.50 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	94.51 83	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		8.20 381	10.94 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	98.43 169	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						90.94 318								88.66 342

MSC_two_6792asdad				99.62 6	99.17 94	99.08 11		98.63 139					99.94 8	98.53 34	99.80 22	99.86 8

No_MVS				99.62 6	99.17 94	99.08 11		98.63 139					99.94 8	98.53 34	99.80 22	99.86 8

eth-test2					0.00 419

eth-test					0.00 419

OPU-MVS				99.37 20	99.24 87	99.05 14	99.02 74				99.16 77	97.81 3	99.37 180	97.24 112	99.73 53	99.70 53

save fliter					99.46 49	98.38 35	98.21 226	98.71 116	97.95 13

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 84	98.88 62					99.94 8	98.47 42	99.81 15	99.84 12

GSMVS																99.20 140

sam_mvs1												89.45 188				99.20 140

sam_mvs												88.99 201

MTGPA								98.74 108

test_post1							96.68 361				30.43 412	87.85 234	98.69 264	92.59 269

test_post											31.83 411	88.83 208	98.91 241

patchmatchnet-post											95.10 368	89.42 189	98.89 245

MTMP							98.89 104	94.14 391

gm-plane-assit					95.88 347	87.47 373			89.74 349		96.94 310		99.19 197	93.32 248

test9_res														96.39 149	99.57 85	99.69 56

agg_prior2														95.87 165	99.57 85	99.68 61

test_prior4						98.01 61	97.86 275

test_prior				99.19 40	99.31 64	98.22 48		98.84 79					99.70 119			99.65 69

新几何2							97.64 294

旧先验1					99.29 73	97.48 78		98.70 120			99.09 92	95.56 49			99.47 104	99.61 75

无先验							97.58 299	98.72 113	91.38 313				99.87 58	93.36 247		99.60 77

原ACMM2							97.67 291

testdata2													99.89 47	91.65 295

segment_acmp												96.85 14

testdata1							97.32 318		96.34 97

plane_prior7					97.42 264	94.63 222

plane_prior6					97.35 271	94.61 225						87.09 246

plane_prior5								98.56 156					99.03 221	96.07 155	94.27 254	96.92 272

plane_prior4											98.28 188

plane_prior2							98.80 135		97.28 45

plane_prior1					97.37 270

plane_prior						94.60 227	98.44 201		96.74 78						94.22 256

n2								0.00 420

nn								0.00 420

door-mid								94.37 387

test11								98.66 132

door								94.64 385

HQP5-MVS						94.25 242

BP-MVS														95.30 184

HQP3-MVS								98.46 181							94.18 258

HQP2-MVS												86.75 252

NP-MVS					97.28 273	94.51 230					97.73 236

ACMMP++_ref															92.97 287

ACMMP++															93.61 276

Test By Simon												94.64 80

High-resolution multi-view benchmark