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.

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 2	99.99 1	100.00 1	99.98 8	99.78 6	100.00 1	99.92 1	100.00 1	99.87 9

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 18	99.77 11	99.80 49	99.73 40	99.97 6	99.92 16	99.77 7	99.98 7	99.43 38	100.00 1	99.90 4

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 45	100.00 1	99.90 7	100.00 1	99.97 9	99.61 17	99.97 17	99.75 13	100.00 1	99.84 14

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 21	99.91 2	99.89 4	99.71 95	99.93 4	99.95 10	99.89 25	99.71 9	99.96 35	99.51 31	99.97 30	99.84 14

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 8	99.73 16	99.85 26	99.70 49	99.92 18	99.93 13	99.45 23	99.97 17	99.36 50	100.00 1	99.85 13

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 36	99.72 19	99.88 18	99.92 6	99.98 3	99.93 13	99.94 1	99.98 7	99.77 12	100.00 1	99.92 3

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 42	99.68 31	99.85 26	99.95 3	99.98 3	99.92 16	99.28 41	99.98 7	99.75 13	100.00 1	99.94 2

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 39	99.70 22	99.86 22	99.89 11	99.98 3	99.90 21	99.94 1	99.98 7	99.75 13	100.00 1	99.90 4

PS-CasMVS		99.66 25	99.58 37	99.89 7	99.80 56	99.85 13	99.66 40	99.73 83	99.62 69	99.84 43	99.71 100	98.62 124	99.96 35	99.30 60	99.96 42	99.86 11

PEN-MVS		99.66 25	99.59 34	99.89 7	99.83 38	99.87 9	99.66 40	99.73 83	99.70 49	99.84 43	99.73 87	98.56 132	99.96 35	99.29 63	99.94 62	99.83 18

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 18	99.82 8	99.82 39	99.84 23	99.94 11	99.91 19	99.13 58	99.96 35	99.83 9	99.99 12	99.83 18

DTE-MVSNet		99.68 23	99.61 31	99.88 11	99.80 56	99.87 9	99.67 36	99.71 95	99.72 43	99.84 43	99.78 66	98.67 118	99.97 17	99.30 60	99.95 49	99.80 24

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 11	99.91 15	99.90 4	99.96 1	99.92 6	99.90 7	99.97 6	99.87 31	99.81 5	99.95 45	99.54 26	99.99 12	99.80 24
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
CP-MVSNet		99.54 47	99.43 62	99.87 14	99.76 84	99.82 26	99.57 62	99.61 146	99.54 83	99.80 60	99.64 141	97.79 210	99.95 45	99.21 70	99.94 62	99.84 14

WR-MVS_H		99.61 36	99.53 49	99.87 14	99.80 56	99.83 22	99.67 36	99.75 75	99.58 82	99.85 40	99.69 113	98.18 181	99.94 57	99.28 65	99.95 49	99.83 18

UA-Net		99.78 13	99.76 14	99.86 16	99.72 108	99.71 67	99.91 3	99.95 4	99.96 2	99.71 100	99.91 19	99.15 54	99.97 17	99.50 33	100.00 1	99.90 4

FC-MVSNet-test		99.70 19	99.65 23	99.86 16	99.88 24	99.86 12	99.72 19	99.78 60	99.90 7	99.82 50	99.83 43	98.45 150	99.87 170	99.51 31	99.97 30	99.86 11

APDe-MVS		99.48 54	99.36 76	99.85 18	99.55 176	99.81 29	99.50 68	99.69 105	98.99 165	99.75 80	99.71 100	98.79 101	99.93 71	98.46 143	99.85 119	99.80 24

FIs		99.65 30	99.58 37	99.84 19	99.84 34	99.85 13	99.66 40	99.75 75	99.86 16	99.74 89	99.79 60	98.27 170	99.85 210	99.37 49	99.93 70	99.83 18

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 22	99.83 6	99.85 26	99.80 32	99.93 14	99.93 13	98.54 135	99.93 71	99.59 20	99.98 21	99.76 37

test_0728_SECOND				99.83 21	99.70 118	99.79 36	99.14 161	99.61 146					99.92 90	97.88 190	99.72 198	99.77 33

pmmvs6		99.86 6	99.86 6	99.83 21	99.94 10	99.90 4	99.83 6	99.91 9	99.85 20	99.94 11	99.95 11	99.73 8	99.90 129	99.65 16	99.97 30	99.69 52

DPE-MVS		99.14 151	98.92 181	99.82 23	99.57 165	99.77 42	98.74 236	99.60 157	98.55 215	99.76 75	99.69 113	98.23 175	99.92 90	96.39 290	99.75 177	99.76 37
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
nrg030		99.70 19	99.66 21	99.82 23	99.76 84	99.84 18	99.61 53	99.70 99	99.93 4	99.78 68	99.68 124	99.10 59	99.78 275	99.45 36	99.96 42	99.83 18

Baseline_NR-MVSNet		99.49 52	99.37 73	99.82 23	99.91 15	99.84 18	98.83 220	99.86 22	99.68 53	99.65 119	99.88 28	97.67 218	99.87 170	99.03 98	99.86 116	99.76 37

MSP-MVS		99.04 172	98.79 199	99.81 26	99.78 72	99.73 60	99.35 95	99.57 175	98.54 218	99.54 162	98.99 316	96.81 255	99.93 71	96.97 258	99.53 257	99.77 33
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
TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 13	99.75 14	99.86 22	99.70 49	99.91 20	99.89 25	99.60 19	99.87 170	99.59 20	99.74 185	99.71 46

XXY-MVS		99.71 18	99.67 20	99.81 26	99.89 21	99.72 64	99.59 59	99.82 39	99.39 111	99.82 50	99.84 42	99.38 29	99.91 108	99.38 47	99.93 70	99.80 24

MP-MVS-pluss		99.14 151	98.92 181	99.80 29	99.83 38	99.83 22	98.61 243	99.63 136	96.84 317	99.44 184	99.58 184	98.81 94	99.91 108	97.70 208	99.82 142	99.67 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS		99.30 104	99.14 117	99.80 29	99.81 51	99.81 29	98.73 238	99.53 200	99.27 126	99.42 190	99.63 151	98.21 176	99.95 45	97.83 199	99.79 161	99.65 83

MTAPA		99.35 90	99.20 109	99.80 29	99.81 51	99.81 29	99.33 99	99.53 200	99.27 126	99.42 190	99.63 151	98.21 176	99.95 45	97.83 199	99.79 161	99.65 83

HPM-MVS_fast		99.43 66	99.30 89	99.80 29	99.83 38	99.81 29	99.52 66	99.70 99	98.35 240	99.51 174	99.50 216	99.31 37	99.88 157	98.18 167	99.84 123	99.69 52

MIMVSNet1		99.66 25	99.62 26	99.80 29	99.94 10	99.87 9	99.69 28	99.77 63	99.78 35	99.93 14	99.89 25	97.94 197	99.92 90	99.65 16	99.98 21	99.62 106

ACMMP_NAP		99.28 107	99.11 127	99.79 34	99.75 95	99.81 29	98.95 206	99.53 200	98.27 249	99.53 167	99.73 87	98.75 109	99.87 170	97.70 208	99.83 133	99.68 58

VPA-MVSNet		99.66 25	99.62 26	99.79 34	99.68 130	99.75 51	99.62 48	99.69 105	99.85 20	99.80 60	99.81 52	98.81 94	99.91 108	99.47 35	99.88 100	99.70 49

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 24	99.66 85	99.69 28	99.92 6	99.67 57	99.77 73	99.75 80	99.61 17	99.98 7	99.35 51	99.98 21	99.72 43
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		99.69 21	99.66 21	99.78 37	99.76 84	99.76 48	99.60 58	99.82 39	99.46 100	99.75 80	99.56 195	99.63 14	99.95 45	99.43 38	99.88 100	99.62 106

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 22	99.76 13	99.87 20	99.73 40	99.89 26	99.87 31	99.63 14	99.87 170	99.54 26	99.92 74	99.63 95

HPM-MVS		99.25 114	99.07 142	99.78 37	99.81 51	99.75 51	99.61 53	99.67 113	97.72 280	99.35 210	99.25 279	99.23 46	99.92 90	97.21 247	99.82 142	99.67 65
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SED-MVS		99.40 76	99.28 96	99.77 40	99.69 121	99.82 26	99.20 140	99.54 191	99.13 151	99.82 50	99.63 151	98.91 83	99.92 90	97.85 196	99.70 204	99.58 133

ZNCC-MVS		99.22 127	99.04 154	99.77 40	99.76 84	99.73 60	99.28 117	99.56 180	98.19 254	99.14 251	99.29 269	98.84 92	99.92 90	97.53 224	99.80 156	99.64 90

DVP-MVS		99.32 101	99.17 112	99.77 40	99.69 121	99.80 34	99.14 161	99.31 272	99.16 145	99.62 132	99.61 169	98.35 162	99.91 108	97.88 190	99.72 198	99.61 115
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
region2R		99.23 118	99.05 148	99.77 40	99.76 84	99.70 74	99.31 106	99.59 164	98.41 229	99.32 217	99.36 252	98.73 112	99.93 71	97.29 236	99.74 185	99.67 65

PGM-MVS		99.20 134	99.01 160	99.77 40	99.75 95	99.71 67	99.16 157	99.72 92	97.99 264	99.42 190	99.60 176	98.81 94	99.93 71	96.91 261	99.74 185	99.66 75

TDRefinement		99.72 17	99.70 17	99.77 40	99.90 19	99.85 13	99.86 5	99.92 6	99.69 52	99.78 68	99.92 16	99.37 31	99.88 157	98.93 113	99.95 49	99.60 119

DIV-MVS_2432*1600		99.63 31	99.59 34	99.76 46	99.84 34	99.90 4	99.37 90	99.79 55	99.83 26	99.88 32	99.85 37	98.42 153	99.90 129	99.60 19	99.73 192	99.49 181

Anonymous20231211		99.62 34	99.57 40	99.76 46	99.61 147	99.60 106	99.81 9	99.73 83	99.82 28	99.90 22	99.90 21	97.97 196	99.86 191	99.42 43	99.96 42	99.80 24

HFP-MVS		99.25 114	99.08 138	99.76 46	99.73 104	99.70 74	99.31 106	99.59 164	98.36 235	99.36 208	99.37 247	98.80 98	99.91 108	97.43 229	99.75 177	99.68 58

#test#		99.12 155	98.90 185	99.76 46	99.73 104	99.70 74	99.10 174	99.59 164	97.60 285	99.36 208	99.37 247	98.80 98	99.91 108	96.84 267	99.75 177	99.68 58

ACMMPR		99.23 118	99.06 144	99.76 46	99.74 101	99.69 78	99.31 106	99.59 164	98.36 235	99.35 210	99.38 246	98.61 126	99.93 71	97.43 229	99.75 177	99.67 65

MP-MVS		99.06 166	98.83 194	99.76 46	99.76 84	99.71 67	99.32 102	99.50 215	98.35 240	98.97 267	99.48 224	98.37 160	99.92 90	95.95 310	99.75 177	99.63 95
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 47	99.47 53	99.76 46	99.58 155	99.64 92	99.30 109	99.63 136	99.61 73	99.71 100	99.56 195	98.76 107	99.96 35	99.14 90	99.92 74	99.68 58

mPP-MVS		99.19 138	99.00 163	99.76 46	99.76 84	99.68 81	99.38 86	99.54 191	98.34 244	99.01 264	99.50 216	98.53 139	99.93 71	97.18 249	99.78 167	99.66 75

SixPastTwentyTwo		99.42 69	99.30 89	99.76 46	99.92 14	99.67 83	99.70 22	99.14 301	99.65 63	99.89 26	99.90 21	96.20 272	99.94 57	99.42 43	99.92 74	99.67 65

SteuartSystems-ACMMP		99.30 104	99.14 117	99.76 46	99.87 28	99.66 85	99.18 146	99.60 157	98.55 215	99.57 148	99.67 130	99.03 71	99.94 57	97.01 256	99.80 156	99.69 52
Skip Steuart: Steuart Systems R&D Blog.
GST-MVS		99.16 147	98.96 174	99.75 56	99.73 104	99.73 60	99.20 140	99.55 186	98.22 251	99.32 217	99.35 257	98.65 122	99.91 108	96.86 264	99.74 185	99.62 106

test_part1		98.63 226	98.26 249	99.75 56	99.40 236	99.49 123	99.67 36	99.68 108	99.86 16	99.88 32	99.86 36	86.73 354	99.93 71	99.34 52	99.97 30	99.81 23

XVS		99.27 111	99.11 127	99.75 56	99.71 111	99.71 67	99.37 90	99.61 146	99.29 122	98.76 294	99.47 229	98.47 146	99.88 157	97.62 216	99.73 192	99.67 65

X-MVStestdata		96.09 324	94.87 334	99.75 56	99.71 111	99.71 67	99.37 90	99.61 146	99.29 122	98.76 294	61.30 372	98.47 146	99.88 157	97.62 216	99.73 192	99.67 65

abl_6		99.36 88	99.23 106	99.75 56	99.71 111	99.74 57	99.33 99	99.76 68	99.07 158	99.65 119	99.63 151	99.09 61	99.92 90	97.13 252	99.76 174	99.58 133

CP-MVS		99.23 118	99.05 148	99.75 56	99.66 136	99.66 85	99.38 86	99.62 139	98.38 233	99.06 262	99.27 273	98.79 101	99.94 57	97.51 225	99.82 142	99.66 75

test1172		99.23 118	99.05 148	99.74 62	99.52 187	99.75 51	99.20 140	99.61 146	98.97 167	99.48 177	99.58 184	98.41 154	99.91 108	97.15 251	99.55 248	99.57 139

SR-MVS		99.19 138	99.00 163	99.74 62	99.51 192	99.72 64	99.18 146	99.60 157	98.85 185	99.47 179	99.58 184	98.38 159	99.92 90	96.92 260	99.54 255	99.57 139

HPM-MVS++		98.96 188	98.70 206	99.74 62	99.52 187	99.71 67	98.86 215	99.19 296	98.47 225	98.59 306	99.06 305	98.08 187	99.91 108	96.94 259	99.60 239	99.60 119

APD-MVS_3200maxsize		99.31 103	99.16 113	99.74 62	99.53 182	99.75 51	99.27 120	99.61 146	99.19 139	99.57 148	99.64 141	98.76 107	99.90 129	97.29 236	99.62 229	99.56 142

LPG-MVS_test		99.22 127	99.05 148	99.74 62	99.82 44	99.63 96	99.16 157	99.73 83	97.56 286	99.64 121	99.69 113	99.37 31	99.89 143	96.66 277	99.87 109	99.69 52

LGP-MVS_train				99.74 62	99.82 44	99.63 96		99.73 83	97.56 286	99.64 121	99.69 113	99.37 31	99.89 143	96.66 277	99.87 109	99.69 52

DP-MVS		99.48 54	99.39 68	99.74 62	99.57 165	99.62 98	99.29 116	99.61 146	99.87 14	99.74 89	99.76 76	98.69 114	99.87 170	98.20 163	99.80 156	99.75 40

ACMMP		99.25 114	99.08 138	99.74 62	99.79 66	99.68 81	99.50 68	99.65 128	98.07 260	99.52 169	99.69 113	98.57 130	99.92 90	97.18 249	99.79 161	99.63 95
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
SR-MVS-dyc-post		99.27 111	99.11 127	99.73 70	99.54 177	99.74 57	99.26 122	99.62 139	99.16 145	99.52 169	99.64 141	98.41 154	99.91 108	97.27 239	99.61 236	99.54 153

SMA-MVS		99.19 138	99.00 163	99.73 70	99.46 219	99.73 60	99.13 167	99.52 208	97.40 296	99.57 148	99.64 141	98.93 80	99.83 238	97.61 218	99.79 161	99.63 95
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
GBi-Net		99.42 69	99.31 84	99.73 70	99.49 203	99.77 42	99.68 31	99.70 99	99.44 103	99.62 132	99.83 43	97.21 241	99.90 129	98.96 107	99.90 84	99.53 158

test1		99.42 69	99.31 84	99.73 70	99.49 203	99.77 42	99.68 31	99.70 99	99.44 103	99.62 132	99.83 43	97.21 241	99.90 129	98.96 107	99.90 84	99.53 158

FMVSNet1		99.66 25	99.63 25	99.73 70	99.78 72	99.77 42	99.68 31	99.70 99	99.67 57	99.82 50	99.83 43	98.98 74	99.90 129	99.24 67	99.97 30	99.53 158

HyFIR lowres test		98.91 194	98.64 209	99.73 70	99.85 33	99.47 126	98.07 297	99.83 34	98.64 206	99.89 26	99.60 176	92.57 308	100.00 1	99.33 55	99.97 30	99.72 43

testtj		98.56 236	98.17 259	99.72 76	99.45 222	99.60 106	98.88 211	99.50 215	96.88 314	99.18 246	99.48 224	97.08 248	99.92 90	93.69 347	99.38 279	99.63 95

UniMVSNet_NR-MVSNet		99.37 85	99.25 103	99.72 76	99.47 214	99.56 115	98.97 204	99.61 146	99.43 108	99.67 111	99.28 271	97.85 206	99.95 45	99.17 80	99.81 151	99.65 83

ACMM	98.09 11	99.46 61	99.38 70	99.72 76	99.80 56	99.69 78	99.13 167	99.65 128	98.99 165	99.64 121	99.72 93	99.39 25	99.86 191	98.23 160	99.81 151	99.60 119
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	98.42 6	99.59 38	99.54 45	99.72 76	99.86 30	99.62 98	99.56 64	99.79 55	98.77 196	99.80 60	99.85 37	99.64 13	99.85 210	98.70 131	99.89 92	99.70 49
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		99.46 61	99.37 73	99.71 80	99.82 44	99.59 109	99.48 72	99.70 99	99.81 29	99.69 105	99.58 184	97.66 222	99.86 191	99.17 80	99.44 270	99.67 65

DU-MVS		99.33 99	99.21 108	99.71 80	99.43 227	99.56 115	98.83 220	99.53 200	99.38 112	99.67 111	99.36 252	97.67 218	99.95 45	99.17 80	99.81 151	99.63 95

APD-MVS		98.87 202	98.59 214	99.71 80	99.50 198	99.62 98	99.01 191	99.57 175	96.80 319	99.54 162	99.63 151	98.29 168	99.91 108	95.24 327	99.71 202	99.61 115
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMH+	98.40 8	99.50 50	99.43 62	99.71 80	99.86 30	99.76 48	99.32 102	99.77 63	99.53 85	99.77 73	99.76 76	99.26 45	99.78 275	97.77 201	99.88 100	99.60 119

COLMAP_ROB	98.06 12	99.45 63	99.37 73	99.70 84	99.83 38	99.70 74	99.38 86	99.78 60	99.53 85	99.67 111	99.78 66	99.19 50	99.86 191	97.32 234	99.87 109	99.55 145
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
K. test v3		98.87 202	98.60 212	99.69 85	99.93 13	99.46 130	99.74 15	94.97 361	99.78 35	99.88 32	99.88 28	93.66 300	99.97 17	99.61 18	99.95 49	99.64 90

UniMVSNet (Re)		99.37 85	99.26 101	99.68 86	99.51 192	99.58 112	98.98 202	99.60 157	99.43 108	99.70 102	99.36 252	97.70 213	99.88 157	99.20 73	99.87 109	99.59 128

NR-MVSNet		99.40 76	99.31 84	99.68 86	99.43 227	99.55 118	99.73 16	99.50 215	99.46 100	99.88 32	99.36 252	97.54 226	99.87 170	98.97 105	99.87 109	99.63 95

LCM-MVSNet-Re		99.28 107	99.15 116	99.67 88	99.33 263	99.76 48	99.34 97	99.97 2	98.93 175	99.91 20	99.79 60	98.68 115	99.93 71	96.80 269	99.56 244	99.30 237

casdiffmvs		99.63 31	99.61 31	99.67 88	99.79 66	99.59 109	99.13 167	99.85 26	99.79 34	99.76 75	99.72 93	99.33 36	99.82 248	99.21 70	99.94 62	99.59 128

1112_ss		99.05 169	98.84 192	99.67 88	99.66 136	99.29 173	98.52 258	99.82 39	97.65 283	99.43 188	99.16 293	96.42 264	99.91 108	99.07 96	99.84 123	99.80 24

DeepPCF-MVS	98.42 6	99.18 142	99.02 157	99.67 88	99.22 284	99.75 51	97.25 345	99.47 226	98.72 201	99.66 115	99.70 107	99.29 39	99.63 338	98.07 176	99.81 151	99.62 106

DeepC-MVS	98.90 4	99.62 34	99.61 31	99.67 88	99.72 108	99.44 137	99.24 130	99.71 95	99.27 126	99.93 14	99.90 21	99.70 11	99.93 71	98.99 101	99.99 12	99.64 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP	97.51 14	99.05 169	98.84 192	99.67 88	99.78 72	99.55 118	98.88 211	99.66 117	97.11 311	99.47 179	99.60 176	99.07 66	99.89 143	96.18 299	99.85 119	99.58 133
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator+	98.92 3	99.35 90	99.24 104	99.67 88	99.35 248	99.47 126	99.62 48	99.50 215	99.44 103	99.12 254	99.78 66	98.77 106	99.94 57	97.87 193	99.72 198	99.62 106

v10		99.69 21	99.69 18	99.66 95	99.81 51	99.39 151	99.66 40	99.75 75	99.60 79	99.92 18	99.87 31	98.75 109	99.86 191	99.90 2	99.99 12	99.73 42

WR-MVS		99.11 159	98.93 177	99.66 95	99.30 269	99.42 144	98.42 268	99.37 259	99.04 163	99.57 148	99.20 290	96.89 253	99.86 191	98.66 135	99.87 109	99.70 49

XVG-OURS-SEG-HR		99.16 147	98.99 168	99.66 95	99.84 34	99.64 92	98.25 280	99.73 83	98.39 232	99.63 125	99.43 237	99.70 11	99.90 129	97.34 233	98.64 329	99.44 202

baseline		99.63 31	99.62 26	99.66 95	99.80 56	99.62 98	99.44 78	99.80 49	99.71 44	99.72 95	99.69 113	99.15 54	99.83 238	99.32 57	99.94 62	99.53 158

EPP-MVSNet		99.17 146	99.00 163	99.66 95	99.80 56	99.43 141	99.70 22	99.24 289	99.48 90	99.56 155	99.77 73	94.89 286	99.93 71	98.72 130	99.89 92	99.63 95

Anonymous20240529		99.42 69	99.34 78	99.65 100	99.53 182	99.60 106	99.63 47	99.39 252	99.47 95	99.76 75	99.78 66	98.13 183	99.86 191	98.70 131	99.68 210	99.49 181

v8		99.68 23	99.69 18	99.65 100	99.80 56	99.40 149	99.66 40	99.76 68	99.64 65	99.93 14	99.85 37	98.66 120	99.84 227	99.88 6	99.99 12	99.71 46

MCST-MVS		99.02 175	98.81 196	99.65 100	99.58 155	99.49 123	98.58 247	99.07 304	98.40 231	99.04 263	99.25 279	98.51 144	99.80 269	97.31 235	99.51 260	99.65 83

XVG-OURS		99.21 132	99.06 144	99.65 100	99.82 44	99.62 98	97.87 318	99.74 80	98.36 235	99.66 115	99.68 124	99.71 9	99.90 129	96.84 267	99.88 100	99.43 208

CHOSEN 1792x2688		99.39 81	99.30 89	99.65 100	99.88 24	99.25 183	98.78 232	99.88 18	98.66 204	99.96 8	99.79 60	97.45 229	99.93 71	99.34 52	99.99 12	99.78 32

QAPM		98.40 256	97.99 267	99.65 100	99.39 238	99.47 126	99.67 36	99.52 208	91.70 356	98.78 292	99.80 54	98.55 133	99.95 45	94.71 335	99.75 177	99.53 158

3Dnovator	99.15 2	99.43 66	99.36 76	99.65 100	99.39 238	99.42 144	99.70 22	99.56 180	99.23 134	99.35 210	99.80 54	99.17 52	99.95 45	98.21 162	99.84 123	99.59 128

lessismore_v0				99.64 107	99.86 30	99.38 154		90.66 368		99.89 26	99.83 43	94.56 291	99.97 17	99.56 25	99.92 74	99.57 139

114514_t		98.49 247	98.11 262	99.64 107	99.73 104	99.58 112	99.24 130	99.76 68	89.94 359	99.42 190	99.56 195	97.76 212	99.86 191	97.74 204	99.82 142	99.47 191

CPTT-MVS		98.74 216	98.44 231	99.64 107	99.61 147	99.38 154	99.18 146	99.55 186	96.49 322	99.27 228	99.37 247	97.11 247	99.92 90	95.74 317	99.67 217	99.62 106

RPSCF		99.18 142	99.02 157	99.64 107	99.83 38	99.85 13	99.44 78	99.82 39	98.33 245	99.50 175	99.78 66	97.90 200	99.65 335	96.78 270	99.83 133	99.44 202

Anonymous202405211		98.75 214	98.46 228	99.63 111	99.34 258	99.66 85	99.47 74	97.65 346	99.28 125	99.56 155	99.50 216	93.15 303	99.84 227	98.62 136	99.58 242	99.40 214

TSAR-MVS + MP.		99.34 95	99.24 104	99.63 111	99.82 44	99.37 157	99.26 122	99.35 263	98.77 196	99.57 148	99.70 107	99.27 44	99.88 157	97.71 206	99.75 177	99.65 83
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		99.26 113	99.13 120	99.63 111	99.70 118	99.61 104	98.58 247	99.48 222	98.50 221	99.52 169	99.63 151	99.14 56	99.76 285	97.89 189	99.77 171	99.51 170

AllTest		99.21 132	99.07 142	99.63 111	99.78 72	99.64 92	99.12 171	99.83 34	98.63 207	99.63 125	99.72 93	98.68 115	99.75 289	96.38 291	99.83 133	99.51 170

TestCases				99.63 111	99.78 72	99.64 92		99.83 34	98.63 207	99.63 125	99.72 93	98.68 115	99.75 289	96.38 291	99.83 133	99.51 170

V42		99.56 42	99.54 45	99.63 111	99.79 66	99.46 130	99.39 84	99.59 164	99.24 132	99.86 39	99.70 107	98.55 133	99.82 248	99.79 11	99.95 49	99.60 119

XVG-ACMP-BASELINE		99.23 118	99.10 135	99.63 111	99.82 44	99.58 112	98.83 220	99.72 92	98.36 235	99.60 140	99.71 100	98.92 81	99.91 108	97.08 254	99.84 123	99.40 214

Test_1112_low_res		98.95 191	98.73 201	99.63 111	99.68 130	99.15 203	98.09 294	99.80 49	97.14 309	99.46 182	99.40 241	96.11 274	99.89 143	99.01 100	99.84 123	99.84 14

TAMVS		99.49 52	99.45 57	99.63 111	99.48 209	99.42 144	99.45 75	99.57 175	99.66 61	99.78 68	99.83 43	97.85 206	99.86 191	99.44 37	99.96 42	99.61 115

SF-MVS		99.10 163	98.93 177	99.62 120	99.58 155	99.51 121	99.13 167	99.65 128	97.97 266	99.42 190	99.61 169	98.86 89	99.87 170	96.45 288	99.68 210	99.49 181

EG-PatchMatch MVS		99.57 39	99.56 44	99.62 120	99.77 80	99.33 167	99.26 122	99.76 68	99.32 120	99.80 60	99.78 66	99.29 39	99.87 170	99.15 84	99.91 83	99.66 75

F-COLMAP		98.74 216	98.45 229	99.62 120	99.57 165	99.47 126	98.84 218	99.65 128	96.31 326	98.93 271	99.19 292	97.68 217	99.87 170	96.52 283	99.37 283	99.53 158

CDPH-MVS		98.56 236	98.20 254	99.61 123	99.50 198	99.46 130	98.32 274	99.41 242	95.22 340	99.21 240	99.10 302	98.34 164	99.82 248	95.09 330	99.66 221	99.56 142

LS3D		99.24 117	99.11 127	99.61 123	98.38 351	99.79 36	99.57 62	99.68 108	99.61 73	99.15 249	99.71 100	98.70 113	99.91 108	97.54 222	99.68 210	99.13 273

tfpnnormal		99.43 66	99.38 70	99.60 125	99.87 28	99.75 51	99.59 59	99.78 60	99.71 44	99.90 22	99.69 113	98.85 91	99.90 129	97.25 244	99.78 167	99.15 266

CSCG		99.37 85	99.29 94	99.60 125	99.71 111	99.46 130	99.43 80	99.85 26	98.79 193	99.41 198	99.60 176	98.92 81	99.92 90	98.02 177	99.92 74	99.43 208

ETH3D-3000-0.1		98.77 211	98.50 226	99.59 127	99.47 214	99.53 120	98.77 233	99.60 157	97.33 300	99.23 234	99.50 216	97.91 199	99.83 238	95.02 331	99.67 217	99.41 212

v1144		99.54 47	99.53 49	99.59 127	99.79 66	99.28 175	99.10 174	99.61 146	99.20 138	99.84 43	99.73 87	98.67 118	99.84 227	99.86 8	99.98 21	99.64 90

UnsupCasMVSNet_eth		98.83 205	98.57 218	99.59 127	99.68 130	99.45 135	98.99 198	99.67 113	99.48 90	99.55 160	99.36 252	94.92 285	99.86 191	98.95 111	96.57 358	99.45 197

DROMVSNet		99.61 36	99.62 26	99.59 127	99.63 142	99.89 7	99.68 31	99.95 4	99.77 38	99.40 203	99.27 273	99.48 22	99.91 108	99.54 26	99.82 142	98.98 298

PHI-MVS		99.11 159	98.95 176	99.59 127	99.13 299	99.59 109	99.17 151	99.65 128	97.88 272	99.25 230	99.46 232	98.97 76	99.80 269	97.26 241	99.82 142	99.37 222

v144192		99.55 45	99.54 45	99.58 132	99.78 72	99.20 198	99.11 173	99.62 139	99.18 140	99.89 26	99.72 93	98.66 120	99.87 170	99.88 6	99.97 30	99.66 75

v2v482		99.50 50	99.47 53	99.58 132	99.78 72	99.25 183	99.14 161	99.58 173	99.25 130	99.81 57	99.62 160	98.24 172	99.84 227	99.83 9	99.97 30	99.64 90

test20.03		99.55 45	99.54 45	99.58 132	99.79 66	99.37 157	99.02 189	99.89 15	99.60 79	99.82 50	99.62 160	98.81 94	99.89 143	99.43 38	99.86 116	99.47 191

PM-MVS		99.36 88	99.29 94	99.58 132	99.83 38	99.66 85	98.95 206	99.86 22	98.85 185	99.81 57	99.73 87	98.40 158	99.92 90	98.36 148	99.83 133	99.17 262

NCCC		98.82 207	98.57 218	99.58 132	99.21 286	99.31 170	98.61 243	99.25 286	98.65 205	98.43 316	99.26 277	97.86 204	99.81 264	96.55 281	99.27 296	99.61 115

train_agg		98.35 261	97.95 271	99.57 137	99.35 248	99.35 164	98.11 292	99.41 242	94.90 344	97.92 337	98.99 316	98.02 191	99.85 210	95.38 325	99.44 270	99.50 176

agg_prior1		98.33 263	97.92 277	99.57 137	99.35 248	99.36 160	97.99 306	99.39 252	94.85 347	97.76 346	98.98 319	98.03 189	99.85 210	95.49 321	99.44 270	99.51 170

v1192		99.57 39	99.57 40	99.57 137	99.77 80	99.22 192	99.04 186	99.60 157	99.18 140	99.87 38	99.72 93	99.08 64	99.85 210	99.89 5	99.98 21	99.66 75

PMMVS2		99.48 54	99.45 57	99.57 137	99.76 84	98.99 218	98.09 294	99.90 13	98.95 171	99.78 68	99.58 184	99.57 20	99.93 71	99.48 34	99.95 49	99.79 30

VNet		99.18 142	99.06 144	99.56 141	99.24 282	99.36 160	99.33 99	99.31 272	99.67 57	99.47 179	99.57 192	96.48 261	99.84 227	99.15 84	99.30 291	99.47 191

CNVR-MVS		98.99 184	98.80 198	99.56 141	99.25 280	99.43 141	98.54 256	99.27 281	98.58 212	98.80 289	99.43 237	98.53 139	99.70 302	97.22 246	99.59 241	99.54 153

DeepC-MVS_fast	98.47 5	99.23 118	99.12 124	99.56 141	99.28 275	99.22 192	98.99 198	99.40 249	99.08 156	99.58 145	99.64 141	98.90 86	99.83 238	97.44 228	99.75 177	99.63 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v1921920		99.56 42	99.57 40	99.55 144	99.75 95	99.11 206	99.05 184	99.61 146	99.15 149	99.88 32	99.71 100	99.08 64	99.87 170	99.90 2	99.97 30	99.66 75

HQP_MVS		98.90 196	98.68 208	99.55 144	99.58 155	99.24 188	98.80 228	99.54 191	98.94 172	99.14 251	99.25 279	97.24 239	99.82 248	95.84 313	99.78 167	99.60 119

FMVSNet2		99.35 90	99.28 96	99.55 144	99.49 203	99.35 164	99.45 75	99.57 175	99.44 103	99.70 102	99.74 83	97.21 241	99.87 170	99.03 98	99.94 62	99.44 202

IS-MVSNet		99.03 173	98.85 190	99.55 144	99.80 56	99.25 183	99.73 16	99.15 300	99.37 113	99.61 138	99.71 100	94.73 289	99.81 264	97.70 208	99.88 100	99.58 133

xxxxxxxxxxxxxcwj		99.11 159	98.96 174	99.54 148	99.53 182	99.25 183	98.29 276	99.76 68	99.07 158	99.42 190	99.61 169	98.86 89	99.87 170	96.45 288	99.68 210	99.49 181

test12				99.54 148	99.29 272	99.33 167		99.16 299		98.43 316		97.54 226	99.82 248		99.47 267	99.48 186

Regformer-2		99.34 95	99.27 99	99.53 150	99.41 233	99.10 210	98.99 198	99.53 200	99.47 95	99.66 115	99.52 209	98.80 98	99.89 143	98.31 154	99.74 185	99.60 119

Effi-MVS+-dtu		99.07 165	98.92 181	99.52 151	98.89 325	99.78 39	99.15 159	99.66 117	99.34 116	98.92 274	99.24 284	97.69 215	99.98 7	98.11 173	99.28 293	98.81 312

新几何1				99.52 151	99.50 198	99.22 192		99.26 283	95.66 336	98.60 305	99.28 271	97.67 218	99.89 143	95.95 310	99.32 289	99.45 197

1121		98.56 236	98.24 250	99.52 151	99.49 203	99.24 188	99.30 109	99.22 291	95.77 333	98.52 311	99.29 269	97.39 233	99.85 210	95.79 315	99.34 286	99.46 195

ETH3D cwj APD-0.16		98.50 244	98.16 260	99.51 154	99.04 314	99.39 151	98.47 262	99.47 226	96.70 321	98.78 292	99.33 261	97.62 225	99.86 191	94.69 336	99.38 279	99.28 242

pmmvs-eth3d		99.48 54	99.47 53	99.51 154	99.77 80	99.41 148	98.81 225	99.66 117	99.42 110	99.75 80	99.66 134	99.20 49	99.76 285	98.98 103	99.99 12	99.36 225

v1240		99.56 42	99.58 37	99.51 154	99.80 56	99.00 217	99.00 193	99.65 128	99.15 149	99.90 22	99.75 80	99.09 61	99.88 157	99.90 2	99.96 42	99.67 65

ETH3 D test6400		97.76 285	97.19 300	99.50 157	99.38 241	99.26 179	98.34 271	99.49 220	92.99 353	98.54 310	99.20 290	95.92 278	99.82 248	91.14 354	99.66 221	99.40 214

Regformer-4		99.45 63	99.44 59	99.50 157	99.52 187	98.94 225	99.17 151	99.53 200	99.64 65	99.76 75	99.60 176	98.96 79	99.90 129	98.91 114	99.84 123	99.67 65

CDS-MVSNet		99.22 127	99.13 120	99.50 157	99.35 248	99.11 206	98.96 205	99.54 191	99.46 100	99.61 138	99.70 107	96.31 269	99.83 238	99.34 52	99.88 100	99.55 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20240521		99.44 65	99.42 65	99.49 160	99.89 21	98.96 223	99.62 48	99.76 68	99.85 20	99.82 50	99.88 28	96.39 267	99.97 17	99.59 20	99.98 21	99.55 145

Patchmtry		98.78 210	98.54 222	99.49 160	98.89 325	99.19 199	99.32 102	99.67 113	99.65 63	99.72 95	99.79 60	91.87 317	99.95 45	98.00 181	99.97 30	99.33 231

UGNet		99.38 83	99.34 78	99.49 160	98.90 322	98.90 233	99.70 22	99.35 263	99.86 16	98.57 308	99.81 52	98.50 145	99.93 71	99.38 47	99.98 21	99.66 75
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
Gipuma		99.57 39	99.59 34	99.49 160	99.98 3	99.71 67	99.72 19	99.84 32	99.81 29	99.94 11	99.78 66	98.91 83	99.71 300	98.41 145	99.95 49	99.05 289
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DELS-MVS		99.34 95	99.30 89	99.48 164	99.51 192	99.36 160	98.12 290	99.53 200	99.36 115	99.41 198	99.61 169	99.22 47	99.87 170	99.21 70	99.68 210	99.20 256
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
PLC	97.35 16	98.36 258	97.99 267	99.48 164	99.32 264	99.24 188	98.50 260	99.51 211	95.19 342	98.58 307	98.96 324	96.95 252	99.83 238	95.63 318	99.25 297	99.37 222
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous20231206		99.35 90	99.31 84	99.47 166	99.74 101	99.06 216	99.28 117	99.74 80	99.23 134	99.72 95	99.53 207	97.63 224	99.88 157	99.11 92	99.84 123	99.48 186

Regformer-1		99.32 101	99.27 99	99.47 166	99.41 233	98.95 224	98.99 198	99.48 222	99.48 90	99.66 115	99.52 209	98.78 103	99.87 170	98.36 148	99.74 185	99.60 119

ab-mvs		99.33 99	99.28 96	99.47 166	99.57 165	99.39 151	99.78 10	99.43 239	98.87 183	99.57 148	99.82 49	98.06 188	99.87 170	98.69 133	99.73 192	99.15 266

Fast-Effi-MVS+		99.02 175	98.87 188	99.46 169	99.38 241	99.50 122	99.04 186	99.79 55	97.17 307	98.62 303	98.74 340	99.34 35	99.95 45	98.32 153	99.41 276	98.92 303

test_prior3		98.62 227	98.34 242	99.46 169	99.35 248	99.22 192	97.95 311	99.39 252	97.87 273	98.05 332	99.05 306	97.90 200	99.69 308	95.99 306	99.49 264	99.48 186

test_prior				99.46 169	99.35 248	99.22 192		99.39 252					99.69 308			99.48 186

TAPA-MVS	97.92 13	98.03 277	97.55 291	99.46 169	99.47 214	99.44 137	98.50 260	99.62 139	86.79 360	99.07 261	99.26 277	98.26 171	99.62 339	97.28 238	99.73 192	99.31 236
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EIA-MVS		99.12 155	99.01 160	99.45 173	99.36 246	99.62 98	99.34 97	99.79 55	98.41 229	98.84 284	98.89 331	98.75 109	99.84 227	98.15 171	99.51 260	98.89 305

test_0402		99.22 127	99.14 117	99.45 173	99.79 66	99.43 141	99.28 117	99.68 108	99.54 83	99.40 203	99.56 195	99.07 66	99.82 248	96.01 304	99.96 42	99.11 274

hse-mvs3		98.61 228	98.34 242	99.44 175	99.60 149	98.67 245	99.27 120	99.44 235	99.68 53	99.32 217	99.49 221	92.50 311	100.00 1	99.24 67	96.51 359	99.65 83

VDD-MVS		99.20 134	99.11 127	99.44 175	99.43 227	98.98 219	99.50 68	98.32 337	99.80 32	99.56 155	99.69 113	96.99 251	99.85 210	98.99 101	99.73 192	99.50 176

PVSNet_Blended_VisFu		99.40 76	99.38 70	99.44 175	99.90 19	98.66 247	98.94 208	99.91 9	97.97 266	99.79 65	99.73 87	99.05 69	99.97 17	99.15 84	99.99 12	99.68 58

OMC-MVS		98.90 196	98.72 202	99.44 175	99.39 238	99.42 144	98.58 247	99.64 134	97.31 301	99.44 184	99.62 160	98.59 128	99.69 308	96.17 300	99.79 161	99.22 251

Fast-Effi-MVS+-dtu		99.20 134	99.12 124	99.43 179	99.25 280	99.69 78	99.05 184	99.82 39	99.50 88	98.97 267	99.05 306	98.98 74	99.98 7	98.20 163	99.24 299	98.62 318

MVP-Stereo		99.16 147	99.08 138	99.43 179	99.48 209	99.07 214	99.08 181	99.55 186	98.63 207	99.31 221	99.68 124	98.19 179	99.78 275	98.18 167	99.58 242	99.45 197
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs5		99.19 138	99.11 127	99.42 181	99.76 84	98.88 234	98.55 253	99.73 83	98.82 189	99.72 95	99.62 160	96.56 258	99.82 248	99.32 57	99.95 49	99.56 142

bset_n11_16_dypcd		98.69 222	98.45 229	99.42 181	99.69 121	98.52 256	96.06 359	96.80 354	99.71 44	99.73 93	99.54 204	95.14 284	99.96 35	99.39 46	99.95 49	99.79 30

EI-MVSNet-UG-set		99.48 54	99.50 51	99.42 181	99.57 165	98.65 250	99.24 130	99.46 230	99.68 53	99.80 60	99.66 134	98.99 73	99.89 143	99.19 75	99.90 84	99.72 43

EI-MVSNet-Vis-set		99.47 60	99.49 52	99.42 181	99.57 165	98.66 247	99.24 130	99.46 230	99.67 57	99.79 65	99.65 139	98.97 76	99.89 143	99.15 84	99.89 92	99.71 46

testdata				99.42 181	99.51 192	98.93 229		99.30 275	96.20 327	98.87 281	99.40 241	98.33 166	99.89 143	96.29 294	99.28 293	99.44 202

VDDNet		98.97 185	98.82 195	99.42 181	99.71 111	98.81 237	99.62 48	98.68 321	99.81 29	99.38 206	99.80 54	94.25 293	99.85 210	98.79 122	99.32 289	99.59 128

FMVSNet5		97.80 283	97.25 297	99.42 181	98.83 331	98.97 221	99.38 86	99.80 49	98.87 183	99.25 230	99.69 113	80.60 366	99.91 108	98.96 107	99.90 84	99.38 219

MVS_111021_LR		99.13 153	99.03 156	99.42 181	99.58 155	99.32 169	97.91 317	99.73 83	98.68 203	99.31 221	99.48 224	99.09 61	99.66 328	97.70 208	99.77 171	99.29 240

RRT_MVS		98.75 214	98.54 222	99.41 189	98.14 360	98.61 251	98.98 202	99.66 117	99.31 121	99.84 43	99.75 80	91.98 314	99.98 7	99.20 73	99.95 49	99.62 106

CMPMVS	77.52 23	98.50 244	98.19 257	99.41 189	98.33 353	99.56 115	99.01 191	99.59 164	95.44 337	99.57 148	99.80 54	95.64 280	99.46 357	96.47 287	99.92 74	99.21 253
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Regformer-3		99.41 73	99.41 66	99.40 191	99.52 187	98.70 243	99.17 151	99.44 235	99.62 69	99.75 80	99.60 176	98.90 86	99.85 210	98.89 115	99.84 123	99.65 83

UnsupCasMVSNet_bld		98.55 239	98.27 248	99.40 191	99.56 175	99.37 157	97.97 310	99.68 108	97.49 292	99.08 258	99.35 257	95.41 283	99.82 248	97.70 208	98.19 342	99.01 296

MVS_111021_HR		99.12 155	99.02 157	99.40 191	99.50 198	99.11 206	97.92 315	99.71 95	98.76 199	99.08 258	99.47 229	99.17 52	99.54 348	97.85 196	99.76 174	99.54 153

MVS_0304		98.88 200	98.71 203	99.39 194	98.85 329	98.91 232	99.45 75	99.30 275	98.56 213	97.26 352	99.68 124	96.18 273	99.96 35	99.17 80	99.94 62	99.29 240

v148		99.40 76	99.41 66	99.39 194	99.76 84	98.94 225	99.09 178	99.59 164	99.17 143	99.81 57	99.61 169	98.41 154	99.69 308	99.32 57	99.94 62	99.53 158

diffmvs		99.34 95	99.32 83	99.39 194	99.67 135	98.77 240	98.57 251	99.81 48	99.61 73	99.48 177	99.41 239	98.47 146	99.86 191	98.97 105	99.90 84	99.53 158

HQP-MVS		98.36 258	98.02 266	99.39 194	99.31 265	98.94 225	97.98 307	99.37 259	97.45 293	98.15 326	98.83 334	96.67 256	99.70 302	94.73 333	99.67 217	99.53 158

TSAR-MVS + GP.		99.12 155	99.04 154	99.38 198	99.34 258	99.16 201	98.15 286	99.29 277	98.18 255	99.63 125	99.62 160	99.18 51	99.68 319	98.20 163	99.74 185	99.30 237

AdaColmap		98.60 230	98.35 241	99.38 198	99.12 301	99.22 192	98.67 242	99.42 241	97.84 277	98.81 287	99.27 273	97.32 237	99.81 264	95.14 328	99.53 257	99.10 276

ITE_SJBPF				99.38 198	99.63 142	99.44 137		99.73 83	98.56 213	99.33 215	99.53 207	98.88 88	99.68 319	96.01 304	99.65 224	99.02 295

原ACMM1				99.37 201	99.47 214	98.87 236		99.27 281	96.74 320	98.26 321	99.32 262	97.93 198	99.82 248	95.96 309	99.38 279	99.43 208

testgi		99.29 106	99.26 101	99.37 201	99.75 95	98.81 237	98.84 218	99.89 15	98.38 233	99.75 80	99.04 309	99.36 34	99.86 191	99.08 95	99.25 297	99.45 197

MSDG		99.08 164	98.98 171	99.37 201	99.60 149	99.13 204	97.54 331	99.74 80	98.84 188	99.53 167	99.55 202	99.10 59	99.79 272	97.07 255	99.86 116	99.18 260

pmmvs4		99.13 153	99.06 144	99.36 204	99.57 165	99.10 210	98.01 302	99.25 286	98.78 195	99.58 145	99.44 236	98.24 172	99.76 285	98.74 128	99.93 70	99.22 251

N_pmnet		98.73 218	98.53 224	99.35 205	99.72 108	98.67 245	98.34 271	94.65 362	98.35 240	99.79 65	99.68 124	98.03 189	99.93 71	98.28 156	99.92 74	99.44 202

Effi-MVS+		99.06 166	98.97 172	99.34 206	99.31 265	98.98 219	98.31 275	99.91 9	98.81 190	98.79 290	98.94 326	99.14 56	99.84 227	98.79 122	98.74 325	99.20 256

Vis-MVSNet (Re-imp)		98.77 211	98.58 217	99.34 206	99.78 72	98.88 234	99.61 53	99.56 180	99.11 155	99.24 233	99.56 195	93.00 306	99.78 275	97.43 229	99.89 92	99.35 228

Patchmatch-RL test		98.60 230	98.36 239	99.33 208	99.77 80	99.07 214	98.27 278	99.87 20	98.91 178	99.74 89	99.72 93	90.57 334	99.79 272	98.55 139	99.85 119	99.11 274

PAPM_NR		98.36 258	98.04 265	99.33 208	99.48 209	98.93 229	98.79 231	99.28 280	97.54 288	98.56 309	98.57 345	97.12 246	99.69 308	94.09 342	98.90 316	99.38 219

PCF-MVS	96.03 18	96.73 312	95.86 323	99.33 208	99.44 224	99.16 201	96.87 354	99.44 235	86.58 361	98.95 269	99.40 241	94.38 292	99.88 157	87.93 359	99.80 156	98.95 300
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		98.76 213	98.57 218	99.33 208	99.57 165	98.97 221	97.53 333	99.55 186	96.41 323	99.27 228	99.13 295	99.07 66	99.78 275	96.73 273	99.89 92	99.23 249
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DPM-MVS		98.28 264	97.94 275	99.32 212	99.36 246	99.11 206	97.31 343	98.78 318	96.88 314	98.84 284	99.11 301	97.77 211	99.61 343	94.03 344	99.36 284	99.23 249

jason		99.16 147	99.11 127	99.32 212	99.75 95	98.44 261	98.26 279	99.39 252	98.70 202	99.74 89	99.30 266	98.54 135	99.97 17	98.48 142	99.82 142	99.55 145
jason: jason.
FMVSNet3		98.80 209	98.63 211	99.32 212	99.13 299	98.72 242	99.10 174	99.48 222	99.23 134	99.62 132	99.64 141	92.57 308	99.86 191	98.96 107	99.90 84	99.39 217

MVSFormer		99.41 73	99.44 59	99.31 215	99.57 165	98.40 264	99.77 11	99.80 49	99.73 40	99.63 125	99.30 266	98.02 191	99.98 7	99.43 38	99.69 207	99.55 145

DP-MVS Recon		98.50 244	98.23 251	99.31 215	99.49 203	99.46 130	98.56 252	99.63 136	94.86 346	98.85 283	99.37 247	97.81 208	99.59 345	96.08 301	99.44 270	98.88 306

PatchMatch-RL		98.68 223	98.47 227	99.30 217	99.44 224	99.28 175	98.14 288	99.54 191	97.12 310	99.11 255	99.25 279	97.80 209	99.70 302	96.51 284	99.30 291	98.93 302

OPU-MVS				99.29 218	99.12 301	99.44 137	99.20 140				99.40 241	99.00 72	98.84 363	96.54 282	99.60 239	99.58 133

CS-MVS		99.40 76	99.43 62	99.29 218	99.44 224	99.72 64	99.36 93	99.91 9	99.71 44	99.28 226	98.83 334	99.22 47	99.86 191	99.40 45	99.77 171	98.29 336

D2MVS		99.22 127	99.19 110	99.29 218	99.69 121	98.74 241	98.81 225	99.41 242	98.55 215	99.68 107	99.69 113	98.13 183	99.87 170	98.82 120	99.98 21	99.24 246

CANet		99.11 159	99.05 148	99.28 221	98.83 331	98.56 253	98.71 241	99.41 242	99.25 130	99.23 234	99.22 286	97.66 222	99.94 57	99.19 75	99.97 30	99.33 231

CNLPA		98.57 235	98.34 242	99.28 221	99.18 293	99.10 210	98.34 271	99.41 242	98.48 224	98.52 311	98.98 319	97.05 249	99.78 275	95.59 319	99.50 262	98.96 299

sss		98.90 196	98.77 200	99.27 223	99.48 209	98.44 261	98.72 239	99.32 268	97.94 270	99.37 207	99.35 257	96.31 269	99.91 108	98.85 117	99.63 228	99.47 191

LF4IMVS		99.01 179	98.92 181	99.27 223	99.71 111	99.28 175	98.59 246	99.77 63	98.32 246	99.39 205	99.41 239	98.62 124	99.84 227	96.62 280	99.84 123	98.69 316

LFMVS		98.46 250	98.19 257	99.26 225	99.24 282	98.52 256	99.62 48	96.94 353	99.87 14	99.31 221	99.58 184	91.04 325	99.81 264	98.68 134	99.42 275	99.45 197

WTY-MVS		98.59 233	98.37 238	99.26 225	99.43 227	98.40 264	98.74 236	99.13 303	98.10 257	99.21 240	99.24 284	94.82 287	99.90 129	97.86 194	98.77 321	99.49 181

OpenMVS	98.12 10	98.23 269	97.89 281	99.26 225	99.19 291	99.26 179	99.65 45	99.69 105	91.33 357	98.14 330	99.77 73	98.28 169	99.96 35	95.41 324	99.55 248	98.58 322

alignmvs		98.28 264	97.96 270	99.25 228	99.12 301	98.93 229	99.03 188	98.42 333	99.64 65	98.72 297	97.85 360	90.86 330	99.62 339	98.88 116	99.13 302	99.19 258

IterMVS-LS		99.41 73	99.47 53	99.25 228	99.81 51	98.09 283	98.85 217	99.76 68	99.62 69	99.83 48	99.64 141	98.54 135	99.97 17	99.15 84	99.99 12	99.68 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
lupinMVS		98.96 188	98.87 188	99.24 230	99.57 165	98.40 264	98.12 290	99.18 297	98.28 248	99.63 125	99.13 295	98.02 191	99.97 17	98.22 161	99.69 207	99.35 228

MVSTER		98.47 249	98.22 252	99.24 230	99.06 311	98.35 269	99.08 181	99.46 230	99.27 126	99.75 80	99.66 134	88.61 344	99.85 210	99.14 90	99.92 74	99.52 168

mvs-test1		98.83 205	98.70 206	99.22 232	98.89 325	99.65 90	98.88 211	99.66 117	99.34 116	98.29 319	98.94 326	97.69 215	99.96 35	98.11 173	98.54 333	98.04 347

EI-MVSNet		99.38 83	99.44 59	99.21 233	99.58 155	98.09 283	99.26 122	99.46 230	99.62 69	99.75 80	99.67 130	98.54 135	99.85 210	99.15 84	99.92 74	99.68 58

BH-RMVSNet		98.41 254	98.14 261	99.21 233	99.21 286	98.47 258	98.60 245	98.26 338	98.35 240	98.93 271	99.31 264	97.20 244	99.66 328	94.32 338	99.10 304	99.51 170

ambc				99.20 235	99.35 248	98.53 254	99.17 151	99.46 230		99.67 111	99.80 54	98.46 149	99.70 302	97.92 187	99.70 204	99.38 219

MVS_Test		99.28 107	99.31 84	99.19 236	99.35 248	98.79 239	99.36 93	99.49 220	99.17 143	99.21 240	99.67 130	98.78 103	99.66 328	99.09 94	99.66 221	99.10 276

MAR-MVS		98.24 268	97.92 277	99.19 236	98.78 339	99.65 90	99.17 151	99.14 301	95.36 338	98.04 334	98.81 337	97.47 228	99.72 296	95.47 323	99.06 305	98.21 341
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
EPNet		98.13 272	97.77 285	99.18 238	94.57 368	97.99 287	99.24 130	97.96 341	99.74 39	97.29 351	99.62 160	93.13 304	99.97 17	98.59 137	99.83 133	99.58 133
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
hse-mvs2		98.52 242	98.30 246	99.16 239	99.29 272	98.60 252	98.77 233	99.02 308	99.68 53	99.32 217	99.04 309	92.50 311	99.85 210	99.24 67	97.87 350	99.03 291

ETV-MVS		99.18 142	99.18 111	99.16 239	99.34 258	99.28 175	99.12 171	99.79 55	99.48 90	98.93 271	98.55 347	99.40 24	99.93 71	98.51 141	99.52 259	98.28 337

CL-MVSNet_2432*1600		98.71 220	98.56 221	99.15 241	99.22 284	98.66 247	97.14 348	99.51 211	98.09 259	99.54 162	99.27 273	96.87 254	99.74 291	98.43 144	98.96 311	99.03 291

AUN-MVS		97.82 282	97.38 293	99.14 242	99.27 277	98.53 254	98.72 239	99.02 308	98.10 257	97.18 354	99.03 313	89.26 343	99.85 210	97.94 186	97.91 348	99.03 291

test_yl		98.25 266	97.95 271	99.13 243	99.17 294	98.47 258	99.00 193	98.67 323	98.97 167	99.22 238	99.02 314	91.31 321	99.69 308	97.26 241	98.93 312	99.24 246

DCV-MVSNet		98.25 266	97.95 271	99.13 243	99.17 294	98.47 258	99.00 193	98.67 323	98.97 167	99.22 238	99.02 314	91.31 321	99.69 308	97.26 241	98.93 312	99.24 246

CS-MVS-test		99.20 134	99.22 107	99.12 245	99.30 269	99.78 39	99.35 95	99.90 13	99.47 95	98.98 266	98.52 349	98.83 93	99.87 170	99.10 93	99.55 248	97.72 351

MIMVSNet		98.43 252	98.20 254	99.11 246	99.53 182	98.38 267	99.58 61	98.61 325	98.96 170	99.33 215	99.76 76	90.92 327	99.81 264	97.38 232	99.76 174	99.15 266

PMMVS		98.49 247	98.29 247	99.11 246	98.96 319	98.42 263	97.54 331	99.32 268	97.53 289	98.47 315	98.15 357	97.88 203	99.82 248	97.46 227	99.24 299	99.09 279

CANet_DTU		98.91 194	98.85 190	99.09 248	98.79 337	98.13 278	98.18 283	99.31 272	99.48 90	98.86 282	99.51 213	96.56 258	99.95 45	99.05 97	99.95 49	99.19 258

MS-PatchMatch		99.00 181	98.97 172	99.09 248	99.11 306	98.19 275	98.76 235	99.33 266	98.49 223	99.44 184	99.58 184	98.21 176	99.69 308	98.20 163	99.62 229	99.39 217

canonicalmvs		99.02 175	99.00 163	99.09 248	99.10 307	98.70 243	99.61 53	99.66 117	99.63 68	98.64 302	97.65 362	99.04 70	99.54 348	98.79 122	98.92 314	99.04 290

PVSNet_BlendedMVS		99.03 173	99.01 160	99.09 248	99.54 177	97.99 287	98.58 247	99.82 39	97.62 284	99.34 213	99.71 100	98.52 142	99.77 283	97.98 182	99.97 30	99.52 168

MDA-MVSNet-bldmvs		99.06 166	99.05 148	99.07 252	99.80 56	97.83 294	98.89 210	99.72 92	99.29 122	99.63 125	99.70 107	96.47 262	99.89 143	98.17 169	99.82 142	99.50 176

TinyColmap		98.97 185	98.93 177	99.07 252	99.46 219	98.19 275	97.75 322	99.75 75	98.79 193	99.54 162	99.70 107	98.97 76	99.62 339	96.63 279	99.83 133	99.41 212

USDC		98.96 188	98.93 177	99.05 254	99.54 177	97.99 287	97.07 351	99.80 49	98.21 252	99.75 80	99.77 73	98.43 151	99.64 337	97.90 188	99.88 100	99.51 170

PAPR		97.56 293	97.07 302	99.04 255	98.80 336	98.11 281	97.63 327	99.25 286	94.56 350	98.02 335	98.25 356	97.43 230	99.68 319	90.90 355	98.74 325	99.33 231

PVSNet_Blended		98.70 221	98.59 214	99.02 256	99.54 177	97.99 287	97.58 330	99.82 39	95.70 335	99.34 213	98.98 319	98.52 142	99.77 283	97.98 182	99.83 133	99.30 237

MVS		95.72 332	94.63 336	98.99 257	98.56 347	97.98 292	99.30 109	98.86 313	72.71 365	97.30 350	99.08 303	98.34 164	99.74 291	89.21 356	98.33 337	99.26 243

HY-MVS	98.23 9	98.21 271	97.95 271	98.99 257	99.03 315	98.24 271	99.61 53	98.72 320	96.81 318	98.73 296	99.51 213	94.06 294	99.86 191	96.91 261	98.20 340	98.86 308

baseline1		97.73 286	97.33 294	98.96 259	99.30 269	97.73 298	99.40 82	98.42 333	99.33 119	99.46 182	99.21 288	91.18 323	99.82 248	98.35 150	91.26 364	99.32 234

DSMNet-mixed		99.48 54	99.65 23	98.95 260	99.71 111	97.27 310	99.50 68	99.82 39	99.59 81	99.41 198	99.85 37	99.62 16	100.00 1	99.53 29	99.89 92	99.59 128

thisisatest0530		97.45 295	96.95 306	98.94 261	99.68 130	97.73 298	99.09 178	94.19 365	98.61 210	99.56 155	99.30 266	84.30 361	99.93 71	98.27 157	99.54 255	99.16 264

mvs_anonymous		99.28 107	99.39 68	98.94 261	99.19 291	97.81 295	99.02 189	99.55 186	99.78 35	99.85 40	99.80 54	98.24 172	99.86 191	99.57 24	99.50 262	99.15 266

MG-MVS		98.52 242	98.39 236	98.94 261	99.15 296	97.39 308	98.18 283	99.21 295	98.89 182	99.23 234	99.63 151	97.37 235	99.74 291	94.22 340	99.61 236	99.69 52

GA-MVS		97.99 280	97.68 288	98.93 264	99.52 187	98.04 286	97.19 347	99.05 307	98.32 246	98.81 287	98.97 322	89.89 341	99.41 358	98.33 152	99.05 306	99.34 230

cl-mvsnet____		98.54 240	98.41 234	98.92 265	99.03 315	97.80 296	97.46 337	99.59 164	98.90 179	99.60 140	99.46 232	93.85 297	99.78 275	97.97 184	99.89 92	99.17 262

cl-mvsnet1		98.54 240	98.42 233	98.92 265	99.03 315	97.80 296	97.46 337	99.59 164	98.90 179	99.60 140	99.46 232	93.87 296	99.78 275	97.97 184	99.89 92	99.18 260

ET-MVSNet_ETH3D		96.78 310	96.07 319	98.91 267	99.26 279	97.92 293	97.70 325	96.05 358	97.96 269	92.37 366	98.43 352	87.06 348	99.90 129	98.27 157	97.56 353	98.91 304

xiu_mvs_v1_base_debu		99.23 118	99.34 78	98.91 267	99.59 152	98.23 272	98.47 262	99.66 117	99.61 73	99.68 107	98.94 326	99.39 25	99.97 17	99.18 77	99.55 248	98.51 326

xiu_mvs_v1_base		99.23 118	99.34 78	98.91 267	99.59 152	98.23 272	98.47 262	99.66 117	99.61 73	99.68 107	98.94 326	99.39 25	99.97 17	99.18 77	99.55 248	98.51 326

xiu_mvs_v1_base_debi		99.23 118	99.34 78	98.91 267	99.59 152	98.23 272	98.47 262	99.66 117	99.61 73	99.68 107	98.94 326	99.39 25	99.97 17	99.18 77	99.55 248	98.51 326

MSLP-MVS++		99.05 169	99.09 136	98.91 267	99.21 286	98.36 268	98.82 224	99.47 226	98.85 185	98.90 277	99.56 195	98.78 103	99.09 361	98.57 138	99.68 210	99.26 243

pmmvs3		98.08 275	97.80 282	98.91 267	99.41 233	97.69 300	97.87 318	99.66 117	95.87 331	99.50 175	99.51 213	90.35 336	99.97 17	98.55 139	99.47 267	99.08 282

tttt0517		97.62 290	97.20 299	98.90 273	99.76 84	97.40 307	99.48 72	94.36 363	99.06 162	99.70 102	99.49 221	84.55 360	99.94 57	98.73 129	99.65 224	99.36 225

OpenMVS_ROB	97.31 17	97.36 299	96.84 310	98.89 274	99.29 272	99.45 135	98.87 214	99.48 222	86.54 362	99.44 184	99.74 83	97.34 236	99.86 191	91.61 351	99.28 293	97.37 356

MDA-MVSNet_test_wron		98.95 191	98.99 168	98.85 275	99.64 140	97.16 313	98.23 281	99.33 266	98.93 175	99.56 155	99.66 134	97.39 233	99.83 238	98.29 155	99.88 100	99.55 145

PMVS	92.94 21	98.82 207	98.81 196	98.85 275	99.84 34	97.99 287	99.20 140	99.47 226	99.71 44	99.42 190	99.82 49	98.09 185	99.47 355	93.88 346	99.85 119	99.07 287
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
YYNet1		98.95 191	98.99 168	98.84 277	99.64 140	97.14 314	98.22 282	99.32 268	98.92 177	99.59 143	99.66 134	97.40 231	99.83 238	98.27 157	99.90 84	99.55 145

new_pmnet		98.88 200	98.89 186	98.84 277	99.70 118	97.62 301	98.15 286	99.50 215	97.98 265	99.62 132	99.54 204	98.15 182	99.94 57	97.55 221	99.84 123	98.95 300

CR-MVSNet		98.35 261	98.20 254	98.83 279	99.05 312	98.12 279	99.30 109	99.67 113	97.39 297	99.16 247	99.79 60	91.87 317	99.91 108	98.78 125	98.77 321	98.44 331

PatchT		98.45 251	98.32 245	98.83 279	98.94 320	98.29 270	99.24 130	98.82 316	99.84 23	99.08 258	99.76 76	91.37 320	99.94 57	98.82 120	99.00 310	98.26 338

RPMNet		98.60 230	98.53 224	98.83 279	99.05 312	98.12 279	99.30 109	99.62 139	99.86 16	99.16 247	99.74 83	92.53 310	99.92 90	98.75 127	98.77 321	98.44 331

miper_lstm_enhance		98.65 225	98.60 212	98.82 282	99.20 289	97.33 309	97.78 321	99.66 117	99.01 164	99.59 143	99.50 216	94.62 290	99.85 210	98.12 172	99.90 84	99.26 243

FPMVS		96.32 320	95.50 328	98.79 283	99.60 149	98.17 277	98.46 267	98.80 317	97.16 308	96.28 357	99.63 151	82.19 362	99.09 361	88.45 358	98.89 317	99.10 276

xiu_mvs_v2_base		99.02 175	99.11 127	98.77 284	99.37 244	98.09 283	98.13 289	99.51 211	99.47 95	99.42 190	98.54 348	99.38 29	99.97 17	98.83 118	99.33 288	98.24 339

PS-MVSNAJ		99.00 181	99.08 138	98.76 285	99.37 244	98.10 282	98.00 304	99.51 211	99.47 95	99.41 198	98.50 351	99.28 41	99.97 17	98.83 118	99.34 286	98.20 343

test0.0.03 1		97.37 298	96.91 309	98.74 286	97.72 361	97.57 302	97.60 329	97.36 352	98.00 262	99.21 240	98.02 358	90.04 339	99.79 272	98.37 147	95.89 362	98.86 308

cl_fuxian		98.72 219	98.71 203	98.72 287	99.12 301	97.22 312	97.68 326	99.56 180	98.90 179	99.54 162	99.48 224	96.37 268	99.73 294	97.88 190	99.88 100	99.21 253

EU-MVSNet		99.39 81	99.62 26	98.72 287	99.88 24	96.44 326	99.56 64	99.85 26	99.90 7	99.90 22	99.85 37	98.09 185	99.83 238	99.58 23	99.95 49	99.90 4

new-patchmatchnet		99.35 90	99.57 40	98.71 289	99.82 44	96.62 324	98.55 253	99.75 75	99.50 88	99.88 32	99.87 31	99.31 37	99.88 157	99.43 38	100.00 1	99.62 106

thisisatest0515		96.98 306	96.42 313	98.66 290	99.42 232	97.47 304	97.27 344	94.30 364	97.24 303	99.15 249	98.86 333	85.01 358	99.87 170	97.10 253	99.39 278	98.63 317

eth_miper_zixun_eth		98.68 223	98.71 203	98.60 291	99.10 307	96.84 321	97.52 335	99.54 191	98.94 172	99.58 145	99.48 224	96.25 271	99.76 285	98.01 180	99.93 70	99.21 253

miper_ehance_all_eth		98.59 233	98.59 214	98.59 292	98.98 318	97.07 315	97.49 336	99.52 208	98.50 221	99.52 169	99.37 247	96.41 266	99.71 300	97.86 194	99.62 229	99.00 297

BH-untuned		98.22 270	98.09 263	98.58 293	99.38 241	97.24 311	98.55 253	98.98 311	97.81 278	99.20 245	98.76 339	97.01 250	99.65 335	94.83 332	98.33 337	98.86 308

IterMVS-SCA-FT		99.00 181	99.16 113	98.51 294	99.75 95	95.90 334	98.07 297	99.84 32	99.84 23	99.89 26	99.73 87	96.01 276	99.99 5	99.33 55	100.00 1	99.63 95

JIA-IIPM		98.06 276	97.92 277	98.50 295	98.59 346	97.02 316	98.80 228	98.51 329	99.88 13	97.89 339	99.87 31	91.89 316	99.90 129	98.16 170	97.68 352	98.59 320

Patchmatch-test		98.10 274	97.98 269	98.48 296	99.27 277	96.48 325	99.40 82	99.07 304	98.81 190	99.23 234	99.57 192	90.11 338	99.87 170	96.69 274	99.64 226	99.09 279

baseline2		96.83 309	96.28 315	98.46 297	99.09 309	96.91 319	98.83 220	93.87 366	97.23 304	96.23 360	98.36 353	88.12 345	99.90 129	96.68 275	98.14 344	98.57 323

IterMVS		98.97 185	99.16 113	98.42 298	99.74 101	95.64 337	98.06 299	99.83 34	99.83 26	99.85 40	99.74 83	96.10 275	99.99 5	99.27 66	100.00 1	99.63 95
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl-mvsnet2		97.56 293	97.28 295	98.40 299	98.37 352	96.75 322	97.24 346	99.37 259	97.31 301	99.41 198	99.22 286	87.30 346	99.37 359	97.70 208	99.62 229	99.08 282

CHOSEN 280x420		98.41 254	98.41 234	98.40 299	99.34 258	95.89 335	96.94 353	99.44 235	98.80 192	99.25 230	99.52 209	93.51 301	99.98 7	98.94 112	99.98 21	99.32 234

API-MVS		98.38 257	98.39 236	98.35 301	98.83 331	99.26 179	99.14 161	99.18 297	98.59 211	98.66 301	98.78 338	98.61 126	99.57 347	94.14 341	99.56 244	96.21 360

PVSNet	97.47 15	98.42 253	98.44 231	98.35 301	99.46 219	96.26 328	96.70 356	99.34 265	97.68 282	99.00 265	99.13 295	97.40 231	99.72 296	97.59 220	99.68 210	99.08 282

miper_enhance_ethall		98.03 277	97.94 275	98.32 303	98.27 354	96.43 327	96.95 352	99.41 242	96.37 325	99.43 188	98.96 324	94.74 288	99.69 308	97.71 206	99.62 229	98.83 311

TR-MVS		97.44 296	97.15 301	98.32 303	98.53 348	97.46 305	98.47 262	97.91 343	96.85 316	98.21 325	98.51 350	96.42 264	99.51 353	92.16 350	97.29 354	97.98 348

PAPM		95.61 333	94.71 335	98.31 305	99.12 301	96.63 323	96.66 357	98.46 332	90.77 358	96.25 358	98.68 342	93.01 305	99.69 308	81.60 365	97.86 351	98.62 318

MVE	92.54 22	96.66 314	96.11 318	98.31 305	99.68 130	97.55 303	97.94 313	95.60 360	99.37 113	90.68 367	98.70 341	96.56 258	98.61 365	86.94 364	99.55 248	98.77 314
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
1314		98.00 279	97.90 280	98.27 307	98.90 322	97.45 306	99.30 109	99.06 306	94.98 343	97.21 353	99.12 299	98.43 151	99.67 324	95.58 320	98.56 332	97.71 352

ppachtmachnet_test		98.89 199	99.12 124	98.20 308	99.66 136	95.24 341	97.63 327	99.68 108	99.08 156	99.78 68	99.62 160	98.65 122	99.88 157	98.02 177	99.96 42	99.48 186

SD-MVS		99.01 179	99.30 89	98.15 309	99.50 198	99.40 149	98.94 208	99.61 146	99.22 137	99.75 80	99.82 49	99.54 21	95.51 367	97.48 226	99.87 109	99.54 153
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
our_test_3		98.85 204	99.09 136	98.13 310	99.66 136	94.90 344	97.72 323	99.58 173	99.07 158	99.64 121	99.62 160	98.19 179	99.93 71	98.41 145	99.95 49	99.55 145

ADS-MVSNet2		97.78 284	97.66 290	98.12 311	99.14 297	95.36 339	99.22 137	98.75 319	96.97 312	98.25 322	99.64 141	90.90 328	99.94 57	96.51 284	99.56 244	99.08 282

DeepMVS_CX				97.98 312	99.69 121	96.95 317		99.26 283	75.51 364	95.74 362	98.28 355	96.47 262	99.62 339	91.23 353	97.89 349	97.38 355

gg-mvs-nofinetune		95.87 329	95.17 333	97.97 313	98.19 356	96.95 317	99.69 28	89.23 370	99.89 11	96.24 359	99.94 12	81.19 363	99.51 353	93.99 345	98.20 340	97.44 354

thres600view7		96.60 315	96.16 317	97.93 314	99.63 142	96.09 332	99.18 146	97.57 347	98.77 196	98.72 297	97.32 366	87.04 349	99.72 296	88.57 357	98.62 330	97.98 348

thres400		96.40 317	95.89 321	97.92 315	99.58 155	96.11 330	99.00 193	97.54 350	98.43 226	98.52 311	96.98 369	86.85 351	99.67 324	87.62 360	98.51 334	97.98 348

ADS-MVSNet		97.72 288	97.67 289	97.86 316	99.14 297	94.65 345	99.22 137	98.86 313	96.97 312	98.25 322	99.64 141	90.90 328	99.84 227	96.51 284	99.56 244	99.08 282

IB-MVS	95.41 20	95.30 334	94.46 337	97.84 317	98.76 341	95.33 340	97.33 342	96.07 357	96.02 329	95.37 364	97.41 365	76.17 372	99.96 35	97.54 222	95.44 363	98.22 340
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
CVMVSNet		98.61 228	98.88 187	97.80 318	99.58 155	93.60 351	99.26 122	99.64 134	99.66 61	99.72 95	99.67 130	93.26 302	99.93 71	99.30 60	99.81 151	99.87 9

BH-w/o		97.20 301	97.01 304	97.76 319	99.08 310	95.69 336	98.03 301	98.52 328	95.76 334	97.96 336	98.02 358	95.62 281	99.47 355	92.82 349	97.25 355	98.12 345

tpm		97.15 302	96.95 306	97.75 320	98.91 321	94.24 347	99.32 102	97.96 341	97.71 281	98.29 319	99.32 262	86.72 355	99.92 90	98.10 175	96.24 361	99.09 279

test-LLR		97.15 302	96.95 306	97.74 321	98.18 357	95.02 342	97.38 339	96.10 355	98.00 262	97.81 343	98.58 343	90.04 339	99.91 108	97.69 214	98.78 319	98.31 334

test-mter		96.23 323	95.73 326	97.74 321	98.18 357	95.02 342	97.38 339	96.10 355	97.90 271	97.81 343	98.58 343	79.12 370	99.91 108	97.69 214	98.78 319	98.31 334

RRT_test8_iter05		97.35 300	97.25 297	97.63 323	98.81 335	93.13 353	99.26 122	99.89 15	99.51 87	99.83 48	99.68 124	79.03 371	99.88 157	99.53 29	99.72 198	99.89 8

tfpn200view9		96.30 321	95.89 321	97.53 324	99.58 155	96.11 330	99.00 193	97.54 350	98.43 226	98.52 311	96.98 369	86.85 351	99.67 324	87.62 360	98.51 334	96.81 358

cascas		96.99 305	96.82 311	97.48 325	97.57 364	95.64 337	96.43 358	99.56 180	91.75 355	97.13 355	97.61 363	95.58 282	98.63 364	96.68 275	99.11 303	98.18 344

thres100view900		96.39 318	96.03 320	97.47 326	99.63 142	95.93 333	99.18 146	97.57 347	98.75 200	98.70 299	97.31 367	87.04 349	99.67 324	87.62 360	98.51 334	96.81 358

PVSNet_0	95.53 19	95.85 330	95.31 332	97.47 326	98.78 339	93.48 352	95.72 360	99.40 249	96.18 328	97.37 349	97.73 361	95.73 279	99.58 346	95.49 321	81.40 365	99.36 225

TESTMET0.1,1		96.24 322	95.84 324	97.41 328	98.24 355	93.84 350	97.38 339	95.84 359	98.43 226	97.81 343	98.56 346	79.77 367	99.89 143	97.77 201	98.77 321	98.52 325

GG-mvs-BLEND				97.36 329	97.59 362	96.87 320	99.70 22	88.49 371		94.64 365	97.26 368	80.66 365	99.12 360	91.50 352	96.50 360	96.08 362

SCA		98.11 273	98.36 239	97.36 329	99.20 289	92.99 354	98.17 285	98.49 331	98.24 250	99.10 257	99.57 192	96.01 276	99.94 57	96.86 264	99.62 229	99.14 270

thres200		96.09 324	95.68 327	97.33 331	99.48 209	96.22 329	98.53 257	97.57 347	98.06 261	98.37 318	96.73 371	86.84 353	99.61 343	86.99 363	98.57 331	96.16 361

KD-MVS_2432*1600		95.89 327	95.41 330	97.31 332	94.96 366	93.89 348	97.09 349	99.22 291	97.23 304	98.88 278	99.04 309	79.23 368	99.54 348	96.24 297	96.81 356	98.50 329

miper_refine_blended		95.89 327	95.41 330	97.31 332	94.96 366	93.89 348	97.09 349	99.22 291	97.23 304	98.88 278	99.04 309	79.23 368	99.54 348	96.24 297	96.81 356	98.50 329

PatchmatchNet		97.65 289	97.80 282	97.18 334	98.82 334	92.49 356	99.17 151	98.39 335	98.12 256	98.79 290	99.58 184	90.71 332	99.89 143	97.23 245	99.41 276	99.16 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.53 316	96.32 314	97.17 335	98.18 357	92.97 355	99.39 84	89.95 369	98.21 252	98.61 304	99.59 182	86.69 356	99.72 296	96.99 257	99.23 301	98.81 312

EPNet_dtu		97.62 290	97.79 284	97.11 336	96.67 365	92.31 357	98.51 259	98.04 339	99.24 132	95.77 361	99.47 229	93.78 299	99.66 328	98.98 103	99.62 229	99.37 222
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt		95.75 331	95.42 329	96.76 337	89.90 370	94.42 346	98.86 215	97.87 344	78.01 363	99.30 225	99.69 113	97.70 213	95.89 366	99.29 63	98.14 344	99.95 1

MVS-HIRNet		97.86 281	98.22 252	96.76 337	99.28 275	91.53 363	98.38 270	92.60 367	99.13 151	99.31 221	99.96 10	97.18 245	99.68 319	98.34 151	99.83 133	99.07 287

tpm2		96.35 319	96.22 316	96.73 339	98.88 328	91.75 361	99.21 139	98.51 329	93.27 352	97.89 339	99.21 288	84.83 359	99.70 302	96.04 303	98.18 343	98.75 315

tpmrst		97.73 286	98.07 264	96.73 339	98.71 343	92.00 358	99.10 174	98.86 313	98.52 219	98.92 274	99.54 204	91.90 315	99.82 248	98.02 177	99.03 308	98.37 333

DWT-MVSNet_test		96.03 326	95.80 325	96.71 341	98.50 349	91.93 359	99.25 129	97.87 344	95.99 330	96.81 356	97.61 363	81.02 364	99.66 328	97.20 248	97.98 347	98.54 324

tpmvs		97.39 297	97.69 287	96.52 342	98.41 350	91.76 360	99.30 109	98.94 312	97.74 279	97.85 342	99.55 202	92.40 313	99.73 294	96.25 296	98.73 327	98.06 346

CostFormer		96.71 313	96.79 312	96.46 343	98.90 322	90.71 367	99.41 81	98.68 321	94.69 349	98.14 330	99.34 260	86.32 357	99.80 269	97.60 219	98.07 346	98.88 306

E-PMN		97.14 304	97.43 292	96.27 344	98.79 337	91.62 362	95.54 361	99.01 310	99.44 103	98.88 278	99.12 299	92.78 307	99.68 319	94.30 339	99.03 308	97.50 353

dp		96.86 308	97.07 302	96.24 345	98.68 345	90.30 369	99.19 145	98.38 336	97.35 299	98.23 324	99.59 182	87.23 347	99.82 248	96.27 295	98.73 327	98.59 320

tpm cat1		96.78 310	96.98 305	96.16 346	98.85 329	90.59 368	99.08 181	99.32 268	92.37 354	97.73 348	99.46 232	91.15 324	99.69 308	96.07 302	98.80 318	98.21 341

EMVS		96.96 307	97.28 295	95.99 347	98.76 341	91.03 365	95.26 362	98.61 325	99.34 116	98.92 274	98.88 332	93.79 298	99.66 328	92.87 348	99.05 306	97.30 357

wuyk23d		97.58 292	99.13 120	92.93 348	99.69 121	99.49 123	99.52 66	99.77 63	97.97 266	99.96 8	99.79 60	99.84 3	99.94 57	95.85 312	99.82 142	79.36 363

test_method		91.72 335	92.32 338	89.91 349	93.49 369	70.18 371	90.28 363	99.56 180	61.71 366	95.39 363	99.52 209	93.90 295	99.94 57	98.76 126	98.27 339	99.62 106

test123		29.31 336	33.05 341	18.08 350	25.93 372	12.24 372	97.53 333	10.93 373	11.78 367	24.21 368	50.08 376	21.04 373	8.60 368	23.51 366	32.43 367	33.39 364

testmvs		28.94 337	33.33 339	15.79 351	26.03 371	9.81 373	96.77 355	15.67 372	11.55 368	23.87 369	50.74 375	19.03 374	8.53 369	23.21 367	33.07 366	29.03 365

uanet_test		8.33 340	11.11 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	100.00 1	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

cdsmvs_eth3d_5k		24.88 338	33.17 340	0.00 352	0.00 373	0.00 374	0.00 364	99.62 139	0.00 369	0.00 370	99.13 295	99.82 4	0.00 370	0.00 368	0.00 368	0.00 366

pcd_1.5k_mvsjas		16.61 339	22.14 342	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	100.00 1	99.28 41	0.00 370	0.00 368	0.00 368	0.00 366

sosnet-low-res		8.33 340	11.11 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	100.00 1	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

sosnet		8.33 340	11.11 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	100.00 1	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uncertanet		8.33 340	11.11 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	100.00 1	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

Regformer		8.33 340	11.11 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	100.00 1	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

ab-mvs-re		8.26 346	11.02 349	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	99.16 293	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uanet		8.33 340	11.11 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	100.00 1	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

eth-test2					0.00 373

eth-test					0.00 373

ZD-MVS					99.43 227	99.61 104		99.43 239	96.38 324	99.11 255	99.07 304	97.86 204	99.92 90	94.04 343	99.49 264

RE-MVS-def			99.13 120		99.54 177	99.74 57	99.26 122	99.62 139	99.16 145	99.52 169	99.64 141	98.57 130		97.27 239	99.61 236	99.54 153

IU-MVS					99.69 121	99.77 42		99.22 291	97.50 291	99.69 105				97.75 203	99.70 204	99.77 33

test_241102_TWO								99.54 191	99.13 151	99.76 75	99.63 151	98.32 167	99.92 90	97.85 196	99.69 207	99.75 40

test_241102_ONE					99.69 121	99.82 26		99.54 191	99.12 154	99.82 50	99.49 221	98.91 83	99.52 352

9.14			98.64 209		99.45 222		98.81 225	99.60 157	97.52 290	99.28 226	99.56 195	98.53 139	99.83 238	95.36 326	99.64 226

save fliter					99.53 182	99.25 183	98.29 276	99.38 258	99.07 158

test_0728_THIRD									99.18 140	99.62 132	99.61 169	98.58 129	99.91 108	97.72 205	99.80 156	99.77 33

test0726					99.69 121	99.80 34	99.24 130	99.57 175	99.16 145	99.73 93	99.65 139	98.35 162

GSMVS																99.14 270

test_part2					99.62 146	99.67 83				99.55 160

sam_mvs1												90.81 331				99.14 270

sam_mvs												90.52 335

MTGPA								99.53 200

test_post1							99.14 161				51.63 374	89.54 342	99.82 248	96.86 264

test_post											52.41 373	90.25 337	99.86 191

patchmatchnet-post											99.62 160	90.58 333	99.94 57

MTMP							99.09 178	98.59 327

gm-plane-assit					97.59 362	89.02 370			93.47 351		98.30 354		99.84 227	96.38 291

test9_res														95.10 329	99.44 270	99.50 176

TEST9					99.35 248	99.35 164	98.11 292	99.41 242	94.83 348	97.92 337	98.99 316	98.02 191	99.85 210

test_8					99.34 258	99.31 170	98.08 296	99.40 249	94.90 344	97.87 341	98.97 322	98.02 191	99.84 227

agg_prior2														94.58 337	99.46 269	99.50 176

agg_prior					99.35 248	99.36 160		99.39 252		97.76 346			99.85 210

test_prior4						99.19 199	98.00 304

test_prior2							97.95 311		97.87 273	98.05 332	99.05 306	97.90 200		95.99 306	99.49 264

旧先验2							97.94 313		95.33 339	98.94 270			99.88 157	96.75 271

新几何2							98.04 300

旧先验1					99.49 203	99.29 173		99.26 283			99.39 245	97.67 218			99.36 284	99.46 195

无先验							98.01 302	99.23 290	95.83 332				99.85 210	95.79 315		99.44 202

原ACMM2							97.92 315

test222					99.51 192	99.08 213	97.83 320	99.29 277	95.21 341	98.68 300	99.31 264	97.28 238			99.38 279	99.43 208

testdata2													99.89 143	95.99 306

segment_acmp												98.37 160

testdata1							97.72 323		97.86 276

plane_prior7					99.58 155	99.38 154

plane_prior6					99.47 214	99.26 179						97.24 239

plane_prior5								99.54 191					99.82 248	95.84 313	99.78 167	99.60 119

plane_prior4											99.25 279

plane_prior3						99.31 170			98.36 235	99.14 251

plane_prior2							98.80 228		98.94 172

plane_prior1					99.51 192

plane_prior						99.24 188	98.42 268		97.87 273						99.71 202

n2								0.00 374

nn								0.00 374

door-mid								99.83 34

test11								99.29 277

door								99.77 63

HQP5-MVS						98.94 225

HQP-NCC					99.31 265		97.98 307		97.45 293	98.15 326

ACMP_Plane					99.31 265		97.98 307		97.45 293	98.15 326

BP-MVS														94.73 333

HQP4-MVS										98.15 326			99.70 302			99.53 158

HQP3-MVS								99.37 259							99.67 217

HQP2-MVS												96.67 256

NP-MVS					99.40 236	99.13 204					98.83 334

MDTV_nov1_ep13_2view						91.44 364	99.14 161		97.37 298	99.21 240		91.78 319		96.75 271		99.03 291

MDTV_nov1_ep13			97.73 286		98.70 344	90.83 366	99.15 159	98.02 340	98.51 220	98.82 286	99.61 169	90.98 326	99.66 328	96.89 263	98.92 314

ACMMP++_ref															99.94 62

ACMMP++															99.79 161

Test By Simon												98.41 154

High-resolution multi-view benchmark