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.

SED-MVS		99.61 2	99.52 6	99.88 6	99.84 32	99.90 1	99.60 71	99.48 139	99.08 11	99.91 1	99.81 62	99.20 5	99.96 18	98.91 67	99.85 58	99.79 53

test_241102_ONE					99.84 32	99.90 1		99.48 139	99.07 13	99.91 1	99.74 116	99.20 5	99.76 173

DVP-MVS		99.57 7	99.47 9	99.88 6	99.85 25	99.89 3	99.57 89	99.37 224	99.10 8	99.81 22	99.80 76	98.94 31	99.96 18	98.93 64	99.86 51	99.81 41
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_SECOND				99.91 2	99.84 32	99.89 3	99.57 89	99.51 101					99.96 18	98.93 64	99.86 51	99.88 5

test0726					99.85 25	99.89 3	99.62 64	99.50 119	99.10 8	99.86 11	99.82 49	98.94 31

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 49	99.89 3	99.75 25	99.56 55	99.02 15	99.88 5	99.85 29	99.18 8	99.96 18	99.22 34	99.92 11	99.90 1

IU-MVS					99.84 32	99.88 7		99.32 249	98.30 85	99.84 13				98.86 77	99.85 58	99.89 2

DPE-MVS		99.46 24	99.32 30	99.91 2	99.78 44	99.88 7	99.36 192	99.51 101	98.73 51	99.88 5	99.84 38	98.72 60	99.96 18	98.16 163	99.87 40	99.88 5

MP-MVS-pluss		99.37 48	99.20 59	99.88 6	99.90 3	99.87 9	99.30 206	99.52 88	97.18 207	99.60 80	99.79 88	98.79 47	99.95 42	98.83 84	99.91 16	99.83 29
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.47 22	99.34 26	99.88 6	99.87 15	99.86 10	99.47 145	99.48 139	98.05 120	99.76 37	99.86 23	98.82 44	99.93 68	98.82 88	99.91 16	99.84 18

zzz-MVS		99.49 15	99.36 21	99.89 4	99.90 3	99.86 10	99.36 192	99.47 157	98.79 47	99.68 53	99.81 62	98.43 81	99.97 10	98.88 70	99.90 23	99.83 29

MTAPA		99.52 13	99.39 17	99.89 4	99.90 3	99.86 10	99.66 46	99.47 157	98.79 47	99.68 53	99.81 62	98.43 81	99.97 10	98.88 70	99.90 23	99.83 29

HPM-MVS++		99.39 46	99.23 57	99.87 11	99.75 62	99.84 13	99.43 159	99.51 101	98.68 55	99.27 153	99.53 208	98.64 68	99.96 18	98.44 141	99.80 84	99.79 53

SR-MVS		99.43 33	99.29 44	99.86 18	99.75 62	99.83 14	99.59 77	99.62 33	98.21 96	99.73 43	99.79 88	98.68 63	99.96 18	98.44 141	99.77 92	99.79 53

SMA-MVS		99.44 30	99.30 40	99.85 25	99.73 75	99.83 14	99.56 96	99.47 157	97.45 181	99.78 31	99.82 49	99.18 8	99.91 90	98.79 90	99.89 33	99.81 41
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
test_part2					99.81 40	99.83 14				99.77 33

XVS		99.53 11	99.42 13	99.87 11	99.85 25	99.83 14	99.69 35	99.68 19	98.98 27	99.37 132	99.74 116	98.81 45	99.94 53	98.79 90	99.86 51	99.84 18

X-MVStestdata		96.55 287	95.45 301	99.87 11	99.85 25	99.83 14	99.69 35	99.68 19	98.98 27	99.37 132	64.01 361	98.81 45	99.94 53	98.79 90	99.86 51	99.84 18

APD-MVS_3200maxsize		99.48 19	99.35 24	99.85 25	99.76 52	99.83 14	99.63 58	99.54 70	98.36 78	99.79 26	99.82 49	98.86 40	99.95 42	98.62 113	99.81 80	99.78 61

test1172		99.43 33	99.29 44	99.85 25	99.75 62	99.82 20	99.60 71	99.56 55	98.28 86	99.74 41	99.79 88	98.53 72	99.95 42	98.55 130	99.78 89	99.79 53

SR-MVS-dyc-post		99.45 26	99.31 37	99.85 25	99.76 52	99.82 20	99.63 58	99.52 88	98.38 75	99.76 37	99.82 49	98.53 72	99.95 42	98.61 116	99.81 80	99.77 63

RE-MVS-def			99.34 26		99.76 52	99.82 20	99.63 58	99.52 88	98.38 75	99.76 37	99.82 49	98.75 56		98.61 116	99.81 80	99.77 63

MP-MVS		99.33 52	99.15 63	99.87 11	99.88 11	99.82 20	99.66 46	99.46 167	98.09 110	99.48 104	99.74 116	98.29 92	99.96 18	97.93 181	99.87 40	99.82 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		99.47 22	99.33 28	99.87 11	99.87 15	99.81 24	99.64 56	99.67 22	98.08 114	99.55 92	99.64 165	98.91 36	99.96 18	98.72 99	99.90 23	99.82 36

SteuartSystems-ACMMP		99.54 9	99.42 13	99.87 11	99.82 37	99.81 24	99.59 77	99.51 101	98.62 57	99.79 26	99.83 42	99.28 3	99.97 10	98.48 135	99.90 23	99.84 18
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		99.42 38	99.27 50	99.88 6	99.89 8	99.80 26	99.67 42	99.50 119	98.70 53	99.77 33	99.49 221	98.21 96	99.95 42	98.46 139	99.77 92	99.88 5
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
HFP-MVS		99.49 15	99.37 19	99.86 18	99.87 15	99.80 26	99.66 46	99.67 22	98.15 101	99.68 53	99.69 139	99.06 13	99.96 18	98.69 104	99.87 40	99.84 18

region2R		99.48 19	99.35 24	99.87 11	99.88 11	99.80 26	99.65 53	99.66 27	98.13 103	99.66 64	99.68 145	98.96 25	99.96 18	98.62 113	99.87 40	99.84 18

#test#		99.43 33	99.29 44	99.86 18	99.87 15	99.80 26	99.55 105	99.67 22	97.83 138	99.68 53	99.69 139	99.06 13	99.96 18	98.39 143	99.87 40	99.84 18

ZD-MVS					99.71 86	99.79 30		99.61 35	96.84 236	99.56 88	99.54 204	98.58 70	99.96 18	96.93 261	99.75 96

testtj		99.12 85	98.87 104	99.86 18	99.72 80	99.79 30	99.44 153	99.51 101	97.29 197	99.59 83	99.74 116	98.15 100	99.96 18	96.74 269	99.69 109	99.81 41

GST-MVS		99.40 45	99.24 55	99.85 25	99.86 21	99.79 30	99.60 71	99.67 22	97.97 126	99.63 70	99.68 145	98.52 74	99.95 42	98.38 145	99.86 51	99.81 41

ACMMPR		99.49 15	99.36 21	99.86 18	99.87 15	99.79 30	99.66 46	99.67 22	98.15 101	99.67 59	99.69 139	98.95 28	99.96 18	98.69 104	99.87 40	99.84 18

mPP-MVS		99.44 30	99.30 40	99.86 18	99.88 11	99.79 30	99.69 35	99.48 139	98.12 105	99.50 100	99.75 110	98.78 48	99.97 10	98.57 124	99.89 33	99.83 29

HPM-MVS_fast		99.51 14	99.40 16	99.85 25	99.91 1	99.79 30	99.76 24	99.56 55	97.72 152	99.76 37	99.75 110	99.13 10	99.92 79	99.07 50	99.92 11	99.85 14

APD-MVS		99.27 60	99.08 72	99.84 32	99.75 62	99.79 30	99.50 124	99.50 119	97.16 209	99.77 33	99.82 49	98.78 48	99.94 53	97.56 217	99.86 51	99.80 49
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		99.45 26	99.31 37	99.86 18	99.87 15	99.78 37	99.58 84	99.65 32	97.84 137	99.71 46	99.80 76	99.12 11	99.97 10	98.33 151	99.87 40	99.83 29

abl_6		99.44 30	99.31 37	99.83 33	99.85 25	99.75 38	99.66 46	99.59 43	98.13 103	99.82 20	99.81 62	98.60 69	99.96 18	98.46 139	99.88 36	99.79 53

CP-MVS		99.45 26	99.32 30	99.85 25	99.83 36	99.75 38	99.69 35	99.52 88	98.07 115	99.53 95	99.63 170	98.93 35	99.97 10	98.74 95	99.91 16	99.83 29

LS3D		99.27 60	99.12 67	99.74 56	99.18 235	99.75 38	99.56 96	99.57 50	98.45 69	99.49 103	99.85 29	97.77 110	99.94 53	98.33 151	99.84 65	99.52 146

MCST-MVS		99.43 33	99.30 40	99.82 35	99.79 42	99.74 41	99.29 210	99.40 205	98.79 47	99.52 97	99.62 176	98.91 36	99.90 105	98.64 111	99.75 96	99.82 36

OPU-MVS				99.64 77	99.56 142	99.72 42	99.60 71				99.70 132	99.27 4	99.42 243	98.24 156	99.80 84	99.79 53

HPM-MVS		99.42 38	99.28 48	99.83 33	99.90 3	99.72 42	99.81 12	99.54 70	97.59 164	99.68 53	99.63 170	98.91 36	99.94 53	98.58 122	99.91 16	99.84 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CDPH-MVS		99.13 79	98.91 99	99.80 40	99.75 62	99.71 44	99.15 245	99.41 199	96.60 254	99.60 80	99.55 199	98.83 43	99.90 105	97.48 224	99.83 72	99.78 61

CNVR-MVS		99.42 38	99.30 40	99.78 45	99.62 125	99.71 44	99.26 225	99.52 88	98.82 42	99.39 127	99.71 128	98.96 25	99.85 131	98.59 121	99.80 84	99.77 63

DP-MVS Recon		99.12 85	98.95 95	99.65 72	99.74 70	99.70 46	99.27 216	99.57 50	96.40 271	99.42 116	99.68 145	98.75 56	99.80 161	97.98 177	99.72 103	99.44 167

ETH3D-3000-0.1		99.21 66	99.02 82	99.77 47	99.73 75	99.69 47	99.38 185	99.51 101	97.45 181	99.61 76	99.75 110	98.51 75	99.91 90	97.45 229	99.83 72	99.71 93

nrg030		98.64 146	98.42 151	99.28 143	99.05 263	99.69 47	99.81 12	99.46 167	98.04 121	99.01 206	99.82 49	96.69 143	99.38 247	99.34 23	94.59 305	98.78 222

SF-MVS		99.38 47	99.24 55	99.79 43	99.79 42	99.68 49	99.57 89	99.54 70	97.82 143	99.71 46	99.80 76	98.95 28	99.93 68	98.19 158	99.84 65	99.74 73

SD-MVS		99.41 42	99.52 6	99.05 164	99.74 70	99.68 49	99.46 148	99.52 88	99.11 7	99.88 5	99.91 5	99.43 1	97.70 342	98.72 99	99.93 10	99.77 63
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
3Dnovator+	97.12 13	99.18 71	98.97 91	99.82 35	99.17 241	99.68 49	99.81 12	99.51 101	99.20 4	98.72 249	99.89 10	95.68 177	99.97 10	98.86 77	99.86 51	99.81 41

QAPM		98.67 143	98.30 160	99.80 40	99.20 230	99.67 52	99.77 21	99.72 11	94.74 316	98.73 248	99.90 7	95.78 173	99.98 5	96.96 258	99.88 36	99.76 68

ACMMP		99.45 26	99.32 30	99.82 35	99.89 8	99.67 52	99.62 64	99.69 18	98.12 105	99.63 70	99.84 38	98.73 59	99.96 18	98.55 130	99.83 72	99.81 41
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
TSAR-MVS + MP.		99.58 4	99.50 8	99.81 38	99.91 1	99.66 54	99.63 58	99.39 209	98.91 36	99.78 31	99.85 29	99.36 2	99.94 53	98.84 81	99.88 36	99.82 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MAR-MVS		98.86 119	98.63 133	99.54 92	99.37 188	99.66 54	99.45 149	99.54 70	96.61 252	99.01 206	99.40 247	97.09 128	99.86 125	97.68 207	99.53 130	99.10 190
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
3Dnovator	97.25 9	99.24 65	99.05 74	99.81 38	99.12 248	99.66 54	99.84 6	99.74 10	99.09 10	98.92 223	99.90 7	95.94 166	99.98 5	98.95 61	99.92 11	99.79 53

TEST9					99.67 100	99.65 57	99.05 264	99.41 199	96.22 282	98.95 218	99.49 221	98.77 51	99.91 90

train_agg		99.02 104	98.77 118	99.77 47	99.67 100	99.65 57	99.05 264	99.41 199	96.28 275	98.95 218	99.49 221	98.76 53	99.91 90	97.63 208	99.72 103	99.75 69

NCCC		99.34 51	99.19 60	99.79 43	99.61 129	99.65 57	99.30 206	99.48 139	98.86 38	99.21 169	99.63 170	98.72 60	99.90 105	98.25 155	99.63 122	99.80 49

test_part1		97.75 239	97.24 271	99.29 140	99.59 134	99.63 60	99.65 53	99.49 127	96.17 286	98.44 281	99.69 139	89.80 315	99.47 230	98.68 106	93.66 318	98.78 222

agg_prior1		99.01 107	98.76 120	99.76 50	99.67 100	99.62 61	98.99 280	99.40 205	96.26 278	98.87 231	99.49 221	98.77 51	99.91 90	97.69 205	99.72 103	99.75 69

agg_prior					99.67 100	99.62 61		99.40 205		98.87 231			99.91 90

test_8					99.67 100	99.61 63	99.03 270	99.41 199	96.28 275	98.93 222	99.48 227	98.76 53	99.91 90

test12				99.75 51	99.64 116	99.61 63		99.29 261		99.21 169		98.38 86	99.89 113		99.74 99	99.74 73

ETH3D cwj APD-0.16		99.06 98	98.84 110	99.72 61	99.51 150	99.60 65	99.23 230	99.44 187	97.04 221	99.39 127	99.67 151	98.30 91	99.92 79	97.27 236	99.69 109	99.64 118

ETH3 D test6400		98.70 139	98.35 155	99.73 58	99.69 95	99.60 65	99.16 241	99.45 179	95.42 304	99.27 153	99.60 183	97.39 117	99.91 90	95.36 302	99.83 72	99.70 95

1121		99.09 94	98.87 104	99.75 51	99.74 70	99.60 65	99.27 216	99.48 139	96.82 239	99.25 160	99.65 158	98.38 86	99.93 68	97.53 220	99.67 116	99.73 80

xxxxxxxxxxxxxcwj		99.43 33	99.32 30	99.75 51	99.76 52	99.59 68	99.14 247	99.53 82	99.00 22	99.71 46	99.80 76	98.95 28	99.93 68	98.19 158	99.84 65	99.74 73

save fliter					99.76 52	99.59 68	99.14 247	99.40 205	99.00 22

新几何1				99.75 51	99.75 62	99.59 68		99.54 70	96.76 240	99.29 148	99.64 165	98.43 81	99.94 53	96.92 263	99.66 117	99.72 86

旧先验1					99.74 70	99.59 68		99.54 70			99.69 139	98.47 78			99.68 114	99.73 80

DeepC-MVS_fast	98.69 1	99.49 15	99.39 17	99.77 47	99.63 119	99.59 68	99.36 192	99.46 167	99.07 13	99.79 26	99.82 49	98.85 41	99.92 79	98.68 106	99.87 40	99.82 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior4						99.56 73	98.99 280

VNet		99.11 90	98.90 100	99.73 58	99.52 148	99.56 73	99.41 168	99.39 209	99.01 18	99.74 41	99.78 95	95.56 180	99.92 79	99.52 6	98.18 207	99.72 86

DPM-MVS		98.95 112	98.71 124	99.66 68	99.63 119	99.55 75	98.64 322	99.10 282	97.93 129	99.42 116	99.55 199	98.67 66	99.80 161	95.80 291	99.68 114	99.61 126

UA-Net		99.42 38	99.29 44	99.80 40	99.62 125	99.55 75	99.50 124	99.70 15	98.79 47	99.77 33	99.96 1	97.45 116	99.96 18	98.92 66	99.90 23	99.89 2

FIs		98.78 134	98.63 133	99.23 150	99.18 235	99.54 77	99.83 9	99.59 43	98.28 86	98.79 243	99.81 62	96.75 141	99.37 250	99.08 49	96.38 267	98.78 222

VPA-MVSNet		98.29 167	97.95 189	99.30 137	99.16 243	99.54 77	99.50 124	99.58 49	98.27 88	99.35 138	99.37 255	92.53 272	99.65 211	99.35 19	94.46 306	98.72 236

AdaColmap		99.01 107	98.80 115	99.66 68	99.56 142	99.54 77	99.18 239	99.70 15	98.18 100	99.35 138	99.63 170	96.32 154	99.90 105	97.48 224	99.77 92	99.55 139

114514_t		98.93 113	98.67 128	99.72 61	99.85 25	99.53 80	99.62 64	99.59 43	92.65 334	99.71 46	99.78 95	98.06 103	99.90 105	98.84 81	99.91 16	99.74 73

DP-MVS		99.16 75	98.95 95	99.78 45	99.77 49	99.53 80	99.41 168	99.50 119	97.03 223	99.04 203	99.88 15	97.39 117	99.92 79	98.66 109	99.90 23	99.87 10

OpenMVS	96.50 16	98.47 151	98.12 169	99.52 103	99.04 264	99.53 80	99.82 10	99.72 11	94.56 319	98.08 299	99.88 15	94.73 211	99.98 5	97.47 226	99.76 95	99.06 200

Regformer-2		99.54 9	99.47 9	99.75 51	99.71 86	99.52 83	99.49 134	99.49 127	98.94 33	99.83 17	99.76 105	99.01 16	99.94 53	99.15 43	99.87 40	99.80 49

PHI-MVS		99.30 55	99.17 62	99.70 64	99.56 142	99.52 83	99.58 84	99.80 8	97.12 213	99.62 74	99.73 123	98.58 70	99.90 105	98.61 116	99.91 16	99.68 102

MVS_111021_LR		99.41 42	99.33 28	99.65 72	99.77 49	99.51 85	98.94 294	99.85 6	98.82 42	99.65 67	99.74 116	98.51 75	99.80 161	98.83 84	99.89 33	99.64 118

test222					99.75 62	99.49 86	98.91 297	99.49 127	96.42 269	99.34 141	99.65 158	98.28 93			99.69 109	99.72 86

test_prior3		99.21 66	99.05 74	99.68 65	99.67 100	99.48 87	98.96 288	99.56 55	98.34 80	99.01 206	99.52 211	98.68 63	99.83 145	97.96 178	99.74 99	99.74 73

test_prior				99.68 65	99.67 100	99.48 87		99.56 55					99.83 145			99.74 73

MVS_111021_HR		99.41 42	99.32 30	99.66 68	99.72 80	99.47 89	98.95 292	99.85 6	98.82 42	99.54 93	99.73 123	98.51 75	99.74 176	98.91 67	99.88 36	99.77 63

CPTT-MVS		99.11 90	98.90 100	99.74 56	99.80 41	99.46 90	99.59 77	99.49 127	97.03 223	99.63 70	99.69 139	97.27 124	99.96 18	97.82 190	99.84 65	99.81 41

FC-MVSNet-test		98.75 137	98.62 138	99.15 157	99.08 257	99.45 91	99.86 5	99.60 40	98.23 93	98.70 256	99.82 49	96.80 137	99.22 278	99.07 50	96.38 267	98.79 221

Regformer-1		99.53 11	99.47 9	99.72 61	99.71 86	99.44 92	99.49 134	99.46 167	98.95 32	99.83 17	99.76 105	99.01 16	99.93 68	99.17 40	99.87 40	99.80 49

PAPM_NR		99.04 101	98.84 110	99.66 68	99.74 70	99.44 92	99.39 180	99.38 215	97.70 154	99.28 150	99.28 278	98.34 89	99.85 131	96.96 258	99.45 132	99.69 98

alignmvs		98.81 130	98.56 145	99.58 87	99.43 173	99.42 94	99.51 118	98.96 297	98.61 58	99.35 138	98.92 315	94.78 205	99.77 170	99.35 19	98.11 213	99.54 141

Regformer-4		99.59 3	99.54 4	99.73 58	99.76 52	99.41 95	99.58 84	99.49 127	99.02 15	99.88 5	99.80 76	99.00 22	99.94 53	99.45 15	99.92 11	99.84 18

CNLPA		99.14 77	98.99 87	99.59 84	99.58 136	99.41 95	99.16 241	99.44 187	98.45 69	99.19 175	99.49 221	98.08 102	99.89 113	97.73 199	99.75 96	99.48 157

DELS-MVS		99.48 19	99.42 13	99.65 72	99.72 80	99.40 97	99.05 264	99.66 27	99.14 6	99.57 87	99.80 76	98.46 79	99.94 53	99.57 3	99.84 65	99.60 128
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
HyFIR lowres test		99.11 90	98.92 97	99.65 72	99.90 3	99.37 98	99.02 273	99.91 3	97.67 159	99.59 83	99.75 110	95.90 169	99.73 183	99.53 5	99.02 165	99.86 11

UniMVSNet (Re)		98.29 167	98.00 182	99.13 158	99.00 269	99.36 99	99.49 134	99.51 101	97.95 127	98.97 216	99.13 297	96.30 155	99.38 247	98.36 149	93.34 321	98.66 266

CS-MVS		99.21 66	99.13 65	99.45 115	99.54 145	99.34 100	99.71 31	99.54 70	98.26 89	98.99 213	99.24 284	98.25 94	99.88 118	98.98 57	99.63 122	99.12 189

原ACMM1				99.65 72	99.73 75	99.33 101		99.47 157	97.46 178	99.12 185	99.66 157	98.67 66	99.91 90	97.70 204	99.69 109	99.71 93

canonicalmvs		99.02 104	98.86 108	99.51 105	99.42 174	99.32 102	99.80 16	99.48 139	98.63 56	99.31 144	98.81 318	97.09 128	99.75 175	99.27 31	97.90 217	99.47 162

XXY-MVS		98.38 160	98.09 173	99.24 148	99.26 216	99.32 102	99.56 96	99.55 63	97.45 181	98.71 250	99.83 42	93.23 250	99.63 218	98.88 70	96.32 269	98.76 228

IS-MVSNet		99.05 100	98.87 104	99.57 88	99.73 75	99.32 102	99.75 25	99.20 271	98.02 124	99.56 88	99.86 23	96.54 147	99.67 204	98.09 167	99.13 154	99.73 80

API-MVS		99.04 101	99.03 79	99.06 162	99.40 182	99.31 105	99.55 105	99.56 55	98.54 61	99.33 142	99.39 251	98.76 53	99.78 168	96.98 256	99.78 89	98.07 326

ETV-MVS		99.26 62	99.21 58	99.40 122	99.46 167	99.30 106	99.56 96	99.52 88	98.52 63	99.44 112	99.27 281	98.41 85	99.86 125	99.10 47	99.59 126	99.04 201

Regformer-3		99.57 7	99.53 5	99.68 65	99.76 52	99.29 107	99.58 84	99.44 187	99.01 18	99.87 10	99.80 76	98.97 24	99.91 90	99.44 17	99.92 11	99.83 29

Fast-Effi-MVS+		98.70 139	98.43 150	99.51 105	99.51 150	99.28 108	99.52 114	99.47 157	96.11 293	99.01 206	99.34 264	96.20 158	99.84 136	97.88 184	98.82 178	99.39 173

PatchMatch-RL		98.84 129	98.62 138	99.52 103	99.71 86	99.28 108	99.06 262	99.77 9	97.74 151	99.50 100	99.53 208	95.41 184	99.84 136	97.17 247	99.64 120	99.44 167

F-COLMAP		99.19 69	99.04 77	99.64 77	99.78 44	99.27 110	99.42 166	99.54 70	97.29 197	99.41 120	99.59 186	98.42 84	99.93 68	98.19 158	99.69 109	99.73 80

NR-MVSNet		97.97 207	97.61 225	99.02 168	98.87 286	99.26 111	99.47 145	99.42 197	97.63 162	97.08 324	99.50 218	95.07 196	99.13 292	97.86 186	93.59 319	98.68 251

WR-MVS		98.06 189	97.73 214	99.06 162	98.86 289	99.25 112	99.19 238	99.35 230	97.30 196	98.66 259	99.43 238	93.94 239	99.21 283	98.58 122	94.28 310	98.71 238

CP-MVSNet		98.09 186	97.78 206	99.01 169	98.97 275	99.24 113	99.67 42	99.46 167	97.25 201	98.48 279	99.64 165	93.79 243	99.06 301	98.63 112	94.10 313	98.74 234

DeepC-MVS	98.35 2	99.30 55	99.19 60	99.64 77	99.82 37	99.23 114	99.62 64	99.55 63	98.94 33	99.63 70	99.95 2	95.82 172	99.94 53	99.37 18	99.97 3	99.73 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tfpnnormal		97.84 223	97.47 239	98.98 173	99.20 230	99.22 115	99.64 56	99.61 35	96.32 273	98.27 293	99.70 132	93.35 249	99.44 238	95.69 293	95.40 291	98.27 319

ab-mvs		98.86 119	98.63 133	99.54 92	99.64 116	99.19 116	99.44 153	99.54 70	97.77 146	99.30 145	99.81 62	94.20 230	99.93 68	99.17 40	98.82 178	99.49 156

MSDG		98.98 109	98.80 115	99.53 98	99.76 52	99.19 116	98.75 312	99.55 63	97.25 201	99.47 105	99.77 101	97.82 108	99.87 122	96.93 261	99.90 23	99.54 141

EIA-MVS		99.18 71	99.09 71	99.45 115	99.49 159	99.18 118	99.67 42	99.53 82	97.66 160	99.40 125	99.44 236	98.10 101	99.81 156	98.94 62	99.62 124	99.35 175

test_yl		98.86 119	98.63 133	99.54 92	99.49 159	99.18 118	99.50 124	99.07 287	98.22 94	99.61 76	99.51 215	95.37 186	99.84 136	98.60 119	98.33 197	99.59 132

DCV-MVSNet		98.86 119	98.63 133	99.54 92	99.49 159	99.18 118	99.50 124	99.07 287	98.22 94	99.61 76	99.51 215	95.37 186	99.84 136	98.60 119	98.33 197	99.59 132

CANet		99.25 64	99.14 64	99.59 84	99.41 177	99.16 121	99.35 197	99.57 50	98.82 42	99.51 99	99.61 180	96.46 149	99.95 42	99.59 1	99.98 2	99.65 112

MSLP-MVS++		99.46 24	99.47 9	99.44 120	99.60 132	99.16 121	99.41 168	99.71 13	98.98 27	99.45 108	99.78 95	99.19 7	99.54 227	99.28 29	99.84 65	99.63 122

casdiffmvs		99.13 79	98.98 90	99.56 90	99.65 114	99.16 121	99.56 96	99.50 119	98.33 83	99.41 120	99.86 23	95.92 167	99.83 145	99.45 15	99.16 150	99.70 95

WTY-MVS		99.06 98	98.88 103	99.61 82	99.62 125	99.16 121	99.37 188	99.56 55	98.04 121	99.53 95	99.62 176	96.84 136	99.94 53	98.85 79	98.49 194	99.72 86

EI-MVSNet-Vis-set		99.58 4	99.56 3	99.64 77	99.78 44	99.15 125	99.61 70	99.45 179	99.01 18	99.89 4	99.82 49	99.01 16	99.92 79	99.56 4	99.95 6	99.85 14

RRT_MVS		98.60 148	98.44 149	99.05 164	98.88 282	99.14 126	99.49 134	99.38 215	97.76 147	99.29 148	99.86 23	95.38 185	99.36 254	98.81 89	97.16 253	98.64 270

EI-MVSNet-UG-set		99.58 4	99.57 1	99.64 77	99.78 44	99.14 126	99.60 71	99.45 179	99.01 18	99.90 3	99.83 42	98.98 23	99.93 68	99.59 1	99.95 6	99.86 11

MVS_Test		99.10 93	98.97 91	99.48 109	99.49 159	99.14 126	99.67 42	99.34 234	97.31 195	99.58 85	99.76 105	97.65 113	99.82 152	98.87 74	99.07 161	99.46 164

baseline		99.15 76	99.02 82	99.53 98	99.66 109	99.14 126	99.72 29	99.48 139	98.35 79	99.42 116	99.84 38	96.07 160	99.79 164	99.51 7	99.14 153	99.67 105

Effi-MVS+		98.81 130	98.59 143	99.48 109	99.46 167	99.12 130	98.08 345	99.50 119	97.50 177	99.38 130	99.41 244	96.37 153	99.81 156	99.11 46	98.54 191	99.51 152

Vis-MVSNet		99.12 85	98.97 91	99.56 90	99.78 44	99.10 131	99.68 40	99.66 27	98.49 65	99.86 11	99.87 20	94.77 208	99.84 136	99.19 37	99.41 135	99.74 73
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PCF-MVS	97.08 14	97.66 256	97.06 276	99.47 112	99.61 129	99.09 132	98.04 346	99.25 265	91.24 339	98.51 276	99.70 132	94.55 220	99.91 90	92.76 332	99.85 58	99.42 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	97.30 7	98.85 127	98.64 132	99.47 112	99.42 174	99.08 133	99.62 64	99.36 225	97.39 190	99.28 150	99.68 145	96.44 151	99.92 79	98.37 147	98.22 203	99.40 172

PVSNet_Blended_VisFu		99.36 49	99.28 48	99.61 82	99.86 21	99.07 134	99.47 145	99.93 2	97.66 160	99.71 46	99.86 23	97.73 111	99.96 18	99.47 13	99.82 78	99.79 53

PS-CasMVS		97.93 209	97.59 228	98.95 177	98.99 270	99.06 135	99.68 40	99.52 88	97.13 211	98.31 290	99.68 145	92.44 278	99.05 302	98.51 133	94.08 314	98.75 230

EPP-MVSNet		99.13 79	98.99 87	99.53 98	99.65 114	99.06 135	99.81 12	99.33 241	97.43 185	99.60 80	99.88 15	97.14 126	99.84 136	99.13 44	98.94 169	99.69 98

PAPR		98.63 147	98.34 156	99.51 105	99.40 182	99.03 137	98.80 307	99.36 225	96.33 272	99.00 211	99.12 300	98.46 79	99.84 136	95.23 304	99.37 140	99.66 108

MVSTER		98.49 150	98.32 158	99.00 171	99.35 191	99.02 138	99.54 108	99.38 215	97.41 188	99.20 172	99.73 123	93.86 242	99.36 254	98.87 74	97.56 229	98.62 280

1112_ss		98.98 109	98.77 118	99.59 84	99.68 99	99.02 138	99.25 227	99.48 139	97.23 204	99.13 183	99.58 189	96.93 135	99.90 105	98.87 74	98.78 181	99.84 18

LFMVS		97.90 214	97.35 259	99.54 92	99.52 148	99.01 140	99.39 180	98.24 334	97.10 217	99.65 67	99.79 88	84.79 345	99.91 90	99.28 29	98.38 196	99.69 98

PLC	97.94 4	99.02 104	98.85 109	99.53 98	99.66 109	99.01 140	99.24 229	99.52 88	96.85 235	99.27 153	99.48 227	98.25 94	99.91 90	97.76 195	99.62 124	99.65 112
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UniMVSNet_NR-MVSNet		98.22 170	97.97 185	98.96 175	98.92 279	98.98 142	99.48 140	99.53 82	97.76 147	98.71 250	99.46 234	96.43 152	99.22 278	98.57 124	92.87 328	98.69 246

DU-MVS		98.08 188	97.79 203	98.96 175	98.87 286	98.98 142	99.41 168	99.45 179	97.87 132	98.71 250	99.50 218	94.82 202	99.22 278	98.57 124	92.87 328	98.68 251

FMVSNet3		98.03 195	97.76 211	98.84 205	99.39 185	98.98 142	99.40 176	99.38 215	96.67 246	99.07 197	99.28 278	92.93 255	98.98 312	97.10 249	96.65 258	98.56 296

xiu_mvs_v1_base_debu		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 249	99.51 101	98.86 38	99.84 13	99.47 230	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

xiu_mvs_v1_base		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 249	99.51 101	98.86 38	99.84 13	99.47 230	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

xiu_mvs_v1_base_debi		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 249	99.51 101	98.86 38	99.84 13	99.47 230	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

sss		99.17 73	99.05 74	99.53 98	99.62 125	98.97 145	99.36 192	99.62 33	97.83 138	99.67 59	99.65 158	97.37 121	99.95 42	99.19 37	99.19 149	99.68 102

anonymousdsp		98.44 153	98.28 161	98.94 178	98.50 323	98.96 149	99.77 21	99.50 119	97.07 218	98.87 231	99.77 101	94.76 209	99.28 268	98.66 109	97.60 225	98.57 295

diffmvs		99.14 77	99.02 82	99.51 105	99.61 129	98.96 149	99.28 212	99.49 127	98.46 68	99.72 45	99.71 128	96.50 148	99.88 118	99.31 26	99.11 155	99.67 105

testdata				99.54 92	99.75 62	98.95 151		99.51 101	97.07 218	99.43 113	99.70 132	98.87 39	99.94 53	97.76 195	99.64 120	99.72 86

MVS		97.28 275	96.55 283	99.48 109	98.78 297	98.95 151	99.27 216	99.39 209	83.53 349	98.08 299	99.54 204	96.97 133	99.87 122	94.23 316	99.16 150	99.63 122

Test_1112_low_res		98.89 115	98.66 131	99.57 88	99.69 95	98.95 151	99.03 270	99.47 157	96.98 225	99.15 181	99.23 286	96.77 140	99.89 113	98.83 84	98.78 181	99.86 11

PS-MVSNAJ		99.32 53	99.32 30	99.30 137	99.57 138	98.94 154	98.97 287	99.46 167	98.92 35	99.71 46	99.24 284	99.01 16	99.98 5	99.35 19	99.66 117	98.97 209

VPNet		97.84 223	97.44 247	99.01 169	99.21 228	98.94 154	99.48 140	99.57 50	98.38 75	99.28 150	99.73 123	88.89 323	99.39 245	99.19 37	93.27 323	98.71 238

MVSFormer		99.17 73	99.12 67	99.29 140	99.51 150	98.94 154	99.88 1	99.46 167	97.55 169	99.80 24	99.65 158	97.39 117	99.28 268	99.03 52	99.85 58	99.65 112

lupinMVS		99.13 79	99.01 86	99.46 114	99.51 150	98.94 154	99.05 264	99.16 276	97.86 133	99.80 24	99.56 196	97.39 117	99.86 125	98.94 62	99.85 58	99.58 136

xiu_mvs_v2_base		99.26 62	99.25 54	99.29 140	99.53 146	98.91 158	99.02 273	99.45 179	98.80 46	99.71 46	99.26 282	98.94 31	99.98 5	99.34 23	99.23 146	98.98 208

test_djsdf		98.67 143	98.57 144	98.98 173	98.70 308	98.91 158	99.88 1	99.46 167	97.55 169	99.22 166	99.88 15	95.73 175	99.28 268	99.03 52	97.62 224	98.75 230

Vis-MVSNet (Re-imp)		98.87 116	98.72 122	99.31 133	99.71 86	98.88 160	99.80 16	99.44 187	97.91 131	99.36 135	99.78 95	95.49 183	99.43 242	97.91 182	99.11 155	99.62 124

pmmvs4		98.13 182	97.90 194	98.81 209	98.61 317	98.87 161	98.99 280	99.21 270	96.44 267	99.06 201	99.58 189	95.90 169	99.11 297	97.18 246	96.11 272	98.46 307

jason		99.13 79	99.03 79	99.45 115	99.46 167	98.87 161	99.12 249	99.26 263	98.03 123	99.79 26	99.65 158	97.02 131	99.85 131	99.02 54	99.90 23	99.65 112
jason: jason.
Patchmtry		97.75 239	97.40 253	98.81 209	99.10 253	98.87 161	99.11 255	99.33 241	94.83 314	98.81 239	99.38 252	94.33 226	99.02 307	96.10 284	95.57 287	98.53 297

TransMVSNet (Re)		97.15 278	96.58 282	98.86 201	99.12 248	98.85 164	99.49 134	98.91 304	95.48 303	97.16 322	99.80 76	93.38 248	99.11 297	94.16 318	91.73 333	98.62 280

V42		98.06 189	97.79 203	98.86 201	98.98 273	98.84 165	99.69 35	99.34 234	96.53 258	99.30 145	99.37 255	94.67 214	99.32 263	97.57 216	94.66 303	98.42 310

WR-MVS_H		98.13 182	97.87 199	98.90 188	99.02 267	98.84 165	99.70 33	99.59 43	97.27 199	98.40 284	99.19 291	95.53 181	99.23 275	98.34 150	93.78 317	98.61 289

FMVSNet2		97.72 245	97.36 257	98.80 211	99.51 150	98.84 165	99.45 149	99.42 197	96.49 260	98.86 236	99.29 276	90.26 308	98.98 312	96.44 279	96.56 261	98.58 294

BH-RMVSNet		98.41 157	98.08 174	99.40 122	99.41 177	98.83 168	99.30 206	98.77 314	97.70 154	98.94 220	99.65 158	92.91 258	99.74 176	96.52 277	99.55 129	99.64 118

ET-MVSNet_ETH3D		96.49 289	95.64 299	99.05 164	99.53 146	98.82 169	98.84 303	97.51 346	97.63 162	84.77 349	99.21 290	92.09 282	98.91 322	98.98 57	92.21 332	99.41 171

v2v482		98.06 189	97.77 208	98.92 182	98.90 280	98.82 169	99.57 89	99.36 225	96.65 248	99.19 175	99.35 261	94.20 230	99.25 273	97.72 201	94.97 300	98.69 246

v8		97.95 208	97.63 224	98.93 180	98.95 277	98.81 171	99.80 16	99.41 199	96.03 298	99.10 190	99.42 241	94.92 198	99.30 266	96.94 260	94.08 314	98.66 266

PVSNet_BlendedMVS		98.86 119	98.80 115	99.03 167	99.76 52	98.79 172	99.28 212	99.91 3	97.42 187	99.67 59	99.37 255	97.53 114	99.88 118	98.98 57	97.29 248	98.42 310

PVSNet_Blended		99.08 96	98.97 91	99.42 121	99.76 52	98.79 172	98.78 309	99.91 3	96.74 241	99.67 59	99.49 221	97.53 114	99.88 118	98.98 57	99.85 58	99.60 128

baseline1		98.31 164	97.95 189	99.38 125	99.50 157	98.74 174	99.59 77	98.93 299	98.41 73	99.14 182	99.60 183	94.59 217	99.79 164	98.48 135	93.29 322	99.61 126

CDS-MVSNet		99.09 94	99.03 79	99.25 146	99.42 174	98.73 175	99.45 149	99.46 167	98.11 107	99.46 107	99.77 101	98.01 104	99.37 250	98.70 101	98.92 172	99.66 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UGNet		98.87 116	98.69 126	99.40 122	99.22 226	98.72 176	99.44 153	99.68 19	99.24 3	99.18 178	99.42 241	92.74 262	99.96 18	99.34 23	99.94 9	99.53 145
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
PMMVS		98.80 133	98.62 138	99.34 127	99.27 214	98.70 177	98.76 311	99.31 252	97.34 192	99.21 169	99.07 302	97.20 125	99.82 152	98.56 127	98.87 175	99.52 146

v1192		97.81 230	97.44 247	98.91 186	98.88 282	98.68 178	99.51 118	99.34 234	96.18 285	99.20 172	99.34 264	94.03 237	99.36 254	95.32 303	95.18 295	98.69 246

v10		97.85 220	97.52 233	98.86 201	98.99 270	98.67 179	99.75 25	99.41 199	95.70 301	98.98 214	99.41 244	94.75 210	99.23 275	96.01 287	94.63 304	98.67 258

v1144		97.98 204	97.69 217	98.85 204	98.87 286	98.66 180	99.54 108	99.35 230	96.27 277	99.23 165	99.35 261	94.67 214	99.23 275	96.73 270	95.16 296	98.68 251

v144192		97.92 212	97.60 226	98.87 198	98.83 292	98.65 181	99.55 105	99.34 234	96.20 283	99.32 143	99.40 247	94.36 225	99.26 272	96.37 282	95.03 299	98.70 242

1314		98.68 142	98.54 146	99.11 159	98.89 281	98.65 181	99.27 216	99.49 127	96.89 233	97.99 304	99.56 196	97.72 112	99.83 145	97.74 198	99.27 144	98.84 218

MG-MVS		99.13 79	99.02 82	99.45 115	99.57 138	98.63 183	99.07 259	99.34 234	98.99 25	99.61 76	99.82 49	97.98 105	99.87 122	97.00 254	99.80 84	99.85 14

pm-mvs1		97.68 252	97.28 268	98.88 194	99.06 260	98.62 184	99.50 124	99.45 179	96.32 273	97.87 307	99.79 88	92.47 274	99.35 258	97.54 219	93.54 320	98.67 258

TranMVSNet+NR-MVSNet		97.93 209	97.66 220	98.76 215	98.78 297	98.62 184	99.65 53	99.49 127	97.76 147	98.49 278	99.60 183	94.23 229	98.97 319	98.00 176	92.90 326	98.70 242

TSAR-MVS + GP.		99.36 49	99.36 21	99.36 126	99.67 100	98.61 186	99.07 259	99.33 241	99.00 22	99.82 20	99.81 62	99.06 13	99.84 136	99.09 48	99.42 134	99.65 112

v7n		97.87 217	97.52 233	98.92 182	98.76 301	98.58 187	99.84 6	99.46 167	96.20 283	98.91 224	99.70 132	94.89 200	99.44 238	96.03 286	93.89 316	98.75 230

thisisatest0530		98.35 162	98.03 179	99.31 133	99.63 119	98.56 188	99.54 108	96.75 351	97.53 174	99.73 43	99.65 158	91.25 301	99.89 113	98.62 113	99.56 127	99.48 157

TAMVS		99.12 85	99.08 72	99.24 148	99.46 167	98.55 189	99.51 118	99.46 167	98.09 110	99.45 108	99.82 49	98.34 89	99.51 228	98.70 101	98.93 170	99.67 105

PEN-MVS		97.76 235	97.44 247	98.72 218	98.77 300	98.54 190	99.78 19	99.51 101	97.06 220	98.29 292	99.64 165	92.63 269	98.89 324	98.09 167	93.16 324	98.72 236

Anonymous20231211		97.88 215	97.54 232	98.90 188	99.71 86	98.53 191	99.48 140	99.57 50	94.16 322	98.81 239	99.68 145	93.23 250	99.42 243	98.84 81	94.42 308	98.76 228

v1921920		97.80 232	97.45 242	98.84 205	98.80 293	98.53 191	99.52 114	99.34 234	96.15 290	99.24 161	99.47 230	93.98 238	99.29 267	95.40 300	95.13 297	98.69 246

PS-MVSNAJss		98.92 114	98.92 97	98.90 188	98.78 297	98.53 191	99.78 19	99.54 70	98.07 115	99.00 211	99.76 105	99.01 16	99.37 250	99.13 44	97.23 249	98.81 219

COLMAP_ROB	97.56 6	98.86 119	98.75 121	99.17 154	99.88 11	98.53 191	99.34 200	99.59 43	97.55 169	98.70 256	99.89 10	95.83 171	99.90 105	98.10 166	99.90 23	99.08 195
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs_anonymous		99.03 103	98.99 87	99.16 155	99.38 186	98.52 195	99.51 118	99.38 215	97.79 144	99.38 130	99.81 62	97.30 122	99.45 233	99.35 19	98.99 167	99.51 152

CHOSEN 1792x2688		99.19 69	99.10 69	99.45 115	99.89 8	98.52 195	99.39 180	99.94 1	98.73 51	99.11 187	99.89 10	95.50 182	99.94 53	99.50 8	99.97 3	99.89 2

mvs_tets		98.40 159	98.23 163	98.91 186	98.67 311	98.51 197	99.66 46	99.53 82	98.19 97	98.65 265	99.81 62	92.75 260	99.44 238	99.31 26	97.48 239	98.77 226

thisisatest0515		98.14 181	97.79 203	99.19 152	99.50 157	98.50 198	98.61 323	96.82 350	96.95 229	99.54 93	99.43 238	91.66 294	99.86 125	98.08 171	99.51 131	99.22 183

CR-MVSNet		98.17 177	97.93 192	98.87 198	99.18 235	98.49 199	99.22 235	99.33 241	96.96 227	99.56 88	99.38 252	94.33 226	99.00 310	94.83 310	98.58 187	99.14 186

RPMNet		96.72 285	95.90 295	99.19 152	99.18 235	98.49 199	99.22 235	99.52 88	88.72 345	99.56 88	97.38 341	94.08 236	99.95 42	86.87 350	98.58 187	99.14 186

AllTest		98.87 116	98.72 122	99.31 133	99.86 21	98.48 201	99.56 96	99.61 35	97.85 135	99.36 135	99.85 29	95.95 164	99.85 131	96.66 275	99.83 72	99.59 132

TestCases				99.31 133	99.86 21	98.48 201		99.61 35	97.85 135	99.36 135	99.85 29	95.95 164	99.85 131	96.66 275	99.83 72	99.59 132

Anonymous20240529		98.09 186	97.68 218	99.34 127	99.66 109	98.44 203	99.40 176	99.43 195	93.67 326	99.22 166	99.89 10	90.23 311	99.93 68	99.26 32	98.33 197	99.66 108

jajsoiax		98.43 154	98.28 161	98.88 194	98.60 318	98.43 204	99.82 10	99.53 82	98.19 97	98.63 267	99.80 76	93.22 252	99.44 238	99.22 34	97.50 235	98.77 226

v1240		97.69 250	97.32 265	98.79 212	98.85 290	98.43 204	99.48 140	99.36 225	96.11 293	99.27 153	99.36 258	93.76 245	99.24 274	94.46 313	95.23 294	98.70 242

CANet_DTU		98.97 111	98.87 104	99.25 146	99.33 196	98.42 206	99.08 258	99.30 256	99.16 5	99.43 113	99.75 110	95.27 190	99.97 10	98.56 127	99.95 6	99.36 174

tttt0517		98.42 155	98.14 167	99.28 143	99.66 109	98.38 207	99.74 28	96.85 349	97.68 156	99.79 26	99.74 116	91.39 298	99.89 113	98.83 84	99.56 127	99.57 137

PatchT		97.03 281	96.44 285	98.79 212	98.99 270	98.34 208	99.16 241	99.07 287	92.13 335	99.52 97	97.31 344	94.54 221	98.98 312	88.54 344	98.73 183	99.03 202

Baseline_NR-MVSNet		97.76 235	97.45 242	98.68 221	99.09 255	98.29 209	99.41 168	98.85 310	95.65 302	98.63 267	99.67 151	94.82 202	99.10 299	98.07 174	92.89 327	98.64 270

CSCG		99.32 53	99.32 30	99.32 132	99.85 25	98.29 209	99.71 31	99.66 27	98.11 107	99.41 120	99.80 76	98.37 88	99.96 18	98.99 56	99.96 5	99.72 86

bset_n11_16_dypcd		98.16 178	97.97 185	98.73 216	98.26 328	98.28 211	97.99 347	98.01 339	97.68 156	99.10 190	99.63 170	95.68 177	99.15 288	98.78 93	96.55 262	98.75 230

PAPM		97.59 260	97.09 275	99.07 161	99.06 260	98.26 212	98.30 340	99.10 282	94.88 313	98.08 299	99.34 264	96.27 156	99.64 213	89.87 340	98.92 172	99.31 179

OMC-MVS		99.08 96	99.04 77	99.20 151	99.67 100	98.22 213	99.28 212	99.52 88	98.07 115	99.66 64	99.81 62	97.79 109	99.78 168	97.79 192	99.81 80	99.60 128

EPNet		98.86 119	98.71 124	99.30 137	97.20 342	98.18 214	99.62 64	98.91 304	99.28 2	98.63 267	99.81 62	95.96 163	99.99 1	99.24 33	99.72 103	99.73 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous202405211		98.30 166	97.98 184	99.26 145	99.57 138	98.16 215	99.41 168	98.55 330	96.03 298	99.19 175	99.74 116	91.87 285	99.92 79	99.16 42	98.29 202	99.70 95

GG-mvs-BLEND				98.45 244	98.55 321	98.16 215	99.43 159	93.68 359		97.23 320	98.46 329	89.30 320	99.22 278	95.43 299	98.22 203	97.98 333

gg-mvs-nofinetune		96.17 295	95.32 303	98.73 216	98.79 294	98.14 217	99.38 185	94.09 358	91.07 341	98.07 302	91.04 354	89.62 318	99.35 258	96.75 268	99.09 159	98.68 251

DTE-MVSNet		97.51 266	97.19 273	98.46 243	98.63 314	98.13 218	99.84 6	99.48 139	96.68 245	97.97 305	99.67 151	92.92 256	98.56 328	96.88 265	92.60 331	98.70 242

VDDNet		97.55 261	97.02 277	99.16 155	99.49 159	98.12 219	99.38 185	99.30 256	95.35 305	99.68 53	99.90 7	82.62 349	99.93 68	99.31 26	98.13 212	99.42 169

thres200		97.61 259	97.28 268	98.62 223	99.64 116	98.03 220	99.26 225	98.74 318	97.68 156	99.09 195	98.32 334	91.66 294	99.81 156	92.88 329	98.22 203	98.03 328

baseline2		97.87 217	97.55 229	98.82 207	99.18 235	98.02 221	99.41 168	96.58 353	96.97 226	96.51 329	99.17 292	93.43 247	99.57 223	97.71 202	99.03 164	98.86 216

IterMVS-LS		98.46 152	98.42 151	98.58 227	99.59 134	98.00 222	99.37 188	99.43 195	96.94 231	99.07 197	99.59 186	97.87 106	99.03 305	98.32 153	95.62 286	98.71 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GA-MVS		97.85 220	97.47 239	99.00 171	99.38 186	97.99 223	98.57 326	99.15 277	97.04 221	98.90 226	99.30 274	89.83 314	99.38 247	96.70 272	98.33 197	99.62 124

cl-mvsnet_		98.01 200	97.84 201	98.55 232	99.25 220	97.97 224	98.71 316	99.34 234	96.47 266	98.59 273	99.54 204	95.65 179	99.21 283	97.21 240	95.77 281	98.46 307

EI-MVSNet		98.67 143	98.67 128	98.68 221	99.35 191	97.97 224	99.50 124	99.38 215	96.93 232	99.20 172	99.83 42	97.87 106	99.36 254	98.38 145	97.56 229	98.71 238

tfpn200view9		97.72 245	97.38 255	98.72 218	99.69 95	97.96 226	99.50 124	98.73 323	97.83 138	99.17 179	98.45 330	91.67 292	99.83 145	93.22 325	98.18 207	98.37 316

thres400		97.77 234	97.38 255	98.92 182	99.69 95	97.96 226	99.50 124	98.73 323	97.83 138	99.17 179	98.45 330	91.67 292	99.83 145	93.22 325	98.18 207	98.96 211

cl-mvsnet1		98.01 200	97.85 200	98.48 238	99.24 221	97.95 228	98.71 316	99.35 230	96.50 259	98.60 272	99.54 204	95.72 176	99.03 305	97.21 240	95.77 281	98.46 307

thres600view7		97.86 219	97.51 235	98.92 182	99.72 80	97.95 228	99.59 77	98.74 318	97.94 128	99.27 153	98.62 325	91.75 288	99.86 125	93.73 321	98.19 206	98.96 211

CHOSEN 280x420		99.12 85	99.13 65	99.08 160	99.66 109	97.89 230	98.43 333	99.71 13	98.88 37	99.62 74	99.76 105	96.63 144	99.70 199	99.46 14	99.99 1	99.66 108

cl-mvsnet2		97.85 220	97.64 223	98.48 238	99.09 255	97.87 231	98.60 325	99.33 241	97.11 216	98.87 231	99.22 287	92.38 279	99.17 287	98.21 157	95.99 275	98.42 310

TR-MVS		97.76 235	97.41 252	98.82 207	99.06 260	97.87 231	98.87 301	98.56 329	96.63 251	98.68 258	99.22 287	92.49 273	99.65 211	95.40 300	97.79 219	98.95 214

thres100view900		97.76 235	97.45 242	98.69 220	99.72 80	97.86 233	99.59 77	98.74 318	97.93 129	99.26 158	98.62 325	91.75 288	99.83 145	93.22 325	98.18 207	98.37 316

test0.0.03 1		97.71 249	97.42 251	98.56 230	98.41 326	97.82 234	98.78 309	98.63 327	97.34 192	98.05 303	98.98 312	94.45 223	98.98 312	95.04 307	97.15 254	98.89 215

JIA-IIPM		97.50 267	97.02 277	98.93 180	98.73 303	97.80 235	99.30 206	98.97 295	91.73 337	98.91 224	94.86 349	95.10 195	99.71 193	97.58 212	97.98 215	99.28 181

mvs-test1		98.86 119	98.84 110	98.89 191	99.33 196	97.77 236	99.44 153	99.30 256	98.47 66	99.10 190	99.43 238	96.78 138	99.95 42	98.73 97	99.02 165	98.96 211

XVG-OURS-SEG-HR		98.69 141	98.62 138	98.89 191	99.71 86	97.74 237	99.12 249	99.54 70	98.44 72	99.42 116	99.71 128	94.20 230	99.92 79	98.54 132	98.90 174	99.00 205

XVG-OURS		98.73 138	98.68 127	98.88 194	99.70 93	97.73 238	98.92 295	99.55 63	98.52 63	99.45 108	99.84 38	95.27 190	99.91 90	98.08 171	98.84 177	99.00 205

miper_ehance_all_eth		98.18 176	98.10 170	98.41 249	99.23 222	97.72 239	98.72 315	99.31 252	96.60 254	98.88 229	99.29 276	97.29 123	99.13 292	97.60 210	95.99 275	98.38 315

miper_enhance_ethall		98.16 178	98.08 174	98.41 249	98.96 276	97.72 239	98.45 332	99.32 249	96.95 229	98.97 216	99.17 292	97.06 130	99.22 278	97.86 186	95.99 275	98.29 318

v148		97.79 233	97.55 229	98.50 235	98.74 302	97.72 239	99.54 108	99.33 241	96.26 278	98.90 226	99.51 215	94.68 213	99.14 289	97.83 189	93.15 325	98.63 278

cl_fuxian		98.12 184	98.04 178	98.38 253	99.30 205	97.69 242	98.81 306	99.33 241	96.67 246	98.83 237	99.34 264	97.11 127	98.99 311	97.58 212	95.34 292	98.48 301

TAPA-MVS	97.07 15	97.74 242	97.34 262	98.94 178	99.70 93	97.53 243	99.25 227	99.51 101	91.90 336	99.30 145	99.63 170	98.78 48	99.64 213	88.09 346	99.87 40	99.65 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		97.73 243	97.45 242	98.57 228	99.45 172	97.50 244	99.02 273	98.98 294	96.11 293	99.41 120	99.14 296	90.28 307	98.74 326	95.74 292	98.93 170	99.47 162

UniMVSNet_ETH3D		97.32 274	96.81 280	98.87 198	99.40 182	97.46 245	99.51 118	99.53 82	95.86 300	98.54 275	99.77 101	82.44 350	99.66 207	98.68 106	97.52 232	99.50 155

miper_lstm_enhance		98.00 202	97.91 193	98.28 264	99.34 195	97.43 246	98.88 299	99.36 225	96.48 264	98.80 241	99.55 199	95.98 162	98.91 322	97.27 236	95.50 290	98.51 299

eth_miper_zixun_eth		98.05 194	97.96 187	98.33 256	99.26 216	97.38 247	98.56 328	99.31 252	96.65 248	98.88 229	99.52 211	96.58 145	99.12 296	97.39 233	95.53 289	98.47 303

cascas		97.69 250	97.43 250	98.48 238	98.60 318	97.30 248	98.18 344	99.39 209	92.96 333	98.41 283	98.78 321	93.77 244	99.27 271	98.16 163	98.61 184	98.86 216

PVSNet	96.02 17	98.85 127	98.84 110	98.89 191	99.73 75	97.28 249	98.32 339	99.60 40	97.86 133	99.50 100	99.57 193	96.75 141	99.86 125	98.56 127	99.70 108	99.54 141

MDA-MVSNet-bldmvs		94.96 307	93.98 313	97.92 285	98.24 329	97.27 250	99.15 245	99.33 241	93.80 325	80.09 355	99.03 307	88.31 330	97.86 339	93.49 323	94.36 309	98.62 280

GBi-Net		97.68 252	97.48 237	98.29 261	99.51 150	97.26 251	99.43 159	99.48 139	96.49 260	99.07 197	99.32 271	90.26 308	98.98 312	97.10 249	96.65 258	98.62 280

test1		97.68 252	97.48 237	98.29 261	99.51 150	97.26 251	99.43 159	99.48 139	96.49 260	99.07 197	99.32 271	90.26 308	98.98 312	97.10 249	96.65 258	98.62 280

FMVSNet1		96.84 283	96.36 286	98.29 261	99.32 203	97.26 251	99.43 159	99.48 139	95.11 308	98.55 274	99.32 271	83.95 346	98.98 312	95.81 290	96.26 270	98.62 280

MDA-MVSNet_test_wron		95.45 302	94.60 308	98.01 279	98.16 330	97.21 254	99.11 255	99.24 266	93.49 329	80.73 354	98.98 312	93.02 253	98.18 330	94.22 317	94.45 307	98.64 270

VDD-MVS		97.73 243	97.35 259	98.88 194	99.47 166	97.12 255	99.34 200	98.85 310	98.19 97	99.67 59	99.85 29	82.98 347	99.92 79	99.49 12	98.32 201	99.60 128

test-LLR		98.06 189	97.90 194	98.55 232	98.79 294	97.10 256	98.67 318	97.75 342	97.34 192	98.61 270	98.85 316	94.45 223	99.45 233	97.25 238	99.38 136	99.10 190

test-mter		97.49 269	97.13 274	98.55 232	98.79 294	97.10 256	98.67 318	97.75 342	96.65 248	98.61 270	98.85 316	88.23 331	99.45 233	97.25 238	99.38 136	99.10 190

YYNet1		95.36 304	94.51 310	97.92 285	97.89 332	97.10 256	99.10 257	99.23 267	93.26 332	80.77 353	99.04 306	92.81 259	98.02 334	94.30 314	94.18 312	98.64 270

ACMM	97.58 5	98.37 161	98.34 156	98.48 238	99.41 177	97.10 256	99.56 96	99.45 179	98.53 62	99.04 203	99.85 29	93.00 254	99.71 193	98.74 95	97.45 240	98.64 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		98.19 174	98.10 170	98.45 244	98.88 282	97.07 260	99.28 212	99.38 215	98.57 60	99.22 166	99.81 62	92.12 281	99.66 207	98.08 171	97.54 231	98.61 289

Patchmatch-test		97.93 209	97.65 221	98.77 214	99.18 235	97.07 260	99.03 270	99.14 279	96.16 288	98.74 247	99.57 193	94.56 219	99.72 187	93.36 324	99.11 155	99.52 146

LPG-MVS_test		98.22 170	98.13 168	98.49 236	99.33 196	97.05 262	99.58 84	99.55 63	97.46 178	99.24 161	99.83 42	92.58 270	99.72 187	98.09 167	97.51 233	98.68 251

LGP-MVS_train				98.49 236	99.33 196	97.05 262		99.55 63	97.46 178	99.24 161	99.83 42	92.58 270	99.72 187	98.09 167	97.51 233	98.68 251

AUN-MVS		96.88 282	96.31 287	98.59 225	99.48 165	97.04 264	99.27 216	99.22 268	97.44 184	98.51 276	99.41 244	91.97 283	99.66 207	97.71 202	83.83 345	99.07 199

plane_prior7					99.29 209	97.03 265

ACMP	97.20 11	98.06 189	97.94 191	98.45 244	99.37 188	97.01 266	99.44 153	99.49 127	97.54 172	98.45 280	99.79 88	91.95 284	99.72 187	97.91 182	97.49 238	98.62 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior3						97.00 267			98.69 54	99.11 187

Fast-Effi-MVS+-dtu		98.77 136	98.83 114	98.60 224	99.41 177	96.99 268	99.52 114	99.49 127	98.11 107	99.24 161	99.34 264	96.96 134	99.79 164	97.95 180	99.45 132	99.02 204

plane_prior6					99.27 214	96.98 269						92.71 265

HQP_MVS		98.27 169	98.22 164	98.44 247	99.29 209	96.97 270	99.39 180	99.47 157	98.97 30	99.11 187	99.61 180	92.71 265	99.69 202	97.78 193	97.63 222	98.67 258

plane_prior						96.97 270	99.21 237		98.45 69						97.60 225

ACMH	97.28 8	98.10 185	97.99 183	98.44 247	99.41 177	96.96 272	99.60 71	99.56 55	98.09 110	98.15 297	99.91 5	90.87 305	99.70 199	98.88 70	97.45 240	98.67 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
NP-MVS					99.23 222	96.92 273					99.40 247

Effi-MVS+-dtu		98.78 134	98.89 102	98.47 242	99.33 196	96.91 274	99.57 89	99.30 256	98.47 66	99.41 120	98.99 309	96.78 138	99.74 176	98.73 97	99.38 136	98.74 234

HQP5-MVS						96.83 275

HQP-MVS		98.02 197	97.90 194	98.37 254	99.19 232	96.83 275	98.98 284	99.39 209	98.24 90	98.66 259	99.40 247	92.47 274	99.64 213	97.19 244	97.58 227	98.64 270

CLD-MVS		98.16 178	98.10 170	98.33 256	99.29 209	96.82 277	98.75 312	99.44 187	97.83 138	99.13 183	99.55 199	92.92 256	99.67 204	98.32 153	97.69 221	98.48 301
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LTVRE_ROB	97.16 12	98.02 197	97.90 194	98.40 251	99.23 222	96.80 278	99.70 33	99.60 40	97.12 213	98.18 296	99.70 132	91.73 290	99.72 187	98.39 143	97.45 240	98.68 251
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
pmmvs5		97.52 264	97.30 267	98.16 270	98.57 320	96.73 279	99.27 216	98.90 306	96.14 291	98.37 286	99.53 208	91.54 297	99.14 289	97.51 222	95.87 279	98.63 278

BH-untuned		98.42 155	98.36 153	98.59 225	99.49 159	96.70 280	99.27 216	99.13 280	97.24 203	98.80 241	99.38 252	95.75 174	99.74 176	97.07 252	99.16 150	99.33 178

IB-MVS	95.67 18	96.22 293	95.44 302	98.57 228	99.21 228	96.70 280	98.65 321	97.74 344	96.71 243	97.27 319	98.54 328	86.03 341	99.92 79	98.47 138	86.30 343	99.10 190
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
ACMH+	97.24 10	97.92 212	97.78 206	98.32 258	99.46 167	96.68 282	99.56 96	99.54 70	98.41 73	97.79 311	99.87 20	90.18 312	99.66 207	98.05 175	97.18 252	98.62 280

EU-MVSNet		97.98 204	98.03 179	97.81 293	98.72 305	96.65 283	99.66 46	99.66 27	98.09 110	98.35 288	99.82 49	95.25 193	98.01 335	97.41 232	95.30 293	98.78 222

D2MVS		98.41 157	98.50 147	98.15 271	99.26 216	96.62 284	99.40 176	99.61 35	97.71 153	98.98 214	99.36 258	96.04 161	99.67 204	98.70 101	97.41 244	98.15 324

MVP-Stereo		97.81 230	97.75 212	97.99 281	97.53 335	96.60 285	98.96 288	98.85 310	97.22 205	97.23 320	99.36 258	95.28 189	99.46 232	95.51 297	99.78 89	97.92 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TESTMET0.1,1		97.55 261	97.27 270	98.40 251	98.93 278	96.53 286	98.67 318	97.61 345	96.96 227	98.64 266	99.28 278	88.63 327	99.45 233	97.30 235	99.38 136	99.21 184

OurMVSNet-221017-0		97.88 215	97.77 208	98.19 268	98.71 307	96.53 286	99.88 1	99.00 292	97.79 144	98.78 244	99.94 3	91.68 291	99.35 258	97.21 240	96.99 256	98.69 246

ADS-MVSNet		98.20 173	98.08 174	98.56 230	99.33 196	96.48 288	99.23 230	99.15 277	96.24 280	99.10 190	99.67 151	94.11 234	99.71 193	96.81 266	99.05 162	99.48 157

testgi		97.65 257	97.50 236	98.13 272	99.36 190	96.45 289	99.42 166	99.48 139	97.76 147	97.87 307	99.45 235	91.09 302	98.81 325	94.53 312	98.52 192	99.13 188

test_0402		96.64 286	96.24 288	97.85 289	98.85 290	96.43 290	99.44 153	99.26 263	93.52 328	96.98 326	99.52 211	88.52 328	99.20 285	92.58 334	97.50 235	97.93 336

ITE_SJBPF				98.08 273	99.29 209	96.37 291		98.92 301	98.34 80	98.83 237	99.75 110	91.09 302	99.62 219	95.82 289	97.40 245	98.25 321

IterMVS-SCA-FT		97.82 228	97.75 212	98.06 275	99.57 138	96.36 292	99.02 273	99.49 127	97.18 207	98.71 250	99.72 127	92.72 263	99.14 289	97.44 230	95.86 280	98.67 258

K. test v3		97.10 280	96.79 281	98.01 279	98.72 305	96.33 293	99.87 4	97.05 348	97.59 164	96.16 333	99.80 76	88.71 324	99.04 303	96.69 273	96.55 262	98.65 268

XVG-ACMP-BASELINE		97.83 225	97.71 216	98.20 267	99.11 250	96.33 293	99.41 168	99.52 88	98.06 119	99.05 202	99.50 218	89.64 317	99.73 183	97.73 199	97.38 246	98.53 297

IterMVS		97.83 225	97.77 208	98.02 278	99.58 136	96.27 295	99.02 273	99.48 139	97.22 205	98.71 250	99.70 132	92.75 260	99.13 292	97.46 227	96.00 274	98.67 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		97.50 267	97.33 264	98.03 276	98.65 312	96.23 296	99.77 21	98.68 326	97.14 210	97.90 306	99.93 4	90.45 306	99.18 286	97.00 254	96.43 266	98.67 258

BH-w/o		98.00 202	97.89 198	98.32 258	99.35 191	96.20 297	99.01 278	98.90 306	96.42 269	98.38 285	99.00 308	95.26 192	99.72 187	96.06 285	98.61 184	99.03 202

TDRefinement		95.42 303	94.57 309	97.97 282	89.83 356	96.11 298	99.48 140	98.75 315	96.74 241	96.68 328	99.88 15	88.65 326	99.71 193	98.37 147	82.74 346	98.09 325

RRT_test8_iter05		97.72 245	97.60 226	98.08 273	99.23 222	96.08 299	99.63 58	99.49 127	97.54 172	98.94 220	99.81 62	87.99 334	99.35 258	99.21 36	96.51 264	98.81 219

EPMVS		97.82 228	97.65 221	98.35 255	98.88 282	95.98 300	99.49 134	94.71 357	97.57 167	99.26 158	99.48 227	92.46 277	99.71 193	97.87 185	99.08 160	99.35 175

pmmvs-eth3d		95.34 305	94.73 307	97.15 309	95.53 349	95.94 301	99.35 197	99.10 282	95.13 306	93.55 342	97.54 339	88.15 333	97.91 337	94.58 311	89.69 339	97.61 340

FMVSNet5		96.43 291	96.19 289	97.15 309	99.11 250	95.89 302	99.32 202	99.52 88	94.47 321	98.34 289	99.07 302	87.54 338	97.07 346	92.61 333	95.72 284	98.47 303

KD-MVS_2432*1600		94.62 309	93.72 315	97.31 307	97.19 343	95.82 303	98.34 336	99.20 271	95.00 311	97.57 313	98.35 332	87.95 335	98.10 332	92.87 330	77.00 351	98.01 329

miper_refine_blended		94.62 309	93.72 315	97.31 307	97.19 343	95.82 303	98.34 336	99.20 271	95.00 311	97.57 313	98.35 332	87.95 335	98.10 332	92.87 330	77.00 351	98.01 329

UnsupCasMVSNet_eth		96.44 290	96.12 290	97.40 306	98.65 312	95.65 305	99.36 192	99.51 101	97.13 211	96.04 335	98.99 309	88.40 329	98.17 331	96.71 271	90.27 336	98.40 313

MIMVSNet1		95.51 301	95.04 305	96.92 316	97.38 337	95.60 306	99.52 114	99.50 119	93.65 327	96.97 327	99.17 292	85.28 344	96.56 350	88.36 345	95.55 288	98.60 292

CVMVSNet		98.57 149	98.67 128	98.30 260	99.35 191	95.59 307	99.50 124	99.55 63	98.60 59	99.39 127	99.83 42	94.48 222	99.45 233	98.75 94	98.56 190	99.85 14

SCA		98.19 174	98.16 165	98.27 265	99.30 205	95.55 308	99.07 259	98.97 295	97.57 167	99.43 113	99.57 193	92.72 263	99.74 176	97.58 212	99.20 148	99.52 146

LF4IMVS		97.52 264	97.46 241	97.70 298	98.98 273	95.55 308	99.29 210	98.82 313	98.07 115	98.66 259	99.64 165	89.97 313	99.61 220	97.01 253	96.68 257	97.94 335

EPNet_dtu		98.03 195	97.96 187	98.23 266	98.27 327	95.54 310	99.23 230	98.75 315	99.02 15	97.82 309	99.71 128	96.11 159	99.48 229	93.04 328	99.65 119	99.69 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		97.12 279	96.89 279	97.83 291	99.07 258	95.52 311	98.57 326	98.74 318	97.58 166	97.81 310	99.79 88	88.16 332	99.56 224	95.10 305	97.21 250	98.39 314

pmmvs6		96.53 288	96.09 291	97.82 292	98.69 309	95.47 312	99.37 188	99.47 157	93.46 330	97.41 316	99.78 95	87.06 339	99.33 262	96.92 263	92.70 330	98.65 268

test20.03		96.12 296	95.96 294	96.63 320	97.44 336	95.45 313	99.51 118	99.38 215	96.55 257	96.16 333	99.25 283	93.76 245	96.17 351	87.35 348	94.22 311	98.27 319

lessismore_v0				97.79 294	98.69 309	95.44 314		94.75 356		95.71 336	99.87 20	88.69 325	99.32 263	95.89 288	94.93 302	98.62 280

DIV-MVS_2432*1600		95.00 306	94.34 311	96.96 314	97.07 345	95.39 315	99.56 96	99.44 187	95.11 308	97.13 323	97.32 343	91.86 286	97.27 345	90.35 339	81.23 348	98.23 322

PatchmatchNet		98.31 164	98.36 153	98.19 268	99.16 243	95.32 316	99.27 216	98.92 301	97.37 191	99.37 132	99.58 189	94.90 199	99.70 199	97.43 231	99.21 147	99.54 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ppachtmachnet_test		97.49 269	97.45 242	97.61 299	98.62 315	95.24 317	98.80 307	99.46 167	96.11 293	98.22 294	99.62 176	96.45 150	98.97 319	93.77 320	95.97 278	98.61 289

USDC		97.34 273	97.20 272	97.75 295	99.07 258	95.20 318	98.51 330	99.04 290	97.99 125	98.31 290	99.86 23	89.02 321	99.55 226	95.67 295	97.36 247	98.49 300

ADS-MVSNet2		98.02 197	98.07 177	97.87 288	99.33 196	95.19 319	99.23 230	99.08 285	96.24 280	99.10 190	99.67 151	94.11 234	98.93 321	96.81 266	99.05 162	99.48 157

MDTV_nov1_ep13_2view						95.18 320	99.35 197		96.84 236	99.58 85		95.19 194		97.82 190		99.46 164

new_pmnet		96.38 292	96.03 292	97.41 305	98.13 331	95.16 321	99.05 264	99.20 271	93.94 323	97.39 317	98.79 319	91.61 296	99.04 303	90.43 338	95.77 281	98.05 327

tpm		97.67 255	97.55 229	98.03 276	99.02 267	95.01 322	99.43 159	98.54 331	96.44 267	99.12 185	99.34 264	91.83 287	99.60 221	97.75 197	96.46 265	99.48 157

our_test_3		97.65 257	97.68 218	97.55 302	98.62 315	94.97 323	98.84 303	99.30 256	96.83 238	98.19 295	99.34 264	97.01 132	99.02 307	95.00 308	96.01 273	98.64 270

MVS_0304		96.79 284	96.52 284	97.59 300	99.22 226	94.92 324	99.04 269	99.59 43	96.49 260	98.43 282	98.99 309	80.48 352	99.39 245	97.15 248	99.27 144	98.47 303

DWT-MVSNet_test		97.53 263	97.40 253	97.93 284	99.03 266	94.86 325	99.57 89	98.63 327	96.59 256	98.36 287	98.79 319	89.32 319	99.74 176	98.14 165	98.16 211	99.20 185

tpmrst		98.33 163	98.48 148	97.90 287	99.16 243	94.78 326	99.31 204	99.11 281	97.27 199	99.45 108	99.59 186	95.33 188	99.84 136	98.48 135	98.61 184	99.09 194

tpmvs		97.98 204	98.02 181	97.84 290	99.04 264	94.73 327	99.31 204	99.20 271	96.10 297	98.76 246	99.42 241	94.94 197	99.81 156	96.97 257	98.45 195	98.97 209

pmmvs3		94.09 315	93.25 318	96.60 321	94.76 351	94.49 328	98.92 295	98.18 337	89.66 342	96.48 330	98.06 337	86.28 340	97.33 344	89.68 341	87.20 342	97.97 334

MDTV_nov1_ep13			98.32 158		99.11 250	94.44 329	99.27 216	98.74 318	97.51 176	99.40 125	99.62 176	94.78 205	99.76 173	97.59 211	98.81 180

tpm2		97.44 271	97.34 262	97.74 296	99.15 246	94.36 330	99.45 149	98.94 298	93.45 331	98.90 226	99.44 236	91.35 299	99.59 222	97.31 234	98.07 214	99.29 180

PVSNet_0	94.43 19	96.09 297	95.47 300	97.94 283	99.31 204	94.34 331	97.81 348	99.70 15	97.12 213	97.46 315	98.75 322	89.71 316	99.79 164	97.69 205	81.69 347	99.68 102

Anonymous20231206		96.22 293	96.03 292	96.79 319	97.31 340	94.14 332	99.63 58	99.08 285	96.17 286	97.04 325	99.06 304	93.94 239	97.76 341	86.96 349	95.06 298	98.47 303

CostFormer		97.72 245	97.73 214	97.71 297	99.15 246	94.02 333	99.54 108	99.02 291	94.67 317	99.04 203	99.35 261	92.35 280	99.77 170	98.50 134	97.94 216	99.34 177

UnsupCasMVSNet_bld		93.53 316	92.51 319	96.58 322	97.38 337	93.82 334	98.24 341	99.48 139	91.10 340	93.10 344	96.66 345	74.89 353	98.37 329	94.03 319	87.71 341	97.56 342

tpm cat1		97.39 272	97.36 257	97.50 304	99.17 241	93.73 335	99.43 159	99.31 252	91.27 338	98.71 250	99.08 301	94.31 228	99.77 170	96.41 281	98.50 193	99.00 205

dp		97.75 239	97.80 202	97.59 300	99.10 253	93.71 336	99.32 202	98.88 308	96.48 264	99.08 196	99.55 199	92.67 268	99.82 152	96.52 277	98.58 187	99.24 182

MVS-HIRNet		95.75 300	95.16 304	97.51 303	99.30 205	93.69 337	98.88 299	95.78 354	85.09 348	98.78 244	92.65 351	91.29 300	99.37 250	94.85 309	99.85 58	99.46 164

CL-MVSNet_2432*1600		94.49 311	93.97 314	96.08 324	96.16 346	93.67 338	98.33 338	99.38 215	95.13 306	97.33 318	98.15 336	92.69 267	96.57 349	88.67 343	79.87 349	97.99 332

DSMNet-mixed		97.25 276	97.35 259	96.95 315	97.84 333	93.61 339	99.57 89	96.63 352	96.13 292	98.87 231	98.61 327	94.59 217	97.70 342	95.08 306	98.86 176	99.55 139

MS-PatchMatch		97.24 277	97.32 265	96.99 312	98.45 325	93.51 340	98.82 305	99.32 249	97.41 188	98.13 298	99.30 274	88.99 322	99.56 224	95.68 294	99.80 84	97.90 338

OpenMVS_ROB	92.34 20	94.38 313	93.70 317	96.41 323	97.38 337	93.17 341	99.06 262	98.75 315	86.58 346	94.84 340	98.26 335	81.53 351	99.32 263	89.01 342	97.87 218	96.76 344

gm-plane-assit					98.54 322	92.96 342			94.65 318		99.15 295		99.64 213	97.56 217

EG-PatchMatch MVS		95.97 298	95.69 298	96.81 318	97.78 334	92.79 343	99.16 241	98.93 299	96.16 288	94.08 341	99.22 287	82.72 348	99.47 230	95.67 295	97.50 235	98.17 323

new-patchmatchnet		94.48 312	94.08 312	95.67 326	95.08 350	92.41 344	99.18 239	99.28 262	94.55 320	93.49 343	97.37 342	87.86 337	97.01 347	91.57 335	88.36 340	97.61 340

LCM-MVSNet-Re		97.83 225	98.15 166	96.87 317	99.30 205	92.25 345	99.59 77	98.26 333	97.43 185	96.20 332	99.13 297	96.27 156	98.73 327	98.17 162	98.99 167	99.64 118

DeepPCF-MVS	98.18 3	98.81 130	99.37 19	97.12 311	99.60 132	91.75 346	98.61 323	99.44 187	99.35 1	99.83 17	99.85 29	98.70 62	99.81 156	99.02 54	99.91 16	99.81 41

RPSCF		98.22 170	98.62 138	96.99 312	99.82 37	91.58 347	99.72 29	99.44 187	96.61 252	99.66 64	99.89 10	95.92 167	99.82 152	97.46 227	99.10 158	99.57 137

Patchmatch-RL test		95.84 299	95.81 297	95.95 325	95.61 347	90.57 348	98.24 341	98.39 332	95.10 310	95.20 337	98.67 324	94.78 205	97.77 340	96.28 283	90.02 337	99.51 152

Gipuma		90.99 318	90.15 321	93.51 328	98.73 303	90.12 349	93.98 353	99.45 179	79.32 351	92.28 345	94.91 348	69.61 354	97.98 336	87.42 347	95.67 285	92.45 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PM-MVS		92.96 317	92.23 320	95.14 327	95.61 347	89.98 350	99.37 188	98.21 335	94.80 315	95.04 339	97.69 338	65.06 355	97.90 338	94.30 314	89.98 338	97.54 343

PMMVS2		86.87 319	85.37 323	91.35 334	90.21 355	83.80 351	98.89 298	97.45 347	83.13 350	91.67 347	95.03 347	48.49 360	94.70 353	85.86 351	77.62 350	95.54 347

ambc				93.06 330	92.68 352	82.36 352	98.47 331	98.73 323		95.09 338	97.41 340	55.55 358	99.10 299	96.42 280	91.32 334	97.71 339

DeepMVS_CX				93.34 329	99.29 209	82.27 353		99.22 268	85.15 347	96.33 331	99.05 305	90.97 304	99.73 183	93.57 322	97.77 220	98.01 329

LCM-MVSNet		86.80 320	85.22 324	91.53 333	87.81 357	80.96 354	98.23 343	98.99 293	71.05 353	90.13 348	96.51 346	48.45 361	96.88 348	90.51 337	85.30 344	96.76 344

CMPMVS	69.68 23	94.13 314	94.90 306	91.84 332	97.24 341	80.01 355	98.52 329	99.48 139	89.01 343	91.99 346	99.67 151	85.67 343	99.13 292	95.44 298	97.03 255	96.39 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
N_pmnet		94.95 308	95.83 296	92.31 331	98.47 324	79.33 356	99.12 249	92.81 362	93.87 324	97.68 312	99.13 297	93.87 241	99.01 309	91.38 336	96.19 271	98.59 293

ANet_high		77.30 325	74.86 329	84.62 337	75.88 361	77.61 357	97.63 350	93.15 361	88.81 344	64.27 358	89.29 355	36.51 362	83.93 359	75.89 354	52.31 356	92.33 351

EMVS		80.02 324	79.22 327	82.43 340	91.19 353	76.40 358	97.55 351	92.49 363	66.36 357	83.01 352	91.27 353	64.63 356	85.79 358	65.82 357	60.65 355	85.08 354

E-PMN		80.61 323	79.88 326	82.81 338	90.75 354	76.38 359	97.69 349	95.76 355	66.44 356	83.52 350	92.25 352	62.54 357	87.16 357	68.53 356	61.40 354	84.89 355

MVE	76.82 21	76.91 326	74.31 330	84.70 336	85.38 360	76.05 360	96.88 352	93.17 360	67.39 355	71.28 357	89.01 356	21.66 367	87.69 356	71.74 355	72.29 353	90.35 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		82.80 322	81.52 325	86.66 335	66.61 363	68.44 361	92.79 355	97.92 340	68.96 354	80.04 356	99.85 29	85.77 342	96.15 352	97.86 186	43.89 357	95.39 348

FPMVS		84.93 321	85.65 322	82.75 339	86.77 358	63.39 362	98.35 335	98.92 301	74.11 352	83.39 351	98.98 312	50.85 359	92.40 355	84.54 352	94.97 300	92.46 349

PMVS	70.75 22	75.98 327	74.97 328	79.01 341	70.98 362	55.18 363	93.37 354	98.21 335	65.08 358	61.78 359	93.83 350	21.74 366	92.53 354	78.59 353	91.12 335	89.34 353
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 328	41.29 333	36.84 342	86.18 359	49.12 364	79.73 356	22.81 365	27.64 359	25.46 362	28.45 362	21.98 365	48.89 360	55.80 358	23.56 360	12.51 358

test123		39.01 330	42.50 332	28.53 343	39.17 364	20.91 365	98.75 312	19.17 366	19.83 361	38.57 360	66.67 358	33.16 363	15.42 361	37.50 360	29.66 359	49.26 356

testmvs		39.17 329	43.78 331	25.37 344	36.04 365	16.84 366	98.36 334	26.56 364	20.06 360	38.51 361	67.32 357	29.64 364	15.30 362	37.59 359	39.90 358	43.98 357

uanet_test		0.02 334	0.03 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.27 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		24.64 331	32.85 334	0.00 345	0.00 366	0.00 367	0.00 357	99.51 101	0.00 362	0.00 363	99.56 196	96.58 145	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		8.27 333	11.03 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.27 363	99.01 16	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.02 334	0.03 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.27 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.02 334	0.03 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.27 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.02 334	0.03 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.27 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.02 334	0.03 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.27 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		8.30 332	11.06 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	99.58 189	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.02 334	0.03 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.27 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

test_241102_TWO								99.48 139	99.08 11	99.88 5	99.81 62	98.94 31	99.96 18	98.91 67	99.84 65	99.88 5

9.14			99.10 69		99.72 80		99.40 176	99.51 101	97.53 174	99.64 69	99.78 95	98.84 42	99.91 90	97.63 208	99.82 78

test_0728_THIRD									98.99 25	99.81 22	99.80 76	99.09 12	99.96 18	98.85 79	99.90 23	99.88 5

GSMVS																99.52 146

sam_mvs1												94.86 201				99.52 146

sam_mvs												94.72 212

MTGPA								99.47 157

test_post1							99.23 230				65.14 360	94.18 233	99.71 193	97.58 212

test_post											65.99 359	94.65 216	99.73 183

patchmatchnet-post											98.70 323	94.79 204	99.74 176

MTMP							99.54 108	98.88 308

test9_res														97.49 223	99.72 103	99.75 69

agg_prior2														97.21 240	99.73 102	99.75 69

test_prior2							98.96 288		98.34 80	99.01 206	99.52 211	98.68 63		97.96 178	99.74 99

旧先验2							98.96 288		96.70 244	99.47 105			99.94 53	98.19 158

新几何2							99.01 278

无先验							98.99 280	99.51 101	96.89 233				99.93 68	97.53 220		99.72 86

原ACMM2							98.95 292

testdata2													99.95 42	96.67 274

segment_acmp												98.96 25

testdata1							98.85 302		98.32 84

plane_prior5								99.47 157					99.69 202	97.78 193	97.63 222	98.67 258

plane_prior4											99.61 180

plane_prior2							99.39 180		98.97 30

plane_prior1					99.26 216

n2								0.00 367

nn								0.00 367

door-mid								98.05 338

test11								99.35 230

door								97.92 340

HQP-NCC					99.19 232		98.98 284		98.24 90	98.66 259

ACMP_Plane					99.19 232		98.98 284		98.24 90	98.66 259

BP-MVS														97.19 244

HQP4-MVS										98.66 259			99.64 213			98.64 270

HQP3-MVS								99.39 209							97.58 227

HQP2-MVS												92.47 274

ACMMP++_ref															97.19 251

ACMMP++															97.43 243

Test By Simon												98.75 56

High-resolution multi-view benchmark