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		99.09 1	98.91 2	99.63 4	99.71 19	99.24 5	99.02 73	98.87 58	97.65 12	99.73 4	99.48 10	97.53 7	99.94 4	98.43 30	99.81 12	99.70 46

DVP-MVS++		99.08 2	98.89 3	99.64 3	99.17 89	99.23 7	99.69 1	98.88 51	97.32 31	99.53 16	99.47 12	97.81 3	99.94 4	98.47 26	99.72 47	99.74 30

DVP-MVS		99.03 3	98.83 6	99.63 4	99.72 12	99.25 2	98.97 83	98.58 138	97.62 14	99.45 18	99.46 15	97.42 9	99.94 4	98.47 26	99.81 12	99.69 49
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
APDe-MVS		99.02 4	98.84 5	99.55 9	99.57 33	98.96 16	99.39 12	98.93 39	97.38 28	99.41 20	99.54 1	96.66 17	99.84 55	98.86 9	99.85 5	99.87 1

DPE-MVS		98.92 5	98.67 9	99.65 2	99.58 32	99.20 9	98.42 187	98.91 45	97.58 17	99.54 15	99.46 15	97.10 12	99.94 4	97.64 74	99.84 10	99.83 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SteuartSystems-ACMMP		98.90 6	98.75 7	99.36 21	99.22 84	98.43 33	99.10 57	98.87 58	97.38 28	99.35 24	99.40 19	97.78 5	99.87 46	97.77 64	99.85 5	99.78 15
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_1920		98.87 7	99.01 1	98.45 86	99.42 54	96.43 119	98.96 87	99.36 7	98.63 1	99.86 2	99.51 5	95.91 37	99.97 1	99.72 1	99.75 38	98.94 163

TSAR-MVS + MP.		98.78 8	98.62 10	99.24 35	99.69 24	98.28 45	99.14 48	98.66 121	96.84 59	99.56 13	99.31 39	96.34 23	99.70 107	98.32 36	99.73 44	99.73 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		98.78 8	98.56 13	99.45 15	99.32 60	98.87 19	98.47 179	98.81 75	97.72 8	98.76 58	99.16 65	97.05 13	99.78 89	98.06 45	99.66 55	99.69 49

MSP-MVS		98.74 10	98.55 14	99.29 28	99.75 3	98.23 46	99.26 27	98.88 51	97.52 19	99.41 20	98.78 118	96.00 33	99.79 86	97.79 63	99.59 68	99.85 4
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
XVS		98.70 11	98.49 16	99.34 23	99.70 22	98.35 41	99.29 22	98.88 51	97.40 25	98.46 75	99.20 55	95.90 39	99.89 37	97.85 58	99.74 42	99.78 15

MCST-MVS		98.65 12	98.37 22	99.48 13	99.60 31	98.87 19	98.41 188	98.68 113	97.04 51	98.52 74	98.80 116	96.78 16	99.83 57	97.93 52	99.61 65	99.74 30

SD-MVS		98.64 13	98.68 8	98.53 78	99.33 57	98.36 40	98.90 95	98.85 67	97.28 34	99.72 6	99.39 20	96.63 19	97.60 329	98.17 40	99.85 5	99.64 64
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
HFP-MVS		98.63 14	98.40 19	99.32 27	99.72 12	98.29 44	99.23 31	98.96 34	96.10 92	98.94 44	99.17 62	96.06 30	99.92 24	97.62 75	99.78 26	99.75 28

ACMMP_NAP		98.61 15	98.30 34	99.55 9	99.62 30	98.95 17	98.82 114	98.81 75	95.80 104	99.16 35	99.47 12	95.37 54	99.92 24	97.89 55	99.75 38	99.79 13

region2R		98.61 15	98.38 21	99.29 28	99.74 7	98.16 51	99.23 31	98.93 39	96.15 89	98.94 44	99.17 62	95.91 37	99.94 4	97.55 82	99.79 23	99.78 15

NCCC		98.61 15	98.35 25	99.38 18	99.28 72	98.61 26	98.45 180	98.76 94	97.82 7	98.45 78	98.93 102	96.65 18	99.83 57	97.38 91	99.41 95	99.71 42

SF-MVS		98.59 18	98.32 33	99.41 17	99.54 35	98.71 22	99.04 67	98.81 75	95.12 139	99.32 25	99.39 20	96.22 24	99.84 55	97.72 67	99.73 44	99.67 58

ACMMPR		98.59 18	98.36 23	99.29 28	99.74 7	98.15 52	99.23 31	98.95 35	96.10 92	98.93 48	99.19 60	95.70 43	99.94 4	97.62 75	99.79 23	99.78 15

SMA-MVS		98.58 20	98.25 37	99.56 8	99.51 39	99.04 15	98.95 88	98.80 83	93.67 208	99.37 23	99.52 3	96.52 21	99.89 37	98.06 45	99.81 12	99.76 27
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
MTAPA		98.58 20	98.29 35	99.46 14	99.76 2	98.64 25	98.90 95	98.74 98	97.27 38	98.02 99	99.39 20	94.81 74	99.96 3	97.91 53	99.79 23	99.77 21

HPM-MVS++		98.58 20	98.25 37	99.55 9	99.50 41	99.08 11	98.72 140	98.66 121	97.51 20	98.15 88	98.83 113	95.70 43	99.92 24	97.53 84	99.67 53	99.66 61

SR-MVS		98.57 23	98.35 25	99.24 35	99.53 36	98.18 49	99.09 58	98.82 70	96.58 71	99.10 37	99.32 37	95.39 52	99.82 64	97.70 71	99.63 62	99.72 38

CP-MVS		98.57 23	98.36 23	99.19 38	99.66 26	97.86 61	99.34 18	98.87 58	95.96 97	98.60 71	99.13 70	96.05 31	99.94 4	97.77 64	99.86 1	99.77 21

MSLP-MVS++		98.56 25	98.57 12	98.55 74	99.26 75	96.80 98	98.71 141	99.05 26	97.28 34	98.84 52	99.28 42	96.47 22	99.40 155	98.52 24	99.70 50	99.47 92

DeepC-MVS_fast	96.70 1	98.55 26	98.34 28	99.18 40	99.25 76	98.04 56	98.50 176	98.78 90	97.72 8	98.92 49	99.28 42	95.27 60	99.82 64	97.55 82	99.77 28	99.69 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		98.54 27	98.35 25	99.13 45	99.49 45	97.86 61	99.11 54	98.80 83	96.49 74	99.17 33	99.35 32	95.34 56	99.82 64	97.72 67	99.65 57	99.71 42

APD-MVS_3200maxsize		98.53 28	98.33 32	99.15 44	99.50 41	97.92 60	99.15 46	98.81 75	96.24 85	99.20 30	99.37 26	95.30 58	99.80 76	97.73 66	99.67 53	99.72 38

mPP-MVS		98.51 29	98.26 36	99.25 34	99.75 3	98.04 56	99.28 24	98.81 75	96.24 85	98.35 84	99.23 50	95.46 49	99.94 4	97.42 89	99.81 12	99.77 21

ZNCC-MVS		98.49 30	98.20 42	99.35 22	99.73 11	98.39 34	99.19 41	98.86 64	95.77 105	98.31 87	99.10 74	95.46 49	99.93 19	97.57 81	99.81 12	99.74 30

CS-MVS-test		98.49 30	98.50 15	98.46 85	99.20 87	97.05 89	99.64 4	98.50 158	97.45 24	98.88 50	99.14 69	95.25 62	99.15 177	98.83 10	99.56 78	99.20 128

PGM-MVS		98.49 30	98.23 40	99.27 33	99.72 12	98.08 55	98.99 80	99.49 5	95.43 121	99.03 38	99.32 37	95.56 46	99.94 4	96.80 120	99.77 28	99.78 15

EI-MVSNet-Vis-set		98.47 33	98.39 20	98.69 65	99.46 49	96.49 116	98.30 199	98.69 110	97.21 40	98.84 52	99.36 30	95.41 51	99.78 89	98.62 14	99.65 57	99.80 12

MVS_111021_HR		98.47 33	98.34 28	98.88 60	99.22 84	97.32 77	97.91 241	99.58 3	97.20 41	98.33 85	99.00 91	95.99 34	99.64 119	98.05 47	99.76 34	99.69 49

CS-MVS		98.44 35	98.49 16	98.31 98	99.08 100	96.73 102	99.67 3	98.47 164	97.17 43	98.94 44	99.10 74	95.73 42	99.13 180	98.71 12	99.49 86	99.09 146

GST-MVS		98.43 36	98.12 45	99.34 23	99.72 12	98.38 35	99.09 58	98.82 70	95.71 109	98.73 61	99.06 84	95.27 60	99.93 19	97.07 100	99.63 62	99.72 38

EI-MVSNet-UG-set		98.41 37	98.34 28	98.61 70	99.45 52	96.32 126	98.28 202	98.68 113	97.17 43	98.74 59	99.37 26	95.25 62	99.79 86	98.57 15	99.54 81	99.73 35

DELS-MVS		98.40 38	98.20 42	98.99 52	99.00 107	97.66 66	97.75 257	98.89 48	97.71 10	98.33 85	98.97 93	94.97 71	99.88 45	98.42 32	99.76 34	99.42 103
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
TSAR-MVS + GP.		98.38 39	98.24 39	98.81 61	99.22 84	97.25 84	98.11 224	98.29 200	97.19 42	98.99 43	99.02 86	96.22 24	99.67 114	98.52 24	98.56 137	99.51 82

HPM-MVS_fast		98.38 39	98.13 44	99.12 47	99.75 3	97.86 61	99.44 11	98.82 70	94.46 169	98.94 44	99.20 55	95.16 66	99.74 99	97.58 78	99.85 5	99.77 21

patch_mono-2		98.36 41	98.87 4	96.82 198	99.53 36	90.68 301	98.64 155	99.29 9	97.88 6	99.19 32	99.52 3	96.80 15	99.97 1	99.11 4	99.86 1	99.82 10

HPM-MVS		98.36 41	98.10 47	99.13 45	99.74 7	97.82 65	99.53 8	98.80 83	94.63 162	98.61 70	98.97 93	95.13 67	99.77 94	97.65 73	99.83 11	99.79 13
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS		98.35 43	98.00 51	99.42 16	99.51 39	98.72 21	98.80 121	98.82 70	94.52 166	99.23 29	99.25 49	95.54 48	99.80 76	96.52 128	99.77 28	99.74 30
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 44	98.23 40	98.67 67	99.27 73	96.90 95	97.95 237	99.58 3	97.14 46	98.44 79	99.01 90	95.03 70	99.62 125	97.91 53	99.75 38	99.50 84

PHI-MVS		98.34 44	98.06 48	99.18 40	99.15 95	98.12 54	99.04 67	99.09 22	93.32 222	98.83 54	99.10 74	96.54 20	99.83 57	97.70 71	99.76 34	99.59 72

MP-MVS		98.33 46	98.01 50	99.28 31	99.75 3	98.18 49	99.22 35	98.79 88	96.13 90	97.92 110	99.23 50	94.54 77	99.94 4	96.74 123	99.78 26	99.73 35
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		98.31 47	97.92 53	99.49 12	99.72 12	98.88 18	98.43 185	98.78 90	94.10 177	97.69 123	99.42 18	95.25 62	99.92 24	98.09 44	99.80 19	99.67 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP		98.23 48	97.95 52	99.09 48	99.74 7	97.62 69	99.03 70	99.41 6	95.98 95	97.60 131	99.36 30	94.45 82	99.93 19	97.14 97	98.85 123	99.70 46
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
EC-MVSNet		98.21 49	98.11 46	98.49 82	98.34 166	97.26 83	99.61 5	98.43 173	96.78 62	98.87 51	98.84 111	93.72 93	99.01 201	98.91 8	99.50 84	99.19 132

dcpmvs_2		98.08 50	98.59 11	96.56 222	99.57 33	90.34 308	99.15 46	98.38 182	96.82 61	99.29 26	99.49 9	95.78 41	99.57 130	98.94 7	99.86 1	99.77 21

CANet		98.05 51	97.76 56	98.90 59	98.73 129	97.27 79	98.35 190	98.78 90	97.37 30	97.72 121	98.96 98	91.53 132	99.92 24	98.79 11	99.65 57	99.51 82

train_agg		97.97 52	97.52 67	99.33 26	99.31 62	98.50 29	97.92 239	98.73 101	92.98 235	97.74 118	98.68 128	96.20 26	99.80 76	96.59 124	99.57 72	99.68 54

ETV-MVS		97.96 53	97.81 54	98.40 93	98.42 155	97.27 79	98.73 136	98.55 144	96.84 59	98.38 81	97.44 247	95.39 52	99.35 158	97.62 75	98.89 119	98.58 190

UA-Net		97.96 53	97.62 60	98.98 53	98.86 120	97.47 74	98.89 99	99.08 23	96.67 68	98.72 62	99.54 1	93.15 98	99.81 69	94.87 176	98.83 124	99.65 62

CDPH-MVS		97.94 55	97.49 68	99.28 31	99.47 47	98.44 31	97.91 241	98.67 118	92.57 250	98.77 57	98.85 110	95.93 36	99.72 101	95.56 160	99.69 51	99.68 54

DeepPCF-MVS	96.37 2	97.93 56	98.48 18	96.30 248	99.00 107	89.54 319	97.43 278	98.87 58	98.16 3	99.26 28	99.38 25	96.12 29	99.64 119	98.30 37	99.77 28	99.72 38

DeepC-MVS	95.98 3	97.88 57	97.58 62	98.77 62	99.25 76	96.93 93	98.83 112	98.75 96	96.96 55	96.89 155	99.50 7	90.46 153	99.87 46	97.84 60	99.76 34	99.52 79
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS Recon		97.86 58	97.46 71	99.06 50	99.53 36	98.35 41	98.33 192	98.89 48	92.62 247	98.05 94	98.94 101	95.34 56	99.65 117	96.04 143	99.42 94	99.19 132

CSCG		97.85 59	97.74 57	98.20 106	99.67 25	95.16 178	99.22 35	99.32 8	93.04 233	97.02 148	98.92 104	95.36 55	99.91 32	97.43 88	99.64 61	99.52 79

MG-MVS		97.81 60	97.60 61	98.44 88	99.12 97	95.97 142	97.75 257	98.78 90	96.89 58	98.46 75	99.22 52	93.90 92	99.68 113	94.81 180	99.52 83	99.67 58

VNet		97.79 61	97.40 75	98.96 55	98.88 118	97.55 71	98.63 157	98.93 39	96.74 65	99.02 39	98.84 111	90.33 156	99.83 57	98.53 18	96.66 191	99.50 84

EIA-MVS		97.75 62	97.58 62	98.27 100	98.38 158	96.44 118	99.01 75	98.60 131	95.88 101	97.26 137	97.53 241	94.97 71	99.33 160	97.38 91	99.20 106	99.05 152

PS-MVSNAJ		97.73 63	97.77 55	97.62 150	98.68 137	95.58 161	97.34 287	98.51 153	97.29 33	98.66 67	97.88 208	94.51 78	99.90 35	97.87 57	99.17 108	97.39 224

casdiffmvs_mvg		97.72 64	97.48 70	98.44 88	98.42 155	96.59 110	98.92 93	98.44 169	96.20 87	97.76 115	99.20 55	91.66 126	99.23 167	98.27 39	98.41 146	99.49 89
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CPTT-MVS		97.72 64	97.32 79	98.92 57	99.64 28	97.10 88	99.12 52	98.81 75	92.34 258	98.09 92	99.08 82	93.01 99	99.92 24	96.06 142	99.77 28	99.75 28

PVSNet_Blended_VisFu		97.70 66	97.46 71	98.44 88	99.27 73	95.91 150	98.63 157	99.16 19	94.48 168	97.67 124	98.88 107	92.80 101	99.91 32	97.11 98	99.12 109	99.50 84

mvsany_test1		97.69 67	97.70 58	97.66 148	98.24 173	94.18 226	97.53 273	97.53 286	95.52 117	99.66 8	99.51 5	94.30 85	99.56 133	98.38 33	98.62 133	99.23 125

canonicalmvs		97.67 68	97.23 82	98.98 53	98.70 134	98.38 35	99.34 18	98.39 179	96.76 64	97.67 124	97.40 250	92.26 109	99.49 146	98.28 38	96.28 207	99.08 150

xiu_mvs_v2_base		97.66 69	97.70 58	97.56 154	98.61 144	95.46 167	97.44 276	98.46 165	97.15 45	98.65 68	98.15 185	94.33 84	99.80 76	97.84 60	98.66 132	97.41 222

baseline		97.64 70	97.44 73	98.25 103	98.35 161	96.20 130	99.00 77	98.32 190	96.33 84	98.03 97	99.17 62	91.35 135	99.16 174	98.10 43	98.29 153	99.39 104

casdiffmvs		97.63 71	97.41 74	98.28 99	98.33 168	96.14 133	98.82 114	98.32 190	96.38 82	97.95 105	99.21 53	91.23 139	99.23 167	98.12 42	98.37 147	99.48 90
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
xiu_mvs_v1_base_debu		97.60 72	97.56 64	97.72 139	98.35 161	95.98 137	97.86 248	98.51 153	97.13 47	99.01 40	98.40 158	91.56 128	99.80 76	98.53 18	98.68 128	97.37 226

xiu_mvs_v1_base		97.60 72	97.56 64	97.72 139	98.35 161	95.98 137	97.86 248	98.51 153	97.13 47	99.01 40	98.40 158	91.56 128	99.80 76	98.53 18	98.68 128	97.37 226

xiu_mvs_v1_base_debi		97.60 72	97.56 64	97.72 139	98.35 161	95.98 137	97.86 248	98.51 153	97.13 47	99.01 40	98.40 158	91.56 128	99.80 76	98.53 18	98.68 128	97.37 226

diffmvs		97.58 75	97.40 75	98.13 111	98.32 170	95.81 155	98.06 227	98.37 183	96.20 87	98.74 59	98.89 106	91.31 137	99.25 164	98.16 41	98.52 138	99.34 107
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVSFormer		97.57 76	97.49 68	97.84 127	98.07 191	95.76 156	99.47 9	98.40 177	94.98 147	98.79 55	98.83 113	92.34 106	98.41 275	96.91 106	99.59 68	99.34 107

alignmvs		97.56 77	97.07 89	99.01 51	98.66 139	98.37 39	98.83 112	98.06 246	96.74 65	98.00 103	97.65 230	90.80 147	99.48 150	98.37 34	96.56 195	99.19 132

DPM-MVS		97.55 78	96.99 92	99.23 37	99.04 102	98.55 27	97.17 301	98.35 186	94.85 154	97.93 109	98.58 139	95.07 69	99.71 106	92.60 247	99.34 101	99.43 101

OMC-MVS		97.55 78	97.34 78	98.20 106	99.33 57	95.92 149	98.28 202	98.59 133	95.52 117	97.97 104	99.10 74	93.28 97	99.49 146	95.09 173	98.88 120	99.19 132

PAPM_NR		97.46 80	97.11 86	98.50 80	99.50 41	96.41 122	98.63 157	98.60 131	95.18 136	97.06 146	98.06 191	94.26 87	99.57 130	93.80 215	98.87 122	99.52 79

EPP-MVSNet		97.46 80	97.28 80	97.99 120	98.64 141	95.38 169	99.33 21	98.31 192	93.61 212	97.19 139	99.07 83	94.05 89	99.23 167	96.89 110	98.43 145	99.37 106

3Dnovator	94.51 5	97.46 80	96.93 94	99.07 49	97.78 208	97.64 67	99.35 17	99.06 24	97.02 52	93.75 257	99.16 65	89.25 176	99.92 24	97.22 96	99.75 38	99.64 64

CNLPA		97.45 83	97.03 90	98.73 63	99.05 101	97.44 76	98.07 226	98.53 148	95.32 129	96.80 160	98.53 143	93.32 96	99.72 101	94.31 198	99.31 103	99.02 154

lupinMVS		97.44 84	97.22 83	98.12 113	98.07 191	95.76 156	97.68 262	97.76 267	94.50 167	98.79 55	98.61 134	92.34 106	99.30 161	97.58 78	99.59 68	99.31 113

3Dnovator+	94.38 6	97.43 85	96.78 102	99.38 18	97.83 206	98.52 28	99.37 14	98.71 106	97.09 50	92.99 282	99.13 70	89.36 172	99.89 37	96.97 103	99.57 72	99.71 42

Vis-MVSNet		97.42 86	97.11 86	98.34 96	98.66 139	96.23 129	99.22 35	99.00 29	96.63 70	98.04 96	99.21 53	88.05 209	99.35 158	96.01 145	99.21 105	99.45 98
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 87	97.25 81	97.91 124	98.70 134	96.80 98	98.82 114	98.69 110	94.53 164	98.11 90	98.28 173	94.50 81	99.57 130	94.12 204	99.49 86	97.37 226

sss		97.39 88	96.98 93	98.61 70	98.60 145	96.61 107	98.22 207	98.93 39	93.97 185	98.01 102	98.48 148	91.98 119	99.85 51	96.45 130	98.15 155	99.39 104

test_cas_vis1_n_1920		97.38 89	97.36 77	97.45 157	98.95 113	93.25 259	99.00 77	98.53 148	97.70 11	99.77 3	99.35 32	84.71 272	99.85 51	98.57 15	99.66 55	99.26 122

PVSNet_Blended		97.38 89	97.12 85	98.14 109	99.25 76	95.35 172	97.28 292	99.26 10	93.13 230	97.94 107	98.21 181	92.74 102	99.81 69	96.88 112	99.40 97	99.27 120

WTY-MVS		97.37 91	96.92 95	98.72 64	98.86 120	96.89 97	98.31 197	98.71 106	95.26 132	97.67 124	98.56 142	92.21 112	99.78 89	95.89 147	96.85 186	99.48 90

jason		97.32 92	97.08 88	98.06 117	97.45 237	95.59 160	97.87 247	97.91 261	94.79 155	98.55 73	98.83 113	91.12 140	99.23 167	97.58 78	99.60 66	99.34 107
jason: jason.
MVS_Test		97.28 93	97.00 91	98.13 111	98.33 168	95.97 142	98.74 132	98.07 241	94.27 173	98.44 79	98.07 190	92.48 104	99.26 163	96.43 131	98.19 154	99.16 138

EPNet		97.28 93	96.87 97	98.51 79	94.98 339	96.14 133	98.90 95	97.02 318	98.28 2	95.99 188	99.11 72	91.36 134	99.89 37	96.98 102	99.19 107	99.50 84
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_yl		97.22 95	96.78 102	98.54 76	98.73 129	96.60 108	98.45 180	98.31 192	94.70 156	98.02 99	98.42 156	90.80 147	99.70 107	96.81 118	96.79 188	99.34 107

DCV-MVSNet		97.22 95	96.78 102	98.54 76	98.73 129	96.60 108	98.45 180	98.31 192	94.70 156	98.02 99	98.42 156	90.80 147	99.70 107	96.81 118	96.79 188	99.34 107

IS-MVSNet		97.22 95	96.88 96	98.25 103	98.85 122	96.36 124	99.19 41	97.97 254	95.39 123	97.23 138	98.99 92	91.11 141	98.93 213	94.60 187	98.59 135	99.47 92

PLC	95.07 4	97.20 98	96.78 102	98.44 88	99.29 68	96.31 128	98.14 219	98.76 94	92.41 256	96.39 178	98.31 171	94.92 73	99.78 89	94.06 207	98.77 127	99.23 125
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 99	97.18 84	97.20 170	98.81 125	93.27 257	95.78 346	99.15 20	95.25 133	96.79 161	98.11 188	92.29 108	99.07 191	98.56 17	99.85 5	99.25 124

LS3D		97.16 100	96.66 110	98.68 66	98.53 149	97.19 86	98.93 92	98.90 46	92.83 242	95.99 188	99.37 26	92.12 115	99.87 46	93.67 219	99.57 72	98.97 159

AdaColmap		97.15 101	96.70 106	98.48 83	99.16 93	96.69 104	98.01 232	98.89 48	94.44 170	96.83 156	98.68 128	90.69 150	99.76 95	94.36 194	99.29 104	98.98 158

Effi-MVS+		97.12 102	96.69 107	98.39 94	98.19 181	96.72 103	97.37 283	98.43 173	93.71 201	97.65 127	98.02 194	92.20 113	99.25 164	96.87 115	97.79 167	99.19 132

CHOSEN 1792x2688		97.12 102	96.80 99	98.08 115	99.30 66	94.56 211	98.05 228	99.71 1	93.57 213	97.09 142	98.91 105	88.17 204	99.89 37	96.87 115	99.56 78	99.81 11

F-COLMAP		97.09 104	96.80 99	97.97 121	99.45 52	94.95 191	98.55 170	98.62 130	93.02 234	96.17 183	98.58 139	94.01 90	99.81 69	93.95 209	98.90 118	99.14 141

TAMVS		97.02 105	96.79 101	97.70 142	98.06 193	95.31 174	98.52 171	98.31 192	93.95 186	97.05 147	98.61 134	93.49 95	98.52 255	95.33 165	97.81 166	99.29 118

CDS-MVSNet		96.99 106	96.69 107	97.90 125	98.05 194	95.98 137	98.20 210	98.33 189	93.67 208	96.95 149	98.49 147	93.54 94	98.42 267	95.24 171	97.74 170	99.31 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 107	96.55 113	98.21 105	98.17 185	96.07 135	97.98 235	98.21 209	97.24 39	97.13 141	98.93 102	86.88 233	99.91 32	95.00 175	99.37 100	98.66 184

114514_t		96.93 108	96.27 123	98.92 57	99.50 41	97.63 68	98.85 108	98.90 46	84.80 353	97.77 114	99.11 72	92.84 100	99.66 116	94.85 177	99.77 28	99.47 92

MAR-MVS		96.91 109	96.40 118	98.45 86	98.69 136	96.90 95	98.66 153	98.68 113	92.40 257	97.07 145	97.96 201	91.54 131	99.75 97	93.68 217	98.92 117	98.69 180
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
HyFIR lowres test		96.90 110	96.49 116	98.14 109	99.33 57	95.56 162	97.38 281	99.65 2	92.34 258	97.61 130	98.20 182	89.29 174	99.10 188	96.97 103	97.60 175	99.77 21

Vis-MVSNet (Re-imp)		96.87 111	96.55 113	97.83 128	98.73 129	95.46 167	99.20 39	98.30 198	94.96 149	96.60 167	98.87 108	90.05 159	98.59 247	93.67 219	98.60 134	99.46 96

PAPR		96.84 112	96.24 125	98.65 68	98.72 133	96.92 94	97.36 285	98.57 140	93.33 221	96.67 163	97.57 238	94.30 85	99.56 133	91.05 284	98.59 135	99.47 92

HY-MVS	93.96 8	96.82 113	96.23 126	98.57 72	98.46 153	97.00 90	98.14 219	98.21 209	93.95 186	96.72 162	97.99 198	91.58 127	99.76 95	94.51 191	96.54 196	98.95 162

UGNet		96.78 114	96.30 122	98.19 108	98.24 173	95.89 152	98.88 102	98.93 39	97.39 27	96.81 159	97.84 212	82.60 296	99.90 35	96.53 127	99.49 86	98.79 172
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
PVSNet_BlendedMVS		96.73 115	96.60 111	97.12 177	99.25 76	95.35 172	98.26 205	99.26 10	94.28 172	97.94 107	97.46 244	92.74 102	99.81 69	96.88 112	93.32 256	96.20 319

test_vis1_n_1920		96.71 116	96.84 98	96.31 247	99.11 98	89.74 314	99.05 64	98.58 138	98.08 4	99.87 1	99.37 26	78.48 322	99.93 19	99.29 2	99.69 51	99.27 120

mvs_anonymous		96.70 117	96.53 115	97.18 172	98.19 181	93.78 235	98.31 197	98.19 213	94.01 182	94.47 217	98.27 176	92.08 117	98.46 262	97.39 90	97.91 162	99.31 113

1112_ss		96.63 118	96.00 134	98.50 80	98.56 146	96.37 123	98.18 216	98.10 234	92.92 238	94.84 205	98.43 154	92.14 114	99.58 129	94.35 195	96.51 197	99.56 78

PMMVS		96.60 119	96.33 120	97.41 160	97.90 203	93.93 231	97.35 286	98.41 175	92.84 241	97.76 115	97.45 246	91.10 142	99.20 171	96.26 135	97.91 162	99.11 144

DP-MVS		96.59 120	95.93 137	98.57 72	99.34 55	96.19 132	98.70 145	98.39 179	89.45 326	94.52 215	99.35 32	91.85 121	99.85 51	92.89 243	98.88 120	99.68 54

PatchMatch-RL		96.59 120	96.03 133	98.27 100	99.31 62	96.51 115	97.91 241	99.06 24	93.72 200	96.92 153	98.06 191	88.50 199	99.65 117	91.77 271	99.00 115	98.66 184

GeoE		96.58 122	96.07 130	98.10 114	98.35 161	95.89 152	99.34 18	98.12 228	93.12 231	96.09 184	98.87 108	89.71 165	98.97 203	92.95 239	98.08 158	99.43 101

mvsmamba		96.57 123	96.32 121	97.32 166	96.60 288	96.43 119	99.54 7	97.98 252	96.49 74	95.20 198	98.64 132	90.82 145	98.55 251	97.97 49	93.65 246	96.98 237

XVG-OURS		96.55 124	96.41 117	96.99 184	98.75 128	93.76 236	97.50 275	98.52 151	95.67 111	96.83 156	99.30 40	88.95 189	99.53 141	95.88 148	96.26 208	97.69 218

FIs		96.51 125	96.12 128	97.67 145	97.13 259	97.54 72	99.36 15	99.22 16	95.89 99	94.03 243	98.35 164	91.98 119	98.44 265	96.40 132	92.76 264	97.01 235

XVG-OURS-SEG-HR		96.51 125	96.34 119	97.02 183	98.77 127	93.76 236	97.79 255	98.50 158	95.45 120	96.94 150	99.09 80	87.87 214	99.55 140	96.76 122	95.83 217	97.74 215

PS-MVSNAJss		96.43 127	96.26 124	96.92 193	95.84 323	95.08 183	99.16 45	98.50 158	95.87 102	93.84 252	98.34 168	94.51 78	98.61 244	96.88 112	93.45 253	97.06 232

test_fmvs1		96.42 128	96.67 109	95.66 273	98.82 124	88.53 336	98.80 121	98.20 211	96.39 81	99.64 10	99.20 55	80.35 312	99.67 114	99.04 5	99.57 72	98.78 175

iter_conf_final		96.42 128	96.12 128	97.34 165	98.46 153	96.55 114	99.08 60	98.06 246	96.03 94	95.63 192	98.46 152	87.72 216	98.59 247	97.84 60	93.80 241	96.87 253

FC-MVSNet-test		96.42 128	96.05 131	97.53 155	96.95 268	97.27 79	99.36 15	99.23 14	95.83 103	93.93 246	98.37 162	92.00 118	98.32 284	96.02 144	92.72 265	97.00 236

ab-mvs		96.42 128	95.71 148	98.55 74	98.63 142	96.75 101	97.88 246	98.74 98	93.84 191	96.54 172	98.18 184	85.34 261	99.75 97	95.93 146	96.35 201	99.15 139

FA-MVS(test-final)		96.41 132	95.94 136	97.82 130	98.21 177	95.20 177	97.80 253	97.58 277	93.21 227	97.36 135	97.70 224	89.47 169	99.56 133	94.12 204	97.99 159	98.71 179

PVSNet	91.96 18	96.35 133	96.15 127	96.96 188	99.17 89	92.05 276	96.08 339	98.68 113	93.69 204	97.75 117	97.80 218	88.86 190	99.69 112	94.26 200	99.01 114	99.15 139

Test_1112_low_res		96.34 134	95.66 153	98.36 95	98.56 146	95.94 145	97.71 260	98.07 241	92.10 267	94.79 209	97.29 255	91.75 123	99.56 133	94.17 202	96.50 198	99.58 76

Effi-MVS+-dtu		96.29 135	96.56 112	95.51 277	97.89 204	90.22 309	98.80 121	98.10 234	96.57 73	96.45 177	96.66 303	90.81 146	98.91 215	95.72 154	97.99 159	97.40 223

QAPM		96.29 135	95.40 157	98.96 55	97.85 205	97.60 70	99.23 31	98.93 39	89.76 321	93.11 279	99.02 86	89.11 181	99.93 19	91.99 266	99.62 64	99.34 107

Fast-Effi-MVS+		96.28 137	95.70 150	98.03 118	98.29 172	95.97 142	98.58 163	98.25 206	91.74 275	95.29 197	97.23 259	91.03 144	99.15 177	92.90 241	97.96 161	98.97 159

nrg030		96.28 137	95.72 145	97.96 123	96.90 273	98.15 52	99.39 12	98.31 192	95.47 119	94.42 223	98.35 164	92.09 116	98.69 237	97.50 86	89.05 310	97.04 233

1314		96.25 139	95.73 144	97.79 132	97.13 259	95.55 164	98.19 213	98.59 133	93.47 216	92.03 307	97.82 216	91.33 136	99.49 146	94.62 186	98.44 143	98.32 200

h-mvs33		96.17 140	95.62 154	97.81 131	99.03 103	94.45 213	98.64 155	98.75 96	97.48 21	98.67 63	98.72 125	89.76 163	99.86 50	97.95 50	81.59 352	99.11 144

HQP_MVS		96.14 141	95.90 138	96.85 196	97.42 239	94.60 209	98.80 121	98.56 142	97.28 34	95.34 195	98.28 173	87.09 228	99.03 196	96.07 139	94.27 224	96.92 242

iter_conf05		96.13 142	95.79 141	97.15 174	98.16 186	95.99 136	98.88 102	97.98 252	95.91 98	95.58 193	98.46 152	85.53 256	98.59 247	97.88 56	93.75 242	96.86 256

tttt0517		96.07 143	95.51 156	97.78 133	98.41 157	94.84 195	99.28 24	94.33 360	94.26 174	97.64 128	98.64 132	84.05 285	99.47 152	95.34 164	97.60 175	99.03 153

MVSTER		96.06 144	95.72 145	97.08 180	98.23 175	95.93 148	98.73 136	98.27 201	94.86 153	95.07 200	98.09 189	88.21 203	98.54 253	96.59 124	93.46 251	96.79 262

thisisatest0530		96.01 145	95.36 162	97.97 121	98.38 158	95.52 165	98.88 102	94.19 362	94.04 179	97.64 128	98.31 171	83.82 292	99.46 153	95.29 168	97.70 172	98.93 164

test_djsdf		96.00 146	95.69 151	96.93 190	95.72 325	95.49 166	99.47 9	98.40 177	94.98 147	94.58 213	97.86 209	89.16 179	98.41 275	96.91 106	94.12 232	96.88 251

RRT_MVS		95.98 147	95.78 142	96.56 222	96.48 296	94.22 225	99.57 6	97.92 259	95.89 99	93.95 245	98.70 126	89.27 175	98.42 267	97.23 95	93.02 260	97.04 233

EI-MVSNet		95.96 148	95.83 140	96.36 243	97.93 201	93.70 242	98.12 222	98.27 201	93.70 203	95.07 200	99.02 86	92.23 111	98.54 253	94.68 182	93.46 251	96.84 258

ECVR-MVS		95.95 149	95.71 148	96.65 208	99.02 104	90.86 296	99.03 70	91.80 371	96.96 55	98.10 91	99.26 45	81.31 302	99.51 145	96.90 109	99.04 111	99.59 72

BH-untuned		95.95 149	95.72 145	96.65 208	98.55 148	92.26 272	98.23 206	97.79 266	93.73 199	94.62 212	98.01 196	88.97 188	99.00 202	93.04 236	98.51 139	98.68 181

test1111		95.94 151	95.78 142	96.41 240	98.99 110	90.12 310	99.04 67	92.45 370	96.99 54	98.03 97	99.27 44	81.40 301	99.48 150	96.87 115	99.04 111	99.63 66

MSDG		95.93 152	95.30 169	97.83 128	98.90 116	95.36 170	96.83 326	98.37 183	91.32 290	94.43 222	98.73 124	90.27 157	99.60 127	90.05 298	98.82 125	98.52 191

BH-RMVSNet		95.92 153	95.32 166	97.69 143	98.32 170	94.64 203	98.19 213	97.45 294	94.56 163	96.03 186	98.61 134	85.02 265	99.12 182	90.68 289	99.06 110	99.30 116

test_fmvs1_n		95.90 154	95.99 135	95.63 274	98.67 138	88.32 340	99.26 27	98.22 208	96.40 80	99.67 7	99.26 45	73.91 349	99.70 107	99.02 6	99.50 84	98.87 167

Fast-Effi-MVS+-dtu		95.87 155	95.85 139	95.91 263	97.74 213	91.74 282	98.69 147	98.15 224	95.56 115	94.92 203	97.68 229	88.98 187	98.79 231	93.19 231	97.78 168	97.20 230

LFMVS		95.86 156	94.98 184	98.47 84	98.87 119	96.32 126	98.84 111	96.02 341	93.40 219	98.62 69	99.20 55	74.99 344	99.63 122	97.72 67	97.20 181	99.46 96

baseline1		95.84 157	95.12 177	98.01 119	98.49 152	95.98 137	98.73 136	97.03 316	95.37 126	96.22 181	98.19 183	89.96 161	99.16 174	94.60 187	87.48 326	98.90 166

OpenMVS	93.04 13	95.83 158	95.00 182	98.32 97	97.18 256	97.32 77	99.21 38	98.97 32	89.96 317	91.14 315	99.05 85	86.64 236	99.92 24	93.38 225	99.47 89	97.73 216

VDD-MVS		95.82 159	95.23 171	97.61 151	98.84 123	93.98 230	98.68 148	97.40 298	95.02 146	97.95 105	99.34 36	74.37 348	99.78 89	98.64 13	96.80 187	99.08 150

UniMVSNet (Re)		95.78 160	95.19 173	97.58 152	96.99 266	97.47 74	98.79 126	99.18 18	95.60 113	93.92 247	97.04 278	91.68 124	98.48 258	95.80 152	87.66 325	96.79 262

VPA-MVSNet		95.75 161	95.11 178	97.69 143	97.24 248	97.27 79	98.94 90	99.23 14	95.13 138	95.51 194	97.32 253	85.73 252	98.91 215	97.33 93	89.55 302	96.89 250

bld_raw_dy_0_64		95.74 162	95.31 168	97.03 182	96.35 302	95.76 156	99.12 52	97.37 301	95.97 96	94.70 211	98.48 148	85.80 251	98.49 257	96.55 126	93.48 250	96.84 258

HQP-MVS		95.72 163	95.40 157	96.69 206	97.20 252	94.25 223	98.05 228	98.46 165	96.43 77	94.45 218	97.73 221	86.75 234	98.96 207	95.30 166	94.18 228	96.86 256

hse-mvs2		95.71 164	95.30 169	96.93 190	98.50 150	93.53 247	98.36 189	98.10 234	97.48 21	98.67 63	97.99 198	89.76 163	99.02 199	97.95 50	80.91 356	98.22 202

UniMVSNet_NR-MVSNet		95.71 164	95.15 174	97.40 162	96.84 276	96.97 91	98.74 132	99.24 12	95.16 137	93.88 249	97.72 223	91.68 124	98.31 286	95.81 150	87.25 330	96.92 242

PatchmatchNet		95.71 164	95.52 155	96.29 249	97.58 222	90.72 300	96.84 325	97.52 287	94.06 178	97.08 143	96.96 287	89.24 177	98.90 218	92.03 265	98.37 147	99.26 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 167	95.33 165	96.76 201	96.16 311	94.63 204	98.43 185	98.39 179	96.64 69	95.02 202	98.78 118	85.15 264	99.05 192	95.21 172	94.20 227	96.60 285
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 167	95.38 161	96.61 215	97.61 220	93.84 234	98.91 94	98.44 169	95.25 133	94.28 229	98.47 150	86.04 249	99.12 182	95.50 162	93.95 237	96.87 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 169	95.69 151	95.44 281	97.54 227	88.54 335	96.97 311	97.56 279	93.50 215	97.52 133	96.93 291	89.49 167	99.16 174	95.25 170	96.42 200	98.64 186

FE-MVS		95.62 170	94.90 188	97.78 133	98.37 160	94.92 192	97.17 301	97.38 300	90.95 302	97.73 120	97.70 224	85.32 263	99.63 122	91.18 279	98.33 150	98.79 172

LPG-MVS_test		95.62 170	95.34 163	96.47 234	97.46 233	93.54 245	98.99 80	98.54 146	94.67 160	94.36 225	98.77 120	85.39 258	99.11 184	95.71 155	94.15 230	96.76 265

CLD-MVS		95.62 170	95.34 163	96.46 237	97.52 230	93.75 238	97.27 293	98.46 165	95.53 116	94.42 223	98.00 197	86.21 244	98.97 203	96.25 137	94.37 222	96.66 280
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 173	94.89 189	97.76 136	98.15 187	95.15 180	96.77 327	94.41 358	92.95 237	97.18 140	97.43 248	84.78 270	99.45 154	94.63 184	97.73 171	98.68 181

thres600view7		95.49 174	94.77 192	97.67 145	98.98 111	95.02 184	98.85 108	96.90 324	95.38 124	96.63 165	96.90 292	84.29 278	99.59 128	88.65 319	96.33 202	98.40 195

test_vis1_n		95.47 175	95.13 175	96.49 231	97.77 209	90.41 306	99.27 26	98.11 231	96.58 71	99.66 8	99.18 61	67.00 359	99.62 125	99.21 3	99.40 97	99.44 99

SCA		95.46 176	95.13 175	96.46 237	97.67 216	91.29 291	97.33 288	97.60 276	94.68 159	96.92 153	97.10 265	83.97 287	98.89 219	92.59 249	98.32 152	99.20 128

IterMVS-LS		95.46 176	95.21 172	96.22 251	98.12 188	93.72 241	98.32 196	98.13 227	93.71 201	94.26 230	97.31 254	92.24 110	98.10 302	94.63 184	90.12 293	96.84 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
jajsoiax		95.45 178	95.03 181	96.73 202	95.42 336	94.63 204	99.14 48	98.52 151	95.74 106	93.22 273	98.36 163	83.87 290	98.65 242	96.95 105	94.04 233	96.91 247

CVMVSNet		95.43 179	96.04 132	93.57 320	97.93 201	83.62 357	98.12 222	98.59 133	95.68 110	96.56 168	99.02 86	87.51 221	97.51 334	93.56 223	97.44 177	99.60 70

anonymousdsp		95.42 180	94.91 187	96.94 189	95.10 338	95.90 151	99.14 48	98.41 175	93.75 196	93.16 275	97.46 244	87.50 223	98.41 275	95.63 159	94.03 234	96.50 304

DU-MVS		95.42 180	94.76 193	97.40 162	96.53 292	96.97 91	98.66 153	98.99 31	95.43 121	93.88 249	97.69 226	88.57 195	98.31 286	95.81 150	87.25 330	96.92 242

mvs_tets		95.41 182	95.00 182	96.65 208	95.58 329	94.42 215	99.00 77	98.55 144	95.73 108	93.21 274	98.38 161	83.45 294	98.63 243	97.09 99	94.00 235	96.91 247

thres100view900		95.38 183	94.70 196	97.41 160	98.98 111	94.92 192	98.87 105	96.90 324	95.38 124	96.61 166	96.88 293	84.29 278	99.56 133	88.11 320	96.29 204	97.76 213

thres400		95.38 183	94.62 199	97.65 149	98.94 114	94.98 188	98.68 148	96.93 322	95.33 127	96.55 170	96.53 309	84.23 281	99.56 133	88.11 320	96.29 204	98.40 195

BH-w/o		95.38 183	95.08 179	96.26 250	98.34 166	91.79 279	97.70 261	97.43 296	92.87 240	94.24 232	97.22 260	88.66 193	98.84 225	91.55 275	97.70 172	98.16 205

VDDNet		95.36 186	94.53 203	97.86 126	98.10 190	95.13 181	98.85 108	97.75 268	90.46 308	98.36 82	99.39 20	73.27 351	99.64 119	97.98 48	96.58 194	98.81 171

TAPA-MVS	93.98 7	95.35 187	94.56 202	97.74 138	99.13 96	94.83 197	98.33 192	98.64 126	86.62 341	96.29 180	98.61 134	94.00 91	99.29 162	80.00 358	99.41 95	99.09 146
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 188	94.98 184	96.43 239	97.67 216	93.48 249	98.73 136	98.44 169	94.94 152	92.53 295	98.53 143	84.50 277	99.14 179	95.48 163	94.00 235	96.66 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 189	94.87 190	96.71 203	99.29 68	93.24 260	98.58 163	98.11 231	89.92 318	93.57 261	99.10 74	86.37 242	99.79 86	90.78 287	98.10 157	97.09 231
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpn200view9		95.32 190	94.62 199	97.43 159	98.94 114	94.98 188	98.68 148	96.93 322	95.33 127	96.55 170	96.53 309	84.23 281	99.56 133	88.11 320	96.29 204	97.76 213

Anonymous202405211		95.28 191	94.49 205	97.67 145	99.00 107	93.75 238	98.70 145	97.04 315	90.66 304	96.49 174	98.80 116	78.13 326	99.83 57	96.21 138	95.36 220	99.44 99

thres200		95.25 192	94.57 201	97.28 167	98.81 125	94.92 192	98.20 210	97.11 311	95.24 135	96.54 172	96.22 320	84.58 275	99.53 141	87.93 324	96.50 198	97.39 224

AllTest		95.24 193	94.65 198	96.99 184	99.25 76	93.21 261	98.59 161	98.18 216	91.36 286	93.52 263	98.77 120	84.67 273	99.72 101	89.70 305	97.87 164	98.02 208

LCM-MVSNet-Re		95.22 194	95.32 166	94.91 295	98.18 183	87.85 346	98.75 129	95.66 347	95.11 140	88.96 333	96.85 296	90.26 158	97.65 327	95.65 158	98.44 143	99.22 127

EPNet_dtu		95.21 195	94.95 186	95.99 258	96.17 309	90.45 305	98.16 218	97.27 306	96.77 63	93.14 278	98.33 169	90.34 155	98.42 267	85.57 337	98.81 126	99.09 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 196	94.45 210	97.46 156	96.75 281	96.56 112	98.86 107	98.65 125	93.30 224	93.27 272	98.27 176	84.85 269	98.87 222	94.82 179	91.26 281	96.96 239

D2MVS		95.18 197	95.08 179	95.48 278	97.10 261	92.07 275	98.30 199	99.13 21	94.02 181	92.90 283	96.73 300	89.48 168	98.73 235	94.48 192	93.60 249	95.65 332

WR-MVS		95.15 198	94.46 208	97.22 169	96.67 286	96.45 117	98.21 208	98.81 75	94.15 175	93.16 275	97.69 226	87.51 221	98.30 288	95.29 168	88.62 316	96.90 249

TranMVSNet+NR-MVSNet		95.14 199	94.48 206	97.11 178	96.45 298	96.36 124	99.03 70	99.03 27	95.04 145	93.58 260	97.93 203	88.27 202	98.03 309	94.13 203	86.90 335	96.95 241

baseline2		95.11 200	94.52 204	96.87 195	96.65 287	93.56 244	98.27 204	94.10 364	93.45 217	92.02 308	97.43 248	87.45 225	99.19 172	93.88 212	97.41 179	97.87 211

miper_enhance_ethall		95.10 201	94.75 194	96.12 255	97.53 229	93.73 240	96.61 333	98.08 239	92.20 266	93.89 248	96.65 305	92.44 105	98.30 288	94.21 201	91.16 282	96.34 312

Anonymous20240529		95.10 201	94.22 218	97.75 137	99.01 106	94.26 222	98.87 105	98.83 69	85.79 349	96.64 164	98.97 93	78.73 320	99.85 51	96.27 134	94.89 221	99.12 143

test-LLR		95.10 201	94.87 190	95.80 268	96.77 278	89.70 315	96.91 316	95.21 350	95.11 140	94.83 207	95.72 331	87.71 217	98.97 203	93.06 234	98.50 140	98.72 177

WR-MVS_H		95.05 204	94.46 208	96.81 199	96.86 275	95.82 154	99.24 30	99.24 12	93.87 190	92.53 295	96.84 297	90.37 154	98.24 294	93.24 229	87.93 322	96.38 311

miper_ehance_all_eth		95.01 205	94.69 197	95.97 260	97.70 215	93.31 256	97.02 309	98.07 241	92.23 263	93.51 265	96.96 287	91.85 121	98.15 298	93.68 217	91.16 282	96.44 309

ADS-MVSNet		95.00 206	94.45 210	96.63 212	98.00 195	91.91 278	96.04 340	97.74 269	90.15 314	96.47 175	96.64 306	87.89 212	98.96 207	90.08 296	97.06 182	99.02 154

VPNet		94.99 207	94.19 220	97.40 162	97.16 257	96.57 111	98.71 141	98.97 32	95.67 111	94.84 205	98.24 180	80.36 311	98.67 241	96.46 129	87.32 329	96.96 239

EPMVS		94.99 207	94.48 206	96.52 229	97.22 250	91.75 281	97.23 294	91.66 372	94.11 176	97.28 136	96.81 298	85.70 253	98.84 225	93.04 236	97.28 180	98.97 159

NR-MVSNet		94.98 209	94.16 222	97.44 158	96.53 292	97.22 85	98.74 132	98.95 35	94.96 149	89.25 332	97.69 226	89.32 173	98.18 296	94.59 189	87.40 328	96.92 242

FMVSNet3		94.97 210	94.26 217	97.11 178	98.18 183	96.62 105	98.56 169	98.26 205	93.67 208	94.09 239	97.10 265	84.25 280	98.01 310	92.08 261	92.14 268	96.70 274

CostFormer		94.95 211	94.73 195	95.60 276	97.28 246	89.06 326	97.53 273	96.89 326	89.66 323	96.82 158	96.72 301	86.05 247	98.95 212	95.53 161	96.13 213	98.79 172

PAPM		94.95 211	94.00 232	97.78 133	97.04 263	95.65 159	96.03 342	98.25 206	91.23 295	94.19 235	97.80 218	91.27 138	98.86 224	82.61 352	97.61 174	98.84 170

CP-MVSNet		94.94 213	94.30 216	96.83 197	96.72 283	95.56 162	99.11 54	98.95 35	93.89 188	92.42 300	97.90 205	87.19 227	98.12 301	94.32 197	88.21 319	96.82 261

TR-MVS		94.94 213	94.20 219	97.17 173	97.75 210	94.14 227	97.59 270	97.02 318	92.28 262	95.75 191	97.64 232	83.88 289	98.96 207	89.77 302	96.15 212	98.40 195

RPSCF		94.87 215	95.40 157	93.26 326	98.89 117	82.06 362	98.33 192	98.06 246	90.30 313	96.56 168	99.26 45	87.09 228	99.49 146	93.82 214	96.32 203	98.24 201

GA-MVS		94.81 216	94.03 228	97.14 175	97.15 258	93.86 233	96.76 328	97.58 277	94.00 183	94.76 210	97.04 278	80.91 306	98.48 258	91.79 270	96.25 209	99.09 146

c3_l		94.79 217	94.43 212	95.89 265	97.75 210	93.12 264	97.16 303	98.03 249	92.23 263	93.46 268	97.05 277	91.39 133	98.01 310	93.58 222	89.21 308	96.53 296

V42		94.78 218	94.14 224	96.70 205	96.33 304	95.22 176	98.97 83	98.09 238	92.32 260	94.31 228	97.06 275	88.39 200	98.55 251	92.90 241	88.87 314	96.34 312

CR-MVSNet		94.76 219	94.15 223	96.59 218	97.00 264	93.43 250	94.96 352	97.56 279	92.46 251	96.93 151	96.24 316	88.15 205	97.88 322	87.38 326	96.65 192	98.46 193

v2v482		94.69 220	94.03 228	96.65 208	96.17 309	94.79 200	98.67 151	98.08 239	92.72 244	94.00 244	97.16 263	87.69 220	98.45 263	92.91 240	88.87 314	96.72 270

pmmvs4		94.69 220	93.99 234	96.81 199	95.74 324	95.94 145	97.40 279	97.67 271	90.42 310	93.37 269	97.59 236	89.08 182	98.20 295	92.97 238	91.67 275	96.30 316

cl22		94.68 222	94.19 220	96.13 254	98.11 189	93.60 243	96.94 313	98.31 192	92.43 255	93.32 271	96.87 295	86.51 237	98.28 292	94.10 206	91.16 282	96.51 302

eth_miper_zixun_eth		94.68 222	94.41 213	95.47 279	97.64 218	91.71 283	96.73 330	98.07 241	92.71 245	93.64 258	97.21 261	90.54 152	98.17 297	93.38 225	89.76 297	96.54 294

PCF-MVS	93.45 11	94.68 222	93.43 268	98.42 92	98.62 143	96.77 100	95.48 350	98.20 211	84.63 354	93.34 270	98.32 170	88.55 197	99.81 69	84.80 344	98.96 116	98.68 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 225	93.54 264	98.08 115	96.88 274	96.56 112	98.19 213	98.50 158	78.05 363	92.69 290	98.02 194	91.07 143	99.63 122	90.09 295	98.36 149	98.04 207

PS-CasMVS		94.67 225	93.99 234	96.71 203	96.68 285	95.26 175	99.13 51	99.03 27	93.68 206	92.33 301	97.95 202	85.35 260	98.10 302	93.59 221	88.16 321	96.79 262

cascas		94.63 227	93.86 243	96.93 190	96.91 272	94.27 221	96.00 343	98.51 153	85.55 350	94.54 214	96.23 318	84.20 283	98.87 222	95.80 152	96.98 185	97.66 219

tpmvs		94.60 228	94.36 215	95.33 284	97.46 233	88.60 334	96.88 322	97.68 270	91.29 292	93.80 254	96.42 313	88.58 194	99.24 166	91.06 282	96.04 215	98.17 204

LTVRE_ROB	92.95 15	94.60 228	93.90 240	96.68 207	97.41 242	94.42 215	98.52 171	98.59 133	91.69 278	91.21 314	98.35 164	84.87 268	99.04 195	91.06 282	93.44 254	96.60 285
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
v1144		94.59 230	93.92 237	96.60 217	96.21 306	94.78 201	98.59 161	98.14 226	91.86 274	94.21 234	97.02 280	87.97 210	98.41 275	91.72 272	89.57 300	96.61 284

ADS-MVSNet2		94.58 231	94.40 214	95.11 290	98.00 195	88.74 332	96.04 340	97.30 303	90.15 314	96.47 175	96.64 306	87.89 212	97.56 332	90.08 296	97.06 182	99.02 154

ACMH	92.88 16	94.55 232	93.95 236	96.34 245	97.63 219	93.26 258	98.81 120	98.49 163	93.43 218	89.74 327	98.53 143	81.91 298	99.08 190	93.69 216	93.30 257	96.70 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 233	93.85 244	96.63 212	97.98 199	93.06 266	98.77 128	97.84 264	93.67 208	93.80 254	98.04 193	76.88 337	98.96 207	94.79 181	92.86 263	97.86 212

XVG-ACMP-BASELINE		94.54 233	94.14 224	95.75 271	96.55 291	91.65 284	98.11 224	98.44 169	94.96 149	94.22 233	97.90 205	79.18 319	99.11 184	94.05 208	93.85 239	96.48 306

AUN-MVS		94.53 235	93.73 254	96.92 193	98.50 150	93.52 248	98.34 191	98.10 234	93.83 193	95.94 190	97.98 200	85.59 255	99.03 196	94.35 195	80.94 355	98.22 202

DIV-MVS_self_test		94.52 236	94.03 228	95.99 258	97.57 226	93.38 254	97.05 307	97.94 257	91.74 275	92.81 285	97.10 265	89.12 180	98.07 306	92.60 247	90.30 290	96.53 296

cl____		94.51 237	94.01 231	96.02 257	97.58 222	93.40 253	97.05 307	97.96 256	91.73 277	92.76 287	97.08 271	89.06 183	98.13 300	92.61 246	90.29 291	96.52 299

GBi-Net		94.49 238	93.80 247	96.56 222	98.21 177	95.00 185	98.82 114	98.18 216	92.46 251	94.09 239	97.07 272	81.16 303	97.95 314	92.08 261	92.14 268	96.72 270

test1		94.49 238	93.80 247	96.56 222	98.21 177	95.00 185	98.82 114	98.18 216	92.46 251	94.09 239	97.07 272	81.16 303	97.95 314	92.08 261	92.14 268	96.72 270

v8		94.47 240	93.77 250	96.57 221	96.36 301	94.83 197	99.05 64	98.19 213	91.92 271	93.16 275	96.97 285	88.82 192	98.48 258	91.69 273	87.79 323	96.39 310

FMVSNet2		94.47 240	93.61 260	97.04 181	98.21 177	96.43 119	98.79 126	98.27 201	92.46 251	93.50 266	97.09 269	81.16 303	98.00 312	91.09 280	91.93 271	96.70 274

test2506		94.44 242	93.91 239	96.04 256	99.02 104	88.99 329	99.06 62	79.47 383	96.96 55	98.36 82	99.26 45	77.21 334	99.52 144	96.78 121	99.04 111	99.59 72

Patchmatch-test		94.42 243	93.68 258	96.63 212	97.60 221	91.76 280	94.83 356	97.49 291	89.45 326	94.14 237	97.10 265	88.99 184	98.83 227	85.37 340	98.13 156	99.29 118

PEN-MVS		94.42 243	93.73 254	96.49 231	96.28 305	94.84 195	99.17 44	99.00 29	93.51 214	92.23 303	97.83 215	86.10 246	97.90 318	92.55 252	86.92 334	96.74 267

v144192		94.39 245	93.70 256	96.48 233	96.06 314	94.35 219	98.58 163	98.16 223	91.45 283	94.33 227	97.02 280	87.50 223	98.45 263	91.08 281	89.11 309	96.63 282

Baseline_NR-MVSNet		94.35 246	93.81 246	95.96 261	96.20 307	94.05 229	98.61 160	96.67 335	91.44 284	93.85 251	97.60 235	88.57 195	98.14 299	94.39 193	86.93 333	95.68 331

miper_lstm_enhance		94.33 247	94.07 227	95.11 290	97.75 210	90.97 295	97.22 295	98.03 249	91.67 279	92.76 287	96.97 285	90.03 160	97.78 325	92.51 254	89.64 299	96.56 291

v1192		94.32 248	93.58 261	96.53 228	96.10 312	94.45 213	98.50 176	98.17 221	91.54 281	94.19 235	97.06 275	86.95 232	98.43 266	90.14 294	89.57 300	96.70 274

ACMH+	92.99 14	94.30 249	93.77 250	95.88 266	97.81 207	92.04 277	98.71 141	98.37 183	93.99 184	90.60 321	98.47 150	80.86 308	99.05 192	92.75 245	92.40 267	96.55 293

v148		94.29 250	93.76 252	95.91 263	96.10 312	92.93 267	98.58 163	97.97 254	92.59 249	93.47 267	96.95 289	88.53 198	98.32 284	92.56 251	87.06 332	96.49 305

v10		94.29 250	93.55 263	96.51 230	96.39 300	94.80 199	98.99 80	98.19 213	91.35 288	93.02 281	96.99 283	88.09 207	98.41 275	90.50 291	88.41 318	96.33 314

MVP-Stereo		94.28 252	93.92 237	95.35 283	94.95 340	92.60 270	97.97 236	97.65 272	91.61 280	90.68 320	97.09 269	86.32 243	98.42 267	89.70 305	99.34 101	95.02 343
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 253	93.33 270	96.97 187	97.19 255	93.38 254	98.74 132	98.57 140	91.21 297	93.81 253	98.58 139	72.85 352	98.77 233	95.05 174	93.93 238	98.77 176

OurMVSNet-221017-0		94.21 254	94.00 232	94.85 298	95.60 328	89.22 324	98.89 99	97.43 296	95.29 130	92.18 304	98.52 146	82.86 295	98.59 247	93.46 224	91.76 273	96.74 267

v1921920		94.20 255	93.47 267	96.40 242	95.98 317	94.08 228	98.52 171	98.15 224	91.33 289	94.25 231	97.20 262	86.41 241	98.42 267	90.04 299	89.39 306	96.69 279

v7n		94.19 256	93.43 268	96.47 234	95.90 320	94.38 218	99.26 27	98.34 188	91.99 269	92.76 287	97.13 264	88.31 201	98.52 255	89.48 310	87.70 324	96.52 299

tpm2		94.19 256	93.76 252	95.46 280	97.23 249	89.04 327	97.31 290	96.85 330	87.08 340	96.21 182	96.79 299	83.75 293	98.74 234	92.43 257	96.23 210	98.59 188

TESTMET0.1,1		94.18 258	93.69 257	95.63 274	96.92 270	89.12 325	96.91 316	94.78 355	93.17 229	94.88 204	96.45 312	78.52 321	98.92 214	93.09 233	98.50 140	98.85 168

dp		94.15 259	93.90 240	94.90 296	97.31 245	86.82 351	96.97 311	97.19 310	91.22 296	96.02 187	96.61 308	85.51 257	99.02 199	90.00 300	94.30 223	98.85 168

ET-MVSNet_ETH3D		94.13 260	92.98 276	97.58 152	98.22 176	96.20 130	97.31 290	95.37 349	94.53 164	79.56 362	97.63 234	86.51 237	97.53 333	96.91 106	90.74 286	99.02 154

tpm		94.13 260	93.80 247	95.12 289	96.50 294	87.91 345	97.44 276	95.89 346	92.62 247	96.37 179	96.30 315	84.13 284	98.30 288	93.24 229	91.66 276	99.14 141

IterMVS-SCA-FT		94.11 262	93.87 242	94.85 298	97.98 199	90.56 304	97.18 299	98.11 231	93.75 196	92.58 293	97.48 243	83.97 287	97.41 336	92.48 256	91.30 279	96.58 287

Anonymous20231211		94.10 263	93.26 273	96.61 215	99.11 98	94.28 220	99.01 75	98.88 51	86.43 343	92.81 285	97.57 238	81.66 300	98.68 240	94.83 178	89.02 312	96.88 251

IterMVS		94.09 264	93.85 244	94.80 301	97.99 197	90.35 307	97.18 299	98.12 228	93.68 206	92.46 299	97.34 251	84.05 285	97.41 336	92.51 254	91.33 278	96.62 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 265	93.51 265	95.80 268	96.77 278	89.70 315	96.91 316	95.21 350	92.89 239	94.83 207	95.72 331	77.69 329	98.97 203	93.06 234	98.50 140	98.72 177

test0.0.03 1		94.08 265	93.51 265	95.80 268	95.53 331	92.89 268	97.38 281	95.97 343	95.11 140	92.51 297	96.66 303	87.71 217	96.94 343	87.03 328	93.67 244	97.57 220

v1240		94.06 267	93.29 272	96.34 245	96.03 316	93.90 232	98.44 183	98.17 221	91.18 298	94.13 238	97.01 282	86.05 247	98.42 267	89.13 315	89.50 304	96.70 274

X-MVStestdata		94.06 267	92.30 288	99.34 23	99.70 22	98.35 41	99.29 22	98.88 51	97.40 25	98.46 75	43.50 376	95.90 39	99.89 37	97.85 58	99.74 42	99.78 15

DTE-MVSNet		93.98 269	93.26 273	96.14 253	96.06 314	94.39 217	99.20 39	98.86 64	93.06 232	91.78 309	97.81 217	85.87 250	97.58 331	90.53 290	86.17 339	96.46 308

pm-mvs1		93.94 270	93.06 275	96.59 218	96.49 295	95.16 178	98.95 88	98.03 249	92.32 260	91.08 316	97.84 212	84.54 276	98.41 275	92.16 259	86.13 341	96.19 320

MS-PatchMatch		93.84 271	93.63 259	94.46 312	96.18 308	89.45 320	97.76 256	98.27 201	92.23 263	92.13 305	97.49 242	79.50 316	98.69 237	89.75 303	99.38 99	95.25 336

tfpnnormal		93.66 272	92.70 282	96.55 227	96.94 269	95.94 145	98.97 83	99.19 17	91.04 300	91.38 313	97.34 251	84.94 267	98.61 244	85.45 339	89.02 312	95.11 340

EU-MVSNet		93.66 272	94.14 224	92.25 335	95.96 319	83.38 358	98.52 171	98.12 228	94.69 158	92.61 292	98.13 187	87.36 226	96.39 354	91.82 269	90.00 295	96.98 237

our_test_3		93.65 274	93.30 271	94.69 303	95.45 334	89.68 317	96.91 316	97.65 272	91.97 270	91.66 311	96.88 293	89.67 166	97.93 317	88.02 323	91.49 277	96.48 306

pmmvs5		93.65 274	92.97 277	95.68 272	95.49 332	92.37 271	98.20 210	97.28 305	89.66 323	92.58 293	97.26 256	82.14 297	98.09 304	93.18 232	90.95 285	96.58 287

test_fmvs2		93.43 276	93.58 261	92.95 330	96.97 267	83.91 356	99.19 41	97.24 308	95.74 106	95.20 198	98.27 176	69.65 354	98.72 236	96.26 135	93.73 243	96.24 317

tpm cat1		93.36 277	92.80 279	95.07 292	97.58 222	87.97 344	96.76 328	97.86 263	82.17 359	93.53 262	96.04 324	86.13 245	99.13 180	89.24 313	95.87 216	98.10 206

JIA-IIPM		93.35 278	92.49 285	95.92 262	96.48 296	90.65 302	95.01 351	96.96 320	85.93 347	96.08 185	87.33 366	87.70 219	98.78 232	91.35 277	95.58 218	98.34 198

SixPastTwentyTwo		93.34 279	92.86 278	94.75 302	95.67 326	89.41 322	98.75 129	96.67 335	93.89 188	90.15 325	98.25 179	80.87 307	98.27 293	90.90 285	90.64 287	96.57 289

USDC		93.33 280	92.71 281	95.21 286	96.83 277	90.83 298	96.91 316	97.50 289	93.84 191	90.72 319	98.14 186	77.69 329	98.82 228	89.51 309	93.21 259	95.97 325

IB-MVS	91.98 17	93.27 281	91.97 292	97.19 171	97.47 232	93.41 252	97.09 306	95.99 342	93.32 222	92.47 298	95.73 329	78.06 327	99.53 141	94.59 189	82.98 347	98.62 187
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
MIMVSNet		93.26 282	92.21 289	96.41 240	97.73 214	93.13 263	95.65 347	97.03 316	91.27 294	94.04 242	96.06 323	75.33 342	97.19 339	86.56 330	96.23 210	98.92 165

ppachtmachnet_test		93.22 283	92.63 283	94.97 294	95.45 334	90.84 297	96.88 322	97.88 262	90.60 305	92.08 306	97.26 256	88.08 208	97.86 323	85.12 341	90.33 289	96.22 318

Patchmtry		93.22 283	92.35 287	95.84 267	96.77 278	93.09 265	94.66 359	97.56 279	87.37 339	92.90 283	96.24 316	88.15 205	97.90 318	87.37 327	90.10 294	96.53 296

FMVSNet1		93.19 285	92.07 290	96.56 222	97.54 227	95.00 185	98.82 114	98.18 216	90.38 311	92.27 302	97.07 272	73.68 350	97.95 314	89.36 312	91.30 279	96.72 270

LF4IMVS		93.14 286	92.79 280	94.20 315	95.88 321	88.67 333	97.66 264	97.07 313	93.81 194	91.71 310	97.65 230	77.96 328	98.81 229	91.47 276	91.92 272	95.12 339

testgi		93.06 287	92.45 286	94.88 297	96.43 299	89.90 311	98.75 129	97.54 285	95.60 113	91.63 312	97.91 204	74.46 347	97.02 341	86.10 333	93.67 244	97.72 217

PatchT		93.06 287	91.97 292	96.35 244	96.69 284	92.67 269	94.48 360	97.08 312	86.62 341	97.08 143	92.23 360	87.94 211	97.90 318	78.89 362	96.69 190	98.49 192

MVS_0304		92.81 289	92.01 291	95.23 285	97.46 233	91.33 289	98.17 217	98.81 75	91.13 299	93.80 254	95.68 334	66.08 361	98.06 307	90.79 286	96.13 213	96.32 315

RPMNet		92.81 289	91.34 297	97.24 168	97.00 264	93.43 250	94.96 352	98.80 83	82.27 358	96.93 151	92.12 361	86.98 231	99.82 64	76.32 366	96.65 192	98.46 193

TransMVSNet (Re)		92.67 291	91.51 296	96.15 252	96.58 290	94.65 202	98.90 95	96.73 331	90.86 303	89.46 331	97.86 209	85.62 254	98.09 304	86.45 331	81.12 353	95.71 330

K. test v3		92.55 292	91.91 294	94.48 310	95.64 327	89.24 323	99.07 61	94.88 354	94.04 179	86.78 346	97.59 236	77.64 332	97.64 328	92.08 261	89.43 305	96.57 289

DSMNet-mixed		92.52 293	92.58 284	92.33 333	94.15 348	82.65 360	98.30 199	94.26 361	89.08 331	92.65 291	95.73 329	85.01 266	95.76 357	86.24 332	97.76 169	98.59 188

TinyColmap		92.31 294	91.53 295	94.65 305	96.92 270	89.75 313	96.92 314	96.68 334	90.45 309	89.62 328	97.85 211	76.06 340	98.81 229	86.74 329	92.51 266	95.41 334

gg-mvs-nofinetune		92.21 295	90.58 303	97.13 176	96.75 281	95.09 182	95.85 344	89.40 376	85.43 351	94.50 216	81.98 369	80.80 309	98.40 281	92.16 259	98.33 150	97.88 210

FMVSNet5		91.81 296	90.92 299	94.49 309	97.21 251	92.09 274	98.00 234	97.55 284	89.31 329	90.86 318	95.61 335	74.48 346	95.32 361	85.57 337	89.70 298	96.07 323

pmmvs6		91.77 297	90.63 302	95.17 288	94.69 346	91.24 292	98.67 151	97.92 259	86.14 345	89.62 328	97.56 240	75.79 341	98.34 282	90.75 288	84.56 343	95.94 326

Anonymous20231206		91.66 298	91.10 298	93.33 324	94.02 352	87.35 348	98.58 163	97.26 307	90.48 307	90.16 324	96.31 314	83.83 291	96.53 352	79.36 360	89.90 296	96.12 321

Patchmatch-RL test		91.49 299	90.85 300	93.41 322	91.37 361	84.40 354	92.81 364	95.93 345	91.87 273	87.25 343	94.87 341	88.99 184	96.53 352	92.54 253	82.00 349	99.30 116

test_0402		91.32 300	90.27 306	94.48 310	96.60 288	91.12 293	98.50 176	97.22 309	86.10 346	88.30 339	96.98 284	77.65 331	97.99 313	78.13 364	92.94 262	94.34 347

test_vis1_rt		91.29 301	90.65 301	93.19 328	97.45 237	86.25 352	98.57 168	90.90 374	93.30 224	86.94 345	93.59 351	62.07 364	99.11 184	97.48 87	95.58 218	94.22 350

PVSNet_0	88.72 19	91.28 302	90.03 308	95.00 293	97.99 197	87.29 349	94.84 355	98.50 158	92.06 268	89.86 326	95.19 337	79.81 315	99.39 156	92.27 258	69.79 369	98.33 199

Anonymous20240521		91.18 303	90.44 304	93.42 321	93.70 353	88.47 337	98.94 90	97.56 279	88.46 334	89.56 330	95.08 340	77.15 336	96.97 342	83.92 347	89.55 302	94.82 345

EG-PatchMatch MVS		91.13 304	90.12 307	94.17 317	94.73 345	89.00 328	98.13 221	97.81 265	89.22 330	85.32 355	96.46 311	67.71 357	98.42 267	87.89 325	93.82 240	95.08 341

TDRefinement		91.06 305	89.68 310	95.21 286	85.35 374	91.49 287	98.51 175	97.07 313	91.47 282	88.83 337	97.84 212	77.31 333	99.09 189	92.79 244	77.98 362	95.04 342

UnsupCasMVSNet_eth		90.99 306	89.92 309	94.19 316	94.08 349	89.83 312	97.13 305	98.67 118	93.69 204	85.83 352	96.19 321	75.15 343	96.74 346	89.14 314	79.41 358	96.00 324

test20.03		90.89 307	90.38 305	92.43 332	93.48 354	88.14 343	98.33 192	97.56 279	93.40 219	87.96 340	96.71 302	80.69 310	94.13 366	79.15 361	86.17 339	95.01 344

MDA-MVSNet_test_wron		90.71 308	89.38 313	94.68 304	94.83 342	90.78 299	97.19 298	97.46 292	87.60 337	72.41 369	95.72 331	86.51 237	96.71 349	85.92 335	86.80 336	96.56 291

YYNet1		90.70 309	89.39 312	94.62 306	94.79 344	90.65 302	97.20 297	97.46 292	87.54 338	72.54 368	95.74 327	86.51 237	96.66 350	86.00 334	86.76 337	96.54 294

KD-MVS_self_test		90.38 310	89.38 313	93.40 323	92.85 357	88.94 330	97.95 237	97.94 257	90.35 312	90.25 323	93.96 348	79.82 314	95.94 356	84.62 346	76.69 364	95.33 335

pmmvs-eth3d		90.36 311	89.05 316	94.32 314	91.10 363	92.12 273	97.63 269	96.95 321	88.86 332	84.91 356	93.13 355	78.32 323	96.74 346	88.70 318	81.81 351	94.09 353

CL-MVSNet_self_test		90.11 312	89.14 315	93.02 329	91.86 360	88.23 342	96.51 336	98.07 241	90.49 306	90.49 322	94.41 343	84.75 271	95.34 360	80.79 356	74.95 366	95.50 333

new_pmnet		90.06 313	89.00 317	93.22 327	94.18 347	88.32 340	96.42 338	96.89 326	86.19 344	85.67 353	93.62 350	77.18 335	97.10 340	81.61 354	89.29 307	94.23 349

MDA-MVSNet-bldmvs		89.97 314	88.35 319	94.83 300	95.21 337	91.34 288	97.64 266	97.51 288	88.36 335	71.17 370	96.13 322	79.22 318	96.63 351	83.65 348	86.27 338	96.52 299

CMPMVS	66.06 21	89.70 315	89.67 311	89.78 340	93.19 355	76.56 365	97.00 310	98.35 186	80.97 360	81.57 360	97.75 220	74.75 345	98.61 244	89.85 301	93.63 247	94.17 351
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 316	88.28 320	93.82 318	92.81 358	91.08 294	98.01 232	97.45 294	87.95 336	87.90 341	95.87 326	67.63 358	94.56 365	78.73 363	88.18 320	95.83 328

KD-MVS_2432*1600		89.61 317	87.96 323	94.54 307	94.06 350	91.59 285	95.59 348	97.63 274	89.87 319	88.95 334	94.38 345	78.28 324	96.82 344	84.83 342	68.05 370	95.21 337

miper_refine_blended		89.61 317	87.96 323	94.54 307	94.06 350	91.59 285	95.59 348	97.63 274	89.87 319	88.95 334	94.38 345	78.28 324	96.82 344	84.83 342	68.05 370	95.21 337

MVS-HIRNet		89.46 319	88.40 318	92.64 331	97.58 222	82.15 361	94.16 363	93.05 369	75.73 365	90.90 317	82.52 368	79.42 317	98.33 283	83.53 349	98.68 128	97.43 221

OpenMVS_ROB	86.42 20	89.00 320	87.43 327	93.69 319	93.08 356	89.42 321	97.91 241	96.89 326	78.58 362	85.86 351	94.69 342	69.48 355	98.29 291	77.13 365	93.29 258	93.36 359

mvsany_test3		88.80 321	88.04 321	91.09 339	89.78 366	81.57 363	97.83 252	95.49 348	93.81 194	87.53 342	93.95 349	56.14 367	97.43 335	94.68 182	83.13 346	94.26 348

new-patchmatchnet		88.50 322	87.45 326	91.67 337	90.31 365	85.89 353	97.16 303	97.33 302	89.47 325	83.63 358	92.77 357	76.38 338	95.06 363	82.70 351	77.29 363	94.06 355

APD_test1		88.22 323	88.01 322	88.86 342	95.98 317	74.66 371	97.21 296	96.44 339	83.96 356	86.66 348	97.90 205	60.95 365	97.84 324	82.73 350	90.23 292	94.09 353

PM-MVS		87.77 324	86.55 329	91.40 338	91.03 364	83.36 359	96.92 314	95.18 352	91.28 293	86.48 350	93.42 352	53.27 368	96.74 346	89.43 311	81.97 350	94.11 352

test_fmvs3		87.17 325	87.06 328	87.50 344	91.21 362	75.66 367	99.05 64	96.61 337	92.79 243	88.85 336	92.78 356	43.72 371	93.49 367	93.95 209	84.56 343	93.34 360

UnsupCasMVSNet_bld		87.17 325	85.12 331	93.31 325	91.94 359	88.77 331	94.92 354	98.30 198	84.30 355	82.30 359	90.04 363	63.96 363	97.25 338	85.85 336	74.47 368	93.93 357

N_pmnet		87.12 327	87.77 325	85.17 348	95.46 333	61.92 378	97.37 283	70.66 384	85.83 348	88.73 338	96.04 324	85.33 262	97.76 326	80.02 357	90.48 288	95.84 327

pmmvs3		86.67 328	84.86 332	92.11 336	88.16 368	87.19 350	96.63 332	94.75 356	79.88 361	87.22 344	92.75 358	66.56 360	95.20 362	81.24 355	76.56 365	93.96 356

test_f		86.07 329	85.39 330	88.10 343	89.28 367	75.57 368	97.73 259	96.33 340	89.41 328	85.35 354	91.56 362	43.31 373	95.53 358	91.32 278	84.23 345	93.21 361

test_vis3_rt		79.22 330	77.40 336	84.67 349	86.44 372	74.85 370	97.66 264	81.43 381	84.98 352	67.12 372	81.91 370	28.09 381	97.60 329	88.96 316	80.04 357	81.55 370

test_method		79.03 331	78.17 333	81.63 353	86.06 373	54.40 383	82.75 372	96.89 326	39.54 376	80.98 361	95.57 336	58.37 366	94.73 364	84.74 345	78.61 359	95.75 329

testf1		79.02 332	77.70 334	82.99 351	88.10 369	66.90 375	94.67 357	93.11 366	71.08 367	74.02 365	93.41 353	34.15 377	93.25 368	72.25 369	78.50 360	88.82 365

APD_test2		79.02 332	77.70 334	82.99 351	88.10 369	66.90 375	94.67 357	93.11 366	71.08 367	74.02 365	93.41 353	34.15 377	93.25 368	72.25 369	78.50 360	88.82 365

LCM-MVSNet		78.70 334	76.24 339	86.08 346	77.26 380	71.99 373	94.34 361	96.72 332	61.62 371	76.53 363	89.33 364	33.91 379	92.78 371	81.85 353	74.60 367	93.46 358

Gipuma		78.40 335	76.75 338	83.38 350	95.54 330	80.43 364	79.42 373	97.40 298	64.67 370	73.46 367	80.82 371	45.65 370	93.14 370	66.32 372	87.43 327	76.56 373
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 336	75.44 340	85.46 347	82.54 375	74.95 369	94.23 362	93.08 368	72.80 366	74.68 364	87.38 365	36.36 376	91.56 372	73.95 367	63.94 372	89.87 364

FPMVS		77.62 337	77.14 337	79.05 355	79.25 378	60.97 379	95.79 345	95.94 344	65.96 369	67.93 371	94.40 344	37.73 375	88.88 374	68.83 371	88.46 317	87.29 367

EGC-MVSNET		75.22 338	69.54 341	92.28 334	94.81 343	89.58 318	97.64 266	96.50 338	1.82 381	5.57 382	95.74 327	68.21 356	96.26 355	73.80 368	91.71 274	90.99 363

ANet_high		69.08 339	65.37 343	80.22 354	65.99 382	71.96 374	90.91 368	90.09 375	82.62 357	49.93 377	78.39 372	29.36 380	81.75 375	62.49 373	38.52 376	86.95 369

tmp_tt		68.90 340	66.97 342	74.68 357	50.78 384	59.95 380	87.13 369	83.47 380	38.80 377	62.21 373	96.23 318	64.70 362	76.91 379	88.91 317	30.49 377	87.19 368

PMVS	61.03 23	65.95 341	63.57 345	73.09 358	57.90 383	51.22 384	85.05 371	93.93 365	54.45 372	44.32 378	83.57 367	13.22 382	89.15 373	58.68 374	81.00 354	78.91 372
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 342	64.25 344	67.02 359	82.28 376	59.36 381	91.83 367	85.63 378	52.69 373	60.22 374	77.28 373	41.06 374	80.12 377	46.15 376	41.14 374	61.57 375

EMVS		64.07 343	63.26 346	66.53 360	81.73 377	58.81 382	91.85 366	84.75 379	51.93 375	59.09 375	75.13 374	43.32 372	79.09 378	42.03 377	39.47 375	61.69 374

MVE	62.14 22	63.28 344	59.38 347	74.99 356	74.33 381	65.47 377	85.55 370	80.50 382	52.02 374	51.10 376	75.00 375	10.91 385	80.50 376	51.60 375	53.40 373	78.99 371
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 345	30.18 349	30.16 361	78.61 379	43.29 385	66.79 374	14.21 385	17.31 378	14.82 381	11.93 381	11.55 384	41.43 380	37.08 378	19.30 378	5.76 378

cdsmvs_eth3d_5k		23.98 346	31.98 348	0.00 364	0.00 387	0.00 388	0.00 375	98.59 133	0.00 382	0.00 383	98.61 134	90.60 151	0.00 383	0.00 381	0.00 381	0.00 379

testmvs		21.48 347	24.95 350	11.09 363	14.89 385	6.47 387	96.56 334	9.87 386	7.55 379	17.93 379	39.02 377	9.43 386	5.90 382	16.56 380	12.72 379	20.91 377

test123		20.95 348	23.72 351	12.64 362	13.54 386	8.19 386	96.55 335	6.13 387	7.48 380	16.74 380	37.98 378	12.97 383	6.05 381	16.69 379	5.43 380	23.68 376

ab-mvs-re		8.20 349	10.94 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	98.43 154	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		7.88 350	10.50 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	94.51 78	0.00 383	0.00 381	0.00 381	0.00 379

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					99.82 1	98.66 24	99.69 1	98.95 35	97.46 23	99.39 22

MSC_two_6792asdad				99.62 6	99.17 89	99.08 11		98.63 128					99.94 4	98.53 18	99.80 19	99.86 2

PC_three_1452									95.08 144	99.60 12	99.16 65	97.86 2	98.47 261	97.52 85	99.72 47	99.74 30

No_MVS				99.62 6	99.17 89	99.08 11		98.63 128					99.94 4	98.53 18	99.80 19	99.86 2

test_one_0601					99.66 26	99.25 2		98.86 64	97.55 18	99.20 30	99.47 12	97.57 6

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					99.46 49	98.70 23		98.79 88	93.21 227	98.67 63	98.97 93	95.70 43	99.83 57	96.07 139	99.58 71

RE-MVS-def			98.34 28		99.49 45	97.86 61	99.11 54	98.80 83	96.49 74	99.17 33	99.35 32	95.29 59		97.72 67	99.65 57	99.71 42

IU-MVS					99.71 19	99.23 7		98.64 126	95.28 131	99.63 11				98.35 35	99.81 12	99.83 7

OPU-MVS				99.37 20	99.24 82	99.05 14	99.02 73				99.16 65	97.81 3	99.37 157	97.24 94	99.73 44	99.70 46

test_241102_TWO								98.87 58	97.65 12	99.53 16	99.48 10	97.34 11	99.94 4	98.43 30	99.80 19	99.83 7

test_241102_ONE					99.71 19	99.24 5		98.87 58	97.62 14	99.73 4	99.39 20	97.53 7	99.74 99

9.14			98.06 48		99.47 47		98.71 141	98.82 70	94.36 171	99.16 35	99.29 41	96.05 31	99.81 69	97.00 101	99.71 49

save fliter					99.46 49	98.38 35	98.21 208	98.71 106	97.95 5

test_0728_THIRD									97.32 31	99.45 18	99.46 15	97.88 1	99.94 4	98.47 26	99.86 1	99.85 4

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 83	98.88 51					99.94 4	98.47 26	99.81 12	99.84 6

test0726					99.72 12	99.25 2	99.06 62	98.88 51	97.62 14	99.56 13	99.50 7	97.42 9

GSMVS																99.20 128

test_part2					99.63 29	99.18 10				99.27 27

sam_mvs1												89.45 170				99.20 128

sam_mvs												88.99 184

ambc				89.49 341	86.66 371	75.78 366	92.66 365	96.72 332		86.55 349	92.50 359	46.01 369	97.90 318	90.32 292	82.09 348	94.80 346

MTGPA								98.74 98

test_post1							96.68 331				30.43 380	87.85 215	98.69 237	92.59 249

test_post											31.83 379	88.83 191	98.91 215

patchmatchnet-post											95.10 339	89.42 171	98.89 219

GG-mvs-BLEND				96.59 218	96.34 303	94.98 188	96.51 336	88.58 377		93.10 280	94.34 347	80.34 313	98.05 308	89.53 308	96.99 184	96.74 267

MTMP							98.89 99	94.14 363

gm-plane-assit					95.88 321	87.47 347			89.74 322		96.94 290		99.19 172	93.32 228

test9_res														96.39 133	99.57 72	99.69 49

TEST9					99.31 62	98.50 29	97.92 239	98.73 101	92.63 246	97.74 118	98.68 128	96.20 26	99.80 76

test_8					99.29 68	98.44 31	97.89 245	98.72 103	92.98 235	97.70 122	98.66 131	96.20 26	99.80 76

agg_prior2														95.87 149	99.57 72	99.68 54

agg_prior					99.30 66	98.38 35		98.72 103		97.57 132			99.81 69

TestCases				96.99 184	99.25 76	93.21 261		98.18 216	91.36 286	93.52 263	98.77 120	84.67 273	99.72 101	89.70 305	97.87 164	98.02 208

test_prior4						98.01 58	97.86 248

test_prior2							97.80 253		96.12 91	97.89 112	98.69 127	95.96 35		96.89 110	99.60 66

test_prior				99.19 38	99.31 62	98.22 47		98.84 68					99.70 107			99.65 62

旧先验2							97.57 272		91.30 291	98.67 63			99.80 76	95.70 157

新几何2							97.64 266

新几何1				99.16 43	99.34 55	98.01 58		98.69 110	90.06 316	98.13 89	98.95 100	94.60 76	99.89 37	91.97 267	99.47 89	99.59 72

旧先验1					99.29 68	97.48 73		98.70 109			99.09 80	95.56 46			99.47 89	99.61 68

无先验							97.58 271	98.72 103	91.38 285				99.87 46	93.36 227		99.60 70

原ACMM2							97.67 263

原ACMM1				98.65 68	99.32 60	96.62 105		98.67 118	93.27 226	97.81 113	98.97 93	95.18 65	99.83 57	93.84 213	99.46 92	99.50 84

test222					99.23 83	97.17 87	97.40 279	98.66 121	88.68 333	98.05 94	98.96 98	94.14 88			99.53 82	99.61 68

testdata2													99.89 37	91.65 274

segment_acmp												96.85 14

testdata				98.26 102	99.20 87	95.36 170		98.68 113	91.89 272	98.60 71	99.10 74	94.44 83	99.82 64	94.27 199	99.44 93	99.58 76

testdata1							97.32 289		96.34 83

test12				99.18 40	99.16 93	98.19 48		98.53 148		98.07 93		95.13 67	99.72 101		99.56 78	99.63 66

plane_prior7					97.42 239	94.63 204

plane_prior6					97.35 244	94.61 207						87.09 228

plane_prior5								98.56 142					99.03 196	96.07 139	94.27 224	96.92 242

plane_prior4											98.28 173

plane_prior3						94.61 207			97.02 52	95.34 195

plane_prior2							98.80 121		97.28 34

plane_prior1					97.37 243

plane_prior						94.60 209	98.44 183		96.74 65						94.22 226

n2								0.00 388

nn								0.00 388

door-mid								94.37 359

lessismore_v0				94.45 313	94.93 341	88.44 338		91.03 373		86.77 347	97.64 232	76.23 339	98.42 267	90.31 293	85.64 342	96.51 302

LGP-MVS_train				96.47 234	97.46 233	93.54 245		98.54 146	94.67 160	94.36 225	98.77 120	85.39 258	99.11 184	95.71 155	94.15 230	96.76 265

test11								98.66 121

door								94.64 357

HQP5-MVS						94.25 223

HQP-NCC					97.20 252		98.05 228		96.43 77	94.45 218

ACMP_Plane					97.20 252		98.05 228		96.43 77	94.45 218

BP-MVS														95.30 166

HQP4-MVS										94.45 218			98.96 207			96.87 253

HQP3-MVS								98.46 165							94.18 228

HQP2-MVS												86.75 234

NP-MVS					97.28 246	94.51 212					97.73 221

MDTV_nov1_ep13_2view						84.26 355	96.89 321		90.97 301	97.90 111		89.89 162		93.91 211		99.18 137

MDTV_nov1_ep13			95.40 157		97.48 231	88.34 339	96.85 324	97.29 304	93.74 198	97.48 134	97.26 256	89.18 178	99.05 192	91.92 268	97.43 178

ACMMP++_ref															92.97 261

ACMMP++															93.61 248

Test By Simon												94.64 75

ITE_SJBPF				95.44 281	97.42 239	91.32 290		97.50 289	95.09 143	93.59 259	98.35 164	81.70 299	98.88 221	89.71 304	93.39 255	96.12 321

DeepMVS_CX				86.78 345	97.09 262	72.30 372		95.17 353	75.92 364	84.34 357	95.19 337	70.58 353	95.35 359	79.98 359	89.04 311	92.68 362

High-resolution multi-view benchmark