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.

DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 61	95.39 11	99.29 1	98.28 39	94.78 48	98.93 13	98.87 22	96.04 2	99.86 9	97.45 36	99.58 23	99.59 25

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 35	98.27 42	95.13 30	99.19 7	98.89 20	95.54 5	99.85 18	97.52 32	99.66 10	99.56 32

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 42	97.85 123	94.92 39	98.73 22	98.87 22	95.08 8	99.84 23	97.52 32	99.67 6	99.48 48
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
DPE-MVS		97.86 4	97.65 8	98.47 5	99.17 32	95.78 7	97.21 170	98.35 30	95.16 29	98.71 24	98.80 29	95.05 10	99.89 3	96.70 53	99.73 1	99.73 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 5	97.73 7	98.08 18	99.15 33	94.82 28	98.81 7	98.30 35	94.76 50	98.30 30	98.90 19	93.77 17	99.68 61	97.93 20	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		97.68 6	97.44 17	98.37 7	98.90 53	95.86 6	97.27 162	98.08 80	95.81 13	97.87 44	98.31 67	94.26 13	99.68 61	97.02 44	99.49 38	99.57 29

fmvsm_l_conf0.5_n		97.65 7	97.75 6	97.34 56	98.21 95	92.75 84	97.83 89	98.73 9	95.04 35	99.30 3	98.84 27	93.34 22	99.78 40	99.32 3	99.13 85	99.50 44

fmvsm_l_conf0.5_n_3		97.64 8	97.60 9	97.79 30	98.14 102	93.94 52	97.93 75	98.65 17	96.70 3	99.38 1	99.07 7	89.92 86	99.81 30	99.16 7	99.43 48	99.61 23

fmvsm_l_conf0.5_n_a		97.63 9	97.76 5	97.26 63	98.25 89	92.59 90	97.81 93	98.68 13	94.93 37	99.24 6	98.87 22	93.52 20	99.79 37	99.32 3	99.21 75	99.40 58

SteuartSystems-ACMMP		97.62 10	97.53 12	97.87 24	98.39 80	94.25 40	98.43 22	98.27 42	95.34 24	98.11 33	98.56 37	94.53 12	99.71 53	96.57 57	99.62 17	99.65 17
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		97.59 11	97.54 11	97.73 38	99.40 11	93.77 57	98.53 14	98.29 37	95.55 20	98.56 26	97.81 108	93.90 15	99.65 65	96.62 54	99.21 75	99.77 2
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
test_fmvsm_n_1920		97.55 12	97.89 3	96.53 89	98.41 77	91.73 117	98.01 60	99.02 1	96.37 8	99.30 3	98.92 17	92.39 41	99.79 37	99.16 7	99.46 41	98.08 181

reproduce-ours		97.53 13	97.51 14	97.60 47	98.97 47	93.31 69	97.71 106	98.20 56	95.80 14	97.88 41	98.98 13	92.91 27	99.81 30	97.68 24	99.43 48	99.67 13

our_new_method		97.53 13	97.51 14	97.60 47	98.97 47	93.31 69	97.71 106	98.20 56	95.80 14	97.88 41	98.98 13	92.91 27	99.81 30	97.68 24	99.43 48	99.67 13

reproduce_model		97.51 15	97.51 14	97.50 50	98.99 46	93.01 78	97.79 95	98.21 54	95.73 17	97.99 37	99.03 10	92.63 36	99.82 28	97.80 22	99.42 51	99.67 13

test_fmvsmconf_n		97.49 16	97.56 10	97.29 59	97.44 150	92.37 96	97.91 77	98.88 4	95.83 12	98.92 16	99.05 9	91.45 57	99.80 34	99.12 9	99.46 41	99.69 12

TSAR-MVS + MP.		97.42 17	97.33 20	97.69 42	99.25 27	94.24 41	98.07 55	97.85 123	93.72 86	98.57 25	98.35 58	93.69 18	99.40 117	97.06 43	99.46 41	99.44 53
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		97.41 18	97.53 12	97.06 74	98.57 72	94.46 34	97.92 76	98.14 70	94.82 45	99.01 10	98.55 39	94.18 14	97.41 341	96.94 45	99.64 14	99.32 66
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
SF-MVS		97.39 19	97.13 21	98.17 15	99.02 42	95.28 19	98.23 39	98.27 42	92.37 139	98.27 31	98.65 35	93.33 23	99.72 52	96.49 59	99.52 30	99.51 41

SMA-MVS		97.35 20	97.03 29	98.30 8	99.06 38	95.42 10	97.94 73	98.18 63	90.57 205	98.85 19	98.94 16	93.33 23	99.83 26	96.72 52	99.68 4	99.63 19
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
HPM-MVS++		97.34 21	96.97 32	98.47 5	99.08 36	96.16 4	97.55 130	97.97 107	95.59 18	96.61 83	97.89 97	92.57 38	99.84 23	95.95 81	99.51 33	99.40 58

NCCC		97.30 22	97.03 29	98.11 17	98.77 56	95.06 25	97.34 155	98.04 95	95.96 10	97.09 65	97.88 99	93.18 25	99.71 53	95.84 86	99.17 80	99.56 32

MM		97.29 23	96.98 31	98.23 11	98.01 112	95.03 26	98.07 55	95.76 298	97.78 1	97.52 48	98.80 29	88.09 108	99.86 9	99.44 1	99.37 62	99.80 1

ACMMP_NAP		97.20 24	96.86 37	98.23 11	99.09 34	95.16 22	97.60 122	98.19 61	92.82 130	97.93 40	98.74 32	91.60 55	99.86 9	96.26 62	99.52 30	99.67 13

XVS		97.18 25	96.96 33	97.81 28	99.38 14	94.03 50	98.59 12	98.20 56	94.85 41	96.59 85	98.29 70	91.70 52	99.80 34	95.66 90	99.40 56	99.62 20

MCST-MVS		97.18 25	96.84 39	98.20 14	99.30 24	95.35 15	97.12 177	98.07 85	93.54 95	96.08 107	97.69 115	93.86 16	99.71 53	96.50 58	99.39 58	99.55 35

fmvsm_s_conf0.5_n_3		97.15 27	97.36 19	96.52 90	97.98 115	91.19 145	97.84 86	98.65 17	97.08 2	99.25 5	99.10 3	87.88 114	99.79 37	99.32 3	99.18 79	98.59 136

HFP-MVS		97.14 28	96.92 35	97.83 26	99.42 7	94.12 46	98.52 15	98.32 33	93.21 107	97.18 59	98.29 70	92.08 46	99.83 26	95.63 95	99.59 19	99.54 37

test_fmvsmconf0.1_n		97.09 29	97.06 24	97.19 68	95.67 257	92.21 103	97.95 72	98.27 42	95.78 16	98.40 29	99.00 11	89.99 84	99.78 40	99.06 10	99.41 54	99.59 25

MTAPA		97.08 30	96.78 46	97.97 23	99.37 16	94.42 36	97.24 164	98.08 80	95.07 34	96.11 105	98.59 36	90.88 74	99.90 2	96.18 74	99.50 35	99.58 28

region2R		97.07 31	96.84 39	97.77 34	99.46 2	93.79 55	98.52 15	98.24 50	93.19 110	97.14 62	98.34 61	91.59 56	99.87 7	95.46 101	99.59 19	99.64 18

ACMMPR		97.07 31	96.84 39	97.79 30	99.44 6	93.88 53	98.52 15	98.31 34	93.21 107	97.15 61	98.33 64	91.35 61	99.86 9	95.63 95	99.59 19	99.62 20

CP-MVS		97.02 33	96.81 44	97.64 45	99.33 21	93.54 60	98.80 8	98.28 39	92.99 119	96.45 93	98.30 69	91.90 49	99.85 18	95.61 97	99.68 4	99.54 37

SR-MVS		97.01 34	96.86 37	97.47 52	99.09 34	93.27 71	97.98 63	98.07 85	93.75 85	97.45 50	98.48 47	91.43 59	99.59 81	96.22 65	99.27 68	99.54 37

ZNCC-MVS		96.96 35	96.67 51	97.85 25	99.37 16	94.12 46	98.49 19	98.18 63	92.64 135	96.39 95	98.18 77	91.61 54	99.88 4	95.59 100	99.55 26	99.57 29

APD-MVS		96.95 36	96.60 53	98.01 20	99.03 41	94.93 27	97.72 104	98.10 78	91.50 163	98.01 36	98.32 66	92.33 42	99.58 84	94.85 113	99.51 33	99.53 40
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 37	97.06 24	96.59 86	98.72 58	91.86 115	97.67 110	98.49 22	94.66 55	97.24 58	98.41 53	92.31 44	98.94 175	96.61 55	99.46 41	98.96 99

DeepC-MVS_fast	93.89 2	96.93 38	96.64 52	97.78 32	98.64 67	94.30 37	97.41 145	98.04 95	94.81 46	96.59 85	98.37 56	91.24 64	99.64 73	95.16 106	99.52 30	99.42 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		96.89 39	97.04 28	96.45 101	98.29 85	91.66 123	99.03 4	97.85 123	95.84 11	96.90 69	97.97 93	91.24 64	98.75 196	96.92 46	99.33 64	98.94 102

SR-MVS-dyc-post		96.88 40	96.80 45	97.11 71	99.02 42	92.34 97	97.98 63	98.03 97	93.52 97	97.43 53	98.51 42	91.40 60	99.56 92	96.05 76	99.26 70	99.43 55

CS-MVS		96.86 41	97.06 24	96.26 117	98.16 101	91.16 150	99.09 3	97.87 118	95.30 25	97.06 66	98.03 87	91.72 50	98.71 203	97.10 42	99.17 80	98.90 109

mPP-MVS		96.86 41	96.60 53	97.64 45	99.40 11	93.44 62	98.50 18	98.09 79	93.27 106	95.95 113	98.33 64	91.04 69	99.88 4	95.20 104	99.57 25	99.60 24

fmvsm_s_conf0.5_n		96.85 43	97.13 21	96.04 130	98.07 109	90.28 179	97.97 69	98.76 8	94.93 37	98.84 20	99.06 8	88.80 98	99.65 65	99.06 10	98.63 108	98.18 170

GST-MVS		96.85 43	96.52 57	97.82 27	99.36 18	94.14 45	98.29 29	98.13 71	92.72 132	96.70 77	98.06 84	91.35 61	99.86 9	94.83 115	99.28 67	99.47 50

balanced_conf03		96.84 45	96.89 36	96.68 80	97.63 139	92.22 102	98.17 48	97.82 129	94.44 65	98.23 32	97.36 140	90.97 71	99.22 134	97.74 23	99.66 10	98.61 133

patch_mono-2		96.83 46	97.44 17	95.01 185	99.05 39	85.39 312	96.98 189	98.77 7	94.70 52	97.99 37	98.66 33	93.61 19	99.91 1	97.67 28	99.50 35	99.72 11

APD-MVS_3200maxsize		96.81 47	96.71 50	97.12 70	99.01 45	92.31 99	97.98 63	98.06 88	93.11 116	97.44 51	98.55 39	90.93 72	99.55 94	96.06 75	99.25 72	99.51 41

PGM-MVS		96.81 47	96.53 56	97.65 43	99.35 20	93.53 61	97.65 113	98.98 2	92.22 141	97.14 62	98.44 50	91.17 67	99.85 18	94.35 128	99.46 41	99.57 29

MP-MVS		96.77 49	96.45 64	97.72 39	99.39 13	93.80 54	98.41 23	98.06 88	93.37 102	95.54 128	98.34 61	90.59 78	99.88 4	94.83 115	99.54 28	99.49 46
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 49	96.46 63	97.71 41	98.40 78	94.07 48	98.21 42	98.45 25	89.86 222	97.11 64	98.01 90	92.52 39	99.69 59	96.03 79	99.53 29	99.36 64

fmvsm_s_conf0.5_n_a		96.75 51	96.93 34	96.20 122	97.64 137	90.72 165	98.00 61	98.73 9	94.55 59	98.91 17	99.08 4	88.22 107	99.63 74	98.91 13	98.37 121	98.25 165

MVS_0304		96.74 52	96.31 68	98.02 19	96.87 178	94.65 30	97.58 123	94.39 358	96.47 7	97.16 60	98.39 54	87.53 123	99.87 7	98.97 12	99.41 54	99.55 35

test_fmvsmvis_n_1920		96.70 53	96.84 39	96.31 111	96.62 198	91.73 117	97.98 63	98.30 35	96.19 9	96.10 106	98.95 15	89.42 89	99.76 43	98.90 14	99.08 89	97.43 217

MP-MVS-pluss		96.70 53	96.27 70	97.98 22	99.23 30	94.71 29	96.96 191	98.06 88	90.67 196	95.55 126	98.78 31	91.07 68	99.86 9	96.58 56	99.55 26	99.38 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 55	96.49 58	97.27 62	98.31 84	93.39 63	96.79 204	96.72 249	94.17 73	97.44 51	97.66 119	92.76 31	99.33 122	96.86 48	97.76 144	99.08 88

HPM-MVS		96.69 55	96.45 64	97.40 54	99.36 18	93.11 76	98.87 6	98.06 88	91.17 179	96.40 94	97.99 91	90.99 70	99.58 84	95.61 97	99.61 18	99.49 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 57	96.58 55	96.99 76	98.46 73	92.31 99	96.20 258	98.90 3	94.30 72	95.86 115	97.74 113	92.33 42	99.38 120	96.04 78	99.42 51	99.28 69

fmvsm_s_conf0.5_n_2		96.62 58	96.82 43	96.02 132	97.98 115	90.43 175	97.50 134	98.59 19	96.59 5	99.31 2	99.08 4	84.47 166	99.75 46	99.37 2	98.45 118	97.88 191

DELS-MVS		96.61 59	96.38 67	97.30 58	97.79 128	93.19 74	95.96 269	98.18 63	95.23 26	95.87 114	97.65 120	91.45 57	99.70 58	95.87 82	99.44 47	99.00 97
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
DeepPCF-MVS	93.97 1	96.61 59	97.09 23	95.15 177	98.09 105	86.63 288	96.00 267	98.15 68	95.43 21	97.95 39	98.56 37	93.40 21	99.36 121	96.77 49	99.48 39	99.45 51

fmvsm_s_conf0.1_n		96.58 61	96.77 47	96.01 135	96.67 196	90.25 180	97.91 77	98.38 26	94.48 63	98.84 20	99.14 1	88.06 109	99.62 75	98.82 15	98.60 110	98.15 174

MVSMamba_PlusPlus		96.51 62	96.48 59	96.59 86	98.07 109	91.97 112	98.14 49	97.79 131	90.43 209	97.34 56	97.52 133	91.29 63	99.19 137	98.12 19	99.64 14	98.60 134

EI-MVSNet-Vis-set		96.51 62	96.47 60	96.63 83	98.24 90	91.20 144	96.89 195	97.73 137	94.74 51	96.49 89	98.49 44	90.88 74	99.58 84	96.44 60	98.32 123	99.13 81

HPM-MVS_fast		96.51 62	96.27 70	97.22 65	99.32 22	92.74 85	98.74 9	98.06 88	90.57 205	96.77 74	98.35 58	90.21 81	99.53 98	94.80 118	99.63 16	99.38 62

EC-MVSNet		96.42 65	96.47 60	96.26 117	97.01 173	91.52 129	98.89 5	97.75 134	94.42 66	96.64 82	97.68 116	89.32 90	98.60 213	97.45 36	99.11 88	98.67 131

fmvsm_s_conf0.1_n_a		96.40 66	96.47 60	96.16 124	95.48 265	90.69 166	97.91 77	98.33 32	94.07 75	98.93 13	99.14 1	87.44 127	99.61 76	98.63 17	98.32 123	98.18 170

CANet		96.39 67	96.02 74	97.50 50	97.62 140	93.38 64	97.02 183	97.96 108	95.42 22	94.86 139	97.81 108	87.38 129	99.82 28	96.88 47	99.20 77	99.29 67

dcpmvs_2		96.37 68	97.05 27	94.31 227	98.96 49	84.11 333	97.56 126	97.51 166	93.92 80	97.43 53	98.52 41	92.75 32	99.32 124	97.32 41	99.50 35	99.51 41

EI-MVSNet-UG-set		96.34 69	96.30 69	96.47 98	98.20 96	90.93 157	96.86 197	97.72 139	94.67 54	96.16 104	98.46 48	90.43 79	99.58 84	96.23 64	97.96 137	98.90 109

fmvsm_s_conf0.1_n_2		96.33 70	96.44 66	96.00 136	97.30 153	90.37 178	97.53 131	97.92 113	96.52 6	99.14 9	99.08 4	83.21 188	99.74 47	99.22 6	98.06 134	97.88 191

train_agg		96.30 71	95.83 79	97.72 39	98.70 59	94.19 42	96.41 237	98.02 100	88.58 267	96.03 108	97.56 130	92.73 34	99.59 81	95.04 108	99.37 62	99.39 60

ACMMP		96.27 72	95.93 75	97.28 61	99.24 28	92.62 88	98.25 35	98.81 5	92.99 119	94.56 146	98.39 54	88.96 95	99.85 18	94.57 126	97.63 145	99.36 64
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
MVS_111021_LR		96.24 73	96.19 72	96.39 106	98.23 94	91.35 137	96.24 256	98.79 6	93.99 78	95.80 117	97.65 120	89.92 86	99.24 132	95.87 82	99.20 77	98.58 137

test_fmvsmconf0.01_n		96.15 74	95.85 78	97.03 75	92.66 373	91.83 116	97.97 69	97.84 127	95.57 19	97.53 47	99.00 11	84.20 172	99.76 43	98.82 15	99.08 89	99.48 48

DeepC-MVS	93.07 3	96.06 75	95.66 80	97.29 59	97.96 117	93.17 75	97.30 160	98.06 88	93.92 80	93.38 175	98.66 33	86.83 135	99.73 49	95.60 99	99.22 74	98.96 99
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		96.05 76	95.91 76	96.46 100	99.24 28	90.47 172	98.30 28	98.57 21	89.01 250	93.97 162	97.57 128	92.62 37	99.76 43	94.66 121	99.27 68	99.15 79

sasdasda		96.02 77	95.45 86	97.75 36	97.59 143	95.15 23	98.28 30	97.60 153	94.52 61	96.27 99	96.12 210	87.65 118	99.18 140	96.20 70	94.82 209	98.91 106

ETV-MVS		96.02 77	95.89 77	96.40 104	97.16 159	92.44 94	97.47 141	97.77 133	94.55 59	96.48 90	94.51 289	91.23 66	98.92 177	95.65 93	98.19 128	97.82 198

canonicalmvs		96.02 77	95.45 86	97.75 36	97.59 143	95.15 23	98.28 30	97.60 153	94.52 61	96.27 99	96.12 210	87.65 118	99.18 140	96.20 70	94.82 209	98.91 106

CDPH-MVS		95.97 80	95.38 91	97.77 34	98.93 50	94.44 35	96.35 245	97.88 116	86.98 312	96.65 81	97.89 97	91.99 48	99.47 109	92.26 163	99.46 41	99.39 60

UA-Net		95.95 81	95.53 82	97.20 67	97.67 133	92.98 80	97.65 113	98.13 71	94.81 46	96.61 83	98.35 58	88.87 96	99.51 103	90.36 205	97.35 155	99.11 85

MGCFI-Net		95.94 82	95.40 90	97.56 49	97.59 143	94.62 31	98.21 42	97.57 158	94.41 67	96.17 103	96.16 208	87.54 122	99.17 142	96.19 72	94.73 214	98.91 106

BP-MVS1		95.89 83	95.49 83	97.08 73	96.67 196	93.20 73	98.08 53	96.32 273	94.56 58	96.32 96	97.84 105	84.07 175	99.15 146	96.75 50	98.78 102	98.90 109

VNet		95.89 83	95.45 86	97.21 66	98.07 109	92.94 81	97.50 134	98.15 68	93.87 82	97.52 48	97.61 126	85.29 155	99.53 98	95.81 87	95.27 200	99.16 77

alignmvs		95.87 85	95.23 95	97.78 32	97.56 148	95.19 21	97.86 82	97.17 208	94.39 69	96.47 91	96.40 196	85.89 148	99.20 136	96.21 69	95.11 205	98.95 101

casdiffmvs_mvg		95.81 86	95.57 81	96.51 94	96.87 178	91.49 130	97.50 134	97.56 162	93.99 78	95.13 135	97.92 96	87.89 113	98.78 191	95.97 80	97.33 156	99.26 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		95.69 87	94.92 102	98.01 20	98.08 108	95.71 9	95.27 306	97.62 152	90.43 209	95.55 126	97.07 156	91.72 50	99.50 106	89.62 221	98.94 97	98.82 121

DP-MVS Recon		95.68 88	95.12 100	97.37 55	99.19 31	94.19 42	97.03 181	98.08 80	88.35 276	95.09 136	97.65 120	89.97 85	99.48 108	92.08 172	98.59 111	98.44 154

casdiffmvs		95.64 89	95.49 83	96.08 126	96.76 194	90.45 173	97.29 161	97.44 184	94.00 77	95.46 130	97.98 92	87.52 125	98.73 199	95.64 94	97.33 156	99.08 88
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GDP-MVS		95.62 90	95.13 98	97.09 72	96.79 188	93.26 72	97.89 80	97.83 128	93.58 90	96.80 71	97.82 107	83.06 195	99.16 144	94.40 127	97.95 138	98.87 115

MG-MVS		95.61 91	95.38 91	96.31 111	98.42 76	90.53 170	96.04 264	97.48 170	93.47 99	95.67 123	98.10 80	89.17 92	99.25 131	91.27 190	98.77 103	99.13 81

baseline		95.58 92	95.42 89	96.08 126	96.78 189	90.41 176	97.16 174	97.45 180	93.69 89	95.65 124	97.85 103	87.29 130	98.68 205	95.66 90	97.25 161	99.13 81

CPTT-MVS		95.57 93	95.19 96	96.70 79	99.27 26	91.48 131	98.33 26	98.11 76	87.79 293	95.17 134	98.03 87	87.09 133	99.61 76	93.51 143	99.42 51	99.02 91

EIA-MVS		95.53 94	95.47 85	95.71 152	97.06 167	89.63 196	97.82 91	97.87 118	93.57 91	93.92 163	95.04 263	90.61 77	98.95 173	94.62 123	98.68 106	98.54 139

3Dnovator+	91.43 4	95.40 95	94.48 118	98.16 16	96.90 177	95.34 16	98.48 20	97.87 118	94.65 56	88.53 301	98.02 89	83.69 179	99.71 53	93.18 150	98.96 96	99.44 53

PS-MVSNAJ		95.37 96	95.33 93	95.49 165	97.35 152	90.66 168	95.31 303	97.48 170	93.85 83	96.51 88	95.70 235	88.65 101	99.65 65	94.80 118	98.27 125	96.17 255

MVSFormer		95.37 96	95.16 97	95.99 137	96.34 226	91.21 142	98.22 40	97.57 158	91.42 167	96.22 101	97.32 141	86.20 145	97.92 293	94.07 131	99.05 91	98.85 117

xiu_mvs_v2_base		95.32 98	95.29 94	95.40 170	97.22 155	90.50 171	95.44 297	97.44 184	93.70 88	96.46 92	96.18 205	88.59 104	99.53 98	94.79 120	97.81 141	96.17 255

PVSNet_Blended_VisFu		95.27 99	94.91 103	96.38 107	98.20 96	90.86 159	97.27 162	98.25 48	90.21 213	94.18 156	97.27 145	87.48 126	99.73 49	93.53 142	97.77 143	98.55 138

diffmvs		95.25 100	95.13 98	95.63 155	96.43 221	89.34 212	95.99 268	97.35 197	92.83 129	96.31 97	97.37 139	86.44 140	98.67 206	96.26 62	97.19 163	98.87 115
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		95.23 101	94.81 104	96.51 94	97.18 158	91.58 127	98.26 34	98.12 73	94.38 70	94.90 138	98.15 79	82.28 214	98.92 177	91.45 187	98.58 112	99.01 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 102	95.04 101	95.76 145	97.49 149	89.56 200	98.67 10	97.00 227	90.69 194	94.24 154	97.62 125	89.79 88	98.81 188	93.39 148	96.49 178	98.92 105

EPNet		95.20 103	94.56 112	97.14 69	92.80 370	92.68 87	97.85 85	94.87 346	96.64 4	92.46 191	97.80 110	86.23 142	99.65 65	93.72 141	98.62 109	99.10 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 104	94.44 120	97.44 53	96.56 205	93.36 66	98.65 11	98.36 27	94.12 74	89.25 285	98.06 84	82.20 216	99.77 42	93.41 147	99.32 65	99.18 76

OMC-MVS		95.09 105	94.70 108	96.25 120	98.46 73	91.28 138	96.43 235	97.57 158	92.04 150	94.77 142	97.96 94	87.01 134	99.09 156	91.31 189	96.77 170	98.36 161

xiu_mvs_v1_base_debu		95.01 106	94.76 105	95.75 147	96.58 202	91.71 119	96.25 253	97.35 197	92.99 119	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 260

xiu_mvs_v1_base		95.01 106	94.76 105	95.75 147	96.58 202	91.71 119	96.25 253	97.35 197	92.99 119	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 260

xiu_mvs_v1_base_debi		95.01 106	94.76 105	95.75 147	96.58 202	91.71 119	96.25 253	97.35 197	92.99 119	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 260

PAPM_NR		95.01 106	94.59 110	96.26 117	98.89 54	90.68 167	97.24 164	97.73 137	91.80 155	92.93 188	96.62 186	89.13 93	99.14 149	89.21 234	97.78 142	98.97 98

lupinMVS		94.99 110	94.56 112	96.29 115	96.34 226	91.21 142	95.83 276	96.27 277	88.93 255	96.22 101	96.88 166	86.20 145	98.85 184	95.27 103	99.05 91	98.82 121

Effi-MVS+		94.93 111	94.45 119	96.36 109	96.61 199	91.47 132	96.41 237	97.41 189	91.02 185	94.50 148	95.92 219	87.53 123	98.78 191	93.89 137	96.81 169	98.84 120

IS-MVSNet		94.90 112	94.52 116	96.05 129	97.67 133	90.56 169	98.44 21	96.22 280	93.21 107	93.99 160	97.74 113	85.55 153	98.45 225	89.98 210	97.86 139	99.14 80

MVS_Test		94.89 113	94.62 109	95.68 153	96.83 183	89.55 201	96.70 213	97.17 208	91.17 179	95.60 125	96.11 214	87.87 115	98.76 195	93.01 158	97.17 164	98.72 126

PVSNet_Blended		94.87 114	94.56 112	95.81 144	98.27 86	89.46 207	95.47 296	98.36 27	88.84 258	94.36 151	96.09 215	88.02 110	99.58 84	93.44 145	98.18 129	98.40 157

jason		94.84 115	94.39 121	96.18 123	95.52 263	90.93 157	96.09 262	96.52 264	89.28 241	96.01 111	97.32 141	84.70 162	98.77 194	95.15 107	98.91 99	98.85 117
jason: jason.
API-MVS		94.84 115	94.49 117	95.90 139	97.90 123	92.00 111	97.80 94	97.48 170	89.19 244	94.81 140	96.71 172	88.84 97	99.17 142	88.91 241	98.76 104	96.53 244

test_yl		94.78 117	94.23 123	96.43 102	97.74 130	91.22 140	96.85 198	97.10 213	91.23 176	95.71 120	96.93 161	84.30 169	99.31 126	93.10 151	95.12 203	98.75 123

DCV-MVSNet		94.78 117	94.23 123	96.43 102	97.74 130	91.22 140	96.85 198	97.10 213	91.23 176	95.71 120	96.93 161	84.30 169	99.31 126	93.10 151	95.12 203	98.75 123

WTY-MVS		94.71 119	94.02 126	96.79 78	97.71 132	92.05 109	96.59 228	97.35 197	90.61 202	94.64 144	96.93 161	86.41 141	99.39 118	91.20 192	94.71 215	98.94 102

mamv4		94.66 120	96.10 73	90.37 356	98.01 112	73.41 404	96.82 202	97.78 132	89.95 220	94.52 147	97.43 137	92.91 27	99.09 156	98.28 18	99.16 82	98.60 134

mvsmamba		94.57 121	94.14 125	95.87 140	97.03 171	89.93 191	97.84 86	95.85 294	91.34 170	94.79 141	96.80 168	80.67 240	98.81 188	94.85 113	98.12 132	98.85 117

RRT-MVS		94.51 122	94.35 122	94.98 188	96.40 222	86.55 291	97.56 126	97.41 189	93.19 110	94.93 137	97.04 158	79.12 269	99.30 128	96.19 72	97.32 158	99.09 87

sss		94.51 122	93.80 130	96.64 81	97.07 164	91.97 112	96.32 248	98.06 88	88.94 254	94.50 148	96.78 169	84.60 163	99.27 130	91.90 173	96.02 183	98.68 130

test_cas_vis1_n_1920		94.48 124	94.55 115	94.28 229	96.78 189	86.45 293	97.63 119	97.64 149	93.32 105	97.68 46	98.36 57	73.75 329	99.08 159	96.73 51	99.05 91	97.31 224

CANet_DTU		94.37 125	93.65 134	96.55 88	96.46 219	92.13 107	96.21 257	96.67 256	94.38 70	93.53 171	97.03 159	79.34 265	99.71 53	90.76 198	98.45 118	97.82 198

AdaColmap		94.34 126	93.68 133	96.31 111	98.59 69	91.68 122	96.59 228	97.81 130	89.87 221	92.15 202	97.06 157	83.62 182	99.54 96	89.34 228	98.07 133	97.70 203

CNLPA		94.28 127	93.53 139	96.52 90	98.38 81	92.55 91	96.59 228	96.88 240	90.13 217	91.91 209	97.24 147	85.21 156	99.09 156	87.64 266	97.83 140	97.92 188

MAR-MVS		94.22 128	93.46 144	96.51 94	98.00 114	92.19 106	97.67 110	97.47 173	88.13 283	93.00 183	95.84 223	84.86 161	99.51 103	87.99 253	98.17 130	97.83 197
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
PAPR		94.18 129	93.42 148	96.48 97	97.64 137	91.42 135	95.55 291	97.71 143	88.99 251	92.34 198	95.82 225	89.19 91	99.11 152	86.14 292	97.38 153	98.90 109

SDMVSNet		94.17 130	93.61 135	95.86 142	98.09 105	91.37 136	97.35 154	98.20 56	93.18 112	91.79 213	97.28 143	79.13 268	98.93 176	94.61 124	92.84 244	97.28 225

test_vis1_n_1920		94.17 130	94.58 111	92.91 291	97.42 151	82.02 358	97.83 89	97.85 123	94.68 53	98.10 34	98.49 44	70.15 352	99.32 124	97.91 21	98.82 100	97.40 219

h-mvs33		94.15 132	93.52 141	96.04 130	97.81 127	90.22 181	97.62 121	97.58 157	95.19 27	96.74 75	97.45 134	83.67 180	99.61 76	95.85 84	79.73 380	98.29 164

CHOSEN 1792x2688		94.15 132	93.51 142	96.06 128	98.27 86	89.38 210	95.18 312	98.48 24	85.60 335	93.76 166	97.11 154	83.15 191	99.61 76	91.33 188	98.72 105	99.19 75

Vis-MVSNet (Re-imp)		94.15 132	93.88 129	94.95 192	97.61 141	87.92 256	98.10 51	95.80 297	92.22 141	93.02 182	97.45 134	84.53 165	97.91 296	88.24 249	97.97 136	99.02 91

CDS-MVSNet		94.14 135	93.54 138	95.93 138	96.18 233	91.46 133	96.33 247	97.04 223	88.97 253	93.56 168	96.51 190	87.55 121	97.89 297	89.80 215	95.95 185	98.44 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 136	93.43 146	96.13 125	98.58 71	91.15 151	96.69 215	97.39 191	87.29 307	91.37 223	96.71 172	88.39 105	99.52 102	87.33 273	97.13 165	97.73 201
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 137	93.70 132	95.27 173	95.70 255	92.03 110	98.10 51	98.68 13	93.36 104	90.39 244	96.70 174	87.63 120	97.94 290	92.25 165	90.50 285	95.84 268

PVSNet_BlendedMVS		94.06 138	93.92 128	94.47 216	98.27 86	89.46 207	96.73 209	98.36 27	90.17 214	94.36 151	95.24 257	88.02 110	99.58 84	93.44 145	90.72 281	94.36 351

nrg030		94.05 139	93.31 150	96.27 116	95.22 287	94.59 32	98.34 25	97.46 175	92.93 126	91.21 233	96.64 179	87.23 132	98.22 244	94.99 111	85.80 328	95.98 264

UGNet		94.04 140	93.28 151	96.31 111	96.85 180	91.19 145	97.88 81	97.68 144	94.40 68	93.00 183	96.18 205	73.39 331	99.61 76	91.72 179	98.46 117	98.13 175
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
TAMVS		94.01 141	93.46 144	95.64 154	96.16 235	90.45 173	96.71 212	96.89 239	89.27 242	93.46 173	96.92 164	87.29 130	97.94 290	88.70 245	95.74 190	98.53 140

114514_t		93.95 142	93.06 155	96.63 83	99.07 37	91.61 124	97.46 143	97.96 108	77.99 398	93.00 183	97.57 128	86.14 147	99.33 122	89.22 233	99.15 83	98.94 102

FC-MVSNet-test		93.94 143	93.57 136	95.04 183	95.48 265	91.45 134	98.12 50	98.71 11	93.37 102	90.23 247	96.70 174	87.66 117	97.85 299	91.49 185	90.39 286	95.83 269

mvsany_test1		93.93 144	93.98 127	93.78 257	94.94 303	86.80 281	94.62 324	92.55 389	88.77 264	96.85 70	98.49 44	88.98 94	98.08 262	95.03 109	95.62 194	96.46 249

GeoE		93.89 145	93.28 151	95.72 151	96.96 176	89.75 195	98.24 38	96.92 236	89.47 235	92.12 204	97.21 149	84.42 167	98.39 232	87.71 260	96.50 177	99.01 94

HY-MVS	89.66 9	93.87 146	92.95 158	96.63 83	97.10 163	92.49 93	95.64 289	96.64 257	89.05 249	93.00 183	95.79 229	85.77 151	99.45 112	89.16 237	94.35 217	97.96 186

XVG-OURS-SEG-HR		93.86 147	93.55 137	94.81 198	97.06 167	88.53 238	95.28 304	97.45 180	91.68 159	94.08 159	97.68 116	82.41 212	98.90 180	93.84 139	92.47 250	96.98 232

VDD-MVS		93.82 148	93.08 154	96.02 132	97.88 124	89.96 190	97.72 104	95.85 294	92.43 137	95.86 115	98.44 50	68.42 366	99.39 118	96.31 61	94.85 207	98.71 128

mvs_anonymous		93.82 148	93.74 131	94.06 237	96.44 220	85.41 310	95.81 277	97.05 221	89.85 224	90.09 257	96.36 198	87.44 127	97.75 311	93.97 133	96.69 174	99.02 91

HQP_MVS		93.78 150	93.43 146	94.82 196	96.21 230	89.99 186	97.74 99	97.51 166	94.85 41	91.34 224	96.64 179	81.32 230	98.60 213	93.02 156	92.23 253	95.86 265

PS-MVSNAJss		93.74 151	93.51 142	94.44 218	93.91 341	89.28 217	97.75 98	97.56 162	92.50 136	89.94 260	96.54 189	88.65 101	98.18 249	93.83 140	90.90 279	95.86 265

XVG-OURS		93.72 152	93.35 149	94.80 201	97.07 164	88.61 233	94.79 321	97.46 175	91.97 153	93.99 160	97.86 102	81.74 225	98.88 181	92.64 162	92.67 249	96.92 236

HyFIR lowres test		93.66 153	92.92 159	95.87 140	98.24 90	89.88 192	94.58 326	98.49 22	85.06 345	93.78 165	95.78 230	82.86 200	98.67 206	91.77 178	95.71 192	99.07 90

LFMVS		93.60 154	92.63 172	96.52 90	98.13 104	91.27 139	97.94 73	93.39 378	90.57 205	96.29 98	98.31 67	69.00 359	99.16 144	94.18 130	95.87 187	99.12 84

F-COLMAP		93.58 155	92.98 157	95.37 171	98.40 78	88.98 226	97.18 172	97.29 202	87.75 296	90.49 242	97.10 155	85.21 156	99.50 106	86.70 283	96.72 173	97.63 205

ab-mvs		93.57 156	92.55 176	96.64 81	97.28 154	91.96 114	95.40 298	97.45 180	89.81 226	93.22 181	96.28 201	79.62 262	99.46 110	90.74 199	93.11 241	98.50 144

LS3D		93.57 156	92.61 174	96.47 98	97.59 143	91.61 124	97.67 110	97.72 139	85.17 343	90.29 246	98.34 61	84.60 163	99.73 49	83.85 326	98.27 125	98.06 182

FA-MVS(test-final)		93.52 158	92.92 159	95.31 172	96.77 191	88.54 237	94.82 320	96.21 282	89.61 230	94.20 155	95.25 256	83.24 187	99.14 149	90.01 209	96.16 182	98.25 165

Fast-Effi-MVS+		93.46 159	92.75 167	95.59 158	96.77 191	90.03 183	96.81 203	97.13 210	88.19 279	91.30 227	94.27 306	86.21 144	98.63 210	87.66 265	96.46 180	98.12 176

hse-mvs2		93.45 160	92.99 156	94.81 198	97.02 172	88.59 234	96.69 215	96.47 267	95.19 27	96.74 75	96.16 208	83.67 180	98.48 224	95.85 84	79.13 384	97.35 222

QAPM		93.45 160	92.27 186	96.98 77	96.77 191	92.62 88	98.39 24	98.12 73	84.50 353	88.27 309	97.77 111	82.39 213	99.81 30	85.40 305	98.81 101	98.51 143

UniMVSNet_NR-MVSNet		93.37 162	92.67 171	95.47 168	95.34 276	92.83 82	97.17 173	98.58 20	92.98 124	90.13 252	95.80 226	88.37 106	97.85 299	91.71 180	83.93 357	95.73 279

1112_ss		93.37 162	92.42 183	96.21 121	97.05 169	90.99 153	96.31 249	96.72 249	86.87 315	89.83 264	96.69 176	86.51 139	99.14 149	88.12 250	93.67 235	98.50 144

UniMVSNet (Re)		93.31 164	92.55 176	95.61 157	95.39 270	93.34 67	97.39 150	98.71 11	93.14 115	90.10 256	94.83 273	87.71 116	98.03 273	91.67 183	83.99 356	95.46 288

OPM-MVS		93.28 165	92.76 165	94.82 196	94.63 319	90.77 163	96.65 219	97.18 206	93.72 86	91.68 217	97.26 146	79.33 266	98.63 210	92.13 169	92.28 252	95.07 314
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet		93.24 166	92.48 181	95.51 163	95.70 255	92.39 95	97.86 82	98.66 16	92.30 140	92.09 206	95.37 249	80.49 244	98.40 228	93.95 134	85.86 327	95.75 277

test_fmvs1		93.21 167	93.53 139	92.25 312	96.55 207	81.20 365	97.40 149	96.96 229	90.68 195	96.80 71	98.04 86	69.25 358	98.40 228	97.58 31	98.50 113	97.16 229

MVSTER		93.20 168	92.81 164	94.37 221	96.56 205	89.59 199	97.06 180	97.12 211	91.24 175	91.30 227	95.96 217	82.02 219	98.05 269	93.48 144	90.55 283	95.47 287

test1111		93.19 169	92.82 163	94.30 228	97.58 147	84.56 327	98.21 42	89.02 408	93.53 96	94.58 145	98.21 74	72.69 332	99.05 166	93.06 154	98.48 116	99.28 69

ECVR-MVS		93.19 169	92.73 169	94.57 213	97.66 135	85.41 310	98.21 42	88.23 410	93.43 100	94.70 143	98.21 74	72.57 333	99.07 163	93.05 155	98.49 114	99.25 72

HQP-MVS		93.19 169	92.74 168	94.54 214	95.86 247	89.33 213	96.65 219	97.39 191	93.55 92	90.14 248	95.87 221	80.95 234	98.50 221	92.13 169	92.10 258	95.78 273

CHOSEN 280x420		93.12 172	92.72 170	94.34 224	96.71 195	87.27 269	90.29 398	97.72 139	86.61 319	91.34 224	95.29 251	84.29 171	98.41 227	93.25 149	98.94 97	97.35 222

sd_testset		93.10 173	92.45 182	95.05 182	98.09 105	89.21 219	96.89 195	97.64 149	93.18 112	91.79 213	97.28 143	75.35 315	98.65 208	88.99 239	92.84 244	97.28 225

Effi-MVS+-dtu		93.08 174	93.21 153	92.68 302	96.02 244	83.25 343	97.14 176	96.72 249	93.85 83	91.20 234	93.44 343	83.08 193	98.30 239	91.69 182	95.73 191	96.50 246

test_djsdf		93.07 175	92.76 165	94.00 241	93.49 355	88.70 232	98.22 40	97.57 158	91.42 167	90.08 258	95.55 243	82.85 201	97.92 293	94.07 131	91.58 265	95.40 293

VDDNet		93.05 176	92.07 190	96.02 132	96.84 181	90.39 177	98.08 53	95.85 294	86.22 327	95.79 118	98.46 48	67.59 369	99.19 137	94.92 112	94.85 207	98.47 149

thisisatest0530		93.03 177	92.21 188	95.49 165	97.07 164	89.11 224	97.49 140	92.19 391	90.16 215	94.09 158	96.41 195	76.43 306	99.05 166	90.38 204	95.68 193	98.31 163

EI-MVSNet		93.03 177	92.88 161	93.48 271	95.77 253	86.98 278	96.44 233	97.12 211	90.66 198	91.30 227	97.64 123	86.56 137	98.05 269	89.91 212	90.55 283	95.41 290

CLD-MVS		92.98 179	92.53 178	94.32 225	96.12 240	89.20 220	95.28 304	97.47 173	92.66 133	89.90 261	95.62 239	80.58 242	98.40 228	92.73 161	92.40 251	95.38 295
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 180	92.33 185	94.87 195	97.11 162	87.16 275	97.97 69	92.09 392	90.63 200	93.88 164	97.01 160	76.50 303	99.06 165	90.29 207	95.45 197	98.38 159

ACMM	89.79 8	92.96 180	92.50 180	94.35 222	96.30 228	88.71 231	97.58 123	97.36 196	91.40 169	90.53 241	96.65 178	79.77 258	98.75 196	91.24 191	91.64 263	95.59 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 182	92.56 175	94.10 235	96.16 235	88.26 245	97.65 113	97.46 175	91.29 171	90.12 254	97.16 151	79.05 271	98.73 199	92.25 165	91.89 261	95.31 300

BH-untuned		92.94 182	92.62 173	93.92 251	97.22 155	86.16 301	96.40 241	96.25 279	90.06 218	89.79 265	96.17 207	83.19 189	98.35 235	87.19 276	97.27 160	97.24 227

DU-MVS		92.90 184	92.04 191	95.49 165	94.95 301	92.83 82	97.16 174	98.24 50	93.02 118	90.13 252	95.71 233	83.47 183	97.85 299	91.71 180	83.93 357	95.78 273

PatchMatch-RL		92.90 184	92.02 193	95.56 159	98.19 98	90.80 161	95.27 306	97.18 206	87.96 285	91.86 212	95.68 236	80.44 245	98.99 171	84.01 321	97.54 147	96.89 237

PMMVS		92.86 186	92.34 184	94.42 220	94.92 304	86.73 284	94.53 328	96.38 271	84.78 350	94.27 153	95.12 262	83.13 192	98.40 228	91.47 186	96.49 178	98.12 176

OpenMVS	89.19 12	92.86 186	91.68 205	96.40 104	95.34 276	92.73 86	98.27 32	98.12 73	84.86 348	85.78 349	97.75 112	78.89 278	99.74 47	87.50 270	98.65 107	96.73 241

Test_1112_low_res		92.84 188	91.84 199	95.85 143	97.04 170	89.97 189	95.53 293	96.64 257	85.38 338	89.65 270	95.18 258	85.86 149	99.10 153	87.70 261	93.58 240	98.49 146

baseline1		92.82 189	91.90 197	95.55 161	97.20 157	90.77 163	97.19 171	94.58 352	92.20 143	92.36 195	96.34 199	84.16 173	98.21 245	89.20 235	83.90 360	97.68 204

1314		92.81 190	92.03 192	95.14 178	95.33 279	89.52 204	96.04 264	97.44 184	87.72 297	86.25 346	95.33 250	83.84 177	98.79 190	89.26 231	97.05 166	97.11 230

DP-MVS		92.76 191	91.51 213	96.52 90	98.77 56	90.99 153	97.38 152	96.08 286	82.38 374	89.29 282	97.87 100	83.77 178	99.69 59	81.37 348	96.69 174	98.89 113

test_fmvs1_n		92.73 192	92.88 161	92.29 310	96.08 243	81.05 366	97.98 63	97.08 216	90.72 193	96.79 73	98.18 77	63.07 391	98.45 225	97.62 30	98.42 120	97.36 220

BH-RMVSNet		92.72 193	91.97 195	94.97 190	97.16 159	87.99 254	96.15 260	95.60 308	90.62 201	91.87 211	97.15 153	78.41 284	98.57 217	83.16 328	97.60 146	98.36 161

ACMP	89.59 10	92.62 194	92.14 189	94.05 238	96.40 222	88.20 248	97.36 153	97.25 205	91.52 162	88.30 307	96.64 179	78.46 283	98.72 202	91.86 176	91.48 267	95.23 307
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 195	92.52 179	92.44 304	96.82 185	81.89 359	96.92 193	93.71 375	92.41 138	84.30 362	94.60 284	85.08 158	97.03 354	91.51 184	97.36 154	98.40 157

TranMVSNet+NR-MVSNet		92.50 195	91.63 206	95.14 178	94.76 312	92.07 108	97.53 131	98.11 76	92.90 128	89.56 273	96.12 210	83.16 190	97.60 324	89.30 229	83.20 366	95.75 277

thres600view7		92.49 197	91.60 207	95.18 176	97.91 122	89.47 205	97.65 113	94.66 349	92.18 147	93.33 176	94.91 268	78.06 291	99.10 153	81.61 342	94.06 230	96.98 232

thres100view900		92.43 198	91.58 208	94.98 188	97.92 121	89.37 211	97.71 106	94.66 349	92.20 143	93.31 177	94.90 269	78.06 291	99.08 159	81.40 345	94.08 226	96.48 247

jajsoiax		92.42 199	91.89 198	94.03 240	93.33 361	88.50 239	97.73 101	97.53 164	92.00 152	88.85 293	96.50 191	75.62 313	98.11 256	93.88 138	91.56 266	95.48 285

thres400		92.42 199	91.52 211	95.12 180	97.85 125	89.29 215	97.41 145	94.88 343	92.19 145	93.27 179	94.46 294	78.17 287	99.08 159	81.40 345	94.08 226	96.98 232

tfpn200view9		92.38 201	91.52 211	94.95 192	97.85 125	89.29 215	97.41 145	94.88 343	92.19 145	93.27 179	94.46 294	78.17 287	99.08 159	81.40 345	94.08 226	96.48 247

test_vis1_n		92.37 202	92.26 187	92.72 299	94.75 313	82.64 348	98.02 59	96.80 246	91.18 178	97.77 45	97.93 95	58.02 400	98.29 240	97.63 29	98.21 127	97.23 228

WR-MVS		92.34 203	91.53 210	94.77 203	95.13 294	90.83 160	96.40 241	97.98 106	91.88 154	89.29 282	95.54 244	82.50 209	97.80 305	89.79 216	85.27 336	95.69 280

NR-MVSNet		92.34 203	91.27 221	95.53 162	94.95 301	93.05 77	97.39 150	98.07 85	92.65 134	84.46 360	95.71 233	85.00 159	97.77 309	89.71 217	83.52 363	95.78 273

mvs_tets		92.31 205	91.76 201	93.94 248	93.41 358	88.29 243	97.63 119	97.53 164	92.04 150	88.76 296	96.45 193	74.62 321	98.09 261	93.91 136	91.48 267	95.45 289

TAPA-MVS	90.10 7	92.30 206	91.22 224	95.56 159	98.33 83	89.60 198	96.79 204	97.65 147	81.83 378	91.52 219	97.23 148	87.94 112	98.91 179	71.31 400	98.37 121	98.17 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 207	91.30 219	95.25 174	96.60 200	88.90 228	94.36 336	92.32 390	87.92 286	93.43 174	94.57 285	77.28 298	99.00 170	89.42 226	95.86 188	97.86 194

Fast-Effi-MVS+-dtu		92.29 207	91.99 194	93.21 282	95.27 283	85.52 308	97.03 181	96.63 260	92.09 148	89.11 288	95.14 260	80.33 248	98.08 262	87.54 269	94.74 213	96.03 263

IterMVS-LS		92.29 207	91.94 196	93.34 276	96.25 229	86.97 279	96.57 231	97.05 221	90.67 196	89.50 276	94.80 275	86.59 136	97.64 319	89.91 212	86.11 326	95.40 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 210	91.74 204	93.73 258	97.77 129	83.69 340	92.88 378	96.72 249	87.91 287	93.00 183	94.86 271	78.51 282	99.05 166	86.53 284	97.45 152	98.47 149

VPNet		92.23 211	91.31 218	94.99 186	95.56 261	90.96 155	97.22 169	97.86 122	92.96 125	90.96 235	96.62 186	75.06 316	98.20 246	91.90 173	83.65 362	95.80 271

thres200		92.23 211	91.39 214	94.75 205	97.61 141	89.03 225	96.60 227	95.09 333	92.08 149	93.28 178	94.00 320	78.39 285	99.04 169	81.26 351	94.18 222	96.19 254

anonymousdsp		92.16 213	91.55 209	93.97 244	92.58 375	89.55 201	97.51 133	97.42 188	89.42 238	88.40 303	94.84 272	80.66 241	97.88 298	91.87 175	91.28 271	94.48 346

XXY-MVS		92.16 213	91.23 223	94.95 192	94.75 313	90.94 156	97.47 141	97.43 187	89.14 245	88.90 290	96.43 194	79.71 259	98.24 242	89.56 222	87.68 310	95.67 281

BH-w/o		92.14 215	91.75 202	93.31 277	96.99 175	85.73 305	95.67 284	95.69 303	88.73 265	89.26 284	94.82 274	82.97 198	98.07 266	85.26 307	96.32 181	96.13 259

Anonymous202405211		92.07 216	90.83 239	95.76 145	98.19 98	88.75 230	97.58 123	95.00 336	86.00 330	93.64 167	97.45 134	66.24 381	99.53 98	90.68 201	92.71 247	99.01 94

FE-MVS		92.05 217	91.05 229	95.08 181	96.83 183	87.93 255	93.91 354	95.70 301	86.30 324	94.15 157	94.97 264	76.59 302	99.21 135	84.10 319	96.86 167	98.09 180

WR-MVS_H		92.00 218	91.35 215	93.95 246	95.09 296	89.47 205	98.04 58	98.68 13	91.46 165	88.34 305	94.68 280	85.86 149	97.56 326	85.77 300	84.24 354	94.82 331

Anonymous20240529		91.98 219	90.73 245	95.73 150	98.14 102	89.40 209	97.99 62	97.72 139	79.63 392	93.54 170	97.41 138	69.94 354	99.56 92	91.04 195	91.11 274	98.22 167

MonoMVSNet		91.92 220	91.77 200	92.37 306	92.94 367	83.11 344	97.09 179	95.55 311	92.91 127	90.85 237	94.55 286	81.27 232	96.52 366	93.01 158	87.76 309	97.47 216

PatchmatchNet		91.91 221	91.35 215	93.59 266	95.38 271	84.11 333	93.15 373	95.39 316	89.54 232	92.10 205	93.68 333	82.82 202	98.13 252	84.81 311	95.32 199	98.52 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing91		91.90 222	91.02 230	94.53 215	96.54 208	86.55 291	95.86 274	95.64 307	91.77 156	91.89 210	93.47 342	69.94 354	98.86 182	90.23 208	93.86 233	98.18 170

CP-MVSNet		91.89 223	91.24 222	93.82 254	95.05 297	88.57 235	97.82 91	98.19 61	91.70 158	88.21 311	95.76 231	81.96 220	97.52 332	87.86 255	84.65 345	95.37 296

SCA		91.84 224	91.18 226	93.83 253	95.59 259	84.95 323	94.72 322	95.58 310	90.82 188	92.25 200	93.69 331	75.80 310	98.10 257	86.20 290	95.98 184	98.45 151

FMVSNet3		91.78 225	90.69 248	95.03 184	96.53 210	92.27 101	97.02 183	96.93 232	89.79 227	89.35 279	94.65 282	77.01 299	97.47 335	86.12 293	88.82 298	95.35 297

AUN-MVS		91.76 226	90.75 243	94.81 198	97.00 174	88.57 235	96.65 219	96.49 266	89.63 229	92.15 202	96.12 210	78.66 280	98.50 221	90.83 196	79.18 383	97.36 220

X-MVStestdata		91.71 227	89.67 291	97.81 28	99.38 14	94.03 50	98.59 12	98.20 56	94.85 41	96.59 85	32.69 424	91.70 52	99.80 34	95.66 90	99.40 56	99.62 20

MVS		91.71 227	90.44 255	95.51 163	95.20 289	91.59 126	96.04 264	97.45 180	73.44 408	87.36 328	95.60 240	85.42 154	99.10 153	85.97 297	97.46 148	95.83 269

EPNet_dtu		91.71 227	91.28 220	92.99 288	93.76 346	83.71 339	96.69 215	95.28 323	93.15 114	87.02 336	95.95 218	83.37 186	97.38 343	79.46 363	96.84 168	97.88 191
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		91.68 230	90.75 243	94.47 216	96.53 210	86.56 290	95.76 281	94.51 355	91.10 183	91.24 232	93.59 337	68.59 363	98.86 182	91.10 193	94.29 219	98.00 185

baseline2		91.63 231	90.86 235	93.94 248	94.33 330	86.32 295	95.92 271	91.64 396	89.37 239	86.94 339	94.69 279	81.62 227	98.69 204	88.64 246	94.57 216	96.81 239

testing99		91.62 232	90.72 246	94.32 225	96.48 216	86.11 302	95.81 277	94.76 347	91.55 161	91.75 215	93.44 343	68.55 364	98.82 186	90.43 202	93.69 234	98.04 183

test2506		91.60 233	90.78 240	94.04 239	97.66 135	83.81 336	98.27 32	75.53 425	93.43 100	95.23 132	98.21 74	67.21 372	99.07 163	93.01 158	98.49 114	99.25 72

miper_ehance_all_eth		91.59 234	91.13 227	92.97 289	95.55 262	86.57 289	94.47 330	96.88 240	87.77 294	88.88 292	94.01 319	86.22 143	97.54 328	89.49 223	86.93 318	94.79 336

v2v482		91.59 234	90.85 237	93.80 255	93.87 343	88.17 250	96.94 192	96.88 240	89.54 232	89.53 274	94.90 269	81.70 226	98.02 274	89.25 232	85.04 342	95.20 308

V42		91.58 236	90.87 234	93.73 258	94.05 338	88.50 239	97.32 158	96.97 228	88.80 263	89.71 266	94.33 301	82.54 208	98.05 269	89.01 238	85.07 340	94.64 344

PCF-MVS	89.48 11	91.56 237	89.95 279	96.36 109	96.60 200	92.52 92	92.51 383	97.26 203	79.41 393	88.90 290	96.56 188	84.04 176	99.55 94	77.01 377	97.30 159	97.01 231
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG		91.55 238	90.76 241	93.94 248	96.52 212	85.06 319	95.22 309	94.54 353	90.47 208	91.98 208	92.71 353	72.02 336	98.74 198	88.10 251	95.26 201	98.01 184

PS-CasMVS		91.55 238	90.84 238	93.69 262	94.96 300	88.28 244	97.84 86	98.24 50	91.46 165	88.04 315	95.80 226	79.67 260	97.48 334	87.02 280	84.54 351	95.31 300

miper_enhance_ethall		91.54 240	91.01 231	93.15 283	95.35 275	87.07 277	93.97 349	96.90 237	86.79 316	89.17 286	93.43 346	86.55 138	97.64 319	89.97 211	86.93 318	94.74 340

PAPM		91.52 241	90.30 261	95.20 175	95.30 282	89.83 193	93.38 369	96.85 243	86.26 326	88.59 299	95.80 226	84.88 160	98.15 251	75.67 382	95.93 186	97.63 205

ET-MVSNet_ETH3D		91.49 242	90.11 271	95.63 155	96.40 222	91.57 128	95.34 300	93.48 377	90.60 204	75.58 400	95.49 246	80.08 252	96.79 363	94.25 129	89.76 291	98.52 141

TR-MVS		91.48 243	90.59 251	94.16 233	96.40 222	87.33 266	95.67 284	95.34 322	87.68 298	91.46 221	95.52 245	76.77 301	98.35 235	82.85 333	93.61 238	96.79 240

tpmrst		91.44 244	91.32 217	91.79 326	95.15 292	79.20 389	93.42 368	95.37 318	88.55 270	93.49 172	93.67 334	82.49 210	98.27 241	90.41 203	89.34 295	97.90 189

test-LLR		91.42 245	91.19 225	92.12 314	94.59 320	80.66 369	94.29 341	92.98 382	91.11 181	90.76 239	92.37 361	79.02 273	98.07 266	88.81 242	96.74 171	97.63 205

MSDG		91.42 245	90.24 265	94.96 191	97.15 161	88.91 227	93.69 361	96.32 273	85.72 334	86.93 340	96.47 192	80.24 249	98.98 172	80.57 354	95.05 206	96.98 232

c3_l		91.38 247	90.89 233	92.88 293	95.58 260	86.30 296	94.68 323	96.84 244	88.17 280	88.83 295	94.23 309	85.65 152	97.47 335	89.36 227	84.63 346	94.89 326

GA-MVS		91.38 247	90.31 260	94.59 208	94.65 318	87.62 264	94.34 337	96.19 283	90.73 192	90.35 245	93.83 324	71.84 338	97.96 285	87.22 275	93.61 238	98.21 168

v1144		91.37 249	90.60 250	93.68 263	93.89 342	88.23 247	96.84 200	97.03 225	88.37 275	89.69 268	94.39 296	82.04 218	97.98 278	87.80 257	85.37 333	94.84 328

GBi-Net		91.35 250	90.27 263	94.59 208	96.51 213	91.18 147	97.50 134	96.93 232	88.82 260	89.35 279	94.51 289	73.87 325	97.29 347	86.12 293	88.82 298	95.31 300

test1		91.35 250	90.27 263	94.59 208	96.51 213	91.18 147	97.50 134	96.93 232	88.82 260	89.35 279	94.51 289	73.87 325	97.29 347	86.12 293	88.82 298	95.31 300

UniMVSNet_ETH3D		91.34 252	90.22 268	94.68 206	94.86 308	87.86 259	97.23 168	97.46 175	87.99 284	89.90 261	96.92 164	66.35 379	98.23 243	90.30 206	90.99 277	97.96 186

FMVSNet2		91.31 253	90.08 272	94.99 186	96.51 213	92.21 103	97.41 145	96.95 230	88.82 260	88.62 298	94.75 277	73.87 325	97.42 340	85.20 308	88.55 303	95.35 297

reproduce_monomvs		91.30 254	91.10 228	91.92 318	96.82 185	82.48 352	97.01 186	97.49 169	94.64 57	88.35 304	95.27 254	70.53 347	98.10 257	95.20 104	84.60 348	95.19 311

D2MVS		91.30 254	90.95 232	92.35 307	94.71 316	85.52 308	96.18 259	98.21 54	88.89 256	86.60 343	93.82 326	79.92 256	97.95 289	89.29 230	90.95 278	93.56 364

v8		91.29 256	90.53 254	93.57 268	94.15 334	88.12 252	97.34 155	97.06 220	88.99 251	88.32 306	94.26 308	83.08 193	98.01 275	87.62 267	83.92 359	94.57 345

CVMVSNet		91.23 257	91.75 202	89.67 364	95.77 253	74.69 400	96.44 233	94.88 343	85.81 332	92.18 201	97.64 123	79.07 270	95.58 383	88.06 252	95.86 188	98.74 125

cl22		91.21 258	90.56 253	93.14 284	96.09 242	86.80 281	94.41 334	96.58 263	87.80 292	88.58 300	93.99 321	80.85 239	97.62 322	89.87 214	86.93 318	94.99 317

PEN-MVS		91.20 259	90.44 255	93.48 271	94.49 324	87.91 258	97.76 97	98.18 63	91.29 171	87.78 319	95.74 232	80.35 247	97.33 345	85.46 304	82.96 367	95.19 311

Baseline_NR-MVSNet		91.20 259	90.62 249	92.95 290	93.83 344	88.03 253	97.01 186	95.12 332	88.42 274	89.70 267	95.13 261	83.47 183	97.44 338	89.66 220	83.24 365	93.37 368

cascas		91.20 259	90.08 272	94.58 212	94.97 299	89.16 223	93.65 363	97.59 156	79.90 391	89.40 277	92.92 351	75.36 314	98.36 234	92.14 168	94.75 212	96.23 251

CostFormer		91.18 262	90.70 247	92.62 303	94.84 309	81.76 360	94.09 347	94.43 356	84.15 356	92.72 190	93.77 328	79.43 264	98.20 246	90.70 200	92.18 256	97.90 189

tt0805		91.09 263	90.07 275	94.16 233	95.61 258	88.31 242	97.56 126	96.51 265	89.56 231	89.17 286	95.64 238	67.08 376	98.38 233	91.07 194	88.44 304	95.80 271

v1192		91.07 264	90.23 266	93.58 267	93.70 347	87.82 261	96.73 209	97.07 218	87.77 294	89.58 271	94.32 303	80.90 238	97.97 281	86.52 285	85.48 331	94.95 318

v144192		91.06 265	90.28 262	93.39 274	93.66 350	87.23 272	96.83 201	97.07 218	87.43 303	89.69 268	94.28 305	81.48 228	98.00 276	87.18 277	84.92 344	94.93 322

v10		91.04 266	90.23 266	93.49 270	94.12 335	88.16 251	97.32 158	97.08 216	88.26 278	88.29 308	94.22 311	82.17 217	97.97 281	86.45 287	84.12 355	94.33 352

eth_miper_zixun_eth		91.02 267	90.59 251	92.34 309	95.33 279	84.35 329	94.10 346	96.90 237	88.56 269	88.84 294	94.33 301	84.08 174	97.60 324	88.77 244	84.37 353	95.06 315

v148		90.99 268	90.38 257	92.81 296	93.83 344	85.80 304	96.78 206	96.68 254	89.45 237	88.75 297	93.93 323	82.96 199	97.82 303	87.83 256	83.25 364	94.80 334

LTVRE_ROB	88.41 13	90.99 268	89.92 281	94.19 231	96.18 233	89.55 201	96.31 249	97.09 215	87.88 288	85.67 350	95.91 220	78.79 279	98.57 217	81.50 343	89.98 288	94.44 349
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
DIV-MVS_self_test		90.97 270	90.33 258	92.88 293	95.36 274	86.19 300	94.46 332	96.63 260	87.82 290	88.18 312	94.23 309	82.99 196	97.53 330	87.72 258	85.57 330	94.93 322

cl____		90.96 271	90.32 259	92.89 292	95.37 273	86.21 299	94.46 332	96.64 257	87.82 290	88.15 313	94.18 312	82.98 197	97.54 328	87.70 261	85.59 329	94.92 324

pmmvs4		90.93 272	89.85 283	94.17 232	93.34 360	90.79 162	94.60 325	96.02 287	84.62 351	87.45 324	95.15 259	81.88 223	97.45 337	87.70 261	87.87 308	94.27 356

XVG-ACMP-BASELINE		90.93 272	90.21 269	93.09 285	94.31 332	85.89 303	95.33 301	97.26 203	91.06 184	89.38 278	95.44 248	68.61 362	98.60 213	89.46 224	91.05 275	94.79 336

v1921920		90.85 274	90.03 277	93.29 278	93.55 351	86.96 280	96.74 208	97.04 223	87.36 305	89.52 275	94.34 300	80.23 250	97.97 281	86.27 288	85.21 337	94.94 320

CR-MVSNet		90.82 275	89.77 287	93.95 246	94.45 326	87.19 273	90.23 399	95.68 305	86.89 314	92.40 192	92.36 364	80.91 236	97.05 353	81.09 352	93.95 231	97.60 210

v7n		90.76 276	89.86 282	93.45 273	93.54 352	87.60 265	97.70 109	97.37 194	88.85 257	87.65 321	94.08 317	81.08 233	98.10 257	84.68 313	83.79 361	94.66 343

RPSCF		90.75 277	90.86 235	90.42 355	96.84 181	76.29 398	95.61 290	96.34 272	83.89 359	91.38 222	97.87 100	76.45 304	98.78 191	87.16 278	92.23 253	96.20 253

MVP-Stereo		90.74 278	90.08 272	92.71 300	93.19 363	88.20 248	95.86 274	96.27 277	86.07 329	84.86 358	94.76 276	77.84 294	97.75 311	83.88 325	98.01 135	92.17 389
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 279	89.65 293	93.96 245	94.29 333	89.63 196	97.79 95	96.82 245	89.07 247	86.12 348	95.48 247	78.61 281	97.78 307	86.97 281	81.67 372	94.46 347

v1240		90.70 280	89.85 283	93.23 280	93.51 354	86.80 281	96.61 225	97.02 226	87.16 310	89.58 271	94.31 304	79.55 263	97.98 278	85.52 303	85.44 332	94.90 325

EPMVS		90.70 280	89.81 285	93.37 275	94.73 315	84.21 331	93.67 362	88.02 411	89.50 234	92.38 194	93.49 340	77.82 295	97.78 307	86.03 296	92.68 248	98.11 179

WBMVS		90.69 282	89.99 278	92.81 296	96.48 216	85.00 320	95.21 311	96.30 275	89.46 236	89.04 289	94.05 318	72.45 335	97.82 303	89.46 224	87.41 315	95.61 282

Anonymous20231211		90.63 283	89.42 298	94.27 230	98.24 90	89.19 222	98.05 57	97.89 114	79.95 390	88.25 310	94.96 265	72.56 334	98.13 252	89.70 218	85.14 338	95.49 284

DTE-MVSNet		90.56 284	89.75 289	93.01 287	93.95 339	87.25 270	97.64 117	97.65 147	90.74 191	87.12 331	95.68 236	79.97 255	97.00 357	83.33 327	81.66 373	94.78 338

ACMH	87.59 16	90.53 285	89.42 298	93.87 252	96.21 230	87.92 256	97.24 164	96.94 231	88.45 273	83.91 370	96.27 202	71.92 337	98.62 212	84.43 316	89.43 294	95.05 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		90.52 286	89.14 305	94.67 207	96.81 187	87.85 260	95.91 272	93.97 369	89.71 228	92.34 198	92.48 359	65.41 386	97.96 285	81.37 348	94.27 220	98.21 168

OurMVSNet-221017-0		90.51 287	90.19 270	91.44 335	93.41 358	81.25 363	96.98 189	96.28 276	91.68 159	86.55 344	96.30 200	74.20 324	97.98 278	88.96 240	87.40 316	95.09 313

miper_lstm_enhance		90.50 288	90.06 276	91.83 323	95.33 279	83.74 337	93.86 355	96.70 253	87.56 301	87.79 318	93.81 327	83.45 185	96.92 359	87.39 271	84.62 347	94.82 331

COLMAP_ROB	87.81 15	90.40 289	89.28 301	93.79 256	97.95 118	87.13 276	96.92 193	95.89 293	82.83 371	86.88 342	97.18 150	73.77 328	99.29 129	78.44 368	93.62 237	94.95 318
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 290	88.96 307	94.35 222	96.54 208	87.29 267	95.50 294	93.84 373	90.97 186	91.75 215	92.96 350	62.18 396	98.00 276	82.86 331	94.08 226	97.76 200

IterMVS-SCA-FT		90.31 290	89.81 285	91.82 324	95.52 263	84.20 332	94.30 340	96.15 284	90.61 202	87.39 327	94.27 306	75.80 310	96.44 367	87.34 272	86.88 322	94.82 331

MS-PatchMatch		90.27 292	89.77 287	91.78 327	94.33 330	84.72 326	95.55 291	96.73 248	86.17 328	86.36 345	95.28 253	71.28 342	97.80 305	84.09 320	98.14 131	92.81 374

tpm		90.25 293	89.74 290	91.76 329	93.92 340	79.73 383	93.98 348	93.54 376	88.28 277	91.99 207	93.25 347	77.51 297	97.44 338	87.30 274	87.94 307	98.12 176

AllTest		90.23 294	88.98 306	93.98 242	97.94 119	86.64 285	96.51 232	95.54 312	85.38 338	85.49 352	96.77 170	70.28 349	99.15 146	80.02 358	92.87 242	96.15 257

dmvs_re		90.21 295	89.50 296	92.35 307	95.47 268	85.15 316	95.70 283	94.37 360	90.94 187	88.42 302	93.57 338	74.63 320	95.67 380	82.80 334	89.57 293	96.22 252

ACMH+	87.92 14	90.20 296	89.18 303	93.25 279	96.48 216	86.45 293	96.99 188	96.68 254	88.83 259	84.79 359	96.22 204	70.16 351	98.53 219	84.42 317	88.04 306	94.77 339

test-mter		90.19 297	89.54 295	92.12 314	94.59 320	80.66 369	94.29 341	92.98 382	87.68 298	90.76 239	92.37 361	67.67 368	98.07 266	88.81 242	96.74 171	97.63 205

IterMVS		90.15 298	89.67 291	91.61 331	95.48 265	83.72 338	94.33 338	96.12 285	89.99 219	87.31 330	94.15 314	75.78 312	96.27 370	86.97 281	86.89 321	94.83 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 299	89.42 298	91.97 317	94.41 328	80.62 371	94.29 341	91.97 394	87.28 308	90.44 243	92.47 360	68.79 360	97.67 316	88.50 248	96.60 176	97.61 209

tpm2		89.96 300	89.21 302	92.23 313	94.91 306	81.25 363	93.78 357	94.42 357	80.62 388	91.56 218	93.44 343	76.44 305	97.94 290	85.60 302	92.08 260	97.49 214

UWE-MVS		89.91 301	89.48 297	91.21 339	95.88 246	78.23 394	94.91 319	90.26 404	89.11 246	92.35 197	94.52 288	68.76 361	97.96 285	83.95 323	95.59 195	97.42 218

IB-MVS	87.33 17	89.91 301	88.28 317	94.79 202	95.26 286	87.70 263	95.12 314	93.95 370	89.35 240	87.03 335	92.49 358	70.74 346	99.19 137	89.18 236	81.37 374	97.49 214
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
ADS-MVSNet		89.89 303	88.68 312	93.53 269	95.86 247	84.89 324	90.93 394	95.07 334	83.23 369	91.28 230	91.81 373	79.01 275	97.85 299	79.52 360	91.39 269	97.84 195

WB-MVSnew		89.88 304	89.56 294	90.82 347	94.57 323	83.06 345	95.65 288	92.85 384	87.86 289	90.83 238	94.10 315	79.66 261	96.88 360	76.34 378	94.19 221	92.54 380

FMVSNet1		89.88 304	88.31 316	94.59 208	95.41 269	91.18 147	97.50 134	96.93 232	86.62 318	87.41 326	94.51 289	65.94 384	97.29 347	83.04 330	87.43 313	95.31 300

pmmvs5		89.86 306	88.87 310	92.82 295	92.86 368	86.23 298	96.26 252	95.39 316	84.24 355	87.12 331	94.51 289	74.27 323	97.36 344	87.61 268	87.57 311	94.86 327

tpmvs		89.83 307	89.15 304	91.89 321	94.92 304	80.30 376	93.11 374	95.46 315	86.28 325	88.08 314	92.65 354	80.44 245	98.52 220	81.47 344	89.92 289	96.84 238

test_fmvs2		89.77 308	89.93 280	89.31 370	93.68 349	76.37 397	97.64 117	95.90 291	89.84 225	91.49 220	96.26 203	58.77 399	97.10 351	94.65 122	91.13 273	94.46 347

mmtdpeth		89.70 309	88.96 307	91.90 320	95.84 252	84.42 328	97.46 143	95.53 314	90.27 212	94.46 150	90.50 381	69.74 357	98.95 173	97.39 40	69.48 406	92.34 383

tfpnnormal		89.70 309	88.40 315	93.60 265	95.15 292	90.10 182	97.56 126	98.16 67	87.28 308	86.16 347	94.63 283	77.57 296	98.05 269	74.48 386	84.59 349	92.65 377

ADS-MVSNet2		89.45 311	88.59 313	92.03 316	95.86 247	82.26 356	90.93 394	94.32 363	83.23 369	91.28 230	91.81 373	79.01 275	95.99 372	79.52 360	91.39 269	97.84 195

Patchmatch-test		89.42 312	87.99 319	93.70 261	95.27 283	85.11 317	88.98 406	94.37 360	81.11 382	87.10 334	93.69 331	82.28 214	97.50 333	74.37 388	94.76 211	98.48 148

test0.0.03 1		89.37 313	88.70 311	91.41 336	92.47 377	85.63 306	95.22 309	92.70 387	91.11 181	86.91 341	93.65 335	79.02 273	93.19 405	78.00 370	89.18 296	95.41 290

SixPastTwentyTwo		89.15 314	88.54 314	90.98 343	93.49 355	80.28 377	96.70 213	94.70 348	90.78 189	84.15 365	95.57 241	71.78 339	97.71 314	84.63 314	85.07 340	94.94 320

RPMNet		88.98 315	87.05 329	94.77 203	94.45 326	87.19 273	90.23 399	98.03 97	77.87 400	92.40 192	87.55 404	80.17 251	99.51 103	68.84 405	93.95 231	97.60 210

TransMVSNet (Re)		88.94 316	87.56 322	93.08 286	94.35 329	88.45 241	97.73 101	95.23 327	87.47 302	84.26 363	95.29 251	79.86 257	97.33 345	79.44 364	74.44 397	93.45 367

USDC		88.94 316	87.83 321	92.27 311	94.66 317	84.96 322	93.86 355	95.90 291	87.34 306	83.40 372	95.56 242	67.43 370	98.19 248	82.64 338	89.67 292	93.66 363

dp		88.90 318	88.26 318	90.81 348	94.58 322	76.62 396	92.85 379	94.93 340	85.12 344	90.07 259	93.07 348	75.81 309	98.12 255	80.53 355	87.42 314	97.71 202

PatchT		88.87 319	87.42 323	93.22 281	94.08 337	85.10 318	89.51 404	94.64 351	81.92 377	92.36 195	88.15 400	80.05 253	97.01 356	72.43 396	93.65 236	97.54 213

our_test_3		88.78 320	87.98 320	91.20 341	92.45 378	82.53 350	93.61 365	95.69 303	85.77 333	84.88 357	93.71 329	79.99 254	96.78 364	79.47 362	86.24 323	94.28 355

EU-MVSNet		88.72 321	88.90 309	88.20 374	93.15 364	74.21 401	96.63 224	94.22 365	85.18 342	87.32 329	95.97 216	76.16 307	94.98 389	85.27 306	86.17 324	95.41 290

Patchmtry		88.64 322	87.25 325	92.78 298	94.09 336	86.64 285	89.82 403	95.68 305	80.81 386	87.63 322	92.36 364	80.91 236	97.03 354	78.86 366	85.12 339	94.67 342

MIMVSNet		88.50 323	86.76 333	93.72 260	94.84 309	87.77 262	91.39 389	94.05 366	86.41 322	87.99 316	92.59 357	63.27 390	95.82 377	77.44 371	92.84 244	97.57 212

tpm cat1		88.36 324	87.21 327	91.81 325	95.13 294	80.55 372	92.58 382	95.70 301	74.97 404	87.45 324	91.96 371	78.01 293	98.17 250	80.39 356	88.74 301	96.72 242

ppachtmachnet_test		88.35 325	87.29 324	91.53 332	92.45 378	83.57 341	93.75 358	95.97 288	84.28 354	85.32 355	94.18 312	79.00 277	96.93 358	75.71 381	84.99 343	94.10 357

JIA-IIPM		88.26 326	87.04 330	91.91 319	93.52 353	81.42 362	89.38 405	94.38 359	80.84 385	90.93 236	80.74 412	79.22 267	97.92 293	82.76 335	91.62 264	96.38 250

testgi		87.97 327	87.21 327	90.24 358	92.86 368	80.76 367	96.67 218	94.97 338	91.74 157	85.52 351	95.83 224	62.66 394	94.47 393	76.25 379	88.36 305	95.48 285

LF4IMVS		87.94 328	87.25 325	89.98 361	92.38 380	80.05 381	94.38 335	95.25 326	87.59 300	84.34 361	94.74 278	64.31 388	97.66 318	84.83 310	87.45 312	92.23 386

gg-mvs-nofinetune		87.82 329	85.61 341	94.44 218	94.46 325	89.27 218	91.21 393	84.61 419	80.88 384	89.89 263	74.98 415	71.50 340	97.53 330	85.75 301	97.21 162	96.51 245

pmmvs6		87.81 330	86.19 337	92.69 301	91.32 385	86.30 296	97.34 155	96.41 270	80.59 389	84.05 369	94.37 298	67.37 371	97.67 316	84.75 312	79.51 382	94.09 359

testing3		87.67 331	86.88 332	90.05 360	96.14 238	80.71 368	97.10 178	92.85 384	90.15 216	87.54 323	94.55 286	55.70 405	94.10 396	73.77 392	94.10 225	95.35 297

K. test v3		87.64 332	86.75 334	90.32 357	93.02 366	79.48 387	96.61 225	92.08 393	90.66 198	80.25 389	94.09 316	67.21 372	96.65 365	85.96 298	80.83 376	94.83 329

Patchmatch-RL test		87.38 333	86.24 336	90.81 348	88.74 403	78.40 393	88.12 411	93.17 380	87.11 311	82.17 380	89.29 392	81.95 221	95.60 382	88.64 246	77.02 388	98.41 156

FMVSNet5		87.29 334	85.79 340	91.78 327	94.80 311	87.28 268	95.49 295	95.28 323	84.09 357	83.85 371	91.82 372	62.95 392	94.17 395	78.48 367	85.34 335	93.91 361

myMVS_eth3d		87.18 335	86.38 335	89.58 365	95.16 290	79.53 384	95.00 316	93.93 371	88.55 270	86.96 337	91.99 369	56.23 404	94.00 397	75.47 384	94.11 223	95.20 308

Syy-MVS		87.13 336	87.02 331	87.47 377	95.16 290	73.21 405	95.00 316	93.93 371	88.55 270	86.96 337	91.99 369	75.90 308	94.00 397	61.59 411	94.11 223	95.20 308

Anonymous20231206		87.09 337	86.14 338	89.93 362	91.22 386	80.35 374	96.11 261	95.35 319	83.57 366	84.16 364	93.02 349	73.54 330	95.61 381	72.16 397	86.14 325	93.84 362

EG-PatchMatch MVS		87.02 338	85.44 342	91.76 329	92.67 372	85.00 320	96.08 263	96.45 268	83.41 368	79.52 391	93.49 340	57.10 402	97.72 313	79.34 365	90.87 280	92.56 379

TinyColmap		86.82 339	85.35 345	91.21 339	94.91 306	82.99 346	93.94 351	94.02 368	83.58 365	81.56 381	94.68 280	62.34 395	98.13 252	75.78 380	87.35 317	92.52 381

mvs5depth		86.53 340	85.08 347	90.87 345	88.74 403	82.52 351	91.91 387	94.23 364	86.35 323	87.11 333	93.70 330	66.52 377	97.76 310	81.37 348	75.80 393	92.31 385

TDRefinement		86.53 340	84.76 352	91.85 322	82.23 418	84.25 330	96.38 243	95.35 319	84.97 347	84.09 367	94.94 266	65.76 385	98.34 238	84.60 315	74.52 396	92.97 371

test_0402		86.46 342	84.79 351	91.45 334	95.02 298	85.55 307	96.29 251	94.89 342	80.90 383	82.21 379	93.97 322	68.21 367	97.29 347	62.98 409	88.68 302	91.51 394

Anonymous20240521		86.42 343	85.44 342	89.34 369	90.33 390	79.79 382	96.73 209	95.92 289	83.71 364	83.25 374	91.36 377	63.92 389	96.01 371	78.39 369	85.36 334	92.22 387

DSMNet-mixed		86.34 344	86.12 339	87.00 381	89.88 394	70.43 407	94.93 318	90.08 405	77.97 399	85.42 354	92.78 352	74.44 322	93.96 399	74.43 387	95.14 202	96.62 243

CL-MVSNet_self_test		86.31 345	85.15 346	89.80 363	88.83 401	81.74 361	93.93 352	96.22 280	86.67 317	85.03 356	90.80 380	78.09 290	94.50 391	74.92 385	71.86 402	93.15 370

pmmvs-eth3d		86.22 346	84.45 354	91.53 332	88.34 405	87.25 270	94.47 330	95.01 335	83.47 367	79.51 392	89.61 390	69.75 356	95.71 378	83.13 329	76.73 391	91.64 391

test_vis1_rt		86.16 347	85.06 348	89.46 366	93.47 357	80.46 373	96.41 237	86.61 416	85.22 341	79.15 393	88.64 395	52.41 408	97.06 352	93.08 153	90.57 282	90.87 399

test20.03		86.14 348	85.40 344	88.35 372	90.12 391	80.06 380	95.90 273	95.20 328	88.59 266	81.29 382	93.62 336	71.43 341	92.65 406	71.26 401	81.17 375	92.34 383

UnsupCasMVSNet_eth		85.99 349	84.45 354	90.62 352	89.97 393	82.40 355	93.62 364	97.37 194	89.86 222	78.59 395	92.37 361	65.25 387	95.35 387	82.27 340	70.75 403	94.10 357

KD-MVS_self_test		85.95 350	84.95 349	88.96 371	89.55 397	79.11 390	95.13 313	96.42 269	85.91 331	84.07 368	90.48 382	70.03 353	94.82 390	80.04 357	72.94 400	92.94 372

ttmdpeth		85.91 351	84.76 352	89.36 368	89.14 398	80.25 378	95.66 287	93.16 381	83.77 362	83.39 373	95.26 255	66.24 381	95.26 388	80.65 353	75.57 394	92.57 378

YYNet1		85.87 352	84.23 356	90.78 351	92.38 380	82.46 354	93.17 371	95.14 331	82.12 376	67.69 408	92.36 364	78.16 289	95.50 385	77.31 373	79.73 380	94.39 350

MDA-MVSNet_test_wron		85.87 352	84.23 356	90.80 350	92.38 380	82.57 349	93.17 371	95.15 330	82.15 375	67.65 410	92.33 367	78.20 286	95.51 384	77.33 372	79.74 379	94.31 354

CMPMVS	62.92 21	85.62 354	84.92 350	87.74 376	89.14 398	73.12 406	94.17 344	96.80 246	73.98 405	73.65 404	94.93 267	66.36 378	97.61 323	83.95 323	91.28 271	92.48 382
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 355	83.64 358	90.92 344	95.27 283	79.49 386	90.55 397	95.60 308	83.76 363	83.00 377	89.95 387	71.09 343	97.97 281	82.75 336	60.79 417	95.31 300

MDA-MVSNet-bldmvs		85.00 356	82.95 361	91.17 342	93.13 365	83.33 342	94.56 327	95.00 336	84.57 352	65.13 414	92.65 354	70.45 348	95.85 375	73.57 393	77.49 387	94.33 352

MIMVSNet1		84.93 357	83.05 359	90.56 353	89.56 396	84.84 325	95.40 298	95.35 319	83.91 358	80.38 387	92.21 368	57.23 401	93.34 403	70.69 403	82.75 370	93.50 365

KD-MVS_2432*1600		84.81 358	82.64 362	91.31 337	91.07 387	85.34 314	91.22 391	95.75 299	85.56 336	83.09 375	90.21 385	67.21 372	95.89 373	77.18 375	62.48 415	92.69 375

miper_refine_blended		84.81 358	82.64 362	91.31 337	91.07 387	85.34 314	91.22 391	95.75 299	85.56 336	83.09 375	90.21 385	67.21 372	95.89 373	77.18 375	62.48 415	92.69 375

OpenMVS_ROB	81.14 20	84.42 360	82.28 366	90.83 346	90.06 392	84.05 335	95.73 282	94.04 367	73.89 407	80.17 390	91.53 376	59.15 398	97.64 319	66.92 407	89.05 297	90.80 400

mvsany_test3		83.59 361	82.44 365	87.03 380	83.80 413	73.82 402	93.70 359	90.92 402	86.42 321	82.51 378	90.26 384	46.76 413	95.71 378	90.82 197	76.76 390	91.57 393

PM-MVS		83.48 362	81.86 368	88.31 373	87.83 407	77.59 395	93.43 367	91.75 395	86.91 313	80.63 385	89.91 388	44.42 414	95.84 376	85.17 309	76.73 391	91.50 395

test_fmvs3		83.21 363	83.02 360	83.78 386	86.77 410	68.34 412	96.76 207	94.91 341	86.49 320	84.14 366	89.48 391	36.04 418	91.73 408	91.86 176	80.77 377	91.26 398

new-patchmatchnet		83.18 364	81.87 367	87.11 379	86.88 409	75.99 399	93.70 359	95.18 329	85.02 346	77.30 398	88.40 397	65.99 383	93.88 400	74.19 390	70.18 404	91.47 396

new_pmnet		82.89 365	81.12 370	88.18 375	89.63 395	80.18 379	91.77 388	92.57 388	76.79 402	75.56 401	88.23 399	61.22 397	94.48 392	71.43 399	82.92 368	89.87 403

MVS-HIRNet		82.47 366	81.21 369	86.26 383	95.38 271	69.21 410	88.96 407	89.49 406	66.28 412	80.79 384	74.08 417	68.48 365	97.39 342	71.93 398	95.47 196	92.18 388

MVStest1		82.38 367	80.04 371	89.37 367	87.63 408	82.83 347	95.03 315	93.37 379	73.90 406	73.50 405	94.35 299	62.89 393	93.25 404	73.80 391	65.92 412	92.04 390

UnsupCasMVSNet_bld		82.13 368	79.46 373	90.14 359	88.00 406	82.47 353	90.89 396	96.62 262	78.94 395	75.61 399	84.40 410	56.63 403	96.31 369	77.30 374	66.77 411	91.63 392

dmvs_testset		81.38 369	82.60 364	77.73 392	91.74 384	51.49 427	93.03 376	84.21 420	89.07 247	78.28 396	91.25 378	76.97 300	88.53 415	56.57 415	82.24 371	93.16 369

test_f		80.57 370	79.62 372	83.41 387	83.38 416	67.80 414	93.57 366	93.72 374	80.80 387	77.91 397	87.63 403	33.40 419	92.08 407	87.14 279	79.04 385	90.34 402

pmmvs3		79.97 371	77.50 376	87.39 378	82.80 417	79.38 388	92.70 381	90.75 403	70.69 409	78.66 394	87.47 405	51.34 409	93.40 402	73.39 394	69.65 405	89.38 404

APD_test1		79.31 372	77.70 375	84.14 385	89.11 400	69.07 411	92.36 386	91.50 397	69.07 410	73.87 403	92.63 356	39.93 416	94.32 394	70.54 404	80.25 378	89.02 405

N_pmnet		78.73 373	78.71 374	78.79 391	92.80 370	46.50 430	94.14 345	43.71 432	78.61 396	80.83 383	91.66 375	74.94 318	96.36 368	67.24 406	84.45 352	93.50 365

WB-MVS		76.77 374	76.63 377	77.18 393	85.32 411	56.82 425	94.53 328	89.39 407	82.66 373	71.35 406	89.18 393	75.03 317	88.88 413	35.42 422	66.79 410	85.84 407

SSC-MVS		76.05 375	75.83 378	76.72 397	84.77 412	56.22 426	94.32 339	88.96 409	81.82 379	70.52 407	88.91 394	74.79 319	88.71 414	33.69 423	64.71 413	85.23 408

test_vis3_rt		72.73 376	70.55 379	79.27 390	80.02 419	68.13 413	93.92 353	74.30 427	76.90 401	58.99 418	73.58 418	20.29 427	95.37 386	84.16 318	72.80 401	74.31 415

LCM-MVSNet		72.55 377	69.39 381	82.03 388	70.81 428	65.42 417	90.12 401	94.36 362	55.02 418	65.88 412	81.72 411	24.16 426	89.96 409	74.32 389	68.10 409	90.71 401

FPMVS		71.27 378	69.85 380	75.50 398	74.64 423	59.03 423	91.30 390	91.50 397	58.80 415	57.92 419	88.28 398	29.98 422	85.53 418	53.43 416	82.84 369	81.95 411

PMMVS2		70.19 379	66.92 383	80.01 389	76.35 422	65.67 416	86.22 412	87.58 413	64.83 414	62.38 415	80.29 414	26.78 424	88.49 416	63.79 408	54.07 419	85.88 406

dongtai		69.99 380	69.33 382	71.98 401	88.78 402	61.64 421	89.86 402	59.93 431	75.67 403	74.96 402	85.45 407	50.19 410	81.66 420	43.86 419	55.27 418	72.63 416

testf1		69.31 381	66.76 384	76.94 395	78.61 420	61.93 419	88.27 409	86.11 417	55.62 416	59.69 416	85.31 408	20.19 428	89.32 410	57.62 412	69.44 407	79.58 412

APD_test2		69.31 381	66.76 384	76.94 395	78.61 420	61.93 419	88.27 409	86.11 417	55.62 416	59.69 416	85.31 408	20.19 428	89.32 410	57.62 412	69.44 407	79.58 412

EGC-MVSNET		68.77 383	63.01 389	86.07 384	92.49 376	82.24 357	93.96 350	90.96 401	0.71 429	2.62 430	90.89 379	53.66 406	93.46 401	57.25 414	84.55 350	82.51 410

Gipuma		67.86 384	65.41 386	75.18 399	92.66 373	73.45 403	66.50 420	94.52 354	53.33 419	57.80 420	66.07 420	30.81 420	89.20 412	48.15 418	78.88 386	62.90 420
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 385	64.89 387	69.79 402	72.62 426	35.23 434	65.19 421	92.83 386	20.35 424	65.20 413	88.08 401	43.14 415	82.70 419	73.12 395	63.46 414	91.45 397

kuosan		65.27 386	64.66 388	67.11 404	83.80 413	61.32 422	88.53 408	60.77 430	68.22 411	67.67 409	80.52 413	49.12 411	70.76 426	29.67 425	53.64 420	69.26 418

ANet_high		63.94 387	59.58 390	77.02 394	61.24 430	66.06 415	85.66 414	87.93 412	78.53 397	42.94 422	71.04 419	25.42 425	80.71 421	52.60 417	30.83 423	84.28 409

PMVS	53.92 22	58.58 388	55.40 391	68.12 403	51.00 431	48.64 428	78.86 417	87.10 415	46.77 420	35.84 426	74.28 416	8.76 430	86.34 417	42.07 420	73.91 398	69.38 417
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 389	52.56 393	55.43 406	74.43 424	47.13 429	83.63 416	76.30 424	42.23 421	42.59 423	62.22 422	28.57 423	74.40 423	31.53 424	31.51 422	44.78 421

MVE	50.73 23	53.25 390	48.81 395	66.58 405	65.34 429	57.50 424	72.49 419	70.94 428	40.15 423	39.28 425	63.51 421	6.89 432	73.48 425	38.29 421	42.38 421	68.76 419
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 391	51.31 394	54.39 407	72.62 426	45.39 431	83.84 415	75.51 426	41.13 422	40.77 424	59.65 423	30.08 421	73.60 424	28.31 426	29.90 424	44.18 422

tmp_tt		51.94 392	53.82 392	46.29 408	33.73 432	45.30 432	78.32 418	67.24 429	18.02 425	50.93 421	87.05 406	52.99 407	53.11 427	70.76 402	25.29 425	40.46 423

wuyk23d		25.11 393	24.57 397	26.74 409	73.98 425	39.89 433	57.88 422	9.80 433	12.27 426	10.39 427	6.97 429	7.03 431	36.44 428	25.43 427	17.39 426	3.89 426

cdsmvs_eth3d_5k		23.24 394	30.99 396	0.00 412	0.00 435	0.00 437	0.00 423	97.63 151	0.00 430	0.00 431	96.88 166	84.38 168	0.00 431	0.00 430	0.00 429	0.00 427

testmvs		13.36 395	16.33 398	4.48 411	5.04 433	2.26 436	93.18 370	3.28 434	2.70 427	8.24 428	21.66 425	2.29 434	2.19 429	7.58 428	2.96 427	9.00 425

test123		13.04 396	15.66 399	5.18 410	4.51 434	3.45 435	92.50 384	1.81 435	2.50 428	7.58 429	20.15 426	3.67 433	2.18 430	7.13 429	1.07 428	9.90 424

ab-mvs-re		8.06 397	10.74 400	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	96.69 176	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		7.39 398	9.85 401	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	88.65 101	0.00 431	0.00 430	0.00 429	0.00 427

mmdepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

monomultidepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

test_blank		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet_test		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

DCPMVS		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet-low-res		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uncertanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

Regformer		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

WAC-MVS						79.53 384								75.56 383

FOURS1					99.55 1	93.34 67	99.29 1	98.35 30	94.98 36	98.49 27

MSC_two_6792asdad				98.86 1	98.67 61	96.94 1		97.93 111					99.86 9	97.68 24	99.67 6	99.77 2

PC_three_1452									90.77 190	98.89 18	98.28 72	96.24 1	98.35 235	95.76 88	99.58 23	99.59 25

No_MVS				98.86 1	98.67 61	96.94 1		97.93 111					99.86 9	97.68 24	99.67 6	99.77 2

test_one_0601					99.32 22	95.20 20		98.25 48	95.13 30	98.48 28	98.87 22	95.16 7

eth-test2					0.00 435

eth-test					0.00 435

ZD-MVS					99.05 39	94.59 32		98.08 80	89.22 243	97.03 67	98.10 80	92.52 39	99.65 65	94.58 125	99.31 66

RE-MVS-def			96.72 49		99.02 42	92.34 97	97.98 63	98.03 97	93.52 97	97.43 53	98.51 42	90.71 76		96.05 76	99.26 70	99.43 55

IU-MVS					99.42 7	95.39 11		97.94 110	90.40 211	98.94 12				97.41 39	99.66 10	99.74 8

OPU-MVS				98.55 3	98.82 55	96.86 3	98.25 35				98.26 73	96.04 2	99.24 132	95.36 102	99.59 19	99.56 32

test_241102_TWO								98.27 42	95.13 30	98.93 13	98.89 20	94.99 11	99.85 18	97.52 32	99.65 13	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 42	95.09 33	99.19 7	98.81 28	95.54 5	99.65 65

9.14			96.75 48		98.93 50		97.73 101	98.23 53	91.28 174	97.88 41	98.44 50	93.00 26	99.65 65	95.76 88	99.47 40

save fliter					98.91 52	94.28 38	97.02 183	98.02 100	95.35 23

test_0728_THIRD									94.78 48	98.73 22	98.87 22	95.87 4	99.84 23	97.45 36	99.72 2	99.77 2

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 42	98.28 39					99.86 9	97.52 32	99.67 6	99.75 6

test0726					99.45 3	95.36 13	98.31 27	98.29 37	94.92 39	98.99 11	98.92 17	95.08 8

GSMVS																98.45 151

test_part2					99.28 25	95.74 8				98.10 34

sam_mvs1												82.76 203				98.45 151

sam_mvs												81.94 222

ambc				86.56 382	83.60 415	70.00 409	85.69 413	94.97 338		80.60 386	88.45 396	37.42 417	96.84 362	82.69 337	75.44 395	92.86 373

MTGPA								98.08 80

test_post1							92.81 380				16.58 428	80.53 243	97.68 315	86.20 290

test_post											17.58 427	81.76 224	98.08 262

patchmatchnet-post											90.45 383	82.65 207	98.10 257

GG-mvs-BLEND				93.62 264	93.69 348	89.20 220	92.39 385	83.33 421		87.98 317	89.84 389	71.00 344	96.87 361	82.08 341	95.40 198	94.80 334

MTMP							97.86 82	82.03 422

gm-plane-assit					93.22 362	78.89 392			84.82 349		93.52 339		98.64 209	87.72 258

test9_res														94.81 117	99.38 59	99.45 51

TEST9					98.70 59	94.19 42	96.41 237	98.02 100	88.17 280	96.03 108	97.56 130	92.74 33	99.59 81

test_8					98.67 61	94.06 49	96.37 244	98.01 103	88.58 267	95.98 112	97.55 132	92.73 34	99.58 84

agg_prior2														93.94 135	99.38 59	99.50 44

agg_prior					98.67 61	93.79 55		98.00 104		95.68 122			99.57 91

TestCases				93.98 242	97.94 119	86.64 285		95.54 312	85.38 338	85.49 352	96.77 170	70.28 349	99.15 146	80.02 358	92.87 242	96.15 257

test_prior4						93.66 58	96.42 236

test_prior2							96.35 245		92.80 131	96.03 108	97.59 127	92.01 47		95.01 110	99.38 59

test_prior				97.23 64	98.67 61	92.99 79		98.00 104					99.41 116			99.29 67

旧先验2							95.94 270		81.66 380	97.34 56			98.82 186	92.26 163

新几何2							95.79 279

新几何1				97.32 57	98.60 68	93.59 59		97.75 134	81.58 381	95.75 119	97.85 103	90.04 83	99.67 63	86.50 286	99.13 85	98.69 129

旧先验1					98.38 81	93.38 64		97.75 134			98.09 82	92.30 45			99.01 94	99.16 77

无先验							95.79 279	97.87 118	83.87 361				99.65 65	87.68 264		98.89 113

原ACMM2							95.67 284

原ACMM1				96.38 107	98.59 69	91.09 152		97.89 114	87.41 304	95.22 133	97.68 116	90.25 80	99.54 96	87.95 254	99.12 87	98.49 146

test222					98.24 90	92.21 103	95.33 301	97.60 153	79.22 394	95.25 131	97.84 105	88.80 98			99.15 83	98.72 126

testdata2													99.67 63	85.96 298

segment_acmp												92.89 30

testdata				95.46 169	98.18 100	88.90 228		97.66 145	82.73 372	97.03 67	98.07 83	90.06 82	98.85 184	89.67 219	98.98 95	98.64 132

testdata1							95.26 308		93.10 117

test12				97.65 43	98.46 73	94.26 39		97.66 145		95.52 129		90.89 73	99.46 110		99.25 72	99.22 74

plane_prior7					96.21 230	89.98 188

plane_prior6					96.10 241	90.00 184						81.32 230

plane_prior5								97.51 166					98.60 213	93.02 156	92.23 253	95.86 265

plane_prior4											96.64 179

plane_prior3						90.00 184			94.46 64	91.34 224

plane_prior2							97.74 99		94.85 41

plane_prior1					96.14 238

plane_prior						89.99 186	97.24 164		94.06 76						92.16 257

n2								0.00 436

nn								0.00 436

door-mid								91.06 400

lessismore_v0				90.45 354	91.96 383	79.09 391		87.19 414		80.32 388	94.39 296	66.31 380	97.55 327	84.00 322	76.84 389	94.70 341

LGP-MVS_train				94.10 235	96.16 235	88.26 245		97.46 175	91.29 171	90.12 254	97.16 151	79.05 271	98.73 199	92.25 165	91.89 261	95.31 300

test11								97.88 116

door								91.13 399

HQP5-MVS						89.33 213

HQP-NCC					95.86 247		96.65 219		93.55 92	90.14 248

ACMP_Plane					95.86 247		96.65 219		93.55 92	90.14 248

BP-MVS														92.13 169

HQP4-MVS										90.14 248			98.50 221			95.78 273

HQP3-MVS								97.39 191							92.10 258

HQP2-MVS												80.95 234

NP-MVS					95.99 245	89.81 194					95.87 221

MDTV_nov1_ep13_2view						70.35 408	93.10 375		83.88 360	93.55 169		82.47 211		86.25 289		98.38 159

MDTV_nov1_ep13			90.76 241		95.22 287	80.33 375	93.03 376	95.28 323	88.14 282	92.84 189	93.83 324	81.34 229	98.08 262	82.86 331	94.34 218

ACMMP++_ref															90.30 287

ACMMP++															91.02 276

Test By Simon												88.73 100

ITE_SJBPF				92.43 305	95.34 276	85.37 313		95.92 289	91.47 164	87.75 320	96.39 197	71.00 344	97.96 285	82.36 339	89.86 290	93.97 360

DeepMVS_CX				74.68 400	90.84 389	64.34 418		81.61 423	65.34 413	67.47 411	88.01 402	48.60 412	80.13 422	62.33 410	73.68 399	79.58 412

High-resolution multi-view benchmark