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	97.21 171	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	97.27 163	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	98.08 182

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	97.44 151	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	97.41 344	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	92.37 140	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	90.57 208	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	97.34 156	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	95.76 299	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	92.82 131	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	97.12 178	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	93.21 108	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	95.67 259	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	97.24 165	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	93.19 111	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	93.21 108	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	92.99 120	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	92.64 136	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	91.50 166	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	98.94 176	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	97.41 146	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	98.75 197	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 98	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	98.71 204	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	93.27 107	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	92.72 133	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	97.63 140	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	96.98 190	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	93.11 117	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	92.22 143	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	93.37 103	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	89.86 225	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	97.64 138	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	96.87 180	94.65 30	97.58 123	94.39 361	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	96.62 200	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	97.43 219

MP-MVS-pluss		96.70 53	96.27 70	97.98 22	99.23 30	94.71 29	96.96 192	98.06 88	90.67 199	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	96.79 205	96.72 250	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	91.17 182	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	96.20 259	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	97.88 192

DELS-MVS		96.61 59	96.38 67	97.30 58	97.79 128	93.19 74	95.96 270	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	96.00 268	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	96.67 198	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	90.43 212	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	96.89 196	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	90.57 208	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	97.01 174	91.52 129	98.89 5	97.75 134	94.42 66	96.64 82	97.68 116	89.32 90	98.60 214	97.45 36	99.11 88	98.67 131

fmvsm_s_conf0.1_n_a		96.40 66	96.47 60	96.16 124	95.48 267	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	97.62 141	93.38 64	97.02 184	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	96.86 198	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	97.30 154	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	97.88 192

train_agg		96.30 71	95.83 79	97.72 39	98.70 59	94.19 42	96.41 238	98.02 100	88.58 270	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	92.99 120	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	96.24 257	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	92.66 377	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	97.30 161	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	89.01 253	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	97.59 144	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	94.82 210	98.91 106

ETV-MVS		96.02 77	95.89 77	96.40 104	97.16 160	92.44 94	97.47 141	97.77 133	94.55 59	96.48 90	94.51 291	91.23 66	98.92 178	95.65 93	98.19 128	97.82 200

canonicalmvs		96.02 77	95.45 86	97.75 36	97.59 144	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	94.82 210	98.91 106

CDPH-MVS		95.97 80	95.38 91	97.77 34	98.93 50	94.44 35	96.35 246	97.88 116	86.98 316	96.65 81	97.89 97	91.99 48	99.47 109	92.26 164	99.46 41	99.39 60

UA-Net		95.95 81	95.53 82	97.20 67	97.67 134	92.98 80	97.65 113	98.13 71	94.81 46	96.61 83	98.35 58	88.87 96	99.51 103	90.36 206	97.35 155	99.11 85

MGCFI-Net		95.94 82	95.40 90	97.56 49	97.59 144	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	94.73 215	98.91 106

BP-MVS1		95.89 83	95.49 83	97.08 73	96.67 198	93.20 73	98.08 53	96.32 274	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	95.27 201	99.16 77

alignmvs		95.87 85	95.23 95	97.78 32	97.56 149	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	95.11 206	98.95 101

casdiffmvs_mvg		95.81 86	95.57 81	96.51 94	96.87 180	91.49 130	97.50 134	97.56 162	93.99 78	95.13 135	97.92 96	87.89 113	98.78 192	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	95.27 309	97.62 152	90.43 212	95.55 126	97.07 156	91.72 50	99.50 106	89.62 222	98.94 97	98.82 121

DP-MVS Recon		95.68 88	95.12 100	97.37 55	99.19 31	94.19 42	97.03 182	98.08 80	88.35 279	95.09 136	97.65 120	89.97 85	99.48 108	92.08 173	98.59 111	98.44 154

casdiffmvs		95.64 89	95.49 83	96.08 126	96.76 196	90.45 173	97.29 162	97.44 184	94.00 77	95.46 130	97.98 92	87.52 125	98.73 200	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	96.79 190	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	96.04 265	97.48 170	93.47 100	95.67 123	98.10 80	89.17 92	99.25 131	91.27 191	98.77 103	99.13 81

baseline		95.58 92	95.42 89	96.08 126	96.78 191	90.41 176	97.16 175	97.45 180	93.69 89	95.65 124	97.85 103	87.29 130	98.68 206	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	87.79 297	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	97.06 168	89.63 196	97.82 91	97.87 118	93.57 91	93.92 163	95.04 264	90.61 77	98.95 174	94.62 123	98.68 106	98.54 139

3Dnovator+	91.43 4	95.40 95	94.48 118	98.16 16	96.90 179	95.34 16	98.48 20	97.87 118	94.65 56	88.53 304	98.02 89	83.69 179	99.71 53	93.18 151	98.96 96	99.44 53

PS-MVSNAJ		95.37 96	95.33 93	95.49 165	97.35 153	90.66 168	95.31 306	97.48 170	93.85 83	96.51 88	95.70 235	88.65 101	99.65 65	94.80 118	98.27 125	96.17 257

MVSFormer		95.37 96	95.16 97	95.99 137	96.34 228	91.21 142	98.22 40	97.57 158	91.42 170	96.22 101	97.32 141	86.20 145	97.92 295	94.07 131	99.05 91	98.85 117

xiu_mvs_v2_base		95.32 98	95.29 94	95.40 170	97.22 156	90.50 171	95.44 299	97.44 184	93.70 88	96.46 92	96.18 205	88.59 104	99.53 98	94.79 120	97.81 141	96.17 257

PVSNet_Blended_VisFu		95.27 99	94.91 103	96.38 107	98.20 96	90.86 159	97.27 163	98.25 48	90.21 216	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	96.43 223	89.34 212	95.99 269	97.35 197	92.83 130	96.31 97	97.37 139	86.44 140	98.67 207	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	97.18 159	91.58 127	98.26 34	98.12 73	94.38 70	94.90 138	98.15 79	82.28 214	98.92 178	91.45 188	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	97.49 150	89.56 200	98.67 10	97.00 228	90.69 197	94.24 154	97.62 125	89.79 88	98.81 189	93.39 148	96.49 178	98.92 105

EPNet		95.20 103	94.56 112	97.14 69	92.80 374	92.68 87	97.85 85	94.87 348	96.64 4	92.46 192	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	96.56 207	93.36 66	98.65 11	98.36 27	94.12 74	89.25 287	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	96.43 236	97.57 158	92.04 152	94.77 142	97.96 94	87.01 134	99.09 157	91.31 190	96.77 170	98.36 161

xiu_mvs_v1_base_debu		95.01 106	94.76 105	95.75 147	96.58 204	91.71 119	96.25 254	97.35 197	92.99 120	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 263

xiu_mvs_v1_base		95.01 106	94.76 105	95.75 147	96.58 204	91.71 119	96.25 254	97.35 197	92.99 120	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 263

xiu_mvs_v1_base_debi		95.01 106	94.76 105	95.75 147	96.58 204	91.71 119	96.25 254	97.35 197	92.99 120	96.70 77	96.63 183	82.67 204	99.44 113	96.22 65	97.46 148	96.11 263

PAPM_NR		95.01 106	94.59 110	96.26 117	98.89 54	90.68 167	97.24 165	97.73 137	91.80 157	92.93 189	96.62 186	89.13 93	99.14 149	89.21 235	97.78 142	98.97 98

lupinMVS		94.99 110	94.56 112	96.29 115	96.34 228	91.21 142	95.83 277	96.27 278	88.93 258	96.22 101	96.88 166	86.20 145	98.85 185	95.27 103	99.05 91	98.82 121

Effi-MVS+		94.93 111	94.45 119	96.36 109	96.61 201	91.47 132	96.41 238	97.41 189	91.02 188	94.50 148	95.92 219	87.53 123	98.78 192	93.89 137	96.81 169	98.84 120

IS-MVSNet		94.90 112	94.52 116	96.05 129	97.67 134	90.56 169	98.44 21	96.22 281	93.21 108	93.99 160	97.74 113	85.55 153	98.45 226	89.98 211	97.86 139	99.14 80

MVS_Test		94.89 113	94.62 109	95.68 153	96.83 185	89.55 201	96.70 214	97.17 208	91.17 182	95.60 125	96.11 214	87.87 115	98.76 196	93.01 159	97.17 164	98.72 126

PVSNet_Blended		94.87 114	94.56 112	95.81 144	98.27 86	89.46 207	95.47 298	98.36 27	88.84 261	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	95.52 265	90.93 157	96.09 263	96.52 265	89.28 244	96.01 111	97.32 141	84.70 162	98.77 195	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	89.19 247	94.81 140	96.71 172	88.84 97	99.17 142	88.91 242	98.76 104	96.53 246

test_yl		94.78 117	94.23 123	96.43 102	97.74 130	91.22 140	96.85 199	97.10 213	91.23 179	95.71 120	96.93 161	84.30 169	99.31 126	93.10 152	95.12 204	98.75 123

DCV-MVSNet		94.78 117	94.23 123	96.43 102	97.74 130	91.22 140	96.85 199	97.10 213	91.23 179	95.71 120	96.93 161	84.30 169	99.31 126	93.10 152	95.12 204	98.75 123

WTY-MVS		94.71 119	94.02 126	96.79 78	97.71 132	92.05 109	96.59 229	97.35 197	90.61 205	94.64 144	96.93 161	86.41 141	99.39 118	91.20 193	94.71 216	98.94 102

mamv4		94.66 120	96.10 73	90.37 359	98.01 112	73.41 408	96.82 203	97.78 132	89.95 223	94.52 147	97.43 137	92.91 27	99.09 157	98.28 18	99.16 82	98.60 134

mvsmamba		94.57 121	94.14 125	95.87 140	97.03 172	89.93 191	97.84 86	95.85 295	91.34 173	94.79 141	96.80 168	80.67 240	98.81 189	94.85 113	98.12 132	98.85 117

RRT-MVS		94.51 122	94.35 122	94.98 188	96.40 224	86.55 291	97.56 126	97.41 189	93.19 111	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	97.07 165	91.97 112	96.32 249	98.06 88	88.94 257	94.50 148	96.78 169	84.60 163	99.27 130	91.90 174	96.02 183	98.68 130

test_cas_vis1_n_1920		94.48 124	94.55 115	94.28 229	96.78 191	86.45 293	97.63 119	97.64 149	93.32 106	97.68 46	98.36 57	73.75 329	99.08 160	96.73 51	99.05 91	97.31 226

CANet_DTU		94.37 125	93.65 134	96.55 88	96.46 221	92.13 107	96.21 258	96.67 257	94.38 70	93.53 171	97.03 159	79.34 265	99.71 53	90.76 199	98.45 118	97.82 200

AdaColmap		94.34 126	93.68 133	96.31 111	98.59 69	91.68 122	96.59 229	97.81 130	89.87 224	92.15 203	97.06 157	83.62 182	99.54 96	89.34 229	98.07 133	97.70 205

CNLPA		94.28 127	93.53 139	96.52 90	98.38 81	92.55 91	96.59 229	96.88 241	90.13 220	91.91 211	97.24 147	85.21 156	99.09 157	87.64 268	97.83 140	97.92 189

MAR-MVS		94.22 128	93.46 144	96.51 94	98.00 114	92.19 106	97.67 110	97.47 173	88.13 287	93.00 184	95.84 223	84.86 161	99.51 103	87.99 255	98.17 130	97.83 199
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	97.64 138	91.42 135	95.55 293	97.71 143	88.99 254	92.34 199	95.82 225	89.19 91	99.11 152	86.14 294	97.38 153	98.90 109

SDMVSNet		94.17 130	93.61 135	95.86 142	98.09 105	91.37 136	97.35 155	98.20 56	93.18 113	91.79 215	97.28 143	79.13 268	98.93 177	94.61 124	92.84 247	97.28 227

test_vis1_n_1920		94.17 130	94.58 111	92.91 292	97.42 152	82.02 359	97.83 89	97.85 123	94.68 53	98.10 34	98.49 44	70.15 353	99.32 124	97.91 21	98.82 100	97.40 221

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	79.73 384	98.29 164

CHOSEN 1792x2688		94.15 132	93.51 142	96.06 128	98.27 86	89.38 210	95.18 315	98.48 24	85.60 339	93.76 166	97.11 154	83.15 191	99.61 76	91.33 189	98.72 105	99.19 75

Vis-MVSNet (Re-imp)		94.15 132	93.88 129	94.95 192	97.61 142	87.92 256	98.10 51	95.80 298	92.22 143	93.02 183	97.45 134	84.53 165	97.91 298	88.24 251	97.97 136	99.02 91

CDS-MVSNet		94.14 135	93.54 138	95.93 138	96.18 235	91.46 133	96.33 248	97.04 223	88.97 256	93.56 168	96.51 190	87.55 121	97.89 299	89.80 216	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	96.69 216	97.39 191	87.29 311	91.37 225	96.71 172	88.39 105	99.52 102	87.33 275	97.13 165	97.73 203
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	95.70 257	92.03 110	98.10 51	98.68 13	93.36 105	90.39 246	96.70 174	87.63 120	97.94 292	92.25 166	90.50 288	95.84 271

PVSNet_BlendedMVS		94.06 138	93.92 128	94.47 216	98.27 86	89.46 207	96.73 210	98.36 27	90.17 217	94.36 151	95.24 258	88.02 110	99.58 84	93.44 145	90.72 284	94.36 355

nrg030		94.05 139	93.31 150	96.27 116	95.22 289	94.59 32	98.34 25	97.46 175	92.93 127	91.21 235	96.64 179	87.23 132	98.22 246	94.99 111	85.80 332	95.98 267

UGNet		94.04 140	93.28 151	96.31 111	96.85 182	91.19 145	97.88 81	97.68 144	94.40 68	93.00 184	96.18 205	73.39 331	99.61 76	91.72 180	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	96.16 237	90.45 173	96.71 213	96.89 240	89.27 245	93.46 173	96.92 164	87.29 130	97.94 292	88.70 247	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	77.99 402	93.00 184	97.57 128	86.14 147	99.33 122	89.22 234	99.15 83	98.94 102

FC-MVSNet-test		93.94 143	93.57 136	95.04 183	95.48 267	91.45 134	98.12 50	98.71 11	93.37 103	90.23 249	96.70 174	87.66 117	97.85 301	91.49 186	90.39 289	95.83 272

mvsany_test1		93.93 144	93.98 127	93.78 257	94.94 306	86.80 281	94.62 327	92.55 392	88.77 267	96.85 70	98.49 44	88.98 94	98.08 264	95.03 109	95.62 195	96.46 251

GeoE		93.89 145	93.28 151	95.72 151	96.96 177	89.75 195	98.24 38	96.92 237	89.47 238	92.12 205	97.21 149	84.42 167	98.39 234	87.71 262	96.50 177	99.01 94

HY-MVS	89.66 9	93.87 146	92.95 158	96.63 83	97.10 164	92.49 93	95.64 290	96.64 258	89.05 252	93.00 184	95.79 229	85.77 151	99.45 112	89.16 238	94.35 218	97.96 187

XVG-OURS-SEG-HR		93.86 147	93.55 137	94.81 198	97.06 168	88.53 238	95.28 307	97.45 180	91.68 162	94.08 159	97.68 116	82.41 212	98.90 181	93.84 139	92.47 253	96.98 234

VDD-MVS		93.82 148	93.08 154	96.02 132	97.88 124	89.96 190	97.72 104	95.85 295	92.43 138	95.86 115	98.44 50	68.42 370	99.39 118	96.31 61	94.85 208	98.71 128

mvs_anonymous		93.82 148	93.74 131	94.06 237	96.44 222	85.41 310	95.81 278	97.05 221	89.85 227	90.09 259	96.36 198	87.44 127	97.75 314	93.97 133	96.69 174	99.02 91

HQP_MVS		93.78 150	93.43 146	94.82 196	96.21 232	89.99 186	97.74 99	97.51 166	94.85 41	91.34 226	96.64 179	81.32 230	98.60 214	93.02 157	92.23 256	95.86 268

PS-MVSNAJss		93.74 151	93.51 142	94.44 218	93.91 344	89.28 217	97.75 98	97.56 162	92.50 137	89.94 262	96.54 189	88.65 101	98.18 251	93.83 140	90.90 282	95.86 268

XVG-OURS		93.72 152	93.35 149	94.80 201	97.07 165	88.61 233	94.79 324	97.46 175	91.97 155	93.99 160	97.86 102	81.74 225	98.88 182	92.64 163	92.67 252	96.92 238

HyFIR lowres test		93.66 153	92.92 159	95.87 140	98.24 90	89.88 192	94.58 329	98.49 22	85.06 349	93.78 165	95.78 230	82.86 200	98.67 207	91.77 179	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	93.39 381	90.57 208	96.29 98	98.31 67	69.00 363	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	97.18 173	97.29 202	87.75 300	90.49 244	97.10 155	85.21 156	99.50 106	86.70 285	96.72 173	97.63 207

ab-mvs		93.57 156	92.55 176	96.64 81	97.28 155	91.96 114	95.40 300	97.45 180	89.81 229	93.22 181	96.28 201	79.62 262	99.46 110	90.74 200	93.11 244	98.50 144

LS3D		93.57 156	92.61 174	96.47 98	97.59 144	91.61 124	97.67 110	97.72 139	85.17 347	90.29 248	98.34 61	84.60 163	99.73 49	83.85 329	98.27 125	98.06 183

FA-MVS(test-final)		93.52 158	92.92 159	95.31 172	96.77 193	88.54 237	94.82 323	96.21 283	89.61 233	94.20 155	95.25 257	83.24 187	99.14 149	90.01 210	96.16 182	98.25 165

Fast-Effi-MVS+		93.46 159	92.75 167	95.59 158	96.77 193	90.03 183	96.81 204	97.13 210	88.19 282	91.30 229	94.27 308	86.21 144	98.63 211	87.66 267	96.46 180	98.12 177

hse-mvs2		93.45 160	92.99 156	94.81 198	97.02 173	88.59 234	96.69 216	96.47 268	95.19 27	96.74 75	96.16 208	83.67 180	98.48 225	95.85 84	79.13 388	97.35 224

QAPM		93.45 160	92.27 186	96.98 77	96.77 193	92.62 88	98.39 24	98.12 73	84.50 357	88.27 312	97.77 111	82.39 213	99.81 30	85.40 307	98.81 101	98.51 143

UniMVSNet_NR-MVSNet		93.37 162	92.67 171	95.47 168	95.34 278	92.83 82	97.17 174	98.58 20	92.98 125	90.13 254	95.80 226	88.37 106	97.85 301	91.71 181	83.93 361	95.73 282

1112_ss		93.37 162	92.42 183	96.21 121	97.05 170	90.99 153	96.31 250	96.72 250	86.87 319	89.83 266	96.69 176	86.51 139	99.14 149	88.12 252	93.67 238	98.50 144

UniMVSNet (Re)		93.31 164	92.55 176	95.61 157	95.39 272	93.34 67	97.39 151	98.71 11	93.14 116	90.10 258	94.83 274	87.71 116	98.03 275	91.67 184	83.99 360	95.46 291

OPM-MVS		93.28 165	92.76 165	94.82 196	94.63 322	90.77 163	96.65 220	97.18 206	93.72 86	91.68 219	97.26 146	79.33 266	98.63 211	92.13 170	92.28 255	95.07 318
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	95.70 257	92.39 95	97.86 82	98.66 16	92.30 141	92.09 207	95.37 250	80.49 244	98.40 229	93.95 134	85.86 331	95.75 280

test_fmvs1		93.21 167	93.53 139	92.25 314	96.55 209	81.20 366	97.40 150	96.96 230	90.68 198	96.80 71	98.04 86	69.25 361	98.40 229	97.58 31	98.50 113	97.16 231

MVSTER		93.20 168	92.81 164	94.37 221	96.56 207	89.59 199	97.06 181	97.12 211	91.24 178	91.30 229	95.96 217	82.02 219	98.05 271	93.48 144	90.55 286	95.47 290

test1111		93.19 169	92.82 163	94.30 228	97.58 148	84.56 327	98.21 42	89.02 411	93.53 96	94.58 145	98.21 74	72.69 332	99.05 167	93.06 155	98.48 116	99.28 69

ECVR-MVS		93.19 169	92.73 169	94.57 213	97.66 136	85.41 310	98.21 42	88.23 413	93.43 101	94.70 143	98.21 74	72.57 333	99.07 164	93.05 156	98.49 114	99.25 72

HQP-MVS		93.19 169	92.74 168	94.54 214	95.86 249	89.33 213	96.65 220	97.39 191	93.55 92	90.14 250	95.87 221	80.95 234	98.50 222	92.13 170	92.10 261	95.78 276

CHOSEN 280x420		93.12 172	92.72 170	94.34 224	96.71 197	87.27 269	90.29 402	97.72 139	86.61 323	91.34 226	95.29 252	84.29 171	98.41 228	93.25 149	98.94 97	97.35 224

sd_testset		93.10 173	92.45 182	95.05 182	98.09 105	89.21 219	96.89 196	97.64 149	93.18 113	91.79 215	97.28 143	75.35 315	98.65 209	88.99 240	92.84 247	97.28 227

Effi-MVS+-dtu		93.08 174	93.21 153	92.68 303	96.02 246	83.25 343	97.14 177	96.72 250	93.85 83	91.20 236	93.44 346	83.08 193	98.30 241	91.69 183	95.73 191	96.50 248

test_djsdf		93.07 175	92.76 165	94.00 241	93.49 358	88.70 232	98.22 40	97.57 158	91.42 170	90.08 260	95.55 243	82.85 201	97.92 295	94.07 131	91.58 268	95.40 297

VDDNet		93.05 176	92.07 190	96.02 132	96.84 183	90.39 177	98.08 53	95.85 295	86.22 331	95.79 118	98.46 48	67.59 373	99.19 137	94.92 112	94.85 208	98.47 149

thisisatest0530		93.03 177	92.21 188	95.49 165	97.07 165	89.11 224	97.49 140	92.19 394	90.16 218	94.09 158	96.41 195	76.43 306	99.05 167	90.38 205	95.68 193	98.31 163

EI-MVSNet		93.03 177	92.88 161	93.48 271	95.77 255	86.98 278	96.44 234	97.12 211	90.66 201	91.30 229	97.64 123	86.56 137	98.05 271	89.91 213	90.55 286	95.41 294

CLD-MVS		92.98 179	92.53 178	94.32 225	96.12 242	89.20 220	95.28 307	97.47 173	92.66 134	89.90 263	95.62 239	80.58 242	98.40 229	92.73 162	92.40 254	95.38 299
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	97.11 163	87.16 275	97.97 69	92.09 395	90.63 203	93.88 164	97.01 160	76.50 303	99.06 166	90.29 208	95.45 198	98.38 159

ACMM	89.79 8	92.96 180	92.50 180	94.35 222	96.30 230	88.71 231	97.58 123	97.36 196	91.40 172	90.53 243	96.65 178	79.77 258	98.75 197	91.24 192	91.64 266	95.59 286
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	96.16 237	88.26 245	97.65 113	97.46 175	91.29 174	90.12 256	97.16 151	79.05 271	98.73 200	92.25 166	91.89 264	95.31 304

BH-untuned		92.94 182	92.62 173	93.92 251	97.22 156	86.16 301	96.40 242	96.25 280	90.06 221	89.79 267	96.17 207	83.19 189	98.35 237	87.19 278	97.27 160	97.24 229

DU-MVS		92.90 184	92.04 192	95.49 165	94.95 304	92.83 82	97.16 175	98.24 50	93.02 119	90.13 254	95.71 233	83.47 183	97.85 301	91.71 181	83.93 361	95.78 276

PatchMatch-RL		92.90 184	92.02 194	95.56 159	98.19 98	90.80 161	95.27 309	97.18 206	87.96 289	91.86 214	95.68 236	80.44 245	98.99 172	84.01 324	97.54 147	96.89 239

PMMVS		92.86 186	92.34 184	94.42 220	94.92 307	86.73 284	94.53 331	96.38 272	84.78 354	94.27 153	95.12 263	83.13 192	98.40 229	91.47 187	96.49 178	98.12 177

OpenMVS	89.19 12	92.86 186	91.68 206	96.40 104	95.34 278	92.73 86	98.27 32	98.12 73	84.86 352	85.78 353	97.75 112	78.89 278	99.74 47	87.50 272	98.65 107	96.73 243

Test_1112_low_res		92.84 188	91.84 200	95.85 143	97.04 171	89.97 189	95.53 295	96.64 258	85.38 342	89.65 272	95.18 259	85.86 149	99.10 154	87.70 263	93.58 243	98.49 146

baseline1		92.82 189	91.90 198	95.55 161	97.20 158	90.77 163	97.19 172	94.58 354	92.20 145	92.36 196	96.34 199	84.16 173	98.21 247	89.20 236	83.90 364	97.68 206

1314		92.81 190	92.03 193	95.14 178	95.33 281	89.52 204	96.04 265	97.44 184	87.72 301	86.25 350	95.33 251	83.84 177	98.79 191	89.26 232	97.05 166	97.11 232

DP-MVS		92.76 191	91.51 214	96.52 90	98.77 56	90.99 153	97.38 153	96.08 287	82.38 378	89.29 284	97.87 100	83.77 178	99.69 59	81.37 351	96.69 174	98.89 113

test_fmvs1_n		92.73 192	92.88 161	92.29 311	96.08 245	81.05 367	97.98 63	97.08 216	90.72 196	96.79 73	98.18 77	63.07 395	98.45 226	97.62 30	98.42 120	97.36 222

BH-RMVSNet		92.72 193	91.97 196	94.97 190	97.16 160	87.99 254	96.15 261	95.60 309	90.62 204	91.87 213	97.15 153	78.41 284	98.57 218	83.16 331	97.60 146	98.36 161

ACMP	89.59 10	92.62 194	92.14 189	94.05 238	96.40 224	88.20 248	97.36 154	97.25 205	91.52 165	88.30 310	96.64 179	78.46 283	98.72 203	91.86 177	91.48 270	95.23 311
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 195	92.52 179	92.44 305	96.82 187	81.89 360	96.92 194	93.71 378	92.41 139	84.30 366	94.60 286	85.08 158	97.03 357	91.51 185	97.36 154	98.40 157

TranMVSNet+NR-MVSNet		92.50 195	91.63 207	95.14 178	94.76 315	92.07 108	97.53 131	98.11 76	92.90 129	89.56 275	96.12 210	83.16 190	97.60 327	89.30 230	83.20 370	95.75 280

thres600view7		92.49 197	91.60 208	95.18 176	97.91 122	89.47 205	97.65 113	94.66 351	92.18 149	93.33 176	94.91 269	78.06 291	99.10 154	81.61 345	94.06 233	96.98 234

thres100view900		92.43 198	91.58 209	94.98 188	97.92 121	89.37 211	97.71 106	94.66 351	92.20 145	93.31 177	94.90 270	78.06 291	99.08 160	81.40 348	94.08 229	96.48 249

jajsoiax		92.42 199	91.89 199	94.03 240	93.33 364	88.50 239	97.73 101	97.53 164	92.00 154	88.85 296	96.50 191	75.62 313	98.11 258	93.88 138	91.56 269	95.48 288

thres400		92.42 199	91.52 212	95.12 180	97.85 125	89.29 215	97.41 146	94.88 345	92.19 147	93.27 179	94.46 296	78.17 287	99.08 160	81.40 348	94.08 229	96.98 234

tfpn200view9		92.38 201	91.52 212	94.95 192	97.85 125	89.29 215	97.41 146	94.88 345	92.19 147	93.27 179	94.46 296	78.17 287	99.08 160	81.40 348	94.08 229	96.48 249

test_vis1_n		92.37 202	92.26 187	92.72 300	94.75 316	82.64 349	98.02 59	96.80 247	91.18 181	97.77 45	97.93 95	58.02 404	98.29 242	97.63 29	98.21 127	97.23 230

WR-MVS		92.34 203	91.53 211	94.77 203	95.13 297	90.83 160	96.40 242	97.98 106	91.88 156	89.29 284	95.54 244	82.50 209	97.80 308	89.79 217	85.27 340	95.69 283

NR-MVSNet		92.34 203	91.27 222	95.53 162	94.95 304	93.05 77	97.39 151	98.07 85	92.65 135	84.46 364	95.71 233	85.00 159	97.77 312	89.71 218	83.52 367	95.78 276

mvs_tets		92.31 205	91.76 202	93.94 248	93.41 361	88.29 243	97.63 119	97.53 164	92.04 152	88.76 299	96.45 193	74.62 321	98.09 263	93.91 136	91.48 270	95.45 292

TAPA-MVS	90.10 7	92.30 206	91.22 225	95.56 159	98.33 83	89.60 198	96.79 205	97.65 147	81.83 382	91.52 221	97.23 148	87.94 112	98.91 180	71.31 403	98.37 121	98.17 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 207	91.30 220	95.25 174	96.60 202	88.90 228	94.36 340	92.32 393	87.92 290	93.43 174	94.57 287	77.28 298	99.00 171	89.42 227	95.86 188	97.86 196

Fast-Effi-MVS+-dtu		92.29 207	91.99 195	93.21 282	95.27 285	85.52 308	97.03 182	96.63 261	92.09 150	89.11 290	95.14 261	80.33 248	98.08 264	87.54 271	94.74 214	96.03 266

IterMVS-LS		92.29 207	91.94 197	93.34 276	96.25 231	86.97 279	96.57 232	97.05 221	90.67 199	89.50 278	94.80 276	86.59 136	97.64 322	89.91 213	86.11 330	95.40 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 210	91.74 205	93.73 258	97.77 129	83.69 340	92.88 382	96.72 250	87.91 291	93.00 184	94.86 272	78.51 282	99.05 167	86.53 286	97.45 152	98.47 149

VPNet		92.23 211	91.31 219	94.99 186	95.56 263	90.96 155	97.22 170	97.86 122	92.96 126	90.96 237	96.62 186	75.06 316	98.20 248	91.90 174	83.65 366	95.80 274

thres200		92.23 211	91.39 215	94.75 205	97.61 142	89.03 225	96.60 228	95.09 334	92.08 151	93.28 178	94.00 323	78.39 285	99.04 170	81.26 354	94.18 225	96.19 256

anonymousdsp		92.16 213	91.55 210	93.97 244	92.58 379	89.55 201	97.51 133	97.42 188	89.42 241	88.40 306	94.84 273	80.66 241	97.88 300	91.87 176	91.28 274	94.48 350

XXY-MVS		92.16 213	91.23 224	94.95 192	94.75 316	90.94 156	97.47 141	97.43 187	89.14 248	88.90 292	96.43 194	79.71 259	98.24 244	89.56 223	87.68 313	95.67 284

BH-w/o		92.14 215	91.75 203	93.31 277	96.99 176	85.73 305	95.67 285	95.69 304	88.73 268	89.26 286	94.82 275	82.97 198	98.07 268	85.26 310	96.32 181	96.13 262

testing3-2		92.10 216	92.05 191	92.27 312	97.71 132	79.56 386	97.42 145	94.41 360	93.53 96	93.22 181	95.49 246	69.16 362	99.11 152	93.25 149	94.22 223	98.13 175

Anonymous202405211		92.07 217	90.83 241	95.76 145	98.19 98	88.75 230	97.58 123	95.00 337	86.00 334	93.64 167	97.45 134	66.24 385	99.53 98	90.68 202	92.71 250	99.01 94

FE-MVS		92.05 218	91.05 230	95.08 181	96.83 185	87.93 255	93.91 358	95.70 302	86.30 328	94.15 157	94.97 265	76.59 302	99.21 135	84.10 322	96.86 167	98.09 181

WR-MVS_H		92.00 219	91.35 216	93.95 246	95.09 299	89.47 205	98.04 58	98.68 13	91.46 168	88.34 308	94.68 281	85.86 149	97.56 329	85.77 302	84.24 358	94.82 335

Anonymous20240529		91.98 220	90.73 247	95.73 150	98.14 102	89.40 209	97.99 62	97.72 139	79.63 396	93.54 170	97.41 138	69.94 355	99.56 92	91.04 196	91.11 277	98.22 167

MonoMVSNet		91.92 221	91.77 201	92.37 307	92.94 370	83.11 345	97.09 180	95.55 312	92.91 128	90.85 239	94.55 288	81.27 232	96.52 369	93.01 159	87.76 312	97.47 218

PatchmatchNet		91.91 222	91.35 216	93.59 266	95.38 273	84.11 333	93.15 377	95.39 317	89.54 235	92.10 206	93.68 336	82.82 202	98.13 254	84.81 314	95.32 200	98.52 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing91		91.90 223	91.02 231	94.53 215	96.54 210	86.55 291	95.86 275	95.64 308	91.77 159	91.89 212	93.47 345	69.94 355	98.86 183	90.23 209	93.86 236	98.18 170

CP-MVSNet		91.89 224	91.24 223	93.82 254	95.05 300	88.57 235	97.82 91	98.19 61	91.70 161	88.21 314	95.76 231	81.96 220	97.52 335	87.86 257	84.65 349	95.37 300

SCA		91.84 225	91.18 227	93.83 253	95.59 261	84.95 323	94.72 325	95.58 311	90.82 191	92.25 201	93.69 334	75.80 310	98.10 259	86.20 292	95.98 184	98.45 151

FMVSNet3		91.78 226	90.69 250	95.03 184	96.53 212	92.27 101	97.02 184	96.93 233	89.79 230	89.35 281	94.65 284	77.01 299	97.47 338	86.12 295	88.82 301	95.35 301

AUN-MVS		91.76 227	90.75 245	94.81 198	97.00 175	88.57 235	96.65 220	96.49 267	89.63 232	92.15 203	96.12 210	78.66 280	98.50 222	90.83 197	79.18 387	97.36 222

X-MVStestdata		91.71 228	89.67 293	97.81 28	99.38 14	94.03 50	98.59 12	98.20 56	94.85 41	96.59 85	32.69 428	91.70 52	99.80 34	95.66 90	99.40 56	99.62 20

MVS		91.71 228	90.44 257	95.51 163	95.20 291	91.59 126	96.04 265	97.45 180	73.44 412	87.36 331	95.60 240	85.42 154	99.10 154	85.97 299	97.46 148	95.83 272

EPNet_dtu		91.71 228	91.28 221	92.99 289	93.76 349	83.71 339	96.69 216	95.28 324	93.15 115	87.02 340	95.95 218	83.37 186	97.38 346	79.46 366	96.84 168	97.88 192
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		91.68 231	90.75 245	94.47 216	96.53 212	86.56 290	95.76 282	94.51 357	91.10 186	91.24 234	93.59 340	68.59 367	98.86 183	91.10 194	94.29 221	98.00 186

baseline2		91.63 232	90.86 237	93.94 248	94.33 333	86.32 295	95.92 272	91.64 399	89.37 242	86.94 343	94.69 280	81.62 227	98.69 205	88.64 248	94.57 217	96.81 241

testing99		91.62 233	90.72 248	94.32 225	96.48 218	86.11 302	95.81 278	94.76 349	91.55 164	91.75 217	93.44 346	68.55 368	98.82 187	90.43 203	93.69 237	98.04 184

test2506		91.60 234	90.78 242	94.04 239	97.66 136	83.81 336	98.27 32	75.53 429	93.43 101	95.23 132	98.21 74	67.21 376	99.07 164	93.01 159	98.49 114	99.25 72

miper_ehance_all_eth		91.59 235	91.13 228	92.97 290	95.55 264	86.57 289	94.47 334	96.88 241	87.77 298	88.88 294	94.01 322	86.22 143	97.54 331	89.49 224	86.93 321	94.79 340

v2v482		91.59 235	90.85 239	93.80 255	93.87 346	88.17 250	96.94 193	96.88 241	89.54 235	89.53 276	94.90 270	81.70 226	98.02 276	89.25 233	85.04 346	95.20 312

V42		91.58 237	90.87 236	93.73 258	94.05 341	88.50 239	97.32 159	96.97 229	88.80 266	89.71 268	94.33 303	82.54 208	98.05 271	89.01 239	85.07 344	94.64 348

PCF-MVS	89.48 11	91.56 238	89.95 281	96.36 109	96.60 202	92.52 92	92.51 387	97.26 203	79.41 397	88.90 292	96.56 188	84.04 176	99.55 94	77.01 380	97.30 159	97.01 233
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG		91.55 239	90.76 243	93.94 248	96.52 214	85.06 319	95.22 312	94.54 355	90.47 211	91.98 209	92.71 357	72.02 336	98.74 199	88.10 253	95.26 202	98.01 185

PS-CasMVS		91.55 239	90.84 240	93.69 262	94.96 303	88.28 244	97.84 86	98.24 50	91.46 168	88.04 318	95.80 226	79.67 260	97.48 337	87.02 282	84.54 355	95.31 304

miper_enhance_ethall		91.54 241	91.01 232	93.15 284	95.35 277	87.07 277	93.97 353	96.90 238	86.79 320	89.17 288	93.43 349	86.55 138	97.64 322	89.97 212	86.93 321	94.74 344

myMVS_eth3d28		91.52 242	90.97 233	93.17 283	96.91 178	83.24 344	95.61 291	94.96 341	92.24 142	91.98 209	93.28 350	69.31 360	98.40 229	88.71 246	95.68 193	97.88 192

PAPM		91.52 242	90.30 263	95.20 175	95.30 284	89.83 193	93.38 373	96.85 244	86.26 330	88.59 302	95.80 226	84.88 160	98.15 253	75.67 385	95.93 186	97.63 207

ET-MVSNet_ETH3D		91.49 244	90.11 273	95.63 155	96.40 224	91.57 128	95.34 303	93.48 380	90.60 207	75.58 404	95.49 246	80.08 252	96.79 366	94.25 129	89.76 294	98.52 141

TR-MVS		91.48 245	90.59 253	94.16 233	96.40 224	87.33 266	95.67 285	95.34 323	87.68 302	91.46 223	95.52 245	76.77 301	98.35 237	82.85 336	93.61 241	96.79 242

tpmrst		91.44 246	91.32 218	91.79 328	95.15 295	79.20 392	93.42 372	95.37 319	88.55 273	93.49 172	93.67 337	82.49 210	98.27 243	90.41 204	89.34 298	97.90 190

test-LLR		91.42 247	91.19 226	92.12 316	94.59 323	80.66 370	94.29 345	92.98 385	91.11 184	90.76 241	92.37 365	79.02 273	98.07 268	88.81 243	96.74 171	97.63 207

MSDG		91.42 247	90.24 267	94.96 191	97.15 162	88.91 227	93.69 365	96.32 274	85.72 338	86.93 344	96.47 192	80.24 249	98.98 173	80.57 357	95.05 207	96.98 234

c3_l		91.38 249	90.89 235	92.88 294	95.58 262	86.30 296	94.68 326	96.84 245	88.17 283	88.83 298	94.23 311	85.65 152	97.47 338	89.36 228	84.63 350	94.89 330

GA-MVS		91.38 249	90.31 262	94.59 208	94.65 321	87.62 264	94.34 341	96.19 284	90.73 195	90.35 247	93.83 327	71.84 338	97.96 287	87.22 277	93.61 241	98.21 168

v1144		91.37 251	90.60 252	93.68 263	93.89 345	88.23 247	96.84 201	97.03 225	88.37 278	89.69 270	94.39 298	82.04 218	97.98 280	87.80 259	85.37 337	94.84 332

GBi-Net		91.35 252	90.27 265	94.59 208	96.51 215	91.18 147	97.50 134	96.93 233	88.82 263	89.35 281	94.51 291	73.87 325	97.29 350	86.12 295	88.82 301	95.31 304

test1		91.35 252	90.27 265	94.59 208	96.51 215	91.18 147	97.50 134	96.93 233	88.82 263	89.35 281	94.51 291	73.87 325	97.29 350	86.12 295	88.82 301	95.31 304

UniMVSNet_ETH3D		91.34 254	90.22 270	94.68 206	94.86 311	87.86 259	97.23 169	97.46 175	87.99 288	89.90 263	96.92 164	66.35 383	98.23 245	90.30 207	90.99 280	97.96 187

FMVSNet2		91.31 255	90.08 274	94.99 186	96.51 215	92.21 103	97.41 146	96.95 231	88.82 263	88.62 301	94.75 278	73.87 325	97.42 343	85.20 311	88.55 306	95.35 301

reproduce_monomvs		91.30 256	91.10 229	91.92 320	96.82 187	82.48 353	97.01 187	97.49 169	94.64 57	88.35 307	95.27 255	70.53 348	98.10 259	95.20 104	84.60 352	95.19 315

D2MVS		91.30 256	90.95 234	92.35 308	94.71 319	85.52 308	96.18 260	98.21 54	88.89 259	86.60 347	93.82 329	79.92 256	97.95 291	89.29 231	90.95 281	93.56 368

v8		91.29 258	90.53 256	93.57 268	94.15 337	88.12 252	97.34 156	97.06 220	88.99 254	88.32 309	94.26 310	83.08 193	98.01 277	87.62 269	83.92 363	94.57 349

CVMVSNet		91.23 259	91.75 203	89.67 367	95.77 255	74.69 403	96.44 234	94.88 345	85.81 336	92.18 202	97.64 123	79.07 270	95.58 386	88.06 254	95.86 188	98.74 125

cl22		91.21 260	90.56 255	93.14 285	96.09 244	86.80 281	94.41 338	96.58 264	87.80 296	88.58 303	93.99 324	80.85 239	97.62 325	89.87 215	86.93 321	94.99 321

PEN-MVS		91.20 261	90.44 257	93.48 271	94.49 327	87.91 258	97.76 97	98.18 63	91.29 174	87.78 322	95.74 232	80.35 247	97.33 348	85.46 306	82.96 371	95.19 315

Baseline_NR-MVSNet		91.20 261	90.62 251	92.95 291	93.83 347	88.03 253	97.01 187	95.12 333	88.42 277	89.70 269	95.13 262	83.47 183	97.44 341	89.66 221	83.24 369	93.37 372

cascas		91.20 261	90.08 274	94.58 212	94.97 302	89.16 223	93.65 367	97.59 156	79.90 395	89.40 279	92.92 355	75.36 314	98.36 236	92.14 169	94.75 213	96.23 253

CostFormer		91.18 264	90.70 249	92.62 304	94.84 312	81.76 361	94.09 351	94.43 358	84.15 360	92.72 191	93.77 331	79.43 264	98.20 248	90.70 201	92.18 259	97.90 190

tt0805		91.09 265	90.07 277	94.16 233	95.61 260	88.31 242	97.56 126	96.51 266	89.56 234	89.17 288	95.64 238	67.08 380	98.38 235	91.07 195	88.44 307	95.80 274

v1192		91.07 266	90.23 268	93.58 267	93.70 350	87.82 261	96.73 210	97.07 218	87.77 298	89.58 273	94.32 305	80.90 238	97.97 283	86.52 287	85.48 335	94.95 322

v144192		91.06 267	90.28 264	93.39 274	93.66 353	87.23 272	96.83 202	97.07 218	87.43 307	89.69 270	94.28 307	81.48 228	98.00 278	87.18 279	84.92 348	94.93 326

v10		91.04 268	90.23 268	93.49 270	94.12 338	88.16 251	97.32 159	97.08 216	88.26 281	88.29 311	94.22 313	82.17 217	97.97 283	86.45 289	84.12 359	94.33 356

eth_miper_zixun_eth		91.02 269	90.59 253	92.34 310	95.33 281	84.35 329	94.10 350	96.90 238	88.56 272	88.84 297	94.33 303	84.08 174	97.60 327	88.77 245	84.37 357	95.06 319

v148		90.99 270	90.38 259	92.81 297	93.83 347	85.80 304	96.78 207	96.68 255	89.45 240	88.75 300	93.93 326	82.96 199	97.82 305	87.83 258	83.25 368	94.80 338

LTVRE_ROB	88.41 13	90.99 270	89.92 283	94.19 231	96.18 235	89.55 201	96.31 250	97.09 215	87.88 292	85.67 354	95.91 220	78.79 279	98.57 218	81.50 346	89.98 291	94.44 353
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		90.97 272	90.33 260	92.88 294	95.36 276	86.19 300	94.46 336	96.63 261	87.82 294	88.18 315	94.23 311	82.99 196	97.53 333	87.72 260	85.57 334	94.93 326

cl____		90.96 273	90.32 261	92.89 293	95.37 275	86.21 299	94.46 336	96.64 258	87.82 294	88.15 316	94.18 314	82.98 197	97.54 331	87.70 263	85.59 333	94.92 328

pmmvs4		90.93 274	89.85 285	94.17 232	93.34 363	90.79 162	94.60 328	96.02 288	84.62 355	87.45 327	95.15 260	81.88 223	97.45 340	87.70 263	87.87 311	94.27 360

XVG-ACMP-BASELINE		90.93 274	90.21 271	93.09 286	94.31 335	85.89 303	95.33 304	97.26 203	91.06 187	89.38 280	95.44 249	68.61 366	98.60 214	89.46 225	91.05 278	94.79 340

v1921920		90.85 276	90.03 279	93.29 278	93.55 354	86.96 280	96.74 209	97.04 223	87.36 309	89.52 277	94.34 302	80.23 250	97.97 283	86.27 290	85.21 341	94.94 324

CR-MVSNet		90.82 277	89.77 289	93.95 246	94.45 329	87.19 273	90.23 403	95.68 306	86.89 318	92.40 193	92.36 368	80.91 236	97.05 356	81.09 355	93.95 234	97.60 212

v7n		90.76 278	89.86 284	93.45 273	93.54 355	87.60 265	97.70 109	97.37 194	88.85 260	87.65 324	94.08 320	81.08 233	98.10 259	84.68 316	83.79 365	94.66 347

RPSCF		90.75 279	90.86 237	90.42 358	96.84 183	76.29 401	95.61 291	96.34 273	83.89 363	91.38 224	97.87 100	76.45 304	98.78 192	87.16 280	92.23 256	96.20 255

MVP-Stereo		90.74 280	90.08 274	92.71 301	93.19 366	88.20 248	95.86 275	96.27 278	86.07 333	84.86 362	94.76 277	77.84 294	97.75 314	83.88 328	98.01 135	92.17 393
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 281	89.65 295	93.96 245	94.29 336	89.63 196	97.79 95	96.82 246	89.07 250	86.12 352	95.48 248	78.61 281	97.78 310	86.97 283	81.67 376	94.46 351

v1240		90.70 282	89.85 285	93.23 280	93.51 357	86.80 281	96.61 226	97.02 227	87.16 314	89.58 273	94.31 306	79.55 263	97.98 280	85.52 305	85.44 336	94.90 329

EPMVS		90.70 282	89.81 287	93.37 275	94.73 318	84.21 331	93.67 366	88.02 414	89.50 237	92.38 195	93.49 343	77.82 295	97.78 310	86.03 298	92.68 251	98.11 180

WBMVS		90.69 284	89.99 280	92.81 297	96.48 218	85.00 320	95.21 314	96.30 276	89.46 239	89.04 291	94.05 321	72.45 335	97.82 305	89.46 225	87.41 318	95.61 285

Anonymous20231211		90.63 285	89.42 300	94.27 230	98.24 90	89.19 222	98.05 57	97.89 114	79.95 394	88.25 313	94.96 266	72.56 334	98.13 254	89.70 219	85.14 342	95.49 287

DTE-MVSNet		90.56 286	89.75 291	93.01 288	93.95 342	87.25 270	97.64 117	97.65 147	90.74 194	87.12 335	95.68 236	79.97 255	97.00 360	83.33 330	81.66 377	94.78 342

ACMH	87.59 16	90.53 287	89.42 300	93.87 252	96.21 232	87.92 256	97.24 165	96.94 232	88.45 276	83.91 374	96.27 202	71.92 337	98.62 213	84.43 319	89.43 297	95.05 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		90.52 288	89.14 308	94.67 207	96.81 189	87.85 260	95.91 273	93.97 372	89.71 231	92.34 199	92.48 363	65.41 390	97.96 287	81.37 351	94.27 222	98.21 168

OurMVSNet-221017-0		90.51 289	90.19 272	91.44 337	93.41 361	81.25 364	96.98 190	96.28 277	91.68 162	86.55 348	96.30 200	74.20 324	97.98 280	88.96 241	87.40 319	95.09 317

miper_lstm_enhance		90.50 290	90.06 278	91.83 325	95.33 281	83.74 337	93.86 359	96.70 254	87.56 305	87.79 321	93.81 330	83.45 185	96.92 362	87.39 273	84.62 351	94.82 335

COLMAP_ROB	87.81 15	90.40 291	89.28 303	93.79 256	97.95 118	87.13 276	96.92 194	95.89 294	82.83 375	86.88 346	97.18 150	73.77 328	99.29 129	78.44 371	93.62 240	94.95 322
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 292	88.96 310	94.35 222	96.54 210	87.29 267	95.50 296	93.84 376	90.97 189	91.75 217	92.96 354	62.18 400	98.00 278	82.86 334	94.08 229	97.76 202

IterMVS-SCA-FT		90.31 292	89.81 287	91.82 326	95.52 265	84.20 332	94.30 344	96.15 285	90.61 205	87.39 330	94.27 308	75.80 310	96.44 370	87.34 274	86.88 325	94.82 335

MS-PatchMatch		90.27 294	89.77 289	91.78 329	94.33 333	84.72 326	95.55 293	96.73 249	86.17 332	86.36 349	95.28 254	71.28 342	97.80 308	84.09 323	98.14 131	92.81 378

tpm		90.25 295	89.74 292	91.76 331	93.92 343	79.73 385	93.98 352	93.54 379	88.28 280	91.99 208	93.25 351	77.51 297	97.44 341	87.30 276	87.94 310	98.12 177

AllTest		90.23 296	88.98 309	93.98 242	97.94 119	86.64 285	96.51 233	95.54 313	85.38 342	85.49 356	96.77 170	70.28 350	99.15 146	80.02 361	92.87 245	96.15 260

dmvs_re		90.21 297	89.50 298	92.35 308	95.47 270	85.15 316	95.70 284	94.37 363	90.94 190	88.42 305	93.57 341	74.63 320	95.67 383	82.80 337	89.57 296	96.22 254

ACMH+	87.92 14	90.20 298	89.18 306	93.25 279	96.48 218	86.45 293	96.99 189	96.68 255	88.83 262	84.79 363	96.22 204	70.16 352	98.53 220	84.42 320	88.04 309	94.77 343

test-mter		90.19 299	89.54 297	92.12 316	94.59 323	80.66 370	94.29 345	92.98 385	87.68 302	90.76 241	92.37 365	67.67 372	98.07 268	88.81 243	96.74 171	97.63 207

IterMVS		90.15 300	89.67 293	91.61 333	95.48 267	83.72 338	94.33 342	96.12 286	89.99 222	87.31 333	94.15 316	75.78 312	96.27 373	86.97 283	86.89 324	94.83 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 301	89.42 300	91.97 319	94.41 331	80.62 372	94.29 345	91.97 397	87.28 312	90.44 245	92.47 364	68.79 364	97.67 319	88.50 250	96.60 176	97.61 211

tpm2		89.96 302	89.21 305	92.23 315	94.91 309	81.25 364	93.78 361	94.42 359	80.62 392	91.56 220	93.44 346	76.44 305	97.94 292	85.60 304	92.08 263	97.49 216

UWE-MVS		89.91 303	89.48 299	91.21 341	95.88 248	78.23 397	94.91 322	90.26 407	89.11 249	92.35 198	94.52 290	68.76 365	97.96 287	83.95 326	95.59 196	97.42 220

IB-MVS	87.33 17	89.91 303	88.28 320	94.79 202	95.26 288	87.70 263	95.12 317	93.95 373	89.35 243	87.03 339	92.49 362	70.74 347	99.19 137	89.18 237	81.37 378	97.49 216
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		89.89 305	88.68 315	93.53 269	95.86 249	84.89 324	90.93 398	95.07 335	83.23 373	91.28 232	91.81 377	79.01 275	97.85 301	79.52 363	91.39 272	97.84 197

WB-MVSnew		89.88 306	89.56 296	90.82 350	94.57 326	83.06 346	95.65 289	92.85 387	87.86 293	90.83 240	94.10 317	79.66 261	96.88 363	76.34 381	94.19 224	92.54 384

FMVSNet1		89.88 306	88.31 319	94.59 208	95.41 271	91.18 147	97.50 134	96.93 233	86.62 322	87.41 329	94.51 291	65.94 388	97.29 350	83.04 333	87.43 316	95.31 304

pmmvs5		89.86 308	88.87 313	92.82 296	92.86 372	86.23 298	96.26 253	95.39 317	84.24 359	87.12 335	94.51 291	74.27 323	97.36 347	87.61 270	87.57 314	94.86 331

tpmvs		89.83 309	89.15 307	91.89 323	94.92 307	80.30 377	93.11 378	95.46 316	86.28 329	88.08 317	92.65 358	80.44 245	98.52 221	81.47 347	89.92 292	96.84 240

test_fmvs2		89.77 310	89.93 282	89.31 373	93.68 352	76.37 400	97.64 117	95.90 292	89.84 228	91.49 222	96.26 203	58.77 403	97.10 354	94.65 122	91.13 276	94.46 351

SSC-MVS3.2		89.74 311	89.26 304	91.19 344	95.16 292	80.29 378	94.53 331	97.03 225	91.79 158	88.86 295	94.10 317	69.94 355	97.82 305	85.29 308	86.66 326	95.45 292

mmtdpeth		89.70 312	88.96 310	91.90 322	95.84 254	84.42 328	97.46 143	95.53 315	90.27 215	94.46 150	90.50 385	69.74 359	98.95 174	97.39 40	69.48 410	92.34 387

tfpnnormal		89.70 312	88.40 318	93.60 265	95.15 295	90.10 182	97.56 126	98.16 67	87.28 312	86.16 351	94.63 285	77.57 296	98.05 271	74.48 389	84.59 353	92.65 381

ADS-MVSNet2		89.45 314	88.59 316	92.03 318	95.86 249	82.26 357	90.93 398	94.32 366	83.23 373	91.28 232	91.81 377	79.01 275	95.99 375	79.52 363	91.39 272	97.84 197

Patchmatch-test		89.42 315	87.99 322	93.70 261	95.27 285	85.11 317	88.98 410	94.37 363	81.11 386	87.10 338	93.69 334	82.28 214	97.50 336	74.37 391	94.76 212	98.48 148

test0.0.03 1		89.37 316	88.70 314	91.41 338	92.47 381	85.63 306	95.22 312	92.70 390	91.11 184	86.91 345	93.65 338	79.02 273	93.19 409	78.00 373	89.18 299	95.41 294

SixPastTwentyTwo		89.15 317	88.54 317	90.98 346	93.49 358	80.28 379	96.70 214	94.70 350	90.78 192	84.15 369	95.57 241	71.78 339	97.71 317	84.63 317	85.07 344	94.94 324

RPMNet		88.98 318	87.05 332	94.77 203	94.45 329	87.19 273	90.23 403	98.03 97	77.87 404	92.40 193	87.55 408	80.17 251	99.51 103	68.84 408	93.95 234	97.60 212

TransMVSNet (Re)		88.94 319	87.56 325	93.08 287	94.35 332	88.45 241	97.73 101	95.23 328	87.47 306	84.26 367	95.29 252	79.86 257	97.33 348	79.44 367	74.44 401	93.45 371

USDC		88.94 319	87.83 324	92.27 312	94.66 320	84.96 322	93.86 359	95.90 292	87.34 310	83.40 376	95.56 242	67.43 374	98.19 250	82.64 341	89.67 295	93.66 367

dp		88.90 321	88.26 321	90.81 351	94.58 325	76.62 399	92.85 383	94.93 342	85.12 348	90.07 261	93.07 352	75.81 309	98.12 257	80.53 358	87.42 317	97.71 204

PatchT		88.87 322	87.42 326	93.22 281	94.08 340	85.10 318	89.51 408	94.64 353	81.92 381	92.36 196	88.15 404	80.05 253	97.01 359	72.43 399	93.65 239	97.54 215

our_test_3		88.78 323	87.98 323	91.20 343	92.45 382	82.53 351	93.61 369	95.69 304	85.77 337	84.88 361	93.71 332	79.99 254	96.78 367	79.47 365	86.24 327	94.28 359

EU-MVSNet		88.72 324	88.90 312	88.20 377	93.15 367	74.21 405	96.63 225	94.22 368	85.18 346	87.32 332	95.97 216	76.16 307	94.98 392	85.27 309	86.17 328	95.41 294

Patchmtry		88.64 325	87.25 328	92.78 299	94.09 339	86.64 285	89.82 407	95.68 306	80.81 390	87.63 325	92.36 368	80.91 236	97.03 357	78.86 369	85.12 343	94.67 346

MIMVSNet		88.50 326	86.76 336	93.72 260	94.84 312	87.77 262	91.39 393	94.05 369	86.41 326	87.99 319	92.59 361	63.27 394	95.82 380	77.44 374	92.84 247	97.57 214

tpm cat1		88.36 327	87.21 330	91.81 327	95.13 297	80.55 373	92.58 386	95.70 302	74.97 408	87.45 327	91.96 375	78.01 293	98.17 252	80.39 359	88.74 304	96.72 244

ppachtmachnet_test		88.35 328	87.29 327	91.53 334	92.45 382	83.57 341	93.75 362	95.97 289	84.28 358	85.32 359	94.18 314	79.00 277	96.93 361	75.71 384	84.99 347	94.10 361

JIA-IIPM		88.26 329	87.04 333	91.91 321	93.52 356	81.42 363	89.38 409	94.38 362	80.84 389	90.93 238	80.74 416	79.22 267	97.92 295	82.76 338	91.62 267	96.38 252

testgi		87.97 330	87.21 330	90.24 361	92.86 372	80.76 368	96.67 219	94.97 339	91.74 160	85.52 355	95.83 224	62.66 398	94.47 396	76.25 382	88.36 308	95.48 288

LF4IMVS		87.94 331	87.25 328	89.98 364	92.38 384	80.05 383	94.38 339	95.25 327	87.59 304	84.34 365	94.74 279	64.31 392	97.66 321	84.83 313	87.45 315	92.23 390

gg-mvs-nofinetune		87.82 332	85.61 345	94.44 218	94.46 328	89.27 218	91.21 397	84.61 423	80.88 388	89.89 265	74.98 419	71.50 340	97.53 333	85.75 303	97.21 162	96.51 247

pmmvs6		87.81 333	86.19 341	92.69 302	91.32 389	86.30 296	97.34 156	96.41 271	80.59 393	84.05 373	94.37 300	67.37 375	97.67 319	84.75 315	79.51 386	94.09 363

testing3		87.67 334	86.88 335	90.05 363	96.14 240	80.71 369	97.10 179	92.85 387	90.15 219	87.54 326	94.55 288	55.70 409	94.10 399	73.77 395	94.10 228	95.35 301

K. test v3		87.64 335	86.75 337	90.32 360	93.02 369	79.48 390	96.61 226	92.08 396	90.66 201	80.25 393	94.09 319	67.21 376	96.65 368	85.96 300	80.83 380	94.83 333

Patchmatch-RL test		87.38 336	86.24 340	90.81 351	88.74 407	78.40 396	88.12 415	93.17 383	87.11 315	82.17 384	89.29 396	81.95 221	95.60 385	88.64 248	77.02 392	98.41 156

FMVSNet5		87.29 337	85.79 344	91.78 329	94.80 314	87.28 268	95.49 297	95.28 324	84.09 361	83.85 375	91.82 376	62.95 396	94.17 398	78.48 370	85.34 339	93.91 365

myMVS_eth3d		87.18 338	86.38 339	89.58 368	95.16 292	79.53 387	95.00 319	93.93 374	88.55 273	86.96 341	91.99 373	56.23 408	94.00 400	75.47 387	94.11 226	95.20 312

Syy-MVS		87.13 339	87.02 334	87.47 381	95.16 292	73.21 409	95.00 319	93.93 374	88.55 273	86.96 341	91.99 373	75.90 308	94.00 400	61.59 415	94.11 226	95.20 312

Anonymous20231206		87.09 340	86.14 342	89.93 365	91.22 390	80.35 375	96.11 262	95.35 320	83.57 370	84.16 368	93.02 353	73.54 330	95.61 384	72.16 400	86.14 329	93.84 366

EG-PatchMatch MVS		87.02 341	85.44 346	91.76 331	92.67 376	85.00 320	96.08 264	96.45 269	83.41 372	79.52 395	93.49 343	57.10 406	97.72 316	79.34 368	90.87 283	92.56 383

TinyColmap		86.82 342	85.35 349	91.21 341	94.91 309	82.99 347	93.94 355	94.02 371	83.58 369	81.56 385	94.68 281	62.34 399	98.13 254	75.78 383	87.35 320	92.52 385

UWE-MVS-28		86.81 343	86.41 338	88.02 379	92.87 371	74.60 404	95.38 302	86.70 419	88.17 283	87.28 334	94.67 283	70.83 346	93.30 407	67.45 409	94.31 220	96.17 257

mvs5depth		86.53 344	85.08 351	90.87 348	88.74 407	82.52 352	91.91 391	94.23 367	86.35 327	87.11 337	93.70 333	66.52 381	97.76 313	81.37 351	75.80 397	92.31 389

TDRefinement		86.53 344	84.76 356	91.85 324	82.23 422	84.25 330	96.38 244	95.35 320	84.97 351	84.09 371	94.94 267	65.76 389	98.34 240	84.60 318	74.52 400	92.97 375

test_0402		86.46 346	84.79 355	91.45 336	95.02 301	85.55 307	96.29 252	94.89 344	80.90 387	82.21 383	93.97 325	68.21 371	97.29 350	62.98 413	88.68 305	91.51 398

Anonymous20240521		86.42 347	85.44 346	89.34 372	90.33 394	79.79 384	96.73 210	95.92 290	83.71 368	83.25 378	91.36 381	63.92 393	96.01 374	78.39 372	85.36 338	92.22 391

DSMNet-mixed		86.34 348	86.12 343	87.00 385	89.88 398	70.43 411	94.93 321	90.08 408	77.97 403	85.42 358	92.78 356	74.44 322	93.96 402	74.43 390	95.14 203	96.62 245

CL-MVSNet_self_test		86.31 349	85.15 350	89.80 366	88.83 405	81.74 362	93.93 356	96.22 281	86.67 321	85.03 360	90.80 384	78.09 290	94.50 394	74.92 388	71.86 406	93.15 374

pmmvs-eth3d		86.22 350	84.45 358	91.53 334	88.34 409	87.25 270	94.47 334	95.01 336	83.47 371	79.51 396	89.61 394	69.75 358	95.71 381	83.13 332	76.73 395	91.64 395

test_vis1_rt		86.16 351	85.06 352	89.46 369	93.47 360	80.46 374	96.41 238	86.61 420	85.22 345	79.15 397	88.64 399	52.41 412	97.06 355	93.08 154	90.57 285	90.87 403

test20.03		86.14 352	85.40 348	88.35 375	90.12 395	80.06 382	95.90 274	95.20 329	88.59 269	81.29 386	93.62 339	71.43 341	92.65 410	71.26 404	81.17 379	92.34 387

UnsupCasMVSNet_eth		85.99 353	84.45 358	90.62 355	89.97 397	82.40 356	93.62 368	97.37 194	89.86 225	78.59 399	92.37 365	65.25 391	95.35 390	82.27 343	70.75 407	94.10 361

KD-MVS_self_test		85.95 354	84.95 353	88.96 374	89.55 401	79.11 393	95.13 316	96.42 270	85.91 335	84.07 372	90.48 386	70.03 354	94.82 393	80.04 360	72.94 404	92.94 376

ttmdpeth		85.91 355	84.76 356	89.36 371	89.14 402	80.25 380	95.66 288	93.16 384	83.77 366	83.39 377	95.26 256	66.24 385	95.26 391	80.65 356	75.57 398	92.57 382

YYNet1		85.87 356	84.23 360	90.78 354	92.38 384	82.46 355	93.17 375	95.14 332	82.12 380	67.69 412	92.36 368	78.16 289	95.50 388	77.31 376	79.73 384	94.39 354

MDA-MVSNet_test_wron		85.87 356	84.23 360	90.80 353	92.38 384	82.57 350	93.17 375	95.15 331	82.15 379	67.65 414	92.33 371	78.20 286	95.51 387	77.33 375	79.74 383	94.31 358

CMPMVS	62.92 21	85.62 358	84.92 354	87.74 380	89.14 402	73.12 410	94.17 348	96.80 247	73.98 409	73.65 408	94.93 268	66.36 382	97.61 326	83.95 326	91.28 274	92.48 386
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 359	83.64 362	90.92 347	95.27 285	79.49 389	90.55 401	95.60 309	83.76 367	83.00 381	89.95 391	71.09 343	97.97 283	82.75 339	60.79 421	95.31 304

MDA-MVSNet-bldmvs		85.00 360	82.95 365	91.17 345	93.13 368	83.33 342	94.56 330	95.00 337	84.57 356	65.13 418	92.65 358	70.45 349	95.85 378	73.57 396	77.49 391	94.33 356

MIMVSNet1		84.93 361	83.05 363	90.56 356	89.56 400	84.84 325	95.40 300	95.35 320	83.91 362	80.38 391	92.21 372	57.23 405	93.34 406	70.69 406	82.75 374	93.50 369

KD-MVS_2432*1600		84.81 362	82.64 366	91.31 339	91.07 391	85.34 314	91.22 395	95.75 300	85.56 340	83.09 379	90.21 389	67.21 376	95.89 376	77.18 378	62.48 419	92.69 379

miper_refine_blended		84.81 362	82.64 366	91.31 339	91.07 391	85.34 314	91.22 395	95.75 300	85.56 340	83.09 379	90.21 389	67.21 376	95.89 376	77.18 378	62.48 419	92.69 379

OpenMVS_ROB	81.14 20	84.42 364	82.28 370	90.83 349	90.06 396	84.05 335	95.73 283	94.04 370	73.89 411	80.17 394	91.53 380	59.15 402	97.64 322	66.92 411	89.05 300	90.80 404

mvsany_test3		83.59 365	82.44 369	87.03 384	83.80 417	73.82 406	93.70 363	90.92 405	86.42 325	82.51 382	90.26 388	46.76 417	95.71 381	90.82 198	76.76 394	91.57 397

PM-MVS		83.48 366	81.86 372	88.31 376	87.83 411	77.59 398	93.43 371	91.75 398	86.91 317	80.63 389	89.91 392	44.42 418	95.84 379	85.17 312	76.73 395	91.50 399

test_fmvs3		83.21 367	83.02 364	83.78 390	86.77 414	68.34 416	96.76 208	94.91 343	86.49 324	84.14 370	89.48 395	36.04 422	91.73 412	91.86 177	80.77 381	91.26 402

new-patchmatchnet		83.18 368	81.87 371	87.11 383	86.88 413	75.99 402	93.70 363	95.18 330	85.02 350	77.30 402	88.40 401	65.99 387	93.88 403	74.19 393	70.18 408	91.47 400

new_pmnet		82.89 369	81.12 374	88.18 378	89.63 399	80.18 381	91.77 392	92.57 391	76.79 406	75.56 405	88.23 403	61.22 401	94.48 395	71.43 402	82.92 372	89.87 407

MVS-HIRNet		82.47 370	81.21 373	86.26 387	95.38 273	69.21 414	88.96 411	89.49 409	66.28 416	80.79 388	74.08 421	68.48 369	97.39 345	71.93 401	95.47 197	92.18 392

MVStest1		82.38 371	80.04 375	89.37 370	87.63 412	82.83 348	95.03 318	93.37 382	73.90 410	73.50 409	94.35 301	62.89 397	93.25 408	73.80 394	65.92 416	92.04 394

UnsupCasMVSNet_bld		82.13 372	79.46 377	90.14 362	88.00 410	82.47 354	90.89 400	96.62 263	78.94 399	75.61 403	84.40 414	56.63 407	96.31 372	77.30 377	66.77 415	91.63 396

dmvs_testset		81.38 373	82.60 368	77.73 396	91.74 388	51.49 431	93.03 380	84.21 424	89.07 250	78.28 400	91.25 382	76.97 300	88.53 419	56.57 419	82.24 375	93.16 373

test_f		80.57 374	79.62 376	83.41 391	83.38 420	67.80 418	93.57 370	93.72 377	80.80 391	77.91 401	87.63 407	33.40 423	92.08 411	87.14 281	79.04 389	90.34 406

pmmvs3		79.97 375	77.50 380	87.39 382	82.80 421	79.38 391	92.70 385	90.75 406	70.69 413	78.66 398	87.47 409	51.34 413	93.40 405	73.39 397	69.65 409	89.38 408

APD_test1		79.31 376	77.70 379	84.14 389	89.11 404	69.07 415	92.36 390	91.50 400	69.07 414	73.87 407	92.63 360	39.93 420	94.32 397	70.54 407	80.25 382	89.02 409

N_pmnet		78.73 377	78.71 378	78.79 395	92.80 374	46.50 434	94.14 349	43.71 436	78.61 400	80.83 387	91.66 379	74.94 318	96.36 371	67.24 410	84.45 356	93.50 369

WB-MVS		76.77 378	76.63 381	77.18 397	85.32 415	56.82 429	94.53 331	89.39 410	82.66 377	71.35 410	89.18 397	75.03 317	88.88 417	35.42 426	66.79 414	85.84 411

SSC-MVS		76.05 379	75.83 382	76.72 401	84.77 416	56.22 430	94.32 343	88.96 412	81.82 383	70.52 411	88.91 398	74.79 319	88.71 418	33.69 427	64.71 417	85.23 412

test_vis3_rt		72.73 380	70.55 383	79.27 394	80.02 423	68.13 417	93.92 357	74.30 431	76.90 405	58.99 422	73.58 422	20.29 431	95.37 389	84.16 321	72.80 405	74.31 419

LCM-MVSNet		72.55 381	69.39 385	82.03 392	70.81 432	65.42 421	90.12 405	94.36 365	55.02 422	65.88 416	81.72 415	24.16 430	89.96 413	74.32 392	68.10 413	90.71 405

FPMVS		71.27 382	69.85 384	75.50 402	74.64 427	59.03 427	91.30 394	91.50 400	58.80 419	57.92 423	88.28 402	29.98 426	85.53 422	53.43 420	82.84 373	81.95 415

PMMVS2		70.19 383	66.92 387	80.01 393	76.35 426	65.67 420	86.22 416	87.58 416	64.83 418	62.38 419	80.29 418	26.78 428	88.49 420	63.79 412	54.07 423	85.88 410

dongtai		69.99 384	69.33 386	71.98 405	88.78 406	61.64 425	89.86 406	59.93 435	75.67 407	74.96 406	85.45 411	50.19 414	81.66 424	43.86 423	55.27 422	72.63 420

testf1		69.31 385	66.76 388	76.94 399	78.61 424	61.93 423	88.27 413	86.11 421	55.62 420	59.69 420	85.31 412	20.19 432	89.32 414	57.62 416	69.44 411	79.58 416

APD_test2		69.31 385	66.76 388	76.94 399	78.61 424	61.93 423	88.27 413	86.11 421	55.62 420	59.69 420	85.31 412	20.19 432	89.32 414	57.62 416	69.44 411	79.58 416

EGC-MVSNET		68.77 387	63.01 393	86.07 388	92.49 380	82.24 358	93.96 354	90.96 404	0.71 433	2.62 434	90.89 383	53.66 410	93.46 404	57.25 418	84.55 354	82.51 414

Gipuma		67.86 388	65.41 390	75.18 403	92.66 377	73.45 407	66.50 424	94.52 356	53.33 423	57.80 424	66.07 424	30.81 424	89.20 416	48.15 422	78.88 390	62.90 424
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 389	64.89 391	69.79 406	72.62 430	35.23 438	65.19 425	92.83 389	20.35 428	65.20 417	88.08 405	43.14 419	82.70 423	73.12 398	63.46 418	91.45 401

kuosan		65.27 390	64.66 392	67.11 408	83.80 417	61.32 426	88.53 412	60.77 434	68.22 415	67.67 413	80.52 417	49.12 415	70.76 430	29.67 429	53.64 424	69.26 422

ANet_high		63.94 391	59.58 394	77.02 398	61.24 434	66.06 419	85.66 418	87.93 415	78.53 401	42.94 426	71.04 423	25.42 429	80.71 425	52.60 421	30.83 427	84.28 413

PMVS	53.92 22	58.58 392	55.40 395	68.12 407	51.00 435	48.64 432	78.86 421	87.10 418	46.77 424	35.84 430	74.28 420	8.76 434	86.34 421	42.07 424	73.91 402	69.38 421
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 393	52.56 397	55.43 410	74.43 428	47.13 433	83.63 420	76.30 428	42.23 425	42.59 427	62.22 426	28.57 427	74.40 427	31.53 428	31.51 426	44.78 425

MVE	50.73 23	53.25 394	48.81 399	66.58 409	65.34 433	57.50 428	72.49 423	70.94 432	40.15 427	39.28 429	63.51 425	6.89 436	73.48 429	38.29 425	42.38 425	68.76 423
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 395	51.31 398	54.39 411	72.62 430	45.39 435	83.84 419	75.51 430	41.13 426	40.77 428	59.65 427	30.08 425	73.60 428	28.31 430	29.90 428	44.18 426

tmp_tt		51.94 396	53.82 396	46.29 412	33.73 436	45.30 436	78.32 422	67.24 433	18.02 429	50.93 425	87.05 410	52.99 411	53.11 431	70.76 405	25.29 429	40.46 427

wuyk23d		25.11 397	24.57 401	26.74 413	73.98 429	39.89 437	57.88 426	9.80 437	12.27 430	10.39 431	6.97 433	7.03 435	36.44 432	25.43 431	17.39 430	3.89 430

cdsmvs_eth3d_5k		23.24 398	30.99 400	0.00 416	0.00 439	0.00 441	0.00 427	97.63 151	0.00 434	0.00 435	96.88 166	84.38 168	0.00 435	0.00 434	0.00 433	0.00 431

testmvs		13.36 399	16.33 402	4.48 415	5.04 437	2.26 440	93.18 374	3.28 438	2.70 431	8.24 432	21.66 429	2.29 438	2.19 433	7.58 432	2.96 431	9.00 429

test123		13.04 400	15.66 403	5.18 414	4.51 438	3.45 439	92.50 388	1.81 439	2.50 432	7.58 433	20.15 430	3.67 437	2.18 434	7.13 433	1.07 432	9.90 428

ab-mvs-re		8.06 401	10.74 404	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	96.69 176	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

pcd_1.5k_mvsjas		7.39 402	9.85 405	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	88.65 101	0.00 435	0.00 434	0.00 433	0.00 431

mmdepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

monomultidepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

test_blank		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet_test		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

DCPMVS		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet-low-res		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uncertanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

Regformer		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

WAC-MVS						79.53 387								75.56 386

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									90.77 193	98.89 18	98.28 72	96.24 1	98.35 237	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					0.00 439

eth-test					0.00 439

ZD-MVS					99.05 39	94.59 32		98.08 80	89.22 246	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 98	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	90.40 214	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	91.28 177	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	97.02 184	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				86.56 386	83.60 419	70.00 413	85.69 417	94.97 339		80.60 390	88.45 400	37.42 421	96.84 365	82.69 340	75.44 399	92.86 377

MTGPA								98.08 80

test_post1							92.81 384				16.58 432	80.53 243	97.68 318	86.20 292

test_post											17.58 431	81.76 224	98.08 264

patchmatchnet-post											90.45 387	82.65 207	98.10 259

GG-mvs-BLEND				93.62 264	93.69 351	89.20 220	92.39 389	83.33 425		87.98 320	89.84 393	71.00 344	96.87 364	82.08 344	95.40 199	94.80 338

MTMP							97.86 82	82.03 426

gm-plane-assit					93.22 365	78.89 395			84.82 353		93.52 342		98.64 210	87.72 260

test9_res														94.81 117	99.38 59	99.45 51

TEST9					98.70 59	94.19 42	96.41 238	98.02 100	88.17 283	96.03 108	97.56 130	92.74 33	99.59 81

test_8					98.67 61	94.06 49	96.37 245	98.01 103	88.58 270	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		95.54 313	85.38 342	85.49 356	96.77 170	70.28 350	99.15 146	80.02 361	92.87 245	96.15 260

test_prior4						93.66 58	96.42 237

test_prior2							96.35 246		92.80 132	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							95.94 271		81.66 384	97.34 56			98.82 187	92.26 164

新几何2							95.79 280

新几何1				97.32 57	98.60 68	93.59 59		97.75 134	81.58 385	95.75 119	97.85 103	90.04 83	99.67 63	86.50 288	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							95.79 280	97.87 118	83.87 365				99.65 65	87.68 266		98.89 113

原ACMM2							95.67 285

原ACMM1				96.38 107	98.59 69	91.09 152		97.89 114	87.41 308	95.22 133	97.68 116	90.25 80	99.54 96	87.95 256	99.12 87	98.49 146

test222					98.24 90	92.21 103	95.33 304	97.60 153	79.22 398	95.25 131	97.84 105	88.80 98			99.15 83	98.72 126

testdata2													99.67 63	85.96 300

segment_acmp												92.89 30

testdata				95.46 169	98.18 100	88.90 228		97.66 145	82.73 376	97.03 67	98.07 83	90.06 82	98.85 185	89.67 220	98.98 95	98.64 132

testdata1							95.26 311		93.10 118

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					96.21 232	89.98 188

plane_prior6					96.10 243	90.00 184						81.32 230

plane_prior5								97.51 166					98.60 214	93.02 157	92.23 256	95.86 268

plane_prior4											96.64 179

plane_prior3						90.00 184			94.46 64	91.34 226

plane_prior2							97.74 99		94.85 41

plane_prior1					96.14 240

plane_prior						89.99 186	97.24 165		94.06 76						92.16 260

n2								0.00 440

nn								0.00 440

door-mid								91.06 403

lessismore_v0				90.45 357	91.96 387	79.09 394		87.19 417		80.32 392	94.39 298	66.31 384	97.55 330	84.00 325	76.84 393	94.70 345

LGP-MVS_train				94.10 235	96.16 237	88.26 245		97.46 175	91.29 174	90.12 256	97.16 151	79.05 271	98.73 200	92.25 166	91.89 264	95.31 304

test11								97.88 116

door								91.13 402

HQP5-MVS						89.33 213

HQP-NCC					95.86 249		96.65 220		93.55 92	90.14 250

ACMP_Plane					95.86 249		96.65 220		93.55 92	90.14 250

BP-MVS														92.13 170

HQP4-MVS										90.14 250			98.50 222			95.78 276

HQP3-MVS								97.39 191							92.10 261

HQP2-MVS												80.95 234

NP-MVS					95.99 247	89.81 194					95.87 221

MDTV_nov1_ep13_2view						70.35 412	93.10 379		83.88 364	93.55 169		82.47 211		86.25 291		98.38 159

MDTV_nov1_ep13			90.76 243		95.22 289	80.33 376	93.03 380	95.28 324	88.14 286	92.84 190	93.83 327	81.34 229	98.08 264	82.86 334	94.34 219

ACMMP++_ref															90.30 290

ACMMP++															91.02 279

Test By Simon												88.73 100

ITE_SJBPF				92.43 306	95.34 278	85.37 313		95.92 290	91.47 167	87.75 323	96.39 197	71.00 344	97.96 287	82.36 342	89.86 293	93.97 364

DeepMVS_CX				74.68 404	90.84 393	64.34 422		81.61 427	65.34 417	67.47 415	88.01 406	48.60 416	80.13 426	62.33 414	73.68 403	79.58 416

High-resolution multi-view benchmark