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.

fmvsm_l_conf0.5_n_a		99.09 1	99.08 1	99.11 55	99.43 57	97.48 83	98.88 119	99.30 13	98.47 14	99.85 8	99.43 38	96.71 17	99.96 4	99.86 1	99.80 24	99.89 5

SED-MVS		99.09 1	98.91 4	99.63 4	99.71 19	99.24 5	99.02 79	98.87 77	97.65 33	99.73 18	99.48 29	97.53 7	99.94 12	98.43 62	99.81 15	99.70 59

DVP-MVS++		99.08 3	98.89 5	99.64 3	99.17 101	99.23 7	99.69 1	98.88 70	97.32 56	99.53 32	99.47 31	97.81 3	99.94 12	98.47 58	99.72 60	99.74 42

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 51	99.41 59	97.54 81	98.89 112	99.31 12	98.49 13	99.86 5	99.42 39	96.45 24	99.96 4	99.86 1	99.74 52	99.90 4

DVP-MVS		99.03 5	98.83 9	99.63 4	99.72 12	99.25 2	98.97 90	98.58 167	97.62 35	99.45 34	99.46 35	97.42 9	99.94 12	98.47 58	99.81 15	99.69 62
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
APDe-MVS		99.02 6	98.84 8	99.55 9	99.57 33	98.96 16	99.39 10	98.93 58	97.38 53	99.41 37	99.54 17	96.66 18	99.84 79	98.86 34	99.85 6	99.87 8
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
reproduce_model		98.94 7	98.81 10	99.34 26	99.52 39	98.26 49	98.94 99	98.84 87	98.06 21	99.35 41	99.61 4	96.39 27	99.94 12	98.77 37	99.82 14	99.83 14

reproduce-ours		98.93 8	98.78 11	99.38 18	99.49 46	98.38 35	98.86 126	98.83 89	98.06 21	99.29 45	99.58 13	96.40 25	99.94 12	98.68 40	99.81 15	99.81 20

our_new_method		98.93 8	98.78 11	99.38 18	99.49 46	98.38 35	98.86 126	98.83 89	98.06 21	99.29 45	99.58 13	96.40 25	99.94 12	98.68 40	99.81 15	99.81 20

test_fmvsmconf_n		98.92 10	98.87 6	99.04 61	98.88 136	97.25 105	98.82 138	99.34 10	98.75 7	99.80 10	99.61 4	95.16 73	99.95 9	99.70 13	99.80 24	99.93 1

DPE-MVS		98.92 10	98.67 16	99.65 2	99.58 32	99.20 9	98.42 232	98.91 64	97.58 38	99.54 31	99.46 35	97.10 12	99.94 12	97.64 109	99.84 11	99.83 14
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_3		98.90 12	98.74 14	99.37 22	99.36 60	98.25 50	98.89 112	99.24 18	98.77 6	99.89 1	99.59 11	93.39 107	99.96 4	99.78 6	99.76 42	99.89 5

SteuartSystems-ACMMP		98.90 12	98.75 13	99.36 24	99.22 96	98.43 33	99.10 63	98.87 77	97.38 53	99.35 41	99.40 42	97.78 5	99.87 70	97.77 97	99.85 6	99.78 26
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_1920		98.87 14	99.01 3	98.45 112	99.42 58	96.43 145	98.96 95	99.36 9	98.63 9	99.86 5	99.51 23	95.91 43	99.97 1	99.72 10	99.75 48	98.94 194

TSAR-MVS + MP.		98.78 15	98.62 17	99.24 40	99.69 24	98.28 48	99.14 54	98.66 145	96.84 87	99.56 29	99.31 62	96.34 28	99.70 131	98.32 68	99.73 55	99.73 47
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		98.78 15	98.56 21	99.45 15	99.32 67	98.87 19	98.47 222	98.81 98	97.72 28	98.76 85	99.16 90	97.05 13	99.78 113	98.06 79	99.66 71	99.69 62

MSP-MVS		98.74 17	98.55 22	99.29 33	99.75 3	98.23 51	99.26 27	98.88 70	97.52 41	99.41 37	98.78 151	96.00 39	99.79 110	97.79 96	99.59 88	99.85 11
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
fmvsm_s_conf0.5_n_8		98.73 18	98.62 17	99.05 60	99.35 61	97.27 99	98.80 147	99.23 23	98.93 2	99.79 11	99.59 11	92.34 123	99.95 9	99.82 4	99.71 62	99.92 2

XVS		98.70 19	98.49 28	99.34 26	99.70 22	98.35 44	99.29 22	98.88 70	97.40 50	98.46 106	99.20 80	95.90 45	99.89 59	97.85 92	99.74 52	99.78 26

fmvsm_s_conf0.5_n_6		98.65 20	98.55 22	98.95 70	98.50 176	97.30 95	98.79 155	99.16 33	98.14 19	99.86 5	99.41 41	93.71 104	99.91 48	99.71 11	99.64 79	99.65 75

MCST-MVS		98.65 20	98.37 37	99.48 13	99.60 31	98.87 19	98.41 233	98.68 137	97.04 79	98.52 104	98.80 149	96.78 16	99.83 81	97.93 86	99.61 84	99.74 42

SD-MVS		98.64 22	98.68 15	98.53 101	99.33 64	98.36 43	98.90 108	98.85 86	97.28 60	99.72 21	99.39 43	96.63 20	97.60 386	98.17 74	99.85 6	99.64 78
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HFP-MVS		98.63 23	98.40 34	99.32 32	99.72 12	98.29 47	99.23 32	98.96 53	96.10 126	98.94 68	99.17 87	96.06 36	99.92 38	97.62 110	99.78 34	99.75 40

ACMMP_NAP		98.61 24	98.30 52	99.55 9	99.62 30	98.95 17	98.82 138	98.81 98	95.80 138	99.16 57	99.47 31	95.37 60	99.92 38	97.89 90	99.75 48	99.79 24

region2R		98.61 24	98.38 36	99.29 33	99.74 7	98.16 57	99.23 32	98.93 58	96.15 122	98.94 68	99.17 87	95.91 43	99.94 12	97.55 118	99.79 30	99.78 26

NCCC		98.61 24	98.35 40	99.38 18	99.28 82	98.61 26	98.45 223	98.76 116	97.82 27	98.45 109	98.93 130	96.65 19	99.83 81	97.38 128	99.41 119	99.71 55

SF-MVS		98.59 27	98.32 51	99.41 17	99.54 35	98.71 22	99.04 73	98.81 98	95.12 175	99.32 44	99.39 43	96.22 30	99.84 79	97.72 100	99.73 55	99.67 71

ACMMPR		98.59 27	98.36 38	99.29 33	99.74 7	98.15 58	99.23 32	98.95 54	96.10 126	98.93 72	99.19 85	95.70 49	99.94 12	97.62 110	99.79 30	99.78 26

test_fmvsmconf0.1_n		98.58 29	98.44 32	98.99 63	97.73 262	97.15 110	98.84 134	98.97 50	98.75 7	99.43 36	99.54 17	93.29 109	99.93 31	99.64 16	99.79 30	99.89 5

SMA-MVS		98.58 29	98.25 55	99.56 8	99.51 40	99.04 15	98.95 96	98.80 105	93.67 262	99.37 40	99.52 20	96.52 22	99.89 59	98.06 79	99.81 15	99.76 39
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MTAPA		98.58 29	98.29 53	99.46 14	99.76 2	98.64 25	98.90 108	98.74 120	97.27 64	98.02 133	99.39 43	94.81 83	99.96 4	97.91 88	99.79 30	99.77 32

HPM-MVS++		98.58 29	98.25 55	99.55 9	99.50 42	99.08 11	98.72 173	98.66 145	97.51 42	98.15 120	98.83 146	95.70 49	99.92 38	97.53 120	99.67 68	99.66 74

SR-MVS		98.57 33	98.35 40	99.24 40	99.53 36	98.18 55	99.09 64	98.82 92	96.58 103	99.10 59	99.32 60	95.39 58	99.82 88	97.70 105	99.63 81	99.72 51

CP-MVS		98.57 33	98.36 38	99.19 44	99.66 26	97.86 69	99.34 16	98.87 77	95.96 130	98.60 100	99.13 95	96.05 37	99.94 12	97.77 97	99.86 2	99.77 32

MSLP-MVS++		98.56 35	98.57 20	98.55 97	99.26 85	96.80 125	98.71 174	99.05 43	97.28 60	98.84 78	99.28 65	96.47 23	99.40 195	98.52 56	99.70 64	99.47 107

DeepC-MVS_fast	96.70 1	98.55 36	98.34 46	99.18 46	99.25 86	98.04 63	98.50 219	98.78 112	97.72 28	98.92 74	99.28 65	95.27 66	99.82 88	97.55 118	99.77 36	99.69 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		98.54 37	98.35 40	99.13 52	99.49 46	97.86 69	99.11 60	98.80 105	96.49 107	99.17 54	99.35 55	95.34 62	99.82 88	97.72 100	99.65 74	99.71 55

fmvsm_s_conf0.5_n_5		98.53 38	98.35 40	99.08 57	99.07 116	97.46 87	98.68 182	99.20 28	97.50 43	99.87 2	99.50 25	91.96 142	99.96 4	99.76 7	99.65 74	99.82 18

fmvsm_s_conf0.5_n_3		98.53 38	98.45 31	98.79 78	99.23 94	97.32 92	98.80 147	99.26 15	98.82 3	99.87 2	99.60 8	90.95 170	99.93 31	99.76 7	99.73 55	99.12 169

APD-MVS_3200maxsize		98.53 38	98.33 50	99.15 50	99.50 42	97.92 68	99.15 51	98.81 98	96.24 118	99.20 51	99.37 49	95.30 64	99.80 100	97.73 99	99.67 68	99.72 51

MM		98.51 41	98.24 57	99.33 30	99.12 110	98.14 60	98.93 103	97.02 371	98.96 1	99.17 54	99.47 31	91.97 141	99.94 12	99.85 3	99.69 65	99.91 3

mPP-MVS		98.51 41	98.26 54	99.25 39	99.75 3	98.04 63	99.28 24	98.81 98	96.24 118	98.35 116	99.23 75	95.46 55	99.94 12	97.42 126	99.81 15	99.77 32

ZNCC-MVS		98.49 43	98.20 63	99.35 25	99.73 11	98.39 34	99.19 44	98.86 83	95.77 140	98.31 119	99.10 99	95.46 55	99.93 31	97.57 117	99.81 15	99.74 42

SPE-MVS-test		98.49 43	98.50 26	98.46 111	99.20 99	97.05 115	99.64 4	98.50 189	97.45 49	98.88 75	99.14 94	95.25 68	99.15 225	98.83 35	99.56 98	99.20 154

PGM-MVS		98.49 43	98.23 59	99.27 38	99.72 12	98.08 62	98.99 86	99.49 5	95.43 156	99.03 60	99.32 60	95.56 52	99.94 12	96.80 157	99.77 36	99.78 26

EI-MVSNet-Vis-set		98.47 46	98.39 35	98.69 86	99.46 52	96.49 142	98.30 244	98.69 134	97.21 67	98.84 78	99.36 53	95.41 57	99.78 113	98.62 44	99.65 74	99.80 23

MVS_111021_HR		98.47 46	98.34 46	98.88 75	99.22 96	97.32 92	97.91 298	99.58 3	97.20 68	98.33 117	99.00 119	95.99 40	99.64 145	98.05 81	99.76 42	99.69 62

balanced_conf03		98.45 48	98.35 40	98.74 82	98.65 165	97.55 79	99.19 44	98.60 156	96.72 97	99.35 41	98.77 154	95.06 78	99.55 168	98.95 31	99.87 1	99.12 169

test_fmvsmvis_n_1920		98.44 49	98.51 24	98.23 133	98.33 198	96.15 159	98.97 90	99.15 35	98.55 12	98.45 109	99.55 15	94.26 96	99.97 1	99.65 14	99.66 71	98.57 238

CS-MVS		98.44 49	98.49 28	98.31 125	99.08 115	96.73 129	99.67 3	98.47 196	97.17 71	98.94 68	99.10 99	95.73 48	99.13 228	98.71 39	99.49 109	99.09 174

GST-MVS		98.43 51	98.12 67	99.34 26	99.72 12	98.38 35	99.09 64	98.82 92	95.71 144	98.73 88	99.06 110	95.27 66	99.93 31	97.07 136	99.63 81	99.72 51

fmvsm_s_conf0.5_n		98.42 52	98.51 24	98.13 143	99.30 73	95.25 206	98.85 130	99.39 7	97.94 25	99.74 17	99.62 3	92.59 118	99.91 48	99.65 14	99.52 104	99.25 147

EI-MVSNet-UG-set		98.41 53	98.34 46	98.61 92	99.45 55	96.32 152	98.28 247	98.68 137	97.17 71	98.74 86	99.37 49	95.25 68	99.79 110	98.57 47	99.54 101	99.73 47

DELS-MVS		98.40 54	98.20 63	98.99 63	99.00 123	97.66 74	97.75 319	98.89 67	97.71 30	98.33 117	98.97 121	94.97 80	99.88 68	98.42 64	99.76 42	99.42 118
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
fmvsm_s_conf0.5_n_a		98.38 55	98.42 33	98.27 127	99.09 114	95.41 196	98.86 126	99.37 8	97.69 32	99.78 13	99.61 4	92.38 121	99.91 48	99.58 19	99.43 117	99.49 103

TSAR-MVS + GP.		98.38 55	98.24 57	98.81 77	99.22 96	97.25 105	98.11 273	98.29 236	97.19 69	98.99 66	99.02 113	96.22 30	99.67 138	98.52 56	98.56 172	99.51 96

HPM-MVS_fast		98.38 55	98.13 66	99.12 54	99.75 3	97.86 69	99.44 9	98.82 92	94.46 217	98.94 68	99.20 80	95.16 73	99.74 123	97.58 113	99.85 6	99.77 32

patch_mono-2		98.36 58	98.87 6	96.82 240	99.53 36	90.68 349	98.64 193	99.29 14	97.88 26	99.19 53	99.52 20	96.80 15	99.97 1	99.11 27	99.86 2	99.82 18

HPM-MVS		98.36 58	98.10 70	99.13 52	99.74 7	97.82 73	99.53 6	98.80 105	94.63 206	98.61 99	98.97 121	95.13 75	99.77 118	97.65 108	99.83 13	99.79 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_4		98.35 60	98.50 26	97.90 160	99.16 105	95.08 215	98.75 159	99.24 18	98.39 15	99.81 9	99.52 20	92.35 122	99.90 56	99.74 9	99.51 106	98.71 219

APD-MVS		98.35 60	98.00 76	99.42 16	99.51 40	98.72 21	98.80 147	98.82 92	94.52 214	99.23 50	99.25 74	95.54 54	99.80 100	96.52 164	99.77 36	99.74 42
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 62	98.23 59	98.67 88	99.27 83	96.90 121	97.95 291	99.58 3	97.14 74	98.44 111	99.01 117	95.03 79	99.62 152	97.91 88	99.75 48	99.50 98

PHI-MVS		98.34 62	98.06 71	99.18 46	99.15 108	98.12 61	99.04 73	99.09 38	93.32 277	98.83 80	99.10 99	96.54 21	99.83 81	97.70 105	99.76 42	99.59 86

MP-MVS		98.33 64	98.01 75	99.28 36	99.75 3	98.18 55	99.22 36	98.79 110	96.13 123	97.92 144	99.23 75	94.54 86	99.94 12	96.74 160	99.78 34	99.73 47
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSMamba_PlusPlus		98.31 65	98.19 65	98.67 88	98.96 130	97.36 90	99.24 30	98.57 169	94.81 198	98.99 66	98.90 134	95.22 71	99.59 155	99.15 26	99.84 11	99.07 182

MP-MVS-pluss		98.31 65	97.92 78	99.49 12	99.72 12	98.88 18	98.43 229	98.78 112	94.10 227	97.69 161	99.42 39	95.25 68	99.92 38	98.09 78	99.80 24	99.67 71
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_2		98.30 67	98.21 61	98.57 94	99.25 86	97.11 112	98.66 189	99.20 28	98.82 3	99.79 11	99.60 8	89.38 205	99.92 38	99.80 5	99.38 124	98.69 221

fmvsm_s_conf0.5_n_7		98.23 68	98.35 40	97.89 162	98.86 140	94.99 221	98.58 202	99.00 46	98.29 16	99.73 18	99.60 8	91.70 146	99.92 38	99.63 17	99.73 55	98.76 213

MVS_0304		98.23 68	97.91 79	99.21 43	98.06 230	97.96 67	98.58 202	95.51 409	98.58 10	98.87 76	99.26 69	92.99 113	99.95 9	99.62 18	99.67 68	99.73 47

ACMMP		98.23 68	97.95 77	99.09 56	99.74 7	97.62 77	99.03 76	99.41 6	95.98 129	97.60 170	99.36 53	94.45 91	99.93 31	97.14 133	98.85 157	99.70 59
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
EC-MVSNet		98.21 71	98.11 68	98.49 108	98.34 195	97.26 104	99.61 5	98.43 205	96.78 90	98.87 76	98.84 142	93.72 103	99.01 250	98.91 33	99.50 107	99.19 158

fmvsm_s_conf0.1_n		98.18 72	98.21 61	98.11 147	98.54 174	95.24 207	98.87 122	99.24 18	97.50 43	99.70 22	99.67 1	91.33 159	99.89 59	99.47 21	99.54 101	99.21 153

fmvsm_s_conf0.1_n_2		98.14 73	98.02 74	98.53 101	98.88 136	97.07 114	98.69 180	98.82 92	98.78 5	99.77 14	99.61 4	88.83 224	99.91 48	99.71 11	99.07 140	98.61 231

fmvsm_s_conf0.1_n_a		98.08 74	98.04 73	98.21 134	97.66 268	95.39 197	98.89 112	99.17 32	97.24 65	99.76 16	99.67 1	91.13 164	99.88 68	99.39 22	99.41 119	99.35 126

dcpmvs_2		98.08 74	98.59 19	96.56 265	99.57 33	90.34 361	99.15 51	98.38 215	96.82 89	99.29 45	99.49 28	95.78 47	99.57 158	98.94 32	99.86 2	99.77 32

CANet		98.05 76	97.76 82	98.90 74	98.73 150	97.27 99	98.35 235	98.78 112	97.37 55	97.72 158	98.96 126	91.53 155	99.92 38	98.79 36	99.65 74	99.51 96

train_agg		97.97 77	97.52 94	99.33 30	99.31 69	98.50 29	97.92 296	98.73 123	92.98 293	97.74 155	98.68 165	96.20 32	99.80 100	96.59 161	99.57 92	99.68 67

ETV-MVS		97.96 78	97.81 80	98.40 120	98.42 182	97.27 99	98.73 169	98.55 174	96.84 87	98.38 113	97.44 286	95.39 58	99.35 200	97.62 110	98.89 151	98.58 237

UA-Net		97.96 78	97.62 86	98.98 65	98.86 140	97.47 85	98.89 112	99.08 39	96.67 100	98.72 90	99.54 17	93.15 111	99.81 93	94.87 220	98.83 158	99.65 75

CDPH-MVS		97.94 80	97.49 96	99.28 36	99.47 50	98.44 31	97.91 298	98.67 142	92.57 309	98.77 84	98.85 141	95.93 42	99.72 125	95.56 198	99.69 65	99.68 67

DeepPCF-MVS	96.37 2	97.93 81	98.48 30	96.30 291	99.00 123	89.54 376	97.43 341	98.87 77	98.16 18	99.26 49	99.38 48	96.12 35	99.64 145	98.30 69	99.77 36	99.72 51

DeepC-MVS	95.98 3	97.88 82	97.58 88	98.77 80	99.25 86	96.93 119	98.83 136	98.75 118	96.96 83	96.89 196	99.50 25	90.46 178	99.87 70	97.84 94	99.76 42	99.52 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf0.01_n		97.86 83	97.54 93	98.83 76	95.48 391	96.83 124	98.95 96	98.60 156	98.58 10	98.93 72	99.55 15	88.57 229	99.91 48	99.54 20	99.61 84	99.77 32

DP-MVS Recon		97.86 83	97.46 99	99.06 59	99.53 36	98.35 44	98.33 237	98.89 67	92.62 306	98.05 128	98.94 129	95.34 62	99.65 142	96.04 180	99.42 118	99.19 158

CSCG		97.85 85	97.74 83	98.20 136	99.67 25	95.16 210	99.22 36	99.32 11	93.04 291	97.02 189	98.92 132	95.36 61	99.91 48	97.43 125	99.64 79	99.52 93

BP-MVS1		97.82 86	97.51 95	98.76 81	98.25 206	97.39 89	99.15 51	97.68 303	96.69 98	98.47 105	99.10 99	90.29 182	99.51 175	98.60 45	99.35 127	99.37 123

MG-MVS		97.81 87	97.60 87	98.44 114	99.12 110	95.97 168	97.75 319	98.78 112	96.89 86	98.46 106	99.22 77	93.90 102	99.68 137	94.81 224	99.52 104	99.67 71

VNet		97.79 88	97.40 103	98.96 68	98.88 136	97.55 79	98.63 196	98.93 58	96.74 94	99.02 61	98.84 142	90.33 181	99.83 81	98.53 50	96.66 237	99.50 98

EIA-MVS		97.75 89	97.58 88	98.27 127	98.38 186	96.44 144	99.01 81	98.60 156	95.88 134	97.26 177	97.53 280	94.97 80	99.33 203	97.38 128	99.20 136	99.05 183

PS-MVSNAJ		97.73 90	97.77 81	97.62 190	98.68 160	95.58 187	97.34 350	98.51 184	97.29 58	98.66 96	97.88 244	94.51 87	99.90 56	97.87 91	99.17 138	97.39 281

casdiffmvs_mvg		97.72 91	97.48 98	98.44 114	98.42 182	96.59 137	98.92 105	98.44 201	96.20 120	97.76 152	99.20 80	91.66 149	99.23 215	98.27 73	98.41 182	99.49 103
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CPTT-MVS		97.72 91	97.32 108	98.92 71	99.64 28	97.10 113	99.12 58	98.81 98	92.34 317	98.09 125	99.08 108	93.01 112	99.92 38	96.06 179	99.77 36	99.75 40

PVSNet_Blended_VisFu		97.70 93	97.46 99	98.44 114	99.27 83	95.91 176	98.63 196	99.16 33	94.48 216	97.67 162	98.88 138	92.80 115	99.91 48	97.11 134	99.12 139	99.50 98

mvsany_test1		97.69 94	97.70 84	97.66 187	98.24 207	94.18 262	97.53 335	97.53 324	95.52 152	99.66 24	99.51 23	94.30 94	99.56 161	98.38 65	98.62 167	99.23 149

sasdasda		97.67 95	97.23 113	98.98 65	98.70 155	98.38 35	99.34 16	98.39 211	96.76 92	97.67 162	97.40 290	92.26 127	99.49 179	98.28 70	96.28 255	99.08 178

canonicalmvs		97.67 95	97.23 113	98.98 65	98.70 155	98.38 35	99.34 16	98.39 211	96.76 92	97.67 162	97.40 290	92.26 127	99.49 179	98.28 70	96.28 255	99.08 178

xiu_mvs_v2_base		97.66 97	97.70 84	97.56 194	98.61 169	95.46 194	97.44 339	98.46 197	97.15 73	98.65 97	98.15 219	94.33 93	99.80 100	97.84 94	98.66 166	97.41 279

GDP-MVS		97.64 98	97.28 109	98.71 85	98.30 203	97.33 91	99.05 69	98.52 181	96.34 115	98.80 81	99.05 111	89.74 192	99.51 175	96.86 154	98.86 155	99.28 141

baseline		97.64 98	97.44 101	98.25 131	98.35 190	96.20 156	99.00 83	98.32 225	96.33 117	98.03 131	99.17 87	91.35 158	99.16 222	98.10 77	98.29 189	99.39 120

casdiffmvs		97.63 100	97.41 102	98.28 126	98.33 198	96.14 160	98.82 138	98.32 225	96.38 114	97.95 139	99.21 78	91.23 163	99.23 215	98.12 76	98.37 183	99.48 105
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCFI-Net		97.62 101	97.19 116	98.92 71	98.66 162	98.20 53	99.32 21	98.38 215	96.69 98	97.58 171	97.42 289	92.10 135	99.50 178	98.28 70	96.25 258	99.08 178

xiu_mvs_v1_base_debu		97.60 102	97.56 90	97.72 177	98.35 190	95.98 163	97.86 308	98.51 184	97.13 75	99.01 63	98.40 192	91.56 151	99.80 100	98.53 50	98.68 162	97.37 283

xiu_mvs_v1_base		97.60 102	97.56 90	97.72 177	98.35 190	95.98 163	97.86 308	98.51 184	97.13 75	99.01 63	98.40 192	91.56 151	99.80 100	98.53 50	98.68 162	97.37 283

xiu_mvs_v1_base_debi		97.60 102	97.56 90	97.72 177	98.35 190	95.98 163	97.86 308	98.51 184	97.13 75	99.01 63	98.40 192	91.56 151	99.80 100	98.53 50	98.68 162	97.37 283

diffmvs		97.58 105	97.40 103	98.13 143	98.32 201	95.81 182	98.06 279	98.37 217	96.20 120	98.74 86	98.89 137	91.31 161	99.25 212	98.16 75	98.52 174	99.34 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
guyue		97.57 106	97.37 105	98.20 136	98.50 176	95.86 180	98.89 112	97.03 368	97.29 58	98.73 88	98.90 134	89.41 204	99.32 204	98.68 40	98.86 155	99.42 118

MVSFormer		97.57 106	97.49 96	97.84 164	98.07 227	95.76 183	99.47 7	98.40 209	94.98 187	98.79 82	98.83 146	92.34 123	98.41 324	96.91 142	99.59 88	99.34 128

alignmvs		97.56 108	97.07 123	99.01 62	98.66 162	98.37 42	98.83 136	98.06 283	96.74 94	98.00 137	97.65 267	90.80 172	99.48 184	98.37 66	96.56 241	99.19 158

DPM-MVS		97.55 109	96.99 127	99.23 42	99.04 118	98.55 27	97.17 365	98.35 220	94.85 197	97.93 143	98.58 175	95.07 77	99.71 130	92.60 294	99.34 128	99.43 116

OMC-MVS		97.55 109	97.34 107	98.20 136	99.33 64	95.92 175	98.28 247	98.59 162	95.52 152	97.97 138	99.10 99	93.28 110	99.49 179	95.09 215	98.88 152	99.19 158

LuminaMVS		97.49 111	97.18 117	98.42 118	97.50 283	97.15 110	98.45 223	97.68 303	96.56 106	98.68 91	98.78 151	89.84 189	99.32 204	98.60 45	98.57 171	98.79 205

KinetiMVS		97.48 112	97.05 124	98.78 79	98.37 188	97.30 95	98.99 86	98.70 132	97.18 70	99.02 61	99.01 117	87.50 258	99.67 138	95.33 205	99.33 130	99.37 123

PAPM_NR		97.46 113	97.11 120	98.50 106	99.50 42	96.41 147	98.63 196	98.60 156	95.18 172	97.06 187	98.06 225	94.26 96	99.57 158	93.80 262	98.87 154	99.52 93

EPP-MVSNet		97.46 113	97.28 109	97.99 155	98.64 166	95.38 198	99.33 20	98.31 227	93.61 266	97.19 180	99.07 109	94.05 99	99.23 215	96.89 146	98.43 181	99.37 123

3Dnovator	94.51 5	97.46 113	96.93 130	99.07 58	97.78 256	97.64 75	99.35 15	99.06 41	97.02 80	93.75 309	99.16 90	89.25 209	99.92 38	97.22 132	99.75 48	99.64 78

CNLPA		97.45 116	97.03 125	98.73 83	99.05 117	97.44 88	98.07 278	98.53 178	95.32 165	96.80 201	98.53 180	93.32 108	99.72 125	94.31 243	99.31 131	99.02 185

lupinMVS		97.44 117	97.22 115	98.12 146	98.07 227	95.76 183	97.68 324	97.76 300	94.50 215	98.79 82	98.61 170	92.34 123	99.30 208	97.58 113	99.59 88	99.31 134

3Dnovator+	94.38 6	97.43 118	96.78 138	99.38 18	97.83 253	98.52 28	99.37 12	98.71 128	97.09 78	92.99 338	99.13 95	89.36 206	99.89 59	96.97 139	99.57 92	99.71 55

Vis-MVSNet		97.42 119	97.11 120	98.34 123	98.66 162	96.23 155	99.22 36	99.00 46	96.63 102	98.04 130	99.21 78	88.05 245	99.35 200	96.01 182	99.21 135	99.45 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 120	97.25 111	97.91 159	98.70 155	96.80 125	98.82 138	98.69 134	94.53 212	98.11 123	98.28 207	94.50 90	99.57 158	94.12 251	99.49 109	97.37 283

sss		97.39 121	96.98 129	98.61 92	98.60 170	96.61 134	98.22 253	98.93 58	93.97 237	98.01 136	98.48 185	91.98 139	99.85 75	96.45 166	98.15 191	99.39 120

test_cas_vis1_n_1920		97.38 122	97.36 106	97.45 197	98.95 131	93.25 300	99.00 83	98.53 178	97.70 31	99.77 14	99.35 55	84.71 311	99.85 75	98.57 47	99.66 71	99.26 145

PVSNet_Blended		97.38 122	97.12 119	98.14 140	99.25 86	95.35 201	97.28 355	99.26 15	93.13 287	97.94 141	98.21 215	92.74 116	99.81 93	96.88 148	99.40 122	99.27 142

WTY-MVS		97.37 124	96.92 131	98.72 84	98.86 140	96.89 123	98.31 242	98.71 128	95.26 168	97.67 162	98.56 179	92.21 131	99.78 113	95.89 184	96.85 231	99.48 105

AstraMVS		97.34 125	97.24 112	97.65 188	98.13 223	94.15 263	98.94 99	96.25 400	97.47 47	98.60 100	99.28 65	89.67 194	99.41 194	98.73 38	98.07 195	99.38 122

jason		97.32 126	97.08 122	98.06 151	97.45 289	95.59 186	97.87 306	97.91 294	94.79 199	98.55 103	98.83 146	91.12 165	99.23 215	97.58 113	99.60 86	99.34 128
jason: jason.
MVS_Test		97.28 127	97.00 126	98.13 143	98.33 198	95.97 168	98.74 163	98.07 278	94.27 222	98.44 111	98.07 224	92.48 119	99.26 211	96.43 167	98.19 190	99.16 164

EPNet		97.28 127	96.87 133	98.51 103	94.98 400	96.14 160	98.90 108	97.02 371	98.28 17	95.99 233	99.11 97	91.36 157	99.89 59	96.98 138	99.19 137	99.50 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsmamba		97.25 129	96.99 127	98.02 153	98.34 195	95.54 191	99.18 48	97.47 330	95.04 181	98.15 120	98.57 178	89.46 201	99.31 207	97.68 107	99.01 145	99.22 151

test_yl		97.22 130	96.78 138	98.54 99	98.73 150	96.60 135	98.45 223	98.31 227	94.70 200	98.02 133	98.42 190	90.80 172	99.70 131	96.81 155	96.79 233	99.34 128

DCV-MVSNet		97.22 130	96.78 138	98.54 99	98.73 150	96.60 135	98.45 223	98.31 227	94.70 200	98.02 133	98.42 190	90.80 172	99.70 131	96.81 155	96.79 233	99.34 128

IS-MVSNet		97.22 130	96.88 132	98.25 131	98.85 143	96.36 150	99.19 44	97.97 288	95.39 159	97.23 178	98.99 120	91.11 166	98.93 262	94.60 231	98.59 169	99.47 107

PLC	95.07 4	97.20 133	96.78 138	98.44 114	99.29 78	96.31 154	98.14 268	98.76 116	92.41 315	96.39 221	98.31 205	94.92 82	99.78 113	94.06 254	98.77 161	99.23 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 134	97.18 117	97.20 210	98.81 146	93.27 297	95.78 410	99.15 35	95.25 169	96.79 202	98.11 222	92.29 126	99.07 240	98.56 49	99.85 6	99.25 147

LS3D		97.16 135	96.66 147	98.68 87	98.53 175	97.19 108	98.93 103	98.90 65	92.83 300	95.99 233	99.37 49	92.12 134	99.87 70	93.67 266	99.57 92	98.97 190

AdaColmap		97.15 136	96.70 143	98.48 109	99.16 105	96.69 131	98.01 285	98.89 67	94.44 218	96.83 197	98.68 165	90.69 175	99.76 119	94.36 239	99.29 132	98.98 189

mamv4		97.13 137	98.11 68	94.17 375	98.97 129	83.70 418	98.66 189	98.71 128	94.63 206	97.83 149	98.90 134	96.25 29	99.55 168	99.27 24	99.76 42	99.27 142

Effi-MVS+		97.12 138	96.69 144	98.39 121	98.19 215	96.72 130	97.37 346	98.43 205	93.71 255	97.65 166	98.02 228	92.20 132	99.25 212	96.87 151	97.79 204	99.19 158

CHOSEN 1792x2688		97.12 138	96.80 135	98.08 149	99.30 73	94.56 246	98.05 280	99.71 1	93.57 267	97.09 183	98.91 133	88.17 239	99.89 59	96.87 151	99.56 98	99.81 20

F-COLMAP		97.09 140	96.80 135	97.97 156	99.45 55	94.95 225	98.55 211	98.62 155	93.02 292	96.17 228	98.58 175	94.01 100	99.81 93	93.95 256	98.90 150	99.14 167

RRT-MVS		97.03 141	96.78 138	97.77 173	97.90 249	94.34 255	99.12 58	98.35 220	95.87 135	98.06 127	98.70 163	86.45 277	99.63 148	98.04 82	98.54 173	99.35 126

TAMVS		97.02 142	96.79 137	97.70 180	98.06 230	95.31 204	98.52 213	98.31 227	93.95 238	97.05 188	98.61 170	93.49 106	98.52 306	95.33 205	97.81 203	99.29 139

CDS-MVSNet		96.99 143	96.69 144	97.90 160	98.05 232	95.98 163	98.20 256	98.33 224	93.67 262	96.95 190	98.49 184	93.54 105	98.42 317	95.24 212	97.74 207	99.31 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 144	96.55 150	98.21 134	98.17 220	96.07 162	97.98 289	98.21 245	97.24 65	97.13 182	98.93 130	86.88 269	99.91 48	95.00 218	99.37 126	98.66 227

114514_t		96.93 145	96.27 160	98.92 71	99.50 42	97.63 76	98.85 130	98.90 65	84.80 414	97.77 151	99.11 97	92.84 114	99.66 141	94.85 221	99.77 36	99.47 107

MAR-MVS		96.91 146	96.40 156	98.45 112	98.69 158	96.90 121	98.66 189	98.68 137	92.40 316	97.07 186	97.96 235	91.54 154	99.75 121	93.68 264	98.92 149	98.69 221
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
HyFIR lowres test		96.90 147	96.49 153	98.14 140	99.33 64	95.56 188	97.38 344	99.65 2	92.34 317	97.61 169	98.20 216	89.29 208	99.10 237	96.97 139	97.60 212	99.77 32

Vis-MVSNet (Re-imp)		96.87 148	96.55 150	97.83 165	98.73 150	95.46 194	99.20 42	98.30 234	94.96 189	96.60 209	98.87 139	90.05 185	98.59 301	93.67 266	98.60 168	99.46 111

SDMVSNet		96.85 149	96.42 154	98.14 140	99.30 73	96.38 148	99.21 39	99.23 23	95.92 131	95.96 235	98.76 159	85.88 287	99.44 191	97.93 86	95.59 270	98.60 232

PAPR		96.84 150	96.24 162	98.65 90	98.72 154	96.92 120	97.36 348	98.57 169	93.33 276	96.67 204	97.57 276	94.30 94	99.56 161	91.05 337	98.59 169	99.47 107

HY-MVS	93.96 8	96.82 151	96.23 163	98.57 94	98.46 181	97.00 116	98.14 268	98.21 245	93.95 238	96.72 203	97.99 232	91.58 150	99.76 119	94.51 235	96.54 242	98.95 193

UGNet		96.78 152	96.30 159	98.19 139	98.24 207	95.89 178	98.88 119	98.93 58	97.39 52	96.81 200	97.84 248	82.60 340	99.90 56	96.53 163	99.49 109	98.79 205
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
PVSNet_BlendedMVS		96.73 153	96.60 148	97.12 219	99.25 86	95.35 201	98.26 250	99.26 15	94.28 221	97.94 141	97.46 283	92.74 116	99.81 93	96.88 148	93.32 306	96.20 376

test_vis1_n_1920		96.71 154	96.84 134	96.31 290	99.11 112	89.74 369	99.05 69	98.58 167	98.08 20	99.87 2	99.37 49	78.48 372	99.93 31	99.29 23	99.69 65	99.27 142

mvs_anonymous		96.70 155	96.53 152	97.18 213	98.19 215	93.78 273	98.31 242	98.19 249	94.01 234	94.47 267	98.27 210	92.08 137	98.46 312	97.39 127	97.91 199	99.31 134

ElysianMVS		96.64 156	96.02 170	98.51 103	98.04 234	97.30 95	98.74 163	98.60 156	95.04 181	97.91 145	98.84 142	83.59 335	99.48 184	94.20 247	99.25 133	98.75 214

StellarMVS		96.64 156	96.02 170	98.51 103	98.04 234	97.30 95	98.74 163	98.60 156	95.04 181	97.91 145	98.84 142	83.59 335	99.48 184	94.20 247	99.25 133	98.75 214

1112_ss		96.63 158	96.00 172	98.50 106	98.56 171	96.37 149	98.18 264	98.10 271	92.92 296	94.84 255	98.43 188	92.14 133	99.58 157	94.35 240	96.51 243	99.56 92

PMMVS		96.60 159	96.33 158	97.41 201	97.90 249	93.93 269	97.35 349	98.41 207	92.84 299	97.76 152	97.45 285	91.10 167	99.20 219	96.26 172	97.91 199	99.11 172

DP-MVS		96.59 160	95.93 175	98.57 94	99.34 62	96.19 158	98.70 178	98.39 211	89.45 386	94.52 265	99.35 55	91.85 143	99.85 75	92.89 290	98.88 152	99.68 67

PatchMatch-RL		96.59 160	96.03 169	98.27 127	99.31 69	96.51 141	97.91 298	99.06 41	93.72 254	96.92 194	98.06 225	88.50 234	99.65 142	91.77 319	99.00 147	98.66 227

GeoE		96.58 162	96.07 166	98.10 148	98.35 190	95.89 178	99.34 16	98.12 265	93.12 288	96.09 229	98.87 139	89.71 193	98.97 252	92.95 286	98.08 194	99.43 116

XVG-OURS		96.55 163	96.41 155	96.99 226	98.75 149	93.76 274	97.50 338	98.52 181	95.67 146	96.83 197	99.30 63	88.95 222	99.53 171	95.88 185	96.26 257	97.69 272

FIs		96.51 164	96.12 165	97.67 184	97.13 313	97.54 81	99.36 13	99.22 27	95.89 133	94.03 295	98.35 198	91.98 139	98.44 315	96.40 168	92.76 314	97.01 291

XVG-OURS-SEG-HR		96.51 164	96.34 157	97.02 225	98.77 148	93.76 274	97.79 317	98.50 189	95.45 155	96.94 191	99.09 106	87.87 250	99.55 168	96.76 159	95.83 269	97.74 269

PS-MVSNAJss		96.43 166	96.26 161	96.92 235	95.84 380	95.08 215	99.16 50	98.50 189	95.87 135	93.84 304	98.34 202	94.51 87	98.61 297	96.88 148	93.45 303	97.06 289

test_fmvs1		96.42 167	96.67 146	95.66 320	98.82 145	88.53 396	98.80 147	98.20 247	96.39 113	99.64 26	99.20 80	80.35 360	99.67 138	99.04 29	99.57 92	98.78 209

FC-MVSNet-test		96.42 167	96.05 167	97.53 195	96.95 322	97.27 99	99.36 13	99.23 23	95.83 137	93.93 298	98.37 196	92.00 138	98.32 336	96.02 181	92.72 315	97.00 292

ab-mvs		96.42 167	95.71 186	98.55 97	98.63 167	96.75 128	97.88 305	98.74 120	93.84 244	96.54 214	98.18 218	85.34 297	99.75 121	95.93 183	96.35 247	99.15 165

FA-MVS(test-final)		96.41 170	95.94 174	97.82 167	98.21 211	95.20 209	97.80 315	97.58 314	93.21 282	97.36 175	97.70 260	89.47 199	99.56 161	94.12 251	97.99 196	98.71 219

PVSNet	91.96 18	96.35 171	96.15 164	96.96 230	99.17 101	92.05 322	96.08 403	98.68 137	93.69 258	97.75 154	97.80 254	88.86 223	99.69 136	94.26 245	99.01 145	99.15 165

Test_1112_low_res		96.34 172	95.66 191	98.36 122	98.56 171	95.94 171	97.71 322	98.07 278	92.10 326	94.79 259	97.29 298	91.75 145	99.56 161	94.17 249	96.50 244	99.58 90

Effi-MVS+-dtu		96.29 173	96.56 149	95.51 325	97.89 251	90.22 362	98.80 147	98.10 271	96.57 105	96.45 219	96.66 355	90.81 171	98.91 265	95.72 192	97.99 196	97.40 280

QAPM		96.29 173	95.40 196	98.96 68	97.85 252	97.60 78	99.23 32	98.93 58	89.76 380	93.11 335	99.02 113	89.11 214	99.93 31	91.99 313	99.62 83	99.34 128

Fast-Effi-MVS+		96.28 175	95.70 188	98.03 152	98.29 204	95.97 168	98.58 202	98.25 242	91.74 334	95.29 248	97.23 303	91.03 169	99.15 225	92.90 288	97.96 198	98.97 190

nrg030		96.28 175	95.72 183	97.96 158	96.90 327	98.15 58	99.39 10	98.31 227	95.47 154	94.42 273	98.35 198	92.09 136	98.69 289	97.50 123	89.05 365	97.04 290

1314		96.25 177	95.73 182	97.79 169	97.13 313	95.55 190	98.19 259	98.59 162	93.47 271	92.03 364	97.82 252	91.33 159	99.49 179	94.62 230	98.44 179	98.32 252

sd_testset		96.17 178	95.76 181	97.42 200	99.30 73	94.34 255	98.82 138	99.08 39	95.92 131	95.96 235	98.76 159	82.83 339	99.32 204	95.56 198	95.59 270	98.60 232

h-mvs33		96.17 178	95.62 192	97.81 168	99.03 119	94.45 248	98.64 193	98.75 118	97.48 45	98.67 92	98.72 162	89.76 190	99.86 74	97.95 84	81.59 414	99.11 172

HQP_MVS		96.14 180	95.90 176	96.85 238	97.42 291	94.60 244	98.80 147	98.56 172	97.28 60	95.34 244	98.28 207	87.09 264	99.03 245	96.07 176	94.27 278	96.92 299

tttt0517		96.07 181	95.51 194	97.78 170	98.41 184	94.84 229	99.28 24	94.33 422	94.26 223	97.64 167	98.64 169	84.05 326	99.47 188	95.34 204	97.60 212	99.03 184

MVSTER		96.06 182	95.72 183	97.08 222	98.23 209	95.93 174	98.73 169	98.27 237	94.86 195	95.07 250	98.09 223	88.21 238	98.54 304	96.59 161	93.46 301	96.79 318

thisisatest0530		96.01 183	95.36 201	97.97 156	98.38 186	95.52 192	98.88 119	94.19 424	94.04 229	97.64 167	98.31 205	83.82 333	99.46 189	95.29 209	97.70 209	98.93 195

test_djsdf		96.00 184	95.69 189	96.93 232	95.72 382	95.49 193	99.47 7	98.40 209	94.98 187	94.58 263	97.86 245	89.16 212	98.41 324	96.91 142	94.12 286	96.88 308

EI-MVSNet		95.96 185	95.83 178	96.36 286	97.93 247	93.70 280	98.12 271	98.27 237	93.70 257	95.07 250	99.02 113	92.23 130	98.54 304	94.68 226	93.46 301	96.84 314

VortexMVS		95.95 186	95.79 179	96.42 282	98.29 204	93.96 268	98.68 182	98.31 227	96.02 128	94.29 280	97.57 276	89.47 199	98.37 331	97.51 122	91.93 322	96.94 297

ECVR-MVS		95.95 186	95.71 186	96.65 250	99.02 120	90.86 344	99.03 76	91.80 435	96.96 83	98.10 124	99.26 69	81.31 346	99.51 175	96.90 145	99.04 142	99.59 86

BH-untuned		95.95 186	95.72 183	96.65 250	98.55 173	92.26 316	98.23 252	97.79 299	93.73 252	94.62 262	98.01 230	88.97 221	99.00 251	93.04 283	98.51 175	98.68 223

test1111		95.94 189	95.78 180	96.41 283	98.99 126	90.12 363	99.04 73	92.45 434	96.99 82	98.03 131	99.27 68	81.40 345	99.48 184	96.87 151	99.04 142	99.63 80

MSDG		95.93 190	95.30 208	97.83 165	98.90 134	95.36 199	96.83 390	98.37 217	91.32 349	94.43 272	98.73 161	90.27 183	99.60 154	90.05 351	98.82 159	98.52 240

BH-RMVSNet		95.92 191	95.32 206	97.69 181	98.32 201	94.64 238	98.19 259	97.45 335	94.56 210	96.03 231	98.61 170	85.02 302	99.12 231	90.68 342	99.06 141	99.30 137

test_fmvs1_n		95.90 192	95.99 173	95.63 321	98.67 161	88.32 400	99.26 27	98.22 244	96.40 112	99.67 23	99.26 69	73.91 409	99.70 131	99.02 30	99.50 107	98.87 199

Fast-Effi-MVS+-dtu		95.87 193	95.85 177	95.91 307	97.74 261	91.74 328	98.69 180	98.15 261	95.56 150	94.92 253	97.68 265	88.98 220	98.79 283	93.19 278	97.78 205	97.20 287

LFMVS		95.86 194	94.98 223	98.47 110	98.87 139	96.32 152	98.84 134	96.02 401	93.40 274	98.62 98	99.20 80	74.99 403	99.63 148	97.72 100	97.20 219	99.46 111

baseline1		95.84 195	95.12 216	98.01 154	98.49 180	95.98 163	98.73 169	97.03 368	95.37 162	96.22 224	98.19 217	89.96 187	99.16 222	94.60 231	87.48 381	98.90 198

OpenMVS	93.04 13	95.83 196	95.00 221	98.32 124	97.18 310	97.32 92	99.21 39	98.97 50	89.96 376	91.14 373	99.05 111	86.64 272	99.92 38	93.38 272	99.47 112	97.73 270

VDD-MVS		95.82 197	95.23 210	97.61 191	98.84 144	93.98 267	98.68 182	97.40 339	95.02 185	97.95 139	99.34 59	74.37 408	99.78 113	98.64 43	96.80 232	99.08 178

UniMVSNet (Re)		95.78 198	95.19 212	97.58 192	96.99 320	97.47 85	98.79 155	99.18 31	95.60 148	93.92 299	97.04 325	91.68 147	98.48 308	95.80 189	87.66 380	96.79 318

VPA-MVSNet		95.75 199	95.11 217	97.69 181	97.24 302	97.27 99	98.94 99	99.23 23	95.13 174	95.51 242	97.32 296	85.73 289	98.91 265	97.33 130	89.55 356	96.89 307

HQP-MVS		95.72 200	95.40 196	96.69 248	97.20 306	94.25 260	98.05 280	98.46 197	96.43 109	94.45 268	97.73 257	86.75 270	98.96 256	95.30 207	94.18 282	96.86 313

hse-mvs2		95.71 201	95.30 208	96.93 232	98.50 176	93.53 285	98.36 234	98.10 271	97.48 45	98.67 92	97.99 232	89.76 190	99.02 248	97.95 84	80.91 419	98.22 255

UniMVSNet_NR-MVSNet		95.71 201	95.15 213	97.40 203	96.84 330	96.97 117	98.74 163	99.24 18	95.16 173	93.88 301	97.72 259	91.68 147	98.31 338	95.81 187	87.25 386	96.92 299

PatchmatchNet		95.71 201	95.52 193	96.29 292	97.58 274	90.72 348	96.84 389	97.52 325	94.06 228	97.08 184	96.96 335	89.24 210	98.90 268	92.03 312	98.37 183	99.26 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 204	95.33 205	96.76 243	96.16 366	94.63 239	98.43 229	98.39 211	96.64 101	95.02 252	98.78 151	85.15 301	99.05 241	95.21 214	94.20 281	96.60 341
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 204	95.38 200	96.61 258	97.61 271	93.84 272	98.91 107	98.44 201	95.25 169	94.28 281	98.47 186	86.04 286	99.12 231	95.50 201	93.95 291	96.87 311
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 206	95.69 189	95.44 329	97.54 279	88.54 395	96.97 375	97.56 317	93.50 269	97.52 173	96.93 339	89.49 197	99.16 222	95.25 211	96.42 246	98.64 229

FE-MVS		95.62 207	94.90 227	97.78 170	98.37 188	94.92 226	97.17 365	97.38 341	90.95 360	97.73 157	97.70 260	85.32 299	99.63 148	91.18 329	98.33 186	98.79 205

LPG-MVS_test		95.62 207	95.34 202	96.47 276	97.46 286	93.54 283	98.99 86	98.54 176	94.67 204	94.36 276	98.77 154	85.39 294	99.11 233	95.71 193	94.15 284	96.76 321

CLD-MVS		95.62 207	95.34 202	96.46 279	97.52 282	93.75 276	97.27 356	98.46 197	95.53 151	94.42 273	98.00 231	86.21 281	98.97 252	96.25 174	94.37 276	96.66 336
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 210	94.89 228	97.76 174	98.15 222	95.15 212	96.77 391	94.41 420	92.95 295	97.18 181	97.43 287	84.78 308	99.45 190	94.63 228	97.73 208	98.68 223

MonoMVSNet		95.51 211	95.45 195	95.68 318	95.54 387	90.87 343	98.92 105	97.37 342	95.79 139	95.53 241	97.38 292	89.58 196	97.68 382	96.40 168	92.59 316	98.49 242

thres600view7		95.49 212	94.77 231	97.67 184	98.98 127	95.02 217	98.85 130	96.90 378	95.38 160	96.63 206	96.90 341	84.29 318	99.59 155	88.65 375	96.33 248	98.40 246

test_vis1_n		95.47 213	95.13 214	96.49 273	97.77 257	90.41 358	99.27 26	98.11 268	96.58 103	99.66 24	99.18 86	67.00 423	99.62 152	99.21 25	99.40 122	99.44 114

SCA		95.46 214	95.13 214	96.46 279	97.67 266	91.29 336	97.33 351	97.60 313	94.68 203	96.92 194	97.10 310	83.97 328	98.89 269	92.59 296	98.32 188	99.20 154

IterMVS-LS		95.46 214	95.21 211	96.22 294	98.12 224	93.72 279	98.32 241	98.13 264	93.71 255	94.26 282	97.31 297	92.24 129	98.10 354	94.63 228	90.12 347	96.84 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-2		95.45 216	95.34 202	95.77 316	98.69 158	88.75 391	98.87 122	97.21 355	96.13 123	97.22 179	97.68 265	77.95 380	99.65 142	97.58 113	96.77 235	98.91 197

jajsoiax		95.45 216	95.03 220	96.73 244	95.42 395	94.63 239	99.14 54	98.52 181	95.74 141	93.22 328	98.36 197	83.87 331	98.65 294	96.95 141	94.04 287	96.91 304

CVMVSNet		95.43 218	96.04 168	93.57 381	97.93 247	83.62 419	98.12 271	98.59 162	95.68 145	96.56 210	99.02 113	87.51 256	97.51 391	93.56 270	97.44 215	99.60 84

anonymousdsp		95.42 219	94.91 226	96.94 231	95.10 399	95.90 177	99.14 54	98.41 207	93.75 249	93.16 331	97.46 283	87.50 258	98.41 324	95.63 197	94.03 288	96.50 360

DU-MVS		95.42 219	94.76 232	97.40 203	96.53 347	96.97 117	98.66 189	98.99 49	95.43 156	93.88 301	97.69 262	88.57 229	98.31 338	95.81 187	87.25 386	96.92 299

mvs_tets		95.41 221	95.00 221	96.65 250	95.58 386	94.42 250	99.00 83	98.55 174	95.73 143	93.21 329	98.38 195	83.45 337	98.63 295	97.09 135	94.00 289	96.91 304

thres100view900		95.38 222	94.70 236	97.41 201	98.98 127	94.92 226	98.87 122	96.90 378	95.38 160	96.61 208	96.88 342	84.29 318	99.56 161	88.11 378	96.29 252	97.76 267

thres400		95.38 222	94.62 240	97.65 188	98.94 132	94.98 222	98.68 182	96.93 376	95.33 163	96.55 212	96.53 361	84.23 322	99.56 161	88.11 378	96.29 252	98.40 246

BH-w/o		95.38 222	95.08 218	96.26 293	98.34 195	91.79 325	97.70 323	97.43 337	92.87 298	94.24 284	97.22 304	88.66 227	98.84 275	91.55 325	97.70 209	98.16 258

VDDNet		95.36 225	94.53 245	97.86 163	98.10 226	95.13 213	98.85 130	97.75 301	90.46 367	98.36 114	99.39 43	73.27 411	99.64 145	97.98 83	96.58 240	98.81 204

TAPA-MVS	93.98 7	95.35 226	94.56 244	97.74 176	99.13 109	94.83 231	98.33 237	98.64 150	86.62 402	96.29 223	98.61 170	94.00 101	99.29 209	80.00 420	99.41 119	99.09 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 227	94.98 223	96.43 281	97.67 266	93.48 287	98.73 169	98.44 201	94.94 193	92.53 351	98.53 180	84.50 317	99.14 227	95.48 202	94.00 289	96.66 336
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 228	94.87 229	96.71 245	99.29 78	93.24 301	98.58 202	98.11 268	89.92 377	93.57 313	99.10 99	86.37 279	99.79 110	90.78 340	98.10 193	97.09 288
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 229	94.72 235	97.13 217	98.05 232	93.26 298	97.87 306	97.20 356	94.96 189	96.18 227	95.66 394	80.97 352	99.35 200	94.47 237	97.08 222	98.78 209

tfpn200view9		95.32 229	94.62 240	97.43 199	98.94 132	94.98 222	98.68 182	96.93 376	95.33 163	96.55 212	96.53 361	84.23 322	99.56 161	88.11 378	96.29 252	97.76 267

Anonymous202405211		95.28 231	94.49 247	97.67 184	99.00 123	93.75 276	98.70 178	97.04 367	90.66 363	96.49 216	98.80 149	78.13 376	99.83 81	96.21 175	95.36 274	99.44 114

thres200		95.25 232	94.57 243	97.28 207	98.81 146	94.92 226	98.20 256	97.11 360	95.24 171	96.54 214	96.22 372	84.58 315	99.53 171	87.93 383	96.50 244	97.39 281

AllTest		95.24 233	94.65 239	96.99 226	99.25 86	93.21 302	98.59 200	98.18 252	91.36 345	93.52 315	98.77 154	84.67 312	99.72 125	89.70 358	97.87 201	98.02 262

LCM-MVSNet-Re		95.22 234	95.32 206	94.91 346	98.18 217	87.85 406	98.75 159	95.66 408	95.11 176	88.96 393	96.85 345	90.26 184	97.65 383	95.65 196	98.44 179	99.22 151

EPNet_dtu		95.21 235	94.95 225	95.99 302	96.17 364	90.45 356	98.16 266	97.27 350	96.77 91	93.14 334	98.33 203	90.34 180	98.42 317	85.57 396	98.81 160	99.09 174
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 236	94.45 252	97.46 196	96.75 337	96.56 139	98.86 126	98.65 149	93.30 279	93.27 327	98.27 210	84.85 306	98.87 272	94.82 223	91.26 333	96.96 294

D2MVS		95.18 237	95.08 218	95.48 326	97.10 315	92.07 321	98.30 244	99.13 37	94.02 231	92.90 339	96.73 351	89.48 198	98.73 287	94.48 236	93.60 300	95.65 390

WR-MVS		95.15 238	94.46 250	97.22 209	96.67 342	96.45 143	98.21 254	98.81 98	94.15 225	93.16 331	97.69 262	87.51 256	98.30 340	95.29 209	88.62 371	96.90 306

TranMVSNet+NR-MVSNet		95.14 239	94.48 248	97.11 220	96.45 353	96.36 150	99.03 76	99.03 44	95.04 181	93.58 312	97.93 238	88.27 237	98.03 360	94.13 250	86.90 391	96.95 296

myMVS_eth3d28		95.12 240	94.62 240	96.64 254	98.17 220	92.17 317	98.02 284	97.32 344	95.41 158	96.22 224	96.05 378	78.01 378	99.13 228	95.22 213	97.16 220	98.60 232

baseline2		95.11 241	94.52 246	96.87 237	96.65 343	93.56 282	98.27 249	94.10 426	93.45 272	92.02 365	97.43 287	87.45 261	99.19 220	93.88 259	97.41 217	97.87 265

miper_enhance_ethall		95.10 242	94.75 233	96.12 298	97.53 281	93.73 278	96.61 397	98.08 276	92.20 325	93.89 300	96.65 357	92.44 120	98.30 340	94.21 246	91.16 334	96.34 369

Anonymous20240529		95.10 242	94.22 262	97.75 175	99.01 122	94.26 259	98.87 122	98.83 89	85.79 410	96.64 205	98.97 121	78.73 369	99.85 75	96.27 171	94.89 275	99.12 169

test-LLR		95.10 242	94.87 229	95.80 313	96.77 334	89.70 371	96.91 380	95.21 412	95.11 176	94.83 257	95.72 391	87.71 252	98.97 252	93.06 281	98.50 176	98.72 216

WR-MVS_H		95.05 245	94.46 250	96.81 241	96.86 329	95.82 181	99.24 30	99.24 18	93.87 243	92.53 351	96.84 346	90.37 179	98.24 346	93.24 276	87.93 377	96.38 368

miper_ehance_all_eth		95.01 246	94.69 237	95.97 304	97.70 264	93.31 296	97.02 373	98.07 278	92.23 322	93.51 317	96.96 335	91.85 143	98.15 350	93.68 264	91.16 334	96.44 366

testing11		95.00 247	94.28 259	97.16 215	97.96 244	93.36 295	98.09 276	97.06 366	94.94 193	95.33 247	96.15 374	76.89 393	99.40 195	95.77 191	96.30 251	98.72 216

ADS-MVSNet		95.00 247	94.45 252	96.63 255	98.00 238	91.91 324	96.04 404	97.74 302	90.15 373	96.47 217	96.64 358	87.89 248	98.96 256	90.08 349	97.06 223	99.02 185

VPNet		94.99 249	94.19 264	97.40 203	97.16 311	96.57 138	98.71 174	98.97 50	95.67 146	94.84 255	98.24 214	80.36 359	98.67 293	96.46 165	87.32 385	96.96 294

EPMVS		94.99 249	94.48 248	96.52 271	97.22 304	91.75 327	97.23 357	91.66 436	94.11 226	97.28 176	96.81 348	85.70 290	98.84 275	93.04 283	97.28 218	98.97 190

testing91		94.98 251	94.25 261	97.20 210	97.94 245	93.41 290	98.00 287	97.58 314	94.99 186	95.45 243	96.04 379	77.20 388	99.42 193	94.97 219	96.02 265	98.78 209

NR-MVSNet		94.98 251	94.16 267	97.44 198	96.53 347	97.22 107	98.74 163	98.95 54	94.96 189	89.25 392	97.69 262	89.32 207	98.18 348	94.59 233	87.40 383	96.92 299

FMVSNet3		94.97 253	94.26 260	97.11 220	98.18 217	96.62 132	98.56 210	98.26 241	93.67 262	94.09 291	97.10 310	84.25 320	98.01 361	92.08 308	92.14 319	96.70 330

CostFormer		94.95 254	94.73 234	95.60 323	97.28 300	89.06 384	97.53 335	96.89 380	89.66 382	96.82 199	96.72 352	86.05 284	98.95 261	95.53 200	96.13 263	98.79 205

PAPM		94.95 254	94.00 280	97.78 170	97.04 317	95.65 185	96.03 406	98.25 242	91.23 354	94.19 287	97.80 254	91.27 162	98.86 274	82.61 413	97.61 211	98.84 202

CP-MVSNet		94.94 256	94.30 258	96.83 239	96.72 339	95.56 188	99.11 60	98.95 54	93.89 241	92.42 356	97.90 241	87.19 263	98.12 353	94.32 242	88.21 374	96.82 317

TR-MVS		94.94 256	94.20 263	97.17 214	97.75 258	94.14 264	97.59 332	97.02 371	92.28 321	95.75 239	97.64 270	83.88 330	98.96 256	89.77 355	96.15 262	98.40 246

RPSCF		94.87 258	95.40 196	93.26 387	98.89 135	82.06 425	98.33 237	98.06 283	90.30 372	96.56 210	99.26 69	87.09 264	99.49 179	93.82 261	96.32 249	98.24 253

testing99		94.83 259	94.08 272	97.07 223	97.94 245	93.13 304	98.10 275	97.17 358	94.86 195	95.34 244	96.00 383	76.31 396	99.40 195	95.08 216	95.90 266	98.68 223

GA-MVS		94.81 260	94.03 276	97.14 216	97.15 312	93.86 271	96.76 392	97.58 314	94.00 235	94.76 261	97.04 325	80.91 353	98.48 308	91.79 318	96.25 258	99.09 174

c3_l		94.79 261	94.43 254	95.89 309	97.75 258	93.12 306	97.16 367	98.03 285	92.23 322	93.46 321	97.05 324	91.39 156	98.01 361	93.58 269	89.21 363	96.53 352

V42		94.78 262	94.14 269	96.70 247	96.33 358	95.22 208	98.97 90	98.09 275	92.32 319	94.31 279	97.06 321	88.39 235	98.55 303	92.90 288	88.87 369	96.34 369

reproduce_monomvs		94.77 263	94.67 238	95.08 341	98.40 185	89.48 377	98.80 147	98.64 150	97.57 39	93.21 329	97.65 267	80.57 358	98.83 278	97.72 100	89.47 359	96.93 298

CR-MVSNet		94.76 264	94.15 268	96.59 261	97.00 318	93.43 288	94.96 417	97.56 317	92.46 310	96.93 192	96.24 368	88.15 240	97.88 374	87.38 385	96.65 238	98.46 244

v2v482		94.69 265	94.03 276	96.65 250	96.17 364	94.79 234	98.67 187	98.08 276	92.72 302	94.00 296	97.16 307	87.69 255	98.45 313	92.91 287	88.87 369	96.72 326

pmmvs4		94.69 265	93.99 282	96.81 241	95.74 381	95.94 171	97.40 342	97.67 306	90.42 369	93.37 324	97.59 274	89.08 215	98.20 347	92.97 285	91.67 327	96.30 372

cl22		94.68 267	94.19 264	96.13 297	98.11 225	93.60 281	96.94 377	98.31 227	92.43 314	93.32 326	96.87 344	86.51 273	98.28 344	94.10 253	91.16 334	96.51 358

eth_miper_zixun_eth		94.68 267	94.41 255	95.47 327	97.64 269	91.71 329	96.73 394	98.07 278	92.71 303	93.64 310	97.21 305	90.54 177	98.17 349	93.38 272	89.76 351	96.54 350

PCF-MVS	93.45 11	94.68 267	93.43 318	98.42 118	98.62 168	96.77 127	95.48 414	98.20 247	84.63 415	93.34 325	98.32 204	88.55 232	99.81 93	84.80 405	98.96 148	98.68 223
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 270	93.54 313	98.08 149	96.88 328	96.56 139	98.19 259	98.50 189	78.05 427	92.69 346	98.02 228	91.07 168	99.63 148	90.09 348	98.36 185	98.04 261

PS-CasMVS		94.67 270	93.99 282	96.71 245	96.68 341	95.26 205	99.13 57	99.03 44	93.68 260	92.33 357	97.95 236	85.35 296	98.10 354	93.59 268	88.16 376	96.79 318

cascas		94.63 272	93.86 292	96.93 232	96.91 326	94.27 258	96.00 407	98.51 184	85.55 411	94.54 264	96.23 370	84.20 324	98.87 272	95.80 189	96.98 228	97.66 273

tpmvs		94.60 273	94.36 257	95.33 333	97.46 286	88.60 394	96.88 386	97.68 303	91.29 351	93.80 306	96.42 365	88.58 228	99.24 214	91.06 335	96.04 264	98.17 257

LTVRE_ROB	92.95 15	94.60 273	93.90 288	96.68 249	97.41 294	94.42 250	98.52 213	98.59 162	91.69 337	91.21 372	98.35 198	84.87 305	99.04 244	91.06 335	93.44 304	96.60 341
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
v1144		94.59 275	93.92 285	96.60 260	96.21 360	94.78 235	98.59 200	98.14 263	91.86 333	94.21 286	97.02 328	87.97 246	98.41 324	91.72 320	89.57 354	96.61 340

ADS-MVSNet2		94.58 276	94.40 256	95.11 339	98.00 238	88.74 392	96.04 404	97.30 346	90.15 373	96.47 217	96.64 358	87.89 248	97.56 389	90.08 349	97.06 223	99.02 185

WBMVS		94.56 277	94.04 274	96.10 299	98.03 236	93.08 308	97.82 314	98.18 252	94.02 231	93.77 308	96.82 347	81.28 347	98.34 333	95.47 203	91.00 337	96.88 308

ACMH	92.88 16	94.55 278	93.95 284	96.34 288	97.63 270	93.26 298	98.81 146	98.49 194	93.43 273	89.74 386	98.53 180	81.91 342	99.08 239	93.69 263	93.30 307	96.70 330
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 279	93.85 293	96.63 255	97.98 242	93.06 309	98.77 158	97.84 297	93.67 262	93.80 306	98.04 227	76.88 394	98.96 256	94.79 225	92.86 312	97.86 266

XVG-ACMP-BASELINE		94.54 279	94.14 269	95.75 317	96.55 346	91.65 330	98.11 273	98.44 201	94.96 189	94.22 285	97.90 241	79.18 368	99.11 233	94.05 255	93.85 293	96.48 363

AUN-MVS		94.53 281	93.73 303	96.92 235	98.50 176	93.52 286	98.34 236	98.10 271	93.83 246	95.94 237	97.98 234	85.59 292	99.03 245	94.35 240	80.94 418	98.22 255

DIV-MVS_self_test		94.52 282	94.03 276	95.99 302	97.57 278	93.38 293	97.05 371	97.94 291	91.74 334	92.81 341	97.10 310	89.12 213	98.07 358	92.60 294	90.30 344	96.53 352

cl____		94.51 283	94.01 279	96.02 301	97.58 274	93.40 292	97.05 371	97.96 290	91.73 336	92.76 343	97.08 316	89.06 216	98.13 352	92.61 293	90.29 345	96.52 355

ETVMVS		94.50 284	93.44 317	97.68 183	98.18 217	95.35 201	98.19 259	97.11 360	93.73 252	96.40 220	95.39 397	74.53 405	98.84 275	91.10 331	96.31 250	98.84 202

GBi-Net		94.49 285	93.80 296	96.56 265	98.21 211	95.00 218	98.82 138	98.18 252	92.46 310	94.09 291	97.07 317	81.16 348	97.95 366	92.08 308	92.14 319	96.72 326

test1		94.49 285	93.80 296	96.56 265	98.21 211	95.00 218	98.82 138	98.18 252	92.46 310	94.09 291	97.07 317	81.16 348	97.95 366	92.08 308	92.14 319	96.72 326

dmvs_re		94.48 287	94.18 266	95.37 331	97.68 265	90.11 364	98.54 212	97.08 362	94.56 210	94.42 273	97.24 302	84.25 320	97.76 380	91.02 338	92.83 313	98.24 253

v8		94.47 288	93.77 299	96.57 264	96.36 356	94.83 231	99.05 69	98.19 249	91.92 330	93.16 331	96.97 333	88.82 226	98.48 308	91.69 321	87.79 378	96.39 367

FMVSNet2		94.47 288	93.61 309	97.04 224	98.21 211	96.43 145	98.79 155	98.27 237	92.46 310	93.50 318	97.09 314	81.16 348	98.00 363	91.09 332	91.93 322	96.70 330

test2506		94.44 290	93.91 287	96.04 300	99.02 120	88.99 387	99.06 67	79.47 448	96.96 83	98.36 114	99.26 69	77.21 387	99.52 174	96.78 158	99.04 142	99.59 86

Patchmatch-test		94.42 291	93.68 307	96.63 255	97.60 272	91.76 326	94.83 421	97.49 329	89.45 386	94.14 289	97.10 310	88.99 217	98.83 278	85.37 399	98.13 192	99.29 139

PEN-MVS		94.42 291	93.73 303	96.49 273	96.28 359	94.84 229	99.17 49	99.00 46	93.51 268	92.23 359	97.83 251	86.10 283	97.90 370	92.55 299	86.92 390	96.74 323

v144192		94.39 293	93.70 305	96.48 275	96.06 370	94.35 254	98.58 202	98.16 260	91.45 342	94.33 278	97.02 328	87.50 258	98.45 313	91.08 334	89.11 364	96.63 338

Baseline_NR-MVSNet		94.35 294	93.81 295	95.96 305	96.20 361	94.05 266	98.61 199	96.67 390	91.44 343	93.85 303	97.60 273	88.57 229	98.14 351	94.39 238	86.93 389	95.68 389

miper_lstm_enhance		94.33 295	94.07 273	95.11 339	97.75 258	90.97 340	97.22 358	98.03 285	91.67 338	92.76 343	96.97 333	90.03 186	97.78 379	92.51 301	89.64 353	96.56 347

v1192		94.32 296	93.58 310	96.53 270	96.10 368	94.45 248	98.50 219	98.17 258	91.54 340	94.19 287	97.06 321	86.95 268	98.43 316	90.14 347	89.57 354	96.70 330

UWE-MVS		94.30 297	93.89 290	95.53 324	97.83 253	88.95 388	97.52 337	93.25 428	94.44 218	96.63 206	97.07 317	78.70 370	99.28 210	91.99 313	97.56 214	98.36 249

ACMH+	92.99 14	94.30 297	93.77 299	95.88 310	97.81 255	92.04 323	98.71 174	98.37 217	93.99 236	90.60 379	98.47 186	80.86 355	99.05 241	92.75 292	92.40 318	96.55 349

v148		94.29 299	93.76 301	95.91 307	96.10 368	92.93 310	98.58 202	97.97 288	92.59 308	93.47 320	96.95 337	88.53 233	98.32 336	92.56 298	87.06 388	96.49 361

v10		94.29 299	93.55 312	96.51 272	96.39 355	94.80 233	98.99 86	98.19 249	91.35 347	93.02 337	96.99 331	88.09 242	98.41 324	90.50 344	88.41 373	96.33 371

MVP-Stereo		94.28 301	93.92 285	95.35 332	94.95 401	92.60 313	97.97 290	97.65 307	91.61 339	90.68 378	97.09 314	86.32 280	98.42 317	89.70 358	99.34 128	95.02 403
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 302	93.33 320	96.97 229	97.19 309	93.38 293	98.74 163	98.57 169	91.21 356	93.81 305	98.58 175	72.85 412	98.77 285	95.05 217	93.93 292	98.77 212

OurMVSNet-221017-0		94.21 303	94.00 280	94.85 351	95.60 385	89.22 382	98.89 112	97.43 337	95.29 166	92.18 361	98.52 183	82.86 338	98.59 301	93.46 271	91.76 325	96.74 323

v1921920		94.20 304	93.47 316	96.40 285	95.98 374	94.08 265	98.52 213	98.15 261	91.33 348	94.25 283	97.20 306	86.41 278	98.42 317	90.04 352	89.39 361	96.69 335

WB-MVSnew		94.19 305	94.04 274	94.66 359	96.82 332	92.14 318	97.86 308	95.96 404	93.50 269	95.64 240	96.77 350	88.06 244	97.99 364	84.87 402	96.86 229	93.85 420

v7n		94.19 305	93.43 318	96.47 276	95.90 377	94.38 253	99.26 27	98.34 223	91.99 328	92.76 343	97.13 309	88.31 236	98.52 306	89.48 363	87.70 379	96.52 355

tpm2		94.19 305	93.76 301	95.46 328	97.23 303	89.04 385	97.31 353	96.85 384	87.08 401	96.21 226	96.79 349	83.75 334	98.74 286	92.43 304	96.23 260	98.59 235

TESTMET0.1,1		94.18 308	93.69 306	95.63 321	96.92 324	89.12 383	96.91 380	94.78 417	93.17 284	94.88 254	96.45 364	78.52 371	98.92 263	93.09 280	98.50 176	98.85 200

dp		94.15 309	93.90 288	94.90 347	97.31 299	86.82 411	96.97 375	97.19 357	91.22 355	96.02 232	96.61 360	85.51 293	99.02 248	90.00 353	94.30 277	98.85 200

ET-MVSNet_ETH3D		94.13 310	92.98 328	97.58 192	98.22 210	96.20 156	97.31 353	95.37 411	94.53 212	79.56 429	97.63 272	86.51 273	97.53 390	96.91 142	90.74 339	99.02 185

tpm		94.13 310	93.80 296	95.12 338	96.50 349	87.91 405	97.44 339	95.89 407	92.62 306	96.37 222	96.30 367	84.13 325	98.30 340	93.24 276	91.66 328	99.14 167

testing222		94.12 312	93.03 327	97.37 206	98.02 237	94.66 236	97.94 294	96.65 392	94.63 206	95.78 238	95.76 386	71.49 413	98.92 263	91.17 330	95.88 267	98.52 240

IterMVS-SCA-FT		94.11 313	93.87 291	94.85 351	97.98 242	90.56 355	97.18 363	98.11 268	93.75 249	92.58 349	97.48 282	83.97 328	97.41 393	92.48 303	91.30 331	96.58 343

Anonymous20231211		94.10 314	93.26 323	96.61 258	99.11 112	94.28 257	99.01 81	98.88 70	86.43 404	92.81 341	97.57 276	81.66 344	98.68 292	94.83 222	89.02 367	96.88 308

IterMVS		94.09 315	93.85 293	94.80 355	97.99 240	90.35 360	97.18 363	98.12 265	93.68 260	92.46 355	97.34 293	84.05 326	97.41 393	92.51 301	91.33 330	96.62 339
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 316	93.51 314	95.80 313	96.77 334	89.70 371	96.91 380	95.21 412	92.89 297	94.83 257	95.72 391	77.69 382	98.97 252	93.06 281	98.50 176	98.72 216

test0.0.03 1		94.08 316	93.51 314	95.80 313	95.53 389	92.89 311	97.38 344	95.97 403	95.11 176	92.51 353	96.66 355	87.71 252	96.94 400	87.03 387	93.67 296	97.57 277

v1240		94.06 318	93.29 322	96.34 288	96.03 372	93.90 270	98.44 227	98.17 258	91.18 357	94.13 290	97.01 330	86.05 284	98.42 317	89.13 369	89.50 358	96.70 330

X-MVStestdata		94.06 318	92.30 344	99.34 26	99.70 22	98.35 44	99.29 22	98.88 70	97.40 50	98.46 106	43.50 443	95.90 45	99.89 59	97.85 92	99.74 52	99.78 26

DTE-MVSNet		93.98 320	93.26 323	96.14 296	96.06 370	94.39 252	99.20 42	98.86 83	93.06 290	91.78 366	97.81 253	85.87 288	97.58 388	90.53 343	86.17 395	96.46 365

pm-mvs1		93.94 321	93.06 326	96.59 261	96.49 350	95.16 210	98.95 96	98.03 285	92.32 319	91.08 374	97.84 248	84.54 316	98.41 324	92.16 306	86.13 398	96.19 377

MS-PatchMatch		93.84 322	93.63 308	94.46 369	96.18 363	89.45 378	97.76 318	98.27 237	92.23 322	92.13 362	97.49 281	79.50 365	98.69 289	89.75 356	99.38 124	95.25 395

tfpnnormal		93.66 323	92.70 334	96.55 269	96.94 323	95.94 171	98.97 90	99.19 30	91.04 358	91.38 371	97.34 293	84.94 304	98.61 297	85.45 398	89.02 367	95.11 399

EU-MVSNet		93.66 323	94.14 269	92.25 397	95.96 376	83.38 421	98.52 213	98.12 265	94.69 202	92.61 348	98.13 221	87.36 262	96.39 413	91.82 317	90.00 349	96.98 293

our_test_3		93.65 325	93.30 321	94.69 357	95.45 393	89.68 373	96.91 380	97.65 307	91.97 329	91.66 369	96.88 342	89.67 194	97.93 369	88.02 381	91.49 329	96.48 363

pmmvs5		93.65 325	92.97 329	95.68 318	95.49 390	92.37 314	98.20 256	97.28 349	89.66 382	92.58 349	97.26 299	82.14 341	98.09 356	93.18 279	90.95 338	96.58 343

SSC-MVS3.2		93.59 327	93.13 325	94.97 344	96.81 333	89.71 370	97.95 291	98.49 194	94.59 209	93.50 318	96.91 340	77.74 381	98.37 331	91.69 321	90.47 342	96.83 316

test_fmvs2		93.43 328	93.58 310	92.95 391	96.97 321	83.91 417	99.19 44	97.24 352	95.74 141	95.20 249	98.27 210	69.65 415	98.72 288	96.26 172	93.73 295	96.24 374

tpm cat1		93.36 329	92.80 331	95.07 342	97.58 274	87.97 404	96.76 392	97.86 296	82.17 422	93.53 314	96.04 379	86.13 282	99.13 228	89.24 367	95.87 268	98.10 260

JIA-IIPM		93.35 330	92.49 340	95.92 306	96.48 351	90.65 350	95.01 416	96.96 374	85.93 408	96.08 230	87.33 433	87.70 254	98.78 284	91.35 327	95.58 272	98.34 250

SixPastTwentyTwo		93.34 331	92.86 330	94.75 356	95.67 383	89.41 380	98.75 159	96.67 390	93.89 241	90.15 384	98.25 213	80.87 354	98.27 345	90.90 339	90.64 340	96.57 345

USDC		93.33 332	92.71 333	95.21 335	96.83 331	90.83 346	96.91 380	97.50 327	93.84 244	90.72 377	98.14 220	77.69 382	98.82 280	89.51 362	93.21 309	95.97 383

IB-MVS	91.98 17	93.27 333	91.97 348	97.19 212	97.47 285	93.41 290	97.09 370	95.99 402	93.32 277	92.47 354	95.73 389	78.06 377	99.53 171	94.59 233	82.98 409	98.62 230
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MIMVSNet		93.26 334	92.21 345	96.41 283	97.73 262	93.13 304	95.65 411	97.03 368	91.27 353	94.04 294	96.06 377	75.33 401	97.19 396	86.56 389	96.23 260	98.92 196

ppachtmachnet_test		93.22 335	92.63 335	94.97 344	95.45 393	90.84 345	96.88 386	97.88 295	90.60 364	92.08 363	97.26 299	88.08 243	97.86 375	85.12 401	90.33 343	96.22 375

Patchmtry		93.22 335	92.35 343	95.84 312	96.77 334	93.09 307	94.66 424	97.56 317	87.37 400	92.90 339	96.24 368	88.15 240	97.90 370	87.37 386	90.10 348	96.53 352

testing3		93.19 337	92.48 341	95.30 334	98.07 227	92.27 315	98.64 193	97.17 358	93.94 240	93.98 297	97.04 325	67.97 420	96.01 417	88.40 376	97.14 221	97.63 274

FMVSNet1		93.19 337	92.07 346	96.56 265	97.54 279	95.00 218	98.82 138	98.18 252	90.38 370	92.27 358	97.07 317	73.68 410	97.95 366	89.36 365	91.30 331	96.72 326

LF4IMVS		93.14 339	92.79 332	94.20 373	95.88 378	88.67 393	97.66 326	97.07 364	93.81 247	91.71 367	97.65 267	77.96 379	98.81 281	91.47 326	91.92 324	95.12 398

mmtdpeth		93.12 340	92.61 336	94.63 361	97.60 272	89.68 373	99.21 39	97.32 344	94.02 231	97.72 158	94.42 408	77.01 392	99.44 191	99.05 28	77.18 430	94.78 408

testgi		93.06 341	92.45 342	94.88 349	96.43 354	89.90 365	98.75 159	97.54 323	95.60 148	91.63 370	97.91 240	74.46 407	97.02 398	86.10 392	93.67 296	97.72 271

PatchT		93.06 341	91.97 348	96.35 287	96.69 340	92.67 312	94.48 427	97.08 362	86.62 402	97.08 184	92.23 427	87.94 247	97.90 370	78.89 424	96.69 236	98.49 242

RPMNet		92.81 343	91.34 354	97.24 208	97.00 318	93.43 288	94.96 417	98.80 105	82.27 421	96.93 192	92.12 428	86.98 267	99.82 88	76.32 429	96.65 238	98.46 244

UWE-MVS-28		92.79 344	92.51 339	93.62 380	96.46 352	86.28 412	97.93 295	92.71 433	94.17 224	94.78 260	97.16 307	81.05 351	96.43 412	81.45 416	96.86 229	98.14 259

myMVS_eth3d		92.73 345	92.01 347	94.89 348	97.39 295	90.94 341	97.91 298	97.46 331	93.16 285	93.42 322	95.37 398	68.09 419	96.12 415	88.34 377	96.99 225	97.60 275

TransMVSNet (Re)		92.67 346	91.51 353	96.15 295	96.58 345	94.65 237	98.90 108	96.73 386	90.86 361	89.46 391	97.86 245	85.62 291	98.09 356	86.45 390	81.12 416	95.71 388

ttmdpeth		92.61 347	91.96 350	94.55 363	94.10 411	90.60 354	98.52 213	97.29 347	92.67 304	90.18 382	97.92 239	79.75 364	97.79 377	91.09 332	86.15 397	95.26 394

Syy-MVS		92.55 348	92.61 336	92.38 394	97.39 295	83.41 420	97.91 298	97.46 331	93.16 285	93.42 322	95.37 398	84.75 309	96.12 415	77.00 428	96.99 225	97.60 275

K. test v3		92.55 348	91.91 351	94.48 367	95.64 384	89.24 381	99.07 66	94.88 416	94.04 229	86.78 408	97.59 274	77.64 385	97.64 384	92.08 308	89.43 360	96.57 345

DSMNet-mixed		92.52 350	92.58 338	92.33 395	94.15 410	82.65 423	98.30 244	94.26 423	89.08 391	92.65 347	95.73 389	85.01 303	95.76 419	86.24 391	97.76 206	98.59 235

TinyColmap		92.31 351	91.53 352	94.65 360	96.92 324	89.75 368	96.92 378	96.68 389	90.45 368	89.62 388	97.85 247	76.06 399	98.81 281	86.74 388	92.51 317	95.41 392

gg-mvs-nofinetune		92.21 352	90.58 360	97.13 217	96.75 337	95.09 214	95.85 408	89.40 441	85.43 412	94.50 266	81.98 436	80.80 356	98.40 330	92.16 306	98.33 186	97.88 264

FMVSNet5		91.81 353	90.92 356	94.49 366	97.21 305	92.09 320	98.00 287	97.55 322	89.31 389	90.86 376	95.61 395	74.48 406	95.32 423	85.57 396	89.70 352	96.07 381

pmmvs6		91.77 354	90.63 359	95.17 337	94.69 407	91.24 337	98.67 187	97.92 293	86.14 406	89.62 388	97.56 279	75.79 400	98.34 333	90.75 341	84.56 402	95.94 384

Anonymous20231206		91.66 355	91.10 355	93.33 385	94.02 415	87.35 408	98.58 202	97.26 351	90.48 366	90.16 383	96.31 366	83.83 332	96.53 410	79.36 422	89.90 350	96.12 379

Patchmatch-RL test		91.49 356	90.85 357	93.41 383	91.37 426	84.40 415	92.81 431	95.93 406	91.87 332	87.25 404	94.87 404	88.99 217	96.53 410	92.54 300	82.00 411	99.30 137

test_0402		91.32 357	90.27 363	94.48 367	96.60 344	91.12 338	98.50 219	97.22 353	86.10 407	88.30 400	96.98 332	77.65 384	97.99 364	78.13 426	92.94 311	94.34 409

test_vis1_rt		91.29 358	90.65 358	93.19 389	97.45 289	86.25 413	98.57 209	90.90 439	93.30 279	86.94 407	93.59 417	62.07 431	99.11 233	97.48 124	95.58 272	94.22 412

PVSNet_0	88.72 19	91.28 359	90.03 366	95.00 343	97.99 240	87.29 409	94.84 420	98.50 189	92.06 327	89.86 385	95.19 400	79.81 363	99.39 198	92.27 305	69.79 436	98.33 251

mvs5depth		91.23 360	90.17 364	94.41 371	92.09 423	89.79 367	95.26 415	96.50 394	90.73 362	91.69 368	97.06 321	76.12 398	98.62 296	88.02 381	84.11 405	94.82 405

Anonymous20240521		91.18 361	90.44 361	93.42 382	93.70 416	88.47 397	98.94 99	97.56 317	88.46 395	89.56 390	95.08 403	77.15 390	96.97 399	83.92 408	89.55 356	94.82 405

EG-PatchMatch MVS		91.13 362	90.12 365	94.17 375	94.73 406	89.00 386	98.13 270	97.81 298	89.22 390	85.32 418	96.46 363	67.71 421	98.42 317	87.89 384	93.82 294	95.08 400

TDRefinement		91.06 363	89.68 368	95.21 335	85.35 441	91.49 333	98.51 218	97.07 364	91.47 341	88.83 397	97.84 248	77.31 386	99.09 238	92.79 291	77.98 428	95.04 402

sc_t1		91.01 364	89.39 370	95.85 311	95.99 373	90.39 359	98.43 229	97.64 309	78.79 425	92.20 360	97.94 237	66.00 425	98.60 300	91.59 324	85.94 399	98.57 238

UnsupCasMVSNet_eth		90.99 365	89.92 367	94.19 374	94.08 412	89.83 366	97.13 369	98.67 142	93.69 258	85.83 414	96.19 373	75.15 402	96.74 404	89.14 368	79.41 423	96.00 382

test20.03		90.89 366	90.38 362	92.43 393	93.48 417	88.14 403	98.33 237	97.56 317	93.40 274	87.96 401	96.71 353	80.69 357	94.13 428	79.15 423	86.17 395	95.01 404

MDA-MVSNet_test_wron		90.71 367	89.38 372	94.68 358	94.83 403	90.78 347	97.19 362	97.46 331	87.60 398	72.41 436	95.72 391	86.51 273	96.71 407	85.92 394	86.80 392	96.56 347

YYNet1		90.70 368	89.39 370	94.62 362	94.79 405	90.65 350	97.20 360	97.46 331	87.54 399	72.54 435	95.74 387	86.51 273	96.66 408	86.00 393	86.76 393	96.54 350

KD-MVS_self_test		90.38 369	89.38 372	93.40 384	92.85 420	88.94 389	97.95 291	97.94 291	90.35 371	90.25 381	93.96 414	79.82 362	95.94 418	84.62 407	76.69 431	95.33 393

pmmvs-eth3d		90.36 370	89.05 375	94.32 372	91.10 428	92.12 319	97.63 331	96.95 375	88.86 393	84.91 419	93.13 422	78.32 373	96.74 404	88.70 373	81.81 413	94.09 415

tt0320		90.26 371	88.73 378	94.86 350	96.12 367	90.62 352	98.17 265	97.63 310	77.46 428	89.68 387	96.04 379	69.19 417	97.79 377	88.98 370	85.29 401	96.16 378

CL-MVSNet_self_test		90.11 372	89.14 374	93.02 390	91.86 425	88.23 402	96.51 400	98.07 278	90.49 365	90.49 380	94.41 409	84.75 309	95.34 422	80.79 418	74.95 433	95.50 391

new_pmnet		90.06 373	89.00 376	93.22 388	94.18 409	88.32 400	96.42 402	96.89 380	86.19 405	85.67 415	93.62 416	77.18 389	97.10 397	81.61 415	89.29 362	94.23 411

MDA-MVSNet-bldmvs		89.97 374	88.35 380	94.83 354	95.21 397	91.34 334	97.64 328	97.51 326	88.36 396	71.17 437	96.13 375	79.22 367	96.63 409	83.65 409	86.27 394	96.52 355

tt0320-xc		89.79 375	88.11 382	94.84 353	96.19 362	90.61 353	98.16 266	97.22 353	77.35 429	88.75 398	96.70 354	65.94 426	97.63 385	89.31 366	83.39 407	96.28 373

CMPMVS	66.06 21	89.70 376	89.67 369	89.78 402	93.19 418	76.56 428	97.00 374	98.35 220	80.97 423	81.57 424	97.75 256	74.75 404	98.61 297	89.85 354	93.63 298	94.17 413
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 377	88.28 381	93.82 378	92.81 421	91.08 339	98.01 285	97.45 335	87.95 397	87.90 402	95.87 385	67.63 422	94.56 427	78.73 425	88.18 375	95.83 386

KD-MVS_2432*1600		89.61 378	87.96 386	94.54 364	94.06 413	91.59 331	95.59 412	97.63 310	89.87 378	88.95 394	94.38 411	78.28 374	96.82 402	84.83 403	68.05 437	95.21 396

miper_refine_blended		89.61 378	87.96 386	94.54 364	94.06 413	91.59 331	95.59 412	97.63 310	89.87 378	88.95 394	94.38 411	78.28 374	96.82 402	84.83 403	68.05 437	95.21 396

MVStest1		89.53 380	87.99 385	94.14 377	94.39 408	90.42 357	98.25 251	96.84 385	82.81 418	81.18 426	97.33 295	77.09 391	96.94 400	85.27 400	78.79 424	95.06 401

MVS-HIRNet		89.46 381	88.40 379	92.64 392	97.58 274	82.15 424	94.16 430	93.05 432	75.73 432	90.90 375	82.52 435	79.42 366	98.33 335	83.53 410	98.68 162	97.43 278

OpenMVS_ROB	86.42 20	89.00 382	87.43 390	93.69 379	93.08 419	89.42 379	97.91 298	96.89 380	78.58 426	85.86 413	94.69 405	69.48 416	98.29 343	77.13 427	93.29 308	93.36 422

mvsany_test3		88.80 383	88.04 383	91.09 401	89.78 431	81.57 426	97.83 313	95.49 410	93.81 247	87.53 403	93.95 415	56.14 434	97.43 392	94.68 226	83.13 408	94.26 410

new-patchmatchnet		88.50 384	87.45 389	91.67 399	90.31 430	85.89 414	97.16 367	97.33 343	89.47 385	83.63 421	92.77 424	76.38 395	95.06 425	82.70 412	77.29 429	94.06 417

APD_test1		88.22 385	88.01 384	88.86 404	95.98 374	74.66 436	97.21 359	96.44 396	83.96 417	86.66 410	97.90 241	60.95 432	97.84 376	82.73 411	90.23 346	94.09 415

PM-MVS		87.77 386	86.55 392	91.40 400	91.03 429	83.36 422	96.92 378	95.18 414	91.28 352	86.48 412	93.42 418	53.27 435	96.74 404	89.43 364	81.97 412	94.11 414

dmvs_testset		87.64 387	88.93 377	83.79 413	95.25 396	63.36 445	97.20 360	91.17 437	93.07 289	85.64 416	95.98 384	85.30 300	91.52 435	69.42 434	87.33 384	96.49 361

test_fmvs3		87.17 388	87.06 391	87.50 406	91.21 427	75.66 431	99.05 69	96.61 393	92.79 301	88.85 396	92.78 423	43.72 438	93.49 429	93.95 256	84.56 402	93.34 423

UnsupCasMVSNet_bld		87.17 388	85.12 395	93.31 386	91.94 424	88.77 390	94.92 419	98.30 234	84.30 416	82.30 422	90.04 430	63.96 429	97.25 395	85.85 395	74.47 435	93.93 419

N_pmnet		87.12 390	87.77 388	85.17 410	95.46 392	61.92 446	97.37 346	70.66 451	85.83 409	88.73 399	96.04 379	85.33 298	97.76 380	80.02 419	90.48 341	95.84 385

pmmvs3		86.67 391	84.86 396	92.11 398	88.16 435	87.19 410	96.63 396	94.75 418	79.88 424	87.22 405	92.75 425	66.56 424	95.20 424	81.24 417	76.56 432	93.96 418

test_f		86.07 392	85.39 393	88.10 405	89.28 433	75.57 432	97.73 321	96.33 398	89.41 388	85.35 417	91.56 429	43.31 440	95.53 420	91.32 328	84.23 404	93.21 424

WB-MVS		84.86 393	85.33 394	83.46 414	89.48 432	69.56 440	98.19 259	96.42 397	89.55 384	81.79 423	94.67 406	84.80 307	90.12 436	52.44 440	80.64 420	90.69 427

SSC-MVS		84.27 394	84.71 397	82.96 418	89.19 434	68.83 441	98.08 277	96.30 399	89.04 392	81.37 425	94.47 407	84.60 314	89.89 437	49.80 442	79.52 422	90.15 428

dongtai		82.47 395	81.88 398	84.22 412	95.19 398	76.03 429	94.59 426	74.14 450	82.63 419	87.19 406	96.09 376	64.10 428	87.85 440	58.91 438	84.11 405	88.78 432

test_vis3_rt		79.22 396	77.40 403	84.67 411	86.44 439	74.85 435	97.66 326	81.43 446	84.98 413	67.12 439	81.91 437	28.09 448	97.60 386	88.96 371	80.04 421	81.55 437

test_method		79.03 397	78.17 399	81.63 419	86.06 440	54.40 451	82.75 439	96.89 380	39.54 443	80.98 427	95.57 396	58.37 433	94.73 426	84.74 406	78.61 425	95.75 387

testf1		79.02 398	77.70 400	82.99 416	88.10 436	66.90 442	94.67 422	93.11 429	71.08 434	74.02 432	93.41 419	34.15 444	93.25 430	72.25 432	78.50 426	88.82 430

APD_test2		79.02 398	77.70 400	82.99 416	88.10 436	66.90 442	94.67 422	93.11 429	71.08 434	74.02 432	93.41 419	34.15 444	93.25 430	72.25 432	78.50 426	88.82 430

LCM-MVSNet		78.70 400	76.24 406	86.08 408	77.26 447	71.99 438	94.34 428	96.72 387	61.62 438	76.53 430	89.33 431	33.91 446	92.78 433	81.85 414	74.60 434	93.46 421

kuosan		78.45 401	77.69 402	80.72 420	92.73 422	75.32 433	94.63 425	74.51 449	75.96 430	80.87 428	93.19 421	63.23 430	79.99 444	42.56 444	81.56 415	86.85 436

Gipuma		78.40 402	76.75 405	83.38 415	95.54 387	80.43 427	79.42 440	97.40 339	64.67 437	73.46 434	80.82 438	45.65 437	93.14 432	66.32 436	87.43 382	76.56 440
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 403	75.44 407	85.46 409	82.54 442	74.95 434	94.23 429	93.08 431	72.80 433	74.68 431	87.38 432	36.36 443	91.56 434	73.95 430	63.94 439	89.87 429

FPMVS		77.62 404	77.14 404	79.05 422	79.25 445	60.97 447	95.79 409	95.94 405	65.96 436	67.93 438	94.40 410	37.73 442	88.88 439	68.83 435	88.46 372	87.29 433

EGC-MVSNET		75.22 405	69.54 408	92.28 396	94.81 404	89.58 375	97.64 328	96.50 394	1.82 448	5.57 449	95.74 387	68.21 418	96.26 414	73.80 431	91.71 326	90.99 426

ANet_high		69.08 406	65.37 410	80.22 421	65.99 449	71.96 439	90.91 435	90.09 440	82.62 420	49.93 444	78.39 439	29.36 447	81.75 441	62.49 437	38.52 443	86.95 435

tmp_tt		68.90 407	66.97 409	74.68 424	50.78 451	59.95 448	87.13 436	83.47 445	38.80 444	62.21 440	96.23 370	64.70 427	76.91 446	88.91 372	30.49 444	87.19 434

PMVS	61.03 23	65.95 408	63.57 412	73.09 425	57.90 450	51.22 452	85.05 438	93.93 427	54.45 439	44.32 445	83.57 434	13.22 449	89.15 438	58.68 439	81.00 417	78.91 439
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 409	64.25 411	67.02 426	82.28 443	59.36 449	91.83 434	85.63 443	52.69 440	60.22 441	77.28 440	41.06 441	80.12 443	46.15 443	41.14 441	61.57 442

EMVS		64.07 410	63.26 413	66.53 427	81.73 444	58.81 450	91.85 433	84.75 444	51.93 442	59.09 442	75.13 441	43.32 439	79.09 445	42.03 445	39.47 442	61.69 441

MVE	62.14 22	63.28 411	59.38 414	74.99 423	74.33 448	65.47 444	85.55 437	80.50 447	52.02 441	51.10 443	75.00 442	10.91 452	80.50 442	51.60 441	53.40 440	78.99 438
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 412	30.18 416	30.16 428	78.61 446	43.29 453	66.79 441	14.21 452	17.31 445	14.82 448	11.93 448	11.55 451	41.43 447	37.08 446	19.30 445	5.76 445

cdsmvs_eth3d_5k		23.98 413	31.98 415	0.00 431	0.00 454	0.00 456	0.00 442	98.59 162	0.00 449	0.00 450	98.61 170	90.60 176	0.00 450	0.00 449	0.00 448	0.00 446

testmvs		21.48 414	24.95 417	11.09 430	14.89 452	6.47 455	96.56 398	9.87 453	7.55 446	17.93 446	39.02 444	9.43 453	5.90 449	16.56 448	12.72 446	20.91 444

test123		20.95 415	23.72 418	12.64 429	13.54 453	8.19 454	96.55 399	6.13 454	7.48 447	16.74 447	37.98 445	12.97 450	6.05 448	16.69 447	5.43 447	23.68 443

ab-mvs-re		8.20 416	10.94 419	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	98.43 188	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

pcd_1.5k_mvsjas		7.88 417	10.50 420	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	94.51 87	0.00 450	0.00 449	0.00 448	0.00 446

mmdepth		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

monomultidepth		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

test_blank		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

uanet_test		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

DCPMVS		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

sosnet-low-res		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

sosnet		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

uncertanet		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

Regformer		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

uanet		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

WAC-MVS						90.94 341								88.66 374

FOURS1					99.82 1	98.66 24	99.69 1	98.95 54	97.46 48	99.39 39

MSC_two_6792asdad				99.62 6	99.17 101	99.08 11		98.63 153					99.94 12	98.53 50	99.80 24	99.86 9

PC_three_1452									95.08 180	99.60 28	99.16 90	97.86 2	98.47 311	97.52 121	99.72 60	99.74 42

No_MVS				99.62 6	99.17 101	99.08 11		98.63 153					99.94 12	98.53 50	99.80 24	99.86 9

test_one_0601					99.66 26	99.25 2		98.86 83	97.55 40	99.20 51	99.47 31	97.57 6

eth-test2					0.00 454

eth-test					0.00 454

ZD-MVS					99.46 52	98.70 23		98.79 110	93.21 282	98.67 92	98.97 121	95.70 49	99.83 81	96.07 176	99.58 91

RE-MVS-def			98.34 46		99.49 46	97.86 69	99.11 60	98.80 105	96.49 107	99.17 54	99.35 55	95.29 65		97.72 100	99.65 74	99.71 55

IU-MVS					99.71 19	99.23 7		98.64 150	95.28 167	99.63 27				98.35 67	99.81 15	99.83 14

OPU-MVS				99.37 22	99.24 93	99.05 14	99.02 79				99.16 90	97.81 3	99.37 199	97.24 131	99.73 55	99.70 59

test_241102_TWO								98.87 77	97.65 33	99.53 32	99.48 29	97.34 11	99.94 12	98.43 62	99.80 24	99.83 14

test_241102_ONE					99.71 19	99.24 5		98.87 77	97.62 35	99.73 18	99.39 43	97.53 7	99.74 123

9.14			98.06 71		99.47 50		98.71 174	98.82 92	94.36 220	99.16 57	99.29 64	96.05 37	99.81 93	97.00 137	99.71 62

save fliter					99.46 52	98.38 35	98.21 254	98.71 128	97.95 24

test_0728_THIRD									97.32 56	99.45 34	99.46 35	97.88 1	99.94 12	98.47 58	99.86 2	99.85 11

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 90	98.88 70					99.94 12	98.47 58	99.81 15	99.84 13

test0726					99.72 12	99.25 2	99.06 67	98.88 70	97.62 35	99.56 29	99.50 25	97.42 9

GSMVS																99.20 154

test_part2					99.63 29	99.18 10				99.27 48

sam_mvs1												89.45 202				99.20 154

sam_mvs												88.99 217

ambc				89.49 403	86.66 438	75.78 430	92.66 432	96.72 387		86.55 411	92.50 426	46.01 436	97.90 370	90.32 345	82.09 410	94.80 407

MTGPA								98.74 120

test_post1							96.68 395				30.43 447	87.85 251	98.69 289	92.59 296

test_post											31.83 446	88.83 224	98.91 265

patchmatchnet-post											95.10 402	89.42 203	98.89 269

GG-mvs-BLEND				96.59 261	96.34 357	94.98 222	96.51 400	88.58 442		93.10 336	94.34 413	80.34 361	98.05 359	89.53 361	96.99 225	96.74 323

MTMP							98.89 112	94.14 425

gm-plane-assit					95.88 378	87.47 407			89.74 381		96.94 338		99.19 220	93.32 275

test9_res														96.39 170	99.57 92	99.69 62

TEST9					99.31 69	98.50 29	97.92 296	98.73 123	92.63 305	97.74 155	98.68 165	96.20 32	99.80 100

test_8					99.29 78	98.44 31	97.89 304	98.72 125	92.98 293	97.70 160	98.66 168	96.20 32	99.80 100

agg_prior2														95.87 186	99.57 92	99.68 67

agg_prior					99.30 73	98.38 35		98.72 125		97.57 172			99.81 93

TestCases				96.99 226	99.25 86	93.21 302		98.18 252	91.36 345	93.52 315	98.77 154	84.67 312	99.72 125	89.70 358	97.87 201	98.02 262

test_prior4						98.01 65	97.86 308

test_prior2							97.80 315		96.12 125	97.89 148	98.69 164	95.96 41		96.89 146	99.60 86

test_prior				99.19 44	99.31 69	98.22 52		98.84 87					99.70 131			99.65 75

旧先验2							97.57 334		91.30 350	98.67 92			99.80 100	95.70 195

新几何2							97.64 328

新几何1				99.16 49	99.34 62	98.01 65		98.69 134	90.06 375	98.13 122	98.95 128	94.60 85	99.89 59	91.97 315	99.47 112	99.59 86

旧先验1					99.29 78	97.48 83		98.70 132			99.09 106	95.56 52			99.47 112	99.61 82

无先验							97.58 333	98.72 125	91.38 344				99.87 70	93.36 274		99.60 84

原ACMM2							97.67 325

原ACMM1				98.65 90	99.32 67	96.62 132		98.67 142	93.27 281	97.81 150	98.97 121	95.18 72	99.83 81	93.84 260	99.46 115	99.50 98

test222					99.23 94	97.17 109	97.40 342	98.66 145	88.68 394	98.05 128	98.96 126	94.14 98			99.53 103	99.61 82

testdata2													99.89 59	91.65 323

segment_acmp												96.85 14

testdata				98.26 130	99.20 99	95.36 199		98.68 137	91.89 331	98.60 100	99.10 99	94.44 92	99.82 88	94.27 244	99.44 116	99.58 90

testdata1							97.32 352		96.34 115

test12				99.18 46	99.16 105	98.19 54		98.53 178		98.07 126		95.13 75	99.72 125		99.56 98	99.63 80

plane_prior7					97.42 291	94.63 239

plane_prior6					97.35 298	94.61 242						87.09 264

plane_prior5								98.56 172					99.03 245	96.07 176	94.27 278	96.92 299

plane_prior4											98.28 207

plane_prior3						94.61 242			97.02 80	95.34 244

plane_prior2							98.80 147		97.28 60

plane_prior1					97.37 297

plane_prior						94.60 244	98.44 227		96.74 94						94.22 280

n2								0.00 455

nn								0.00 455

door-mid								94.37 421

lessismore_v0				94.45 370	94.93 402	88.44 398		91.03 438		86.77 409	97.64 270	76.23 397	98.42 317	90.31 346	85.64 400	96.51 358

LGP-MVS_train				96.47 276	97.46 286	93.54 283		98.54 176	94.67 204	94.36 276	98.77 154	85.39 294	99.11 233	95.71 193	94.15 284	96.76 321

test11								98.66 145

door								94.64 419

HQP5-MVS						94.25 260

HQP-NCC					97.20 306		98.05 280		96.43 109	94.45 268

ACMP_Plane					97.20 306		98.05 280		96.43 109	94.45 268

BP-MVS														95.30 207

HQP4-MVS										94.45 268			98.96 256			96.87 311

HQP3-MVS								98.46 197							94.18 282

HQP2-MVS												86.75 270

NP-MVS					97.28 300	94.51 247					97.73 257

MDTV_nov1_ep13_2view						84.26 416	96.89 385		90.97 359	97.90 147		89.89 188		93.91 258		99.18 163

MDTV_nov1_ep13			95.40 196		97.48 284	88.34 399	96.85 388	97.29 347	93.74 251	97.48 174	97.26 299	89.18 211	99.05 241	91.92 316	97.43 216

ACMMP++_ref															92.97 310

ACMMP++															93.61 299

Test By Simon												94.64 84

ITE_SJBPF				95.44 329	97.42 291	91.32 335		97.50 327	95.09 179	93.59 311	98.35 198	81.70 343	98.88 271	89.71 357	93.39 305	96.12 379

DeepMVS_CX				86.78 407	97.09 316	72.30 437		95.17 415	75.92 431	84.34 420	95.19 400	70.58 414	95.35 421	79.98 421	89.04 366	92.68 425

High-resolution multi-view benchmark