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	98.88 116	99.30 13	98.47 13	99.85 6	99.43 34	96.71 17	99.96 4	99.86 1	99.80 24	99.89 4

SED-MVS		99.09 1	98.91 4	99.63 4	99.71 19	99.24 5	99.02 79	98.87 72	97.65 29	99.73 14	99.48 25	97.53 7	99.94 10	98.43 54	99.81 15	99.70 57

DVP-MVS++		99.08 3	98.89 5	99.64 3	99.17 100	99.23 7	99.69 1	98.88 65	97.32 50	99.53 27	99.47 27	97.81 3	99.94 10	98.47 50	99.72 59	99.74 40

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 51	99.41 59	97.54 81	98.89 110	99.31 12	98.49 12	99.86 4	99.42 35	96.45 24	99.96 4	99.86 1	99.74 52	99.90 3

DVP-MVS		99.03 5	98.83 9	99.63 4	99.72 12	99.25 2	98.97 89	98.58 159	97.62 31	99.45 29	99.46 31	97.42 9	99.94 10	98.47 50	99.81 15	99.69 60
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 53	97.38 47	99.41 32	99.54 15	96.66 18	99.84 74	98.86 29	99.85 6	99.87 7
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 98	98.84 82	98.06 17	99.35 36	99.61 4	96.39 27	99.94 10	98.77 32	99.82 14	99.83 13

reproduce-ours		98.93 8	98.78 11	99.38 18	99.49 46	98.38 35	98.86 122	98.83 84	98.06 17	99.29 40	99.58 11	96.40 25	99.94 10	98.68 34	99.81 15	99.81 18

our_new_method		98.93 8	98.78 11	99.38 18	99.49 46	98.38 35	98.86 122	98.83 84	98.06 17	99.29 40	99.58 11	96.40 25	99.94 10	98.68 34	99.81 15	99.81 18

test_fmvsmconf_n		98.92 10	98.87 6	99.04 59	98.88 133	97.25 99	98.82 134	99.34 10	98.75 6	99.80 7	99.61 4	95.16 73	99.95 8	99.70 9	99.80 24	99.93 1

DPE-MVS		98.92 10	98.67 16	99.65 2	99.58 32	99.20 9	98.42 218	98.91 59	97.58 34	99.54 26	99.46 31	97.10 12	99.94 10	97.64 101	99.84 11	99.83 13
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	98.89 110	99.24 18	98.77 5	99.89 1	99.59 10	93.39 106	99.96 4	99.78 5	99.76 42	99.89 4

SteuartSystems-ACMMP		98.90 12	98.75 13	99.36 24	99.22 95	98.43 33	99.10 63	98.87 72	97.38 47	99.35 36	99.40 37	97.78 5	99.87 65	97.77 89	99.85 6	99.78 24
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_1920		98.87 14	99.01 3	98.45 106	99.42 58	96.43 138	98.96 94	99.36 9	98.63 8	99.86 4	99.51 20	95.91 43	99.97 1	99.72 7	99.75 48	98.94 188

TSAR-MVS + MP.		98.78 15	98.62 17	99.24 40	99.69 24	98.28 48	99.14 54	98.66 139	96.84 79	99.56 24	99.31 57	96.34 28	99.70 126	98.32 60	99.73 55	99.73 45
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 20	99.45 15	99.32 66	98.87 19	98.47 210	98.81 93	97.72 24	98.76 79	99.16 84	97.05 13	99.78 108	98.06 71	99.66 69	99.69 60

MSP-MVS		98.74 17	98.55 21	99.29 33	99.75 3	98.23 51	99.26 27	98.88 65	97.52 37	99.41 32	98.78 141	96.00 39	99.79 105	97.79 88	99.59 84	99.85 10
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
XVS		98.70 18	98.49 25	99.34 26	99.70 22	98.35 44	99.29 22	98.88 65	97.40 44	98.46 97	99.20 74	95.90 45	99.89 54	97.85 84	99.74 52	99.78 24

MCST-MVS		98.65 19	98.37 34	99.48 13	99.60 31	98.87 19	98.41 219	98.68 131	97.04 71	98.52 95	98.80 139	96.78 16	99.83 76	97.93 78	99.61 80	99.74 40

SD-MVS		98.64 20	98.68 15	98.53 97	99.33 63	98.36 43	98.90 106	98.85 81	97.28 53	99.72 16	99.39 38	96.63 20	97.60 368	98.17 66	99.85 6	99.64 75
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 21	98.40 31	99.32 32	99.72 12	98.29 47	99.23 32	98.96 48	96.10 116	98.94 62	99.17 81	96.06 36	99.92 36	97.62 102	99.78 34	99.75 38

ACMMP_NAP		98.61 22	98.30 47	99.55 9	99.62 30	98.95 17	98.82 134	98.81 93	95.80 127	99.16 52	99.47 27	95.37 60	99.92 36	97.89 82	99.75 48	99.79 22

region2R		98.61 22	98.38 33	99.29 33	99.74 7	98.16 57	99.23 32	98.93 53	96.15 113	98.94 62	99.17 81	95.91 43	99.94 10	97.55 109	99.79 30	99.78 24

NCCC		98.61 22	98.35 37	99.38 18	99.28 81	98.61 26	98.45 211	98.76 111	97.82 23	98.45 100	98.93 123	96.65 19	99.83 76	97.38 118	99.41 114	99.71 53

SF-MVS		98.59 25	98.32 46	99.41 17	99.54 35	98.71 22	99.04 73	98.81 93	95.12 163	99.32 39	99.39 38	96.22 30	99.84 74	97.72 92	99.73 55	99.67 69

ACMMPR		98.59 25	98.36 35	99.29 33	99.74 7	98.15 58	99.23 32	98.95 49	96.10 116	98.93 66	99.19 79	95.70 49	99.94 10	97.62 102	99.79 30	99.78 24

test_fmvsmconf0.1_n		98.58 27	98.44 29	98.99 61	97.73 249	97.15 104	98.84 130	98.97 45	98.75 6	99.43 31	99.54 15	93.29 108	99.93 29	99.64 12	99.79 30	99.89 4

SMA-MVS		98.58 27	98.25 50	99.56 8	99.51 40	99.04 15	98.95 95	98.80 100	93.67 246	99.37 35	99.52 18	96.52 22	99.89 54	98.06 71	99.81 15	99.76 37
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 27	98.29 48	99.46 14	99.76 2	98.64 25	98.90 106	98.74 115	97.27 57	98.02 124	99.39 38	94.81 83	99.96 4	97.91 80	99.79 30	99.77 30

HPM-MVS++		98.58 27	98.25 50	99.55 9	99.50 42	99.08 11	98.72 164	98.66 139	97.51 38	98.15 111	98.83 136	95.70 49	99.92 36	97.53 111	99.67 66	99.66 72

SR-MVS		98.57 31	98.35 37	99.24 40	99.53 36	98.18 55	99.09 64	98.82 87	96.58 95	99.10 54	99.32 55	95.39 58	99.82 83	97.70 97	99.63 77	99.72 49

CP-MVS		98.57 31	98.36 35	99.19 44	99.66 26	97.86 69	99.34 16	98.87 72	95.96 119	98.60 92	99.13 89	96.05 37	99.94 10	97.77 89	99.86 2	99.77 30

MSLP-MVS++		98.56 33	98.57 19	98.55 93	99.26 84	96.80 118	98.71 165	99.05 39	97.28 53	98.84 72	99.28 60	96.47 23	99.40 185	98.52 48	99.70 62	99.47 104

DeepC-MVS_fast	96.70 1	98.55 34	98.34 41	99.18 46	99.25 85	98.04 63	98.50 207	98.78 107	97.72 24	98.92 68	99.28 60	95.27 66	99.82 83	97.55 109	99.77 36	99.69 60
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 35	98.35 37	99.13 52	99.49 46	97.86 69	99.11 60	98.80 100	96.49 98	99.17 49	99.35 50	95.34 62	99.82 83	97.72 92	99.65 72	99.71 53

fmvsm_s_conf0.5_n_3		98.53 36	98.45 28	98.79 75	99.23 93	97.32 91	98.80 143	99.26 15	98.82 2	99.87 2	99.60 8	90.95 165	99.93 29	99.76 6	99.73 55	99.12 163

APD-MVS_3200maxsize		98.53 36	98.33 45	99.15 50	99.50 42	97.92 68	99.15 51	98.81 93	96.24 109	99.20 46	99.37 44	95.30 64	99.80 95	97.73 91	99.67 66	99.72 49

MM		98.51 38	98.24 52	99.33 30	99.12 108	98.14 60	98.93 101	97.02 354	98.96 1	99.17 49	99.47 27	91.97 138	99.94 10	99.85 3	99.69 63	99.91 2

mPP-MVS		98.51 38	98.26 49	99.25 39	99.75 3	98.04 63	99.28 24	98.81 93	96.24 109	98.35 107	99.23 69	95.46 55	99.94 10	97.42 116	99.81 15	99.77 30

ZNCC-MVS		98.49 40	98.20 58	99.35 25	99.73 11	98.39 34	99.19 44	98.86 78	95.77 129	98.31 110	99.10 93	95.46 55	99.93 29	97.57 108	99.81 15	99.74 40

SPE-MVS-test		98.49 40	98.50 24	98.46 105	99.20 98	97.05 108	99.64 4	98.50 181	97.45 43	98.88 69	99.14 88	95.25 68	99.15 213	98.83 30	99.56 94	99.20 148

PGM-MVS		98.49 40	98.23 54	99.27 38	99.72 12	98.08 62	98.99 86	99.49 5	95.43 145	99.03 55	99.32 55	95.56 52	99.94 10	96.80 147	99.77 36	99.78 24

EI-MVSNet-Vis-set		98.47 43	98.39 32	98.69 82	99.46 52	96.49 135	98.30 230	98.69 128	97.21 60	98.84 72	99.36 48	95.41 57	99.78 108	98.62 37	99.65 72	99.80 21

MVS_111021_HR		98.47 43	98.34 41	98.88 72	99.22 95	97.32 91	97.91 279	99.58 3	97.20 61	98.33 108	99.00 112	95.99 40	99.64 138	98.05 73	99.76 42	99.69 60

balanced_conf03		98.45 45	98.35 37	98.74 78	98.65 160	97.55 79	99.19 44	98.60 150	96.72 89	99.35 36	98.77 143	95.06 78	99.55 161	98.95 26	99.87 1	99.12 163

test_fmvsmvis_n_1920		98.44 46	98.51 22	98.23 126	98.33 190	96.15 152	98.97 89	99.15 31	98.55 11	98.45 100	99.55 13	94.26 96	99.97 1	99.65 10	99.66 69	98.57 225

CS-MVS		98.44 46	98.49 25	98.31 118	99.08 113	96.73 122	99.67 3	98.47 187	97.17 63	98.94 62	99.10 93	95.73 48	99.13 216	98.71 33	99.49 104	99.09 168

GST-MVS		98.43 48	98.12 62	99.34 26	99.72 12	98.38 35	99.09 64	98.82 87	95.71 133	98.73 82	99.06 104	95.27 66	99.93 29	97.07 126	99.63 77	99.72 49

fmvsm_s_conf0.5_n		98.42 49	98.51 22	98.13 135	99.30 72	95.25 198	98.85 126	99.39 7	97.94 21	99.74 13	99.62 3	92.59 117	99.91 45	99.65 10	99.52 100	99.25 141

EI-MVSNet-UG-set		98.41 50	98.34 41	98.61 88	99.45 55	96.32 145	98.28 233	98.68 131	97.17 63	98.74 80	99.37 44	95.25 68	99.79 105	98.57 39	99.54 97	99.73 45

DELS-MVS		98.40 51	98.20 58	98.99 61	99.00 120	97.66 74	97.75 300	98.89 62	97.71 26	98.33 108	98.97 114	94.97 80	99.88 63	98.42 56	99.76 42	99.42 115
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 52	98.42 30	98.27 120	99.09 112	95.41 188	98.86 122	99.37 8	97.69 28	99.78 9	99.61 4	92.38 120	99.91 45	99.58 14	99.43 112	99.49 100

TSAR-MVS + GP.		98.38 52	98.24 52	98.81 74	99.22 95	97.25 99	98.11 257	98.29 226	97.19 62	98.99 60	99.02 107	96.22 30	99.67 133	98.52 48	98.56 162	99.51 93

HPM-MVS_fast		98.38 52	98.13 61	99.12 54	99.75 3	97.86 69	99.44 9	98.82 87	94.46 202	98.94 62	99.20 74	95.16 73	99.74 118	97.58 105	99.85 6	99.77 30

patch_mono-2		98.36 55	98.87 6	96.82 229	99.53 36	90.68 336	98.64 182	99.29 14	97.88 22	99.19 48	99.52 18	96.80 15	99.97 1	99.11 22	99.86 2	99.82 17

HPM-MVS		98.36 55	98.10 65	99.13 52	99.74 7	97.82 73	99.53 6	98.80 100	94.63 192	98.61 91	98.97 114	95.13 75	99.77 113	97.65 100	99.83 13	99.79 22
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS		98.35 57	98.00 71	99.42 16	99.51 40	98.72 21	98.80 143	98.82 87	94.52 199	99.23 45	99.25 68	95.54 54	99.80 95	96.52 154	99.77 36	99.74 40
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 58	98.23 54	98.67 84	99.27 82	96.90 114	97.95 274	99.58 3	97.14 66	98.44 102	99.01 111	95.03 79	99.62 145	97.91 80	99.75 48	99.50 95

PHI-MVS		98.34 58	98.06 66	99.18 46	99.15 106	98.12 61	99.04 73	99.09 34	93.32 261	98.83 74	99.10 93	96.54 21	99.83 76	97.70 97	99.76 42	99.59 83

MP-MVS		98.33 60	98.01 70	99.28 36	99.75 3	98.18 55	99.22 36	98.79 105	96.13 114	97.92 135	99.23 69	94.54 86	99.94 10	96.74 150	99.78 34	99.73 45
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSMamba_PlusPlus		98.31 61	98.19 60	98.67 84	98.96 127	97.36 89	99.24 30	98.57 161	94.81 184	98.99 60	98.90 127	95.22 71	99.59 148	99.15 21	99.84 11	99.07 176

MP-MVS-pluss		98.31 61	97.92 73	99.49 12	99.72 12	98.88 18	98.43 216	98.78 107	94.10 211	97.69 150	99.42 35	95.25 68	99.92 36	98.09 70	99.80 24	99.67 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_2		98.30 63	98.21 56	98.57 90	99.25 85	97.11 105	98.66 178	99.20 26	98.82 2	99.79 8	99.60 8	89.38 196	99.92 36	99.80 4	99.38 119	98.69 209

MVS_0304		98.23 64	97.91 74	99.21 43	98.06 219	97.96 67	98.58 191	95.51 391	98.58 9	98.87 70	99.26 63	92.99 112	99.95 8	99.62 13	99.67 66	99.73 45

ACMMP		98.23 64	97.95 72	99.09 56	99.74 7	97.62 77	99.03 76	99.41 6	95.98 118	97.60 159	99.36 48	94.45 91	99.93 29	97.14 123	98.85 148	99.70 57
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 66	98.11 63	98.49 102	98.34 187	97.26 98	99.61 5	98.43 196	96.78 82	98.87 70	98.84 134	93.72 103	99.01 237	98.91 28	99.50 102	99.19 152

fmvsm_s_conf0.1_n		98.18 67	98.21 56	98.11 139	98.54 169	95.24 199	98.87 119	99.24 18	97.50 39	99.70 17	99.67 1	91.33 154	99.89 54	99.47 16	99.54 97	99.21 147

fmvsm_s_conf0.1_n_2		98.14 68	98.02 69	98.53 97	98.88 133	97.07 107	98.69 171	98.82 87	98.78 4	99.77 10	99.61 4	88.83 215	99.91 45	99.71 8	99.07 132	98.61 219

fmvsm_s_conf0.1_n_a		98.08 69	98.04 68	98.21 127	97.66 255	95.39 189	98.89 110	99.17 29	97.24 58	99.76 12	99.67 1	91.13 159	99.88 63	99.39 17	99.41 114	99.35 120

dcpmvs_2		98.08 69	98.59 18	96.56 253	99.57 33	90.34 345	99.15 51	98.38 206	96.82 81	99.29 40	99.49 24	95.78 47	99.57 151	98.94 27	99.86 2	99.77 30

CANet		98.05 71	97.76 77	98.90 71	98.73 146	97.27 94	98.35 221	98.78 107	97.37 49	97.72 147	98.96 119	91.53 150	99.92 36	98.79 31	99.65 72	99.51 93

train_agg		97.97 72	97.52 89	99.33 30	99.31 68	98.50 29	97.92 277	98.73 118	92.98 277	97.74 144	98.68 154	96.20 32	99.80 95	96.59 151	99.57 88	99.68 65

ETV-MVS		97.96 73	97.81 75	98.40 113	98.42 175	97.27 94	98.73 160	98.55 166	96.84 79	98.38 104	97.44 272	95.39 58	99.35 190	97.62 102	98.89 143	98.58 224

UA-Net		97.96 73	97.62 81	98.98 63	98.86 137	97.47 85	98.89 110	99.08 35	96.67 92	98.72 83	99.54 15	93.15 110	99.81 88	94.87 208	98.83 149	99.65 73

CDPH-MVS		97.94 75	97.49 91	99.28 36	99.47 50	98.44 31	97.91 279	98.67 136	92.57 293	98.77 78	98.85 133	95.93 42	99.72 120	95.56 188	99.69 63	99.68 65

DeepPCF-MVS	96.37 2	97.93 76	98.48 27	96.30 278	99.00 120	89.54 359	97.43 322	98.87 72	98.16 15	99.26 44	99.38 43	96.12 35	99.64 138	98.30 61	99.77 36	99.72 49

DeepC-MVS	95.98 3	97.88 77	97.58 83	98.77 76	99.25 85	96.93 112	98.83 132	98.75 113	96.96 75	96.89 184	99.50 22	90.46 173	99.87 65	97.84 86	99.76 42	99.52 90
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 78	97.54 88	98.83 73	95.48 372	96.83 117	98.95 95	98.60 150	98.58 9	98.93 66	99.55 13	88.57 220	99.91 45	99.54 15	99.61 80	99.77 30

DP-MVS Recon		97.86 78	97.46 94	99.06 58	99.53 36	98.35 44	98.33 223	98.89 62	92.62 290	98.05 119	98.94 122	95.34 62	99.65 136	96.04 170	99.42 113	99.19 152

CSCG		97.85 80	97.74 78	98.20 129	99.67 25	95.16 202	99.22 36	99.32 11	93.04 275	97.02 177	98.92 125	95.36 61	99.91 45	97.43 115	99.64 76	99.52 90

BP-MVS1		97.82 81	97.51 90	98.76 77	98.25 197	97.39 88	99.15 51	97.68 293	96.69 90	98.47 96	99.10 93	90.29 177	99.51 168	98.60 38	99.35 122	99.37 118

MG-MVS		97.81 82	97.60 82	98.44 108	99.12 108	95.97 161	97.75 300	98.78 107	96.89 78	98.46 97	99.22 71	93.90 102	99.68 132	94.81 212	99.52 100	99.67 69

VNet		97.79 83	97.40 98	98.96 66	98.88 133	97.55 79	98.63 185	98.93 53	96.74 86	99.02 56	98.84 134	90.33 176	99.83 76	98.53 42	96.66 223	99.50 95

EIA-MVS		97.75 84	97.58 83	98.27 120	98.38 179	96.44 137	99.01 81	98.60 150	95.88 123	97.26 166	97.53 266	94.97 80	99.33 193	97.38 118	99.20 128	99.05 177

PS-MVSNAJ		97.73 85	97.77 76	97.62 179	98.68 155	95.58 179	97.34 331	98.51 176	97.29 52	98.66 88	97.88 232	94.51 87	99.90 52	97.87 83	99.17 130	97.39 266

casdiffmvs_mvg		97.72 86	97.48 93	98.44 108	98.42 175	96.59 130	98.92 103	98.44 192	96.20 111	97.76 141	99.20 74	91.66 144	99.23 203	98.27 65	98.41 172	99.49 100
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 86	97.32 102	98.92 68	99.64 28	97.10 106	99.12 58	98.81 93	92.34 301	98.09 116	99.08 102	93.01 111	99.92 36	96.06 169	99.77 36	99.75 38

PVSNet_Blended_VisFu		97.70 88	97.46 94	98.44 108	99.27 82	95.91 169	98.63 185	99.16 30	94.48 201	97.67 151	98.88 130	92.80 114	99.91 45	97.11 124	99.12 131	99.50 95

mvsany_test1		97.69 89	97.70 79	97.66 177	98.24 198	94.18 252	97.53 316	97.53 311	95.52 141	99.66 19	99.51 20	94.30 94	99.56 154	98.38 57	98.62 158	99.23 143

sasdasda		97.67 90	97.23 106	98.98 63	98.70 151	98.38 35	99.34 16	98.39 202	96.76 84	97.67 151	97.40 276	92.26 124	99.49 172	98.28 62	96.28 241	99.08 172

canonicalmvs		97.67 90	97.23 106	98.98 63	98.70 151	98.38 35	99.34 16	98.39 202	96.76 84	97.67 151	97.40 276	92.26 124	99.49 172	98.28 62	96.28 241	99.08 172

xiu_mvs_v2_base		97.66 92	97.70 79	97.56 183	98.61 164	95.46 186	97.44 320	98.46 188	97.15 65	98.65 89	98.15 208	94.33 93	99.80 95	97.84 86	98.66 157	97.41 264

GDP-MVS		97.64 93	97.28 103	98.71 81	98.30 195	97.33 90	99.05 69	98.52 173	96.34 106	98.80 75	99.05 105	89.74 186	99.51 168	96.86 144	98.86 147	99.28 135

baseline		97.64 93	97.44 96	98.25 124	98.35 182	96.20 149	99.00 83	98.32 216	96.33 108	98.03 122	99.17 81	91.35 153	99.16 210	98.10 69	98.29 179	99.39 116

casdiffmvs		97.63 95	97.41 97	98.28 119	98.33 190	96.14 153	98.82 134	98.32 216	96.38 105	97.95 130	99.21 72	91.23 158	99.23 203	98.12 68	98.37 173	99.48 102
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 96	97.19 109	98.92 68	98.66 157	98.20 53	99.32 21	98.38 206	96.69 90	97.58 160	97.42 275	92.10 132	99.50 171	98.28 62	96.25 244	99.08 172

xiu_mvs_v1_base_debu		97.60 97	97.56 85	97.72 167	98.35 182	95.98 156	97.86 289	98.51 176	97.13 67	99.01 57	98.40 181	91.56 146	99.80 95	98.53 42	98.68 153	97.37 268

xiu_mvs_v1_base		97.60 97	97.56 85	97.72 167	98.35 182	95.98 156	97.86 289	98.51 176	97.13 67	99.01 57	98.40 181	91.56 146	99.80 95	98.53 42	98.68 153	97.37 268

xiu_mvs_v1_base_debi		97.60 97	97.56 85	97.72 167	98.35 182	95.98 156	97.86 289	98.51 176	97.13 67	99.01 57	98.40 181	91.56 146	99.80 95	98.53 42	98.68 153	97.37 268

diffmvs		97.58 100	97.40 98	98.13 135	98.32 193	95.81 174	98.06 263	98.37 208	96.20 111	98.74 80	98.89 129	91.31 156	99.25 200	98.16 67	98.52 164	99.34 122
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVSFormer		97.57 101	97.49 91	97.84 154	98.07 216	95.76 175	99.47 7	98.40 200	94.98 173	98.79 76	98.83 136	92.34 121	98.41 310	96.91 132	99.59 84	99.34 122

alignmvs		97.56 102	97.07 115	99.01 60	98.66 157	98.37 42	98.83 132	98.06 273	96.74 86	98.00 128	97.65 254	90.80 167	99.48 177	98.37 58	96.56 227	99.19 152

DPM-MVS		97.55 103	96.99 118	99.23 42	99.04 115	98.55 27	97.17 346	98.35 211	94.85 183	97.93 134	98.58 164	95.07 77	99.71 125	92.60 280	99.34 123	99.43 113

OMC-MVS		97.55 103	97.34 101	98.20 129	99.33 63	95.92 168	98.28 233	98.59 154	95.52 141	97.97 129	99.10 93	93.28 109	99.49 172	95.09 203	98.88 144	99.19 152

PAPM_NR		97.46 105	97.11 112	98.50 100	99.50 42	96.41 140	98.63 185	98.60 150	95.18 160	97.06 175	98.06 214	94.26 96	99.57 151	93.80 248	98.87 146	99.52 90

EPP-MVSNet		97.46 105	97.28 103	97.99 147	98.64 161	95.38 190	99.33 20	98.31 218	93.61 250	97.19 168	99.07 103	94.05 99	99.23 203	96.89 136	98.43 171	99.37 118

3Dnovator	94.51 5	97.46 105	96.93 121	99.07 57	97.78 243	97.64 75	99.35 15	99.06 37	97.02 72	93.75 294	99.16 84	89.25 200	99.92 36	97.22 122	99.75 48	99.64 75

CNLPA		97.45 108	97.03 116	98.73 79	99.05 114	97.44 87	98.07 262	98.53 170	95.32 153	96.80 189	98.53 169	93.32 107	99.72 120	94.31 231	99.31 125	99.02 179

lupinMVS		97.44 109	97.22 108	98.12 138	98.07 216	95.76 175	97.68 305	97.76 290	94.50 200	98.79 76	98.61 159	92.34 121	99.30 196	97.58 105	99.59 84	99.31 128

3Dnovator+	94.38 6	97.43 110	96.78 129	99.38 18	97.83 240	98.52 28	99.37 12	98.71 123	97.09 70	92.99 322	99.13 89	89.36 197	99.89 54	96.97 129	99.57 88	99.71 53

Vis-MVSNet		97.42 111	97.11 112	98.34 116	98.66 157	96.23 148	99.22 36	99.00 42	96.63 94	98.04 121	99.21 72	88.05 236	99.35 190	96.01 172	99.21 127	99.45 110
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 112	97.25 105	97.91 151	98.70 151	96.80 118	98.82 134	98.69 128	94.53 197	98.11 114	98.28 196	94.50 90	99.57 151	94.12 237	99.49 104	97.37 268

sss		97.39 113	96.98 120	98.61 88	98.60 165	96.61 127	98.22 239	98.93 53	93.97 221	98.01 127	98.48 174	91.98 136	99.85 70	96.45 156	98.15 181	99.39 116

test_cas_vis1_n_1920		97.38 114	97.36 100	97.45 186	98.95 128	93.25 288	99.00 83	98.53 170	97.70 27	99.77 10	99.35 50	84.71 301	99.85 70	98.57 39	99.66 69	99.26 139

PVSNet_Blended		97.38 114	97.12 111	98.14 132	99.25 85	95.35 193	97.28 336	99.26 15	93.13 271	97.94 132	98.21 204	92.74 115	99.81 88	96.88 138	99.40 117	99.27 136

WTY-MVS		97.37 116	96.92 122	98.72 80	98.86 137	96.89 116	98.31 228	98.71 123	95.26 156	97.67 151	98.56 168	92.21 128	99.78 108	95.89 174	96.85 218	99.48 102

jason		97.32 117	97.08 114	98.06 143	97.45 275	95.59 178	97.87 287	97.91 284	94.79 185	98.55 94	98.83 136	91.12 160	99.23 203	97.58 105	99.60 82	99.34 122
jason: jason.
MVS_Test		97.28 118	97.00 117	98.13 135	98.33 190	95.97 161	98.74 156	98.07 268	94.27 207	98.44 102	98.07 213	92.48 118	99.26 199	96.43 157	98.19 180	99.16 158

EPNet		97.28 118	96.87 124	98.51 99	94.98 381	96.14 153	98.90 106	97.02 354	98.28 14	95.99 220	99.11 91	91.36 152	99.89 54	96.98 128	99.19 129	99.50 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsmamba		97.25 120	96.99 118	98.02 145	98.34 187	95.54 183	99.18 48	97.47 317	95.04 169	98.15 111	98.57 167	89.46 193	99.31 195	97.68 99	99.01 137	99.22 145

test_yl		97.22 121	96.78 129	98.54 95	98.73 146	96.60 128	98.45 211	98.31 218	94.70 186	98.02 124	98.42 179	90.80 167	99.70 126	96.81 145	96.79 220	99.34 122

DCV-MVSNet		97.22 121	96.78 129	98.54 95	98.73 146	96.60 128	98.45 211	98.31 218	94.70 186	98.02 124	98.42 179	90.80 167	99.70 126	96.81 145	96.79 220	99.34 122

IS-MVSNet		97.22 121	96.88 123	98.25 124	98.85 139	96.36 143	99.19 44	97.97 278	95.39 147	97.23 167	98.99 113	91.11 161	98.93 249	94.60 219	98.59 160	99.47 104

PLC	95.07 4	97.20 124	96.78 129	98.44 108	99.29 77	96.31 147	98.14 252	98.76 111	92.41 299	96.39 209	98.31 194	94.92 82	99.78 108	94.06 240	98.77 152	99.23 143
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 125	97.18 110	97.20 199	98.81 142	93.27 285	95.78 391	99.15 31	95.25 157	96.79 190	98.11 211	92.29 123	99.07 227	98.56 41	99.85 6	99.25 141

LS3D		97.16 126	96.66 138	98.68 83	98.53 170	97.19 102	98.93 101	98.90 60	92.83 284	95.99 220	99.37 44	92.12 131	99.87 65	93.67 252	99.57 88	98.97 184

AdaColmap		97.15 127	96.70 134	98.48 103	99.16 104	96.69 124	98.01 268	98.89 62	94.44 203	96.83 185	98.68 154	90.69 170	99.76 114	94.36 227	99.29 126	98.98 183

mamv4		97.13 128	98.11 63	94.17 357	98.97 126	83.70 399	98.66 178	98.71 123	94.63 192	97.83 138	98.90 127	96.25 29	99.55 161	99.27 19	99.76 42	99.27 136

Effi-MVS+		97.12 129	96.69 135	98.39 114	98.19 206	96.72 123	97.37 327	98.43 196	93.71 239	97.65 155	98.02 217	92.20 129	99.25 200	96.87 141	97.79 193	99.19 152

CHOSEN 1792x2688		97.12 129	96.80 126	98.08 141	99.30 72	94.56 236	98.05 264	99.71 1	93.57 251	97.09 171	98.91 126	88.17 230	99.89 54	96.87 141	99.56 94	99.81 18

F-COLMAP		97.09 131	96.80 126	97.97 148	99.45 55	94.95 215	98.55 199	98.62 149	93.02 276	96.17 215	98.58 164	94.01 100	99.81 88	93.95 242	98.90 142	99.14 161

RRT-MVS		97.03 132	96.78 129	97.77 163	97.90 236	94.34 245	99.12 58	98.35 211	95.87 124	98.06 118	98.70 152	86.45 267	99.63 141	98.04 74	98.54 163	99.35 120

TAMVS		97.02 133	96.79 128	97.70 170	98.06 219	95.31 196	98.52 201	98.31 218	93.95 222	97.05 176	98.61 159	93.49 105	98.52 292	95.33 195	97.81 192	99.29 133

CDS-MVSNet		96.99 134	96.69 135	97.90 152	98.05 221	95.98 156	98.20 242	98.33 215	93.67 246	96.95 178	98.49 173	93.54 104	98.42 303	95.24 201	97.74 196	99.31 128
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 135	96.55 141	98.21 127	98.17 211	96.07 155	97.98 272	98.21 235	97.24 58	97.13 170	98.93 123	86.88 259	99.91 45	95.00 206	99.37 121	98.66 215

114514_t		96.93 136	96.27 151	98.92 68	99.50 42	97.63 76	98.85 126	98.90 60	84.80 398	97.77 140	99.11 91	92.84 113	99.66 135	94.85 209	99.77 36	99.47 104

MAR-MVS		96.91 137	96.40 147	98.45 106	98.69 154	96.90 114	98.66 178	98.68 131	92.40 300	97.07 174	97.96 224	91.54 149	99.75 116	93.68 250	98.92 141	98.69 209
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		96.90 138	96.49 144	98.14 132	99.33 63	95.56 180	97.38 325	99.65 2	92.34 301	97.61 158	98.20 205	89.29 199	99.10 224	96.97 129	97.60 201	99.77 30

Vis-MVSNet (Re-imp)		96.87 139	96.55 141	97.83 155	98.73 146	95.46 186	99.20 42	98.30 224	94.96 175	96.60 197	98.87 131	90.05 180	98.59 287	93.67 252	98.60 159	99.46 108

SDMVSNet		96.85 140	96.42 145	98.14 132	99.30 72	96.38 141	99.21 39	99.23 22	95.92 120	95.96 222	98.76 148	85.88 277	99.44 182	97.93 78	95.59 256	98.60 220

PAPR		96.84 141	96.24 153	98.65 86	98.72 150	96.92 113	97.36 329	98.57 161	93.33 260	96.67 192	97.57 263	94.30 94	99.56 154	91.05 321	98.59 160	99.47 104

HY-MVS	93.96 8	96.82 142	96.23 154	98.57 90	98.46 174	97.00 109	98.14 252	98.21 235	93.95 222	96.72 191	97.99 221	91.58 145	99.76 114	94.51 223	96.54 228	98.95 187

UGNet		96.78 143	96.30 150	98.19 131	98.24 198	95.89 171	98.88 116	98.93 53	97.39 46	96.81 188	97.84 236	82.60 328	99.90 52	96.53 153	99.49 104	98.79 198
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		96.73 144	96.60 139	97.12 208	99.25 85	95.35 193	98.26 236	99.26 15	94.28 206	97.94 132	97.46 269	92.74 115	99.81 88	96.88 138	93.32 292	96.20 358

test_vis1_n_1920		96.71 145	96.84 125	96.31 277	99.11 110	89.74 353	99.05 69	98.58 159	98.08 16	99.87 2	99.37 44	78.48 359	99.93 29	99.29 18	99.69 63	99.27 136

mvs_anonymous		96.70 146	96.53 143	97.18 202	98.19 206	93.78 261	98.31 228	98.19 239	94.01 218	94.47 253	98.27 199	92.08 134	98.46 298	97.39 117	97.91 188	99.31 128

1112_ss		96.63 147	96.00 161	98.50 100	98.56 166	96.37 142	98.18 250	98.10 261	92.92 280	94.84 242	98.43 177	92.14 130	99.58 150	94.35 228	96.51 229	99.56 89

PMMVS		96.60 148	96.33 149	97.41 190	97.90 236	93.93 257	97.35 330	98.41 198	92.84 283	97.76 141	97.45 271	91.10 162	99.20 207	96.26 162	97.91 188	99.11 166

DP-MVS		96.59 149	95.93 164	98.57 90	99.34 61	96.19 151	98.70 169	98.39 202	89.45 370	94.52 251	99.35 50	91.85 139	99.85 70	92.89 276	98.88 144	99.68 65

PatchMatch-RL		96.59 149	96.03 160	98.27 120	99.31 68	96.51 134	97.91 279	99.06 37	93.72 238	96.92 182	98.06 214	88.50 225	99.65 136	91.77 305	99.00 139	98.66 215

GeoE		96.58 151	96.07 157	98.10 140	98.35 182	95.89 171	99.34 16	98.12 255	93.12 272	96.09 216	98.87 131	89.71 187	98.97 239	92.95 272	98.08 184	99.43 113

XVG-OURS		96.55 152	96.41 146	96.99 215	98.75 145	93.76 262	97.50 319	98.52 173	95.67 135	96.83 185	99.30 58	88.95 213	99.53 164	95.88 175	96.26 243	97.69 257

FIs		96.51 153	96.12 156	97.67 174	97.13 299	97.54 81	99.36 13	99.22 25	95.89 122	94.03 280	98.35 187	91.98 136	98.44 301	96.40 158	92.76 300	97.01 276

XVG-OURS-SEG-HR		96.51 153	96.34 148	97.02 214	98.77 144	93.76 262	97.79 298	98.50 181	95.45 144	96.94 179	99.09 100	87.87 241	99.55 161	96.76 149	95.83 255	97.74 254

PS-MVSNAJss		96.43 155	96.26 152	96.92 224	95.84 361	95.08 207	99.16 50	98.50 181	95.87 124	93.84 289	98.34 191	94.51 87	98.61 284	96.88 138	93.45 289	97.06 274

test_fmvs1		96.42 156	96.67 137	95.66 305	98.82 141	88.53 378	98.80 143	98.20 237	96.39 104	99.64 21	99.20 74	80.35 347	99.67 133	99.04 24	99.57 88	98.78 201

FC-MVSNet-test		96.42 156	96.05 158	97.53 184	96.95 308	97.27 94	99.36 13	99.23 22	95.83 126	93.93 283	98.37 185	92.00 135	98.32 320	96.02 171	92.72 301	97.00 277

ab-mvs		96.42 156	95.71 174	98.55 93	98.63 162	96.75 121	97.88 286	98.74 115	93.84 228	96.54 202	98.18 207	85.34 287	99.75 116	95.93 173	96.35 233	99.15 159

FA-MVS(test-final)		96.41 159	95.94 163	97.82 157	98.21 202	95.20 201	97.80 296	97.58 301	93.21 266	97.36 164	97.70 248	89.47 192	99.56 154	94.12 237	97.99 185	98.71 208

PVSNet	91.96 18	96.35 160	96.15 155	96.96 219	99.17 100	92.05 309	96.08 384	98.68 131	93.69 242	97.75 143	97.80 242	88.86 214	99.69 131	94.26 233	99.01 137	99.15 159

Test_1112_low_res		96.34 161	95.66 179	98.36 115	98.56 166	95.94 164	97.71 303	98.07 268	92.10 310	94.79 246	97.29 284	91.75 141	99.56 154	94.17 235	96.50 230	99.58 87

Effi-MVS+-dtu		96.29 162	96.56 140	95.51 310	97.89 238	90.22 346	98.80 143	98.10 261	96.57 97	96.45 207	96.66 338	90.81 166	98.91 252	95.72 182	97.99 185	97.40 265

QAPM		96.29 162	95.40 184	98.96 66	97.85 239	97.60 78	99.23 32	98.93 53	89.76 364	93.11 319	99.02 107	89.11 205	99.93 29	91.99 299	99.62 79	99.34 122

Fast-Effi-MVS+		96.28 164	95.70 176	98.03 144	98.29 196	95.97 161	98.58 191	98.25 232	91.74 318	95.29 235	97.23 289	91.03 164	99.15 213	92.90 274	97.96 187	98.97 184

nrg030		96.28 164	95.72 171	97.96 150	96.90 313	98.15 58	99.39 10	98.31 218	95.47 143	94.42 259	98.35 187	92.09 133	98.69 276	97.50 113	89.05 349	97.04 275

1314		96.25 166	95.73 170	97.79 159	97.13 299	95.55 182	98.19 245	98.59 154	93.47 255	92.03 347	97.82 240	91.33 154	99.49 172	94.62 218	98.44 169	98.32 238

sd_testset		96.17 167	95.76 169	97.42 189	99.30 72	94.34 245	98.82 134	99.08 35	95.92 120	95.96 222	98.76 148	82.83 327	99.32 194	95.56 188	95.59 256	98.60 220

h-mvs33		96.17 167	95.62 180	97.81 158	99.03 116	94.45 238	98.64 182	98.75 113	97.48 40	98.67 84	98.72 151	89.76 184	99.86 69	97.95 76	81.59 395	99.11 166

HQP_MVS		96.14 169	95.90 165	96.85 227	97.42 277	94.60 234	98.80 143	98.56 164	97.28 53	95.34 231	98.28 196	87.09 254	99.03 232	96.07 166	94.27 264	96.92 283

tttt0517		96.07 170	95.51 182	97.78 160	98.41 177	94.84 219	99.28 24	94.33 404	94.26 208	97.64 156	98.64 158	84.05 316	99.47 179	95.34 194	97.60 201	99.03 178

MVSTER		96.06 171	95.72 171	97.08 211	98.23 200	95.93 167	98.73 160	98.27 227	94.86 181	95.07 237	98.09 212	88.21 229	98.54 290	96.59 151	93.46 287	96.79 301

thisisatest0530		96.01 172	95.36 189	97.97 148	98.38 179	95.52 184	98.88 116	94.19 406	94.04 213	97.64 156	98.31 194	83.82 323	99.46 180	95.29 198	97.70 198	98.93 189

test_djsdf		96.00 173	95.69 177	96.93 221	95.72 363	95.49 185	99.47 7	98.40 200	94.98 173	94.58 249	97.86 233	89.16 203	98.41 310	96.91 132	94.12 272	96.88 292

EI-MVSNet		95.96 174	95.83 167	96.36 273	97.93 234	93.70 268	98.12 255	98.27 227	93.70 241	95.07 237	99.02 107	92.23 127	98.54 290	94.68 214	93.46 287	96.84 298

ECVR-MVS		95.95 175	95.71 174	96.65 239	99.02 117	90.86 331	99.03 76	91.80 416	96.96 75	98.10 115	99.26 63	81.31 334	99.51 168	96.90 135	99.04 134	99.59 83

BH-untuned		95.95 175	95.72 171	96.65 239	98.55 168	92.26 304	98.23 238	97.79 289	93.73 236	94.62 248	98.01 219	88.97 212	99.00 238	93.04 269	98.51 165	98.68 211

test1111		95.94 177	95.78 168	96.41 270	98.99 123	90.12 347	99.04 73	92.45 415	96.99 74	98.03 122	99.27 62	81.40 333	99.48 177	96.87 141	99.04 134	99.63 77

MSDG		95.93 178	95.30 195	97.83 155	98.90 131	95.36 191	96.83 371	98.37 208	91.32 333	94.43 258	98.73 150	90.27 178	99.60 147	90.05 335	98.82 150	98.52 226

BH-RMVSNet		95.92 179	95.32 193	97.69 171	98.32 193	94.64 228	98.19 245	97.45 322	94.56 195	96.03 218	98.61 159	85.02 292	99.12 218	90.68 326	99.06 133	99.30 131

test_fmvs1_n		95.90 180	95.99 162	95.63 306	98.67 156	88.32 382	99.26 27	98.22 234	96.40 103	99.67 18	99.26 63	73.91 393	99.70 126	99.02 25	99.50 102	98.87 192

Fast-Effi-MVS+-dtu		95.87 181	95.85 166	95.91 294	97.74 248	91.74 315	98.69 171	98.15 251	95.56 139	94.92 240	97.68 253	88.98 211	98.79 270	93.19 264	97.78 194	97.20 272

LFMVS		95.86 182	94.98 210	98.47 104	98.87 136	96.32 145	98.84 130	96.02 383	93.40 258	98.62 90	99.20 74	74.99 387	99.63 141	97.72 92	97.20 208	99.46 108

baseline1		95.84 183	95.12 203	98.01 146	98.49 173	95.98 156	98.73 160	97.03 352	95.37 150	96.22 212	98.19 206	89.96 182	99.16 210	94.60 219	87.48 365	98.90 191

OpenMVS	93.04 13	95.83 184	95.00 208	98.32 117	97.18 296	97.32 91	99.21 39	98.97 45	89.96 360	91.14 356	99.05 105	86.64 262	99.92 36	93.38 258	99.47 107	97.73 255

VDD-MVS		95.82 185	95.23 197	97.61 180	98.84 140	93.98 256	98.68 173	97.40 326	95.02 171	97.95 130	99.34 54	74.37 392	99.78 108	98.64 36	96.80 219	99.08 172

UniMVSNet (Re)		95.78 186	95.19 199	97.58 181	96.99 306	97.47 85	98.79 150	99.18 28	95.60 137	93.92 284	97.04 310	91.68 142	98.48 294	95.80 179	87.66 364	96.79 301

VPA-MVSNet		95.75 187	95.11 204	97.69 171	97.24 288	97.27 94	98.94 98	99.23 22	95.13 162	95.51 229	97.32 282	85.73 279	98.91 252	97.33 120	89.55 340	96.89 291

HQP-MVS		95.72 188	95.40 184	96.69 237	97.20 292	94.25 250	98.05 264	98.46 188	96.43 100	94.45 254	97.73 245	86.75 260	98.96 243	95.30 196	94.18 268	96.86 297

hse-mvs2		95.71 189	95.30 195	96.93 221	98.50 171	93.53 273	98.36 220	98.10 261	97.48 40	98.67 84	97.99 221	89.76 184	99.02 235	97.95 76	80.91 400	98.22 241

UniMVSNet_NR-MVSNet		95.71 189	95.15 200	97.40 192	96.84 316	96.97 110	98.74 156	99.24 18	95.16 161	93.88 286	97.72 247	91.68 142	98.31 322	95.81 177	87.25 370	96.92 283

PatchmatchNet		95.71 189	95.52 181	96.29 279	97.58 261	90.72 335	96.84 370	97.52 312	94.06 212	97.08 172	96.96 320	89.24 201	98.90 255	92.03 298	98.37 173	99.26 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 192	95.33 192	96.76 232	96.16 349	94.63 229	98.43 216	98.39 202	96.64 93	95.02 239	98.78 141	85.15 291	99.05 228	95.21 202	94.20 267	96.60 324
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 192	95.38 188	96.61 246	97.61 258	93.84 260	98.91 105	98.44 192	95.25 157	94.28 266	98.47 175	86.04 276	99.12 218	95.50 191	93.95 277	96.87 295
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 194	95.69 177	95.44 314	97.54 266	88.54 377	96.97 356	97.56 304	93.50 253	97.52 162	96.93 324	89.49 190	99.16 210	95.25 200	96.42 232	98.64 217

FE-MVS		95.62 195	94.90 214	97.78 160	98.37 181	94.92 216	97.17 346	97.38 328	90.95 344	97.73 146	97.70 248	85.32 289	99.63 141	91.18 313	98.33 176	98.79 198

LPG-MVS_test		95.62 195	95.34 190	96.47 264	97.46 272	93.54 271	98.99 86	98.54 168	94.67 190	94.36 262	98.77 143	85.39 284	99.11 220	95.71 183	94.15 270	96.76 304

CLD-MVS		95.62 195	95.34 190	96.46 267	97.52 269	93.75 264	97.27 337	98.46 188	95.53 140	94.42 259	98.00 220	86.21 271	98.97 239	96.25 164	94.37 262	96.66 319
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 198	94.89 215	97.76 164	98.15 212	95.15 204	96.77 372	94.41 402	92.95 279	97.18 169	97.43 273	84.78 298	99.45 181	94.63 216	97.73 197	98.68 211

MonoMVSNet		95.51 199	95.45 183	95.68 303	95.54 368	90.87 330	98.92 103	97.37 329	95.79 128	95.53 228	97.38 278	89.58 189	97.68 365	96.40 158	92.59 302	98.49 228

thres600view7		95.49 200	94.77 218	97.67 174	98.98 124	95.02 208	98.85 126	96.90 361	95.38 148	96.63 194	96.90 325	84.29 308	99.59 148	88.65 357	96.33 234	98.40 232

test_vis1_n		95.47 201	95.13 201	96.49 261	97.77 244	90.41 343	99.27 26	98.11 258	96.58 95	99.66 19	99.18 80	67.00 406	99.62 145	99.21 20	99.40 117	99.44 111

SCA		95.46 202	95.13 201	96.46 267	97.67 253	91.29 323	97.33 332	97.60 300	94.68 189	96.92 182	97.10 295	83.97 318	98.89 256	92.59 282	98.32 178	99.20 148

IterMVS-LS		95.46 202	95.21 198	96.22 281	98.12 213	93.72 267	98.32 227	98.13 254	93.71 239	94.26 267	97.31 283	92.24 126	98.10 338	94.63 216	90.12 331	96.84 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
jajsoiax		95.45 204	95.03 207	96.73 233	95.42 376	94.63 229	99.14 54	98.52 173	95.74 130	93.22 312	98.36 186	83.87 321	98.65 281	96.95 131	94.04 273	96.91 288

CVMVSNet		95.43 205	96.04 159	93.57 362	97.93 234	83.62 400	98.12 255	98.59 154	95.68 134	96.56 198	99.02 107	87.51 247	97.51 373	93.56 256	97.44 204	99.60 81

anonymousdsp		95.42 206	94.91 213	96.94 220	95.10 380	95.90 170	99.14 54	98.41 198	93.75 233	93.16 315	97.46 269	87.50 249	98.41 310	95.63 187	94.03 274	96.50 343

DU-MVS		95.42 206	94.76 219	97.40 192	96.53 332	96.97 110	98.66 178	98.99 44	95.43 145	93.88 286	97.69 250	88.57 220	98.31 322	95.81 177	87.25 370	96.92 283

mvs_tets		95.41 208	95.00 208	96.65 239	95.58 367	94.42 240	99.00 83	98.55 166	95.73 132	93.21 313	98.38 184	83.45 325	98.63 282	97.09 125	94.00 275	96.91 288

thres100view900		95.38 209	94.70 223	97.41 190	98.98 124	94.92 216	98.87 119	96.90 361	95.38 148	96.61 196	96.88 326	84.29 308	99.56 154	88.11 360	96.29 238	97.76 252

thres400		95.38 209	94.62 227	97.65 178	98.94 129	94.98 212	98.68 173	96.93 359	95.33 151	96.55 200	96.53 344	84.23 312	99.56 154	88.11 360	96.29 238	98.40 232

BH-w/o		95.38 209	95.08 205	96.26 280	98.34 187	91.79 312	97.70 304	97.43 324	92.87 282	94.24 269	97.22 290	88.66 218	98.84 262	91.55 309	97.70 198	98.16 244

VDDNet		95.36 212	94.53 231	97.86 153	98.10 215	95.13 205	98.85 126	97.75 291	90.46 351	98.36 105	99.39 38	73.27 395	99.64 138	97.98 75	96.58 226	98.81 197

TAPA-MVS	93.98 7	95.35 213	94.56 230	97.74 166	99.13 107	94.83 221	98.33 223	98.64 144	86.62 386	96.29 211	98.61 159	94.00 101	99.29 197	80.00 401	99.41 114	99.09 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 214	94.98 210	96.43 269	97.67 253	93.48 275	98.73 160	98.44 192	94.94 179	92.53 335	98.53 169	84.50 307	99.14 215	95.48 192	94.00 275	96.66 319
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 215	94.87 216	96.71 234	99.29 77	93.24 289	98.58 191	98.11 258	89.92 361	93.57 298	99.10 93	86.37 269	99.79 105	90.78 324	98.10 183	97.09 273
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG		95.32 216	94.72 222	97.13 206	98.05 221	93.26 286	97.87 287	97.20 340	94.96 175	96.18 214	95.66 375	80.97 339	99.35 190	94.47 225	97.08 210	98.78 201

tfpn200view9		95.32 216	94.62 227	97.43 188	98.94 129	94.98 212	98.68 173	96.93 359	95.33 151	96.55 200	96.53 344	84.23 312	99.56 154	88.11 360	96.29 238	97.76 252

Anonymous202405211		95.28 218	94.49 233	97.67 174	99.00 120	93.75 264	98.70 169	97.04 351	90.66 347	96.49 204	98.80 139	78.13 363	99.83 76	96.21 165	95.36 260	99.44 111

thres200		95.25 219	94.57 229	97.28 196	98.81 142	94.92 216	98.20 242	97.11 344	95.24 159	96.54 202	96.22 355	84.58 305	99.53 164	87.93 365	96.50 230	97.39 266

AllTest		95.24 220	94.65 226	96.99 215	99.25 85	93.21 290	98.59 189	98.18 242	91.36 329	93.52 300	98.77 143	84.67 302	99.72 120	89.70 342	97.87 190	98.02 247

LCM-MVSNet-Re		95.22 221	95.32 193	94.91 330	98.18 208	87.85 388	98.75 153	95.66 390	95.11 164	88.96 375	96.85 329	90.26 179	97.65 366	95.65 186	98.44 169	99.22 145

EPNet_dtu		95.21 222	94.95 212	95.99 289	96.17 347	90.45 341	98.16 251	97.27 336	96.77 83	93.14 318	98.33 192	90.34 175	98.42 303	85.57 378	98.81 151	99.09 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 223	94.45 238	97.46 185	96.75 322	96.56 132	98.86 122	98.65 143	93.30 263	93.27 311	98.27 199	84.85 296	98.87 259	94.82 211	91.26 318	96.96 279

D2MVS		95.18 224	95.08 205	95.48 311	97.10 301	92.07 308	98.30 230	99.13 33	94.02 215	92.90 323	96.73 335	89.48 191	98.73 274	94.48 224	93.60 286	95.65 371

WR-MVS		95.15 225	94.46 236	97.22 198	96.67 327	96.45 136	98.21 240	98.81 93	94.15 209	93.16 315	97.69 250	87.51 247	98.30 324	95.29 198	88.62 355	96.90 290

TranMVSNet+NR-MVSNet		95.14 226	94.48 234	97.11 209	96.45 337	96.36 143	99.03 76	99.03 40	95.04 169	93.58 297	97.93 226	88.27 228	98.03 344	94.13 236	86.90 375	96.95 281

baseline2		95.11 227	94.52 232	96.87 226	96.65 328	93.56 270	98.27 235	94.10 408	93.45 256	92.02 348	97.43 273	87.45 251	99.19 208	93.88 245	97.41 206	97.87 250

miper_enhance_ethall		95.10 228	94.75 220	96.12 285	97.53 268	93.73 266	96.61 378	98.08 266	92.20 309	93.89 285	96.65 340	92.44 119	98.30 324	94.21 234	91.16 319	96.34 352

Anonymous20240529		95.10 228	94.22 248	97.75 165	99.01 119	94.26 249	98.87 119	98.83 84	85.79 394	96.64 193	98.97 114	78.73 356	99.85 70	96.27 161	94.89 261	99.12 163

test-LLR		95.10 228	94.87 216	95.80 299	96.77 319	89.70 354	96.91 361	95.21 394	95.11 164	94.83 244	95.72 372	87.71 243	98.97 239	93.06 267	98.50 166	98.72 205

WR-MVS_H		95.05 231	94.46 236	96.81 230	96.86 315	95.82 173	99.24 30	99.24 18	93.87 227	92.53 335	96.84 330	90.37 174	98.24 330	93.24 262	87.93 361	96.38 351

miper_ehance_all_eth		95.01 232	94.69 224	95.97 291	97.70 251	93.31 284	97.02 354	98.07 268	92.23 306	93.51 302	96.96 320	91.85 139	98.15 334	93.68 250	91.16 319	96.44 349

testing11		95.00 233	94.28 245	97.16 204	97.96 231	93.36 283	98.09 260	97.06 350	94.94 179	95.33 234	96.15 357	76.89 377	99.40 185	95.77 181	96.30 237	98.72 205

ADS-MVSNet		95.00 233	94.45 238	96.63 243	98.00 225	91.91 311	96.04 385	97.74 292	90.15 357	96.47 205	96.64 341	87.89 239	98.96 243	90.08 333	97.06 211	99.02 179

VPNet		94.99 235	94.19 250	97.40 192	97.16 297	96.57 131	98.71 165	98.97 45	95.67 135	94.84 242	98.24 203	80.36 346	98.67 280	96.46 155	87.32 369	96.96 279

EPMVS		94.99 235	94.48 234	96.52 259	97.22 290	91.75 314	97.23 338	91.66 417	94.11 210	97.28 165	96.81 332	85.70 280	98.84 262	93.04 269	97.28 207	98.97 184

testing91		94.98 237	94.25 247	97.20 199	97.94 232	93.41 278	98.00 270	97.58 301	94.99 172	95.45 230	96.04 361	77.20 372	99.42 184	94.97 207	96.02 251	98.78 201

NR-MVSNet		94.98 237	94.16 253	97.44 187	96.53 332	97.22 101	98.74 156	98.95 49	94.96 175	89.25 374	97.69 250	89.32 198	98.18 332	94.59 221	87.40 367	96.92 283

FMVSNet3		94.97 239	94.26 246	97.11 209	98.18 208	96.62 125	98.56 198	98.26 231	93.67 246	94.09 276	97.10 295	84.25 310	98.01 345	92.08 294	92.14 305	96.70 313

CostFormer		94.95 240	94.73 221	95.60 308	97.28 286	89.06 367	97.53 316	96.89 363	89.66 366	96.82 187	96.72 336	86.05 274	98.95 248	95.53 190	96.13 249	98.79 198

PAPM		94.95 240	94.00 266	97.78 160	97.04 303	95.65 177	96.03 387	98.25 232	91.23 338	94.19 272	97.80 242	91.27 157	98.86 261	82.61 395	97.61 200	98.84 195

CP-MVSNet		94.94 242	94.30 244	96.83 228	96.72 324	95.56 180	99.11 60	98.95 49	93.89 225	92.42 340	97.90 229	87.19 253	98.12 337	94.32 230	88.21 358	96.82 300

TR-MVS		94.94 242	94.20 249	97.17 203	97.75 245	94.14 253	97.59 313	97.02 354	92.28 305	95.75 226	97.64 257	83.88 320	98.96 243	89.77 339	96.15 248	98.40 232

RPSCF		94.87 244	95.40 184	93.26 368	98.89 132	82.06 406	98.33 223	98.06 273	90.30 356	96.56 198	99.26 63	87.09 254	99.49 172	93.82 247	96.32 235	98.24 239

testing99		94.83 245	94.08 258	97.07 212	97.94 232	93.13 292	98.10 259	97.17 342	94.86 181	95.34 231	96.00 364	76.31 380	99.40 185	95.08 204	95.90 252	98.68 211

GA-MVS		94.81 246	94.03 262	97.14 205	97.15 298	93.86 259	96.76 373	97.58 301	94.00 219	94.76 247	97.04 310	80.91 340	98.48 294	91.79 304	96.25 244	99.09 168

c3_l		94.79 247	94.43 240	95.89 296	97.75 245	93.12 294	97.16 348	98.03 275	92.23 306	93.46 305	97.05 309	91.39 151	98.01 345	93.58 255	89.21 347	96.53 335

V42		94.78 248	94.14 255	96.70 236	96.33 342	95.22 200	98.97 89	98.09 265	92.32 303	94.31 265	97.06 306	88.39 226	98.55 289	92.90 274	88.87 353	96.34 352

reproduce_monomvs		94.77 249	94.67 225	95.08 326	98.40 178	89.48 360	98.80 143	98.64 144	97.57 35	93.21 313	97.65 254	80.57 345	98.83 265	97.72 92	89.47 343	96.93 282

CR-MVSNet		94.76 250	94.15 254	96.59 249	97.00 304	93.43 276	94.96 398	97.56 304	92.46 294	96.93 180	96.24 351	88.15 231	97.88 358	87.38 367	96.65 224	98.46 230

v2v482		94.69 251	94.03 262	96.65 239	96.17 347	94.79 224	98.67 176	98.08 266	92.72 286	94.00 281	97.16 293	87.69 246	98.45 299	92.91 273	88.87 353	96.72 309

pmmvs4		94.69 251	93.99 268	96.81 230	95.74 362	95.94 164	97.40 323	97.67 295	90.42 353	93.37 308	97.59 261	89.08 206	98.20 331	92.97 271	91.67 312	96.30 355

cl22		94.68 253	94.19 250	96.13 284	98.11 214	93.60 269	96.94 358	98.31 218	92.43 298	93.32 310	96.87 328	86.51 263	98.28 328	94.10 239	91.16 319	96.51 341

eth_miper_zixun_eth		94.68 253	94.41 241	95.47 312	97.64 256	91.71 316	96.73 375	98.07 268	92.71 287	93.64 295	97.21 291	90.54 172	98.17 333	93.38 258	89.76 335	96.54 333

PCF-MVS	93.45 11	94.68 253	93.43 304	98.42 112	98.62 163	96.77 120	95.48 395	98.20 237	84.63 399	93.34 309	98.32 193	88.55 223	99.81 88	84.80 387	98.96 140	98.68 211
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 256	93.54 299	98.08 141	96.88 314	96.56 132	98.19 245	98.50 181	78.05 410	92.69 330	98.02 217	91.07 163	99.63 141	90.09 332	98.36 175	98.04 246

PS-CasMVS		94.67 256	93.99 268	96.71 234	96.68 326	95.26 197	99.13 57	99.03 40	93.68 244	92.33 341	97.95 225	85.35 286	98.10 338	93.59 254	88.16 360	96.79 301

cascas		94.63 258	93.86 278	96.93 221	96.91 312	94.27 248	96.00 388	98.51 176	85.55 395	94.54 250	96.23 353	84.20 314	98.87 259	95.80 179	96.98 216	97.66 258

tpmvs		94.60 259	94.36 243	95.33 318	97.46 272	88.60 376	96.88 367	97.68 293	91.29 335	93.80 291	96.42 348	88.58 219	99.24 202	91.06 319	96.04 250	98.17 243

LTVRE_ROB	92.95 15	94.60 259	93.90 274	96.68 238	97.41 280	94.42 240	98.52 201	98.59 154	91.69 321	91.21 355	98.35 187	84.87 295	99.04 231	91.06 319	93.44 290	96.60 324
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		94.59 261	93.92 271	96.60 248	96.21 344	94.78 225	98.59 189	98.14 253	91.86 317	94.21 271	97.02 313	87.97 237	98.41 310	91.72 306	89.57 338	96.61 323

ADS-MVSNet2		94.58 262	94.40 242	95.11 324	98.00 225	88.74 374	96.04 385	97.30 332	90.15 357	96.47 205	96.64 341	87.89 239	97.56 371	90.08 333	97.06 211	99.02 179

WBMVS		94.56 263	94.04 260	96.10 286	98.03 223	93.08 296	97.82 295	98.18 242	94.02 215	93.77 293	96.82 331	81.28 335	98.34 317	95.47 193	91.00 322	96.88 292

ACMH	92.88 16	94.55 264	93.95 270	96.34 275	97.63 257	93.26 286	98.81 142	98.49 186	93.43 257	89.74 369	98.53 169	81.91 330	99.08 226	93.69 249	93.30 293	96.70 313
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 265	93.85 279	96.63 243	97.98 229	93.06 297	98.77 152	97.84 287	93.67 246	93.80 291	98.04 216	76.88 378	98.96 243	94.79 213	92.86 298	97.86 251

XVG-ACMP-BASELINE		94.54 265	94.14 255	95.75 302	96.55 331	91.65 317	98.11 257	98.44 192	94.96 175	94.22 270	97.90 229	79.18 355	99.11 220	94.05 241	93.85 279	96.48 346

AUN-MVS		94.53 267	93.73 289	96.92 224	98.50 171	93.52 274	98.34 222	98.10 261	93.83 230	95.94 224	97.98 223	85.59 282	99.03 232	94.35 228	80.94 399	98.22 241

DIV-MVS_self_test		94.52 268	94.03 262	95.99 289	97.57 265	93.38 281	97.05 352	97.94 281	91.74 318	92.81 325	97.10 295	89.12 204	98.07 342	92.60 280	90.30 328	96.53 335

cl____		94.51 269	94.01 265	96.02 288	97.58 261	93.40 280	97.05 352	97.96 280	91.73 320	92.76 327	97.08 301	89.06 207	98.13 336	92.61 279	90.29 329	96.52 338

ETVMVS		94.50 270	93.44 303	97.68 173	98.18 208	95.35 193	98.19 245	97.11 344	93.73 236	96.40 208	95.39 378	74.53 389	98.84 262	91.10 315	96.31 236	98.84 195

GBi-Net		94.49 271	93.80 282	96.56 253	98.21 202	95.00 209	98.82 134	98.18 242	92.46 294	94.09 276	97.07 302	81.16 336	97.95 350	92.08 294	92.14 305	96.72 309

test1		94.49 271	93.80 282	96.56 253	98.21 202	95.00 209	98.82 134	98.18 242	92.46 294	94.09 276	97.07 302	81.16 336	97.95 350	92.08 294	92.14 305	96.72 309

dmvs_re		94.48 273	94.18 252	95.37 316	97.68 252	90.11 348	98.54 200	97.08 346	94.56 195	94.42 259	97.24 288	84.25 310	97.76 363	91.02 322	92.83 299	98.24 239

v8		94.47 274	93.77 285	96.57 252	96.36 340	94.83 221	99.05 69	98.19 239	91.92 314	93.16 315	96.97 318	88.82 217	98.48 294	91.69 307	87.79 362	96.39 350

FMVSNet2		94.47 274	93.61 295	97.04 213	98.21 202	96.43 138	98.79 150	98.27 227	92.46 294	93.50 303	97.09 299	81.16 336	98.00 347	91.09 316	91.93 308	96.70 313

test2506		94.44 276	93.91 273	96.04 287	99.02 117	88.99 370	99.06 67	79.47 429	96.96 75	98.36 105	99.26 63	77.21 371	99.52 167	96.78 148	99.04 134	99.59 83

Patchmatch-test		94.42 277	93.68 293	96.63 243	97.60 259	91.76 313	94.83 402	97.49 316	89.45 370	94.14 274	97.10 295	88.99 208	98.83 265	85.37 381	98.13 182	99.29 133

PEN-MVS		94.42 277	93.73 289	96.49 261	96.28 343	94.84 219	99.17 49	99.00 42	93.51 252	92.23 343	97.83 239	86.10 273	97.90 354	92.55 285	86.92 374	96.74 306

v144192		94.39 279	93.70 291	96.48 263	96.06 352	94.35 244	98.58 191	98.16 250	91.45 326	94.33 264	97.02 313	87.50 249	98.45 299	91.08 318	89.11 348	96.63 321

Baseline_NR-MVSNet		94.35 280	93.81 281	95.96 292	96.20 345	94.05 255	98.61 188	96.67 373	91.44 327	93.85 288	97.60 260	88.57 220	98.14 335	94.39 226	86.93 373	95.68 370

miper_lstm_enhance		94.33 281	94.07 259	95.11 324	97.75 245	90.97 327	97.22 339	98.03 275	91.67 322	92.76 327	96.97 318	90.03 181	97.78 362	92.51 287	89.64 337	96.56 330

v1192		94.32 282	93.58 296	96.53 258	96.10 350	94.45 238	98.50 207	98.17 248	91.54 324	94.19 272	97.06 306	86.95 258	98.43 302	90.14 331	89.57 338	96.70 313

UWE-MVS		94.30 283	93.89 276	95.53 309	97.83 240	88.95 371	97.52 318	93.25 410	94.44 203	96.63 194	97.07 302	78.70 357	99.28 198	91.99 299	97.56 203	98.36 235

ACMH+	92.99 14	94.30 283	93.77 285	95.88 297	97.81 242	92.04 310	98.71 165	98.37 208	93.99 220	90.60 362	98.47 175	80.86 342	99.05 228	92.75 278	92.40 304	96.55 332

v148		94.29 285	93.76 287	95.91 294	96.10 350	92.93 298	98.58 191	97.97 278	92.59 292	93.47 304	96.95 322	88.53 224	98.32 320	92.56 284	87.06 372	96.49 344

v10		94.29 285	93.55 298	96.51 260	96.39 339	94.80 223	98.99 86	98.19 239	91.35 331	93.02 321	96.99 316	88.09 233	98.41 310	90.50 328	88.41 357	96.33 354

MVP-Stereo		94.28 287	93.92 271	95.35 317	94.95 382	92.60 301	97.97 273	97.65 296	91.61 323	90.68 361	97.09 299	86.32 270	98.42 303	89.70 342	99.34 123	95.02 384
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 288	93.33 306	96.97 218	97.19 295	93.38 281	98.74 156	98.57 161	91.21 340	93.81 290	98.58 164	72.85 396	98.77 272	95.05 205	93.93 278	98.77 204

OurMVSNet-221017-0		94.21 289	94.00 266	94.85 334	95.60 366	89.22 365	98.89 110	97.43 324	95.29 154	92.18 344	98.52 172	82.86 326	98.59 287	93.46 257	91.76 310	96.74 306

v1921920		94.20 290	93.47 302	96.40 272	95.98 355	94.08 254	98.52 201	98.15 251	91.33 332	94.25 268	97.20 292	86.41 268	98.42 303	90.04 336	89.39 345	96.69 318

WB-MVSnew		94.19 291	94.04 260	94.66 341	96.82 318	92.14 305	97.86 289	95.96 386	93.50 253	95.64 227	96.77 334	88.06 235	97.99 348	84.87 384	96.86 217	93.85 401

v7n		94.19 291	93.43 304	96.47 264	95.90 358	94.38 243	99.26 27	98.34 214	91.99 312	92.76 327	97.13 294	88.31 227	98.52 292	89.48 347	87.70 363	96.52 338

tpm2		94.19 291	93.76 287	95.46 313	97.23 289	89.04 368	97.31 334	96.85 367	87.08 385	96.21 213	96.79 333	83.75 324	98.74 273	92.43 290	96.23 246	98.59 222

TESTMET0.1,1		94.18 294	93.69 292	95.63 306	96.92 310	89.12 366	96.91 361	94.78 399	93.17 268	94.88 241	96.45 347	78.52 358	98.92 250	93.09 266	98.50 166	98.85 193

dp		94.15 295	93.90 274	94.90 331	97.31 285	86.82 393	96.97 356	97.19 341	91.22 339	96.02 219	96.61 343	85.51 283	99.02 235	90.00 337	94.30 263	98.85 193

ET-MVSNet_ETH3D		94.13 296	92.98 313	97.58 181	98.22 201	96.20 149	97.31 334	95.37 393	94.53 197	79.56 410	97.63 259	86.51 263	97.53 372	96.91 132	90.74 324	99.02 179

tpm		94.13 296	93.80 282	95.12 323	96.50 334	87.91 387	97.44 320	95.89 389	92.62 290	96.37 210	96.30 350	84.13 315	98.30 324	93.24 262	91.66 313	99.14 161

testing222		94.12 298	93.03 312	97.37 195	98.02 224	94.66 226	97.94 276	96.65 375	94.63 192	95.78 225	95.76 367	71.49 397	98.92 250	91.17 314	95.88 253	98.52 226

IterMVS-SCA-FT		94.11 299	93.87 277	94.85 334	97.98 229	90.56 340	97.18 344	98.11 258	93.75 233	92.58 333	97.48 268	83.97 318	97.41 375	92.48 289	91.30 316	96.58 326

Anonymous20231211		94.10 300	93.26 309	96.61 246	99.11 110	94.28 247	99.01 81	98.88 65	86.43 388	92.81 325	97.57 263	81.66 332	98.68 279	94.83 210	89.02 351	96.88 292

IterMVS		94.09 301	93.85 279	94.80 337	97.99 227	90.35 344	97.18 344	98.12 255	93.68 244	92.46 339	97.34 279	84.05 316	97.41 375	92.51 287	91.33 315	96.62 322
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 302	93.51 300	95.80 299	96.77 319	89.70 354	96.91 361	95.21 394	92.89 281	94.83 244	95.72 372	77.69 366	98.97 239	93.06 267	98.50 166	98.72 205

test0.0.03 1		94.08 302	93.51 300	95.80 299	95.53 370	92.89 299	97.38 325	95.97 385	95.11 164	92.51 337	96.66 338	87.71 243	96.94 382	87.03 369	93.67 282	97.57 262

v1240		94.06 304	93.29 308	96.34 275	96.03 354	93.90 258	98.44 214	98.17 248	91.18 341	94.13 275	97.01 315	86.05 274	98.42 303	89.13 352	89.50 342	96.70 313

X-MVStestdata		94.06 304	92.30 328	99.34 26	99.70 22	98.35 44	99.29 22	98.88 65	97.40 44	98.46 97	43.50 424	95.90 45	99.89 54	97.85 84	99.74 52	99.78 24

DTE-MVSNet		93.98 306	93.26 309	96.14 283	96.06 352	94.39 242	99.20 42	98.86 78	93.06 274	91.78 349	97.81 241	85.87 278	97.58 370	90.53 327	86.17 379	96.46 348

pm-mvs1		93.94 307	93.06 311	96.59 249	96.49 335	95.16 202	98.95 95	98.03 275	92.32 303	91.08 357	97.84 236	84.54 306	98.41 310	92.16 292	86.13 382	96.19 359

MS-PatchMatch		93.84 308	93.63 294	94.46 351	96.18 346	89.45 361	97.76 299	98.27 227	92.23 306	92.13 345	97.49 267	79.50 352	98.69 276	89.75 340	99.38 119	95.25 376

tfpnnormal		93.66 309	92.70 319	96.55 257	96.94 309	95.94 164	98.97 89	99.19 27	91.04 342	91.38 354	97.34 279	84.94 294	98.61 284	85.45 380	89.02 351	95.11 380

EU-MVSNet		93.66 309	94.14 255	92.25 378	95.96 357	83.38 402	98.52 201	98.12 255	94.69 188	92.61 332	98.13 210	87.36 252	96.39 394	91.82 303	90.00 333	96.98 278

our_test_3		93.65 311	93.30 307	94.69 339	95.45 374	89.68 356	96.91 361	97.65 296	91.97 313	91.66 352	96.88 326	89.67 188	97.93 353	88.02 363	91.49 314	96.48 346

pmmvs5		93.65 311	92.97 314	95.68 303	95.49 371	92.37 302	98.20 242	97.28 335	89.66 366	92.58 333	97.26 285	82.14 329	98.09 340	93.18 265	90.95 323	96.58 326

test_fmvs2		93.43 313	93.58 296	92.95 372	96.97 307	83.91 398	99.19 44	97.24 338	95.74 130	95.20 236	98.27 199	69.65 399	98.72 275	96.26 162	93.73 281	96.24 356

tpm cat1		93.36 314	92.80 316	95.07 327	97.58 261	87.97 386	96.76 373	97.86 286	82.17 406	93.53 299	96.04 361	86.13 272	99.13 216	89.24 350	95.87 254	98.10 245

JIA-IIPM		93.35 315	92.49 324	95.92 293	96.48 336	90.65 337	95.01 397	96.96 357	85.93 392	96.08 217	87.33 414	87.70 245	98.78 271	91.35 311	95.58 258	98.34 236

SixPastTwentyTwo		93.34 316	92.86 315	94.75 338	95.67 364	89.41 363	98.75 153	96.67 373	93.89 225	90.15 367	98.25 202	80.87 341	98.27 329	90.90 323	90.64 325	96.57 328

USDC		93.33 317	92.71 318	95.21 320	96.83 317	90.83 333	96.91 361	97.50 314	93.84 228	90.72 360	98.14 209	77.69 366	98.82 267	89.51 346	93.21 295	95.97 364

IB-MVS	91.98 17	93.27 318	91.97 332	97.19 201	97.47 271	93.41 278	97.09 351	95.99 384	93.32 261	92.47 338	95.73 370	78.06 364	99.53 164	94.59 221	82.98 390	98.62 218
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		93.26 319	92.21 329	96.41 270	97.73 249	93.13 292	95.65 392	97.03 352	91.27 337	94.04 279	96.06 360	75.33 385	97.19 378	86.56 371	96.23 246	98.92 190

ppachtmachnet_test		93.22 320	92.63 320	94.97 329	95.45 374	90.84 332	96.88 367	97.88 285	90.60 348	92.08 346	97.26 285	88.08 234	97.86 359	85.12 383	90.33 327	96.22 357

Patchmtry		93.22 320	92.35 327	95.84 298	96.77 319	93.09 295	94.66 405	97.56 304	87.37 384	92.90 323	96.24 351	88.15 231	97.90 354	87.37 368	90.10 332	96.53 335

testing3		93.19 322	92.48 325	95.30 319	98.07 216	92.27 303	98.64 182	97.17 342	93.94 224	93.98 282	97.04 310	67.97 403	96.01 398	88.40 358	97.14 209	97.63 259

FMVSNet1		93.19 322	92.07 330	96.56 253	97.54 266	95.00 209	98.82 134	98.18 242	90.38 354	92.27 342	97.07 302	73.68 394	97.95 350	89.36 349	91.30 316	96.72 309

LF4IMVS		93.14 324	92.79 317	94.20 355	95.88 359	88.67 375	97.66 307	97.07 348	93.81 231	91.71 350	97.65 254	77.96 365	98.81 268	91.47 310	91.92 309	95.12 379

mmtdpeth		93.12 325	92.61 321	94.63 343	97.60 259	89.68 356	99.21 39	97.32 331	94.02 215	97.72 147	94.42 389	77.01 376	99.44 182	99.05 23	77.18 411	94.78 389

testgi		93.06 326	92.45 326	94.88 333	96.43 338	89.90 349	98.75 153	97.54 310	95.60 137	91.63 353	97.91 228	74.46 391	97.02 380	86.10 374	93.67 282	97.72 256

PatchT		93.06 326	91.97 332	96.35 274	96.69 325	92.67 300	94.48 408	97.08 346	86.62 386	97.08 172	92.23 408	87.94 238	97.90 354	78.89 405	96.69 222	98.49 228

RPMNet		92.81 328	91.34 338	97.24 197	97.00 304	93.43 276	94.96 398	98.80 100	82.27 405	96.93 180	92.12 409	86.98 257	99.82 83	76.32 410	96.65 224	98.46 230

myMVS_eth3d		92.73 329	92.01 331	94.89 332	97.39 281	90.94 328	97.91 279	97.46 318	93.16 269	93.42 306	95.37 379	68.09 402	96.12 396	88.34 359	96.99 213	97.60 260

TransMVSNet (Re)		92.67 330	91.51 337	96.15 282	96.58 330	94.65 227	98.90 106	96.73 369	90.86 345	89.46 373	97.86 233	85.62 281	98.09 340	86.45 372	81.12 397	95.71 369

ttmdpeth		92.61 331	91.96 334	94.55 345	94.10 392	90.60 339	98.52 201	97.29 333	92.67 288	90.18 365	97.92 227	79.75 351	97.79 361	91.09 316	86.15 381	95.26 375

Syy-MVS		92.55 332	92.61 321	92.38 375	97.39 281	83.41 401	97.91 279	97.46 318	93.16 269	93.42 306	95.37 379	84.75 299	96.12 396	77.00 409	96.99 213	97.60 260

K. test v3		92.55 332	91.91 335	94.48 349	95.64 365	89.24 364	99.07 66	94.88 398	94.04 213	86.78 389	97.59 261	77.64 369	97.64 367	92.08 294	89.43 344	96.57 328

DSMNet-mixed		92.52 334	92.58 323	92.33 376	94.15 391	82.65 404	98.30 230	94.26 405	89.08 375	92.65 331	95.73 370	85.01 293	95.76 400	86.24 373	97.76 195	98.59 222

TinyColmap		92.31 335	91.53 336	94.65 342	96.92 310	89.75 352	96.92 359	96.68 372	90.45 352	89.62 370	97.85 235	76.06 383	98.81 268	86.74 370	92.51 303	95.41 373

gg-mvs-nofinetune		92.21 336	90.58 344	97.13 206	96.75 322	95.09 206	95.85 389	89.40 422	85.43 396	94.50 252	81.98 417	80.80 343	98.40 316	92.16 292	98.33 176	97.88 249

FMVSNet5		91.81 337	90.92 340	94.49 348	97.21 291	92.09 307	98.00 270	97.55 309	89.31 373	90.86 359	95.61 376	74.48 390	95.32 404	85.57 378	89.70 336	96.07 362

pmmvs6		91.77 338	90.63 343	95.17 322	94.69 388	91.24 324	98.67 176	97.92 283	86.14 390	89.62 370	97.56 265	75.79 384	98.34 317	90.75 325	84.56 384	95.94 365

Anonymous20231206		91.66 339	91.10 339	93.33 366	94.02 396	87.35 390	98.58 191	97.26 337	90.48 350	90.16 366	96.31 349	83.83 322	96.53 392	79.36 403	89.90 334	96.12 360

Patchmatch-RL test		91.49 340	90.85 341	93.41 364	91.37 407	84.40 396	92.81 412	95.93 388	91.87 316	87.25 385	94.87 385	88.99 208	96.53 392	92.54 286	82.00 392	99.30 131

test_0402		91.32 341	90.27 347	94.48 349	96.60 329	91.12 325	98.50 207	97.22 339	86.10 391	88.30 381	96.98 317	77.65 368	97.99 348	78.13 407	92.94 297	94.34 390

test_vis1_rt		91.29 342	90.65 342	93.19 370	97.45 275	86.25 394	98.57 197	90.90 420	93.30 263	86.94 388	93.59 398	62.07 412	99.11 220	97.48 114	95.58 258	94.22 393

PVSNet_0	88.72 19	91.28 343	90.03 350	95.00 328	97.99 227	87.29 391	94.84 401	98.50 181	92.06 311	89.86 368	95.19 381	79.81 350	99.39 188	92.27 291	69.79 417	98.33 237

mvs5depth		91.23 344	90.17 348	94.41 353	92.09 404	89.79 351	95.26 396	96.50 377	90.73 346	91.69 351	97.06 306	76.12 382	98.62 283	88.02 363	84.11 387	94.82 386

Anonymous20240521		91.18 345	90.44 345	93.42 363	93.70 397	88.47 379	98.94 98	97.56 304	88.46 379	89.56 372	95.08 384	77.15 374	96.97 381	83.92 390	89.55 340	94.82 386

EG-PatchMatch MVS		91.13 346	90.12 349	94.17 357	94.73 387	89.00 369	98.13 254	97.81 288	89.22 374	85.32 399	96.46 346	67.71 404	98.42 303	87.89 366	93.82 280	95.08 381

TDRefinement		91.06 347	89.68 352	95.21 320	85.35 422	91.49 320	98.51 206	97.07 348	91.47 325	88.83 379	97.84 236	77.31 370	99.09 225	92.79 277	77.98 409	95.04 383

UnsupCasMVSNet_eth		90.99 348	89.92 351	94.19 356	94.08 393	89.83 350	97.13 350	98.67 136	93.69 242	85.83 395	96.19 356	75.15 386	96.74 386	89.14 351	79.41 404	96.00 363

test20.03		90.89 349	90.38 346	92.43 374	93.48 398	88.14 385	98.33 223	97.56 304	93.40 258	87.96 382	96.71 337	80.69 344	94.13 409	79.15 404	86.17 379	95.01 385

MDA-MVSNet_test_wron		90.71 350	89.38 355	94.68 340	94.83 384	90.78 334	97.19 343	97.46 318	87.60 382	72.41 417	95.72 372	86.51 263	96.71 389	85.92 376	86.80 376	96.56 330

YYNet1		90.70 351	89.39 354	94.62 344	94.79 386	90.65 337	97.20 341	97.46 318	87.54 383	72.54 416	95.74 368	86.51 263	96.66 390	86.00 375	86.76 377	96.54 333

KD-MVS_self_test		90.38 352	89.38 355	93.40 365	92.85 401	88.94 372	97.95 274	97.94 281	90.35 355	90.25 364	93.96 395	79.82 349	95.94 399	84.62 389	76.69 412	95.33 374

pmmvs-eth3d		90.36 353	89.05 358	94.32 354	91.10 409	92.12 306	97.63 312	96.95 358	88.86 377	84.91 400	93.13 403	78.32 360	96.74 386	88.70 355	81.81 394	94.09 396

CL-MVSNet_self_test		90.11 354	89.14 357	93.02 371	91.86 406	88.23 384	96.51 381	98.07 268	90.49 349	90.49 363	94.41 390	84.75 299	95.34 403	80.79 399	74.95 414	95.50 372

new_pmnet		90.06 355	89.00 359	93.22 369	94.18 390	88.32 382	96.42 383	96.89 363	86.19 389	85.67 396	93.62 397	77.18 373	97.10 379	81.61 397	89.29 346	94.23 392

MDA-MVSNet-bldmvs		89.97 356	88.35 362	94.83 336	95.21 378	91.34 321	97.64 309	97.51 313	88.36 380	71.17 418	96.13 358	79.22 354	96.63 391	83.65 391	86.27 378	96.52 338

CMPMVS	66.06 21	89.70 357	89.67 353	89.78 383	93.19 399	76.56 409	97.00 355	98.35 211	80.97 407	81.57 405	97.75 244	74.75 388	98.61 284	89.85 338	93.63 284	94.17 394
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 358	88.28 363	93.82 360	92.81 402	91.08 326	98.01 268	97.45 322	87.95 381	87.90 383	95.87 366	67.63 405	94.56 408	78.73 406	88.18 359	95.83 367

KD-MVS_2432*1600		89.61 359	87.96 367	94.54 346	94.06 394	91.59 318	95.59 393	97.63 298	89.87 362	88.95 376	94.38 392	78.28 361	96.82 384	84.83 385	68.05 418	95.21 377

miper_refine_blended		89.61 359	87.96 367	94.54 346	94.06 394	91.59 318	95.59 393	97.63 298	89.87 362	88.95 376	94.38 392	78.28 361	96.82 384	84.83 385	68.05 418	95.21 377

MVStest1		89.53 361	87.99 366	94.14 359	94.39 389	90.42 342	98.25 237	96.84 368	82.81 402	81.18 407	97.33 281	77.09 375	96.94 382	85.27 382	78.79 405	95.06 382

MVS-HIRNet		89.46 362	88.40 361	92.64 373	97.58 261	82.15 405	94.16 411	93.05 414	75.73 413	90.90 358	82.52 416	79.42 353	98.33 319	83.53 392	98.68 153	97.43 263

OpenMVS_ROB	86.42 20	89.00 363	87.43 371	93.69 361	93.08 400	89.42 362	97.91 279	96.89 363	78.58 409	85.86 394	94.69 386	69.48 400	98.29 327	77.13 408	93.29 294	93.36 403

mvsany_test3		88.80 364	88.04 364	91.09 382	89.78 412	81.57 407	97.83 294	95.49 392	93.81 231	87.53 384	93.95 396	56.14 415	97.43 374	94.68 214	83.13 389	94.26 391

new-patchmatchnet		88.50 365	87.45 370	91.67 380	90.31 411	85.89 395	97.16 348	97.33 330	89.47 369	83.63 402	92.77 405	76.38 379	95.06 406	82.70 394	77.29 410	94.06 398

APD_test1		88.22 366	88.01 365	88.86 385	95.98 355	74.66 417	97.21 340	96.44 379	83.96 401	86.66 391	97.90 229	60.95 413	97.84 360	82.73 393	90.23 330	94.09 396

PM-MVS		87.77 367	86.55 373	91.40 381	91.03 410	83.36 403	96.92 359	95.18 396	91.28 336	86.48 393	93.42 399	53.27 416	96.74 386	89.43 348	81.97 393	94.11 395

dmvs_testset		87.64 368	88.93 360	83.79 394	95.25 377	63.36 426	97.20 341	91.17 418	93.07 273	85.64 397	95.98 365	85.30 290	91.52 416	69.42 415	87.33 368	96.49 344

test_fmvs3		87.17 369	87.06 372	87.50 387	91.21 408	75.66 412	99.05 69	96.61 376	92.79 285	88.85 378	92.78 404	43.72 419	93.49 410	93.95 242	84.56 384	93.34 404

UnsupCasMVSNet_bld		87.17 369	85.12 376	93.31 367	91.94 405	88.77 373	94.92 400	98.30 224	84.30 400	82.30 403	90.04 411	63.96 410	97.25 377	85.85 377	74.47 416	93.93 400

N_pmnet		87.12 371	87.77 369	85.17 391	95.46 373	61.92 427	97.37 327	70.66 432	85.83 393	88.73 380	96.04 361	85.33 288	97.76 363	80.02 400	90.48 326	95.84 366

pmmvs3		86.67 372	84.86 377	92.11 379	88.16 416	87.19 392	96.63 377	94.75 400	79.88 408	87.22 386	92.75 406	66.56 407	95.20 405	81.24 398	76.56 413	93.96 399

test_f		86.07 373	85.39 374	88.10 386	89.28 414	75.57 413	97.73 302	96.33 381	89.41 372	85.35 398	91.56 410	43.31 421	95.53 401	91.32 312	84.23 386	93.21 405

WB-MVS		84.86 374	85.33 375	83.46 395	89.48 413	69.56 421	98.19 245	96.42 380	89.55 368	81.79 404	94.67 387	84.80 297	90.12 417	52.44 421	80.64 401	90.69 408

SSC-MVS		84.27 375	84.71 378	82.96 399	89.19 415	68.83 422	98.08 261	96.30 382	89.04 376	81.37 406	94.47 388	84.60 304	89.89 418	49.80 423	79.52 403	90.15 409

dongtai		82.47 376	81.88 379	84.22 393	95.19 379	76.03 410	94.59 407	74.14 431	82.63 403	87.19 387	96.09 359	64.10 409	87.85 421	58.91 419	84.11 387	88.78 413

test_vis3_rt		79.22 377	77.40 384	84.67 392	86.44 420	74.85 416	97.66 307	81.43 427	84.98 397	67.12 420	81.91 418	28.09 429	97.60 368	88.96 353	80.04 402	81.55 418

test_method		79.03 378	78.17 380	81.63 400	86.06 421	54.40 432	82.75 420	96.89 363	39.54 424	80.98 408	95.57 377	58.37 414	94.73 407	84.74 388	78.61 406	95.75 368

testf1		79.02 379	77.70 381	82.99 397	88.10 417	66.90 423	94.67 403	93.11 411	71.08 415	74.02 413	93.41 400	34.15 425	93.25 411	72.25 413	78.50 407	88.82 411

APD_test2		79.02 379	77.70 381	82.99 397	88.10 417	66.90 423	94.67 403	93.11 411	71.08 415	74.02 413	93.41 400	34.15 425	93.25 411	72.25 413	78.50 407	88.82 411

LCM-MVSNet		78.70 381	76.24 387	86.08 389	77.26 428	71.99 419	94.34 409	96.72 370	61.62 419	76.53 411	89.33 412	33.91 427	92.78 414	81.85 396	74.60 415	93.46 402

kuosan		78.45 382	77.69 383	80.72 401	92.73 403	75.32 414	94.63 406	74.51 430	75.96 411	80.87 409	93.19 402	63.23 411	79.99 425	42.56 425	81.56 396	86.85 417

Gipuma		78.40 383	76.75 386	83.38 396	95.54 368	80.43 408	79.42 421	97.40 326	64.67 418	73.46 415	80.82 419	45.65 418	93.14 413	66.32 417	87.43 366	76.56 421
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 384	75.44 388	85.46 390	82.54 423	74.95 415	94.23 410	93.08 413	72.80 414	74.68 412	87.38 413	36.36 424	91.56 415	73.95 411	63.94 420	89.87 410

FPMVS		77.62 385	77.14 385	79.05 403	79.25 426	60.97 428	95.79 390	95.94 387	65.96 417	67.93 419	94.40 391	37.73 423	88.88 420	68.83 416	88.46 356	87.29 414

EGC-MVSNET		75.22 386	69.54 389	92.28 377	94.81 385	89.58 358	97.64 309	96.50 377	1.82 429	5.57 430	95.74 368	68.21 401	96.26 395	73.80 412	91.71 311	90.99 407

ANet_high		69.08 387	65.37 391	80.22 402	65.99 430	71.96 420	90.91 416	90.09 421	82.62 404	49.93 425	78.39 420	29.36 428	81.75 422	62.49 418	38.52 424	86.95 416

tmp_tt		68.90 388	66.97 390	74.68 405	50.78 432	59.95 429	87.13 417	83.47 426	38.80 425	62.21 421	96.23 353	64.70 408	76.91 427	88.91 354	30.49 425	87.19 415

PMVS	61.03 23	65.95 389	63.57 393	73.09 406	57.90 431	51.22 433	85.05 419	93.93 409	54.45 420	44.32 426	83.57 415	13.22 430	89.15 419	58.68 420	81.00 398	78.91 420
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 390	64.25 392	67.02 407	82.28 424	59.36 430	91.83 415	85.63 424	52.69 421	60.22 422	77.28 421	41.06 422	80.12 424	46.15 424	41.14 422	61.57 423

EMVS		64.07 391	63.26 394	66.53 408	81.73 425	58.81 431	91.85 414	84.75 425	51.93 423	59.09 423	75.13 422	43.32 420	79.09 426	42.03 426	39.47 423	61.69 422

MVE	62.14 22	63.28 392	59.38 395	74.99 404	74.33 429	65.47 425	85.55 418	80.50 428	52.02 422	51.10 424	75.00 423	10.91 433	80.50 423	51.60 422	53.40 421	78.99 419
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 393	30.18 397	30.16 409	78.61 427	43.29 434	66.79 422	14.21 433	17.31 426	14.82 429	11.93 429	11.55 432	41.43 428	37.08 427	19.30 426	5.76 426

cdsmvs_eth3d_5k		23.98 394	31.98 396	0.00 412	0.00 435	0.00 437	0.00 423	98.59 154	0.00 430	0.00 431	98.61 159	90.60 171	0.00 431	0.00 430	0.00 429	0.00 427

testmvs		21.48 395	24.95 398	11.09 411	14.89 433	6.47 436	96.56 379	9.87 434	7.55 427	17.93 427	39.02 425	9.43 434	5.90 430	16.56 429	12.72 427	20.91 425

test123		20.95 396	23.72 399	12.64 410	13.54 434	8.19 435	96.55 380	6.13 435	7.48 428	16.74 428	37.98 426	12.97 431	6.05 429	16.69 428	5.43 428	23.68 424

ab-mvs-re		8.20 397	10.94 400	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	98.43 177	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		7.88 398	10.50 401	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	94.51 87	0.00 431	0.00 430	0.00 429	0.00 427

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

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

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

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

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

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

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

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

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

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

WAC-MVS						90.94 328								88.66 356

FOURS1					99.82 1	98.66 24	99.69 1	98.95 49	97.46 42	99.39 34

MSC_two_6792asdad				99.62 6	99.17 100	99.08 11		98.63 147					99.94 10	98.53 42	99.80 24	99.86 8

PC_three_1452									95.08 168	99.60 23	99.16 84	97.86 2	98.47 297	97.52 112	99.72 59	99.74 40

No_MVS				99.62 6	99.17 100	99.08 11		98.63 147					99.94 10	98.53 42	99.80 24	99.86 8

test_one_0601					99.66 26	99.25 2		98.86 78	97.55 36	99.20 46	99.47 27	97.57 6

eth-test2					0.00 435

eth-test					0.00 435

ZD-MVS					99.46 52	98.70 23		98.79 105	93.21 266	98.67 84	98.97 114	95.70 49	99.83 76	96.07 166	99.58 87

RE-MVS-def			98.34 41		99.49 46	97.86 69	99.11 60	98.80 100	96.49 98	99.17 49	99.35 50	95.29 65		97.72 92	99.65 72	99.71 53

IU-MVS					99.71 19	99.23 7		98.64 144	95.28 155	99.63 22				98.35 59	99.81 15	99.83 13

OPU-MVS				99.37 22	99.24 92	99.05 14	99.02 79				99.16 84	97.81 3	99.37 189	97.24 121	99.73 55	99.70 57

test_241102_TWO								98.87 72	97.65 29	99.53 27	99.48 25	97.34 11	99.94 10	98.43 54	99.80 24	99.83 13

test_241102_ONE					99.71 19	99.24 5		98.87 72	97.62 31	99.73 14	99.39 38	97.53 7	99.74 118

9.14			98.06 66		99.47 50		98.71 165	98.82 87	94.36 205	99.16 52	99.29 59	96.05 37	99.81 88	97.00 127	99.71 61

save fliter					99.46 52	98.38 35	98.21 240	98.71 123	97.95 20

test_0728_THIRD									97.32 50	99.45 29	99.46 31	97.88 1	99.94 10	98.47 50	99.86 2	99.85 10

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 89	98.88 65					99.94 10	98.47 50	99.81 15	99.84 12

test0726					99.72 12	99.25 2	99.06 67	98.88 65	97.62 31	99.56 24	99.50 22	97.42 9

GSMVS																99.20 148

test_part2					99.63 29	99.18 10				99.27 43

sam_mvs1												89.45 194				99.20 148

sam_mvs												88.99 208

ambc				89.49 384	86.66 419	75.78 411	92.66 413	96.72 370		86.55 392	92.50 407	46.01 417	97.90 354	90.32 329	82.09 391	94.80 388

MTGPA								98.74 115

test_post1							96.68 376				30.43 428	87.85 242	98.69 276	92.59 282

test_post											31.83 427	88.83 215	98.91 252

patchmatchnet-post											95.10 383	89.42 195	98.89 256

GG-mvs-BLEND				96.59 249	96.34 341	94.98 212	96.51 381	88.58 423		93.10 320	94.34 394	80.34 348	98.05 343	89.53 345	96.99 213	96.74 306

MTMP							98.89 110	94.14 407

gm-plane-assit					95.88 359	87.47 389			89.74 365		96.94 323		99.19 208	93.32 261

test9_res														96.39 160	99.57 88	99.69 60

TEST9					99.31 68	98.50 29	97.92 277	98.73 118	92.63 289	97.74 144	98.68 154	96.20 32	99.80 95

test_8					99.29 77	98.44 31	97.89 285	98.72 120	92.98 277	97.70 149	98.66 157	96.20 32	99.80 95

agg_prior2														95.87 176	99.57 88	99.68 65

agg_prior					99.30 72	98.38 35		98.72 120		97.57 161			99.81 88

TestCases				96.99 215	99.25 85	93.21 290		98.18 242	91.36 329	93.52 300	98.77 143	84.67 302	99.72 120	89.70 342	97.87 190	98.02 247

test_prior4						98.01 65	97.86 289

test_prior2							97.80 296		96.12 115	97.89 137	98.69 153	95.96 41		96.89 136	99.60 82

test_prior				99.19 44	99.31 68	98.22 52		98.84 82					99.70 126			99.65 73

旧先验2							97.57 315		91.30 334	98.67 84			99.80 95	95.70 185

新几何2							97.64 309

新几何1				99.16 49	99.34 61	98.01 65		98.69 128	90.06 359	98.13 113	98.95 121	94.60 85	99.89 54	91.97 301	99.47 107	99.59 83

旧先验1					99.29 77	97.48 83		98.70 127			99.09 100	95.56 52			99.47 107	99.61 79

无先验							97.58 314	98.72 120	91.38 328				99.87 65	93.36 260		99.60 81

原ACMM2							97.67 306

原ACMM1				98.65 86	99.32 66	96.62 125		98.67 136	93.27 265	97.81 139	98.97 114	95.18 72	99.83 76	93.84 246	99.46 110	99.50 95

test222					99.23 93	97.17 103	97.40 323	98.66 139	88.68 378	98.05 119	98.96 119	94.14 98			99.53 99	99.61 79

testdata2													99.89 54	91.65 308

segment_acmp												96.85 14

testdata				98.26 123	99.20 98	95.36 191		98.68 131	91.89 315	98.60 92	99.10 93	94.44 92	99.82 83	94.27 232	99.44 111	99.58 87

testdata1							97.32 333		96.34 106

test12				99.18 46	99.16 104	98.19 54		98.53 170		98.07 117		95.13 75	99.72 120		99.56 94	99.63 77

plane_prior7					97.42 277	94.63 229

plane_prior6					97.35 284	94.61 232						87.09 254

plane_prior5								98.56 164					99.03 232	96.07 166	94.27 264	96.92 283

plane_prior4											98.28 196

plane_prior3						94.61 232			97.02 72	95.34 231

plane_prior2							98.80 143		97.28 53

plane_prior1					97.37 283

plane_prior						94.60 234	98.44 214		96.74 86						94.22 266

n2								0.00 436

nn								0.00 436

door-mid								94.37 403

lessismore_v0				94.45 352	94.93 383	88.44 380		91.03 419		86.77 390	97.64 257	76.23 381	98.42 303	90.31 330	85.64 383	96.51 341

LGP-MVS_train				96.47 264	97.46 272	93.54 271		98.54 168	94.67 190	94.36 262	98.77 143	85.39 284	99.11 220	95.71 183	94.15 270	96.76 304

test11								98.66 139

door								94.64 401

HQP5-MVS						94.25 250

HQP-NCC					97.20 292		98.05 264		96.43 100	94.45 254

ACMP_Plane					97.20 292		98.05 264		96.43 100	94.45 254

BP-MVS														95.30 196

HQP4-MVS										94.45 254			98.96 243			96.87 295

HQP3-MVS								98.46 188							94.18 268

HQP2-MVS												86.75 260

NP-MVS					97.28 286	94.51 237					97.73 245

MDTV_nov1_ep13_2view						84.26 397	96.89 366		90.97 343	97.90 136		89.89 183		93.91 244		99.18 157

MDTV_nov1_ep13			95.40 184		97.48 270	88.34 381	96.85 369	97.29 333	93.74 235	97.48 163	97.26 285	89.18 202	99.05 228	91.92 302	97.43 205

ACMMP++_ref															92.97 296

ACMMP++															93.61 285

Test By Simon												94.64 84

ITE_SJBPF				95.44 314	97.42 277	91.32 322		97.50 314	95.09 167	93.59 296	98.35 187	81.70 331	98.88 258	89.71 341	93.39 291	96.12 360

DeepMVS_CX				86.78 388	97.09 302	72.30 418		95.17 397	75.92 412	84.34 401	95.19 381	70.58 398	95.35 402	79.98 402	89.04 350	92.68 406

High-resolution multi-view benchmark