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 51	99.43 54	97.48 76	98.88 108	99.30 13	98.47 9	99.85 4	99.43 28	96.71 17	99.96 4	99.86 1	99.80 19	99.89 5

SED-MVS		99.09 1	98.91 4	99.63 4	99.71 19	99.24 5	99.02 74	98.87 69	97.65 22	99.73 10	99.48 18	97.53 7	99.94 8	98.43 42	99.81 12	99.70 53

DVP-MVS++		99.08 3	98.89 5	99.64 3	99.17 94	99.23 7	99.69 1	98.88 62	97.32 42	99.53 23	99.47 20	97.81 3	99.94 8	98.47 38	99.72 51	99.74 37

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 47	99.41 56	97.54 74	98.89 103	99.31 12	98.49 8	99.86 2	99.42 29	96.45 24	99.96 4	99.86 1	99.74 45	99.90 3

DVP-MVS		99.03 5	98.83 9	99.63 4	99.72 12	99.25 2	98.97 84	98.58 149	97.62 24	99.45 25	99.46 24	97.42 9	99.94 8	98.47 38	99.81 12	99.69 56
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 12	98.93 50	97.38 39	99.41 28	99.54 8	96.66 18	99.84 67	98.86 21	99.85 5	99.87 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_fmvsmconf_n		98.92 7	98.87 6	99.04 55	98.88 126	97.25 88	98.82 126	99.34 10	98.75 3	99.80 5	99.61 4	95.16 68	99.95 7	99.70 6	99.80 19	99.93 1

DPE-MVS		98.92 7	98.67 12	99.65 2	99.58 32	99.20 9	98.42 203	98.91 56	97.58 27	99.54 22	99.46 24	97.10 12	99.94 8	97.64 87	99.84 10	99.83 13
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SteuartSystems-ACMMP		98.90 9	98.75 10	99.36 21	99.22 89	98.43 33	99.10 58	98.87 69	97.38 39	99.35 32	99.40 31	97.78 5	99.87 58	97.77 77	99.85 5	99.78 21
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_1920		98.87 10	99.01 3	98.45 93	99.42 55	96.43 126	98.96 89	99.36 9	98.63 5	99.86 2	99.51 13	95.91 39	99.97 1	99.72 5	99.75 41	98.94 174

TSAR-MVS + MP.		98.78 11	98.62 13	99.24 36	99.69 24	98.28 45	99.14 49	98.66 131	96.84 71	99.56 20	99.31 51	96.34 25	99.70 119	98.32 48	99.73 48	99.73 42
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 11	98.56 16	99.45 15	99.32 62	98.87 19	98.47 195	98.81 86	97.72 17	98.76 68	99.16 77	97.05 13	99.78 101	98.06 57	99.66 61	99.69 56

MSP-MVS		98.74 13	98.55 17	99.29 29	99.75 3	98.23 46	99.26 27	98.88 62	97.52 29	99.41 28	98.78 130	96.00 35	99.79 98	97.79 76	99.59 76	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 14	98.49 21	99.34 23	99.70 22	98.35 41	99.29 22	98.88 62	97.40 36	98.46 86	99.20 67	95.90 41	99.89 47	97.85 71	99.74 45	99.78 21

MCST-MVS		98.65 15	98.37 29	99.48 13	99.60 31	98.87 19	98.41 204	98.68 123	97.04 63	98.52 85	98.80 128	96.78 16	99.83 69	97.93 64	99.61 72	99.74 37

SD-MVS		98.64 16	98.68 11	98.53 85	99.33 59	98.36 40	98.90 99	98.85 78	97.28 45	99.72 12	99.39 32	96.63 20	97.60 346	98.17 52	99.85 5	99.64 71
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 17	98.40 26	99.32 28	99.72 12	98.29 44	99.23 31	98.96 45	96.10 104	98.94 53	99.17 74	96.06 32	99.92 31	97.62 88	99.78 29	99.75 35

ACMMP_NAP		98.61 18	98.30 41	99.55 9	99.62 30	98.95 17	98.82 126	98.81 86	95.80 118	99.16 44	99.47 20	95.37 56	99.92 31	97.89 68	99.75 41	99.79 19

region2R		98.61 18	98.38 28	99.29 29	99.74 7	98.16 51	99.23 31	98.93 50	96.15 101	98.94 53	99.17 74	95.91 39	99.94 8	97.55 95	99.79 25	99.78 21

NCCC		98.61 18	98.35 32	99.38 18	99.28 77	98.61 26	98.45 196	98.76 104	97.82 16	98.45 89	98.93 114	96.65 19	99.83 69	97.38 104	99.41 106	99.71 49

SF-MVS		98.59 21	98.32 40	99.41 17	99.54 35	98.71 22	99.04 68	98.81 86	95.12 153	99.32 33	99.39 32	96.22 26	99.84 67	97.72 80	99.73 48	99.67 65

ACMMPR		98.59 21	98.36 30	99.29 29	99.74 7	98.15 52	99.23 31	98.95 46	96.10 104	98.93 57	99.19 72	95.70 45	99.94 8	97.62 88	99.79 25	99.78 21

test_fmvsmconf0.1_n		98.58 23	98.44 24	98.99 57	97.73 228	97.15 93	98.84 122	98.97 42	98.75 3	99.43 27	99.54 8	93.29 102	99.93 25	99.64 9	99.79 25	99.89 5

SMA-MVS		98.58 23	98.25 44	99.56 8	99.51 39	99.04 15	98.95 90	98.80 93	93.67 226	99.37 31	99.52 11	96.52 22	99.89 47	98.06 57	99.81 12	99.76 34
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 23	98.29 42	99.46 14	99.76 2	98.64 25	98.90 99	98.74 108	97.27 49	98.02 111	99.39 32	94.81 77	99.96 4	97.91 66	99.79 25	99.77 27

HPM-MVS++		98.58 23	98.25 44	99.55 9	99.50 41	99.08 11	98.72 154	98.66 131	97.51 30	98.15 100	98.83 125	95.70 45	99.92 31	97.53 97	99.67 59	99.66 68

SR-MVS		98.57 27	98.35 32	99.24 36	99.53 36	98.18 49	99.09 59	98.82 81	96.58 83	99.10 46	99.32 49	95.39 54	99.82 76	97.70 84	99.63 69	99.72 45

CP-MVS		98.57 27	98.36 30	99.19 40	99.66 26	97.86 62	99.34 18	98.87 69	95.96 109	98.60 81	99.13 82	96.05 33	99.94 8	97.77 77	99.86 1	99.77 27

MSLP-MVS++		98.56 29	98.57 15	98.55 81	99.26 80	96.80 105	98.71 155	99.05 36	97.28 45	98.84 62	99.28 54	96.47 23	99.40 168	98.52 36	99.70 54	99.47 100

DeepC-MVS_fast	96.70 1	98.55 30	98.34 35	99.18 42	99.25 81	98.04 57	98.50 192	98.78 100	97.72 17	98.92 59	99.28 54	95.27 62	99.82 76	97.55 95	99.77 31	99.69 56
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 31	98.35 32	99.13 48	99.49 45	97.86 62	99.11 55	98.80 93	96.49 86	99.17 41	99.35 44	95.34 58	99.82 76	97.72 80	99.65 64	99.71 49

APD-MVS_3200maxsize		98.53 32	98.33 39	99.15 46	99.50 41	97.92 61	99.15 47	98.81 86	96.24 97	99.20 38	99.37 38	95.30 60	99.80 88	97.73 79	99.67 59	99.72 45

MM		98.51 33	98.24 46	99.33 26	99.12 102	98.14 54	98.93 95	97.02 334	98.96 1	99.17 41	99.47 20	91.97 129	99.94 8	99.85 4	99.69 56	99.91 2

mPP-MVS		98.51 33	98.26 43	99.25 35	99.75 3	98.04 57	99.28 24	98.81 86	96.24 97	98.35 96	99.23 62	95.46 51	99.94 8	97.42 102	99.81 12	99.77 27

ZNCC-MVS		98.49 35	98.20 52	99.35 22	99.73 11	98.39 34	99.19 42	98.86 75	95.77 119	98.31 99	99.10 86	95.46 51	99.93 25	97.57 94	99.81 12	99.74 37

CS-MVS-test		98.49 35	98.50 20	98.46 92	99.20 92	97.05 95	99.64 4	98.50 169	97.45 35	98.88 60	99.14 81	95.25 64	99.15 191	98.83 22	99.56 86	99.20 139

PGM-MVS		98.49 35	98.23 48	99.27 34	99.72 12	98.08 56	98.99 81	99.49 5	95.43 135	99.03 47	99.32 49	95.56 48	99.94 8	96.80 133	99.77 31	99.78 21

MVS_0304		98.47 38	98.22 50	99.21 39	99.00 114	97.80 67	98.88 108	95.32 371	98.86 2	98.53 84	99.44 27	94.38 87	99.94 8	99.86 1	99.70 54	99.90 3

EI-MVSNet-Vis-set		98.47 38	98.39 27	98.69 72	99.46 49	96.49 123	98.30 215	98.69 120	97.21 52	98.84 62	99.36 42	95.41 53	99.78 101	98.62 26	99.65 64	99.80 18

MVS_111021_HR		98.47 38	98.34 35	98.88 66	99.22 89	97.32 81	97.91 260	99.58 3	97.20 53	98.33 97	99.00 103	95.99 36	99.64 131	98.05 59	99.76 37	99.69 56

test_fmvsmvis_n_1920		98.44 41	98.51 18	98.23 113	98.33 178	96.15 141	98.97 84	99.15 28	98.55 7	98.45 89	99.55 6	94.26 91	99.97 1	99.65 7	99.66 61	98.57 205

CS-MVS		98.44 41	98.49 21	98.31 105	99.08 107	96.73 109	99.67 3	98.47 175	97.17 55	98.94 53	99.10 86	95.73 44	99.13 194	98.71 24	99.49 96	99.09 157

GST-MVS		98.43 43	98.12 55	99.34 23	99.72 12	98.38 35	99.09 59	98.82 81	95.71 123	98.73 71	99.06 96	95.27 62	99.93 25	97.07 113	99.63 69	99.72 45

fmvsm_s_conf0.5_n		98.42 44	98.51 18	98.13 122	99.30 68	95.25 188	98.85 118	99.39 7	97.94 14	99.74 9	99.62 3	92.59 110	99.91 39	99.65 7	99.52 92	99.25 133

EI-MVSNet-UG-set		98.41 45	98.34 35	98.61 77	99.45 52	96.32 134	98.28 218	98.68 123	97.17 55	98.74 69	99.37 38	95.25 64	99.79 98	98.57 27	99.54 89	99.73 42

DELS-MVS		98.40 46	98.20 52	98.99 57	99.00 114	97.66 68	97.75 279	98.89 59	97.71 19	98.33 97	98.97 105	94.97 74	99.88 56	98.42 44	99.76 37	99.42 111
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 47	98.42 25	98.27 107	99.09 106	95.41 178	98.86 116	99.37 8	97.69 21	99.78 6	99.61 4	92.38 113	99.91 39	99.58 10	99.43 104	99.49 96

TSAR-MVS + GP.		98.38 47	98.24 46	98.81 68	99.22 89	97.25 88	98.11 241	98.29 211	97.19 54	98.99 52	99.02 98	96.22 26	99.67 126	98.52 36	98.56 149	99.51 89

HPM-MVS_fast		98.38 47	98.13 54	99.12 50	99.75 3	97.86 62	99.44 11	98.82 81	94.46 185	98.94 53	99.20 67	95.16 68	99.74 111	97.58 91	99.85 5	99.77 27

patch_mono-2		98.36 50	98.87 6	96.82 214	99.53 36	90.68 320	98.64 169	99.29 14	97.88 15	99.19 40	99.52 11	96.80 15	99.97 1	99.11 16	99.86 1	99.82 16

HPM-MVS		98.36 50	98.10 57	99.13 48	99.74 7	97.82 66	99.53 8	98.80 93	94.63 176	98.61 80	98.97 105	95.13 70	99.77 106	97.65 86	99.83 11	99.79 19
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS		98.35 52	98.00 62	99.42 16	99.51 39	98.72 21	98.80 135	98.82 81	94.52 182	99.23 37	99.25 61	95.54 50	99.80 88	96.52 141	99.77 31	99.74 37
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.34 53	98.23 48	98.67 74	99.27 78	96.90 101	97.95 255	99.58 3	97.14 58	98.44 91	99.01 102	95.03 73	99.62 137	97.91 66	99.75 41	99.50 91

PHI-MVS		98.34 53	98.06 58	99.18 42	99.15 100	98.12 55	99.04 68	99.09 31	93.32 241	98.83 64	99.10 86	96.54 21	99.83 69	97.70 84	99.76 37	99.59 79

MP-MVS		98.33 55	98.01 61	99.28 32	99.75 3	98.18 49	99.22 35	98.79 98	96.13 102	97.92 122	99.23 62	94.54 80	99.94 8	96.74 136	99.78 29	99.73 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		98.31 56	97.92 64	99.49 12	99.72 12	98.88 18	98.43 201	98.78 100	94.10 193	97.69 135	99.42 29	95.25 64	99.92 31	98.09 56	99.80 19	99.67 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP		98.23 57	97.95 63	99.09 52	99.74 7	97.62 71	99.03 71	99.41 6	95.98 107	97.60 143	99.36 42	94.45 85	99.93 25	97.14 110	98.85 135	99.70 53
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 58	98.11 56	98.49 89	98.34 176	97.26 87	99.61 5	98.43 184	96.78 74	98.87 61	98.84 123	93.72 98	99.01 215	98.91 20	99.50 94	99.19 143

fmvsm_s_conf0.1_n		98.18 59	98.21 51	98.11 126	98.54 158	95.24 189	98.87 113	99.24 17	97.50 31	99.70 13	99.67 1	91.33 145	99.89 47	99.47 12	99.54 89	99.21 138

fmvsm_s_conf0.1_n_a		98.08 60	98.04 60	98.21 114	97.66 234	95.39 179	98.89 103	99.17 26	97.24 50	99.76 8	99.67 1	91.13 150	99.88 56	99.39 13	99.41 106	99.35 115

dcpmvs_2		98.08 60	98.59 14	96.56 238	99.57 33	90.34 327	99.15 47	98.38 193	96.82 73	99.29 34	99.49 17	95.78 43	99.57 142	98.94 19	99.86 1	99.77 27

CANet		98.05 62	97.76 67	98.90 65	98.73 138	97.27 83	98.35 206	98.78 100	97.37 41	97.72 133	98.96 110	91.53 141	99.92 31	98.79 23	99.65 64	99.51 89

train_agg		97.97 63	97.52 79	99.33 26	99.31 64	98.50 29	97.92 258	98.73 111	92.98 257	97.74 130	98.68 142	96.20 28	99.80 88	96.59 137	99.57 80	99.68 61

ETV-MVS		97.96 64	97.81 65	98.40 100	98.42 165	97.27 83	98.73 150	98.55 155	96.84 71	98.38 93	97.44 261	95.39 54	99.35 171	97.62 88	98.89 131	98.58 204

UA-Net		97.96 64	97.62 71	98.98 59	98.86 129	97.47 78	98.89 103	99.08 32	96.67 80	98.72 72	99.54 8	93.15 104	99.81 81	94.87 190	98.83 136	99.65 69

CDPH-MVS		97.94 66	97.49 80	99.28 32	99.47 47	98.44 31	97.91 260	98.67 128	92.57 272	98.77 67	98.85 122	95.93 38	99.72 113	95.56 173	99.69 56	99.68 61

DeepPCF-MVS	96.37 2	97.93 67	98.48 23	96.30 264	99.00 114	89.54 339	97.43 300	98.87 69	98.16 11	99.26 36	99.38 37	96.12 31	99.64 131	98.30 49	99.77 31	99.72 45

DeepC-MVS	95.98 3	97.88 68	97.58 73	98.77 69	99.25 81	96.93 99	98.83 124	98.75 106	96.96 67	96.89 167	99.50 15	90.46 164	99.87 58	97.84 73	99.76 37	99.52 86
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 69	97.54 78	98.83 67	95.48 352	96.83 104	98.95 90	98.60 141	98.58 6	98.93 57	99.55 6	88.57 206	99.91 39	99.54 11	99.61 72	99.77 27

DP-MVS Recon		97.86 69	97.46 83	99.06 54	99.53 36	98.35 41	98.33 208	98.89 59	92.62 269	98.05 106	98.94 113	95.34 58	99.65 129	96.04 156	99.42 105	99.19 143

CSCG		97.85 71	97.74 68	98.20 116	99.67 25	95.16 192	99.22 35	99.32 11	93.04 255	97.02 160	98.92 116	95.36 57	99.91 39	97.43 101	99.64 68	99.52 86

MG-MVS		97.81 72	97.60 72	98.44 95	99.12 102	95.97 151	97.75 279	98.78 100	96.89 70	98.46 86	99.22 64	93.90 97	99.68 125	94.81 194	99.52 92	99.67 65

VNet		97.79 73	97.40 87	98.96 61	98.88 126	97.55 73	98.63 172	98.93 50	96.74 77	99.02 48	98.84 123	90.33 167	99.83 69	98.53 30	96.66 207	99.50 91

EIA-MVS		97.75 74	97.58 73	98.27 107	98.38 168	96.44 125	99.01 76	98.60 141	95.88 115	97.26 149	97.53 255	94.97 74	99.33 173	97.38 104	99.20 118	99.05 163

PS-MVSNAJ		97.73 75	97.77 66	97.62 164	98.68 146	95.58 170	97.34 309	98.51 164	97.29 44	98.66 77	97.88 222	94.51 81	99.90 45	97.87 70	99.17 120	97.39 244

casdiffmvs_mvg		97.72 76	97.48 82	98.44 95	98.42 165	96.59 117	98.92 97	98.44 180	96.20 99	97.76 127	99.20 67	91.66 135	99.23 181	98.27 51	98.41 158	99.49 96
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 76	97.32 91	98.92 63	99.64 28	97.10 94	99.12 53	98.81 86	92.34 280	98.09 104	99.08 94	93.01 105	99.92 31	96.06 155	99.77 31	99.75 35

PVSNet_Blended_VisFu		97.70 78	97.46 83	98.44 95	99.27 78	95.91 159	98.63 172	99.16 27	94.48 184	97.67 136	98.88 119	92.80 107	99.91 39	97.11 111	99.12 121	99.50 91

mvsany_test1		97.69 79	97.70 69	97.66 162	98.24 184	94.18 242	97.53 295	97.53 297	95.52 131	99.66 15	99.51 13	94.30 89	99.56 145	98.38 45	98.62 145	99.23 135

canonicalmvs		97.67 80	97.23 94	98.98 59	98.70 143	98.38 35	99.34 18	98.39 190	96.76 76	97.67 136	97.40 264	92.26 117	99.49 158	98.28 50	96.28 224	99.08 161

xiu_mvs_v2_base		97.66 81	97.70 69	97.56 168	98.61 153	95.46 176	97.44 298	98.46 176	97.15 57	98.65 78	98.15 199	94.33 88	99.80 88	97.84 73	98.66 144	97.41 242

baseline		97.64 82	97.44 85	98.25 111	98.35 171	96.20 138	99.00 78	98.32 201	96.33 96	98.03 109	99.17 74	91.35 144	99.16 188	98.10 55	98.29 165	99.39 112

casdiffmvs		97.63 83	97.41 86	98.28 106	98.33 178	96.14 142	98.82 126	98.32 201	96.38 94	97.95 117	99.21 65	91.23 149	99.23 181	98.12 54	98.37 159	99.48 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
xiu_mvs_v1_base_debu		97.60 84	97.56 75	97.72 152	98.35 171	95.98 146	97.86 269	98.51 164	97.13 59	99.01 49	98.40 172	91.56 137	99.80 88	98.53 30	98.68 140	97.37 246

xiu_mvs_v1_base		97.60 84	97.56 75	97.72 152	98.35 171	95.98 146	97.86 269	98.51 164	97.13 59	99.01 49	98.40 172	91.56 137	99.80 88	98.53 30	98.68 140	97.37 246

xiu_mvs_v1_base_debi		97.60 84	97.56 75	97.72 152	98.35 171	95.98 146	97.86 269	98.51 164	97.13 59	99.01 49	98.40 172	91.56 137	99.80 88	98.53 30	98.68 140	97.37 246

diffmvs		97.58 87	97.40 87	98.13 122	98.32 181	95.81 164	98.06 245	98.37 194	96.20 99	98.74 69	98.89 118	91.31 147	99.25 178	98.16 53	98.52 150	99.34 116
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 88	97.49 80	97.84 140	98.07 203	95.76 165	99.47 9	98.40 188	94.98 161	98.79 65	98.83 125	92.34 114	98.41 291	96.91 119	99.59 76	99.34 116

alignmvs		97.56 89	97.07 101	99.01 56	98.66 148	98.37 39	98.83 124	98.06 257	96.74 77	98.00 115	97.65 244	90.80 158	99.48 162	98.37 46	96.56 211	99.19 143

DPM-MVS		97.55 90	96.99 104	99.23 38	99.04 109	98.55 27	97.17 324	98.35 197	94.85 168	97.93 121	98.58 153	95.07 72	99.71 118	92.60 261	99.34 113	99.43 109

OMC-MVS		97.55 90	97.34 90	98.20 116	99.33 59	95.92 158	98.28 218	98.59 144	95.52 131	97.97 116	99.10 86	93.28 103	99.49 158	95.09 187	98.88 132	99.19 143

PAPM_NR		97.46 92	97.11 98	98.50 87	99.50 41	96.41 129	98.63 172	98.60 141	95.18 150	97.06 158	98.06 205	94.26 91	99.57 142	93.80 229	98.87 134	99.52 86

EPP-MVSNet		97.46 92	97.28 92	97.99 133	98.64 150	95.38 180	99.33 21	98.31 203	93.61 230	97.19 151	99.07 95	94.05 94	99.23 181	96.89 123	98.43 157	99.37 114

3Dnovator	94.51 5	97.46 92	96.93 106	99.07 53	97.78 222	97.64 69	99.35 17	99.06 34	97.02 64	93.75 275	99.16 77	89.25 187	99.92 31	97.22 109	99.75 41	99.64 71

CNLPA		97.45 95	97.03 102	98.73 70	99.05 108	97.44 80	98.07 244	98.53 159	95.32 143	96.80 172	98.53 157	93.32 101	99.72 113	94.31 212	99.31 115	99.02 165

lupinMVS		97.44 96	97.22 95	98.12 125	98.07 203	95.76 165	97.68 284	97.76 278	94.50 183	98.79 65	98.61 148	92.34 114	99.30 175	97.58 91	99.59 76	99.31 122

3Dnovator+	94.38 6	97.43 97	96.78 114	99.38 18	97.83 220	98.52 28	99.37 14	98.71 116	97.09 62	92.99 302	99.13 82	89.36 183	99.89 47	96.97 116	99.57 80	99.71 49

Vis-MVSNet		97.42 98	97.11 98	98.34 103	98.66 148	96.23 137	99.22 35	99.00 39	96.63 82	98.04 108	99.21 65	88.05 222	99.35 171	96.01 158	99.21 117	99.45 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		97.41 99	97.25 93	97.91 137	98.70 143	96.80 105	98.82 126	98.69 120	94.53 180	98.11 102	98.28 187	94.50 84	99.57 142	94.12 218	99.49 96	97.37 246

sss		97.39 100	96.98 105	98.61 77	98.60 154	96.61 114	98.22 223	98.93 50	93.97 201	98.01 114	98.48 162	91.98 127	99.85 63	96.45 143	98.15 167	99.39 112

test_cas_vis1_n_1920		97.38 101	97.36 89	97.45 171	98.95 121	93.25 275	99.00 78	98.53 159	97.70 20	99.77 7	99.35 44	84.71 289	99.85 63	98.57 27	99.66 61	99.26 131

PVSNet_Blended		97.38 101	97.12 97	98.14 119	99.25 81	95.35 183	97.28 314	99.26 15	93.13 251	97.94 119	98.21 195	92.74 108	99.81 81	96.88 125	99.40 109	99.27 129

WTY-MVS		97.37 103	96.92 107	98.72 71	98.86 129	96.89 103	98.31 213	98.71 116	95.26 146	97.67 136	98.56 156	92.21 120	99.78 101	95.89 160	96.85 202	99.48 98

jason		97.32 104	97.08 100	98.06 130	97.45 253	95.59 169	97.87 268	97.91 272	94.79 169	98.55 83	98.83 125	91.12 151	99.23 181	97.58 91	99.60 74	99.34 116
jason: jason.
MVS_Test		97.28 105	97.00 103	98.13 122	98.33 178	95.97 151	98.74 146	98.07 252	94.27 189	98.44 91	98.07 204	92.48 111	99.26 177	96.43 144	98.19 166	99.16 149

EPNet		97.28 105	96.87 109	98.51 86	94.98 360	96.14 142	98.90 99	97.02 334	98.28 10	95.99 201	99.11 84	91.36 143	99.89 47	96.98 115	99.19 119	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_yl		97.22 107	96.78 114	98.54 83	98.73 138	96.60 115	98.45 196	98.31 203	94.70 170	98.02 111	98.42 170	90.80 158	99.70 119	96.81 131	96.79 204	99.34 116

DCV-MVSNet		97.22 107	96.78 114	98.54 83	98.73 138	96.60 115	98.45 196	98.31 203	94.70 170	98.02 111	98.42 170	90.80 158	99.70 119	96.81 131	96.79 204	99.34 116

IS-MVSNet		97.22 107	96.88 108	98.25 111	98.85 131	96.36 132	99.19 42	97.97 265	95.39 137	97.23 150	98.99 104	91.11 152	98.93 227	94.60 201	98.59 147	99.47 100

PLC	95.07 4	97.20 110	96.78 114	98.44 95	99.29 73	96.31 136	98.14 236	98.76 104	92.41 278	96.39 191	98.31 185	94.92 76	99.78 101	94.06 221	98.77 139	99.23 135
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		97.18 111	97.18 96	97.20 186	98.81 134	93.27 273	95.78 369	99.15 28	95.25 147	96.79 173	98.11 202	92.29 116	99.07 205	98.56 29	99.85 5	99.25 133

LS3D		97.16 112	96.66 122	98.68 73	98.53 159	97.19 91	98.93 95	98.90 57	92.83 264	95.99 201	99.37 38	92.12 123	99.87 58	93.67 233	99.57 80	98.97 170

AdaColmap		97.15 113	96.70 118	98.48 90	99.16 98	96.69 111	98.01 250	98.89 59	94.44 186	96.83 168	98.68 142	90.69 161	99.76 107	94.36 208	99.29 116	98.98 169

Effi-MVS+		97.12 114	96.69 119	98.39 101	98.19 192	96.72 110	97.37 305	98.43 184	93.71 219	97.65 139	98.02 208	92.20 121	99.25 178	96.87 128	97.79 179	99.19 143

CHOSEN 1792x2688		97.12 114	96.80 111	98.08 128	99.30 68	94.56 226	98.05 246	99.71 1	93.57 231	97.09 154	98.91 117	88.17 216	99.89 47	96.87 128	99.56 86	99.81 17

F-COLMAP		97.09 116	96.80 111	97.97 134	99.45 52	94.95 205	98.55 185	98.62 140	93.02 256	96.17 196	98.58 153	94.01 95	99.81 81	93.95 223	98.90 130	99.14 152

TAMVS		97.02 117	96.79 113	97.70 155	98.06 206	95.31 186	98.52 187	98.31 203	93.95 202	97.05 159	98.61 148	93.49 100	98.52 271	95.33 179	97.81 178	99.29 127

CDS-MVSNet		96.99 118	96.69 119	97.90 138	98.05 207	95.98 146	98.20 226	98.33 200	93.67 226	96.95 161	98.49 161	93.54 99	98.42 283	95.24 185	97.74 182	99.31 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		96.96 119	96.55 125	98.21 114	98.17 197	96.07 144	97.98 253	98.21 220	97.24 50	97.13 153	98.93 114	86.88 246	99.91 39	95.00 189	99.37 112	98.66 196

114514_t		96.93 120	96.27 136	98.92 63	99.50 41	97.63 70	98.85 118	98.90 57	84.80 376	97.77 126	99.11 84	92.84 106	99.66 128	94.85 191	99.77 31	99.47 100

MAR-MVS		96.91 121	96.40 131	98.45 93	98.69 145	96.90 101	98.66 167	98.68 123	92.40 279	97.07 157	97.96 215	91.54 140	99.75 109	93.68 231	98.92 129	98.69 192
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		96.90 122	96.49 128	98.14 119	99.33 59	95.56 171	97.38 303	99.65 2	92.34 280	97.61 142	98.20 196	89.29 185	99.10 202	96.97 116	97.60 187	99.77 27

Vis-MVSNet (Re-imp)		96.87 123	96.55 125	97.83 141	98.73 138	95.46 176	99.20 40	98.30 209	94.96 163	96.60 179	98.87 120	90.05 170	98.59 263	93.67 233	98.60 146	99.46 104

SDMVSNet		96.85 124	96.42 129	98.14 119	99.30 68	96.38 130	99.21 38	99.23 20	95.92 110	95.96 203	98.76 136	85.88 263	99.44 167	97.93 64	95.59 235	98.60 200

PAPR		96.84 125	96.24 138	98.65 75	98.72 142	96.92 100	97.36 307	98.57 151	93.33 240	96.67 175	97.57 252	94.30 89	99.56 145	91.05 300	98.59 147	99.47 100

HY-MVS	93.96 8	96.82 126	96.23 139	98.57 79	98.46 163	97.00 96	98.14 236	98.21 220	93.95 202	96.72 174	97.99 212	91.58 136	99.76 107	94.51 205	96.54 212	98.95 173

UGNet		96.78 127	96.30 135	98.19 118	98.24 184	95.89 161	98.88 108	98.93 50	97.39 38	96.81 171	97.84 226	82.60 316	99.90 45	96.53 140	99.49 96	98.79 184
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		96.73 128	96.60 123	97.12 193	99.25 81	95.35 183	98.26 221	99.26 15	94.28 188	97.94 119	97.46 258	92.74 108	99.81 81	96.88 125	93.32 275	96.20 339

test_vis1_n_1920		96.71 129	96.84 110	96.31 263	99.11 104	89.74 334	99.05 65	98.58 149	98.08 12	99.87 1	99.37 38	78.48 342	99.93 25	99.29 14	99.69 56	99.27 129

mvs_anonymous		96.70 130	96.53 127	97.18 188	98.19 192	93.78 251	98.31 213	98.19 224	94.01 198	94.47 234	98.27 190	92.08 125	98.46 278	97.39 103	97.91 174	99.31 122

1112_ss		96.63 131	96.00 147	98.50 87	98.56 155	96.37 131	98.18 234	98.10 245	92.92 260	94.84 222	98.43 168	92.14 122	99.58 141	94.35 209	96.51 213	99.56 85

PMMVS		96.60 132	96.33 133	97.41 175	97.90 217	93.93 247	97.35 308	98.41 186	92.84 263	97.76 127	97.45 260	91.10 153	99.20 185	96.26 148	97.91 174	99.11 155

DP-MVS		96.59 133	95.93 150	98.57 79	99.34 57	96.19 140	98.70 159	98.39 190	89.45 348	94.52 232	99.35 44	91.85 130	99.85 63	92.89 257	98.88 132	99.68 61

PatchMatch-RL		96.59 133	96.03 146	98.27 107	99.31 64	96.51 122	97.91 260	99.06 34	93.72 218	96.92 165	98.06 205	88.50 211	99.65 129	91.77 285	99.00 127	98.66 196

GeoE		96.58 135	96.07 143	98.10 127	98.35 171	95.89 161	99.34 18	98.12 239	93.12 252	96.09 197	98.87 120	89.71 176	98.97 217	92.95 253	98.08 170	99.43 109

mvsmamba		96.57 136	96.32 134	97.32 182	96.60 307	96.43 126	99.54 7	97.98 263	96.49 86	95.20 215	98.64 146	90.82 156	98.55 267	97.97 61	93.65 265	96.98 257

XVG-OURS		96.55 137	96.41 130	96.99 200	98.75 137	93.76 252	97.50 297	98.52 162	95.67 125	96.83 168	99.30 52	88.95 200	99.53 153	95.88 161	96.26 225	97.69 235

FIs		96.51 138	96.12 141	97.67 159	97.13 277	97.54 74	99.36 15	99.22 23	95.89 113	94.03 261	98.35 178	91.98 127	98.44 281	96.40 145	92.76 284	97.01 255

XVG-OURS-SEG-HR		96.51 138	96.34 132	97.02 199	98.77 136	93.76 252	97.79 277	98.50 169	95.45 134	96.94 162	99.09 92	87.87 227	99.55 152	96.76 135	95.83 234	97.74 232

PS-MVSNAJss		96.43 140	96.26 137	96.92 209	95.84 342	95.08 197	99.16 46	98.50 169	95.87 116	93.84 271	98.34 182	94.51 81	98.61 260	96.88 125	93.45 272	97.06 252

test_fmvs1		96.42 141	96.67 121	95.66 289	98.82 133	88.53 356	98.80 135	98.20 222	96.39 93	99.64 17	99.20 67	80.35 332	99.67 126	99.04 17	99.57 80	98.78 187

iter_conf_final		96.42 141	96.12 141	97.34 181	98.46 163	96.55 121	99.08 61	98.06 257	96.03 106	95.63 209	98.46 166	87.72 229	98.59 263	97.84 73	93.80 260	96.87 273

FC-MVSNet-test		96.42 141	96.05 144	97.53 169	96.95 286	97.27 83	99.36 15	99.23 20	95.83 117	93.93 265	98.37 176	92.00 126	98.32 300	96.02 157	92.72 285	97.00 256

ab-mvs		96.42 141	95.71 162	98.55 81	98.63 151	96.75 108	97.88 267	98.74 108	93.84 208	96.54 184	98.18 198	85.34 275	99.75 109	95.93 159	96.35 217	99.15 150

FA-MVS(test-final)		96.41 145	95.94 149	97.82 143	98.21 188	95.20 191	97.80 275	97.58 288	93.21 246	97.36 147	97.70 238	89.47 180	99.56 145	94.12 218	97.99 171	98.71 191

PVSNet	91.96 18	96.35 146	96.15 140	96.96 204	99.17 94	92.05 294	96.08 362	98.68 123	93.69 222	97.75 129	97.80 232	88.86 201	99.69 124	94.26 214	99.01 126	99.15 150

Test_1112_low_res		96.34 147	95.66 167	98.36 102	98.56 155	95.94 154	97.71 282	98.07 252	92.10 289	94.79 226	97.29 269	91.75 132	99.56 145	94.17 216	96.50 214	99.58 83

Effi-MVS+-dtu		96.29 148	96.56 124	95.51 293	97.89 218	90.22 328	98.80 135	98.10 245	96.57 85	96.45 189	96.66 320	90.81 157	98.91 230	95.72 167	97.99 171	97.40 243

QAPM		96.29 148	95.40 171	98.96 61	97.85 219	97.60 72	99.23 31	98.93 50	89.76 342	93.11 299	99.02 98	89.11 192	99.93 25	91.99 280	99.62 71	99.34 116

Fast-Effi-MVS+		96.28 150	95.70 164	98.03 131	98.29 183	95.97 151	98.58 178	98.25 217	91.74 297	95.29 214	97.23 274	91.03 155	99.15 191	92.90 255	97.96 173	98.97 170

nrg030		96.28 150	95.72 159	97.96 136	96.90 291	98.15 52	99.39 12	98.31 203	95.47 133	94.42 240	98.35 178	92.09 124	98.69 253	97.50 99	89.05 330	97.04 253

1314		96.25 152	95.73 158	97.79 145	97.13 277	95.55 173	98.19 229	98.59 144	93.47 235	92.03 327	97.82 230	91.33 145	99.49 158	94.62 200	98.44 155	98.32 216

sd_testset		96.17 153	95.76 157	97.42 174	99.30 68	94.34 235	98.82 126	99.08 32	95.92 110	95.96 203	98.76 136	82.83 315	99.32 174	95.56 173	95.59 235	98.60 200

h-mvs33		96.17 153	95.62 168	97.81 144	99.03 110	94.45 228	98.64 169	98.75 106	97.48 32	98.67 73	98.72 139	89.76 174	99.86 62	97.95 62	81.59 373	99.11 155

HQP_MVS		96.14 155	95.90 151	96.85 212	97.42 255	94.60 224	98.80 135	98.56 153	97.28 45	95.34 212	98.28 187	87.09 241	99.03 210	96.07 152	94.27 243	96.92 262

iter_conf05		96.13 156	95.79 154	97.15 190	98.16 198	95.99 145	98.88 108	97.98 263	95.91 112	95.58 210	98.46 166	85.53 270	98.59 263	97.88 69	93.75 261	96.86 276

tttt0517		96.07 157	95.51 170	97.78 146	98.41 167	94.84 209	99.28 24	94.33 382	94.26 190	97.64 140	98.64 146	84.05 304	99.47 164	95.34 178	97.60 187	99.03 164

MVSTER		96.06 158	95.72 159	97.08 196	98.23 186	95.93 157	98.73 150	98.27 212	94.86 167	95.07 217	98.09 203	88.21 215	98.54 269	96.59 137	93.46 270	96.79 282

thisisatest0530		96.01 159	95.36 176	97.97 134	98.38 168	95.52 174	98.88 108	94.19 384	94.04 195	97.64 140	98.31 185	83.82 311	99.46 165	95.29 182	97.70 184	98.93 175

test_djsdf		96.00 160	95.69 165	96.93 206	95.72 344	95.49 175	99.47 9	98.40 188	94.98 161	94.58 230	97.86 223	89.16 190	98.41 291	96.91 119	94.12 251	96.88 271

RRT_MVS		95.98 161	95.78 155	96.56 238	96.48 315	94.22 241	99.57 6	97.92 270	95.89 113	93.95 264	98.70 140	89.27 186	98.42 283	97.23 108	93.02 279	97.04 253

EI-MVSNet		95.96 162	95.83 153	96.36 259	97.93 215	93.70 258	98.12 239	98.27 212	93.70 221	95.07 217	99.02 98	92.23 119	98.54 269	94.68 196	93.46 270	96.84 278

ECVR-MVS		95.95 163	95.71 162	96.65 224	99.02 111	90.86 315	99.03 71	91.80 393	96.96 67	98.10 103	99.26 57	81.31 322	99.51 157	96.90 122	99.04 123	99.59 79

BH-untuned		95.95 163	95.72 159	96.65 224	98.55 157	92.26 289	98.23 222	97.79 277	93.73 216	94.62 229	98.01 210	88.97 199	99.00 216	93.04 250	98.51 151	98.68 193

test1111		95.94 165	95.78 155	96.41 256	98.99 118	90.12 329	99.04 68	92.45 392	96.99 66	98.03 109	99.27 56	81.40 321	99.48 162	96.87 128	99.04 123	99.63 73

MSDG		95.93 166	95.30 183	97.83 141	98.90 124	95.36 181	96.83 349	98.37 194	91.32 312	94.43 239	98.73 138	90.27 168	99.60 139	90.05 314	98.82 137	98.52 206

BH-RMVSNet		95.92 167	95.32 180	97.69 156	98.32 181	94.64 218	98.19 229	97.45 307	94.56 178	96.03 199	98.61 148	85.02 280	99.12 196	90.68 305	99.06 122	99.30 125

test_fmvs1_n		95.90 168	95.99 148	95.63 290	98.67 147	88.32 360	99.26 27	98.22 219	96.40 92	99.67 14	99.26 57	73.91 370	99.70 119	99.02 18	99.50 94	98.87 178

Fast-Effi-MVS+-dtu		95.87 169	95.85 152	95.91 279	97.74 227	91.74 300	98.69 161	98.15 235	95.56 129	94.92 220	97.68 243	88.98 198	98.79 247	93.19 245	97.78 180	97.20 250

LFMVS		95.86 170	94.98 198	98.47 91	98.87 128	96.32 134	98.84 122	96.02 361	93.40 238	98.62 79	99.20 67	74.99 364	99.63 134	97.72 80	97.20 193	99.46 104

baseline1		95.84 171	95.12 191	98.01 132	98.49 162	95.98 146	98.73 150	97.03 332	95.37 140	96.22 194	98.19 197	89.96 172	99.16 188	94.60 201	87.48 346	98.90 177

OpenMVS	93.04 13	95.83 172	95.00 196	98.32 104	97.18 274	97.32 81	99.21 38	98.97 42	89.96 338	91.14 335	99.05 97	86.64 249	99.92 31	93.38 239	99.47 99	97.73 233

VDD-MVS		95.82 173	95.23 185	97.61 165	98.84 132	93.98 246	98.68 162	97.40 311	95.02 160	97.95 117	99.34 48	74.37 369	99.78 101	98.64 25	96.80 203	99.08 161

UniMVSNet (Re)		95.78 174	95.19 187	97.58 166	96.99 284	97.47 78	98.79 140	99.18 25	95.60 127	93.92 266	97.04 293	91.68 133	98.48 274	95.80 165	87.66 345	96.79 282

VPA-MVSNet		95.75 175	95.11 192	97.69 156	97.24 266	97.27 83	98.94 93	99.23 20	95.13 152	95.51 211	97.32 267	85.73 266	98.91 230	97.33 106	89.55 322	96.89 270

bld_raw_dy_0_64		95.74 176	95.31 182	97.03 198	96.35 321	95.76 165	99.12 53	97.37 314	95.97 108	94.70 228	98.48 162	85.80 265	98.49 273	96.55 139	93.48 269	96.84 278

HQP-MVS		95.72 177	95.40 171	96.69 222	97.20 270	94.25 239	98.05 246	98.46 176	96.43 89	94.45 235	97.73 235	86.75 247	98.96 221	95.30 180	94.18 247	96.86 276

hse-mvs2		95.71 178	95.30 183	96.93 206	98.50 160	93.53 263	98.36 205	98.10 245	97.48 32	98.67 73	97.99 212	89.76 174	99.02 213	97.95 62	80.91 377	98.22 219

UniMVSNet_NR-MVSNet		95.71 178	95.15 188	97.40 177	96.84 294	96.97 97	98.74 146	99.24 17	95.16 151	93.88 268	97.72 237	91.68 133	98.31 302	95.81 163	87.25 351	96.92 262

PatchmatchNet		95.71 178	95.52 169	96.29 265	97.58 239	90.72 319	96.84 348	97.52 298	94.06 194	97.08 155	96.96 303	89.24 188	98.90 233	92.03 279	98.37 159	99.26 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		95.69 181	95.33 179	96.76 217	96.16 330	94.63 219	98.43 201	98.39 190	96.64 81	95.02 219	98.78 130	85.15 279	99.05 206	95.21 186	94.20 246	96.60 305
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM	93.85 9	95.69 181	95.38 175	96.61 231	97.61 237	93.84 250	98.91 98	98.44 180	95.25 147	94.28 247	98.47 164	86.04 262	99.12 196	95.50 176	93.95 256	96.87 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		95.63 183	95.69 165	95.44 297	97.54 244	88.54 355	96.97 334	97.56 290	93.50 233	97.52 145	96.93 307	89.49 178	99.16 188	95.25 184	96.42 216	98.64 198

FE-MVS		95.62 184	94.90 202	97.78 146	98.37 170	94.92 206	97.17 324	97.38 313	90.95 323	97.73 132	97.70 238	85.32 277	99.63 134	91.18 293	98.33 162	98.79 184

LPG-MVS_test		95.62 184	95.34 177	96.47 250	97.46 250	93.54 261	98.99 81	98.54 157	94.67 174	94.36 243	98.77 132	85.39 272	99.11 198	95.71 168	94.15 249	96.76 285

CLD-MVS		95.62 184	95.34 177	96.46 253	97.52 247	93.75 254	97.27 315	98.46 176	95.53 130	94.42 240	98.00 211	86.21 257	98.97 217	96.25 150	94.37 241	96.66 300
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		95.61 187	94.89 203	97.76 149	98.15 199	95.15 194	96.77 350	94.41 380	92.95 259	97.18 152	97.43 262	84.78 286	99.45 166	94.63 198	97.73 183	98.68 193

thres600view7		95.49 188	94.77 206	97.67 159	98.98 119	95.02 198	98.85 118	96.90 341	95.38 138	96.63 177	96.90 308	84.29 296	99.59 140	88.65 336	96.33 218	98.40 211

test_vis1_n		95.47 189	95.13 189	96.49 247	97.77 223	90.41 325	99.27 26	98.11 242	96.58 83	99.66 15	99.18 73	67.00 383	99.62 137	99.21 15	99.40 109	99.44 107

SCA		95.46 190	95.13 189	96.46 253	97.67 232	91.29 308	97.33 310	97.60 287	94.68 173	96.92 165	97.10 280	83.97 306	98.89 234	92.59 263	98.32 164	99.20 139

IterMVS-LS		95.46 190	95.21 186	96.22 267	98.12 200	93.72 257	98.32 212	98.13 238	93.71 219	94.26 248	97.31 268	92.24 118	98.10 318	94.63 198	90.12 313	96.84 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
jajsoiax		95.45 192	95.03 195	96.73 218	95.42 356	94.63 219	99.14 49	98.52 162	95.74 120	93.22 293	98.36 177	83.87 309	98.65 258	96.95 118	94.04 252	96.91 267

CVMVSNet		95.43 193	96.04 145	93.57 339	97.93 215	83.62 377	98.12 239	98.59 144	95.68 124	96.56 180	99.02 98	87.51 234	97.51 351	93.56 237	97.44 189	99.60 77

anonymousdsp		95.42 194	94.91 201	96.94 205	95.10 359	95.90 160	99.14 49	98.41 186	93.75 213	93.16 295	97.46 258	87.50 236	98.41 291	95.63 172	94.03 253	96.50 324

DU-MVS		95.42 194	94.76 207	97.40 177	96.53 311	96.97 97	98.66 167	98.99 41	95.43 135	93.88 268	97.69 240	88.57 206	98.31 302	95.81 163	87.25 351	96.92 262

mvs_tets		95.41 196	95.00 196	96.65 224	95.58 348	94.42 230	99.00 78	98.55 155	95.73 122	93.21 294	98.38 175	83.45 313	98.63 259	97.09 112	94.00 254	96.91 267

thres100view900		95.38 197	94.70 210	97.41 175	98.98 119	94.92 206	98.87 113	96.90 341	95.38 138	96.61 178	96.88 309	84.29 296	99.56 145	88.11 339	96.29 221	97.76 230

thres400		95.38 197	94.62 213	97.65 163	98.94 122	94.98 202	98.68 162	96.93 339	95.33 141	96.55 182	96.53 326	84.23 300	99.56 145	88.11 339	96.29 221	98.40 211

BH-w/o		95.38 197	95.08 193	96.26 266	98.34 176	91.79 297	97.70 283	97.43 309	92.87 262	94.24 250	97.22 275	88.66 204	98.84 240	91.55 289	97.70 184	98.16 222

VDDNet		95.36 200	94.53 217	97.86 139	98.10 202	95.13 195	98.85 118	97.75 279	90.46 329	98.36 94	99.39 32	73.27 372	99.64 131	97.98 60	96.58 210	98.81 183

TAPA-MVS	93.98 7	95.35 201	94.56 216	97.74 151	99.13 101	94.83 211	98.33 208	98.64 136	86.62 364	96.29 193	98.61 148	94.00 96	99.29 176	80.00 378	99.41 106	99.09 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	93.49 10	95.34 202	94.98 198	96.43 255	97.67 232	93.48 265	98.73 150	98.44 180	94.94 166	92.53 315	98.53 157	84.50 295	99.14 193	95.48 177	94.00 254	96.66 300
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROB	93.27 12	95.33 203	94.87 204	96.71 219	99.29 73	93.24 276	98.58 178	98.11 242	89.92 339	93.57 279	99.10 86	86.37 255	99.79 98	90.78 303	98.10 169	97.09 251
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpn200view9		95.32 204	94.62 213	97.43 173	98.94 122	94.98 202	98.68 162	96.93 339	95.33 141	96.55 182	96.53 326	84.23 300	99.56 145	88.11 339	96.29 221	97.76 230

Anonymous202405211		95.28 205	94.49 219	97.67 159	99.00 114	93.75 254	98.70 159	97.04 331	90.66 325	96.49 186	98.80 128	78.13 346	99.83 69	96.21 151	95.36 239	99.44 107

thres200		95.25 206	94.57 215	97.28 183	98.81 134	94.92 206	98.20 226	97.11 325	95.24 149	96.54 184	96.22 337	84.58 293	99.53 153	87.93 343	96.50 214	97.39 244

AllTest		95.24 207	94.65 212	96.99 200	99.25 81	93.21 277	98.59 176	98.18 227	91.36 308	93.52 281	98.77 132	84.67 290	99.72 113	89.70 321	97.87 176	98.02 225

LCM-MVSNet-Re		95.22 208	95.32 180	94.91 312	98.18 194	87.85 366	98.75 143	95.66 368	95.11 154	88.96 353	96.85 312	90.26 169	97.65 344	95.65 171	98.44 155	99.22 137

EPNet_dtu		95.21 209	94.95 200	95.99 274	96.17 328	90.45 324	98.16 235	97.27 319	96.77 75	93.14 298	98.33 183	90.34 166	98.42 283	85.57 356	98.81 138	99.09 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		95.20 210	94.45 224	97.46 170	96.75 300	96.56 119	98.86 116	98.65 135	93.30 243	93.27 292	98.27 190	84.85 284	98.87 237	94.82 193	91.26 301	96.96 259

D2MVS		95.18 211	95.08 193	95.48 294	97.10 279	92.07 293	98.30 215	99.13 30	94.02 197	92.90 303	96.73 317	89.48 179	98.73 251	94.48 206	93.60 268	95.65 352

WR-MVS		95.15 212	94.46 222	97.22 185	96.67 305	96.45 124	98.21 224	98.81 86	94.15 191	93.16 295	97.69 240	87.51 234	98.30 304	95.29 182	88.62 336	96.90 269

TranMVSNet+NR-MVSNet		95.14 213	94.48 220	97.11 194	96.45 317	96.36 132	99.03 71	99.03 37	95.04 159	93.58 278	97.93 217	88.27 214	98.03 324	94.13 217	86.90 356	96.95 261

baseline2		95.11 214	94.52 218	96.87 211	96.65 306	93.56 260	98.27 220	94.10 386	93.45 236	92.02 328	97.43 262	87.45 238	99.19 186	93.88 226	97.41 191	97.87 228

miper_enhance_ethall		95.10 215	94.75 208	96.12 271	97.53 246	93.73 256	96.61 356	98.08 250	92.20 288	93.89 267	96.65 322	92.44 112	98.30 304	94.21 215	91.16 302	96.34 333

Anonymous20240529		95.10 215	94.22 232	97.75 150	99.01 113	94.26 238	98.87 113	98.83 80	85.79 372	96.64 176	98.97 105	78.73 340	99.85 63	96.27 147	94.89 240	99.12 154

test-LLR		95.10 215	94.87 204	95.80 284	96.77 297	89.70 335	96.91 339	95.21 372	95.11 154	94.83 224	95.72 350	87.71 230	98.97 217	93.06 248	98.50 152	98.72 189

WR-MVS_H		95.05 218	94.46 222	96.81 215	96.86 293	95.82 163	99.24 30	99.24 17	93.87 207	92.53 315	96.84 313	90.37 165	98.24 310	93.24 243	87.93 342	96.38 332

miper_ehance_all_eth		95.01 219	94.69 211	95.97 276	97.70 230	93.31 272	97.02 332	98.07 252	92.23 285	93.51 283	96.96 303	91.85 130	98.15 314	93.68 231	91.16 302	96.44 330

ADS-MVSNet		95.00 220	94.45 224	96.63 228	98.00 209	91.91 296	96.04 363	97.74 280	90.15 335	96.47 187	96.64 323	87.89 225	98.96 221	90.08 312	97.06 195	99.02 165

VPNet		94.99 221	94.19 234	97.40 177	97.16 275	96.57 118	98.71 155	98.97 42	95.67 125	94.84 222	98.24 194	80.36 331	98.67 257	96.46 142	87.32 350	96.96 259

EPMVS		94.99 221	94.48 220	96.52 245	97.22 268	91.75 299	97.23 316	91.66 394	94.11 192	97.28 148	96.81 314	85.70 267	98.84 240	93.04 250	97.28 192	98.97 170

NR-MVSNet		94.98 223	94.16 237	97.44 172	96.53 311	97.22 90	98.74 146	98.95 46	94.96 163	89.25 352	97.69 240	89.32 184	98.18 312	94.59 203	87.40 348	96.92 262

FMVSNet3		94.97 224	94.26 231	97.11 194	98.18 194	96.62 112	98.56 184	98.26 216	93.67 226	94.09 257	97.10 280	84.25 298	98.01 325	92.08 275	92.14 288	96.70 294

CostFormer		94.95 225	94.73 209	95.60 292	97.28 264	89.06 346	97.53 295	96.89 343	89.66 344	96.82 170	96.72 318	86.05 260	98.95 226	95.53 175	96.13 230	98.79 184

PAPM		94.95 225	94.00 248	97.78 146	97.04 281	95.65 168	96.03 365	98.25 217	91.23 317	94.19 253	97.80 232	91.27 148	98.86 239	82.61 372	97.61 186	98.84 181

CP-MVSNet		94.94 227	94.30 230	96.83 213	96.72 302	95.56 171	99.11 55	98.95 46	93.89 205	92.42 320	97.90 219	87.19 240	98.12 317	94.32 211	88.21 339	96.82 281

TR-MVS		94.94 227	94.20 233	97.17 189	97.75 224	94.14 243	97.59 292	97.02 334	92.28 284	95.75 207	97.64 246	83.88 308	98.96 221	89.77 318	96.15 229	98.40 211

RPSCF		94.87 229	95.40 171	93.26 345	98.89 125	82.06 383	98.33 208	98.06 257	90.30 334	96.56 180	99.26 57	87.09 241	99.49 158	93.82 228	96.32 219	98.24 217

GA-MVS		94.81 230	94.03 244	97.14 191	97.15 276	93.86 249	96.76 351	97.58 288	94.00 199	94.76 227	97.04 293	80.91 326	98.48 274	91.79 284	96.25 226	99.09 157

c3_l		94.79 231	94.43 226	95.89 281	97.75 224	93.12 280	97.16 326	98.03 260	92.23 285	93.46 286	97.05 292	91.39 142	98.01 325	93.58 236	89.21 328	96.53 316

V42		94.78 232	94.14 239	96.70 221	96.33 323	95.22 190	98.97 84	98.09 249	92.32 282	94.31 246	97.06 290	88.39 212	98.55 267	92.90 255	88.87 334	96.34 333

CR-MVSNet		94.76 233	94.15 238	96.59 234	97.00 282	93.43 266	94.96 375	97.56 290	92.46 273	96.93 163	96.24 333	88.15 217	97.88 338	87.38 345	96.65 208	98.46 209

v2v482		94.69 234	94.03 244	96.65 224	96.17 328	94.79 214	98.67 165	98.08 250	92.72 266	94.00 262	97.16 278	87.69 233	98.45 279	92.91 254	88.87 334	96.72 290

pmmvs4		94.69 234	93.99 250	96.81 215	95.74 343	95.94 154	97.40 301	97.67 282	90.42 331	93.37 289	97.59 250	89.08 193	98.20 311	92.97 252	91.67 295	96.30 336

cl22		94.68 236	94.19 234	96.13 270	98.11 201	93.60 259	96.94 336	98.31 203	92.43 277	93.32 291	96.87 311	86.51 250	98.28 308	94.10 220	91.16 302	96.51 322

eth_miper_zixun_eth		94.68 236	94.41 227	95.47 295	97.64 235	91.71 301	96.73 353	98.07 252	92.71 267	93.64 276	97.21 276	90.54 163	98.17 313	93.38 239	89.76 317	96.54 314

PCF-MVS	93.45 11	94.68 236	93.43 285	98.42 99	98.62 152	96.77 107	95.48 373	98.20 222	84.63 377	93.34 290	98.32 184	88.55 209	99.81 81	84.80 364	98.96 128	98.68 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		94.67 239	93.54 280	98.08 128	96.88 292	96.56 119	98.19 229	98.50 169	78.05 386	92.69 310	98.02 208	91.07 154	99.63 134	90.09 311	98.36 161	98.04 224

PS-CasMVS		94.67 239	93.99 250	96.71 219	96.68 304	95.26 187	99.13 52	99.03 37	93.68 224	92.33 321	97.95 216	85.35 274	98.10 318	93.59 235	88.16 341	96.79 282

cascas		94.63 241	93.86 259	96.93 206	96.91 290	94.27 237	96.00 366	98.51 164	85.55 373	94.54 231	96.23 335	84.20 302	98.87 237	95.80 165	96.98 200	97.66 236

tpmvs		94.60 242	94.36 229	95.33 301	97.46 250	88.60 354	96.88 345	97.68 281	91.29 314	93.80 273	96.42 330	88.58 205	99.24 180	91.06 298	96.04 231	98.17 221

LTVRE_ROB	92.95 15	94.60 242	93.90 256	96.68 223	97.41 258	94.42 230	98.52 187	98.59 144	91.69 300	91.21 334	98.35 178	84.87 283	99.04 209	91.06 298	93.44 273	96.60 305
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		94.59 244	93.92 253	96.60 233	96.21 325	94.78 215	98.59 176	98.14 237	91.86 296	94.21 252	97.02 296	87.97 223	98.41 291	91.72 286	89.57 320	96.61 304

ADS-MVSNet2		94.58 245	94.40 228	95.11 307	98.00 209	88.74 352	96.04 363	97.30 316	90.15 335	96.47 187	96.64 323	87.89 225	97.56 349	90.08 312	97.06 195	99.02 165

ACMH	92.88 16	94.55 246	93.95 252	96.34 261	97.63 236	93.26 274	98.81 134	98.49 174	93.43 237	89.74 347	98.53 157	81.91 318	99.08 204	93.69 230	93.30 276	96.70 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		94.54 247	93.85 260	96.63 228	97.98 213	93.06 282	98.77 142	97.84 275	93.67 226	93.80 273	98.04 207	76.88 357	98.96 221	94.79 195	92.86 282	97.86 229

XVG-ACMP-BASELINE		94.54 247	94.14 239	95.75 287	96.55 310	91.65 302	98.11 241	98.44 180	94.96 163	94.22 251	97.90 219	79.18 339	99.11 198	94.05 222	93.85 258	96.48 327

AUN-MVS		94.53 249	93.73 270	96.92 209	98.50 160	93.52 264	98.34 207	98.10 245	93.83 210	95.94 205	97.98 214	85.59 269	99.03 210	94.35 209	80.94 376	98.22 219

DIV-MVS_self_test		94.52 250	94.03 244	95.99 274	97.57 243	93.38 270	97.05 330	97.94 268	91.74 297	92.81 305	97.10 280	89.12 191	98.07 322	92.60 261	90.30 310	96.53 316

cl____		94.51 251	94.01 247	96.02 273	97.58 239	93.40 269	97.05 330	97.96 267	91.73 299	92.76 307	97.08 286	89.06 194	98.13 316	92.61 260	90.29 311	96.52 319

ETVMVS		94.50 252	93.44 284	97.68 158	98.18 194	95.35 183	98.19 229	97.11 325	93.73 216	96.40 190	95.39 355	74.53 366	98.84 240	91.10 295	96.31 220	98.84 181

GBi-Net		94.49 253	93.80 263	96.56 238	98.21 188	95.00 199	98.82 126	98.18 227	92.46 273	94.09 257	97.07 287	81.16 323	97.95 330	92.08 275	92.14 288	96.72 290

test1		94.49 253	93.80 263	96.56 238	98.21 188	95.00 199	98.82 126	98.18 227	92.46 273	94.09 257	97.07 287	81.16 323	97.95 330	92.08 275	92.14 288	96.72 290

dmvs_re		94.48 255	94.18 236	95.37 299	97.68 231	90.11 330	98.54 186	97.08 327	94.56 178	94.42 240	97.24 273	84.25 298	97.76 342	91.02 301	92.83 283	98.24 217

v8		94.47 256	93.77 266	96.57 237	96.36 320	94.83 211	99.05 65	98.19 224	91.92 293	93.16 295	96.97 301	88.82 203	98.48 274	91.69 287	87.79 343	96.39 331

FMVSNet2		94.47 256	93.61 276	97.04 197	98.21 188	96.43 126	98.79 140	98.27 212	92.46 273	93.50 284	97.09 284	81.16 323	98.00 327	91.09 296	91.93 291	96.70 294

test2506		94.44 258	93.91 255	96.04 272	99.02 111	88.99 349	99.06 63	79.47 406	96.96 67	98.36 94	99.26 57	77.21 354	99.52 156	96.78 134	99.04 123	99.59 79

Patchmatch-test		94.42 259	93.68 274	96.63 228	97.60 238	91.76 298	94.83 379	97.49 302	89.45 348	94.14 255	97.10 280	88.99 195	98.83 243	85.37 359	98.13 168	99.29 127

PEN-MVS		94.42 259	93.73 270	96.49 247	96.28 324	94.84 209	99.17 45	99.00 39	93.51 232	92.23 323	97.83 229	86.10 259	97.90 334	92.55 266	86.92 355	96.74 287

v144192		94.39 261	93.70 272	96.48 249	96.06 333	94.35 234	98.58 178	98.16 234	91.45 305	94.33 245	97.02 296	87.50 236	98.45 279	91.08 297	89.11 329	96.63 302

Baseline_NR-MVSNet		94.35 262	93.81 262	95.96 277	96.20 326	94.05 245	98.61 175	96.67 352	91.44 306	93.85 270	97.60 249	88.57 206	98.14 315	94.39 207	86.93 354	95.68 351

miper_lstm_enhance		94.33 263	94.07 242	95.11 307	97.75 224	90.97 312	97.22 317	98.03 260	91.67 301	92.76 307	96.97 301	90.03 171	97.78 341	92.51 268	89.64 319	96.56 311

v1192		94.32 264	93.58 277	96.53 244	96.10 331	94.45 228	98.50 192	98.17 232	91.54 303	94.19 253	97.06 290	86.95 245	98.43 282	90.14 310	89.57 320	96.70 294

ACMH+	92.99 14	94.30 265	93.77 266	95.88 282	97.81 221	92.04 295	98.71 155	98.37 194	93.99 200	90.60 341	98.47 164	80.86 328	99.05 206	92.75 259	92.40 287	96.55 313

v148		94.29 266	93.76 268	95.91 279	96.10 331	92.93 283	98.58 178	97.97 265	92.59 271	93.47 285	96.95 305	88.53 210	98.32 300	92.56 265	87.06 353	96.49 325

v10		94.29 266	93.55 279	96.51 246	96.39 319	94.80 213	98.99 81	98.19 224	91.35 310	93.02 301	96.99 299	88.09 219	98.41 291	90.50 307	88.41 338	96.33 335

MVP-Stereo		94.28 268	93.92 253	95.35 300	94.95 361	92.60 286	97.97 254	97.65 283	91.61 302	90.68 340	97.09 284	86.32 256	98.42 283	89.70 321	99.34 113	95.02 363
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D		94.24 269	93.33 287	96.97 203	97.19 273	93.38 270	98.74 146	98.57 151	91.21 319	93.81 272	98.58 153	72.85 373	98.77 249	95.05 188	93.93 257	98.77 188

OurMVSNet-221017-0		94.21 270	94.00 248	94.85 316	95.60 347	89.22 344	98.89 103	97.43 309	95.29 144	92.18 324	98.52 160	82.86 314	98.59 263	93.46 238	91.76 293	96.74 287

v1921920		94.20 271	93.47 283	96.40 258	95.98 336	94.08 244	98.52 187	98.15 235	91.33 311	94.25 249	97.20 277	86.41 254	98.42 283	90.04 315	89.39 326	96.69 299

WB-MVSnew		94.19 272	94.04 243	94.66 323	96.82 296	92.14 290	97.86 269	95.96 364	93.50 233	95.64 208	96.77 316	88.06 221	97.99 328	84.87 361	96.86 201	93.85 378

v7n		94.19 272	93.43 285	96.47 250	95.90 339	94.38 233	99.26 27	98.34 199	91.99 291	92.76 307	97.13 279	88.31 213	98.52 271	89.48 326	87.70 344	96.52 319

tpm2		94.19 272	93.76 268	95.46 296	97.23 267	89.04 347	97.31 312	96.85 347	87.08 363	96.21 195	96.79 315	83.75 312	98.74 250	92.43 271	96.23 227	98.59 202

TESTMET0.1,1		94.18 275	93.69 273	95.63 290	96.92 288	89.12 345	96.91 339	94.78 377	93.17 248	94.88 221	96.45 329	78.52 341	98.92 228	93.09 247	98.50 152	98.85 179

dp		94.15 276	93.90 256	94.90 313	97.31 263	86.82 371	96.97 334	97.19 323	91.22 318	96.02 200	96.61 325	85.51 271	99.02 213	90.00 316	94.30 242	98.85 179

ET-MVSNet_ETH3D		94.13 277	92.98 294	97.58 166	98.22 187	96.20 138	97.31 312	95.37 370	94.53 180	79.56 385	97.63 248	86.51 250	97.53 350	96.91 119	90.74 306	99.02 165

tpm		94.13 277	93.80 263	95.12 306	96.50 313	87.91 365	97.44 298	95.89 367	92.62 269	96.37 192	96.30 332	84.13 303	98.30 304	93.24 243	91.66 296	99.14 152

testing222		94.12 279	93.03 293	97.37 180	98.02 208	94.66 216	97.94 257	96.65 354	94.63 176	95.78 206	95.76 345	71.49 374	98.92 228	91.17 294	95.88 232	98.52 206

IterMVS-SCA-FT		94.11 280	93.87 258	94.85 316	97.98 213	90.56 323	97.18 322	98.11 242	93.75 213	92.58 313	97.48 257	83.97 306	97.41 353	92.48 270	91.30 299	96.58 307

Anonymous20231211		94.10 281	93.26 290	96.61 231	99.11 104	94.28 236	99.01 76	98.88 62	86.43 366	92.81 305	97.57 252	81.66 320	98.68 256	94.83 192	89.02 332	96.88 271

IterMVS		94.09 282	93.85 260	94.80 319	97.99 211	90.35 326	97.18 322	98.12 239	93.68 224	92.46 319	97.34 265	84.05 304	97.41 353	92.51 268	91.33 298	96.62 303
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter		94.08 283	93.51 281	95.80 284	96.77 297	89.70 335	96.91 339	95.21 372	92.89 261	94.83 224	95.72 350	77.69 349	98.97 217	93.06 248	98.50 152	98.72 189

test0.0.03 1		94.08 283	93.51 281	95.80 284	95.53 350	92.89 284	97.38 303	95.97 363	95.11 154	92.51 317	96.66 320	87.71 230	96.94 360	87.03 347	93.67 263	97.57 240

v1240		94.06 285	93.29 289	96.34 261	96.03 335	93.90 248	98.44 199	98.17 232	91.18 320	94.13 256	97.01 298	86.05 260	98.42 283	89.13 331	89.50 324	96.70 294

X-MVStestdata		94.06 285	92.30 308	99.34 23	99.70 22	98.35 41	99.29 22	98.88 62	97.40 36	98.46 86	43.50 399	95.90 41	99.89 47	97.85 71	99.74 45	99.78 21

DTE-MVSNet		93.98 287	93.26 290	96.14 269	96.06 333	94.39 232	99.20 40	98.86 75	93.06 254	91.78 329	97.81 231	85.87 264	97.58 348	90.53 306	86.17 360	96.46 329

pm-mvs1		93.94 288	93.06 292	96.59 234	96.49 314	95.16 192	98.95 90	98.03 260	92.32 282	91.08 336	97.84 226	84.54 294	98.41 291	92.16 273	86.13 362	96.19 340

MS-PatchMatch		93.84 289	93.63 275	94.46 331	96.18 327	89.45 340	97.76 278	98.27 212	92.23 285	92.13 325	97.49 256	79.50 336	98.69 253	89.75 319	99.38 111	95.25 356

tfpnnormal		93.66 290	92.70 300	96.55 243	96.94 287	95.94 154	98.97 84	99.19 24	91.04 321	91.38 333	97.34 265	84.94 282	98.61 260	85.45 358	89.02 332	95.11 360

EU-MVSNet		93.66 290	94.14 239	92.25 355	95.96 338	83.38 379	98.52 187	98.12 239	94.69 172	92.61 312	98.13 201	87.36 239	96.39 371	91.82 283	90.00 315	96.98 257

our_test_3		93.65 292	93.30 288	94.69 321	95.45 354	89.68 337	96.91 339	97.65 283	91.97 292	91.66 331	96.88 309	89.67 177	97.93 333	88.02 342	91.49 297	96.48 327

pmmvs5		93.65 292	92.97 295	95.68 288	95.49 351	92.37 287	98.20 226	97.28 318	89.66 344	92.58 313	97.26 270	82.14 317	98.09 320	93.18 246	90.95 305	96.58 307

test_fmvs2		93.43 294	93.58 277	92.95 349	96.97 285	83.91 376	99.19 42	97.24 321	95.74 120	95.20 215	98.27 190	69.65 376	98.72 252	96.26 148	93.73 262	96.24 337

tpm cat1		93.36 295	92.80 297	95.07 309	97.58 239	87.97 364	96.76 351	97.86 274	82.17 382	93.53 280	96.04 341	86.13 258	99.13 194	89.24 329	95.87 233	98.10 223

JIA-IIPM		93.35 296	92.49 304	95.92 278	96.48 315	90.65 321	95.01 374	96.96 337	85.93 370	96.08 198	87.33 389	87.70 232	98.78 248	91.35 291	95.58 237	98.34 214

SixPastTwentyTwo		93.34 297	92.86 296	94.75 320	95.67 345	89.41 342	98.75 143	96.67 352	93.89 205	90.15 345	98.25 193	80.87 327	98.27 309	90.90 302	90.64 307	96.57 309

USDC		93.33 298	92.71 299	95.21 303	96.83 295	90.83 317	96.91 339	97.50 300	93.84 208	90.72 339	98.14 200	77.69 349	98.82 244	89.51 325	93.21 278	95.97 345

IB-MVS	91.98 17	93.27 299	91.97 312	97.19 187	97.47 249	93.41 268	97.09 329	95.99 362	93.32 241	92.47 318	95.73 348	78.06 347	99.53 153	94.59 203	82.98 368	98.62 199
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		93.26 300	92.21 309	96.41 256	97.73 228	93.13 279	95.65 370	97.03 332	91.27 316	94.04 260	96.06 340	75.33 362	97.19 356	86.56 349	96.23 227	98.92 176

ppachtmachnet_test		93.22 301	92.63 301	94.97 311	95.45 354	90.84 316	96.88 345	97.88 273	90.60 326	92.08 326	97.26 270	88.08 220	97.86 339	85.12 360	90.33 309	96.22 338

Patchmtry		93.22 301	92.35 307	95.84 283	96.77 297	93.09 281	94.66 382	97.56 290	87.37 362	92.90 303	96.24 333	88.15 217	97.90 334	87.37 346	90.10 314	96.53 316

testing3		93.19 303	92.48 305	95.30 302	98.07 203	92.27 288	98.64 169	97.17 324	93.94 204	93.98 263	97.04 293	67.97 380	96.01 375	88.40 337	97.14 194	97.63 237

FMVSNet1		93.19 303	92.07 310	96.56 238	97.54 244	95.00 199	98.82 126	98.18 227	90.38 332	92.27 322	97.07 287	73.68 371	97.95 330	89.36 328	91.30 299	96.72 290

LF4IMVS		93.14 305	92.79 298	94.20 334	95.88 340	88.67 353	97.66 286	97.07 329	93.81 211	91.71 330	97.65 244	77.96 348	98.81 245	91.47 290	91.92 292	95.12 359

testgi		93.06 306	92.45 306	94.88 315	96.43 318	89.90 331	98.75 143	97.54 296	95.60 127	91.63 332	97.91 218	74.46 368	97.02 358	86.10 352	93.67 263	97.72 234

PatchT		93.06 306	91.97 312	96.35 260	96.69 303	92.67 285	94.48 383	97.08 327	86.62 364	97.08 155	92.23 383	87.94 224	97.90 334	78.89 382	96.69 206	98.49 208

RPMNet		92.81 308	91.34 317	97.24 184	97.00 282	93.43 266	94.96 375	98.80 93	82.27 381	96.93 163	92.12 384	86.98 244	99.82 76	76.32 387	96.65 208	98.46 209

myMVS_eth3d		92.73 309	92.01 311	94.89 314	97.39 259	90.94 313	97.91 260	97.46 303	93.16 249	93.42 287	95.37 356	68.09 379	96.12 373	88.34 338	96.99 197	97.60 238

TransMVSNet (Re)		92.67 310	91.51 316	96.15 268	96.58 309	94.65 217	98.90 99	96.73 348	90.86 324	89.46 351	97.86 223	85.62 268	98.09 320	86.45 350	81.12 374	95.71 350

Syy-MVS		92.55 311	92.61 302	92.38 352	97.39 259	83.41 378	97.91 260	97.46 303	93.16 249	93.42 287	95.37 356	84.75 287	96.12 373	77.00 386	96.99 197	97.60 238

K. test v3		92.55 311	91.91 314	94.48 329	95.64 346	89.24 343	99.07 62	94.88 376	94.04 195	86.78 366	97.59 250	77.64 352	97.64 345	92.08 275	89.43 325	96.57 309

DSMNet-mixed		92.52 313	92.58 303	92.33 353	94.15 369	82.65 381	98.30 215	94.26 383	89.08 353	92.65 311	95.73 348	85.01 281	95.76 377	86.24 351	97.76 181	98.59 202

TinyColmap		92.31 314	91.53 315	94.65 324	96.92 288	89.75 333	96.92 337	96.68 351	90.45 330	89.62 348	97.85 225	76.06 360	98.81 245	86.74 348	92.51 286	95.41 354

gg-mvs-nofinetune		92.21 315	90.58 323	97.13 192	96.75 300	95.09 196	95.85 367	89.40 399	85.43 374	94.50 233	81.98 392	80.80 329	98.40 297	92.16 273	98.33 162	97.88 227

FMVSNet5		91.81 316	90.92 319	94.49 328	97.21 269	92.09 292	98.00 252	97.55 295	89.31 351	90.86 338	95.61 353	74.48 367	95.32 381	85.57 356	89.70 318	96.07 343

pmmvs6		91.77 317	90.63 322	95.17 305	94.69 367	91.24 309	98.67 165	97.92 270	86.14 368	89.62 348	97.56 254	75.79 361	98.34 298	90.75 304	84.56 364	95.94 346

Anonymous20231206		91.66 318	91.10 318	93.33 343	94.02 373	87.35 368	98.58 178	97.26 320	90.48 328	90.16 344	96.31 331	83.83 310	96.53 369	79.36 380	89.90 316	96.12 341

Patchmatch-RL test		91.49 319	90.85 320	93.41 341	91.37 382	84.40 374	92.81 387	95.93 366	91.87 295	87.25 363	94.87 362	88.99 195	96.53 369	92.54 267	82.00 370	99.30 125

test_0402		91.32 320	90.27 326	94.48 329	96.60 307	91.12 310	98.50 192	97.22 322	86.10 369	88.30 359	96.98 300	77.65 351	97.99 328	78.13 384	92.94 281	94.34 367

test_vis1_rt		91.29 321	90.65 321	93.19 347	97.45 253	86.25 372	98.57 183	90.90 397	93.30 243	86.94 365	93.59 374	62.07 387	99.11 198	97.48 100	95.58 237	94.22 370

PVSNet_0	88.72 19	91.28 322	90.03 328	95.00 310	97.99 211	87.29 369	94.84 378	98.50 169	92.06 290	89.86 346	95.19 358	79.81 335	99.39 169	92.27 272	69.79 392	98.33 215

Anonymous20240521		91.18 323	90.44 324	93.42 340	93.70 374	88.47 357	98.94 93	97.56 290	88.46 357	89.56 350	95.08 361	77.15 356	96.97 359	83.92 367	89.55 322	94.82 365

EG-PatchMatch MVS		91.13 324	90.12 327	94.17 336	94.73 366	89.00 348	98.13 238	97.81 276	89.22 352	85.32 376	96.46 328	67.71 381	98.42 283	87.89 344	93.82 259	95.08 361

TDRefinement		91.06 325	89.68 330	95.21 303	85.35 397	91.49 305	98.51 191	97.07 329	91.47 304	88.83 357	97.84 226	77.31 353	99.09 203	92.79 258	77.98 385	95.04 362

UnsupCasMVSNet_eth		90.99 326	89.92 329	94.19 335	94.08 370	89.83 332	97.13 328	98.67 128	93.69 222	85.83 372	96.19 338	75.15 363	96.74 363	89.14 330	79.41 381	96.00 344

test20.03		90.89 327	90.38 325	92.43 351	93.48 375	88.14 363	98.33 208	97.56 290	93.40 238	87.96 360	96.71 319	80.69 330	94.13 386	79.15 381	86.17 360	95.01 364

MDA-MVSNet_test_wron		90.71 328	89.38 333	94.68 322	94.83 363	90.78 318	97.19 321	97.46 303	87.60 360	72.41 392	95.72 350	86.51 250	96.71 366	85.92 354	86.80 357	96.56 311

YYNet1		90.70 329	89.39 332	94.62 325	94.79 365	90.65 321	97.20 319	97.46 303	87.54 361	72.54 391	95.74 346	86.51 250	96.66 367	86.00 353	86.76 358	96.54 314

KD-MVS_self_test		90.38 330	89.38 333	93.40 342	92.85 378	88.94 350	97.95 255	97.94 268	90.35 333	90.25 343	93.96 371	79.82 334	95.94 376	84.62 366	76.69 387	95.33 355

pmmvs-eth3d		90.36 331	89.05 336	94.32 333	91.10 384	92.12 291	97.63 291	96.95 338	88.86 355	84.91 377	93.13 378	78.32 343	96.74 363	88.70 334	81.81 372	94.09 373

CL-MVSNet_self_test		90.11 332	89.14 335	93.02 348	91.86 381	88.23 362	96.51 359	98.07 252	90.49 327	90.49 342	94.41 366	84.75 287	95.34 380	80.79 376	74.95 389	95.50 353

new_pmnet		90.06 333	89.00 337	93.22 346	94.18 368	88.32 360	96.42 361	96.89 343	86.19 367	85.67 373	93.62 373	77.18 355	97.10 357	81.61 374	89.29 327	94.23 369

MDA-MVSNet-bldmvs		89.97 334	88.35 340	94.83 318	95.21 358	91.34 306	97.64 288	97.51 299	88.36 358	71.17 393	96.13 339	79.22 338	96.63 368	83.65 368	86.27 359	96.52 319

CMPMVS	66.06 21	89.70 335	89.67 331	89.78 360	93.19 376	76.56 386	97.00 333	98.35 197	80.97 383	81.57 382	97.75 234	74.75 365	98.61 260	89.85 317	93.63 266	94.17 371
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		89.67 336	88.28 341	93.82 337	92.81 379	91.08 311	98.01 250	97.45 307	87.95 359	87.90 361	95.87 344	67.63 382	94.56 385	78.73 383	88.18 340	95.83 348

KD-MVS_2432*1600		89.61 337	87.96 344	94.54 326	94.06 371	91.59 303	95.59 371	97.63 285	89.87 340	88.95 354	94.38 368	78.28 344	96.82 361	84.83 362	68.05 393	95.21 357

miper_refine_blended		89.61 337	87.96 344	94.54 326	94.06 371	91.59 303	95.59 371	97.63 285	89.87 340	88.95 354	94.38 368	78.28 344	96.82 361	84.83 362	68.05 393	95.21 357

MVS-HIRNet		89.46 339	88.40 339	92.64 350	97.58 239	82.15 382	94.16 386	93.05 391	75.73 388	90.90 337	82.52 391	79.42 337	98.33 299	83.53 369	98.68 140	97.43 241

OpenMVS_ROB	86.42 20	89.00 340	87.43 348	93.69 338	93.08 377	89.42 341	97.91 260	96.89 343	78.58 385	85.86 371	94.69 363	69.48 377	98.29 307	77.13 385	93.29 277	93.36 380

mvsany_test3		88.80 341	88.04 342	91.09 359	89.78 387	81.57 384	97.83 274	95.49 369	93.81 211	87.53 362	93.95 372	56.14 390	97.43 352	94.68 196	83.13 367	94.26 368

new-patchmatchnet		88.50 342	87.45 347	91.67 357	90.31 386	85.89 373	97.16 326	97.33 315	89.47 347	83.63 379	92.77 380	76.38 358	95.06 383	82.70 371	77.29 386	94.06 375

APD_test1		88.22 343	88.01 343	88.86 362	95.98 336	74.66 392	97.21 318	96.44 357	83.96 379	86.66 368	97.90 219	60.95 388	97.84 340	82.73 370	90.23 312	94.09 373

PM-MVS		87.77 344	86.55 350	91.40 358	91.03 385	83.36 380	96.92 337	95.18 374	91.28 315	86.48 370	93.42 375	53.27 391	96.74 363	89.43 327	81.97 371	94.11 372

dmvs_testset		87.64 345	88.93 338	83.79 370	95.25 357	63.36 401	97.20 319	91.17 395	93.07 253	85.64 374	95.98 343	85.30 278	91.52 393	69.42 392	87.33 349	96.49 325

test_fmvs3		87.17 346	87.06 349	87.50 364	91.21 383	75.66 388	99.05 65	96.61 355	92.79 265	88.85 356	92.78 379	43.72 394	93.49 387	93.95 223	84.56 364	93.34 381

UnsupCasMVSNet_bld		87.17 346	85.12 353	93.31 344	91.94 380	88.77 351	94.92 377	98.30 209	84.30 378	82.30 380	90.04 386	63.96 386	97.25 355	85.85 355	74.47 391	93.93 377

N_pmnet		87.12 348	87.77 346	85.17 368	95.46 353	61.92 402	97.37 305	70.66 407	85.83 371	88.73 358	96.04 341	85.33 276	97.76 342	80.02 377	90.48 308	95.84 347

pmmvs3		86.67 349	84.86 354	92.11 356	88.16 391	87.19 370	96.63 355	94.75 378	79.88 384	87.22 364	92.75 381	66.56 384	95.20 382	81.24 375	76.56 388	93.96 376

test_f		86.07 350	85.39 351	88.10 363	89.28 389	75.57 389	97.73 281	96.33 359	89.41 350	85.35 375	91.56 385	43.31 396	95.53 378	91.32 292	84.23 366	93.21 382

WB-MVS		84.86 351	85.33 352	83.46 371	89.48 388	69.56 396	98.19 229	96.42 358	89.55 346	81.79 381	94.67 364	84.80 285	90.12 394	52.44 397	80.64 378	90.69 385

SSC-MVS		84.27 352	84.71 355	82.96 375	89.19 390	68.83 397	98.08 243	96.30 360	89.04 354	81.37 383	94.47 365	84.60 292	89.89 395	49.80 399	79.52 380	90.15 386

test_vis3_rt		79.22 353	77.40 359	84.67 369	86.44 395	74.85 391	97.66 286	81.43 404	84.98 375	67.12 395	81.91 393	28.09 404	97.60 346	88.96 332	80.04 379	81.55 393

test_method		79.03 354	78.17 356	81.63 376	86.06 396	54.40 407	82.75 395	96.89 343	39.54 399	80.98 384	95.57 354	58.37 389	94.73 384	84.74 365	78.61 382	95.75 349

testf1		79.02 355	77.70 357	82.99 373	88.10 392	66.90 398	94.67 380	93.11 388	71.08 390	74.02 388	93.41 376	34.15 400	93.25 388	72.25 390	78.50 383	88.82 388

APD_test2		79.02 355	77.70 357	82.99 373	88.10 392	66.90 398	94.67 380	93.11 388	71.08 390	74.02 388	93.41 376	34.15 400	93.25 388	72.25 390	78.50 383	88.82 388

LCM-MVSNet		78.70 357	76.24 362	86.08 366	77.26 403	71.99 394	94.34 384	96.72 349	61.62 394	76.53 386	89.33 387	33.91 402	92.78 391	81.85 373	74.60 390	93.46 379

Gipuma		78.40 358	76.75 361	83.38 372	95.54 349	80.43 385	79.42 396	97.40 311	64.67 393	73.46 390	80.82 394	45.65 393	93.14 390	66.32 394	87.43 347	76.56 396
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.95 359	75.44 363	85.46 367	82.54 398	74.95 390	94.23 385	93.08 390	72.80 389	74.68 387	87.38 388	36.36 399	91.56 392	73.95 388	63.94 395	89.87 387

FPMVS		77.62 360	77.14 360	79.05 378	79.25 401	60.97 403	95.79 368	95.94 365	65.96 392	67.93 394	94.40 367	37.73 398	88.88 397	68.83 393	88.46 337	87.29 390

EGC-MVSNET		75.22 361	69.54 364	92.28 354	94.81 364	89.58 338	97.64 288	96.50 356	1.82 404	5.57 405	95.74 346	68.21 378	96.26 372	73.80 389	91.71 294	90.99 384

ANet_high		69.08 362	65.37 366	80.22 377	65.99 405	71.96 395	90.91 391	90.09 398	82.62 380	49.93 400	78.39 395	29.36 403	81.75 398	62.49 395	38.52 399	86.95 392

tmp_tt		68.90 363	66.97 365	74.68 380	50.78 407	59.95 404	87.13 392	83.47 403	38.80 400	62.21 396	96.23 335	64.70 385	76.91 402	88.91 333	30.49 400	87.19 391

PMVS	61.03 23	65.95 364	63.57 368	73.09 381	57.90 406	51.22 408	85.05 394	93.93 387	54.45 395	44.32 401	83.57 390	13.22 405	89.15 396	58.68 396	81.00 375	78.91 395
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 365	64.25 367	67.02 382	82.28 399	59.36 405	91.83 390	85.63 401	52.69 396	60.22 397	77.28 396	41.06 397	80.12 400	46.15 400	41.14 397	61.57 398

EMVS		64.07 366	63.26 369	66.53 383	81.73 400	58.81 406	91.85 389	84.75 402	51.93 398	59.09 398	75.13 397	43.32 395	79.09 401	42.03 401	39.47 398	61.69 397

MVE	62.14 22	63.28 367	59.38 370	74.99 379	74.33 404	65.47 400	85.55 393	80.50 405	52.02 397	51.10 399	75.00 398	10.91 408	80.50 399	51.60 398	53.40 396	78.99 394
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		30.17 368	30.18 372	30.16 384	78.61 402	43.29 409	66.79 397	14.21 408	17.31 401	14.82 404	11.93 404	11.55 407	41.43 403	37.08 402	19.30 401	5.76 401

cdsmvs_eth3d_5k		23.98 369	31.98 371	0.00 387	0.00 410	0.00 412	0.00 398	98.59 144	0.00 405	0.00 406	98.61 148	90.60 162	0.00 406	0.00 405	0.00 404	0.00 402

testmvs		21.48 370	24.95 373	11.09 386	14.89 408	6.47 411	96.56 357	9.87 409	7.55 402	17.93 402	39.02 400	9.43 409	5.90 405	16.56 404	12.72 402	20.91 400

test123		20.95 371	23.72 374	12.64 385	13.54 409	8.19 410	96.55 358	6.13 410	7.48 403	16.74 403	37.98 401	12.97 406	6.05 404	16.69 403	5.43 403	23.68 399

ab-mvs-re		8.20 372	10.94 375	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	98.43 168	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

pcd_1.5k_mvsjas		7.88 373	10.50 376	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	94.51 81	0.00 406	0.00 405	0.00 404	0.00 402

test_blank		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uanet_test		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

DCPMVS		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

sosnet-low-res		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

sosnet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uncertanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

Regformer		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

uanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 404	0.00 402

WAC-MVS						90.94 313								88.66 335

FOURS1					99.82 1	98.66 24	99.69 1	98.95 46	97.46 34	99.39 30

MSC_two_6792asdad				99.62 6	99.17 94	99.08 11		98.63 138					99.94 8	98.53 30	99.80 19	99.86 8

PC_three_1452									95.08 158	99.60 19	99.16 77	97.86 2	98.47 277	97.52 98	99.72 51	99.74 37

No_MVS				99.62 6	99.17 94	99.08 11		98.63 138					99.94 8	98.53 30	99.80 19	99.86 8

test_one_0601					99.66 26	99.25 2		98.86 75	97.55 28	99.20 38	99.47 20	97.57 6

eth-test2					0.00 410

eth-test					0.00 410

ZD-MVS					99.46 49	98.70 23		98.79 98	93.21 246	98.67 73	98.97 105	95.70 45	99.83 69	96.07 152	99.58 79

RE-MVS-def			98.34 35		99.49 45	97.86 62	99.11 55	98.80 93	96.49 86	99.17 41	99.35 44	95.29 61		97.72 80	99.65 64	99.71 49

IU-MVS					99.71 19	99.23 7		98.64 136	95.28 145	99.63 18				98.35 47	99.81 12	99.83 13

OPU-MVS				99.37 20	99.24 87	99.05 14	99.02 74				99.16 77	97.81 3	99.37 170	97.24 107	99.73 48	99.70 53

test_241102_TWO								98.87 69	97.65 22	99.53 23	99.48 18	97.34 11	99.94 8	98.43 42	99.80 19	99.83 13

test_241102_ONE					99.71 19	99.24 5		98.87 69	97.62 24	99.73 10	99.39 32	97.53 7	99.74 111

9.14			98.06 58		99.47 47		98.71 155	98.82 81	94.36 187	99.16 44	99.29 53	96.05 33	99.81 81	97.00 114	99.71 53

save fliter					99.46 49	98.38 35	98.21 224	98.71 116	97.95 13

test_0728_THIRD									97.32 42	99.45 25	99.46 24	97.88 1	99.94 8	98.47 38	99.86 1	99.85 10

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 84	98.88 62					99.94 8	98.47 38	99.81 12	99.84 12

test0726					99.72 12	99.25 2	99.06 63	98.88 62	97.62 24	99.56 20	99.50 15	97.42 9

GSMVS																99.20 139

test_part2					99.63 29	99.18 10				99.27 35

sam_mvs1												89.45 181				99.20 139

sam_mvs												88.99 195

ambc				89.49 361	86.66 394	75.78 387	92.66 388	96.72 349		86.55 369	92.50 382	46.01 392	97.90 334	90.32 308	82.09 369	94.80 366

MTGPA								98.74 108

test_post1							96.68 354				30.43 403	87.85 228	98.69 253	92.59 263

test_post											31.83 402	88.83 202	98.91 230

patchmatchnet-post											95.10 360	89.42 182	98.89 234

GG-mvs-BLEND				96.59 234	96.34 322	94.98 202	96.51 359	88.58 400		93.10 300	94.34 370	80.34 333	98.05 323	89.53 324	96.99 197	96.74 287

MTMP							98.89 103	94.14 385

gm-plane-assit					95.88 340	87.47 367			89.74 343		96.94 306		99.19 186	93.32 242

test9_res														96.39 146	99.57 80	99.69 56

TEST9					99.31 64	98.50 29	97.92 258	98.73 111	92.63 268	97.74 130	98.68 142	96.20 28	99.80 88

test_8					99.29 73	98.44 31	97.89 266	98.72 113	92.98 257	97.70 134	98.66 145	96.20 28	99.80 88

agg_prior2														95.87 162	99.57 80	99.68 61

agg_prior					99.30 68	98.38 35		98.72 113		97.57 144			99.81 81

TestCases				96.99 200	99.25 81	93.21 277		98.18 227	91.36 308	93.52 281	98.77 132	84.67 290	99.72 113	89.70 321	97.87 176	98.02 225

test_prior4						98.01 59	97.86 269

test_prior2							97.80 275		96.12 103	97.89 124	98.69 141	95.96 37		96.89 123	99.60 74

test_prior				99.19 40	99.31 64	98.22 47		98.84 79					99.70 119			99.65 69

旧先验2							97.57 294		91.30 313	98.67 73			99.80 88	95.70 170

新几何2							97.64 288

新几何1				99.16 45	99.34 57	98.01 59		98.69 120	90.06 337	98.13 101	98.95 112	94.60 79	99.89 47	91.97 281	99.47 99	99.59 79

旧先验1					99.29 73	97.48 76		98.70 119			99.09 92	95.56 48			99.47 99	99.61 75

无先验							97.58 293	98.72 113	91.38 307				99.87 58	93.36 241		99.60 77

原ACMM2							97.67 285

原ACMM1				98.65 75	99.32 62	96.62 112		98.67 128	93.27 245	97.81 125	98.97 105	95.18 67	99.83 69	93.84 227	99.46 102	99.50 91

test222					99.23 88	97.17 92	97.40 301	98.66 131	88.68 356	98.05 106	98.96 110	94.14 93			99.53 91	99.61 75

testdata2													99.89 47	91.65 288

segment_acmp												96.85 14

testdata				98.26 110	99.20 92	95.36 181		98.68 123	91.89 294	98.60 81	99.10 86	94.44 86	99.82 76	94.27 213	99.44 103	99.58 83

testdata1							97.32 311		96.34 95

test12				99.18 42	99.16 98	98.19 48		98.53 159		98.07 105		95.13 70	99.72 113		99.56 86	99.63 73

plane_prior7					97.42 255	94.63 219

plane_prior6					97.35 262	94.61 222						87.09 241

plane_prior5								98.56 153					99.03 210	96.07 152	94.27 243	96.92 262

plane_prior4											98.28 187

plane_prior3						94.61 222			97.02 64	95.34 212

plane_prior2							98.80 135		97.28 45

plane_prior1					97.37 261

plane_prior						94.60 224	98.44 199		96.74 77						94.22 245

n2								0.00 411

nn								0.00 411

door-mid								94.37 381

lessismore_v0				94.45 332	94.93 362	88.44 358		91.03 396		86.77 367	97.64 246	76.23 359	98.42 283	90.31 309	85.64 363	96.51 322

LGP-MVS_train				96.47 250	97.46 250	93.54 261		98.54 157	94.67 174	94.36 243	98.77 132	85.39 272	99.11 198	95.71 168	94.15 249	96.76 285

test11								98.66 131

door								94.64 379

HQP5-MVS						94.25 239

HQP-NCC					97.20 270		98.05 246		96.43 89	94.45 235

ACMP_Plane					97.20 270		98.05 246		96.43 89	94.45 235

BP-MVS														95.30 180

HQP4-MVS										94.45 235			98.96 221			96.87 273

HQP3-MVS								98.46 176							94.18 247

HQP2-MVS												86.75 247

NP-MVS					97.28 264	94.51 227					97.73 235

MDTV_nov1_ep13_2view						84.26 375	96.89 344		90.97 322	97.90 123		89.89 173		93.91 225		99.18 148

MDTV_nov1_ep13			95.40 171		97.48 248	88.34 359	96.85 347	97.29 317	93.74 215	97.48 146	97.26 270	89.18 189	99.05 206	91.92 282	97.43 190

ACMMP++_ref															92.97 280

ACMMP++															93.61 267

Test By Simon												94.64 78

ITE_SJBPF				95.44 297	97.42 255	91.32 307		97.50 300	95.09 157	93.59 277	98.35 178	81.70 319	98.88 236	89.71 320	93.39 274	96.12 341

DeepMVS_CX				86.78 365	97.09 280	72.30 393		95.17 375	75.92 387	84.34 378	95.19 358	70.58 375	95.35 379	79.98 379	89.04 331	92.68 383

High-resolution multi-view benchmark