High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

DVP-MVS++		98.06 1	97.99 1	98.28 10	98.67 67	95.39 12	99.29 1	98.28 52	94.78 61	98.93 20	98.87 31	96.04 2	99.86 9	97.45 45	99.58 23	99.59 32

SED-MVS		98.05 2	97.99 1	98.24 11	99.42 10	95.30 18	98.25 40	98.27 55	95.13 40	99.19 13	98.89 28	95.54 5	99.85 21	97.52 41	99.66 10	99.56 40

TestfortrainingZip a		97.92 3	97.70 10	98.58 3	99.56 1	96.08 5	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51	95.46 7	99.86 9	96.63 69	99.58 23	99.80 1

MED-MVS		97.91 4	97.88 4	98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	94.23 87	98.73 30	98.53 51	95.46 7	99.86 9	97.40 49	99.58 23	99.65 20

DVP-MVS		97.91 4	97.81 5	98.22 14	99.45 6	95.36 14	98.21 48	97.85 137	94.92 50	98.73 30	98.87 31	95.08 10	99.84 26	97.52 41	99.67 6	99.48 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
DPE-MVS		97.86 6	97.65 11	98.47 6	99.17 38	95.78 8	97.21 198	98.35 42	95.16 38	98.71 35	98.80 38	95.05 12	99.89 3	96.70 68	99.73 1	99.73 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 7	97.73 9	98.08 19	99.15 39	94.82 29	98.81 8	98.30 48	94.76 64	98.30 43	98.90 25	93.77 19	99.68 75	97.93 28	99.69 3	99.75 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		97.68 8	97.44 24	98.37 8	98.90 59	95.86 7	97.27 189	98.08 93	95.81 20	97.87 58	98.31 81	94.26 15	99.68 75	97.02 57	99.49 43	99.57 36

fmvsm_l_conf0.5_n		97.65 9	97.75 8	97.34 61	98.21 106	92.75 92	97.83 99	98.73 10	95.04 45	99.30 7	98.84 36	93.34 24	99.78 49	99.32 7	99.13 97	99.50 52

fmvsm_l_conf0.5_n_3		97.64 10	97.60 13	97.79 34	98.14 113	93.94 56	97.93 84	98.65 24	96.70 8	99.38 5	99.07 11	89.92 90	99.81 35	99.16 14	99.43 53	99.61 30

fmvsm_l_conf0.5_n_a		97.63 11	97.76 7	97.26 68	98.25 99	92.59 100	97.81 104	98.68 19	94.93 48	99.24 10	98.87 31	93.52 22	99.79 46	99.32 7	99.21 83	99.40 66

SteuartSystems-ACMMP		97.62 12	97.53 18	97.87 28	98.39 88	94.25 44	98.43 27	98.27 55	95.34 32	98.11 47	98.56 47	94.53 14	99.71 67	96.57 73	99.62 17	99.65 20
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_9		97.59 13	97.79 6	96.97 86	98.28 94	91.49 144	97.61 139	98.71 13	97.10 5	99.70 1	98.93 22	90.95 75	99.77 52	99.35 6	99.53 33	99.65 20

MSP-MVS		97.59 13	97.54 17	97.73 42	99.40 14	93.77 61	98.53 19	98.29 50	95.55 27	98.56 38	97.81 136	93.90 17	99.65 79	96.62 70	99.21 83	99.77 3
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
lecture		97.58 15	97.63 12	97.43 58	99.37 19	92.93 86	98.86 7	98.85 5	95.27 34	98.65 36	98.90 25	91.97 51	99.80 40	97.63 37	99.21 83	99.57 36

test_fmvsm_n_1920		97.55 16	97.89 3	96.53 105	98.41 85	91.73 130	98.01 67	99.02 1	96.37 13	99.30 7	98.92 23	92.39 43	99.79 46	99.16 14	99.46 46	98.08 228

ME-MVS		97.54 17	97.39 27	98.00 23	99.21 36	94.50 35	97.75 111	98.34 44	94.23 87	98.15 46	98.53 51	93.32 27	99.84 26	97.40 49	99.58 23	99.65 20

reproduce-ours		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 121	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 32	99.43 53	99.67 15

our_new_method		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 121	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 32	99.43 53	99.67 15

reproduce_model		97.51 20	97.51 20	97.50 54	98.99 52	93.01 82	97.79 107	98.21 67	95.73 24	97.99 51	99.03 15	92.63 38	99.82 33	97.80 30	99.42 56	99.67 15

test_fmvsmconf_n		97.49 21	97.56 16	97.29 64	97.44 164	92.37 107	97.91 86	98.88 4	95.83 19	98.92 23	99.05 14	91.45 60	99.80 40	99.12 16	99.46 46	99.69 14

TSAR-MVS + MP.		97.42 22	97.33 29	97.69 46	99.25 32	94.24 45	98.07 61	97.85 137	93.72 103	98.57 37	98.35 72	93.69 20	99.40 133	97.06 56	99.46 46	99.44 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		97.41 23	97.53 18	97.06 82	98.57 78	94.46 38	97.92 85	98.14 83	94.82 57	99.01 17	98.55 49	94.18 16	97.41 402	96.94 58	99.64 14	99.32 74
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
SF-MVS		97.39 24	97.13 31	98.17 16	99.02 48	95.28 20	98.23 44	98.27 55	92.37 172	98.27 44	98.65 45	93.33 25	99.72 65	96.49 75	99.52 35	99.51 49

SMA-MVS		97.35 25	97.03 40	98.30 9	99.06 44	95.42 11	97.94 82	98.18 76	90.57 257	98.85 27	98.94 21	93.33 25	99.83 31	96.72 66	99.68 4	99.63 26
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HPM-MVS++		97.34 26	96.97 43	98.47 6	99.08 42	96.16 4	97.55 150	97.97 121	95.59 25	96.61 97	97.89 118	92.57 40	99.84 26	95.95 99	99.51 38	99.40 66

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 101	98.43 82	90.32 204	97.80 105	98.53 30	97.24 4	99.62 2	99.14 2	88.65 108	99.80 40	99.54 1	99.15 94	99.74 9

fmvsm_s_conf0.5_n_8		97.32 28	97.48 23	96.85 88	98.28 94	91.07 169	97.76 109	98.62 26	97.53 2	99.20 12	99.12 5	88.24 116	99.81 35	99.41 3	99.17 91	99.67 15

fmvsm_s_conf0.5_n_11		97.30 29	97.59 14	96.43 119	98.42 83	91.37 151	98.04 64	98.00 117	97.30 3	99.45 4	99.21 1	89.28 96	99.80 40	99.27 10	99.35 69	98.12 220

NCCC		97.30 29	97.03 40	98.11 18	98.77 62	95.06 26	97.34 178	98.04 108	95.96 15	97.09 79	97.88 123	93.18 28	99.71 67	95.84 104	99.17 91	99.56 40

fmvsm_s_conf0.5_n_10		97.29 31	97.40 26	96.97 86	98.24 100	91.96 126	97.89 89	98.72 12	96.77 7	99.46 3	99.06 12	87.78 126	99.84 26	99.40 4	99.27 75	99.12 92

MM		97.29 31	96.98 42	98.23 12	98.01 123	95.03 27	98.07 61	95.76 355	97.78 1	97.52 62	98.80 38	88.09 118	99.86 9	99.44 2	99.37 67	99.80 1

ACMMP_NAP		97.20 33	96.86 49	98.23 12	99.09 40	95.16 23	97.60 140	98.19 74	92.82 155	97.93 54	98.74 42	91.60 58	99.86 9	96.26 80	99.52 35	99.67 15

XVS		97.18 34	96.96 45	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	98.29 84	91.70 55	99.80 40	95.66 108	99.40 61	99.62 27

MCST-MVS		97.18 34	96.84 51	98.20 15	99.30 29	95.35 16	97.12 205	98.07 98	93.54 112	96.08 126	97.69 149	93.86 18	99.71 67	96.50 74	99.39 63	99.55 43

fmvsm_s_conf0.5_n_3		97.15 36	97.36 28	96.52 107	97.98 125	91.19 161	97.84 96	98.65 24	97.08 6	99.25 9	99.10 6	87.88 124	99.79 46	99.32 7	99.18 90	98.59 170

HFP-MVS		97.14 37	96.92 47	97.83 30	99.42 10	94.12 50	98.52 20	98.32 46	93.21 127	97.18 73	98.29 84	92.08 48	99.83 31	95.63 113	99.59 19	99.54 45

test_fmvsmconf0.1_n		97.09 38	97.06 35	97.19 73	95.67 307	92.21 114	97.95 81	98.27 55	95.78 23	98.40 42	99.00 16	89.99 88	99.78 49	99.06 18	99.41 59	99.59 32

fmvsm_s_conf0.5_n_6		97.08 39	97.17 30	96.81 89	97.28 169	91.73 130	97.75 111	98.50 31	94.86 52	99.22 11	98.78 40	89.75 93	99.76 54	99.10 17	99.29 73	98.94 121

MTAPA		97.08 39	96.78 59	97.97 27	99.37 19	94.42 40	97.24 191	98.08 93	95.07 44	96.11 124	98.59 46	90.88 78	99.90 2	96.18 92	99.50 40	99.58 35

region2R		97.07 41	96.84 51	97.77 38	99.46 5	93.79 59	98.52 20	98.24 63	93.19 130	97.14 76	98.34 75	91.59 59	99.87 7	95.46 119	99.59 19	99.64 25

ACMMPR		97.07 41	96.84 51	97.79 34	99.44 9	93.88 57	98.52 20	98.31 47	93.21 127	97.15 75	98.33 78	91.35 64	99.86 9	95.63 113	99.59 19	99.62 27

CP-MVS		97.02 43	96.81 56	97.64 49	99.33 26	93.54 64	98.80 9	98.28 52	92.99 140	96.45 111	98.30 83	91.90 52	99.85 21	95.61 115	99.68 4	99.54 45

SR-MVS		97.01 44	96.86 49	97.47 56	99.09 40	93.27 75	97.98 72	98.07 98	93.75 102	97.45 64	98.48 61	91.43 62	99.59 95	96.22 83	99.27 75	99.54 45

fmvsm_s_conf0.5_n_5		97.00 45	96.97 43	97.09 79	97.58 160	92.56 101	97.68 125	98.47 35	94.02 93	98.90 25	98.89 28	88.94 102	99.78 49	99.18 12	99.03 106	98.93 125

ZNCC-MVS		96.96 46	96.67 64	97.85 29	99.37 19	94.12 50	98.49 24	98.18 76	92.64 162	96.39 113	98.18 91	91.61 57	99.88 4	95.59 118	99.55 30	99.57 36

APD-MVS		96.95 47	96.60 66	98.01 21	99.03 47	94.93 28	97.72 119	98.10 91	91.50 207	98.01 50	98.32 80	92.33 44	99.58 98	94.85 137	99.51 38	99.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 48	97.06 35	96.59 102	98.72 64	91.86 128	97.67 126	98.49 32	94.66 69	97.24 72	98.41 67	92.31 46	98.94 195	96.61 71	99.46 46	98.96 114

DeepC-MVS_fast	93.89 2	96.93 49	96.64 65	97.78 36	98.64 73	94.30 41	97.41 168	98.04 108	94.81 59	96.59 99	98.37 70	91.24 67	99.64 87	95.16 124	99.52 35	99.42 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		96.89 50	97.04 39	96.45 118	98.29 93	91.66 137	99.03 4	97.85 137	95.84 18	96.90 83	97.97 109	91.24 67	98.75 231	96.92 59	99.33 70	98.94 121

SR-MVS-dyc-post		96.88 51	96.80 57	97.11 78	99.02 48	92.34 108	97.98 72	98.03 110	93.52 115	97.43 67	98.51 56	91.40 63	99.56 106	96.05 94	99.26 78	99.43 63

CS-MVS		96.86 52	97.06 35	96.26 135	98.16 112	91.16 166	99.09 3	97.87 132	95.30 33	97.06 80	98.03 101	91.72 53	98.71 241	97.10 55	99.17 91	98.90 130

mPP-MVS		96.86 52	96.60 66	97.64 49	99.40 14	93.44 66	98.50 23	98.09 92	93.27 126	95.95 132	98.33 78	91.04 72	99.88 4	95.20 122	99.57 29	99.60 31

fmvsm_s_conf0.5_n		96.85 54	97.13 31	96.04 150	98.07 120	90.28 205	97.97 78	98.76 9	94.93 48	98.84 28	99.06 12	88.80 105	99.65 79	99.06 18	98.63 123	98.18 213

GST-MVS		96.85 54	96.52 70	97.82 31	99.36 23	94.14 49	98.29 34	98.13 84	92.72 158	96.70 91	98.06 98	91.35 64	99.86 9	94.83 140	99.28 74	99.47 58

balanced_conf03		96.84 56	96.89 48	96.68 93	97.63 152	92.22 113	98.17 54	97.82 143	94.44 79	98.23 45	97.36 178	90.97 74	99.22 152	97.74 31	99.66 10	98.61 168

patch_mono-2		96.83 57	97.44 24	95.01 226	99.05 45	85.39 377	96.98 218	98.77 8	94.70 66	97.99 51	98.66 43	93.61 21	99.91 1	97.67 36	99.50 40	99.72 13

APD-MVS_3200maxsize		96.81 58	96.71 63	97.12 76	99.01 51	92.31 110	97.98 72	98.06 101	93.11 136	97.44 65	98.55 49	90.93 76	99.55 108	96.06 93	99.25 80	99.51 49

PGM-MVS		96.81 58	96.53 69	97.65 47	99.35 25	93.53 65	97.65 130	98.98 2	92.22 178	97.14 76	98.44 64	91.17 70	99.85 21	94.35 163	99.46 46	99.57 36

MP-MVS		96.77 60	96.45 77	97.72 43	99.39 16	93.80 58	98.41 28	98.06 101	93.37 122	95.54 150	98.34 75	90.59 82	99.88 4	94.83 140	99.54 32	99.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 60	96.46 76	97.71 45	98.40 86	94.07 52	98.21 48	98.45 37	89.86 274	97.11 78	98.01 104	92.52 41	99.69 73	96.03 97	99.53 33	99.36 72

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 174	97.76 141	89.57 234	97.66 129	98.66 22	95.36 30	99.03 16	98.90 25	88.39 113	99.73 61	99.17 13	98.66 121	98.08 228

fmvsm_s_conf0.5_n_a		96.75 62	96.93 46	96.20 140	97.64 150	90.72 186	98.00 68	98.73 10	94.55 73	98.91 24	99.08 8	88.22 117	99.63 88	98.91 21	98.37 136	98.25 208

MGCNet		96.74 64	96.31 81	98.02 20	96.87 203	94.65 31	97.58 141	94.39 422	96.47 12	97.16 74	98.39 68	87.53 135	99.87 7	98.97 20	99.41 59	99.55 43

test_fmvsmvis_n_1920		96.70 65	96.84 51	96.31 129	96.62 230	91.73 130	97.98 72	98.30 48	96.19 14	96.10 125	98.95 20	89.42 94	99.76 54	98.90 22	99.08 101	97.43 268

MP-MVS-pluss		96.70 65	96.27 83	97.98 26	99.23 35	94.71 30	96.96 220	98.06 101	90.67 247	95.55 148	98.78 40	91.07 71	99.86 9	96.58 72	99.55 30	99.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 67	96.49 71	97.27 67	98.31 92	93.39 67	96.79 242	96.72 298	94.17 89	97.44 65	97.66 153	92.76 33	99.33 139	96.86 62	97.76 162	99.08 98

HPM-MVS		96.69 67	96.45 77	97.40 59	99.36 23	93.11 80	98.87 6	98.06 101	91.17 226	96.40 112	97.99 107	90.99 73	99.58 98	95.61 115	99.61 18	99.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 69	96.58 68	96.99 84	98.46 79	92.31 110	96.20 305	98.90 3	94.30 86	95.86 135	97.74 144	92.33 44	99.38 136	96.04 96	99.42 56	99.28 77

fmvsm_s_conf0.5_n_2		96.62 70	96.82 55	96.02 152	97.98 125	90.43 196	97.50 154	98.59 27	96.59 10	99.31 6	99.08 8	84.47 203	99.75 58	99.37 5	98.45 133	97.88 241

DELS-MVS		96.61 71	96.38 80	97.30 63	97.79 139	93.19 78	95.96 319	98.18 76	95.23 35	95.87 134	97.65 154	91.45 60	99.70 72	95.87 100	99.44 52	99.00 109
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 217	98.09 116	86.63 343	96.00 317	98.15 81	95.43 28	97.95 53	98.56 47	93.40 23	99.36 137	96.77 63	99.48 44	99.45 59

fmvsm_s_conf0.1_n		96.58 73	96.77 60	96.01 155	96.67 228	90.25 206	97.91 86	98.38 38	94.48 77	98.84 28	99.14 2	88.06 119	99.62 89	98.82 23	98.60 125	98.15 217

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 102	98.07 120	91.97 124	98.14 55	97.79 145	90.43 262	97.34 70	97.52 169	91.29 66	99.19 155	98.12 27	99.64 14	98.60 169

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 98	98.24 100	91.20 160	96.89 228	97.73 151	94.74 65	96.49 106	98.49 58	90.88 78	99.58 98	96.44 76	98.32 138	99.13 89

HPM-MVS_fast		96.51 74	96.27 83	97.22 70	99.32 27	92.74 93	98.74 10	98.06 101	90.57 257	96.77 88	98.35 72	90.21 85	99.53 112	94.80 144	99.63 16	99.38 70

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 209	97.29 168	88.38 287	97.23 195	98.47 35	95.14 39	98.43 41	99.09 7	87.58 132	99.72 65	98.80 25	99.21 83	98.02 232

EC-MVSNet		96.42 78	96.47 73	96.26 135	97.01 192	91.52 143	98.89 5	97.75 148	94.42 80	96.64 96	97.68 150	89.32 95	98.60 257	97.45 45	99.11 100	98.67 166

fmvsm_s_conf0.1_n_a		96.40 79	96.47 73	96.16 142	95.48 316	90.69 187	97.91 86	98.33 45	94.07 91	98.93 20	99.14 2	87.44 140	99.61 90	98.63 26	98.32 138	98.18 213

CANet		96.39 80	96.02 87	97.50 54	97.62 153	93.38 68	97.02 211	97.96 122	95.42 29	94.86 173	97.81 136	87.38 142	99.82 33	96.88 60	99.20 88	99.29 75

dcpmvs_2		96.37 81	97.05 38	94.31 276	98.96 55	84.11 398	97.56 145	97.51 192	93.92 97	97.43 67	98.52 55	92.75 34	99.32 141	97.32 54	99.50 40	99.51 49

NormalMVS		96.36 82	96.11 86	97.12 76	99.37 19	92.90 87	97.99 69	97.63 165	95.92 16	96.57 102	97.93 111	85.34 185	99.50 120	94.99 129	99.21 83	98.97 111

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 115	98.20 107	90.93 176	96.86 231	97.72 153	94.67 68	96.16 123	98.46 62	90.43 83	99.58 98	96.23 82	97.96 155	98.90 130

fmvsm_s_conf0.1_n_2		96.33 84	96.44 79	96.00 156	97.30 167	90.37 202	97.53 151	97.92 127	96.52 11	99.14 15	99.08 8	83.21 226	99.74 59	99.22 11	98.06 150	97.88 241

train_agg		96.30 85	95.83 92	97.72 43	98.70 65	94.19 46	96.41 280	98.02 113	88.58 322	96.03 127	97.56 166	92.73 36	99.59 95	95.04 126	99.37 67	99.39 68

ACMMP		96.27 86	95.93 88	97.28 66	99.24 33	92.62 98	98.25 40	98.81 6	92.99 140	94.56 183	98.39 68	88.96 101	99.85 21	94.57 157	97.63 163	99.36 72
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
MVS_111021_LR		96.24 87	96.19 85	96.39 124	98.23 105	91.35 153	96.24 302	98.79 7	93.99 95	95.80 137	97.65 154	89.92 90	99.24 150	95.87 100	99.20 88	98.58 171

test_fmvsmconf0.01_n		96.15 88	95.85 91	97.03 83	92.66 432	91.83 129	97.97 78	97.84 141	95.57 26	97.53 61	99.00 16	84.20 209	99.76 54	98.82 23	99.08 101	99.48 56

DeepC-MVS	93.07 3	96.06 89	95.66 93	97.29 64	97.96 127	93.17 79	97.30 183	98.06 101	93.92 97	93.38 222	98.66 43	86.83 149	99.73 61	95.60 117	99.22 82	98.96 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		96.05 90	95.91 89	96.46 117	99.24 33	90.47 193	98.30 33	98.57 29	89.01 304	93.97 203	97.57 164	92.62 39	99.76 54	94.66 151	99.27 75	99.15 87

sasdasda		96.02 91	95.45 100	97.75 40	97.59 156	95.15 24	98.28 35	97.60 170	94.52 75	96.27 118	96.12 259	87.65 129	99.18 158	96.20 88	94.82 256	98.91 127

ETV-MVS		96.02 91	95.89 90	96.40 122	97.16 175	92.44 105	97.47 163	97.77 147	94.55 73	96.48 107	94.51 341	91.23 69	98.92 198	95.65 111	98.19 144	97.82 249

canonicalmvs		96.02 91	95.45 100	97.75 40	97.59 156	95.15 24	98.28 35	97.60 170	94.52 75	96.27 118	96.12 259	87.65 129	99.18 158	96.20 88	94.82 256	98.91 127

CDPH-MVS		95.97 94	95.38 106	97.77 38	98.93 56	94.44 39	96.35 289	97.88 130	86.98 369	96.65 95	97.89 118	91.99 50	99.47 125	92.26 203	99.46 46	99.39 68

UA-Net		95.95 95	95.53 96	97.20 72	97.67 146	92.98 84	97.65 130	98.13 84	94.81 59	96.61 97	98.35 72	88.87 103	99.51 117	90.36 255	97.35 174	99.11 94

SymmetryMVS		95.94 96	95.54 95	97.15 74	97.85 135	92.90 87	97.99 69	96.91 285	95.92 16	96.57 102	97.93 111	85.34 185	99.50 120	94.99 129	96.39 221	99.05 102

MGCFI-Net		95.94 96	95.40 104	97.56 53	97.59 156	94.62 32	98.21 48	97.57 177	94.41 81	96.17 122	96.16 257	87.54 134	99.17 160	96.19 90	94.73 261	98.91 127

BP-MVS1		95.89 98	95.49 97	97.08 81	96.67 228	93.20 77	98.08 59	96.32 324	94.56 72	96.32 115	97.84 130	84.07 212	99.15 164	96.75 64	98.78 116	98.90 130

VNet		95.89 98	95.45 100	97.21 71	98.07 120	92.94 85	97.50 154	98.15 81	93.87 99	97.52 62	97.61 160	85.29 187	99.53 112	95.81 105	95.27 247	99.16 85

alignmvs		95.87 100	95.23 111	97.78 36	97.56 162	95.19 22	97.86 92	97.17 248	94.39 83	96.47 108	96.40 244	85.89 168	99.20 154	96.21 87	95.11 252	98.95 118

casdiffmvs_mvg		95.81 101	95.57 94	96.51 111	96.87 203	91.49 144	97.50 154	97.56 185	93.99 95	95.13 163	97.92 114	87.89 123	98.78 215	95.97 98	97.33 175	99.26 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		95.69 102	94.92 124	98.01 21	98.08 119	95.71 10	95.27 362	97.62 169	90.43 262	95.55 148	97.07 199	91.72 53	99.50 120	89.62 271	98.94 110	98.82 146

DP-MVS Recon		95.68 103	95.12 116	97.37 60	99.19 37	94.19 46	97.03 209	98.08 93	88.35 331	95.09 164	97.65 154	89.97 89	99.48 124	92.08 214	98.59 126	98.44 190

casdiffmvs		95.64 104	95.49 97	96.08 145	96.76 225	90.45 194	97.29 184	97.44 212	94.00 94	95.46 153	97.98 108	87.52 137	98.73 235	95.64 112	97.33 175	99.08 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
GDP-MVS		95.62 105	95.13 114	97.09 79	96.79 214	93.26 76	97.89 89	97.83 142	93.58 107	96.80 85	97.82 134	83.06 233	99.16 162	94.40 160	97.95 156	98.87 140

MG-MVS		95.61 106	95.38 106	96.31 129	98.42 83	90.53 191	96.04 314	97.48 197	93.47 117	95.67 145	98.10 94	89.17 98	99.25 149	91.27 232	98.77 117	99.13 89

baseline		95.58 107	95.42 103	96.08 145	96.78 219	90.41 197	97.16 202	97.45 208	93.69 106	95.65 146	97.85 128	87.29 143	98.68 245	95.66 108	97.25 181	99.13 89

CPTT-MVS		95.57 108	95.19 112	96.70 92	99.27 31	91.48 146	98.33 31	98.11 89	87.79 350	95.17 162	98.03 101	87.09 147	99.61 90	93.51 181	99.42 56	99.02 103

balanced_ft_v1		95.56 109	95.40 104	96.07 147	97.16 175	90.36 203	98.23 44	97.31 233	92.89 152	96.36 114	97.11 196	83.28 224	99.26 148	97.40 49	98.80 115	98.58 171

EIA-MVS		95.53 110	95.47 99	95.71 185	97.06 184	89.63 230	97.82 101	97.87 132	93.57 108	93.92 204	95.04 313	90.61 81	98.95 193	94.62 153	98.68 120	98.54 175

3Dnovator+	91.43 4	95.40 111	94.48 150	98.16 17	96.90 201	95.34 17	98.48 25	97.87 132	94.65 70	88.53 354	98.02 103	83.69 216	99.71 67	93.18 189	98.96 109	99.44 61

PS-MVSNAJ		95.37 112	95.33 108	95.49 203	97.35 166	90.66 189	95.31 359	97.48 197	93.85 100	96.51 105	95.70 284	88.65 108	99.65 79	94.80 144	98.27 141	96.17 310

MVSFormer		95.37 112	95.16 113	95.99 157	96.34 267	91.21 158	98.22 46	97.57 177	91.42 211	96.22 120	97.32 179	86.20 163	97.92 344	94.07 166	99.05 103	98.85 142

diffmvs_AUTHOR		95.33 114	95.27 110	95.50 202	96.37 265	89.08 261	96.08 312	97.38 223	93.09 138	96.53 104	97.74 144	86.45 157	98.68 245	96.32 78	97.48 166	98.75 157

xiu_mvs_v2_base		95.32 115	95.29 109	95.40 208	97.22 171	90.50 192	95.44 352	97.44 212	93.70 105	96.46 109	96.18 254	88.59 112	99.53 112	94.79 147	97.81 159	96.17 310

E3new		95.28 116	95.11 117	95.80 171	97.03 189	89.76 224	96.78 246	97.54 189	92.06 188	95.40 154	97.75 141	87.49 138	98.76 225	94.85 137	97.10 187	98.88 138

PVSNet_Blended_VisFu		95.27 117	94.91 125	96.38 125	98.20 107	90.86 179	97.27 189	98.25 61	90.21 266	94.18 196	97.27 185	87.48 139	99.73 61	93.53 180	97.77 161	98.55 174

viewcassd2359sk11		95.26 118	95.09 118	95.80 171	96.95 198	89.72 226	96.80 241	97.56 185	92.21 180	95.37 155	97.80 138	87.17 146	98.77 219	94.82 142	97.10 187	98.90 130

KinetiMVS		95.26 118	94.75 136	96.79 90	96.99 194	92.05 120	97.82 101	97.78 146	94.77 63	96.46 109	97.70 147	80.62 289	99.34 138	92.37 202	98.28 140	98.97 111

diffmvs		95.25 120	95.13 114	95.63 188	96.43 260	89.34 248	95.99 318	97.35 228	92.83 154	96.31 116	97.37 177	86.44 158	98.67 248	96.26 80	97.19 184	98.87 140
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas		95.24 121	95.02 120	95.91 160	96.87 203	89.98 215	96.82 237	97.49 195	92.26 176	95.47 152	97.82 134	86.47 156	98.69 243	94.80 144	97.20 183	99.06 101

Vis-MVSNet		95.23 122	94.81 131	96.51 111	97.18 174	91.58 141	98.26 39	98.12 86	94.38 84	94.90 172	98.15 93	82.28 254	98.92 198	91.45 229	98.58 127	99.01 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 123	95.04 119	95.76 178	97.49 163	89.56 235	98.67 15	97.00 275	90.69 245	94.24 192	97.62 159	89.79 92	98.81 211	93.39 186	96.49 214	98.92 126

E2		95.20 124	95.00 121	95.79 174	96.79 214	89.66 227	96.82 237	97.58 174	92.35 173	95.28 157	97.83 132	86.68 151	98.76 225	94.79 147	96.92 193	98.95 118

E3		95.20 124	95.00 121	95.79 174	96.77 221	89.66 227	96.82 237	97.58 174	92.35 173	95.28 157	97.83 132	86.69 150	98.76 225	94.79 147	96.92 193	98.95 118

EPNet		95.20 124	94.56 143	97.14 75	92.80 429	92.68 97	97.85 95	94.87 406	96.64 9	92.46 239	97.80 138	86.23 160	99.65 79	93.72 176	98.62 124	99.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 127	94.44 152	97.44 57	96.56 243	93.36 70	98.65 16	98.36 39	94.12 90	89.25 335	98.06 98	82.20 256	99.77 52	93.41 185	99.32 71	99.18 84

guyue		95.17 128	94.96 123	95.82 169	96.97 196	89.65 229	97.56 145	95.58 367	94.82 57	95.72 140	97.42 174	82.90 238	98.84 207	96.71 67	96.93 192	98.96 114

E4		95.09 129	94.86 130	95.77 177	96.58 238	89.56 235	96.85 232	97.56 185	92.50 167	95.03 169	97.86 126	86.03 166	98.78 215	94.71 150	96.65 207	98.96 114

OMC-MVS		95.09 129	94.70 137	96.25 138	98.46 79	91.28 154	96.43 276	97.57 177	92.04 189	94.77 178	97.96 110	87.01 148	99.09 175	91.31 231	96.77 198	98.36 197

viewmacassd2359aftdt		95.07 131	94.80 132	95.87 163	96.53 248	89.84 221	96.90 227	97.48 197	92.44 169	95.36 156	97.89 118	85.23 188	98.68 245	94.40 160	97.00 191	99.09 96

E5new		95.04 132	94.88 126	95.52 196	96.62 230	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.89 118	85.65 177	98.77 219	94.92 132	96.44 217	98.78 149

E6new		95.04 132	94.88 126	95.52 196	96.60 234	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.90 116	85.66 175	98.77 219	94.92 132	96.44 217	98.78 149

E6		95.04 132	94.88 126	95.52 196	96.60 234	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.90 116	85.66 175	98.77 219	94.92 132	96.44 217	98.78 149

E5		95.04 132	94.88 126	95.52 196	96.62 230	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.89 118	85.65 177	98.77 219	94.92 132	96.44 217	98.78 149

xiu_mvs_v1_base_debu		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 316

xiu_mvs_v1_base		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 316

xiu_mvs_v1_base_debi		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 316

PAPM_NR		95.01 136	94.59 141	96.26 135	98.89 60	90.68 188	97.24 191	97.73 151	91.80 194	92.93 236	96.62 234	89.13 99	99.14 167	89.21 284	97.78 160	98.97 111

lupinMVS		94.99 140	94.56 143	96.29 133	96.34 267	91.21 158	95.83 327	96.27 331	88.93 310	96.22 120	96.88 213	86.20 163	98.85 205	95.27 121	99.05 103	98.82 146

Effi-MVS+		94.93 141	94.45 151	96.36 127	96.61 233	91.47 147	96.41 280	97.41 218	91.02 234	94.50 185	95.92 268	87.53 135	98.78 215	93.89 172	96.81 197	98.84 145

IS-MVSNet		94.90 142	94.52 147	96.05 149	97.67 146	90.56 190	98.44 26	96.22 336	93.21 127	93.99 201	97.74 144	85.55 182	98.45 271	89.98 260	97.86 157	99.14 88

LuminaMVS		94.89 143	94.35 155	96.53 105	95.48 316	92.80 91	96.88 230	96.18 340	92.85 153	95.92 133	96.87 215	81.44 271	98.83 208	96.43 77	97.10 187	97.94 237

MVS_Test		94.89 143	94.62 140	95.68 186	96.83 209	89.55 237	96.70 254	97.17 248	91.17 226	95.60 147	96.11 263	87.87 125	98.76 225	93.01 197	97.17 185	98.72 161

viewdifsd2359ckpt13		94.87 145	94.52 147	95.90 161	96.88 202	90.19 208	96.92 224	97.36 226	91.26 219	94.65 180	97.46 170	85.79 172	98.64 252	93.64 178	96.76 199	98.88 138

PVSNet_Blended		94.87 145	94.56 143	95.81 170	98.27 96	89.46 243	95.47 350	98.36 39	88.84 313	94.36 188	96.09 264	88.02 120	99.58 98	93.44 183	98.18 145	98.40 193

jason		94.84 147	94.39 153	96.18 141	95.52 314	90.93 176	96.09 311	96.52 313	89.28 295	96.01 130	97.32 179	84.70 199	98.77 219	95.15 125	98.91 112	98.85 142
jason: jason.
API-MVS		94.84 147	94.49 149	95.90 161	97.90 133	92.00 123	97.80 105	97.48 197	89.19 298	94.81 176	96.71 220	88.84 104	99.17 160	88.91 293	98.76 118	96.53 299

AstraMVS		94.82 149	94.64 139	95.34 211	96.36 266	88.09 302	97.58 141	94.56 415	94.98 46	95.70 143	97.92 114	81.93 264	98.93 196	96.87 61	95.88 228	98.99 110

viewdifsd2359ckpt09		94.81 150	94.37 154	96.12 144	96.91 199	90.75 185	96.94 221	97.31 233	90.51 260	94.31 190	97.38 176	85.70 174	98.71 241	93.54 179	96.75 200	98.90 130

test_yl		94.78 151	94.23 158	96.43 119	97.74 142	91.22 156	96.85 232	97.10 256	91.23 223	95.71 141	96.93 208	84.30 206	99.31 143	93.10 190	95.12 250	98.75 157

DCV-MVSNet		94.78 151	94.23 158	96.43 119	97.74 142	91.22 156	96.85 232	97.10 256	91.23 223	95.71 141	96.93 208	84.30 206	99.31 143	93.10 190	95.12 250	98.75 157

viewdifsd2359ckpt07		94.76 153	94.68 138	95.01 226	96.76 225	87.41 319	96.38 286	97.43 215	92.65 160	94.52 184	97.75 141	85.55 182	98.81 211	94.36 162	96.69 204	98.82 146

SSM_0404		94.73 154	94.31 157	95.98 158	97.05 186	90.90 178	97.01 214	97.29 235	91.24 220	94.17 197	97.60 161	85.03 192	98.76 225	92.14 208	97.30 178	98.29 206

WTY-MVS		94.71 155	94.02 163	96.79 90	97.71 144	92.05 120	96.59 269	97.35 228	90.61 253	94.64 181	96.93 208	86.41 159	99.39 134	91.20 234	94.71 262	98.94 121

mvsmamba		94.57 156	94.14 160	95.87 163	97.03 189	89.93 219	97.84 96	95.85 351	91.34 214	94.79 177	96.80 216	80.67 287	98.81 211	94.85 137	98.12 148	98.85 142

SSM_0407		94.54 157	94.12 162	95.80 171	96.79 214	90.38 199	96.79 242	97.29 235	91.24 220	93.68 208	97.60 161	85.03 192	98.67 248	92.14 208	96.51 210	98.35 199

RRT-MVS		94.51 158	94.35 155	94.98 230	96.40 261	86.55 346	97.56 145	97.41 218	93.19 130	94.93 171	97.04 201	79.12 317	99.30 145	96.19 90	97.32 177	99.09 96

sss		94.51 158	93.80 167	96.64 94	97.07 181	91.97 124	96.32 294	98.06 101	88.94 309	94.50 185	96.78 217	84.60 200	99.27 147	91.90 215	96.02 224	98.68 165

test_cas_vis1_n_1920		94.48 160	94.55 146	94.28 278	96.78 219	86.45 349	97.63 136	97.64 163	93.32 125	97.68 60	98.36 71	73.75 380	99.08 177	96.73 65	99.05 103	97.31 275

CANet_DTU		94.37 161	93.65 173	96.55 104	96.46 258	92.13 118	96.21 303	96.67 305	94.38 84	93.53 216	97.03 206	79.34 313	99.71 67	90.76 244	98.45 133	97.82 249

AdaColmap		94.34 162	93.68 172	96.31 129	98.59 75	91.68 136	96.59 269	97.81 144	89.87 273	92.15 250	97.06 200	83.62 219	99.54 110	89.34 278	98.07 149	97.70 254

viewmambaseed2359dif		94.28 163	94.14 160	94.71 248	96.21 271	86.97 333	95.93 321	97.11 255	89.00 305	95.00 170	97.70 147	86.02 167	98.59 261	93.71 177	96.59 209	98.57 173

CNLPA		94.28 163	93.53 178	96.52 107	98.38 89	92.55 102	96.59 269	96.88 289	90.13 270	91.91 258	97.24 187	85.21 189	99.09 175	87.64 326	97.83 158	97.92 238

MAR-MVS		94.22 165	93.46 183	96.51 111	98.00 124	92.19 117	97.67 126	97.47 201	88.13 339	93.00 231	95.84 272	84.86 198	99.51 117	87.99 307	98.17 146	97.83 248
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PAPR		94.18 166	93.42 188	96.48 114	97.64 150	91.42 150	95.55 345	97.71 157	88.99 306	92.34 246	95.82 274	89.19 97	99.11 170	86.14 354	97.38 172	98.90 130

SDMVSNet		94.17 167	93.61 174	95.86 166	98.09 116	91.37 151	97.35 177	98.20 69	93.18 132	91.79 262	97.28 183	79.13 316	98.93 196	94.61 154	92.84 294	97.28 276

test_vis1_n_1920		94.17 167	94.58 142	92.91 354	97.42 165	82.02 425	97.83 99	97.85 137	94.68 67	98.10 48	98.49 58	70.15 409	99.32 141	97.91 29	98.82 113	97.40 270

h-mvs33		94.15 169	93.52 180	96.04 150	97.81 138	90.22 207	97.62 138	97.58 174	95.19 36	96.74 89	97.45 171	83.67 217	99.61 90	95.85 102	79.73 436	98.29 206

CHOSEN 1792x2688		94.15 169	93.51 181	96.06 148	98.27 96	89.38 246	95.18 371	98.48 34	85.60 392	93.76 207	97.11 196	83.15 229	99.61 90	91.33 230	98.72 119	99.19 83

Vis-MVSNet (Re-imp)		94.15 169	93.88 166	94.95 234	97.61 154	87.92 307	98.10 57	95.80 354	92.22 178	93.02 230	97.45 171	84.53 202	97.91 347	88.24 303	97.97 154	99.02 103

CDS-MVSNet		94.14 172	93.54 177	95.93 159	96.18 279	91.46 148	96.33 293	97.04 270	88.97 308	93.56 213	96.51 238	87.55 133	97.89 348	89.80 265	95.95 226	98.44 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 173	93.43 186	96.13 143	98.58 77	91.15 167	96.69 256	97.39 220	87.29 364	91.37 272	96.71 220	88.39 113	99.52 116	87.33 335	97.13 186	97.73 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 174	93.70 171	95.27 213	95.70 305	92.03 122	98.10 57	98.68 19	93.36 124	90.39 293	96.70 222	87.63 131	97.94 341	92.25 205	90.50 335	95.84 324

PVSNet_BlendedMVS		94.06 175	93.92 165	94.47 264	98.27 96	89.46 243	96.73 250	98.36 39	90.17 267	94.36 188	95.24 307	88.02 120	99.58 98	93.44 183	90.72 331	94.36 417

nrg030		94.05 176	93.31 190	96.27 134	95.22 339	94.59 33	98.34 30	97.46 203	92.93 147	91.21 282	96.64 227	87.23 145	98.22 294	94.99 129	85.80 384	95.98 320

UGNet		94.04 177	93.28 191	96.31 129	96.85 206	91.19 161	97.88 91	97.68 158	94.40 82	93.00 231	96.18 254	73.39 384	99.61 90	91.72 221	98.46 132	98.13 218
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
TAMVS		94.01 178	93.46 183	95.64 187	96.16 281	90.45 194	96.71 253	96.89 288	89.27 296	93.46 220	96.92 211	87.29 143	97.94 341	88.70 299	95.74 232	98.53 176

Elysia		94.00 179	93.12 196	96.64 94	96.08 291	92.72 95	97.50 154	97.63 165	91.15 228	94.82 174	97.12 194	74.98 367	99.06 183	90.78 242	98.02 151	98.12 220

StellarMVS		94.00 179	93.12 196	96.64 94	96.08 291	92.72 95	97.50 154	97.63 165	91.15 228	94.82 174	97.12 194	74.98 367	99.06 183	90.78 242	98.02 151	98.12 220

IMVS_0403		93.98 181	93.79 168	94.55 259	96.19 275	86.16 358	96.35 289	97.24 242	91.54 202	93.59 212	97.04 201	85.86 169	98.73 235	90.68 247	95.59 238	98.76 153

114514_t		93.95 182	93.06 199	96.63 98	99.07 43	91.61 138	97.46 165	97.96 122	77.99 465	93.00 231	97.57 164	86.14 165	99.33 139	89.22 283	99.15 94	98.94 121

IMVS_0407		93.94 183	93.75 169	94.49 263	96.19 275	86.16 358	96.35 289	97.24 242	91.54 202	93.50 217	97.04 201	85.64 180	98.54 264	90.68 247	95.59 238	98.76 153

FC-MVSNet-test		93.94 183	93.57 175	95.04 224	95.48 316	91.45 149	98.12 56	98.71 13	93.37 122	90.23 296	96.70 222	87.66 128	97.85 350	91.49 227	90.39 336	95.83 325

mvsany_test1		93.93 185	93.98 164	93.78 311	94.94 356	86.80 336	94.62 386	92.55 456	88.77 319	96.85 84	98.49 58	88.98 100	98.08 312	95.03 127	95.62 237	96.46 304

GeoE		93.89 186	93.28 191	95.72 184	96.96 197	89.75 225	98.24 43	96.92 284	89.47 289	92.12 252	97.21 189	84.42 204	98.39 279	87.71 317	96.50 213	99.01 106

HY-MVS	89.66 9	93.87 187	92.95 204	96.63 98	97.10 180	92.49 104	95.64 342	96.64 306	89.05 303	93.00 231	95.79 278	85.77 173	99.45 128	89.16 287	94.35 264	97.96 235

XVG-OURS-SEG-HR		93.86 188	93.55 176	94.81 240	97.06 184	88.53 282	95.28 360	97.45 208	91.68 199	94.08 200	97.68 150	82.41 252	98.90 201	93.84 174	92.47 300	96.98 284

VDD-MVS		93.82 189	93.08 198	96.02 152	97.88 134	89.96 218	97.72 119	95.85 351	92.43 170	95.86 135	98.44 64	68.42 426	99.39 134	96.31 79	94.85 254	98.71 163

mvs_anonymous		93.82 189	93.74 170	94.06 289	96.44 259	85.41 375	95.81 329	97.05 268	89.85 276	90.09 306	96.36 246	87.44 140	97.75 364	93.97 168	96.69 204	99.02 103

HQP_MVS		93.78 191	93.43 186	94.82 238	96.21 271	89.99 213	97.74 114	97.51 192	94.85 53	91.34 273	96.64 227	81.32 273	98.60 257	93.02 195	92.23 303	95.86 321

PS-MVSNAJss		93.74 192	93.51 181	94.44 266	93.91 394	89.28 253	97.75 111	97.56 185	92.50 167	89.94 309	96.54 237	88.65 108	98.18 299	93.83 175	90.90 329	95.86 321

XVG-OURS		93.72 193	93.35 189	94.80 243	97.07 181	88.61 276	94.79 383	97.46 203	91.97 192	93.99 201	97.86 126	81.74 267	98.88 202	92.64 201	92.67 299	96.92 289

mamba_0408		93.70 194	92.99 200	95.83 168	96.79 214	90.38 199	88.69 478	97.07 262	90.96 236	93.68 208	97.31 181	84.97 195	98.76 225	90.95 238	96.51 210	98.35 199

HyFIR lowres test		93.66 195	92.92 205	95.87 163	98.24 100	89.88 220	94.58 388	98.49 32	85.06 402	93.78 206	95.78 279	82.86 239	98.67 248	91.77 220	95.71 234	99.07 100

LFMVS		93.60 196	92.63 219	96.52 107	98.13 115	91.27 155	97.94 82	93.39 444	90.57 257	96.29 117	98.31 81	69.00 419	99.16 162	94.18 165	95.87 229	99.12 92

icg_test_0407_2		93.58 197	93.46 183	93.94 301	96.19 275	86.16 358	93.73 425	97.24 242	91.54 202	93.50 217	97.04 201	85.64 180	96.91 422	90.68 247	95.59 238	98.76 153

F-COLMAP		93.58 197	92.98 203	95.37 209	98.40 86	88.98 267	97.18 200	97.29 235	87.75 353	90.49 291	97.10 198	85.21 189	99.50 120	86.70 345	96.72 203	97.63 256

ab-mvs		93.57 199	92.55 223	96.64 94	97.28 169	91.96 126	95.40 353	97.45 208	89.81 278	93.22 228	96.28 250	79.62 310	99.46 126	90.74 245	93.11 291	98.50 180

LS3D		93.57 199	92.61 221	96.47 115	97.59 156	91.61 138	97.67 126	97.72 153	85.17 400	90.29 295	98.34 75	84.60 200	99.73 61	83.85 390	98.27 141	98.06 230

FA-MVS(test-final)		93.52 201	92.92 205	95.31 212	96.77 221	88.54 280	94.82 382	96.21 338	89.61 284	94.20 194	95.25 306	83.24 225	99.14 167	90.01 259	96.16 223	98.25 208

SSM_04072		93.51 202	92.99 200	95.05 222	96.79 214	90.38 199	88.69 478	97.07 262	90.96 236	93.68 208	97.31 181	84.97 195	96.42 433	90.95 238	96.51 210	98.35 199

viewdifsd2359ckpt11		93.46 203	93.22 194	94.17 282	96.11 288	85.42 373	96.43 276	97.07 262	92.91 148	94.20 194	98.00 105	80.82 285	98.73 235	94.42 158	89.04 351	98.34 203

viewmsd2359difaftdt		93.46 203	93.23 193	94.17 282	96.12 286	85.42 373	96.43 276	97.08 259	92.91 148	94.21 193	98.00 105	80.82 285	98.74 233	94.41 159	89.05 349	98.34 203

Fast-Effi-MVS+		93.46 203	92.75 213	95.59 191	96.77 221	90.03 210	96.81 240	97.13 250	88.19 334	91.30 276	94.27 359	86.21 162	98.63 254	87.66 325	96.46 216	98.12 220

hse-mvs2		93.45 206	92.99 200	94.81 240	97.02 191	88.59 277	96.69 256	96.47 316	95.19 36	96.74 89	96.16 257	83.67 217	98.48 270	95.85 102	79.13 440	97.35 273

QAPM		93.45 206	92.27 233	96.98 85	96.77 221	92.62 98	98.39 29	98.12 86	84.50 410	88.27 362	97.77 140	82.39 253	99.81 35	85.40 367	98.81 114	98.51 179

UniMVSNet_NR-MVSNet		93.37 208	92.67 217	95.47 206	95.34 328	92.83 89	97.17 201	98.58 28	92.98 145	90.13 301	95.80 275	88.37 115	97.85 350	91.71 222	83.93 413	95.73 335

1112_ss		93.37 208	92.42 230	96.21 139	97.05 186	90.99 170	96.31 295	96.72 298	86.87 372	89.83 313	96.69 224	86.51 155	99.14 167	88.12 304	93.67 285	98.50 180

UniMVSNet (Re)		93.31 210	92.55 223	95.61 190	95.39 322	93.34 71	97.39 173	98.71 13	93.14 135	90.10 305	94.83 324	87.71 127	98.03 323	91.67 225	83.99 412	95.46 344

OPM-MVS		93.28 211	92.76 211	94.82 238	94.63 372	90.77 183	96.65 260	97.18 246	93.72 103	91.68 266	97.26 186	79.33 314	98.63 254	92.13 211	92.28 302	95.07 374
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet		93.24 212	92.48 228	95.51 200	95.70 305	92.39 106	97.86 92	98.66 22	92.30 175	92.09 254	95.37 299	80.49 292	98.40 274	93.95 169	85.86 383	95.75 333

test_fmvs1		93.21 213	93.53 178	92.25 377	96.55 245	81.20 432	97.40 172	96.96 277	90.68 246	96.80 85	98.04 100	69.25 417	98.40 274	97.58 40	98.50 128	97.16 281

MVSTER		93.20 214	92.81 210	94.37 269	96.56 243	89.59 233	97.06 208	97.12 251	91.24 220	91.30 276	95.96 266	82.02 260	98.05 319	93.48 182	90.55 333	95.47 343

test1111		93.19 215	92.82 209	94.30 277	97.58 160	84.56 392	98.21 48	89.02 478	93.53 113	94.58 182	98.21 88	72.69 388	99.05 186	93.06 193	98.48 131	99.28 77

ECVR-MVS		93.19 215	92.73 215	94.57 258	97.66 148	85.41 375	98.21 48	88.23 480	93.43 120	94.70 179	98.21 88	72.57 389	99.07 181	93.05 194	98.49 129	99.25 80

HQP-MVS		93.19 215	92.74 214	94.54 260	95.86 297	89.33 249	96.65 260	97.39 220	93.55 109	90.14 297	95.87 270	80.95 279	98.50 267	92.13 211	92.10 308	95.78 329

CHOSEN 280x420		93.12 218	92.72 216	94.34 272	96.71 227	87.27 323	90.29 468	97.72 153	86.61 376	91.34 273	95.29 301	84.29 208	98.41 273	93.25 187	98.94 110	97.35 273

sd_testset		93.10 219	92.45 229	95.05 222	98.09 116	89.21 255	96.89 228	97.64 163	93.18 132	91.79 262	97.28 183	75.35 364	98.65 251	88.99 290	92.84 294	97.28 276

Effi-MVS+-dtu		93.08 220	93.21 195	92.68 365	96.02 294	83.25 408	97.14 204	96.72 298	93.85 100	91.20 283	93.44 399	83.08 231	98.30 288	91.69 224	95.73 233	96.50 301

test_djsdf		93.07 221	92.76 211	94.00 293	93.49 411	88.70 273	98.22 46	97.57 177	91.42 211	90.08 307	95.55 292	82.85 240	97.92 344	94.07 166	91.58 315	95.40 351

VDDNet		93.05 222	92.07 237	96.02 152	96.84 207	90.39 198	98.08 59	95.85 351	86.22 384	95.79 138	98.46 62	67.59 429	99.19 155	94.92 132	94.85 254	98.47 185

thisisatest0530		93.03 223	92.21 235	95.49 203	97.07 181	89.11 260	97.49 162	92.19 461	90.16 268	94.09 199	96.41 243	76.43 355	99.05 186	90.38 254	95.68 235	98.31 205

EI-MVSNet		93.03 223	92.88 207	93.48 333	95.77 303	86.98 332	96.44 274	97.12 251	90.66 249	91.30 276	97.64 157	86.56 153	98.05 319	89.91 262	90.55 333	95.41 348

CLD-MVS		92.98 225	92.53 225	94.32 274	96.12 286	89.20 256	95.28 360	97.47 201	92.66 159	89.90 310	95.62 288	80.58 290	98.40 274	92.73 200	92.40 301	95.38 353
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 226	92.33 232	94.87 237	97.11 179	87.16 329	97.97 78	92.09 462	90.63 251	93.88 205	97.01 207	76.50 352	99.06 183	90.29 257	95.45 244	98.38 195

ACMM	89.79 8	92.96 226	92.50 227	94.35 270	96.30 269	88.71 272	97.58 141	97.36 226	91.40 213	90.53 290	96.65 226	79.77 306	98.75 231	91.24 233	91.64 313	95.59 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 228	92.56 222	94.10 287	96.16 281	88.26 291	97.65 130	97.46 203	91.29 215	90.12 303	97.16 191	79.05 319	98.73 235	92.25 205	91.89 311	95.31 358

BH-untuned		92.94 228	92.62 220	93.92 305	97.22 171	86.16 358	96.40 284	96.25 335	90.06 271	89.79 314	96.17 256	83.19 227	98.35 282	87.19 338	97.27 180	97.24 278

DU-MVS		92.90 230	92.04 239	95.49 203	94.95 354	92.83 89	97.16 202	98.24 63	93.02 139	90.13 301	95.71 282	83.47 220	97.85 350	91.71 222	83.93 413	95.78 329

PatchMatch-RL		92.90 230	92.02 241	95.56 192	98.19 109	90.80 181	95.27 362	97.18 246	87.96 341	91.86 261	95.68 285	80.44 293	98.99 191	84.01 385	97.54 165	96.89 290

VortexMVS		92.88 232	92.64 218	93.58 326	96.58 238	87.53 318	96.93 223	97.28 238	92.78 157	89.75 315	94.99 314	82.73 243	97.76 362	94.60 155	88.16 360	95.46 344

PMMVS		92.86 233	92.34 231	94.42 268	94.92 357	86.73 339	94.53 390	96.38 322	84.78 407	94.27 191	95.12 312	83.13 230	98.40 274	91.47 228	96.49 214	98.12 220

OpenMVS	89.19 12	92.86 233	91.68 254	96.40 122	95.34 328	92.73 94	98.27 37	98.12 86	84.86 405	85.78 413	97.75 141	78.89 326	99.74 59	87.50 332	98.65 122	96.73 294

Test_1112_low_res		92.84 235	91.84 248	95.85 167	97.04 188	89.97 217	95.53 347	96.64 306	85.38 395	89.65 320	95.18 308	85.86 169	99.10 172	87.70 318	93.58 290	98.49 182

baseline1		92.82 236	91.90 246	95.55 194	97.20 173	90.77 183	97.19 199	94.58 414	92.20 181	92.36 243	96.34 247	84.16 210	98.21 295	89.20 285	83.90 416	97.68 255

1314		92.81 237	92.03 240	95.14 218	95.33 331	89.52 240	96.04 314	97.44 212	87.72 354	86.25 403	95.33 300	83.84 214	98.79 214	89.26 281	97.05 190	97.11 282

DP-MVS		92.76 238	91.51 262	96.52 107	98.77 62	90.99 170	97.38 175	96.08 343	82.38 440	89.29 332	97.87 124	83.77 215	99.69 73	81.37 414	96.69 204	98.89 136

test_fmvs1_n		92.73 239	92.88 207	92.29 374	96.08 291	81.05 433	97.98 72	97.08 259	90.72 244	96.79 87	98.18 91	63.07 456	98.45 271	97.62 39	98.42 135	97.36 271

BH-RMVSNet		92.72 240	91.97 243	94.97 232	97.16 175	87.99 305	96.15 309	95.60 365	90.62 252	91.87 260	97.15 193	78.41 332	98.57 262	83.16 392	97.60 164	98.36 197

ACMP	89.59 10	92.62 241	92.14 236	94.05 290	96.40 261	88.20 297	97.36 176	97.25 241	91.52 206	88.30 360	96.64 227	78.46 331	98.72 240	91.86 218	91.48 317	95.23 365
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 242	92.52 226	92.44 367	96.82 211	81.89 426	96.92 224	93.71 441	92.41 171	84.30 428	94.60 336	85.08 191	97.03 416	91.51 226	97.36 173	98.40 193

TranMVSNet+NR-MVSNet		92.50 242	91.63 255	95.14 218	94.76 365	92.07 119	97.53 151	98.11 89	92.90 151	89.56 323	96.12 259	83.16 228	97.60 379	89.30 279	83.20 422	95.75 333

thres600view7		92.49 244	91.60 256	95.18 216	97.91 132	89.47 241	97.65 130	94.66 411	92.18 185	93.33 223	94.91 319	78.06 339	99.10 172	81.61 407	94.06 279	96.98 284

IMVS_0404		92.44 245	91.92 245	94.00 293	96.19 275	86.16 358	93.84 422	97.24 242	91.54 202	88.17 366	97.04 201	76.96 349	97.09 413	90.68 247	95.59 238	98.76 153

thres100view900		92.43 246	91.58 257	94.98 230	97.92 131	89.37 247	97.71 121	94.66 411	92.20 181	93.31 224	94.90 320	78.06 339	99.08 177	81.40 411	94.08 275	96.48 302

jajsoiax		92.42 247	91.89 247	94.03 292	93.33 419	88.50 283	97.73 116	97.53 190	92.00 191	88.85 346	96.50 239	75.62 362	98.11 306	93.88 173	91.56 316	95.48 341

thres400		92.42 247	91.52 260	95.12 220	97.85 135	89.29 251	97.41 168	94.88 403	92.19 183	93.27 226	94.46 346	78.17 335	99.08 177	81.40 411	94.08 275	96.98 284

tfpn200view9		92.38 249	91.52 260	94.95 234	97.85 135	89.29 251	97.41 168	94.88 403	92.19 183	93.27 226	94.46 346	78.17 335	99.08 177	81.40 411	94.08 275	96.48 302

test_vis1_n		92.37 250	92.26 234	92.72 362	94.75 366	82.64 415	98.02 66	96.80 295	91.18 225	97.77 59	97.93 111	58.02 467	98.29 289	97.63 37	98.21 143	97.23 279

WR-MVS		92.34 251	91.53 259	94.77 245	95.13 347	90.83 180	96.40 284	97.98 120	91.88 193	89.29 332	95.54 293	82.50 249	97.80 357	89.79 266	85.27 392	95.69 336

NR-MVSNet		92.34 251	91.27 270	95.53 195	94.95 354	93.05 81	97.39 173	98.07 98	92.65 160	84.46 425	95.71 282	85.00 194	97.77 361	89.71 267	83.52 419	95.78 329

mvs_tets		92.31 253	91.76 250	93.94 301	93.41 416	88.29 289	97.63 136	97.53 190	92.04 189	88.76 349	96.45 241	74.62 372	98.09 311	93.91 171	91.48 317	95.45 346

TAPA-MVS	90.10 7	92.30 254	91.22 273	95.56 192	98.33 91	89.60 232	96.79 242	97.65 161	81.83 444	91.52 268	97.23 188	87.94 122	98.91 200	71.31 468	98.37 136	98.17 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 255	91.30 268	95.25 214	96.60 234	88.90 269	94.36 401	92.32 459	87.92 342	93.43 221	94.57 337	77.28 346	99.00 190	89.42 276	95.86 230	97.86 245

Fast-Effi-MVS+-dtu		92.29 255	91.99 242	93.21 344	95.27 335	85.52 371	97.03 209	96.63 309	92.09 186	89.11 340	95.14 310	80.33 296	98.08 312	87.54 329	94.74 260	96.03 319

IterMVS-LS		92.29 255	91.94 244	93.34 338	96.25 270	86.97 333	96.57 272	97.05 268	90.67 247	89.50 326	94.80 326	86.59 152	97.64 374	89.91 262	86.11 382	95.40 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 258	91.74 253	93.73 312	97.77 140	83.69 405	92.88 445	96.72 298	87.91 343	93.00 231	94.86 322	78.51 330	99.05 186	86.53 346	97.45 171	98.47 185

VPNet		92.23 259	91.31 267	94.99 228	95.56 312	90.96 172	97.22 197	97.86 136	92.96 146	90.96 284	96.62 234	75.06 365	98.20 296	91.90 215	83.65 418	95.80 327

thres200		92.23 259	91.39 263	94.75 247	97.61 154	89.03 262	96.60 268	95.09 392	92.08 187	93.28 225	94.00 374	78.39 333	99.04 189	81.26 417	94.18 271	96.19 309

anonymousdsp		92.16 261	91.55 258	93.97 297	92.58 434	89.55 237	97.51 153	97.42 217	89.42 292	88.40 356	94.84 323	80.66 288	97.88 349	91.87 217	91.28 321	94.48 412

XXY-MVS		92.16 261	91.23 272	94.95 234	94.75 366	90.94 175	97.47 163	97.43 215	89.14 299	88.90 342	96.43 242	79.71 307	98.24 292	89.56 272	87.68 365	95.67 337

BH-w/o		92.14 263	91.75 251	93.31 339	96.99 194	85.73 368	95.67 337	95.69 360	88.73 320	89.26 334	94.82 325	82.97 236	98.07 316	85.26 370	96.32 222	96.13 315

testing3-2		92.10 264	92.05 238	92.27 375	97.71 144	79.56 453	97.42 167	94.41 421	93.53 113	93.22 228	95.49 295	69.16 418	99.11 170	93.25 187	94.22 269	98.13 218

Anonymous202405211		92.07 265	90.83 289	95.76 178	98.19 109	88.75 271	97.58 141	95.00 395	86.00 387	93.64 211	97.45 171	66.24 441	99.53 112	90.68 247	92.71 297	99.01 106

FE-MVS		92.05 266	91.05 278	95.08 221	96.83 209	87.93 306	93.91 419	95.70 358	86.30 381	94.15 198	94.97 315	76.59 351	99.21 153	84.10 383	96.86 195	98.09 227

WR-MVS_H		92.00 267	91.35 264	93.95 299	95.09 349	89.47 241	98.04 64	98.68 19	91.46 209	88.34 358	94.68 331	85.86 169	97.56 382	85.77 362	84.24 410	94.82 396

Anonymous20240529		91.98 268	90.73 295	95.73 183	98.14 113	89.40 245	97.99 69	97.72 153	79.63 458	93.54 215	97.41 175	69.94 411	99.56 106	91.04 237	91.11 324	98.22 210

MonoMVSNet		91.92 269	91.77 249	92.37 369	92.94 425	83.11 411	97.09 207	95.55 369	92.91 148	90.85 286	94.55 338	81.27 275	96.52 431	93.01 197	87.76 364	97.47 267

PatchmatchNet		91.91 270	91.35 264	93.59 325	95.38 323	84.11 398	93.15 440	95.39 375	89.54 286	92.10 253	93.68 387	82.82 241	98.13 302	84.81 374	95.32 246	98.52 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing91		91.90 271	91.02 279	94.53 261	96.54 246	86.55 346	95.86 325	95.64 364	91.77 196	91.89 259	93.47 398	69.94 411	98.86 203	90.23 258	93.86 282	98.18 213

CP-MVSNet		91.89 272	91.24 271	93.82 308	95.05 350	88.57 278	97.82 101	98.19 74	91.70 198	88.21 364	95.76 280	81.96 261	97.52 393	87.86 309	84.65 401	95.37 354

SCA		91.84 273	91.18 275	93.83 307	95.59 310	84.95 388	94.72 384	95.58 367	90.82 239	92.25 248	93.69 385	75.80 359	98.10 307	86.20 352	95.98 225	98.45 187

FMVSNet3		91.78 274	90.69 298	95.03 225	96.53 248	92.27 112	97.02 211	96.93 280	89.79 279	89.35 329	94.65 334	77.01 347	97.47 396	86.12 355	88.82 352	95.35 355

AUN-MVS		91.76 275	90.75 293	94.81 240	97.00 193	88.57 278	96.65 260	96.49 315	89.63 283	92.15 250	96.12 259	78.66 328	98.50 267	90.83 240	79.18 439	97.36 271

X-MVStestdata		91.71 276	89.67 344	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	32.69 495	91.70 55	99.80 40	95.66 108	99.40 61	99.62 27

MVS		91.71 276	90.44 307	95.51 200	95.20 341	91.59 140	96.04 314	97.45 208	73.44 475	87.36 382	95.60 289	85.42 184	99.10 172	85.97 359	97.46 167	95.83 325

EPNet_dtu		91.71 276	91.28 269	92.99 351	93.76 399	83.71 404	96.69 256	95.28 382	93.15 134	87.02 391	95.95 267	83.37 223	97.38 404	79.46 430	96.84 196	97.88 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		91.68 279	90.75 293	94.47 264	96.53 248	86.56 345	95.76 333	94.51 418	91.10 232	91.24 281	93.59 393	68.59 423	98.86 203	91.10 235	94.29 267	98.00 234

usedtu_dtu_shiyan1		91.65 280	90.67 299	94.60 251	93.65 405	90.95 173	94.86 380	97.12 251	89.69 281	89.21 336	93.62 390	81.17 276	97.67 369	87.54 329	89.14 347	95.17 371

FE-MVSNET3		91.65 280	90.67 299	94.60 251	93.65 405	90.95 173	94.86 380	97.12 251	89.69 281	89.21 336	93.62 390	81.17 276	97.67 369	87.54 329	89.14 347	95.17 371

baseline2		91.63 282	90.86 285	93.94 301	94.33 383	86.32 351	95.92 322	91.64 466	89.37 293	86.94 394	94.69 330	81.62 269	98.69 243	88.64 300	94.57 263	96.81 292

testing99		91.62 283	90.72 296	94.32 274	96.48 255	86.11 363	95.81 329	94.76 408	91.55 201	91.75 264	93.44 399	68.55 424	98.82 209	90.43 252	93.69 284	98.04 231

test2506		91.60 284	90.78 290	94.04 291	97.66 148	83.81 401	98.27 37	75.53 496	93.43 120	95.23 160	98.21 88	67.21 432	99.07 181	93.01 197	98.49 129	99.25 80

miper_ehance_all_eth		91.59 285	91.13 276	92.97 352	95.55 313	86.57 344	94.47 395	96.88 289	87.77 351	88.88 344	94.01 373	86.22 161	97.54 389	89.49 273	86.93 373	94.79 401

v2v482		91.59 285	90.85 287	93.80 309	93.87 396	88.17 299	96.94 221	96.88 289	89.54 286	89.53 324	94.90 320	81.70 268	98.02 324	89.25 282	85.04 398	95.20 366

V42		91.58 287	90.87 284	93.73 312	94.05 391	88.50 283	97.32 181	96.97 276	88.80 318	89.71 316	94.33 354	82.54 248	98.05 319	89.01 289	85.07 396	94.64 410

PCF-MVS	89.48 11	91.56 288	89.95 332	96.36 127	96.60 234	92.52 103	92.51 451	97.26 239	79.41 459	88.90 342	96.56 236	84.04 213	99.55 108	77.01 444	97.30 178	97.01 283
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG		91.55 289	90.76 291	93.94 301	96.52 251	85.06 384	95.22 366	94.54 416	90.47 261	91.98 256	92.71 410	72.02 392	98.74 233	88.10 305	95.26 248	98.01 233

PS-CasMVS		91.55 289	90.84 288	93.69 316	94.96 353	88.28 290	97.84 96	98.24 63	91.46 209	88.04 369	95.80 275	79.67 308	97.48 395	87.02 342	84.54 407	95.31 358

miper_enhance_ethall		91.54 291	91.01 280	93.15 346	95.35 327	87.07 331	93.97 414	96.90 286	86.79 373	89.17 338	93.43 402	86.55 154	97.64 374	89.97 261	86.93 373	94.74 406

myMVS_eth3d28		91.52 292	90.97 281	93.17 345	96.91 199	83.24 409	95.61 343	94.96 399	92.24 177	91.98 256	93.28 403	69.31 416	98.40 274	88.71 298	95.68 235	97.88 241

PAPM		91.52 292	90.30 313	95.20 215	95.30 334	89.83 222	93.38 436	96.85 292	86.26 383	88.59 352	95.80 275	84.88 197	98.15 301	75.67 449	95.93 227	97.63 256

ET-MVSNet_ETH3D		91.49 294	90.11 323	95.63 188	96.40 261	91.57 142	95.34 356	93.48 443	90.60 255	75.58 471	95.49 295	80.08 300	96.79 427	94.25 164	89.76 341	98.52 177

TR-MVS		91.48 295	90.59 303	94.16 285	96.40 261	87.33 320	95.67 337	95.34 381	87.68 355	91.46 270	95.52 294	76.77 350	98.35 282	82.85 397	93.61 288	96.79 293

tpmrst		91.44 296	91.32 266	91.79 392	95.15 345	79.20 459	93.42 435	95.37 377	88.55 325	93.49 219	93.67 388	82.49 250	98.27 291	90.41 253	89.34 345	97.90 239

test-LLR		91.42 297	91.19 274	92.12 380	94.59 373	80.66 436	94.29 406	92.98 449	91.11 230	90.76 288	92.37 418	79.02 321	98.07 316	88.81 295	96.74 201	97.63 256

MSDG		91.42 297	90.24 317	94.96 233	97.15 178	88.91 268	93.69 428	96.32 324	85.72 391	86.93 395	96.47 240	80.24 297	98.98 192	80.57 421	95.05 253	96.98 284

c3_l		91.38 299	90.89 283	92.88 356	95.58 311	86.30 352	94.68 385	96.84 293	88.17 335	88.83 348	94.23 362	85.65 177	97.47 396	89.36 277	84.63 402	94.89 386

GA-MVS		91.38 299	90.31 312	94.59 253	94.65 371	87.62 316	94.34 402	96.19 339	90.73 243	90.35 294	93.83 378	71.84 394	97.96 335	87.22 337	93.61 288	98.21 211

v1144		91.37 301	90.60 302	93.68 318	93.89 395	88.23 293	96.84 235	97.03 272	88.37 330	89.69 318	94.39 348	82.04 259	97.98 328	87.80 312	85.37 389	94.84 390

GBi-Net		91.35 302	90.27 315	94.59 253	96.51 252	91.18 163	97.50 154	96.93 280	88.82 315	89.35 329	94.51 341	73.87 376	97.29 408	86.12 355	88.82 352	95.31 358

test1		91.35 302	90.27 315	94.59 253	96.51 252	91.18 163	97.50 154	96.93 280	88.82 315	89.35 329	94.51 341	73.87 376	97.29 408	86.12 355	88.82 352	95.31 358

UniMVSNet_ETH3D		91.34 304	90.22 320	94.68 249	94.86 361	87.86 310	97.23 195	97.46 203	87.99 340	89.90 310	96.92 211	66.35 439	98.23 293	90.30 256	90.99 327	97.96 235

FMVSNet2		91.31 305	90.08 324	94.99 228	96.51 252	92.21 114	97.41 168	96.95 278	88.82 315	88.62 351	94.75 328	73.87 376	97.42 401	85.20 371	88.55 357	95.35 355

reproduce_monomvs		91.30 306	91.10 277	91.92 384	96.82 211	82.48 419	97.01 214	97.49 195	94.64 71	88.35 357	95.27 304	70.53 404	98.10 307	95.20 122	84.60 404	95.19 369

D2MVS		91.30 306	90.95 282	92.35 370	94.71 369	85.52 371	96.18 307	98.21 67	88.89 311	86.60 398	93.82 380	79.92 304	97.95 339	89.29 280	90.95 328	93.56 433

v8		91.29 308	90.53 306	93.57 328	94.15 387	88.12 301	97.34 178	97.06 267	88.99 306	88.32 359	94.26 361	83.08 231	98.01 325	87.62 327	83.92 415	94.57 411

CVMVSNet		91.23 309	91.75 251	89.67 432	95.77 303	74.69 471	96.44 274	94.88 403	85.81 389	92.18 249	97.64 157	79.07 318	95.58 449	88.06 306	95.86 230	98.74 160

cl22		91.21 310	90.56 305	93.14 347	96.09 290	86.80 336	94.41 399	96.58 312	87.80 349	88.58 353	93.99 375	80.85 284	97.62 377	89.87 264	86.93 373	94.99 377

PEN-MVS		91.20 311	90.44 307	93.48 333	94.49 377	87.91 309	97.76 109	98.18 76	91.29 215	87.78 373	95.74 281	80.35 295	97.33 406	85.46 366	82.96 423	95.19 369

Baseline_NR-MVSNet		91.20 311	90.62 301	92.95 353	93.83 397	88.03 303	97.01 214	95.12 391	88.42 329	89.70 317	95.13 311	83.47 220	97.44 399	89.66 270	83.24 421	93.37 437

cascas		91.20 311	90.08 324	94.58 257	94.97 352	89.16 259	93.65 430	97.59 173	79.90 457	89.40 327	92.92 408	75.36 363	98.36 281	92.14 208	94.75 259	96.23 306

CostFormer		91.18 314	90.70 297	92.62 366	94.84 362	81.76 427	94.09 412	94.43 419	84.15 413	92.72 238	93.77 382	79.43 312	98.20 296	90.70 246	92.18 306	97.90 239

tt0805		91.09 315	90.07 327	94.16 285	95.61 309	88.31 288	97.56 145	96.51 314	89.56 285	89.17 338	95.64 287	67.08 436	98.38 280	91.07 236	88.44 358	95.80 327

v1192		91.07 316	90.23 318	93.58 326	93.70 400	87.82 312	96.73 250	97.07 262	87.77 351	89.58 321	94.32 356	80.90 283	97.97 331	86.52 347	85.48 387	94.95 378

v144192		91.06 317	90.28 314	93.39 336	93.66 403	87.23 326	96.83 236	97.07 262	87.43 360	89.69 318	94.28 358	81.48 270	98.00 326	87.18 339	84.92 400	94.93 382

v10		91.04 318	90.23 318	93.49 332	94.12 388	88.16 300	97.32 181	97.08 259	88.26 333	88.29 361	94.22 364	82.17 257	97.97 331	86.45 349	84.12 411	94.33 418

eth_miper_zixun_eth		91.02 319	90.59 303	92.34 372	95.33 331	84.35 394	94.10 411	96.90 286	88.56 324	88.84 347	94.33 354	84.08 211	97.60 379	88.77 297	84.37 409	95.06 375

v148		90.99 320	90.38 309	92.81 359	93.83 397	85.80 365	96.78 246	96.68 303	89.45 291	88.75 350	93.93 377	82.96 237	97.82 354	87.83 310	83.25 420	94.80 399

LTVRE_ROB	88.41 13	90.99 320	89.92 334	94.19 281	96.18 279	89.55 237	96.31 295	97.09 258	87.88 344	85.67 414	95.91 269	78.79 327	98.57 262	81.50 408	89.98 338	94.44 415
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DIV-MVS_self_test		90.97 322	90.33 310	92.88 356	95.36 326	86.19 357	94.46 397	96.63 309	87.82 347	88.18 365	94.23 362	82.99 234	97.53 391	87.72 315	85.57 386	94.93 382

cl____		90.96 323	90.32 311	92.89 355	95.37 325	86.21 355	94.46 397	96.64 306	87.82 347	88.15 367	94.18 365	82.98 235	97.54 389	87.70 318	85.59 385	94.92 384

pmmvs4		90.93 324	89.85 336	94.17 282	93.34 418	90.79 182	94.60 387	96.02 344	84.62 408	87.45 378	95.15 309	81.88 265	97.45 398	87.70 318	87.87 363	94.27 422

XVG-ACMP-BASELINE		90.93 324	90.21 321	93.09 348	94.31 385	85.89 364	95.33 357	97.26 239	91.06 233	89.38 328	95.44 298	68.61 422	98.60 257	89.46 274	91.05 325	94.79 401

v1921920		90.85 326	90.03 329	93.29 340	93.55 407	86.96 335	96.74 249	97.04 270	87.36 362	89.52 325	94.34 353	80.23 298	97.97 331	86.27 350	85.21 393	94.94 380

CR-MVSNet		90.82 327	89.77 340	93.95 299	94.45 379	87.19 327	90.23 469	95.68 362	86.89 371	92.40 240	92.36 421	80.91 281	97.05 415	81.09 418	93.95 280	97.60 261

v7n		90.76 328	89.86 335	93.45 335	93.54 408	87.60 317	97.70 124	97.37 224	88.85 312	87.65 375	94.08 371	81.08 278	98.10 307	84.68 376	83.79 417	94.66 409

RPSCF		90.75 329	90.86 285	90.42 423	96.84 207	76.29 469	95.61 343	96.34 323	83.89 417	91.38 271	97.87 124	76.45 353	98.78 215	87.16 340	92.23 303	96.20 308

MVP-Stereo		90.74 330	90.08 324	92.71 363	93.19 421	88.20 297	95.86 325	96.27 331	86.07 386	84.86 423	94.76 327	77.84 342	97.75 364	83.88 389	98.01 153	92.17 458
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 331	89.65 346	93.96 298	94.29 386	89.63 230	97.79 107	96.82 294	89.07 301	86.12 407	95.48 297	78.61 329	97.78 359	86.97 343	81.67 428	94.46 413

v1240		90.70 332	89.85 336	93.23 342	93.51 410	86.80 336	96.61 266	97.02 274	87.16 367	89.58 321	94.31 357	79.55 311	97.98 328	85.52 365	85.44 388	94.90 385

EPMVS		90.70 332	89.81 338	93.37 337	94.73 368	84.21 396	93.67 429	88.02 481	89.50 288	92.38 242	93.49 396	77.82 343	97.78 359	86.03 358	92.68 298	98.11 226

WBMVS		90.69 334	89.99 331	92.81 359	96.48 255	85.00 385	95.21 368	96.30 326	89.46 290	89.04 341	94.05 372	72.45 391	97.82 354	89.46 274	87.41 370	95.61 338

Anonymous20231211		90.63 335	89.42 351	94.27 279	98.24 100	89.19 258	98.05 63	97.89 128	79.95 456	88.25 363	94.96 316	72.56 390	98.13 302	89.70 268	85.14 394	95.49 340

DTE-MVSNet		90.56 336	89.75 342	93.01 350	93.95 392	87.25 324	97.64 134	97.65 161	90.74 242	87.12 386	95.68 285	79.97 303	97.00 419	83.33 391	81.66 429	94.78 403

ACMH	87.59 16	90.53 337	89.42 351	93.87 306	96.21 271	87.92 307	97.24 191	96.94 279	88.45 328	83.91 436	96.27 251	71.92 393	98.62 256	84.43 379	89.43 344	95.05 376
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		90.52 338	89.14 359	94.67 250	96.81 213	87.85 311	95.91 323	93.97 435	89.71 280	92.34 246	92.48 416	65.41 447	97.96 335	81.37 414	94.27 268	98.21 211

OurMVSNet-221017-0		90.51 339	90.19 322	91.44 401	93.41 416	81.25 430	96.98 218	96.28 330	91.68 199	86.55 400	96.30 248	74.20 375	97.98 328	88.96 292	87.40 371	95.09 373

miper_lstm_enhance		90.50 340	90.06 328	91.83 389	95.33 331	83.74 402	93.86 420	96.70 302	87.56 358	87.79 372	93.81 381	83.45 222	96.92 421	87.39 333	84.62 403	94.82 396

COLMAP_ROB	87.81 15	90.40 341	89.28 354	93.79 310	97.95 128	87.13 330	96.92 224	95.89 350	82.83 432	86.88 397	97.18 190	73.77 379	99.29 146	78.44 435	93.62 287	94.95 378
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 342	88.96 361	94.35 270	96.54 246	87.29 321	95.50 348	93.84 439	90.97 235	91.75 264	92.96 407	62.18 462	98.00 326	82.86 395	94.08 275	97.76 251

IterMVS-SCA-FT		90.31 342	89.81 338	91.82 390	95.52 314	84.20 397	94.30 405	96.15 341	90.61 253	87.39 381	94.27 359	75.80 359	96.44 432	87.34 334	86.88 377	94.82 396

MS-PatchMatch		90.27 344	89.77 340	91.78 393	94.33 383	84.72 391	95.55 345	96.73 297	86.17 385	86.36 402	95.28 303	71.28 398	97.80 357	84.09 384	98.14 147	92.81 443

tpm		90.25 345	89.74 343	91.76 395	93.92 393	79.73 451	93.98 413	93.54 442	88.28 332	91.99 255	93.25 404	77.51 345	97.44 399	87.30 336	87.94 362	98.12 220

AllTest		90.23 346	88.98 360	93.98 295	97.94 129	86.64 340	96.51 273	95.54 370	85.38 395	85.49 416	96.77 218	70.28 406	99.15 164	80.02 425	92.87 292	96.15 313

dmvs_re		90.21 347	89.50 349	92.35 370	95.47 320	85.15 381	95.70 336	94.37 424	90.94 238	88.42 355	93.57 394	74.63 371	95.67 446	82.80 398	89.57 343	96.22 307

ACMH+	87.92 14	90.20 348	89.18 357	93.25 341	96.48 255	86.45 349	96.99 217	96.68 303	88.83 314	84.79 424	96.22 253	70.16 408	98.53 265	84.42 380	88.04 361	94.77 404

test-mter		90.19 349	89.54 348	92.12 380	94.59 373	80.66 436	94.29 406	92.98 449	87.68 355	90.76 288	92.37 418	67.67 428	98.07 316	88.81 295	96.74 201	97.63 256

IterMVS		90.15 350	89.67 344	91.61 397	95.48 316	83.72 403	94.33 403	96.12 342	89.99 272	87.31 384	94.15 367	75.78 361	96.27 436	86.97 343	86.89 376	94.83 391
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 351	89.42 351	91.97 383	94.41 381	80.62 438	94.29 406	91.97 464	87.28 365	90.44 292	92.47 417	68.79 420	97.67 369	88.50 302	96.60 208	97.61 260

SD_0403		90.01 352	90.02 330	89.96 429	95.65 308	76.76 466	95.76 333	96.46 317	90.58 256	86.59 399	96.29 249	82.12 258	94.78 458	73.00 463	93.76 283	98.35 199

tpm2		89.96 353	89.21 356	92.23 378	94.91 359	81.25 430	93.78 423	94.42 420	80.62 454	91.56 267	93.44 399	76.44 354	97.94 341	85.60 364	92.08 310	97.49 265

UWE-MVS		89.91 354	89.48 350	91.21 406	95.88 296	78.23 464	94.91 379	90.26 474	89.11 300	92.35 245	94.52 340	68.76 421	97.96 335	83.95 387	95.59 238	97.42 269

IB-MVS	87.33 17	89.91 354	88.28 371	94.79 244	95.26 338	87.70 314	95.12 374	93.95 436	89.35 294	87.03 390	92.49 415	70.74 403	99.19 155	89.18 286	81.37 430	97.49 265
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
ADS-MVSNet		89.89 356	88.68 366	93.53 329	95.86 297	84.89 389	90.93 464	95.07 393	83.23 430	91.28 279	91.81 431	79.01 323	97.85 350	79.52 427	91.39 319	97.84 246

WB-MVSnew		89.88 357	89.56 347	90.82 415	94.57 376	83.06 412	95.65 341	92.85 451	87.86 346	90.83 287	94.10 368	79.66 309	96.88 423	76.34 445	94.19 270	92.54 449

FMVSNet1		89.88 357	88.31 370	94.59 253	95.41 321	91.18 163	97.50 154	96.93 280	86.62 375	87.41 380	94.51 341	65.94 444	97.29 408	83.04 394	87.43 368	95.31 358

pmmvs5		89.86 359	88.87 364	92.82 358	92.86 427	86.23 354	96.26 298	95.39 375	84.24 412	87.12 386	94.51 341	74.27 374	97.36 405	87.61 328	87.57 366	94.86 387

tpmvs		89.83 360	89.15 358	91.89 387	94.92 357	80.30 443	93.11 441	95.46 374	86.28 382	88.08 368	92.65 411	80.44 293	98.52 266	81.47 410	89.92 339	96.84 291

test_fmvs2		89.77 361	89.93 333	89.31 439	93.68 402	76.37 468	97.64 134	95.90 348	89.84 277	91.49 269	96.26 252	58.77 465	97.10 412	94.65 152	91.13 323	94.46 413

SSC-MVS3.2		89.74 362	89.26 355	91.19 409	95.16 342	80.29 444	94.53 390	97.03 272	91.79 195	88.86 345	94.10 368	69.94 411	97.82 354	85.29 368	86.66 378	95.45 346

mmtdpeth		89.70 363	88.96 361	91.90 386	95.84 302	84.42 393	97.46 165	95.53 373	90.27 265	94.46 187	90.50 440	69.74 415	98.95 193	97.39 53	69.48 476	92.34 452

tfpnnormal		89.70 363	88.40 369	93.60 324	95.15 345	90.10 209	97.56 145	98.16 80	87.28 365	86.16 405	94.63 335	77.57 344	98.05 319	74.48 453	84.59 405	92.65 446

ADS-MVSNet2		89.45 365	88.59 367	92.03 382	95.86 297	82.26 423	90.93 464	94.32 427	83.23 430	91.28 279	91.81 431	79.01 323	95.99 438	79.52 427	91.39 319	97.84 246

Patchmatch-test		89.42 366	87.99 373	93.70 315	95.27 335	85.11 382	88.98 476	94.37 424	81.11 448	87.10 389	93.69 385	82.28 254	97.50 394	74.37 455	94.76 258	98.48 184

test0.0.03 1		89.37 367	88.70 365	91.41 402	92.47 436	85.63 369	95.22 366	92.70 454	91.11 230	86.91 396	93.65 389	79.02 321	93.19 476	78.00 437	89.18 346	95.41 348

SixPastTwentyTwo		89.15 368	88.54 368	90.98 411	93.49 411	80.28 445	96.70 254	94.70 410	90.78 240	84.15 431	95.57 290	71.78 395	97.71 367	84.63 377	85.07 396	94.94 380

RPMNet		88.98 369	87.05 383	94.77 245	94.45 379	87.19 327	90.23 469	98.03 110	77.87 467	92.40 240	87.55 468	80.17 299	99.51 117	68.84 475	93.95 280	97.60 261

TransMVSNet (Re)		88.94 370	87.56 376	93.08 349	94.35 382	88.45 286	97.73 116	95.23 386	87.47 359	84.26 429	95.29 301	79.86 305	97.33 406	79.44 431	74.44 459	93.45 436

USDC		88.94 370	87.83 375	92.27 375	94.66 370	84.96 387	93.86 420	95.90 348	87.34 363	83.40 438	95.56 291	67.43 430	98.19 298	82.64 402	89.67 342	93.66 432

dp		88.90 372	88.26 372	90.81 416	94.58 375	76.62 467	92.85 446	94.93 400	85.12 401	90.07 308	93.07 405	75.81 358	98.12 305	80.53 422	87.42 369	97.71 253

PatchT		88.87 373	87.42 377	93.22 343	94.08 390	85.10 383	89.51 474	94.64 413	81.92 443	92.36 243	88.15 461	80.05 301	97.01 418	72.43 464	93.65 286	97.54 264

our_test_3		88.78 374	87.98 374	91.20 408	92.45 437	82.53 417	93.61 432	95.69 360	85.77 390	84.88 422	93.71 383	79.99 302	96.78 428	79.47 429	86.24 379	94.28 421

EU-MVSNet		88.72 375	88.90 363	88.20 444	93.15 422	74.21 473	96.63 265	94.22 429	85.18 399	87.32 383	95.97 265	76.16 356	94.98 456	85.27 369	86.17 380	95.41 348

Patchmtry		88.64 376	87.25 379	92.78 361	94.09 389	86.64 340	89.82 473	95.68 362	80.81 452	87.63 376	92.36 421	80.91 281	97.03 416	78.86 433	85.12 395	94.67 408

MIMVSNet		88.50 377	86.76 387	93.72 314	94.84 362	87.77 313	91.39 458	94.05 432	86.41 379	87.99 370	92.59 414	63.27 455	95.82 443	77.44 438	92.84 294	97.57 263

tpm cat1		88.36 378	87.21 381	91.81 391	95.13 347	80.55 439	92.58 450	95.70 358	74.97 471	87.45 378	91.96 429	78.01 341	98.17 300	80.39 423	88.74 355	96.72 295

ppachtmachnet_test		88.35 379	87.29 378	91.53 398	92.45 437	83.57 406	93.75 424	95.97 345	84.28 411	85.32 419	94.18 365	79.00 325	96.93 420	75.71 448	84.99 399	94.10 423

JIA-IIPM		88.26 380	87.04 384	91.91 385	93.52 409	81.42 429	89.38 475	94.38 423	80.84 451	90.93 285	80.74 483	79.22 315	97.92 344	82.76 399	91.62 314	96.38 305

testgi		87.97 381	87.21 381	90.24 425	92.86 427	80.76 434	96.67 259	94.97 397	91.74 197	85.52 415	95.83 273	62.66 460	94.47 461	76.25 446	88.36 359	95.48 341

LF4IMVS		87.94 382	87.25 379	89.98 428	92.38 439	80.05 449	94.38 400	95.25 385	87.59 357	84.34 427	94.74 329	64.31 453	97.66 373	84.83 373	87.45 367	92.23 455

gg-mvs-nofinetune		87.82 383	85.61 396	94.44 266	94.46 378	89.27 254	91.21 462	84.61 490	80.88 450	89.89 312	74.98 486	71.50 396	97.53 391	85.75 363	97.21 182	96.51 300

pmmvs6		87.81 384	86.19 392	92.69 364	91.32 445	86.30 352	97.34 178	96.41 320	80.59 455	84.05 435	94.37 350	67.37 431	97.67 369	84.75 375	79.51 438	94.09 425

testing3		87.67 385	86.88 386	90.05 427	96.14 284	80.71 435	97.10 206	92.85 451	90.15 269	87.54 377	94.55 338	55.70 472	94.10 465	73.77 459	94.10 274	95.35 355

K. test v3		87.64 386	86.75 388	90.32 424	93.02 424	79.48 457	96.61 266	92.08 463	90.66 249	80.25 457	94.09 370	67.21 432	96.65 430	85.96 360	80.83 432	94.83 391

blended_shiyan8		87.58 387	85.55 397	93.66 320	88.76 467	88.54 280	95.21 368	96.29 329	82.81 433	86.25 403	87.73 465	73.70 381	97.58 381	87.81 311	71.42 468	94.85 389

blended_shiyan6		87.55 388	85.52 398	93.64 321	88.78 465	88.50 283	95.23 365	96.30 326	82.80 434	86.09 408	87.70 466	73.69 382	97.56 382	87.70 318	71.36 469	94.86 387

Patchmatch-RL test		87.38 389	86.24 391	90.81 416	88.74 468	78.40 463	88.12 483	93.17 446	87.11 368	82.17 447	89.29 452	81.95 262	95.60 448	88.64 300	77.02 447	98.41 192

wanda-best-256-512		87.29 390	85.21 403	93.53 329	88.54 470	88.21 295	94.51 393	96.27 331	82.69 437	85.92 410	86.89 472	73.04 385	97.55 384	87.68 322	71.36 469	94.83 391

FE-blended-shiyan7		87.29 390	85.21 403	93.53 329	88.54 470	88.21 295	94.51 393	96.27 331	82.69 437	85.92 410	86.89 472	73.03 386	97.55 384	87.68 322	71.36 469	94.83 391

FMVSNet5		87.29 390	85.79 395	91.78 393	94.80 364	87.28 322	95.49 349	95.28 382	84.09 414	83.85 437	91.82 430	62.95 457	94.17 464	78.48 434	85.34 391	93.91 429

myMVS_eth3d		87.18 393	86.38 390	89.58 433	95.16 342	79.53 454	95.00 376	93.93 437	88.55 325	86.96 392	91.99 427	56.23 471	94.00 466	75.47 451	94.11 272	95.20 366

Syy-MVS		87.13 394	87.02 385	87.47 448	95.16 342	73.21 476	95.00 376	93.93 437	88.55 325	86.96 392	91.99 427	75.90 357	94.00 466	61.59 482	94.11 272	95.20 366

Anonymous20231206		87.09 395	86.14 393	89.93 430	91.22 446	80.35 441	96.11 310	95.35 378	83.57 424	84.16 430	93.02 406	73.54 383	95.61 447	72.16 465	86.14 381	93.84 430

usedtu_blend_shiyan5		87.06 396	84.84 410	93.69 316	88.54 470	88.70 273	95.83 327	95.54 370	78.74 462	85.92 410	86.89 472	73.03 386	97.55 384	87.73 313	71.36 469	94.83 391

EG-PatchMatch MVS		87.02 397	85.44 399	91.76 395	92.67 431	85.00 385	96.08 312	96.45 318	83.41 429	79.52 459	93.49 396	57.10 469	97.72 366	79.34 432	90.87 330	92.56 448

blend_shiyan4		86.87 398	84.61 415	93.67 319	88.87 463	88.70 273	95.17 372	96.30 326	82.80 434	86.16 405	87.11 470	65.12 452	97.55 384	87.73 313	72.21 466	94.75 405

0.4-1-1-0.1		86.83 399	84.27 418	94.50 262	91.39 444	88.23 293	92.62 449	92.27 460	84.04 415	86.01 409	83.30 479	65.29 449	98.31 286	89.08 288	74.45 458	96.96 288

TinyColmap		86.82 400	85.35 402	91.21 406	94.91 359	82.99 413	93.94 416	94.02 434	83.58 423	81.56 449	94.68 331	62.34 461	98.13 302	75.78 447	87.35 372	92.52 450

UWE-MVS-28		86.81 401	86.41 389	88.02 446	92.87 426	74.60 472	95.38 355	86.70 486	88.17 335	87.28 385	94.67 333	70.83 402	93.30 474	67.45 476	94.31 266	96.17 310

mvs5depth		86.53 402	85.08 406	90.87 413	88.74 468	82.52 418	91.91 455	94.23 428	86.35 380	87.11 388	93.70 384	66.52 437	97.76 362	81.37 414	75.80 452	92.31 454

TDRefinement		86.53 402	84.76 412	91.85 388	82.23 488	84.25 395	96.38 286	95.35 378	84.97 404	84.09 433	94.94 317	65.76 445	98.34 285	84.60 378	74.52 457	92.97 440

sc_t1		86.48 404	84.10 421	93.63 322	93.45 414	85.76 367	96.79 242	94.71 409	73.06 476	86.45 401	94.35 351	55.13 473	97.95 339	84.38 381	78.55 443	97.18 280

test_0402		86.46 405	84.79 411	91.45 400	95.02 351	85.55 370	96.29 297	94.89 402	80.90 449	82.21 446	93.97 376	68.21 427	97.29 408	62.98 480	88.68 356	91.51 464

Anonymous20240521		86.42 406	85.44 399	89.34 438	90.33 452	79.79 450	96.73 250	95.92 346	83.71 422	83.25 440	91.36 436	63.92 454	96.01 437	78.39 436	85.36 390	92.22 456

FE-MVSNET2		86.36 407	84.68 414	91.39 403	87.67 476	86.47 348	96.21 303	96.41 320	87.87 345	79.31 461	89.64 449	65.29 449	95.58 449	82.42 403	77.28 446	92.14 459

DSMNet-mixed		86.34 408	86.12 394	87.00 452	89.88 456	70.43 478	94.93 378	90.08 475	77.97 466	85.42 418	92.78 409	74.44 373	93.96 468	74.43 454	95.14 249	96.62 298

CL-MVSNet_self_test		86.31 409	85.15 405	89.80 431	88.83 464	81.74 428	93.93 417	96.22 336	86.67 374	85.03 421	90.80 439	78.09 338	94.50 459	74.92 452	71.86 467	93.15 439

0.4-1-1-0.2		86.27 410	83.62 423	94.20 280	90.38 451	87.69 315	91.04 463	92.52 457	83.43 428	85.22 420	81.49 482	65.31 448	98.29 289	88.90 294	74.30 460	96.64 297

pmmvs-eth3d		86.22 411	84.45 416	91.53 398	88.34 473	87.25 324	94.47 395	95.01 394	83.47 426	79.51 460	89.61 450	69.75 414	95.71 444	83.13 393	76.73 450	91.64 461

test_vis1_rt		86.16 412	85.06 407	89.46 435	93.47 413	80.46 440	96.41 280	86.61 487	85.22 398	79.15 462	88.64 456	52.41 477	97.06 414	93.08 192	90.57 332	90.87 470

test20.03		86.14 413	85.40 401	88.35 442	90.12 453	80.06 448	95.90 324	95.20 387	88.59 321	81.29 450	93.62 390	71.43 397	92.65 477	71.26 469	81.17 431	92.34 452

0.3-1-1-0.015		86.11 414	83.37 424	94.34 272	90.58 450	88.02 304	91.64 457	92.45 458	83.56 425	84.46 425	81.84 480	62.73 459	98.31 286	88.98 291	74.09 461	96.70 296

UnsupCasMVSNet_eth		85.99 415	84.45 416	90.62 420	89.97 455	82.40 422	93.62 431	97.37 224	89.86 274	78.59 465	92.37 418	65.25 451	95.35 454	82.27 405	70.75 473	94.10 423

KD-MVS_self_test		85.95 416	84.95 408	88.96 441	89.55 459	79.11 460	95.13 373	96.42 319	85.91 388	84.07 434	90.48 441	70.03 410	94.82 457	80.04 424	72.94 464	92.94 441

ttmdpeth		85.91 417	84.76 412	89.36 437	89.14 460	80.25 446	95.66 340	93.16 448	83.77 420	83.39 439	95.26 305	66.24 441	95.26 455	80.65 420	75.57 453	92.57 447

YYNet1		85.87 418	84.23 419	90.78 419	92.38 439	82.46 421	93.17 438	95.14 390	82.12 442	67.69 479	92.36 421	78.16 337	95.50 452	77.31 440	79.73 436	94.39 416

MDA-MVSNet_test_wron		85.87 418	84.23 419	90.80 418	92.38 439	82.57 416	93.17 438	95.15 389	82.15 441	67.65 481	92.33 424	78.20 334	95.51 451	77.33 439	79.74 435	94.31 420

CMPMVS	62.92 21	85.62 420	84.92 409	87.74 447	89.14 460	73.12 477	94.17 409	96.80 295	73.98 472	73.65 475	94.93 318	66.36 438	97.61 378	83.95 387	91.28 321	92.48 451
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 421	83.64 422	90.92 412	95.27 335	79.49 456	90.55 467	95.60 365	83.76 421	83.00 443	89.95 446	71.09 399	97.97 331	82.75 400	60.79 488	95.31 358

tt0320		85.39 422	83.12 425	92.19 379	93.44 415	85.79 366	96.19 306	94.87 406	71.19 479	82.92 444	91.76 433	58.43 466	96.81 426	81.03 419	78.26 444	93.98 427

MDA-MVSNet-bldmvs		85.00 423	82.95 428	91.17 410	93.13 423	83.33 407	94.56 389	95.00 395	84.57 409	65.13 485	92.65 411	70.45 405	95.85 441	73.57 460	77.49 445	94.33 418

MIMVSNet1		84.93 424	83.05 426	90.56 421	89.56 458	84.84 390	95.40 353	95.35 378	83.91 416	80.38 455	92.21 426	57.23 468	93.34 473	70.69 471	82.75 426	93.50 434

tt0320-xc		84.83 425	82.33 433	92.31 373	93.66 403	86.20 356	96.17 308	94.06 431	71.26 478	82.04 448	92.22 425	55.07 474	96.72 429	81.49 409	75.04 456	94.02 426

KD-MVS_2432*1600		84.81 426	82.64 429	91.31 404	91.07 447	85.34 379	91.22 460	95.75 356	85.56 393	83.09 441	90.21 444	67.21 432	95.89 439	77.18 442	62.48 486	92.69 444

miper_refine_blended		84.81 426	82.64 429	91.31 404	91.07 447	85.34 379	91.22 460	95.75 356	85.56 393	83.09 441	90.21 444	67.21 432	95.89 439	77.18 442	62.48 486	92.69 444

OpenMVS_ROB	81.14 20	84.42 428	82.28 434	90.83 414	90.06 454	84.05 400	95.73 335	94.04 433	73.89 474	80.17 458	91.53 435	59.15 464	97.64 374	66.92 478	89.05 349	90.80 471

FE-MVSNET		83.85 429	81.97 435	89.51 434	87.19 478	83.19 410	95.21 368	93.17 446	83.45 427	78.90 463	89.05 454	65.46 446	93.84 470	69.71 474	75.56 454	91.51 464

mvsany_test3		83.59 430	82.44 432	87.03 451	83.80 483	73.82 474	93.70 426	90.92 472	86.42 378	82.51 445	90.26 443	46.76 482	95.71 444	90.82 241	76.76 449	91.57 463

PM-MVS		83.48 431	81.86 437	88.31 443	87.83 475	77.59 465	93.43 434	91.75 465	86.91 370	80.63 453	89.91 447	44.42 484	95.84 442	85.17 372	76.73 450	91.50 466

test_fmvs3		83.21 432	83.02 427	83.78 457	86.77 480	68.34 483	96.76 248	94.91 401	86.49 377	84.14 432	89.48 451	36.04 488	91.73 479	91.86 218	80.77 433	91.26 469

new-patchmatchnet		83.18 433	81.87 436	87.11 450	86.88 479	75.99 470	93.70 426	95.18 388	85.02 403	77.30 468	88.40 458	65.99 443	93.88 469	74.19 457	70.18 474	91.47 467

new_pmnet		82.89 434	81.12 439	88.18 445	89.63 457	80.18 447	91.77 456	92.57 455	76.79 469	75.56 472	88.23 460	61.22 463	94.48 460	71.43 467	82.92 424	89.87 474

MVS-HIRNet		82.47 435	81.21 438	86.26 454	95.38 323	69.21 481	88.96 477	89.49 476	66.28 483	80.79 452	74.08 488	68.48 425	97.39 403	71.93 466	95.47 243	92.18 457

MVStest1		82.38 436	80.04 440	89.37 436	87.63 477	82.83 414	95.03 375	93.37 445	73.90 473	73.50 476	94.35 351	62.89 458	93.25 475	73.80 458	65.92 483	92.04 460

UnsupCasMVSNet_bld		82.13 437	79.46 442	90.14 426	88.00 474	82.47 420	90.89 466	96.62 311	78.94 461	75.61 470	84.40 478	56.63 470	96.31 435	77.30 441	66.77 482	91.63 462

dmvs_testset		81.38 438	82.60 431	77.73 463	91.74 443	51.49 498	93.03 443	84.21 491	89.07 301	78.28 466	91.25 437	76.97 348	88.53 486	56.57 486	82.24 427	93.16 438

test_f		80.57 439	79.62 441	83.41 458	83.38 486	67.80 485	93.57 433	93.72 440	80.80 453	77.91 467	87.63 467	33.40 489	92.08 478	87.14 341	79.04 441	90.34 473

usedtu_dtu_shiyan2		80.00 440	76.91 446	89.27 440	82.13 489	79.69 452	95.45 351	94.20 430	72.95 477	75.80 469	87.75 464	44.44 483	94.30 463	70.64 472	68.81 479	93.84 430

pmmvs3		79.97 441	77.50 445	87.39 449	82.80 487	79.38 458	92.70 448	90.75 473	70.69 480	78.66 464	87.47 469	51.34 478	93.40 472	73.39 461	69.65 475	89.38 475

APD_test1		79.31 442	77.70 444	84.14 456	89.11 462	69.07 482	92.36 454	91.50 467	69.07 481	73.87 474	92.63 413	39.93 486	94.32 462	70.54 473	80.25 434	89.02 476

N_pmnet		78.73 443	78.71 443	78.79 462	92.80 429	46.50 501	94.14 410	43.71 503	78.61 463	80.83 451	91.66 434	74.94 369	96.36 434	67.24 477	84.45 408	93.50 434

WB-MVS		76.77 444	76.63 447	77.18 464	85.32 481	56.82 496	94.53 390	89.39 477	82.66 439	71.35 477	89.18 453	75.03 366	88.88 484	35.42 493	66.79 481	85.84 478

SSC-MVS		76.05 445	75.83 448	76.72 468	84.77 482	56.22 497	94.32 404	88.96 479	81.82 445	70.52 478	88.91 455	74.79 370	88.71 485	33.69 494	64.71 484	85.23 479

test_vis3_rt		72.73 446	70.55 449	79.27 461	80.02 490	68.13 484	93.92 418	74.30 498	76.90 468	58.99 489	73.58 489	20.29 497	95.37 453	84.16 382	72.80 465	74.31 486

LCM-MVSNet		72.55 447	69.39 451	82.03 459	70.81 499	65.42 488	90.12 471	94.36 426	55.02 489	65.88 483	81.72 481	24.16 496	89.96 480	74.32 456	68.10 480	90.71 472

FPMVS		71.27 448	69.85 450	75.50 469	74.64 494	59.03 494	91.30 459	91.50 467	58.80 486	57.92 490	88.28 459	29.98 492	85.53 489	53.43 487	82.84 425	81.95 482

PMMVS2		70.19 449	66.92 453	80.01 460	76.35 493	65.67 487	86.22 484	87.58 483	64.83 485	62.38 486	80.29 485	26.78 494	88.49 487	63.79 479	54.07 490	85.88 477

dongtai		69.99 450	69.33 452	71.98 472	88.78 465	61.64 492	89.86 472	59.93 502	75.67 470	74.96 473	85.45 475	50.19 479	81.66 491	43.86 490	55.27 489	72.63 487

testf1		69.31 451	66.76 454	76.94 466	78.61 491	61.93 490	88.27 481	86.11 488	55.62 487	59.69 487	85.31 476	20.19 498	89.32 481	57.62 483	69.44 477	79.58 483

APD_test2		69.31 451	66.76 454	76.94 466	78.61 491	61.93 490	88.27 481	86.11 488	55.62 487	59.69 487	85.31 476	20.19 498	89.32 481	57.62 483	69.44 477	79.58 483

EGC-MVSNET		68.77 453	63.01 459	86.07 455	92.49 435	82.24 424	93.96 415	90.96 471	0.71 500	2.62 501	90.89 438	53.66 475	93.46 471	57.25 485	84.55 406	82.51 481

Gipuma		67.86 454	65.41 456	75.18 470	92.66 432	73.45 475	66.50 492	94.52 417	53.33 490	57.80 491	66.07 491	30.81 490	89.20 483	48.15 489	78.88 442	62.90 491
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 455	64.89 457	69.79 473	72.62 497	35.23 505	65.19 493	92.83 453	20.35 495	65.20 484	88.08 462	43.14 485	82.70 490	73.12 462	63.46 485	91.45 468

kuosan		65.27 456	64.66 458	67.11 475	83.80 483	61.32 493	88.53 480	60.77 501	68.22 482	67.67 480	80.52 484	49.12 480	70.76 497	29.67 496	53.64 491	69.26 489

ANet_high		63.94 457	59.58 460	77.02 465	61.24 501	66.06 486	85.66 486	87.93 482	78.53 464	42.94 493	71.04 490	25.42 495	80.71 492	52.60 488	30.83 494	84.28 480

PMVS	53.92 22	58.58 458	55.40 461	68.12 474	51.00 502	48.64 499	78.86 489	87.10 485	46.77 491	35.84 497	74.28 487	8.76 500	86.34 488	42.07 491	73.91 462	69.38 488
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 459	52.56 463	55.43 477	74.43 495	47.13 500	83.63 488	76.30 495	42.23 492	42.59 494	62.22 493	28.57 493	74.40 494	31.53 495	31.51 493	44.78 492

MVE	50.73 23	53.25 460	48.81 465	66.58 476	65.34 500	57.50 495	72.49 491	70.94 499	40.15 494	39.28 496	63.51 492	6.89 502	73.48 496	38.29 492	42.38 492	68.76 490
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 461	51.31 464	54.39 478	72.62 497	45.39 502	83.84 487	75.51 497	41.13 493	40.77 495	59.65 494	30.08 491	73.60 495	28.31 497	29.90 495	44.18 493

tmp_tt		51.94 462	53.82 462	46.29 479	33.73 503	45.30 503	78.32 490	67.24 500	18.02 496	50.93 492	87.05 471	52.99 476	53.11 498	70.76 470	25.29 496	40.46 494

wuyk23d		25.11 463	24.57 467	26.74 480	73.98 496	39.89 504	57.88 494	9.80 504	12.27 497	10.39 498	6.97 500	7.03 501	36.44 499	25.43 498	17.39 497	3.89 497

cdsmvs_eth3d_5k		23.24 464	30.99 466	0.00 483	0.00 506	0.00 508	0.00 495	97.63 165	0.00 501	0.00 502	96.88 213	84.38 205	0.00 502	0.00 501	0.00 500	0.00 498

testmvs		13.36 465	16.33 468	4.48 482	5.04 504	2.26 507	93.18 437	3.28 505	2.70 498	8.24 499	21.66 496	2.29 504	2.19 500	7.58 499	2.96 498	9.00 496

test123		13.04 466	15.66 469	5.18 481	4.51 505	3.45 506	92.50 452	1.81 506	2.50 499	7.58 500	20.15 497	3.67 503	2.18 501	7.13 500	1.07 499	9.90 495

ab-mvs-re		8.06 467	10.74 470	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	96.69 224	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

pcd_1.5k_mvsjas		7.39 468	9.85 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	88.65 108	0.00 502	0.00 501	0.00 500	0.00 498

mmdepth		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

monomultidepth		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

test_blank		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uanet_test		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

DCPMVS		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

sosnet-low-res		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

sosnet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uncertanet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

Regformer		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uanet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

MED-MVS test				98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51		99.86 9	97.40 49	99.58 23	99.65 20

TestfortrainingZip							98.69 11

WAC-MVS						79.53 454								75.56 450

FOURS1					99.55 4	93.34 71	99.29 1	98.35 42	94.98 46	98.49 39

MSC_two_6792asdad				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 32	99.67 6	99.77 3

PC_three_1452									90.77 241	98.89 26	98.28 86	96.24 1	98.35 282	95.76 106	99.58 23	99.59 32

No_MVS				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 32	99.67 6	99.77 3

test_one_0601					99.32 27	95.20 21		98.25 61	95.13 40	98.48 40	98.87 31	95.16 9

eth-test2					0.00 506

eth-test					0.00 506

ZD-MVS					99.05 45	94.59 33		98.08 93	89.22 297	97.03 81	98.10 94	92.52 41	99.65 79	94.58 156	99.31 72

RE-MVS-def			96.72 62		99.02 48	92.34 108	97.98 72	98.03 110	93.52 115	97.43 67	98.51 56	90.71 80		96.05 94	99.26 78	99.43 63

IU-MVS					99.42 10	95.39 12		97.94 124	90.40 264	98.94 19				97.41 48	99.66 10	99.74 9

OPU-MVS				98.55 4	98.82 61	96.86 3	98.25 40				98.26 87	96.04 2	99.24 150	95.36 120	99.59 19	99.56 40

test_241102_TWO								98.27 55	95.13 40	98.93 20	98.89 28	94.99 13	99.85 21	97.52 41	99.65 13	99.74 9

test_241102_ONE					99.42 10	95.30 18		98.27 55	95.09 43	99.19 13	98.81 37	95.54 5	99.65 79

9.14			96.75 61		98.93 56		97.73 116	98.23 66	91.28 218	97.88 55	98.44 64	93.00 29	99.65 79	95.76 106	99.47 45

save fliter					98.91 58	94.28 42	97.02 211	98.02 113	95.35 31

test_0728_THIRD									94.78 61	98.73 30	98.87 31	95.87 4	99.84 26	97.45 45	99.72 2	99.77 3

test_0728_SECOND				98.51 5	99.45 6	95.93 6	98.21 48	98.28 52					99.86 9	97.52 41	99.67 6	99.75 7

test0726					99.45 6	95.36 14	98.31 32	98.29 50	94.92 50	98.99 18	98.92 23	95.08 10

GSMVS																98.45 187

test_part2					99.28 30	95.74 9				98.10 48

sam_mvs1												82.76 242				98.45 187

sam_mvs												81.94 263

ambc				86.56 453	83.60 485	70.00 480	85.69 485	94.97 397		80.60 454	88.45 457	37.42 487	96.84 425	82.69 401	75.44 455	92.86 442

MTGPA								98.08 93

test_post1							92.81 447				16.58 499	80.53 291	97.68 368	86.20 352

test_post											17.58 498	81.76 266	98.08 312

patchmatchnet-post											90.45 442	82.65 247	98.10 307

GG-mvs-BLEND				93.62 323	93.69 401	89.20 256	92.39 453	83.33 492		87.98 371	89.84 448	71.00 400	96.87 424	82.08 406	95.40 245	94.80 399

MTMP							97.86 92	82.03 493

gm-plane-assit					93.22 420	78.89 462			84.82 406		93.52 395		98.64 252	87.72 315

test9_res														94.81 143	99.38 64	99.45 59

TEST9					98.70 65	94.19 46	96.41 280	98.02 113	88.17 335	96.03 127	97.56 166	92.74 35	99.59 95

test_8					98.67 67	94.06 53	96.37 288	98.01 116	88.58 322	95.98 131	97.55 168	92.73 36	99.58 98

agg_prior2														93.94 170	99.38 64	99.50 52

agg_prior					98.67 67	93.79 59		98.00 117		95.68 144			99.57 105

TestCases				93.98 295	97.94 129	86.64 340		95.54 370	85.38 395	85.49 416	96.77 218	70.28 406	99.15 164	80.02 425	92.87 292	96.15 313

test_prior4						93.66 62	96.42 279

test_prior2							96.35 289		92.80 156	96.03 127	97.59 163	92.01 49		95.01 128	99.38 64

test_prior				97.23 69	98.67 67	92.99 83		98.00 117					99.41 132			99.29 75

旧先验2							95.94 320		81.66 446	97.34 70			98.82 209	92.26 203

新几何2							95.79 331

新几何1				97.32 62	98.60 74	93.59 63		97.75 148	81.58 447	95.75 139	97.85 128	90.04 87	99.67 77	86.50 348	99.13 97	98.69 164

旧先验1					98.38 89	93.38 68		97.75 148			98.09 96	92.30 47			99.01 107	99.16 85

无先验							95.79 331	97.87 132	83.87 419				99.65 79	87.68 322		98.89 136

原ACMM2							95.67 337

原ACMM1				96.38 125	98.59 75	91.09 168		97.89 128	87.41 361	95.22 161	97.68 150	90.25 84	99.54 110	87.95 308	99.12 99	98.49 182

test222					98.24 100	92.21 114	95.33 357	97.60 170	79.22 460	95.25 159	97.84 130	88.80 105			99.15 94	98.72 161

testdata2													99.67 77	85.96 360

segment_acmp												92.89 32

testdata				95.46 207	98.18 111	88.90 269		97.66 159	82.73 436	97.03 81	98.07 97	90.06 86	98.85 205	89.67 269	98.98 108	98.64 167

testdata1							95.26 364		93.10 137

test12				97.65 47	98.46 79	94.26 43		97.66 159		95.52 151		90.89 77	99.46 126		99.25 80	99.22 82

plane_prior7					96.21 271	89.98 215

plane_prior6					96.10 289	90.00 211						81.32 273

plane_prior5								97.51 192					98.60 257	93.02 195	92.23 303	95.86 321

plane_prior4											96.64 227

plane_prior3						90.00 211			94.46 78	91.34 273

plane_prior2							97.74 114		94.85 53

plane_prior1					96.14 284

plane_prior						89.99 213	97.24 191		94.06 92						92.16 307

n2								0.00 507

nn								0.00 507

door-mid								91.06 470

lessismore_v0				90.45 422	91.96 442	79.09 461		87.19 484		80.32 456	94.39 348	66.31 440	97.55 384	84.00 386	76.84 448	94.70 407

LGP-MVS_train				94.10 287	96.16 281	88.26 291		97.46 203	91.29 215	90.12 303	97.16 191	79.05 319	98.73 235	92.25 205	91.89 311	95.31 358

test11								97.88 130

door								91.13 469

HQP5-MVS						89.33 249

HQP-NCC					95.86 297		96.65 260		93.55 109	90.14 297

ACMP_Plane					95.86 297		96.65 260		93.55 109	90.14 297

BP-MVS														92.13 211

HQP4-MVS										90.14 297			98.50 267			95.78 329

HQP3-MVS								97.39 220							92.10 308

HQP2-MVS												80.95 279

NP-MVS					95.99 295	89.81 223					95.87 270

MDTV_nov1_ep13_2view						70.35 479	93.10 442		83.88 418	93.55 214		82.47 251		86.25 351		98.38 195

MDTV_nov1_ep13			90.76 291		95.22 339	80.33 442	93.03 443	95.28 382	88.14 338	92.84 237	93.83 378	81.34 272	98.08 312	82.86 395	94.34 265

ACMMP++_ref															90.30 337

ACMMP++															91.02 326

Test By Simon												88.73 107

ITE_SJBPF				92.43 368	95.34 328	85.37 378		95.92 346	91.47 208	87.75 374	96.39 245	71.00 400	97.96 335	82.36 404	89.86 340	93.97 428

DeepMVS_CX				74.68 471	90.84 449	64.34 489		81.61 494	65.34 484	67.47 482	88.01 463	48.60 481	80.13 493	62.33 481	73.68 463	79.58 483

High-resolution multi-view benchmark