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++		95.98 1	96.36 1	94.82 33	97.78 59	86.00 53	98.29 1	97.49 11	90.75 28	97.62 8	98.06 21	92.59 2	99.61 6	95.64 31	99.02 12	98.86 15

SED-MVS		95.91 2	96.28 2	94.80 36	98.77 7	85.99 55	97.13 18	97.44 20	90.31 40	97.71 2	98.07 19	92.31 5	99.58 13	95.66 29	99.13 3	98.84 18

TestfortrainingZip a		95.70 3	95.76 4	95.51 8	98.88 1	87.98 10	97.32 10	97.86 1	88.11 127	97.21 14	97.54 43	92.42 4	99.67 1	93.66 58	98.85 20	98.89 14

DVP-MVS		95.67 4	96.02 3	94.64 42	98.78 5	85.93 58	97.09 20	96.73 96	90.27 44	97.04 21	98.05 24	91.47 9	99.55 19	95.62 33	99.08 7	98.45 40
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		95.57 5	95.67 5	95.25 12	98.36 30	87.28 19	95.56 116	97.51 10	89.13 86	97.14 17	97.91 31	91.64 8	99.62 4	94.61 48	99.17 2	98.86 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		95.46 6	95.64 6	94.91 22	98.26 33	86.29 47	97.46 7	97.40 25	89.03 91	96.20 33	98.10 13	89.39 17	99.34 41	95.88 28	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSP-MVS		95.42 7	95.56 7	94.98 20	98.49 19	86.52 37	96.91 29	97.47 16	91.73 13	96.10 34	96.69 85	89.90 13	99.30 47	94.70 46	98.04 78	99.13 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CNVR-MVS		95.40 8	95.37 10	95.50 9	98.11 41	88.51 7	95.29 128	96.96 67	92.09 9	95.32 47	97.08 68	89.49 16	99.33 44	95.10 42	98.85 20	98.66 25

SMA-MVS		95.20 9	95.07 18	95.59 6	98.14 40	88.48 8	96.26 52	97.28 39	85.90 195	97.67 4	98.10 13	88.41 22	99.56 15	94.66 47	99.19 1	98.71 24
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
SteuartSystems-ACMMP		95.20 9	95.32 12	94.85 26	96.99 80	86.33 43	97.33 8	97.30 36	91.38 18	95.39 46	97.46 48	88.98 21	99.40 33	94.12 52	98.89 18	98.82 20
Skip Steuart: Steuart Systems R&D Blog.
ME-MVS		95.17 11	95.29 13	94.81 34	98.39 27	85.89 64	95.91 86	97.55 8	89.01 93	95.86 40	97.54 43	89.24 18	99.59 10	95.27 39	98.85 20	98.95 11

HPM-MVS++		95.14 12	94.91 24	95.83 4	98.25 34	89.65 4	95.92 85	96.96 67	91.75 12	94.02 69	96.83 80	88.12 26	99.55 19	93.41 64	98.94 16	98.28 60

lecture		95.10 13	95.46 9	94.01 64	98.40 25	84.36 105	97.70 3	97.78 3	91.19 19	96.22 32	98.08 18	86.64 42	99.37 36	94.91 44	98.26 62	98.29 59

MM		95.10 13	94.91 24	95.68 5	96.09 114	88.34 9	96.68 37	94.37 285	95.08 1	94.68 55	97.72 38	82.94 98	99.64 3	97.85 5	98.76 32	99.06 7

fmvsm_s_conf0.5_n_9		94.99 15	95.50 8	93.44 84	96.51 98	82.25 182	95.76 99	96.92 72	93.37 3	97.63 7	98.43 1	84.82 74	99.16 58	98.15 1	97.92 83	98.90 13

SF-MVS		94.97 16	94.90 26	95.20 13	97.84 55	87.76 11	96.65 38	97.48 15	87.76 142	95.71 42	97.70 39	88.28 25	99.35 40	93.89 56	98.78 29	98.48 34

SD-MVS		94.96 17	95.33 11	93.88 69	97.25 77	86.69 29	96.19 55	97.11 57	90.42 36	96.95 23	97.27 56	89.53 15	96.91 302	94.38 50	98.85 20	98.03 88
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
TSAR-MVS + MP.		94.85 18	94.94 22	94.58 45	98.25 34	86.33 43	96.11 65	96.62 105	88.14 124	96.10 34	96.96 74	89.09 20	98.94 90	94.48 49	98.68 40	98.48 34
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
reproduce-ours		94.82 19	94.97 20	94.38 53	97.91 52	85.46 73	95.86 89	97.15 50	89.82 56	95.23 50	98.10 13	87.09 39	99.37 36	95.30 37	98.25 66	98.30 54

our_new_method		94.82 19	94.97 20	94.38 53	97.91 52	85.46 73	95.86 89	97.15 50	89.82 56	95.23 50	98.10 13	87.09 39	99.37 36	95.30 37	98.25 66	98.30 54

NCCC		94.81 21	94.69 30	95.17 15	97.83 56	87.46 18	95.66 107	96.93 71	92.34 7	93.94 70	96.58 95	87.74 29	99.44 32	92.83 73	98.40 57	98.62 26

fmvsm_l_conf0.5_n_3		94.80 22	95.01 19	94.15 62	95.64 140	85.08 80	96.09 66	97.36 27	90.98 23	97.09 19	98.12 9	84.98 71	98.94 90	97.07 16	97.80 90	98.43 42

reproduce_model		94.76 23	94.92 23	94.29 59	97.92 48	85.18 79	95.95 83	97.19 43	89.67 66	95.27 49	98.16 5	86.53 46	99.36 39	95.42 36	98.15 71	98.33 49

ACMMP_NAP		94.74 24	94.56 31	95.28 11	98.02 46	87.70 12	95.68 104	97.34 29	88.28 118	95.30 48	97.67 40	85.90 53	99.54 23	93.91 55	98.95 15	98.60 27

test_fmvsm_n_1920		94.71 25	95.11 17	93.50 83	95.79 130	84.62 90	96.15 60	97.64 5	89.85 55	97.19 16	97.89 32	86.28 49	98.71 119	97.11 15	98.08 77	97.17 150

fmvsm_l_conf0.5_n_9		94.65 26	95.28 14	92.77 122	95.95 126	81.83 192	95.53 117	97.12 54	91.68 15	97.89 1	98.06 21	85.71 55	98.65 123	97.32 12	98.26 62	97.83 107

test_fmvsmconf_n		94.60 27	94.81 28	93.98 65	94.62 201	84.96 83	96.15 60	97.35 28	89.37 75	96.03 37	98.11 10	86.36 47	99.01 73	97.45 10	97.83 88	97.96 92

fmvsm_s_conf0.5_n_8		94.56 28	95.12 16	92.87 116	95.96 125	81.32 207	95.76 99	97.57 7	93.48 2	97.53 10	98.32 2	81.78 124	99.13 60	97.91 2	97.81 89	98.16 73

HFP-MVS		94.52 29	94.40 36	94.86 25	98.61 12	86.81 26	96.94 24	97.34 29	88.63 106	93.65 75	97.21 60	86.10 51	99.49 29	92.35 87	98.77 31	98.30 54

fmvsm_s_conf0.5_n_3		94.49 30	95.13 15	92.56 139	95.49 148	81.10 217	95.93 84	97.16 49	92.96 4	97.39 12	98.13 6	83.63 86	98.80 108	97.89 3	97.61 97	97.78 111

ZNCC-MVS		94.47 31	94.28 43	95.03 17	98.52 17	86.96 21	96.85 32	97.32 33	88.24 119	93.15 85	97.04 71	86.17 50	99.62 4	92.40 84	98.81 26	98.52 30

XVS		94.45 32	94.32 39	94.85 26	98.54 15	86.60 35	96.93 26	97.19 43	90.66 33	92.85 93	97.16 66	85.02 67	99.49 29	91.99 103	98.56 53	98.47 37

MCST-MVS		94.45 32	94.20 49	95.19 14	98.46 21	87.50 17	95.00 151	97.12 54	87.13 161	92.51 110	96.30 102	89.24 18	99.34 41	93.46 61	98.62 49	98.73 22

fmvsm_s_conf0.5_n_10		94.43 34	94.84 27	93.20 93	95.73 133	83.19 142	95.99 77	97.31 35	91.08 20	97.67 4	98.11 10	81.87 121	99.22 51	97.86 4	97.91 85	97.20 148

region2R		94.43 34	94.27 45	94.92 21	98.65 10	86.67 31	96.92 28	97.23 42	88.60 109	93.58 77	97.27 56	85.22 62	99.54 23	92.21 92	98.74 34	98.56 29

ACMMPR		94.43 34	94.28 43	94.91 22	98.63 11	86.69 29	96.94 24	97.32 33	88.63 106	93.53 80	97.26 58	85.04 66	99.54 23	92.35 87	98.78 29	98.50 31

MTAPA		94.42 37	94.22 46	95.00 19	98.42 23	86.95 22	94.36 201	96.97 64	91.07 21	93.14 86	97.56 42	84.30 79	99.56 15	93.43 62	98.75 33	98.47 37

CP-MVS		94.34 38	94.21 48	94.74 40	98.39 27	86.64 33	97.60 5	97.24 40	88.53 111	92.73 101	97.23 59	85.20 63	99.32 45	92.15 95	98.83 25	98.25 67

fmvsm_l_conf0.5_n		94.29 39	94.46 34	93.79 75	95.28 155	85.43 75	95.68 104	96.43 118	86.56 179	96.84 25	97.81 36	87.56 34	98.77 112	97.14 14	96.82 116	97.16 156

MP-MVS		94.25 40	94.07 54	94.77 38	98.47 20	86.31 45	96.71 35	96.98 63	89.04 89	91.98 121	97.19 63	85.43 60	99.56 15	92.06 101	98.79 27	98.44 41
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 41	94.07 54	94.75 39	98.06 44	86.90 24	95.88 88	96.94 70	85.68 202	95.05 53	97.18 64	87.31 37	99.07 63	91.90 109	98.61 51	98.28 60
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 42	94.17 52	94.43 50	98.21 37	85.78 68	96.40 42	96.90 75	88.20 122	94.33 59	97.40 51	84.75 75	99.03 68	93.35 65	97.99 80	98.48 34

GST-MVS		94.21 43	93.97 58	94.90 24	98.41 24	86.82 25	96.54 40	97.19 43	88.24 119	93.26 82	96.83 80	85.48 59	99.59 10	91.43 118	98.40 57	98.30 54

MP-MVS-pluss		94.21 43	94.00 57	94.85 26	98.17 38	86.65 32	94.82 164	97.17 48	86.26 187	92.83 95	97.87 33	85.57 58	99.56 15	94.37 51	98.92 17	98.34 47
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_l_conf0.5_n_a		94.20 45	94.40 36	93.60 81	95.29 154	84.98 82	95.61 112	96.28 132	86.31 185	96.75 27	97.86 34	87.40 35	98.74 116	97.07 16	97.02 109	97.07 161

test_fmvsmconf0.1_n		94.20 45	94.31 41	93.88 69	92.46 314	84.80 86	96.18 57	96.82 84	89.29 80	95.68 43	98.11 10	85.10 64	98.99 80	97.38 11	97.75 94	97.86 102

DeepPCF-MVS	89.96 1	94.20 45	94.77 29	92.49 145	96.52 96	80.00 258	94.00 230	97.08 58	90.05 48	95.65 44	97.29 55	89.66 14	98.97 85	93.95 54	98.71 35	98.50 31

MGCNet		94.18 48	93.80 62	95.34 10	94.91 180	87.62 15	95.97 80	93.01 329	92.58 6	94.22 60	97.20 62	80.56 134	99.59 10	97.04 19	98.68 40	98.81 21

CS-MVS		94.12 49	94.44 35	93.17 97	96.55 93	83.08 151	97.63 4	96.95 69	91.71 14	93.50 81	96.21 105	85.61 56	98.24 166	93.64 59	98.17 69	98.19 70

fmvsm_s_conf0.5_n_6		94.11 50	94.56 31	92.76 124	94.98 173	81.96 190	95.79 95	97.29 38	89.31 78	97.52 11	97.61 41	83.25 92	98.88 96	97.05 18	98.22 68	97.43 133

DeepC-MVS_fast	89.43 2	94.04 51	93.79 63	94.80 36	97.48 69	86.78 27	95.65 109	96.89 76	89.40 74	92.81 96	96.97 73	85.37 61	99.24 50	90.87 127	98.69 38	98.38 46
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		94.02 52	94.29 42	93.24 91	96.69 86	83.24 139	97.49 6	96.92 72	92.14 8	92.90 91	95.77 140	85.02 67	98.33 161	93.03 70	98.62 49	98.13 76

HPM-MVS		94.02 52	93.88 59	94.43 50	98.39 27	85.78 68	97.25 14	97.07 59	86.90 171	92.62 107	96.80 84	84.85 73	99.17 55	92.43 82	98.65 47	98.33 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 54	93.78 64	94.63 43	98.50 18	85.90 63	96.87 30	96.91 74	88.70 104	91.83 130	97.17 65	83.96 83	99.55 19	91.44 117	98.64 48	98.43 42

balanced_conf03		93.98 55	94.22 46	93.26 90	96.13 108	83.29 138	96.27 51	96.52 113	89.82 56	95.56 45	95.51 150	84.50 77	98.79 110	94.83 45	98.86 19	97.72 115

fmvsm_s_conf0.5_n_5		93.96 56	94.18 51	93.30 87	94.79 187	83.81 120	95.77 97	96.74 95	88.02 129	96.23 31	97.84 35	83.36 91	98.83 106	97.49 8	97.34 103	97.25 143

PGM-MVS		93.96 56	93.72 68	94.68 41	98.43 22	86.22 48	95.30 126	97.78 3	87.45 152	93.26 82	97.33 54	84.62 76	99.51 27	90.75 129	98.57 52	98.32 53

PHI-MVS		93.89 58	93.65 72	94.62 44	96.84 83	86.43 40	96.69 36	97.49 11	85.15 226	93.56 79	96.28 103	85.60 57	99.31 46	92.45 81	98.79 27	98.12 79

fmvsm_s_conf0.5_n_4		93.86 59	94.37 38	92.33 158	95.13 166	80.95 224	95.64 110	96.97 64	89.60 68	96.85 24	97.77 37	83.08 96	98.92 93	97.49 8	96.78 117	97.13 157

SR-MVS-dyc-post		93.82 60	93.82 61	93.82 72	97.92 48	84.57 92	96.28 49	96.76 91	87.46 150	93.75 73	97.43 49	84.24 80	99.01 73	92.73 74	97.80 90	97.88 100

APD-MVS_3200maxsize		93.78 61	93.77 65	93.80 74	97.92 48	84.19 109	96.30 45	96.87 78	86.96 167	93.92 71	97.47 47	83.88 84	98.96 87	92.71 77	97.87 86	98.26 66

fmvsm_s_conf0.5_n		93.76 62	94.06 56	92.86 117	95.62 142	83.17 143	96.14 62	96.12 153	88.13 125	95.82 41	98.04 27	83.43 87	98.48 139	96.97 20	96.23 130	96.92 176

patch_mono-2		93.74 63	94.32 39	92.01 170	97.54 65	78.37 300	93.40 262	97.19 43	88.02 129	94.99 54	97.21 60	88.35 23	98.44 149	94.07 53	98.09 75	99.23 1

MSLP-MVS++		93.72 64	94.08 53	92.65 134	97.31 73	83.43 132	95.79 95	97.33 31	90.03 49	93.58 77	96.96 74	84.87 72	97.76 217	92.19 94	98.66 44	96.76 186

TSAR-MVS + GP.		93.66 65	93.41 76	94.41 52	96.59 90	86.78 27	94.40 193	93.93 303	89.77 63	94.21 61	95.59 147	87.35 36	98.61 131	92.72 76	96.15 133	97.83 107

fmvsm_s_conf0.5_n_a		93.57 66	93.76 66	93.00 108	95.02 168	83.67 124	96.19 55	96.10 155	87.27 156	95.98 38	98.05 24	83.07 97	98.45 147	96.68 22	95.51 144	96.88 179

CANet		93.54 67	93.20 81	94.55 46	95.65 139	85.73 70	94.94 154	96.69 101	91.89 11	90.69 154	95.88 128	81.99 119	99.54 23	93.14 68	97.95 82	98.39 44

dcpmvs_2		93.49 68	94.19 50	91.38 208	97.69 62	76.78 342	94.25 206	96.29 129	88.33 115	94.46 57	96.88 77	88.07 27	98.64 126	93.62 60	98.09 75	98.73 22

fmvsm_s_conf0.5_n_2		93.47 69	93.83 60	92.39 152	95.36 151	81.19 213	95.20 140	96.56 110	90.37 38	97.13 18	98.03 28	77.47 183	98.96 87	97.79 6	96.58 122	97.03 165

NormalMVS		93.46 70	93.16 82	94.37 55	98.40 25	86.20 49	96.30 45	96.27 133	91.65 16	92.68 103	96.13 112	77.97 174	98.84 103	90.75 129	98.26 62	98.07 81

fmvsm_s_conf0.1_n		93.46 70	93.66 71	92.85 118	93.75 262	83.13 145	96.02 75	95.74 189	87.68 145	95.89 39	98.17 4	82.78 101	98.46 143	96.71 21	96.17 132	96.98 170

MVS_111021_HR		93.45 72	93.31 77	93.84 71	96.99 80	84.84 84	93.24 275	97.24 40	88.76 101	91.60 136	95.85 132	86.07 52	98.66 121	91.91 107	98.16 70	98.03 88

MVSMamba_PlusPlus		93.44 73	93.54 74	93.14 99	96.58 92	83.05 152	96.06 71	96.50 115	84.42 247	94.09 65	95.56 149	85.01 70	98.69 120	94.96 43	98.66 44	97.67 118

test_fmvsmvis_n_1920		93.44 73	93.55 73	93.10 101	93.67 270	84.26 107	95.83 93	96.14 149	89.00 94	92.43 112	97.50 46	83.37 90	98.72 117	96.61 23	97.44 99	96.32 203

train_agg		93.44 73	93.08 83	94.52 47	97.53 66	86.49 38	94.07 221	96.78 88	81.86 311	92.77 98	96.20 106	87.63 31	99.12 61	92.14 96	98.69 38	97.94 93

EC-MVSNet		93.44 73	93.71 69	92.63 135	95.21 160	82.43 175	97.27 13	96.71 99	90.57 35	92.88 92	95.80 136	83.16 93	98.16 172	93.68 57	98.14 72	97.31 135

DELS-MVS		93.43 77	93.25 79	93.97 66	95.42 150	85.04 81	93.06 284	97.13 53	90.74 30	91.84 128	95.09 175	86.32 48	99.21 53	91.22 119	98.45 55	97.65 119
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
HPM-MVS_fast		93.40 78	93.22 80	93.94 68	98.36 30	84.83 85	97.15 17	96.80 87	85.77 199	92.47 111	97.13 67	82.38 105	99.07 63	90.51 134	98.40 57	97.92 97

DeepC-MVS	88.79 3	93.31 79	92.99 86	94.26 60	96.07 116	85.83 66	94.89 157	96.99 62	89.02 92	89.56 178	97.37 53	82.51 104	99.38 34	92.20 93	98.30 60	97.57 125
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
sasdasda		93.27 80	92.75 90	94.85 26	95.70 136	87.66 13	96.33 43	96.41 120	90.00 50	94.09 65	94.60 200	82.33 107	98.62 129	92.40 84	92.86 220	98.27 62

canonicalmvs		93.27 80	92.75 90	94.85 26	95.70 136	87.66 13	96.33 43	96.41 120	90.00 50	94.09 65	94.60 200	82.33 107	98.62 129	92.40 84	92.86 220	98.27 62

ACMMP		93.24 82	92.88 88	94.30 58	98.09 43	85.33 77	96.86 31	97.45 19	88.33 115	90.15 169	97.03 72	81.44 126	99.51 27	90.85 128	95.74 140	98.04 87
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
CSCG		93.23 83	93.05 84	93.76 76	98.04 45	84.07 111	96.22 54	97.37 26	84.15 250	90.05 170	95.66 144	87.77 28	99.15 59	89.91 139	98.27 61	98.07 81

fmvsm_s_conf0.1_n_a		93.19 84	93.26 78	92.97 110	92.49 312	83.62 127	96.02 75	95.72 193	86.78 173	96.04 36	98.19 3	82.30 109	98.43 151	96.38 24	95.42 150	96.86 180

test_fmvsmconf0.01_n		93.19 84	93.02 85	93.71 79	89.25 411	84.42 103	96.06 71	96.29 129	89.06 87	94.68 55	98.13 6	79.22 158	98.98 84	97.22 13	97.24 104	97.74 113

fmvsm_s_conf0.1_n_2		93.16 86	93.42 75	92.37 153	94.62 201	81.13 215	95.23 133	95.89 177	90.30 42	96.74 28	98.02 29	76.14 195	98.95 89	97.64 7	96.21 131	97.03 165

fmvsm_s_conf0.5_n_7		93.15 87	93.76 66	91.31 211	94.42 222	79.48 270	94.52 183	97.14 52	89.33 77	94.17 63	98.09 17	81.83 122	97.49 243	96.33 25	98.02 79	96.95 172

alignmvs		93.08 88	92.50 96	94.81 34	95.62 142	87.61 16	95.99 77	96.07 158	89.77 63	94.12 64	94.87 184	80.56 134	98.66 121	92.42 83	93.10 216	98.15 74

MGCFI-Net		93.03 89	92.63 93	94.23 61	95.62 142	85.92 60	96.08 67	96.33 127	89.86 54	93.89 72	94.66 197	82.11 114	98.50 137	92.33 89	92.82 223	98.27 62

EI-MVSNet-Vis-set		93.01 90	92.92 87	93.29 88	95.01 169	83.51 131	94.48 185	95.77 186	90.87 24	92.52 109	96.67 87	84.50 77	99.00 78	91.99 103	94.44 178	97.36 134

casdiffmvs_mvg		92.96 91	92.83 89	93.35 86	94.59 205	83.40 134	95.00 151	96.34 126	90.30 42	92.05 119	96.05 116	83.43 87	98.15 173	92.07 98	95.67 141	98.49 33
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		92.83 92	92.54 95	93.68 80	96.10 113	84.71 88	95.66 107	96.39 122	91.92 10	93.22 84	96.49 98	83.16 93	98.87 97	84.47 226	95.47 147	97.45 131

CDPH-MVS		92.83 92	92.30 99	94.44 48	97.79 57	86.11 52	94.06 223	96.66 102	80.09 342	92.77 98	96.63 92	86.62 43	99.04 67	87.40 179	98.66 44	98.17 72

SymmetryMVS		92.81 94	92.31 98	94.32 57	96.15 106	86.20 49	96.30 45	94.43 281	91.65 16	92.68 103	96.13 112	77.97 174	98.84 103	90.75 129	94.72 165	97.92 97

ETV-MVS		92.74 95	92.66 92	92.97 110	95.20 161	84.04 115	95.07 147	96.51 114	90.73 31	92.96 90	91.19 328	84.06 81	98.34 159	91.72 112	96.54 123	96.54 198

EI-MVSNet-UG-set		92.74 95	92.62 94	93.12 100	94.86 183	83.20 141	94.40 193	95.74 189	90.71 32	92.05 119	96.60 94	84.00 82	98.99 80	91.55 115	93.63 195	97.17 150

DPM-MVS		92.58 97	91.74 107	95.08 16	96.19 105	89.31 5	92.66 301	96.56 110	83.44 269	91.68 135	95.04 176	86.60 45	98.99 80	85.60 206	97.92 83	96.93 175

casdiffmvs		92.51 98	92.43 97	92.74 128	94.41 223	81.98 188	94.54 182	96.23 141	89.57 69	91.96 123	96.17 110	82.58 103	98.01 194	90.95 125	95.45 149	98.23 68
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BP-MVS1		92.48 99	92.07 102	93.72 78	94.50 214	84.39 104	95.90 87	94.30 288	90.39 37	92.67 105	95.94 124	74.46 227	98.65 123	93.14 68	97.35 102	98.13 76

MVS_111021_LR		92.47 100	92.29 100	92.98 109	95.99 122	84.43 101	93.08 281	96.09 156	88.20 122	91.12 149	95.72 143	81.33 128	97.76 217	91.74 111	97.37 101	96.75 187

3Dnovator+	87.14 4	92.42 101	91.37 116	95.55 7	95.63 141	88.73 6	97.07 22	96.77 90	90.84 25	84.02 321	96.62 93	75.95 204	99.34 41	87.77 172	97.68 95	98.59 28

baseline		92.39 102	92.29 100	92.69 132	94.46 218	81.77 194	94.14 212	96.27 133	89.22 82	91.88 126	96.00 119	82.35 106	97.99 196	91.05 121	95.27 155	98.30 54

VNet		92.24 103	91.91 104	93.24 91	96.59 90	83.43 132	94.84 163	96.44 117	89.19 84	94.08 68	95.90 126	77.85 180	98.17 171	88.90 156	93.38 205	98.13 76

GDP-MVS		92.04 104	91.46 113	93.75 77	94.55 211	84.69 89	95.60 115	96.56 110	87.83 139	93.07 89	95.89 127	73.44 248	98.65 123	90.22 137	96.03 135	97.91 99

CPTT-MVS		91.99 105	91.80 105	92.55 140	98.24 36	81.98 188	96.76 34	96.49 116	81.89 310	90.24 162	96.44 100	78.59 166	98.61 131	89.68 143	97.85 87	97.06 162

EIA-MVS		91.95 106	91.94 103	91.98 174	95.16 163	80.01 257	95.36 121	96.73 96	88.44 112	89.34 183	92.16 291	83.82 85	98.45 147	89.35 146	97.06 107	97.48 129

DP-MVS Recon		91.95 106	91.28 119	93.96 67	98.33 32	85.92 60	94.66 176	96.66 102	82.69 289	90.03 171	95.82 135	82.30 109	99.03 68	84.57 224	96.48 126	96.91 177

KinetiMVS		91.82 108	91.30 117	93.39 85	94.72 194	83.36 136	95.45 119	96.37 124	90.33 39	92.17 116	96.03 118	72.32 265	98.75 113	87.94 169	96.34 128	98.07 81

viewcassd2359sk11		91.79 109	91.62 109	92.29 163	94.62 201	80.88 227	93.70 251	96.18 147	87.38 154	91.13 148	95.85 132	81.62 125	98.06 188	89.71 141	94.40 179	97.94 93

EPNet		91.79 109	91.02 125	94.10 63	90.10 398	85.25 78	96.03 74	92.05 356	92.83 5	87.39 227	95.78 139	79.39 156	99.01 73	88.13 166	97.48 98	98.05 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
E3		91.78 111	91.61 110	92.30 161	94.48 215	80.86 229	93.73 247	96.19 146	87.63 148	91.16 146	95.95 123	81.30 129	98.06 188	89.76 140	94.29 182	97.99 90

viewmanbaseed2359cas		91.78 111	91.58 111	92.37 153	94.32 229	81.07 218	93.76 245	95.96 169	87.26 157	91.50 138	95.88 128	80.92 133	97.97 201	89.70 142	94.92 161	98.07 81

MG-MVS		91.77 113	91.70 108	92.00 173	97.08 79	80.03 256	93.60 255	95.18 237	87.85 138	90.89 152	96.47 99	82.06 117	98.36 156	85.07 212	97.04 108	97.62 120

Vis-MVSNet		91.75 114	91.23 120	93.29 88	95.32 153	83.78 121	96.14 62	95.98 165	89.89 52	90.45 158	96.58 95	75.09 216	98.31 164	84.75 218	96.90 112	97.78 111
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 115	90.82 131	94.44 48	94.59 205	86.37 42	97.18 16	97.02 61	89.20 83	84.31 316	96.66 88	73.74 244	99.17 55	86.74 189	97.96 81	97.79 110

EPP-MVSNet		91.70 116	91.56 112	92.13 169	95.88 127	80.50 240	97.33 8	95.25 233	86.15 190	89.76 176	95.60 146	83.42 89	98.32 163	87.37 181	93.25 209	97.56 126

MVSFormer		91.68 117	91.30 117	92.80 120	93.86 255	83.88 118	95.96 81	95.90 175	84.66 243	91.76 132	94.91 181	77.92 177	97.30 269	89.64 144	97.11 105	97.24 144

viewmacassd2359aftdt		91.67 118	91.43 115	92.37 153	93.95 253	81.00 221	93.90 240	95.97 168	87.75 143	91.45 141	96.04 117	79.92 143	97.97 201	89.26 149	94.67 167	98.14 75

Effi-MVS+		91.59 119	91.11 122	93.01 107	94.35 228	83.39 135	94.60 178	95.10 241	87.10 162	90.57 157	93.10 262	81.43 127	98.07 187	89.29 148	94.48 176	97.59 124

diffmvs_AUTHOR		91.51 120	91.44 114	91.73 193	93.09 289	80.27 244	92.51 306	95.58 205	87.22 158	91.80 131	95.57 148	79.96 142	97.48 244	92.23 91	94.97 159	97.45 131

IS-MVSNet		91.43 121	91.09 124	92.46 146	95.87 129	81.38 206	96.95 23	93.69 315	89.72 65	89.50 181	95.98 121	78.57 167	97.77 216	83.02 246	96.50 125	98.22 69

PVSNet_Blended_VisFu		91.38 122	90.91 128	92.80 120	96.39 100	83.17 143	94.87 159	96.66 102	83.29 274	89.27 185	94.46 209	80.29 137	99.17 55	87.57 176	95.37 151	96.05 222

diffmvs		91.37 123	91.23 120	91.77 192	93.09 289	80.27 244	92.36 311	95.52 211	87.03 164	91.40 143	94.93 180	80.08 140	97.44 252	92.13 97	94.56 173	97.61 121
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		91.31 124	91.11 122	91.93 179	94.37 224	80.14 249	93.46 260	95.80 184	86.46 182	91.35 144	93.77 240	82.21 112	98.09 184	87.57 176	94.95 160	97.55 127

OMC-MVS		91.23 125	90.62 136	93.08 103	96.27 103	84.07 111	93.52 257	95.93 171	86.95 168	89.51 179	96.13 112	78.50 168	98.35 158	85.84 204	92.90 219	96.83 185

PAPM_NR		91.22 126	90.78 132	92.52 143	97.60 64	81.46 203	94.37 199	96.24 140	86.39 184	87.41 224	94.80 189	82.06 117	98.48 139	82.80 252	95.37 151	97.61 121

viewdifsd2359ckpt13		91.20 127	90.75 133	92.54 141	94.30 230	82.13 184	94.03 225	95.89 177	85.60 205	90.20 164	95.36 157	79.69 151	97.90 211	87.85 171	93.86 190	97.61 121

viewdifsd2359ckpt09		91.18 128	90.65 135	92.75 126	94.61 204	82.36 180	94.32 202	95.74 189	84.72 240	89.66 177	95.15 173	79.69 151	98.04 191	87.70 173	94.27 183	97.85 105

PS-MVSNAJ		91.18 128	90.92 127	91.96 176	95.26 158	82.60 174	92.09 324	95.70 195	86.27 186	91.84 128	92.46 281	79.70 148	98.99 80	89.08 151	95.86 137	94.29 296

xiu_mvs_v2_base		91.13 130	90.89 129	91.86 185	94.97 174	82.42 176	92.24 317	95.64 202	86.11 194	91.74 134	93.14 260	79.67 153	98.89 95	89.06 152	95.46 148	94.28 297

guyue		91.12 131	90.84 130	91.96 176	94.59 205	80.57 238	94.87 159	93.71 314	88.96 95	91.14 147	95.22 165	73.22 252	97.76 217	92.01 102	93.81 193	97.54 128

viewdifsd2359ckpt07		91.11 132	91.02 125	91.41 206	94.21 235	78.37 300	92.91 292	95.71 194	87.50 149	90.32 161	95.88 128	80.27 138	97.99 196	88.78 159	93.55 197	97.86 102

nrg030		91.08 133	90.39 137	93.17 97	93.07 291	86.91 23	96.41 41	96.26 137	88.30 117	88.37 203	94.85 187	82.19 113	97.64 228	91.09 120	82.95 354	94.96 263

mamv4		90.92 134	91.78 106	88.33 334	95.67 138	70.75 419	92.92 291	96.02 164	81.90 307	88.11 206	95.34 160	85.88 54	96.97 297	95.22 41	95.01 158	97.26 142

lupinMVS		90.92 134	90.21 141	93.03 106	93.86 255	83.88 118	92.81 296	93.86 307	79.84 345	91.76 132	94.29 214	77.92 177	98.04 191	90.48 135	97.11 105	97.17 150

RRT-MVS		90.85 136	90.70 134	91.30 212	94.25 232	76.83 341	94.85 162	96.13 152	89.04 89	90.23 163	94.88 183	70.15 293	98.72 117	91.86 110	94.88 162	98.34 47

h-mvs33		90.80 137	90.15 144	92.75 126	96.01 118	82.66 168	95.43 120	95.53 210	89.80 59	93.08 87	95.64 145	75.77 205	99.00 78	92.07 98	78.05 411	96.60 193

jason		90.80 137	90.10 145	92.90 114	93.04 294	83.53 130	93.08 281	94.15 296	80.22 339	91.41 142	94.91 181	76.87 187	97.93 207	90.28 136	96.90 112	97.24 144
jason: jason.
VDD-MVS		90.74 139	89.92 153	93.20 93	96.27 103	83.02 154	95.73 101	93.86 307	88.42 114	92.53 108	96.84 79	62.09 371	98.64 126	90.95 125	92.62 230	97.93 96

SSM_0404		90.73 140	90.08 146	92.69 132	95.00 172	83.13 145	94.32 202	95.00 249	85.41 214	89.84 172	95.35 158	76.13 196	97.98 199	85.46 209	94.18 185	96.95 172

PVSNet_Blended		90.73 140	90.32 139	91.98 174	96.12 109	81.25 209	92.55 305	96.83 82	82.04 302	89.10 187	92.56 279	81.04 131	98.85 101	86.72 191	95.91 136	95.84 230

AstraMVS		90.69 142	90.30 140	91.84 188	93.81 258	79.85 263	94.76 169	92.39 344	88.96 95	91.01 151	95.87 131	70.69 282	97.94 206	92.49 80	92.70 224	97.73 114

test_yl		90.69 142	90.02 151	92.71 129	95.72 134	82.41 178	94.11 215	95.12 239	85.63 203	91.49 139	94.70 191	74.75 220	98.42 152	86.13 199	92.53 232	97.31 135

DCV-MVSNet		90.69 142	90.02 151	92.71 129	95.72 134	82.41 178	94.11 215	95.12 239	85.63 203	91.49 139	94.70 191	74.75 220	98.42 152	86.13 199	92.53 232	97.31 135

API-MVS		90.66 145	90.07 147	92.45 148	96.36 101	84.57 92	96.06 71	95.22 236	82.39 292	89.13 186	94.27 217	80.32 136	98.46 143	80.16 303	96.71 119	94.33 295

xiu_mvs_v1_base_debu		90.64 146	90.05 148	92.40 149	93.97 250	84.46 98	93.32 266	95.46 214	85.17 221	92.25 113	94.03 222	70.59 284	98.57 134	90.97 122	94.67 167	94.18 298

xiu_mvs_v1_base		90.64 146	90.05 148	92.40 149	93.97 250	84.46 98	93.32 266	95.46 214	85.17 221	92.25 113	94.03 222	70.59 284	98.57 134	90.97 122	94.67 167	94.18 298

xiu_mvs_v1_base_debi		90.64 146	90.05 148	92.40 149	93.97 250	84.46 98	93.32 266	95.46 214	85.17 221	92.25 113	94.03 222	70.59 284	98.57 134	90.97 122	94.67 167	94.18 298

HQP_MVS		90.60 149	90.19 142	91.82 189	94.70 197	82.73 164	95.85 91	96.22 142	90.81 26	86.91 233	94.86 185	74.23 231	98.12 174	88.15 164	89.99 265	94.63 276

LuminaMVS		90.55 150	89.81 155	92.77 122	92.78 307	84.21 108	94.09 219	94.17 295	85.82 196	91.54 137	94.14 221	69.93 294	97.92 208	91.62 114	94.21 184	96.18 211

FIs		90.51 151	90.35 138	90.99 229	93.99 249	80.98 222	95.73 101	97.54 9	89.15 85	86.72 240	94.68 193	81.83 122	97.24 277	85.18 211	88.31 298	94.76 274

SSM_0407		90.47 152	89.80 156	92.46 146	94.76 188	82.66 168	93.98 232	95.00 249	85.41 214	88.96 191	95.35 158	76.13 196	97.88 212	85.46 209	93.15 213	96.85 181

mvsmamba		90.33 153	89.69 159	92.25 167	95.17 162	81.64 196	95.27 131	93.36 320	84.88 233	89.51 179	94.27 217	69.29 309	97.42 254	89.34 147	96.12 134	97.68 117

MAR-MVS		90.30 154	89.37 169	93.07 105	96.61 89	84.48 97	95.68 104	95.67 197	82.36 294	87.85 214	92.85 267	76.63 193	98.80 108	80.01 304	96.68 120	95.91 225
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
FC-MVSNet-test		90.27 155	90.18 143	90.53 246	93.71 267	79.85 263	95.77 97	97.59 6	89.31 78	86.27 251	94.67 196	81.93 120	97.01 295	84.26 228	88.09 301	94.71 275

CANet_DTU		90.26 156	89.41 168	92.81 119	93.46 277	83.01 155	93.48 258	94.47 280	89.43 73	87.76 219	94.23 219	70.54 288	99.03 68	84.97 213	96.39 127	96.38 201

SDMVSNet		90.19 157	89.61 162	91.93 179	96.00 119	83.09 150	92.89 293	95.98 165	88.73 102	86.85 237	95.20 169	72.09 267	97.08 288	88.90 156	89.85 271	95.63 240

Elysia		90.12 158	89.10 176	93.18 95	93.16 284	84.05 113	95.22 135	96.27 133	85.16 224	90.59 155	94.68 193	64.64 354	98.37 154	86.38 195	95.77 138	97.12 158

StellarMVS		90.12 158	89.10 176	93.18 95	93.16 284	84.05 113	95.22 135	96.27 133	85.16 224	90.59 155	94.68 193	64.64 354	98.37 154	86.38 195	95.77 138	97.12 158

OPM-MVS		90.12 158	89.56 163	91.82 189	93.14 286	83.90 117	94.16 211	95.74 189	88.96 95	87.86 213	95.43 155	72.48 262	97.91 209	88.10 168	90.18 263	93.65 333
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 161	89.13 175	92.95 112	96.71 85	82.32 181	96.08 67	89.91 413	86.79 172	92.15 118	96.81 82	62.60 369	98.34 159	87.18 183	93.90 189	98.19 70

GeoE		90.05 162	89.43 167	91.90 184	95.16 163	80.37 243	95.80 94	94.65 273	83.90 255	87.55 223	94.75 190	78.18 173	97.62 230	81.28 283	93.63 195	97.71 116

viewmambaseed2359dif		90.04 163	89.78 157	90.83 235	92.85 304	77.92 312	92.23 318	95.01 245	81.90 307	90.20 164	95.45 152	79.64 155	97.34 267	87.52 178	93.17 211	97.23 147

PAPR		90.02 164	89.27 174	92.29 163	95.78 131	80.95 224	92.68 300	96.22 142	81.91 306	86.66 241	93.75 242	82.23 111	98.44 149	79.40 315	94.79 164	97.48 129

PVSNet_BlendedMVS		89.98 165	89.70 158	90.82 237	96.12 109	81.25 209	93.92 236	96.83 82	83.49 268	89.10 187	92.26 289	81.04 131	98.85 101	86.72 191	87.86 305	92.35 382

IMVS_0403		89.97 166	89.64 160	90.96 232	93.72 263	77.75 323	93.00 286	95.34 228	85.53 209	88.77 196	94.49 205	78.49 169	97.84 213	84.75 218	92.65 225	97.28 138

PS-MVSNAJss		89.97 166	89.62 161	91.02 226	91.90 332	80.85 230	95.26 132	95.98 165	86.26 187	86.21 253	94.29 214	79.70 148	97.65 226	88.87 158	88.10 299	94.57 281

XVG-OURS-SEG-HR		89.95 168	89.45 165	91.47 204	94.00 248	81.21 212	91.87 329	96.06 160	85.78 198	88.55 199	95.73 142	74.67 224	97.27 273	88.71 160	89.64 276	95.91 225

UGNet		89.95 168	88.95 184	92.95 112	94.51 213	83.31 137	95.70 103	95.23 234	89.37 75	87.58 221	93.94 230	64.00 359	98.78 111	83.92 233	96.31 129	96.74 188
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
UniMVSNet_NR-MVSNet		89.92 170	89.29 172	91.81 191	93.39 279	83.72 122	94.43 191	97.12 54	89.80 59	86.46 244	93.32 251	83.16 93	97.23 278	84.92 214	81.02 384	94.49 289

AdaColmap		89.89 171	89.07 178	92.37 153	97.41 70	83.03 153	94.42 192	95.92 172	82.81 286	86.34 250	94.65 198	73.89 240	99.02 71	80.69 294	95.51 144	95.05 258

hse-mvs2		89.88 172	89.34 170	91.51 201	94.83 185	81.12 216	93.94 234	93.91 306	89.80 59	93.08 87	93.60 245	75.77 205	97.66 225	92.07 98	77.07 418	95.74 235

IMVS_0407		89.85 173	89.51 164	90.88 234	93.72 263	77.75 323	93.07 283	95.34 228	85.53 209	88.34 204	94.49 205	77.69 181	97.60 231	84.75 218	92.65 225	97.28 138

UniMVSNet (Re)		89.80 174	89.07 178	92.01 170	93.60 273	84.52 95	94.78 167	97.47 16	89.26 81	86.44 247	92.32 286	82.10 115	97.39 265	84.81 217	80.84 388	94.12 302

HQP-MVS		89.80 174	89.28 173	91.34 210	94.17 237	81.56 197	94.39 195	96.04 161	88.81 98	85.43 279	93.97 229	73.83 242	97.96 203	87.11 186	89.77 274	94.50 287

FA-MVS(test-final)		89.66 176	88.91 186	91.93 179	94.57 209	80.27 244	91.36 341	94.74 269	84.87 234	89.82 173	92.61 278	74.72 223	98.47 142	83.97 232	93.53 199	97.04 164

VPA-MVSNet		89.62 177	88.96 183	91.60 198	93.86 255	82.89 159	95.46 118	97.33 31	87.91 133	88.43 202	93.31 252	74.17 234	97.40 262	87.32 182	82.86 359	94.52 284

WTY-MVS		89.60 178	88.92 185	91.67 196	95.47 149	81.15 214	92.38 310	94.78 267	83.11 278	89.06 189	94.32 212	78.67 165	96.61 318	81.57 279	90.89 252	97.24 144

Vis-MVSNet (Re-imp)		89.59 179	89.44 166	90.03 273	95.74 132	75.85 356	95.61 112	90.80 394	87.66 147	87.83 216	95.40 156	76.79 189	96.46 332	78.37 321	96.73 118	97.80 109

VDDNet		89.56 180	88.49 199	92.76 124	95.07 167	82.09 185	96.30 45	93.19 324	81.05 333	91.88 126	96.86 78	61.16 387	98.33 161	88.43 163	92.49 234	97.84 106

114514_t		89.51 181	88.50 197	92.54 141	98.11 41	81.99 187	95.16 143	96.36 125	70.19 440	85.81 261	95.25 164	76.70 191	98.63 128	82.07 267	96.86 115	97.00 169

QAPM		89.51 181	88.15 208	93.59 82	94.92 178	84.58 91	96.82 33	96.70 100	78.43 369	83.41 337	96.19 109	73.18 253	99.30 47	77.11 337	96.54 123	96.89 178

CLD-MVS		89.47 183	88.90 187	91.18 217	94.22 234	82.07 186	92.13 322	96.09 156	87.90 134	85.37 285	92.45 282	74.38 229	97.56 235	87.15 184	90.43 258	93.93 311
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 184	88.90 187	91.12 218	94.47 216	81.49 201	95.30 126	96.14 149	86.73 175	85.45 276	95.16 171	69.89 296	98.10 176	87.70 173	89.23 283	93.77 326

CDS-MVSNet		89.45 184	88.51 196	92.29 163	93.62 272	83.61 129	93.01 285	94.68 272	81.95 304	87.82 217	93.24 256	78.69 164	96.99 296	80.34 300	93.23 210	96.28 206
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewdifsd2359ckpt11		89.43 186	89.05 180	90.56 244	92.89 302	77.00 337	92.81 296	94.52 277	87.03 164	89.77 174	95.79 137	74.67 224	97.51 239	88.97 154	84.98 331	97.17 150

viewmsd2359difaftdt		89.43 186	89.05 180	90.56 244	92.89 302	77.00 337	92.81 296	94.52 277	87.03 164	89.77 174	95.79 137	74.67 224	97.51 239	88.97 154	84.98 331	97.17 150

Fast-Effi-MVS+		89.41 188	88.64 192	91.71 195	94.74 191	80.81 231	93.54 256	95.10 241	83.11 278	86.82 239	90.67 351	79.74 147	97.75 221	80.51 298	93.55 197	96.57 196

ab-mvs		89.41 188	88.35 201	92.60 136	95.15 165	82.65 172	92.20 320	95.60 204	83.97 254	88.55 199	93.70 244	74.16 235	98.21 170	82.46 257	89.37 279	96.94 174

XVG-OURS		89.40 190	88.70 191	91.52 200	94.06 242	81.46 203	91.27 345	96.07 158	86.14 191	88.89 194	95.77 140	68.73 318	97.26 275	87.39 180	89.96 267	95.83 231

test_vis1_n_1920		89.39 191	89.84 154	88.04 343	92.97 298	72.64 396	94.71 173	96.03 163	86.18 189	91.94 125	96.56 97	61.63 375	95.74 369	93.42 63	95.11 157	95.74 235

mvs_anonymous		89.37 192	89.32 171	89.51 302	93.47 276	74.22 374	91.65 336	94.83 263	82.91 284	85.45 276	93.79 238	81.23 130	96.36 339	86.47 193	94.09 186	97.94 93

DU-MVS		89.34 193	88.50 197	91.85 187	93.04 294	83.72 122	94.47 188	96.59 107	89.50 70	86.46 244	93.29 254	77.25 185	97.23 278	84.92 214	81.02 384	94.59 279

TAMVS		89.21 194	88.29 205	91.96 176	93.71 267	82.62 173	93.30 270	94.19 293	82.22 297	87.78 218	93.94 230	78.83 161	96.95 299	77.70 330	92.98 218	96.32 203

icg_test_0407_2		89.15 195	88.97 182	89.68 295	93.72 263	77.75 323	88.26 408	95.34 228	85.53 209	88.34 204	94.49 205	77.69 181	93.99 405	84.75 218	92.65 225	97.28 138

ACMM	84.12 9	89.14 196	88.48 200	91.12 218	94.65 200	81.22 211	95.31 124	96.12 153	85.31 218	85.92 259	94.34 210	70.19 292	98.06 188	85.65 205	88.86 288	94.08 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 197	88.64 192	90.48 252	95.53 147	74.97 365	96.08 67	84.89 446	88.13 125	90.16 168	96.65 89	63.29 364	98.10 176	86.14 197	96.90 112	98.39 44

EI-MVSNet		89.10 197	88.86 189	89.80 287	91.84 334	78.30 303	93.70 251	95.01 245	85.73 200	87.15 228	95.28 162	79.87 145	97.21 280	83.81 235	87.36 313	93.88 315

ECVR-MVS		89.09 199	88.53 195	90.77 239	95.62 142	75.89 355	96.16 58	84.22 448	87.89 136	90.20 164	96.65 89	63.19 366	98.10 176	85.90 202	96.94 110	98.33 49

CNLPA		89.07 200	87.98 212	92.34 157	96.87 82	84.78 87	94.08 220	93.24 321	81.41 324	84.46 306	95.13 174	75.57 212	96.62 315	77.21 335	93.84 192	95.61 242

mamba_0408		89.06 201	87.92 215	92.50 144	94.76 188	82.66 168	79.84 460	94.64 274	85.18 219	88.96 191	95.00 177	76.00 201	97.98 199	83.74 237	93.15 213	96.85 181

PLC	84.53 7	89.06 201	88.03 210	92.15 168	97.27 76	82.69 167	94.29 204	95.44 219	79.71 347	84.01 322	94.18 220	76.68 192	98.75 113	77.28 334	93.41 204	95.02 259
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 203	88.64 192	90.21 263	90.74 383	79.28 280	95.96 81	95.90 175	84.66 243	85.33 287	92.94 266	74.02 237	97.30 269	89.64 144	88.53 291	94.05 308

HY-MVS	83.01 12	89.03 203	87.94 214	92.29 163	94.86 183	82.77 160	92.08 325	94.49 279	81.52 323	86.93 231	92.79 273	78.32 172	98.23 167	79.93 305	90.55 256	95.88 228

ACMP	84.23 8	89.01 205	88.35 201	90.99 229	94.73 192	81.27 208	95.07 147	95.89 177	86.48 180	83.67 330	94.30 213	69.33 305	97.99 196	87.10 188	88.55 290	93.72 331
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 206	88.26 207	90.94 233	94.05 243	80.78 232	91.71 333	95.38 223	81.55 322	88.63 198	93.91 234	75.04 217	95.47 381	82.47 256	91.61 240	96.57 196

TranMVSNet+NR-MVSNet		88.84 207	87.95 213	91.49 202	92.68 310	83.01 155	94.92 156	96.31 128	89.88 53	85.53 270	93.85 237	76.63 193	96.96 298	81.91 271	79.87 401	94.50 287

CHOSEN 1792x2688		88.84 207	87.69 220	92.30 161	96.14 107	81.42 205	90.01 378	95.86 181	74.52 409	87.41 224	93.94 230	75.46 213	98.36 156	80.36 299	95.53 143	97.12 158

MVSTER		88.84 207	88.29 205	90.51 249	92.95 299	80.44 241	93.73 247	95.01 245	84.66 243	87.15 228	93.12 261	72.79 257	97.21 280	87.86 170	87.36 313	93.87 316

test_cas_vis1_n_1920		88.83 210	88.85 190	88.78 318	91.15 362	76.72 343	93.85 241	94.93 255	83.23 277	92.81 96	96.00 119	61.17 386	94.45 395	91.67 113	94.84 163	95.17 254

OpenMVS	83.78 11	88.74 211	87.29 230	93.08 103	92.70 309	85.39 76	96.57 39	96.43 118	78.74 364	80.85 369	96.07 115	69.64 300	99.01 73	78.01 328	96.65 121	94.83 271

thisisatest0530		88.67 212	87.61 222	91.86 185	94.87 182	80.07 252	94.63 177	89.90 414	84.00 253	88.46 201	93.78 239	66.88 333	98.46 143	83.30 242	92.65 225	97.06 162

Effi-MVS+-dtu		88.65 213	88.35 201	89.54 299	93.33 280	76.39 349	94.47 188	94.36 286	87.70 144	85.43 279	89.56 381	73.45 247	97.26 275	85.57 207	91.28 244	94.97 260

tttt0517		88.61 214	87.78 219	91.11 221	94.96 175	77.81 318	95.35 122	89.69 417	85.09 228	88.05 211	94.59 202	66.93 331	98.48 139	83.27 243	92.13 237	97.03 165

BH-untuned		88.60 215	88.13 209	90.01 276	95.24 159	78.50 296	93.29 271	94.15 296	84.75 239	84.46 306	93.40 248	75.76 207	97.40 262	77.59 331	94.52 175	94.12 302

sd_testset		88.59 216	87.85 218	90.83 235	96.00 119	80.42 242	92.35 312	94.71 270	88.73 102	86.85 237	95.20 169	67.31 325	96.43 334	79.64 309	89.85 271	95.63 240

NR-MVSNet		88.58 217	87.47 226	91.93 179	93.04 294	84.16 110	94.77 168	96.25 139	89.05 88	80.04 383	93.29 254	79.02 160	97.05 293	81.71 278	80.05 398	94.59 279

SSM_04072		88.57 218	87.92 215	90.51 249	94.76 188	82.66 168	79.84 460	94.64 274	85.18 219	88.96 191	95.00 177	76.00 201	92.03 429	83.74 237	93.15 213	96.85 181

VortexMVS		88.42 219	88.01 211	89.63 296	93.89 254	78.82 286	93.82 242	95.47 213	86.67 177	84.53 304	91.99 303	72.62 260	96.65 313	89.02 153	84.09 340	93.41 343

1112_ss		88.42 219	87.33 229	91.72 194	94.92 178	80.98 222	92.97 289	94.54 276	78.16 375	83.82 325	93.88 235	78.78 163	97.91 209	79.45 311	89.41 278	96.26 207

WR-MVS		88.38 221	87.67 221	90.52 248	93.30 281	80.18 247	93.26 273	95.96 169	88.57 110	85.47 275	92.81 271	76.12 198	96.91 302	81.24 284	82.29 364	94.47 292

BH-RMVSNet		88.37 222	87.48 225	91.02 226	95.28 155	79.45 272	92.89 293	93.07 327	85.45 213	86.91 233	94.84 188	70.35 289	97.76 217	73.97 368	94.59 172	95.85 229

IterMVS-LS		88.36 223	87.91 217	89.70 291	93.80 259	78.29 304	93.73 247	95.08 243	85.73 200	84.75 297	91.90 307	79.88 144	96.92 301	83.83 234	82.51 360	93.89 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 224	86.13 273	94.85 26	98.54 15	86.60 35	96.93 26	97.19 43	90.66 33	92.85 93	23.41 473	85.02 67	99.49 29	91.99 103	98.56 53	98.47 37

LCM-MVSNet-Re		88.30 225	88.32 204	88.27 336	94.71 196	72.41 401	93.15 276	90.98 387	87.77 141	79.25 393	91.96 304	78.35 171	95.75 368	83.04 245	95.62 142	96.65 192

jajsoiax		88.24 226	87.50 224	90.48 252	90.89 376	80.14 249	95.31 124	95.65 201	84.97 231	84.24 317	94.02 225	65.31 350	97.42 254	88.56 161	88.52 292	93.89 312

VPNet		88.20 227	87.47 226	90.39 257	93.56 274	79.46 271	94.04 224	95.54 209	88.67 105	86.96 230	94.58 203	69.33 305	97.15 282	84.05 231	80.53 393	94.56 282

TAPA-MVS	84.62 6	88.16 228	87.01 238	91.62 197	96.64 88	80.65 234	94.39 195	96.21 145	76.38 389	86.19 254	95.44 153	79.75 146	98.08 186	62.75 435	95.29 153	96.13 214
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 229	87.28 231	90.57 242	94.96 175	80.07 252	94.27 205	91.29 380	86.74 174	87.41 224	94.00 227	76.77 190	96.20 345	80.77 292	79.31 407	95.44 244

Anonymous20240529		88.09 230	86.59 255	92.58 138	96.53 95	81.92 191	95.99 77	95.84 182	74.11 413	89.06 189	95.21 168	61.44 379	98.81 107	83.67 240	87.47 310	97.01 168

HyFIR lowres test		88.09 230	86.81 243	91.93 179	96.00 119	80.63 235	90.01 378	95.79 185	73.42 420	87.68 220	92.10 297	73.86 241	97.96 203	80.75 293	91.70 239	97.19 149

mvs_tets		88.06 232	87.28 231	90.38 259	90.94 372	79.88 261	95.22 135	95.66 199	85.10 227	84.21 318	93.94 230	63.53 362	97.40 262	88.50 162	88.40 296	93.87 316

F-COLMAP		87.95 233	86.80 244	91.40 207	96.35 102	80.88 227	94.73 171	95.45 217	79.65 348	82.04 356	94.61 199	71.13 274	98.50 137	76.24 347	91.05 250	94.80 273

LS3D		87.89 234	86.32 266	92.59 137	96.07 116	82.92 158	95.23 133	94.92 256	75.66 396	82.89 344	95.98 121	72.48 262	99.21 53	68.43 405	95.23 156	95.64 239

anonymousdsp		87.84 235	87.09 234	90.12 268	89.13 412	80.54 239	94.67 175	95.55 207	82.05 300	83.82 325	92.12 294	71.47 272	97.15 282	87.15 184	87.80 308	92.67 370

v2v482		87.84 235	87.06 235	90.17 264	90.99 368	79.23 283	94.00 230	95.13 238	84.87 234	85.53 270	92.07 300	74.45 228	97.45 249	84.71 223	81.75 372	93.85 319

WR-MVS_H		87.80 237	87.37 228	89.10 311	93.23 282	78.12 307	95.61 112	97.30 36	87.90 134	83.72 328	92.01 302	79.65 154	96.01 354	76.36 344	80.54 392	93.16 354

AUN-MVS		87.78 238	86.54 258	91.48 203	94.82 186	81.05 219	93.91 238	93.93 303	83.00 281	86.93 231	93.53 246	69.50 303	97.67 223	86.14 197	77.12 417	95.73 237

PCF-MVS	84.11 10	87.74 239	86.08 277	92.70 131	94.02 244	84.43 101	89.27 391	95.87 180	73.62 418	84.43 308	94.33 211	78.48 170	98.86 99	70.27 391	94.45 177	94.81 272
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 240	86.13 273	92.31 160	96.66 87	80.74 233	94.87 159	91.49 375	80.47 338	89.46 182	95.44 153	54.72 424	98.23 167	82.19 263	89.89 269	97.97 91

V42		87.68 240	86.86 240	90.15 266	90.58 388	80.14 249	94.24 208	95.28 232	83.66 262	85.67 265	91.33 323	74.73 222	97.41 260	84.43 227	81.83 370	92.89 364

thres600view7		87.65 242	86.67 250	90.59 241	96.08 115	78.72 287	94.88 158	91.58 371	87.06 163	88.08 209	92.30 287	68.91 315	98.10 176	70.05 398	91.10 245	94.96 263

XXY-MVS		87.65 242	86.85 241	90.03 273	92.14 322	80.60 237	93.76 245	95.23 234	82.94 283	84.60 300	94.02 225	74.27 230	95.49 380	81.04 286	83.68 346	94.01 310

Test_1112_low_res		87.65 242	86.51 259	91.08 222	94.94 177	79.28 280	91.77 331	94.30 288	76.04 394	83.51 335	92.37 284	77.86 179	97.73 222	78.69 320	89.13 285	96.22 208

thres100view900		87.63 245	86.71 247	90.38 259	96.12 109	78.55 293	95.03 150	91.58 371	87.15 160	88.06 210	92.29 288	68.91 315	98.10 176	70.13 395	91.10 245	94.48 290

CP-MVSNet		87.63 245	87.26 233	88.74 322	93.12 287	76.59 346	95.29 128	96.58 108	88.43 113	83.49 336	92.98 265	75.28 214	95.83 363	78.97 317	81.15 380	93.79 321

thres400		87.62 247	86.64 251	90.57 242	95.99 122	78.64 290	94.58 179	91.98 360	86.94 169	88.09 207	91.77 309	69.18 311	98.10 176	70.13 395	91.10 245	94.96 263

v1144		87.61 248	86.79 245	90.06 272	91.01 367	79.34 276	93.95 233	95.42 222	83.36 273	85.66 266	91.31 326	74.98 218	97.42 254	83.37 241	82.06 366	93.42 342

IMVS_0404		87.60 249	86.84 242	89.89 280	93.72 263	77.75 323	88.56 403	95.34 228	85.53 209	79.98 384	94.49 205	66.54 341	94.64 394	84.75 218	92.65 225	97.28 138

tfpn200view9		87.58 250	86.64 251	90.41 256	95.99 122	78.64 290	94.58 179	91.98 360	86.94 169	88.09 207	91.77 309	69.18 311	98.10 176	70.13 395	91.10 245	94.48 290

BH-w/o		87.57 251	87.05 236	89.12 310	94.90 181	77.90 314	92.41 308	93.51 317	82.89 285	83.70 329	91.34 322	75.75 208	97.07 290	75.49 352	93.49 201	92.39 380

UniMVSNet_ETH3D		87.53 252	86.37 263	91.00 228	92.44 315	78.96 285	94.74 170	95.61 203	84.07 252	85.36 286	94.52 204	59.78 395	97.34 267	82.93 247	87.88 304	96.71 189

ET-MVSNet_ETH3D		87.51 253	85.91 285	92.32 159	93.70 269	83.93 116	92.33 314	90.94 390	84.16 249	72.09 438	92.52 280	69.90 295	95.85 362	89.20 150	88.36 297	97.17 150

1314		87.51 253	86.57 256	90.34 261	92.42 316	79.74 266	92.63 302	95.35 227	78.35 370	80.14 380	91.62 317	74.05 236	97.15 282	81.05 285	93.53 199	94.12 302

v8		87.50 255	86.71 247	89.89 280	91.37 352	79.40 273	94.50 184	95.38 223	84.81 237	83.60 333	91.33 323	76.05 199	97.42 254	82.84 250	80.51 395	92.84 366

Fast-Effi-MVS+-dtu		87.44 256	86.72 246	89.63 296	92.04 326	77.68 328	94.03 225	93.94 302	85.81 197	82.42 349	91.32 325	70.33 290	97.06 291	80.33 301	90.23 262	94.14 301

MVS		87.44 256	86.10 276	91.44 205	92.61 311	83.62 127	92.63 302	95.66 199	67.26 446	81.47 361	92.15 292	77.95 176	98.22 169	79.71 307	95.48 146	92.47 376

FE-MVS		87.40 258	86.02 279	91.57 199	94.56 210	79.69 267	90.27 365	93.72 313	80.57 336	88.80 195	91.62 317	65.32 349	98.59 133	74.97 360	94.33 181	96.44 199

FMVSNet3		87.40 258	86.11 275	91.30 212	93.79 261	83.64 126	94.20 210	94.81 265	83.89 256	84.37 309	91.87 308	68.45 321	96.56 323	78.23 325	85.36 327	93.70 332

test_fmvs1		87.34 260	87.56 223	86.68 382	90.59 387	71.80 405	94.01 228	94.04 301	78.30 371	91.97 122	95.22 165	56.28 414	93.71 411	92.89 72	94.71 166	94.52 284

thisisatest0515		87.33 261	85.99 280	91.37 209	93.49 275	79.55 268	90.63 359	89.56 422	80.17 340	87.56 222	90.86 341	67.07 330	98.28 165	81.50 280	93.02 217	96.29 205

PS-CasMVS		87.32 262	86.88 239	88.63 325	92.99 297	76.33 351	95.33 123	96.61 106	88.22 121	83.30 341	93.07 263	73.03 255	95.79 367	78.36 322	81.00 386	93.75 328

GBi-Net		87.26 263	85.98 281	91.08 222	94.01 245	83.10 147	95.14 144	94.94 251	83.57 264	84.37 309	91.64 313	66.59 338	96.34 340	78.23 325	85.36 327	93.79 321

test1		87.26 263	85.98 281	91.08 222	94.01 245	83.10 147	95.14 144	94.94 251	83.57 264	84.37 309	91.64 313	66.59 338	96.34 340	78.23 325	85.36 327	93.79 321

v1192		87.25 265	86.33 265	90.00 277	90.76 382	79.04 284	93.80 243	95.48 212	82.57 290	85.48 274	91.18 330	73.38 251	97.42 254	82.30 260	82.06 366	93.53 336

v10		87.25 265	86.38 262	89.85 282	91.19 358	79.50 269	94.48 185	95.45 217	83.79 260	83.62 332	91.19 328	75.13 215	97.42 254	81.94 270	80.60 390	92.63 372

DP-MVS		87.25 265	85.36 302	92.90 114	97.65 63	83.24 139	94.81 165	92.00 358	74.99 404	81.92 358	95.00 177	72.66 258	99.05 65	66.92 417	92.33 235	96.40 200

miper_ehance_all_eth		87.22 268	86.62 254	89.02 314	92.13 323	77.40 332	90.91 354	94.81 265	81.28 327	84.32 314	90.08 367	79.26 157	96.62 315	83.81 235	82.94 355	93.04 359

test2506		87.21 269	86.28 268	90.02 275	95.62 142	73.64 381	96.25 53	71.38 471	87.89 136	90.45 158	96.65 89	55.29 421	98.09 184	86.03 201	96.94 110	98.33 49

thres200		87.21 269	86.24 270	90.12 268	95.36 151	78.53 294	93.26 273	92.10 354	86.42 183	88.00 212	91.11 334	69.24 310	98.00 195	69.58 399	91.04 251	93.83 320

v144192		87.19 271	86.35 264	89.74 288	90.64 386	78.24 305	93.92 236	95.43 220	81.93 305	85.51 272	91.05 337	74.21 233	97.45 249	82.86 249	81.56 374	93.53 336

FMVSNet2		87.19 271	85.82 288	91.30 212	94.01 245	83.67 124	94.79 166	94.94 251	83.57 264	83.88 324	92.05 301	66.59 338	96.51 327	77.56 332	85.01 330	93.73 330

c3_l		87.14 273	86.50 260	89.04 313	92.20 320	77.26 333	91.22 348	94.70 271	82.01 303	84.34 313	90.43 356	78.81 162	96.61 318	83.70 239	81.09 381	93.25 348

testing91		87.11 274	86.18 271	89.92 279	94.43 221	75.38 364	91.53 338	92.27 350	86.48 180	86.50 242	90.24 359	61.19 385	97.53 237	82.10 265	90.88 253	96.84 184

Baseline_NR-MVSNet		87.07 275	86.63 253	88.40 329	91.44 347	77.87 316	94.23 209	92.57 341	84.12 251	85.74 264	92.08 298	77.25 185	96.04 350	82.29 261	79.94 399	91.30 405

v148		87.04 276	86.32 266	89.21 307	90.94 372	77.26 333	93.71 250	94.43 281	84.84 236	84.36 312	90.80 345	76.04 200	97.05 293	82.12 264	79.60 404	93.31 345

test_fmvs1_n		87.03 277	87.04 237	86.97 373	89.74 406	71.86 403	94.55 181	94.43 281	78.47 367	91.95 124	95.50 151	51.16 435	93.81 409	93.02 71	94.56 173	95.26 251

v1921920		86.97 278	86.06 278	89.69 292	90.53 391	78.11 308	93.80 243	95.43 220	81.90 307	85.33 287	91.05 337	72.66 258	97.41 260	82.05 268	81.80 371	93.53 336

tt0805		86.92 279	85.74 294	90.48 252	92.22 319	79.98 259	95.63 111	94.88 259	83.83 258	84.74 298	92.80 272	57.61 409	97.67 223	85.48 208	84.42 336	93.79 321

miper_enhance_ethall		86.90 280	86.18 271	89.06 312	91.66 343	77.58 330	90.22 371	94.82 264	79.16 354	84.48 305	89.10 386	79.19 159	96.66 312	84.06 230	82.94 355	92.94 362

MonoMVSNet		86.89 281	86.55 257	87.92 347	89.46 410	73.75 378	94.12 213	93.10 325	87.82 140	85.10 290	90.76 347	69.59 301	94.94 392	86.47 193	82.50 361	95.07 257

v7n		86.81 282	85.76 292	89.95 278	90.72 384	79.25 282	95.07 147	95.92 172	84.45 246	82.29 350	90.86 341	72.60 261	97.53 237	79.42 314	80.52 394	93.08 358

PEN-MVS		86.80 283	86.27 269	88.40 329	92.32 318	75.71 359	95.18 141	96.38 123	87.97 131	82.82 345	93.15 259	73.39 250	95.92 358	76.15 348	79.03 409	93.59 334

cl22		86.78 284	85.98 281	89.18 309	92.34 317	77.62 329	90.84 355	94.13 298	81.33 326	83.97 323	90.15 364	73.96 238	96.60 320	84.19 229	82.94 355	93.33 344

v1240		86.78 284	85.85 287	89.56 298	90.45 393	77.79 320	93.61 254	95.37 225	81.65 317	85.43 279	91.15 332	71.50 271	97.43 253	81.47 281	82.05 368	93.47 340

TR-MVS		86.78 284	85.76 292	89.82 284	94.37 224	78.41 298	92.47 307	92.83 333	81.11 332	86.36 248	92.40 283	68.73 318	97.48 244	73.75 372	89.85 271	93.57 335

PatchMatch-RL		86.77 287	85.54 296	90.47 255	95.88 127	82.71 166	90.54 362	92.31 348	79.82 346	84.32 314	91.57 321	68.77 317	96.39 336	73.16 374	93.48 203	92.32 383

testing3-2		86.72 288	86.71 247	86.74 381	96.11 112	65.92 441	93.39 263	89.65 420	89.46 71	87.84 215	92.79 273	59.17 401	97.60 231	81.31 282	90.72 254	96.70 190

testing99		86.72 288	85.73 295	89.69 292	94.23 233	74.91 367	91.35 342	90.97 388	86.14 191	86.36 248	90.22 360	59.41 398	97.48 244	82.24 262	90.66 255	96.69 191

PAPM		86.68 290	85.39 300	90.53 246	93.05 293	79.33 279	89.79 381	94.77 268	78.82 361	81.95 357	93.24 256	76.81 188	97.30 269	66.94 415	93.16 212	94.95 267

pm-mvs1		86.61 291	85.54 296	89.82 284	91.44 347	80.18 247	95.28 130	94.85 261	83.84 257	81.66 359	92.62 277	72.45 264	96.48 329	79.67 308	78.06 410	92.82 367

GA-MVS		86.61 291	85.27 305	90.66 240	91.33 355	78.71 289	90.40 364	93.81 310	85.34 217	85.12 289	89.57 380	61.25 382	97.11 287	80.99 289	89.59 277	96.15 212

Anonymous20231211		86.59 293	85.13 308	90.98 231	96.52 96	81.50 199	96.14 62	96.16 148	73.78 416	83.65 331	92.15 292	63.26 365	97.37 266	82.82 251	81.74 373	94.06 307

test_vis1_n		86.56 294	86.49 261	86.78 380	88.51 417	72.69 393	94.68 174	93.78 312	79.55 349	90.70 153	95.31 161	48.75 441	93.28 417	93.15 67	93.99 187	94.38 294

DIV-MVS_self_test		86.53 295	85.78 289	88.75 320	92.02 328	76.45 348	90.74 356	94.30 288	81.83 313	83.34 339	90.82 344	75.75 208	96.57 321	81.73 277	81.52 376	93.24 349

cl____		86.52 296	85.78 289	88.75 320	92.03 327	76.46 347	90.74 356	94.30 288	81.83 313	83.34 339	90.78 346	75.74 210	96.57 321	81.74 276	81.54 375	93.22 350

eth_miper_zixun_eth		86.50 297	85.77 291	88.68 323	91.94 329	75.81 357	90.47 363	94.89 257	82.05 300	84.05 320	90.46 355	75.96 203	96.77 306	82.76 253	79.36 406	93.46 341

baseline2		86.50 297	85.39 300	89.84 283	91.12 363	76.70 344	91.88 328	88.58 426	82.35 295	79.95 385	90.95 339	73.42 249	97.63 229	80.27 302	89.95 268	95.19 253

EPNet_dtu		86.49 299	85.94 284	88.14 341	90.24 396	72.82 391	94.11 215	92.20 352	86.66 178	79.42 392	92.36 285	73.52 245	95.81 365	71.26 383	93.66 194	95.80 233
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		86.44 300	85.35 303	89.69 292	94.29 231	75.40 363	91.30 343	90.53 398	84.76 238	85.06 291	90.13 365	58.95 404	97.45 249	82.08 266	91.09 249	96.21 210

cascas		86.43 301	84.98 311	90.80 238	92.10 325	80.92 226	90.24 369	95.91 174	73.10 423	83.57 334	88.39 399	65.15 351	97.46 248	84.90 216	91.43 242	94.03 309

reproduce_monomvs		86.37 302	85.87 286	87.87 348	93.66 271	73.71 379	93.44 261	95.02 244	88.61 108	82.64 348	91.94 305	57.88 408	96.68 311	89.96 138	79.71 403	93.22 350

SCA		86.32 303	85.18 307	89.73 290	92.15 321	76.60 345	91.12 349	91.69 367	83.53 267	85.50 273	88.81 392	66.79 334	96.48 329	76.65 340	90.35 260	96.12 215

LTVRE_ROB	82.13 13	86.26 304	84.90 314	90.34 261	94.44 220	81.50 199	92.31 316	94.89 257	83.03 280	79.63 390	92.67 275	69.69 299	97.79 215	71.20 384	86.26 322	91.72 393
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
DTE-MVSNet		86.11 305	85.48 298	87.98 344	91.65 344	74.92 366	94.93 155	95.75 188	87.36 155	82.26 351	93.04 264	72.85 256	95.82 364	74.04 367	77.46 415	93.20 352

XVG-ACMP-BASELINE		86.00 306	84.84 316	89.45 303	91.20 357	78.00 310	91.70 334	95.55 207	85.05 229	82.97 343	92.25 290	54.49 425	97.48 244	82.93 247	87.45 312	92.89 364

MVP-Stereo		85.97 307	84.86 315	89.32 305	90.92 374	82.19 183	92.11 323	94.19 293	78.76 363	78.77 399	91.63 316	68.38 322	96.56 323	75.01 359	93.95 188	89.20 433
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 308	85.09 309	88.35 331	90.79 379	77.42 331	91.83 330	95.70 195	80.77 335	80.08 382	90.02 369	66.74 336	96.37 337	81.88 272	87.97 303	91.26 406

test-LLR		85.87 309	85.41 299	87.25 365	90.95 370	71.67 408	89.55 385	89.88 415	83.41 270	84.54 302	87.95 406	67.25 327	95.11 388	81.82 273	93.37 206	94.97 260

FMVSNet1		85.85 310	84.11 330	91.08 222	92.81 305	83.10 147	95.14 144	94.94 251	81.64 318	82.68 346	91.64 313	59.01 403	96.34 340	75.37 354	83.78 343	93.79 321

PatchmatchNet		85.85 310	84.70 318	89.29 306	91.76 338	75.54 360	88.49 404	91.30 379	81.63 319	85.05 292	88.70 396	71.71 268	96.24 344	74.61 364	89.05 286	96.08 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
myMVS_eth3d28		85.80 312	85.26 306	87.42 360	94.73 192	69.92 426	90.60 360	90.95 389	87.21 159	86.06 257	90.04 368	59.47 396	96.02 352	74.89 361	93.35 208	96.33 202

CostFormer		85.77 313	84.94 313	88.26 337	91.16 361	72.58 399	89.47 389	91.04 386	76.26 392	86.45 246	89.97 371	70.74 281	96.86 305	82.35 259	87.07 318	95.34 250

PMMVS		85.71 314	84.96 312	87.95 345	88.90 415	77.09 335	88.68 401	90.06 408	72.32 430	86.47 243	90.76 347	72.15 266	94.40 397	81.78 275	93.49 201	92.36 381

PVSNet	78.82 18	85.55 315	84.65 319	88.23 339	94.72 194	71.93 402	87.12 425	92.75 337	78.80 362	84.95 294	90.53 353	64.43 357	96.71 310	74.74 362	93.86 190	96.06 221

UBG		85.51 316	84.57 323	88.35 331	94.21 235	71.78 406	90.07 376	89.66 419	82.28 296	85.91 260	89.01 388	61.30 380	97.06 291	76.58 343	92.06 238	96.22 208

IterMVS-SCA-FT		85.45 317	84.53 324	88.18 340	91.71 340	76.87 340	90.19 373	92.65 340	85.40 216	81.44 362	90.54 352	66.79 334	95.00 391	81.04 286	81.05 382	92.66 371

pmmvs4		85.43 318	83.86 335	90.16 265	90.02 401	82.97 157	90.27 365	92.67 339	75.93 395	80.73 371	91.74 311	71.05 275	95.73 370	78.85 319	83.46 350	91.78 392

mvsany_test1		85.42 319	85.30 304	85.77 394	87.95 429	75.41 362	87.61 422	80.97 456	76.82 386	88.68 197	95.83 134	77.44 184	90.82 442	85.90 202	86.51 320	91.08 413

ACMH	80.38 17	85.36 320	83.68 337	90.39 257	94.45 219	80.63 235	94.73 171	94.85 261	82.09 299	77.24 408	92.65 276	60.01 393	97.58 233	72.25 379	84.87 333	92.96 361
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 321	84.64 321	87.49 357	90.77 381	72.59 398	94.01 228	94.40 284	84.72 240	79.62 391	93.17 258	61.91 373	96.72 308	81.99 269	81.16 378	93.16 354

CR-MVSNet		85.35 321	83.76 336	90.12 268	90.58 388	79.34 276	85.24 438	91.96 362	78.27 372	85.55 268	87.87 409	71.03 276	95.61 373	73.96 369	89.36 280	95.40 246

tpmrst		85.35 321	84.99 310	86.43 385	90.88 377	67.88 434	88.71 400	91.43 377	80.13 341	86.08 256	88.80 394	73.05 254	96.02 352	82.48 255	83.40 352	95.40 246

miper_lstm_enhance		85.27 324	84.59 322	87.31 362	91.28 356	74.63 369	87.69 419	94.09 300	81.20 331	81.36 364	89.85 375	74.97 219	94.30 400	81.03 288	79.84 402	93.01 360

IB-MVS	80.51 15	85.24 325	83.26 343	91.19 216	92.13 323	79.86 262	91.75 332	91.29 380	83.28 275	80.66 373	88.49 398	61.28 381	98.46 143	80.99 289	79.46 405	95.25 252
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
CHOSEN 280x420		85.15 326	83.99 333	88.65 324	92.47 313	78.40 299	79.68 462	92.76 336	74.90 406	81.41 363	89.59 379	69.85 298	95.51 377	79.92 306	95.29 153	92.03 388

RPSCF		85.07 327	84.27 325	87.48 358	92.91 301	70.62 421	91.69 335	92.46 342	76.20 393	82.67 347	95.22 165	63.94 360	97.29 272	77.51 333	85.80 324	94.53 283

MS-PatchMatch		85.05 328	84.16 328	87.73 350	91.42 350	78.51 295	91.25 346	93.53 316	77.50 378	80.15 379	91.58 319	61.99 372	95.51 377	75.69 351	94.35 180	89.16 434

ACMH+	81.04 14	85.05 328	83.46 340	89.82 284	94.66 199	79.37 274	94.44 190	94.12 299	82.19 298	78.04 402	92.82 270	58.23 406	97.54 236	73.77 371	82.90 358	92.54 373

mmtdpeth		85.04 330	84.15 329	87.72 351	93.11 288	75.74 358	94.37 199	92.83 333	84.98 230	89.31 184	86.41 425	61.61 377	97.14 285	92.63 79	62.11 454	90.29 421

WBMVS		84.97 331	84.18 327	87.34 361	94.14 241	71.62 410	90.20 372	92.35 345	81.61 320	84.06 319	90.76 347	61.82 374	96.52 326	78.93 318	83.81 342	93.89 312

IterMVS		84.88 332	83.98 334	87.60 353	91.44 347	76.03 353	90.18 374	92.41 343	83.24 276	81.06 368	90.42 357	66.60 337	94.28 401	79.46 310	80.98 387	92.48 375
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 333	83.09 346	90.14 267	93.80 259	80.05 254	89.18 394	93.09 326	78.89 358	78.19 400	91.91 306	65.86 348	97.27 273	68.47 404	88.45 294	93.11 356

testing222		84.84 334	83.32 341	89.43 304	94.15 240	75.94 354	91.09 350	89.41 424	84.90 232	85.78 262	89.44 382	52.70 432	96.28 343	70.80 390	91.57 241	96.07 219

tpm		84.73 335	84.02 332	86.87 378	90.33 394	68.90 429	89.06 396	89.94 412	80.85 334	85.75 263	89.86 374	68.54 320	95.97 355	77.76 329	84.05 341	95.75 234

tfpnnormal		84.72 336	83.23 344	89.20 308	92.79 306	80.05 254	94.48 185	95.81 183	82.38 293	81.08 367	91.21 327	69.01 314	96.95 299	61.69 437	80.59 391	90.58 420

SD_0403		84.71 337	84.65 319	84.92 404	92.95 299	65.95 440	92.07 326	93.23 322	83.82 259	79.03 394	93.73 243	73.90 239	92.91 423	63.02 434	90.05 264	95.89 227

CVMVSNet		84.69 338	84.79 317	84.37 409	91.84 334	64.92 447	93.70 251	91.47 376	66.19 449	86.16 255	95.28 162	67.18 329	93.33 416	80.89 291	90.42 259	94.88 269

SSC-MVS3.2		84.60 339	84.19 326	85.85 393	92.74 308	68.07 431	88.15 410	93.81 310	87.42 153	83.76 327	91.07 336	62.91 367	95.73 370	74.56 365	83.24 353	93.75 328

test-mter		84.54 340	83.64 338	87.25 365	90.95 370	71.67 408	89.55 385	89.88 415	79.17 353	84.54 302	87.95 406	55.56 416	95.11 388	81.82 273	93.37 206	94.97 260

ETVMVS		84.43 341	82.92 350	88.97 316	94.37 224	74.67 368	91.23 347	88.35 428	83.37 272	86.06 257	89.04 387	55.38 419	95.67 372	67.12 413	91.34 243	96.58 195

TransMVSNet (Re)		84.43 341	83.06 348	88.54 326	91.72 339	78.44 297	95.18 141	92.82 335	82.73 288	79.67 389	92.12 294	73.49 246	95.96 356	71.10 388	68.73 443	91.21 407

pmmvs5		84.21 343	82.84 353	88.34 333	88.95 414	76.94 339	92.41 308	91.91 364	75.63 397	80.28 377	91.18 330	64.59 356	95.57 374	77.09 338	83.47 349	92.53 374

dmvs_re		84.20 344	83.22 345	87.14 371	91.83 336	77.81 318	90.04 377	90.19 404	84.70 242	81.49 360	89.17 385	64.37 358	91.13 440	71.58 382	85.65 326	92.46 377

tpm2		84.08 345	82.94 349	87.48 358	91.39 351	71.27 411	89.23 393	90.37 400	71.95 432	84.64 299	89.33 383	67.30 326	96.55 325	75.17 356	87.09 317	94.63 276

test_fmvs2		83.98 346	84.03 331	83.83 414	87.16 432	67.53 438	93.93 235	92.89 331	77.62 377	86.89 236	93.53 246	47.18 445	92.02 431	90.54 132	86.51 320	91.93 390

COLMAP_ROB	80.39 16	83.96 347	82.04 356	89.74 288	95.28 155	79.75 265	94.25 206	92.28 349	75.17 402	78.02 403	93.77 240	58.60 405	97.84 213	65.06 426	85.92 323	91.63 395
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 348	81.53 359	91.21 215	90.58 388	79.34 276	85.24 438	96.76 91	71.44 434	85.55 268	82.97 446	70.87 279	98.91 94	61.01 439	89.36 280	95.40 246

SixPastTwentyTwo		83.91 349	82.90 351	86.92 375	90.99 368	70.67 420	93.48 258	91.99 359	85.54 207	77.62 407	92.11 296	60.59 389	96.87 304	76.05 349	77.75 412	93.20 352

EPMVS		83.90 350	82.70 354	87.51 355	90.23 397	72.67 394	88.62 402	81.96 454	81.37 325	85.01 293	88.34 400	66.31 342	94.45 395	75.30 355	87.12 316	95.43 245

WB-MVSnew		83.77 351	83.28 342	85.26 401	91.48 346	71.03 415	91.89 327	87.98 429	78.91 356	84.78 296	90.22 360	69.11 313	94.02 404	64.70 427	90.44 257	90.71 415

TESTMET0.1,1		83.74 352	82.85 352	86.42 386	89.96 402	71.21 413	89.55 385	87.88 430	77.41 379	83.37 338	87.31 414	56.71 412	93.65 413	80.62 296	92.85 222	94.40 293

UWE-MVS		83.69 353	83.09 346	85.48 396	93.06 292	65.27 446	90.92 353	86.14 438	79.90 344	86.26 252	90.72 350	57.17 411	95.81 365	71.03 389	92.62 230	95.35 249

pmmvs6		83.42 354	81.60 358	88.87 317	88.01 427	77.87 316	94.96 153	94.24 292	74.67 408	78.80 398	91.09 335	60.17 392	96.49 328	77.06 339	75.40 424	92.23 385

AllTest		83.42 354	81.39 360	89.52 300	95.01 169	77.79 320	93.12 277	90.89 392	77.41 379	76.12 417	93.34 249	54.08 427	97.51 239	68.31 406	84.27 338	93.26 346

tpmvs		83.35 356	82.07 355	87.20 369	91.07 365	71.00 417	88.31 407	91.70 366	78.91 356	80.49 376	87.18 418	69.30 308	97.08 288	68.12 409	83.56 348	93.51 339

USDC		82.76 357	81.26 362	87.26 364	91.17 359	74.55 370	89.27 391	93.39 319	78.26 373	75.30 424	92.08 298	54.43 426	96.63 314	71.64 381	85.79 325	90.61 417

Patchmtry		82.71 358	80.93 364	88.06 342	90.05 400	76.37 350	84.74 443	91.96 362	72.28 431	81.32 365	87.87 409	71.03 276	95.50 379	68.97 401	80.15 397	92.32 383

PatchT		82.68 359	81.27 361	86.89 377	90.09 399	70.94 418	84.06 445	90.15 405	74.91 405	85.63 267	83.57 441	69.37 304	94.87 393	65.19 423	88.50 293	94.84 270

MIMVSNet		82.59 360	80.53 365	88.76 319	91.51 345	78.32 302	86.57 429	90.13 406	79.32 350	80.70 372	88.69 397	52.98 431	93.07 421	66.03 421	88.86 288	94.90 268

test0.0.03 1		82.41 361	81.69 357	84.59 407	88.23 423	72.89 390	90.24 369	87.83 431	83.41 270	79.86 387	89.78 376	67.25 327	88.99 452	65.18 424	83.42 351	91.90 391

EG-PatchMatch MVS		82.37 362	80.34 368	88.46 328	90.27 395	79.35 275	92.80 299	94.33 287	77.14 383	73.26 435	90.18 363	47.47 444	96.72 308	70.25 392	87.32 315	89.30 430

tpm cat1		81.96 363	80.27 369	87.01 372	91.09 364	71.02 416	87.38 423	91.53 374	66.25 448	80.17 378	86.35 427	68.22 323	96.15 348	69.16 400	82.29 364	93.86 318

our_test_3		81.93 364	80.46 367	86.33 387	88.46 420	73.48 383	88.46 405	91.11 382	76.46 387	76.69 413	88.25 402	66.89 332	94.36 398	68.75 402	79.08 408	91.14 409

ppachtmachnet_test		81.84 365	80.07 373	87.15 370	88.46 420	74.43 373	89.04 397	92.16 353	75.33 400	77.75 405	88.99 389	66.20 344	95.37 383	65.12 425	77.60 413	91.65 394

gg-mvs-nofinetune		81.77 366	79.37 381	88.99 315	90.85 378	77.73 327	86.29 430	79.63 459	74.88 407	83.19 342	69.05 462	60.34 390	96.11 349	75.46 353	94.64 171	93.11 356

CL-MVSNet_self_test		81.74 367	80.53 365	85.36 398	85.96 438	72.45 400	90.25 367	93.07 327	81.24 329	79.85 388	87.29 415	70.93 278	92.52 425	66.95 414	69.23 439	91.11 411

Patchmatch-RL test		81.67 368	79.96 374	86.81 379	85.42 443	71.23 412	82.17 453	87.50 434	78.47 367	77.19 409	82.50 448	70.81 280	93.48 414	82.66 254	72.89 428	95.71 238

ADS-MVSNet2		81.66 369	79.71 378	87.50 356	91.35 353	74.19 375	83.33 448	88.48 427	72.90 425	82.24 352	85.77 431	64.98 352	93.20 419	64.57 428	83.74 344	95.12 255

K. test v3		81.59 370	80.15 372	85.91 392	89.89 404	69.42 428	92.57 304	87.71 432	85.56 206	73.44 434	89.71 378	55.58 415	95.52 376	77.17 336	69.76 437	92.78 368

ADS-MVSNet		81.56 371	79.78 375	86.90 376	91.35 353	71.82 404	83.33 448	89.16 425	72.90 425	82.24 352	85.77 431	64.98 352	93.76 410	64.57 428	83.74 344	95.12 255

sc_t1		81.53 372	78.67 393	90.12 268	90.78 380	78.64 290	93.91 238	90.20 403	68.42 443	80.82 370	89.88 373	46.48 447	96.76 307	76.03 350	71.47 432	94.96 263

FMVSNet5		81.52 373	79.60 379	87.27 363	91.17 359	77.95 311	91.49 339	92.26 351	76.87 385	76.16 416	87.91 408	51.67 433	92.34 427	67.74 410	81.16 378	91.52 398

dp		81.47 374	80.23 370	85.17 402	89.92 403	65.49 444	86.74 427	90.10 407	76.30 391	81.10 366	87.12 419	62.81 368	95.92 358	68.13 408	79.88 400	94.09 305

Patchmatch-test		81.37 375	79.30 382	87.58 354	90.92 374	74.16 376	80.99 455	87.68 433	70.52 438	76.63 414	88.81 392	71.21 273	92.76 424	60.01 443	86.93 319	95.83 231

EU-MVSNet		81.32 376	80.95 363	82.42 422	88.50 419	63.67 451	93.32 266	91.33 378	64.02 453	80.57 375	92.83 269	61.21 384	92.27 428	76.34 345	80.38 396	91.32 404

test_0402		81.30 377	79.17 386	87.67 352	93.19 283	78.17 306	92.98 288	91.71 365	75.25 401	76.02 420	90.31 358	59.23 399	96.37 337	50.22 457	83.63 347	88.47 442

JIA-IIPM		81.04 378	78.98 390	87.25 365	88.64 416	73.48 383	81.75 454	89.61 421	73.19 422	82.05 355	73.71 458	66.07 347	95.87 361	71.18 386	84.60 335	92.41 379

Anonymous20231206		81.03 379	79.77 377	84.82 405	87.85 430	70.26 423	91.42 340	92.08 355	73.67 417	77.75 405	89.25 384	62.43 370	93.08 420	61.50 438	82.00 369	91.12 410

mvs5depth		80.98 380	79.15 387	86.45 384	84.57 446	73.29 386	87.79 415	91.67 368	80.52 337	82.20 354	89.72 377	55.14 422	95.93 357	73.93 370	66.83 446	90.12 423

pmmvs-eth3d		80.97 381	78.72 392	87.74 349	84.99 445	79.97 260	90.11 375	91.65 369	75.36 399	73.51 433	86.03 428	59.45 397	93.96 408	75.17 356	72.21 429	89.29 432

testgi		80.94 382	80.20 371	83.18 415	87.96 428	66.29 439	91.28 344	90.70 397	83.70 261	78.12 401	92.84 268	51.37 434	90.82 442	63.34 431	82.46 362	92.43 378

CMPMVS	59.16 21	80.52 383	79.20 385	84.48 408	83.98 447	67.63 437	89.95 380	93.84 309	64.79 452	66.81 450	91.14 333	57.93 407	95.17 386	76.25 346	88.10 299	90.65 416
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		80.46 384	79.59 380	83.06 417	93.44 278	64.64 448	93.33 265	85.47 443	84.34 248	79.93 386	90.84 343	44.35 453	92.39 426	57.06 451	87.56 309	92.16 387

Anonymous20240521		80.44 385	79.21 384	84.11 412	85.75 441	67.89 433	92.86 295	93.23 322	75.61 398	75.59 423	87.47 413	50.03 436	94.33 399	71.14 387	81.21 377	90.12 423

LF4IMVS		80.37 386	79.07 389	84.27 411	86.64 434	69.87 427	89.39 390	91.05 385	76.38 389	74.97 426	90.00 370	47.85 443	94.25 402	74.55 366	80.82 389	88.69 440

KD-MVS_self_test		80.20 387	79.24 383	83.07 416	85.64 442	65.29 445	91.01 352	93.93 303	78.71 365	76.32 415	86.40 426	59.20 400	92.93 422	72.59 377	69.35 438	91.00 414

tt0320		80.13 388	77.41 397	88.29 335	90.50 392	78.02 309	93.10 280	90.71 396	66.06 450	76.75 412	86.97 421	49.56 439	95.40 382	71.65 380	71.41 433	91.46 402

Syy-MVS		80.07 389	79.78 375	80.94 426	91.92 330	59.93 458	89.75 383	87.40 435	81.72 315	78.82 396	87.20 416	66.29 343	91.29 438	47.06 459	87.84 306	91.60 396

UnsupCasMVSNet_eth		80.07 389	78.27 395	85.46 397	85.24 444	72.63 397	88.45 406	94.87 260	82.99 282	71.64 442	88.07 405	56.34 413	91.75 435	73.48 373	63.36 452	92.01 389

test20.03		79.95 391	79.08 388	82.55 419	85.79 440	67.74 436	91.09 350	91.08 383	81.23 330	74.48 430	89.96 372	61.63 375	90.15 444	60.08 441	76.38 420	89.76 425

TDRefinement		79.81 392	77.34 398	87.22 368	79.24 461	75.48 361	93.12 277	92.03 357	76.45 388	75.01 425	91.58 319	49.19 440	96.44 333	70.22 394	69.18 440	89.75 426

TinyColmap		79.76 393	77.69 396	85.97 389	91.71 340	73.12 387	89.55 385	90.36 401	75.03 403	72.03 439	90.19 362	46.22 450	96.19 347	63.11 432	81.03 383	88.59 441

myMVS_eth3d		79.67 394	78.79 391	82.32 423	91.92 330	64.08 449	89.75 383	87.40 435	81.72 315	78.82 396	87.20 416	45.33 451	91.29 438	59.09 446	87.84 306	91.60 396

tt0320-xc		79.63 395	76.66 404	88.52 327	91.03 366	78.72 287	93.00 286	89.53 423	66.37 447	76.11 419	87.11 420	46.36 449	95.32 385	72.78 376	67.67 444	91.51 399

OpenMVS_ROB	74.94 19	79.51 396	77.03 403	86.93 374	87.00 433	76.23 352	92.33 314	90.74 395	68.93 442	74.52 429	88.23 403	49.58 438	96.62 315	57.64 449	84.29 337	87.94 445

MIMVSNet1		79.38 397	77.28 399	85.69 395	86.35 435	73.67 380	91.61 337	92.75 337	78.11 376	72.64 437	88.12 404	48.16 442	91.97 433	60.32 440	77.49 414	91.43 403

YYNet1		79.22 398	77.20 400	85.28 400	88.20 425	72.66 395	85.87 432	90.05 410	74.33 411	62.70 453	87.61 411	66.09 346	92.03 429	66.94 415	72.97 427	91.15 408

MDA-MVSNet_test_wron		79.21 399	77.19 401	85.29 399	88.22 424	72.77 392	85.87 432	90.06 408	74.34 410	62.62 455	87.56 412	66.14 345	91.99 432	66.90 418	73.01 426	91.10 412

UWE-MVS-28		78.98 400	78.38 394	80.80 427	88.18 426	60.66 457	90.65 358	78.51 461	78.84 360	77.93 404	90.93 340	59.08 402	89.02 451	50.96 456	90.33 261	92.72 369

MDA-MVSNet-bldmvs		78.85 401	76.31 406	86.46 383	89.76 405	73.88 377	88.79 399	90.42 399	79.16 354	59.18 458	88.33 401	60.20 391	94.04 403	62.00 436	68.96 441	91.48 401

KD-MVS_2432*1600		78.50 402	76.02 410	85.93 390	86.22 436	74.47 371	84.80 441	92.33 346	79.29 351	76.98 410	85.92 429	53.81 429	93.97 406	67.39 411	57.42 459	89.36 428

miper_refine_blended		78.50 402	76.02 410	85.93 390	86.22 436	74.47 371	84.80 441	92.33 346	79.29 351	76.98 410	85.92 429	53.81 429	93.97 406	67.39 411	57.42 459	89.36 428

FE-MVSNET		78.19 404	76.03 409	84.69 406	83.70 449	73.31 385	90.58 361	90.00 411	77.11 384	71.91 440	85.47 433	55.53 417	91.94 434	59.69 444	70.24 435	88.83 438

PM-MVS		78.11 405	76.12 408	84.09 413	83.54 450	70.08 424	88.97 398	85.27 445	79.93 343	74.73 428	86.43 424	34.70 461	93.48 414	79.43 313	72.06 430	88.72 439

test_vis1_rt		77.96 406	76.46 405	82.48 421	85.89 439	71.74 407	90.25 367	78.89 460	71.03 437	71.30 443	81.35 450	42.49 455	91.05 441	84.55 225	82.37 363	84.65 448

test_fmvs3		77.67 407	77.16 402	79.22 430	79.52 460	61.14 455	92.34 313	91.64 370	73.98 414	78.86 395	86.59 422	27.38 465	87.03 454	88.12 167	75.97 422	89.50 427

PVSNet_0	73.20 20	77.22 408	74.83 414	84.37 409	90.70 385	71.10 414	83.09 450	89.67 418	72.81 427	73.93 432	83.13 443	60.79 388	93.70 412	68.54 403	50.84 464	88.30 443

DSMNet-mixed		76.94 409	76.29 407	78.89 431	83.10 452	56.11 467	87.78 416	79.77 458	60.65 457	75.64 422	88.71 395	61.56 378	88.34 453	60.07 442	89.29 282	92.21 386

ttmdpeth		76.55 410	74.64 415	82.29 424	82.25 455	67.81 435	89.76 382	85.69 441	70.35 439	75.76 421	91.69 312	46.88 446	89.77 446	66.16 420	63.23 453	89.30 430

new-patchmatchnet		76.41 411	75.17 413	80.13 428	82.65 454	59.61 459	87.66 420	91.08 383	78.23 374	69.85 446	83.22 442	54.76 423	91.63 437	64.14 430	64.89 450	89.16 434

UnsupCasMVSNet_bld		76.23 412	73.27 416	85.09 403	83.79 448	72.92 389	85.65 435	93.47 318	71.52 433	68.84 448	79.08 453	49.77 437	93.21 418	66.81 419	60.52 456	89.13 436

mvsany_test3		74.95 413	73.26 417	80.02 429	74.61 465	63.16 453	85.53 436	78.42 462	74.16 412	74.89 427	86.46 423	36.02 460	89.09 450	82.39 258	66.91 445	87.82 446

dmvs_testset		74.57 414	75.81 412	70.86 441	87.72 431	40.47 476	87.05 426	77.90 466	82.75 287	71.15 444	85.47 433	67.98 324	84.12 463	45.26 460	76.98 419	88.00 444

MVS-HIRNet		73.70 415	72.20 418	78.18 434	91.81 337	56.42 466	82.94 451	82.58 452	55.24 460	68.88 447	66.48 463	55.32 420	95.13 387	58.12 448	88.42 295	83.01 451

MVStest1		72.91 416	69.70 421	82.54 420	78.14 462	73.05 388	88.21 409	86.21 437	60.69 456	64.70 451	90.53 353	46.44 448	85.70 459	58.78 447	53.62 461	88.87 437

new_pmnet		72.15 417	70.13 420	78.20 433	82.95 453	65.68 442	83.91 446	82.40 453	62.94 455	64.47 452	79.82 452	42.85 454	86.26 458	57.41 450	74.44 425	82.65 453

test_f		71.95 418	70.87 419	75.21 437	74.21 467	59.37 460	85.07 440	85.82 440	65.25 451	70.42 445	83.13 443	23.62 466	82.93 465	78.32 323	71.94 431	83.33 450

pmmvs3		71.81 419	68.71 422	81.11 425	75.86 464	70.42 422	86.74 427	83.66 449	58.95 459	68.64 449	80.89 451	36.93 459	89.52 448	63.10 433	63.59 451	83.39 449

APD_test1		69.04 420	66.26 426	77.36 436	80.51 458	62.79 454	85.46 437	83.51 450	54.11 462	59.14 459	84.79 437	23.40 468	89.61 447	55.22 452	70.24 435	79.68 457

N_pmnet		68.89 421	68.44 423	70.23 442	89.07 413	28.79 481	88.06 411	19.50 481	69.47 441	71.86 441	84.93 435	61.24 383	91.75 435	54.70 453	77.15 416	90.15 422

WB-MVS		67.92 422	67.49 424	69.21 445	81.09 456	41.17 475	88.03 412	78.00 465	73.50 419	62.63 454	83.11 445	63.94 360	86.52 456	25.66 471	51.45 463	79.94 456

SSC-MVS		67.06 423	66.56 425	68.56 447	80.54 457	40.06 477	87.77 417	77.37 468	72.38 429	61.75 456	82.66 447	63.37 363	86.45 457	24.48 472	48.69 466	79.16 458

LCM-MVSNet		66.00 424	62.16 429	77.51 435	64.51 475	58.29 461	83.87 447	90.90 391	48.17 464	54.69 461	73.31 459	16.83 474	86.75 455	65.47 422	61.67 455	87.48 447

test_vis3_rt		65.12 425	62.60 427	72.69 439	71.44 468	60.71 456	87.17 424	65.55 472	63.80 454	53.22 462	65.65 465	14.54 475	89.44 449	76.65 340	65.38 448	67.91 463

FPMVS		64.63 426	62.55 428	70.88 440	70.80 469	56.71 462	84.42 444	84.42 447	51.78 463	49.57 463	81.61 449	23.49 467	81.48 466	40.61 466	76.25 421	74.46 459

EGC-MVSNET		61.97 427	56.37 432	78.77 432	89.63 408	73.50 382	89.12 395	82.79 451	0.21 478	1.24 479	84.80 436	39.48 456	90.04 445	44.13 461	75.94 423	72.79 460

PMMVS2		59.60 428	56.40 431	69.21 445	68.83 472	46.58 471	73.02 467	77.48 467	55.07 461	49.21 464	72.95 460	17.43 473	80.04 467	49.32 458	44.33 467	80.99 455

testf1		59.54 429	56.11 433	69.85 443	69.28 470	56.61 464	80.37 457	76.55 469	42.58 467	45.68 466	75.61 454	11.26 476	84.18 461	43.20 463	60.44 457	68.75 461

APD_test2		59.54 429	56.11 433	69.85 443	69.28 470	56.61 464	80.37 457	76.55 469	42.58 467	45.68 466	75.61 454	11.26 476	84.18 461	43.20 463	60.44 457	68.75 461

ANet_high		58.88 431	54.22 436	72.86 438	56.50 478	56.67 463	80.75 456	86.00 439	73.09 424	37.39 470	64.63 466	22.17 469	79.49 468	43.51 462	23.96 472	82.43 454

dongtai		58.82 432	58.24 430	60.56 450	83.13 451	45.09 474	82.32 452	48.22 480	67.61 445	61.70 457	69.15 461	38.75 457	76.05 469	32.01 468	41.31 468	60.55 465

Gipuma		57.99 433	54.91 435	67.24 448	88.51 417	65.59 443	52.21 470	90.33 402	43.58 466	42.84 469	51.18 470	20.29 471	85.07 460	34.77 467	70.45 434	51.05 469
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		53.51 434	53.30 437	54.13 454	76.06 463	45.36 473	80.11 459	48.36 479	59.63 458	54.84 460	63.43 467	37.41 458	62.07 474	20.73 474	39.10 469	54.96 468

PMVS	47.18 22	52.22 435	48.46 439	63.48 449	45.72 480	46.20 472	73.41 466	78.31 463	41.03 469	30.06 472	65.68 464	6.05 478	83.43 464	30.04 469	65.86 447	60.80 464
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 436	48.47 438	56.66 452	52.26 479	18.98 483	41.51 472	81.40 455	10.10 473	44.59 468	75.01 457	28.51 463	68.16 470	53.54 454	49.31 465	82.83 452

MVE	39.65 23	43.39 437	38.59 443	57.77 451	56.52 477	48.77 470	55.38 469	58.64 476	29.33 472	28.96 473	52.65 469	4.68 479	64.62 473	28.11 470	33.07 470	59.93 466
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 438	42.29 440	46.03 455	65.58 474	37.41 478	73.51 465	64.62 473	33.99 470	28.47 474	47.87 471	19.90 472	67.91 471	22.23 473	24.45 471	32.77 470

EMVS		42.07 439	41.12 441	44.92 456	63.45 476	35.56 480	73.65 464	63.48 474	33.05 471	26.88 475	45.45 472	21.27 470	67.14 472	19.80 475	23.02 473	32.06 471

tmp_tt		35.64 440	39.24 442	24.84 457	14.87 481	23.90 482	62.71 468	51.51 478	6.58 475	36.66 471	62.08 468	44.37 452	30.34 477	52.40 455	22.00 474	20.27 472

cdsmvs_eth3d_5k		22.14 441	29.52 444	0.00 461	0.00 484	0.00 486	0.00 473	95.76 187	0.00 479	0.00 480	94.29 214	75.66 211	0.00 480	0.00 479	0.00 478	0.00 476

wuyk23d		21.27 442	20.48 445	23.63 458	68.59 473	36.41 479	49.57 471	6.85 482	9.37 474	7.89 476	4.46 478	4.03 480	31.37 476	17.47 476	16.07 475	3.12 473

testmvs		8.92 443	11.52 446	1.12 460	1.06 482	0.46 485	86.02 431	0.65 483	0.62 476	2.74 477	9.52 476	0.31 482	0.45 479	2.38 477	0.39 476	2.46 475

test123		8.76 444	11.22 447	1.39 459	0.85 483	0.97 484	85.76 434	0.35 484	0.54 477	2.45 478	8.14 477	0.60 481	0.48 478	2.16 478	0.17 477	2.71 474

ab-mvs-re		7.82 445	10.43 448	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	93.88 235	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

pcd_1.5k_mvsjas		6.64 446	8.86 449	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	79.70 148	0.00 480	0.00 479	0.00 478	0.00 476

mmdepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

monomultidepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

test_blank		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uanet_test		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

DCPMVS		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

sosnet-low-res		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

sosnet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uncertanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

Regformer		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

MED-MVS test				94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	88.11 127	97.21 14	97.54 43		99.67 1	95.27 39	98.85 20	98.95 11

TestfortrainingZip							97.32 10

WAC-MVS						64.08 449								59.14 445

FOURS1					98.86 3	85.54 72	98.29 1	97.49 11	89.79 62	96.29 30

MSC_two_6792asdad				96.52 1	97.78 59	90.86 1		96.85 79					99.61 6	96.03 26	99.06 9	99.07 5

PC_three_1452									82.47 291	97.09 19	97.07 70	92.72 1	98.04 191	92.70 78	99.02 12	98.86 15

No_MVS				96.52 1	97.78 59	90.86 1		96.85 79					99.61 6	96.03 26	99.06 9	99.07 5

test_one_0601					98.58 13	85.83 66		97.44 20	91.05 22	96.78 26	98.06 21	91.45 12

eth-test2					0.00 484

eth-test					0.00 484

ZD-MVS					98.15 39	86.62 34		97.07 59	83.63 263	94.19 62	96.91 76	87.57 33	99.26 49	91.99 103	98.44 56

RE-MVS-def			93.68 70		97.92 48	84.57 92	96.28 49	96.76 91	87.46 150	93.75 73	97.43 49	82.94 98		92.73 74	97.80 90	97.88 100

IU-MVS					98.77 7	86.00 53		96.84 81	81.26 328	97.26 13				95.50 35	99.13 3	99.03 8

OPU-MVS				96.21 3	98.00 47	90.85 3	97.13 18				97.08 68	92.59 2	98.94 90	92.25 90	98.99 14	98.84 18

test_241102_TWO								97.44 20	90.31 40	97.62 8	98.07 19	91.46 11	99.58 13	95.66 29	99.12 6	98.98 10

test_241102_ONE					98.77 7	85.99 55		97.44 20	90.26 46	97.71 2	97.96 30	92.31 5	99.38 34

9.14			94.47 33		97.79 57		96.08 67	97.44 20	86.13 193	95.10 52	97.40 51	88.34 24	99.22 51	93.25 66	98.70 37

save fliter					97.85 54	85.63 71	95.21 138	96.82 84	89.44 72

test_0728_THIRD									90.75 28	97.04 21	98.05 24	92.09 7	99.55 19	95.64 31	99.13 3	99.13 2

test_0728_SECOND				95.01 18	98.79 4	86.43 40	97.09 20	97.49 11					99.61 6	95.62 33	99.08 7	98.99 9

test0726					98.78 5	85.93 58	97.19 15	97.47 16	90.27 44	97.64 6	98.13 6	91.47 9

GSMVS																96.12 215

test_part2					98.55 14	87.22 20				96.40 29

sam_mvs1												71.70 269				96.12 215

sam_mvs												70.60 283

ambc				83.06 417	79.99 459	63.51 452	77.47 463	92.86 332		74.34 431	84.45 438	28.74 462	95.06 390	73.06 375	68.89 442	90.61 417

MTGPA								96.97 64

test_post1							88.00 413				9.81 475	69.31 307	95.53 375	76.65 340

test_post											10.29 474	70.57 287	95.91 360

patchmatchnet-post											83.76 440	71.53 270	96.48 329

GG-mvs-BLEND				87.94 346	89.73 407	77.91 313	87.80 414	78.23 464		80.58 374	83.86 439	59.88 394	95.33 384	71.20 384	92.22 236	90.60 419

MTMP							96.16 58	60.64 475

gm-plane-assit					89.60 409	68.00 432			77.28 382		88.99 389		97.57 234	79.44 312

test9_res														91.91 107	98.71 35	98.07 81

TEST9					97.53 66	86.49 38	94.07 221	96.78 88	81.61 320	92.77 98	96.20 106	87.71 30	99.12 61

test_8					97.49 68	86.30 46	94.02 227	96.76 91	81.86 311	92.70 102	96.20 106	87.63 31	99.02 71

agg_prior2														90.54 132	98.68 40	98.27 62

agg_prior					97.38 71	85.92 60		96.72 98		92.16 117			98.97 85

TestCases				89.52 300	95.01 169	77.79 320		90.89 392	77.41 379	76.12 417	93.34 249	54.08 427	97.51 239	68.31 406	84.27 338	93.26 346

test_prior4						85.96 57	94.11 215

test_prior2							94.12 213		87.67 146	92.63 106	96.39 101	86.62 43		91.50 116	98.67 43

test_prior				93.82 72	97.29 75	84.49 96		96.88 77					98.87 97			98.11 80

旧先验2							93.36 264		71.25 435	94.37 58			97.13 286	86.74 189

新几何2							93.11 279

新几何1				93.10 101	97.30 74	84.35 106		95.56 206	71.09 436	91.26 145	96.24 104	82.87 100	98.86 99	79.19 316	98.10 74	96.07 219

旧先验1					96.79 84	81.81 193		95.67 197			96.81 82	86.69 41			97.66 96	96.97 171

无先验							93.28 272	96.26 137	73.95 415				99.05 65	80.56 297		96.59 194

原ACMM2							92.94 290

原ACMM1				92.01 170	97.34 72	81.05 219		96.81 86	78.89 358	90.45 158	95.92 125	82.65 102	98.84 103	80.68 295	98.26 62	96.14 213

test222					96.55 93	81.70 195	92.22 319	95.01 245	68.36 444	90.20 164	96.14 111	80.26 139			97.80 90	96.05 222

testdata2													98.75 113	78.30 324

segment_acmp												87.16 38

testdata				90.49 251	96.40 99	77.89 315		95.37 225	72.51 428	93.63 76	96.69 85	82.08 116	97.65 226	83.08 244	97.39 100	95.94 224

testdata1							92.15 321		87.94 132

test12				94.34 56	97.13 78	86.15 51		96.29 129		91.04 150		85.08 65	99.01 73		98.13 73	97.86 102

plane_prior7					94.70 197	82.74 163

plane_prior6					94.52 212	82.75 161						74.23 231

plane_prior5								96.22 142					98.12 174	88.15 164	89.99 265	94.63 276

plane_prior4											94.86 185

plane_prior3						82.75 161			90.26 46	86.91 233

plane_prior2							95.85 91		90.81 26

plane_prior1					94.59 205

plane_prior						82.73 164	95.21 138		89.66 67						89.88 270

n2								0.00 485

nn								0.00 485

door-mid								85.49 442

lessismore_v0				86.04 388	88.46 420	68.78 430		80.59 457		73.01 436	90.11 366	55.39 418	96.43 334	75.06 358	65.06 449	92.90 363

LGP-MVS_train				91.12 218	94.47 216	81.49 201		96.14 149	86.73 175	85.45 276	95.16 171	69.89 296	98.10 176	87.70 173	89.23 283	93.77 326

test11								96.57 109

door								85.33 444

HQP5-MVS						81.56 197

HQP-NCC					94.17 237		94.39 195		88.81 98	85.43 279

ACMP_Plane					94.17 237		94.39 195		88.81 98	85.43 279

BP-MVS														87.11 186

HQP4-MVS										85.43 279			97.96 203			94.51 286

HQP3-MVS								96.04 161							89.77 274

HQP2-MVS												73.83 242

NP-MVS					94.37 224	82.42 176					93.98 228

MDTV_nov1_ep13_2view						55.91 468	87.62 421		73.32 421	84.59 301		70.33 290		74.65 363		95.50 243

MDTV_nov1_ep13			83.56 339		91.69 342	69.93 425	87.75 418	91.54 373	78.60 366	84.86 295	88.90 391	69.54 302	96.03 351	70.25 392	88.93 287

ACMMP++_ref															87.47 310

ACMMP++															88.01 302

Test By Simon												80.02 141

ITE_SJBPF				88.24 338	91.88 333	77.05 336		92.92 330	85.54 207	80.13 381	93.30 253	57.29 410	96.20 345	72.46 378	84.71 334	91.49 400

DeepMVS_CX				56.31 453	74.23 466	51.81 469		56.67 477	44.85 465	48.54 465	75.16 456	27.87 464	58.74 475	40.92 465	52.22 462	58.39 467

High-resolution multi-view benchmark