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 9	98.67 58	95.39 11	99.29 1	98.28 36	94.78 41	98.93 9	98.87 15	96.04 2	99.86 8	97.45 26	99.58 21	99.59 22

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 36	98.27 39	95.13 23	99.19 4	98.89 13	95.54 5	99.85 18	97.52 22	99.66 10	99.56 29

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 43	97.85 116	94.92 32	98.73 18	98.87 15	95.08 8	99.84 23	97.52 22	99.67 6	99.48 44
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 4	97.65 8	98.47 5	99.17 32	95.78 7	97.21 159	98.35 27	95.16 22	98.71 20	98.80 22	95.05 10	99.89 3	96.70 41	99.73 1	99.73 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 5	97.73 7	98.08 18	99.15 33	94.82 27	98.81 7	98.30 32	94.76 43	98.30 26	98.90 12	93.77 17	99.68 54	97.93 14	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		97.68 6	97.44 13	98.37 7	98.90 50	95.86 6	97.27 151	98.08 74	95.81 9	97.87 36	98.31 60	94.26 13	99.68 54	97.02 33	99.49 36	99.57 26

fmvsm_l_conf0.5_n		97.65 7	97.75 6	97.34 50	98.21 92	92.75 76	97.83 84	98.73 9	95.04 28	99.30 1	98.84 20	93.34 22	99.78 35	99.32 3	99.13 77	99.50 40

fmvsm_l_conf0.5_n_a		97.63 8	97.76 5	97.26 57	98.25 86	92.59 82	97.81 88	98.68 13	94.93 30	99.24 3	98.87 15	93.52 20	99.79 33	99.32 3	99.21 69	99.40 54

SteuartSystems-ACMMP		97.62 9	97.53 11	97.87 24	98.39 77	94.25 38	98.43 24	98.27 39	95.34 17	98.11 28	98.56 31	94.53 12	99.71 46	96.57 45	99.62 15	99.65 15
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		97.59 10	97.54 10	97.73 36	99.40 11	93.77 54	98.53 15	98.29 34	95.55 13	98.56 22	97.81 99	93.90 15	99.65 58	96.62 42	99.21 69	99.77 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
test_fmvsm_n_1920		97.55 11	97.89 3	96.53 79	98.41 74	91.73 107	98.01 57	99.02 1	96.37 4	99.30 1	98.92 10	92.39 37	99.79 33	99.16 5	99.46 39	98.08 165

test_fmvsmconf_n		97.49 12	97.56 9	97.29 53	97.44 139	92.37 88	97.91 75	98.88 4	95.83 8	98.92 12	99.05 5	91.45 53	99.80 30	99.12 6	99.46 39	99.69 12

TSAR-MVS + MP.		97.42 13	97.33 15	97.69 40	99.25 27	94.24 39	98.07 52	97.85 116	93.72 75	98.57 21	98.35 51	93.69 18	99.40 110	97.06 32	99.46 39	99.44 49
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 14	97.53 11	97.06 66	98.57 69	94.46 31	97.92 73	98.14 64	94.82 38	99.01 6	98.55 33	94.18 14	97.41 320	96.94 34	99.64 13	99.32 62
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 15	97.13 16	98.17 15	99.02 42	95.28 19	98.23 40	98.27 39	92.37 129	98.27 27	98.65 29	93.33 23	99.72 45	96.49 47	99.52 28	99.51 37

SMA-MVS		97.35 16	97.03 24	98.30 8	99.06 38	95.42 10	97.94 71	98.18 57	90.57 191	98.85 15	98.94 9	93.33 23	99.83 26	96.72 40	99.68 4	99.63 17
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 17	96.97 26	98.47 5	99.08 36	96.16 4	97.55 122	97.97 101	95.59 11	96.61 72	97.89 90	92.57 34	99.84 23	95.95 66	99.51 31	99.40 54

NCCC		97.30 18	97.03 24	98.11 17	98.77 53	95.06 24	97.34 144	98.04 89	95.96 6	97.09 55	97.88 92	93.18 25	99.71 46	95.84 71	99.17 73	99.56 29

ACMMP_NAP		97.20 19	96.86 30	98.23 11	99.09 34	95.16 22	97.60 115	98.19 55	92.82 118	97.93 34	98.74 26	91.60 51	99.86 8	96.26 50	99.52 28	99.67 13

XVS		97.18 20	96.96 27	97.81 28	99.38 14	94.03 48	98.59 12	98.20 52	94.85 34	96.59 74	98.29 63	91.70 48	99.80 30	95.66 75	99.40 50	99.62 18

MCST-MVS		97.18 20	96.84 32	98.20 14	99.30 24	95.35 15	97.12 166	98.07 79	93.54 83	96.08 94	97.69 106	93.86 16	99.71 46	96.50 46	99.39 52	99.55 32

HFP-MVS		97.14 22	96.92 29	97.83 26	99.42 7	94.12 44	98.52 16	98.32 30	93.21 96	97.18 50	98.29 63	92.08 42	99.83 26	95.63 80	99.59 17	99.54 33

test_fmvsmconf0.1_n		97.09 23	97.06 19	97.19 62	95.67 233	92.21 94	97.95 70	98.27 39	95.78 10	98.40 25	99.00 6	89.99 78	99.78 35	99.06 7	99.41 49	99.59 22

MTAPA		97.08 24	96.78 38	97.97 22	99.37 16	94.42 33	97.24 153	98.08 74	95.07 27	96.11 92	98.59 30	90.88 68	99.90 2	96.18 59	99.50 33	99.58 25

region2R		97.07 25	96.84 32	97.77 33	99.46 2	93.79 52	98.52 16	98.24 47	93.19 99	97.14 52	98.34 54	91.59 52	99.87 7	95.46 87	99.59 17	99.64 16

ACMMPR		97.07 25	96.84 32	97.79 30	99.44 6	93.88 50	98.52 16	98.31 31	93.21 96	97.15 51	98.33 57	91.35 57	99.86 8	95.63 80	99.59 17	99.62 18

MVS_0304		97.04 27	96.73 41	97.96 23	97.60 133	94.36 34	98.01 57	94.09 344	97.33 2	96.29 86	98.79 24	89.73 82	99.86 8	99.36 2	99.42 46	99.67 13

CP-MVS		97.02 28	96.81 36	97.64 43	99.33 21	93.54 57	98.80 8	98.28 36	92.99 107	96.45 82	98.30 62	91.90 45	99.85 18	95.61 82	99.68 4	99.54 33

SR-MVS		97.01 29	96.86 30	97.47 46	99.09 34	93.27 66	97.98 61	98.07 79	93.75 74	97.45 42	98.48 41	91.43 55	99.59 74	96.22 53	99.27 62	99.54 33

ZNCC-MVS		96.96 30	96.67 44	97.85 25	99.37 16	94.12 44	98.49 20	98.18 57	92.64 124	96.39 84	98.18 70	91.61 50	99.88 4	95.59 85	99.55 24	99.57 26

APD-MVS		96.95 31	96.60 46	98.01 19	99.03 41	94.93 26	97.72 98	98.10 72	91.50 151	98.01 31	98.32 59	92.33 38	99.58 77	94.85 100	99.51 31	99.53 36
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		96.94 32	97.06 19	96.59 77	98.72 55	91.86 105	97.67 103	98.49 19	94.66 48	97.24 49	98.41 47	92.31 40	98.94 159	96.61 43	99.46 39	98.96 94

DeepC-MVS_fast	93.89 2	96.93 33	96.64 45	97.78 31	98.64 64	94.30 35	97.41 134	98.04 89	94.81 39	96.59 74	98.37 49	91.24 59	99.64 66	95.16 93	99.52 28	99.42 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS-test		96.89 34	97.04 23	96.45 90	98.29 82	91.66 113	99.03 4	97.85 116	95.84 7	96.90 59	97.97 86	91.24 59	98.75 176	96.92 35	99.33 58	98.94 97

SR-MVS-dyc-post		96.88 35	96.80 37	97.11 65	99.02 42	92.34 89	97.98 61	98.03 91	93.52 85	97.43 45	98.51 36	91.40 56	99.56 85	96.05 61	99.26 64	99.43 51

CS-MVS		96.86 36	97.06 19	96.26 106	98.16 98	91.16 140	99.09 3	97.87 111	95.30 18	97.06 56	98.03 80	91.72 46	98.71 182	97.10 31	99.17 73	98.90 102

mPP-MVS		96.86 36	96.60 46	97.64 43	99.40 11	93.44 59	98.50 19	98.09 73	93.27 95	95.95 100	98.33 57	91.04 64	99.88 4	95.20 92	99.57 23	99.60 21

fmvsm_s_conf0.5_n		96.85 38	97.13 16	96.04 119	98.07 105	90.28 169	97.97 67	98.76 8	94.93 30	98.84 16	99.06 4	88.80 92	99.65 58	99.06 7	98.63 99	98.18 155

GST-MVS		96.85 38	96.52 50	97.82 27	99.36 18	94.14 43	98.29 31	98.13 65	92.72 121	96.70 66	98.06 77	91.35 57	99.86 8	94.83 101	99.28 61	99.47 46

patch_mono-2		96.83 40	97.44 13	95.01 172	99.05 39	85.39 298	96.98 176	98.77 7	94.70 45	97.99 32	98.66 27	93.61 19	99.91 1	97.67 18	99.50 33	99.72 11

APD-MVS_3200maxsize		96.81 41	96.71 43	97.12 64	99.01 45	92.31 91	97.98 61	98.06 82	93.11 104	97.44 43	98.55 33	90.93 66	99.55 87	96.06 60	99.25 66	99.51 37

PGM-MVS		96.81 41	96.53 49	97.65 41	99.35 20	93.53 58	97.65 106	98.98 2	92.22 131	97.14 52	98.44 44	91.17 62	99.85 18	94.35 113	99.46 39	99.57 26

MP-MVS		96.77 43	96.45 56	97.72 37	99.39 13	93.80 51	98.41 25	98.06 82	93.37 91	95.54 115	98.34 54	90.59 72	99.88 4	94.83 101	99.54 26	99.49 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 43	96.46 55	97.71 39	98.40 75	94.07 46	98.21 43	98.45 22	89.86 204	97.11 54	98.01 83	92.52 35	99.69 52	96.03 64	99.53 27	99.36 60

fmvsm_s_conf0.5_n_a		96.75 45	96.93 28	96.20 111	97.64 128	90.72 156	98.00 59	98.73 9	94.55 50	98.91 13	99.08 3	88.22 101	99.63 67	98.91 9	98.37 111	98.25 151

test_fmvsmvis_n_1920		96.70 46	96.84 32	96.31 100	96.62 182	91.73 107	97.98 61	98.30 32	96.19 5	96.10 93	98.95 8	89.42 83	99.76 38	98.90 10	99.08 81	97.43 195

MP-MVS-pluss		96.70 46	96.27 60	97.98 21	99.23 30	94.71 28	96.96 178	98.06 82	90.67 182	95.55 113	98.78 25	91.07 63	99.86 8	96.58 44	99.55 24	99.38 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.		96.69 48	96.49 51	97.27 56	98.31 81	93.39 60	96.79 190	96.72 234	94.17 62	97.44 43	97.66 110	92.76 28	99.33 115	96.86 37	97.76 131	99.08 83

HPM-MVS		96.69 48	96.45 56	97.40 48	99.36 18	93.11 69	98.87 6	98.06 82	91.17 166	96.40 83	97.99 84	90.99 65	99.58 77	95.61 82	99.61 16	99.49 42
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		96.68 50	96.58 48	96.99 68	98.46 70	92.31 91	96.20 244	98.90 3	94.30 60	95.86 102	97.74 104	92.33 38	99.38 113	96.04 63	99.42 46	99.28 65

DELS-MVS		96.61 51	96.38 58	97.30 52	97.79 119	93.19 67	95.96 255	98.18 57	95.23 19	95.87 101	97.65 111	91.45 53	99.70 51	95.87 67	99.44 45	99.00 92
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 51	97.09 18	95.15 163	98.09 101	86.63 278	96.00 253	98.15 62	95.43 14	97.95 33	98.56 31	93.40 21	99.36 114	96.77 38	99.48 37	99.45 47

fmvsm_s_conf0.1_n		96.58 53	96.77 39	96.01 123	96.67 180	90.25 170	97.91 75	98.38 23	94.48 53	98.84 16	99.14 1	88.06 103	99.62 68	98.82 11	98.60 101	98.15 158

EI-MVSNet-Vis-set		96.51 54	96.47 52	96.63 74	98.24 87	91.20 135	96.89 182	97.73 126	94.74 44	96.49 78	98.49 38	90.88 68	99.58 77	96.44 48	98.32 113	99.13 77

HPM-MVS_fast		96.51 54	96.27 60	97.22 59	99.32 22	92.74 77	98.74 9	98.06 82	90.57 191	96.77 63	98.35 51	90.21 75	99.53 91	94.80 104	99.63 14	99.38 58

EC-MVSNet		96.42 56	96.47 52	96.26 106	97.01 161	91.52 119	98.89 5	97.75 123	94.42 55	96.64 71	97.68 107	89.32 84	98.60 192	97.45 26	99.11 80	98.67 121

fmvsm_s_conf0.1_n_a		96.40 57	96.47 52	96.16 113	95.48 241	90.69 157	97.91 75	98.33 29	94.07 64	98.93 9	99.14 1	87.44 117	99.61 69	98.63 13	98.32 113	98.18 155

CANet		96.39 58	96.02 63	97.50 45	97.62 130	93.38 61	97.02 171	97.96 102	95.42 15	94.86 125	97.81 99	87.38 119	99.82 28	96.88 36	99.20 71	99.29 63

dcpmvs_2		96.37 59	97.05 22	94.31 212	98.96 46	84.11 316	97.56 119	97.51 153	93.92 69	97.43 45	98.52 35	92.75 29	99.32 117	97.32 30	99.50 33	99.51 37

EI-MVSNet-UG-set		96.34 60	96.30 59	96.47 87	98.20 93	90.93 147	96.86 184	97.72 128	94.67 47	96.16 91	98.46 42	90.43 73	99.58 77	96.23 52	97.96 125	98.90 102

train_agg		96.30 61	95.83 68	97.72 37	98.70 56	94.19 40	96.41 223	98.02 94	88.58 246	96.03 95	97.56 121	92.73 31	99.59 74	95.04 95	99.37 56	99.39 56

ACMMP		96.27 62	95.93 64	97.28 55	99.24 28	92.62 80	98.25 36	98.81 5	92.99 107	94.56 131	98.39 48	88.96 89	99.85 18	94.57 112	97.63 132	99.36 60
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 63	96.19 62	96.39 95	98.23 91	91.35 127	96.24 242	98.79 6	93.99 67	95.80 104	97.65 111	89.92 80	99.24 124	95.87 67	99.20 71	98.58 123

test_fmvsmconf0.01_n		96.15 64	95.85 67	97.03 67	92.66 351	91.83 106	97.97 67	97.84 120	95.57 12	97.53 39	99.00 6	84.20 161	99.76 38	98.82 11	99.08 81	99.48 44

DeepC-MVS	93.07 3	96.06 65	95.66 69	97.29 53	97.96 108	93.17 68	97.30 149	98.06 82	93.92 69	93.38 158	98.66 27	86.83 125	99.73 42	95.60 84	99.22 68	98.96 94
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 66	95.91 65	96.46 89	99.24 28	90.47 164	98.30 30	98.57 18	89.01 229	93.97 145	97.57 119	92.62 33	99.76 38	94.66 107	99.27 62	99.15 75

ETV-MVS		96.02 67	95.89 66	96.40 93	97.16 147	92.44 86	97.47 131	97.77 122	94.55 50	96.48 79	94.51 274	91.23 61	98.92 161	95.65 78	98.19 118	97.82 177

canonicalmvs		96.02 67	95.45 74	97.75 35	97.59 134	95.15 23	98.28 32	97.60 142	94.52 52	96.27 88	96.12 200	87.65 111	99.18 130	96.20 58	94.82 193	98.91 101

CDPH-MVS		95.97 69	95.38 77	97.77 33	98.93 47	94.44 32	96.35 231	97.88 109	86.98 291	96.65 70	97.89 90	91.99 44	99.47 102	92.26 149	99.46 39	99.39 56

UA-Net		95.95 70	95.53 71	97.20 61	97.67 124	92.98 72	97.65 106	98.13 65	94.81 39	96.61 72	98.35 51	88.87 90	99.51 96	90.36 189	97.35 142	99.11 81

VNet		95.89 71	95.45 74	97.21 60	98.07 105	92.94 73	97.50 125	98.15 62	93.87 71	97.52 40	97.61 117	85.29 145	99.53 91	95.81 72	95.27 185	99.16 73

alignmvs		95.87 72	95.23 81	97.78 31	97.56 137	95.19 21	97.86 79	97.17 193	94.39 57	96.47 80	96.40 187	85.89 138	99.20 127	96.21 57	95.11 189	98.95 96

casdiffmvs_mvg		95.81 73	95.57 70	96.51 83	96.87 166	91.49 120	97.50 125	97.56 149	93.99 67	95.13 122	97.92 89	87.89 107	98.78 171	95.97 65	97.33 143	99.26 67
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 74	94.92 87	98.01 19	98.08 104	95.71 9	95.27 287	97.62 141	90.43 194	95.55 113	97.07 144	91.72 46	99.50 99	89.62 204	98.94 89	98.82 111

DP-MVS Recon		95.68 75	95.12 85	97.37 49	99.19 31	94.19 40	97.03 169	98.08 74	88.35 255	95.09 123	97.65 111	89.97 79	99.48 101	92.08 158	98.59 102	98.44 140

casdiffmvs		95.64 76	95.49 72	96.08 115	96.76 178	90.45 165	97.29 150	97.44 170	94.00 66	95.46 117	97.98 85	87.52 115	98.73 178	95.64 79	97.33 143	99.08 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MG-MVS		95.61 77	95.38 77	96.31 100	98.42 73	90.53 162	96.04 250	97.48 156	93.47 87	95.67 110	98.10 73	89.17 86	99.25 123	91.27 176	98.77 94	99.13 77

baseline		95.58 78	95.42 76	96.08 115	96.78 173	90.41 167	97.16 163	97.45 166	93.69 78	95.65 111	97.85 96	87.29 120	98.68 184	95.66 75	97.25 147	99.13 77

CPTT-MVS		95.57 79	95.19 82	96.70 71	99.27 26	91.48 121	98.33 28	98.11 70	87.79 272	95.17 121	98.03 80	87.09 123	99.61 69	93.51 129	99.42 46	99.02 86

EIA-MVS		95.53 80	95.47 73	95.71 138	97.06 156	89.63 186	97.82 86	97.87 111	93.57 79	93.92 146	95.04 250	90.61 71	98.95 158	94.62 109	98.68 97	98.54 125

3Dnovator+	91.43 4	95.40 81	94.48 103	98.16 16	96.90 165	95.34 16	98.48 21	97.87 111	94.65 49	88.53 281	98.02 82	83.69 167	99.71 46	93.18 136	98.96 88	99.44 49

PS-MVSNAJ		95.37 82	95.33 79	95.49 151	97.35 141	90.66 160	95.31 284	97.48 156	93.85 72	96.51 77	95.70 224	88.65 95	99.65 58	94.80 104	98.27 115	96.17 232

MVSFormer		95.37 82	95.16 83	95.99 124	96.34 204	91.21 133	98.22 41	97.57 146	91.42 155	96.22 89	97.32 129	86.20 135	97.92 274	94.07 117	99.05 83	98.85 108

xiu_mvs_v2_base		95.32 84	95.29 80	95.40 156	97.22 143	90.50 163	95.44 278	97.44 170	93.70 77	96.46 81	96.18 196	88.59 98	99.53 91	94.79 106	97.81 128	96.17 232

PVSNet_Blended_VisFu		95.27 85	94.91 88	96.38 96	98.20 93	90.86 149	97.27 151	98.25 45	90.21 196	94.18 139	97.27 133	87.48 116	99.73 42	93.53 128	97.77 130	98.55 124

diffmvs		95.25 86	95.13 84	95.63 141	96.43 200	89.34 202	95.99 254	97.35 182	92.83 117	96.31 85	97.37 128	86.44 130	98.67 185	96.26 50	97.19 149	98.87 107
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		95.23 87	94.81 89	96.51 83	97.18 146	91.58 117	98.26 35	98.12 67	94.38 58	94.90 124	98.15 72	82.28 201	98.92 161	91.45 173	98.58 103	99.01 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		95.22 88	95.04 86	95.76 131	97.49 138	89.56 190	98.67 10	97.00 212	90.69 180	94.24 137	97.62 116	89.79 81	98.81 169	93.39 134	96.49 164	98.92 100

EPNet		95.20 89	94.56 97	97.14 63	92.80 348	92.68 79	97.85 82	94.87 329	96.64 3	92.46 174	97.80 101	86.23 132	99.65 58	93.72 127	98.62 100	99.10 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator	91.36 5	95.19 90	94.44 105	97.44 47	96.56 189	93.36 63	98.65 11	98.36 24	94.12 63	89.25 266	98.06 77	82.20 203	99.77 37	93.41 133	99.32 59	99.18 72

OMC-MVS		95.09 91	94.70 93	96.25 109	98.46 70	91.28 129	96.43 221	97.57 146	92.04 140	94.77 127	97.96 87	87.01 124	99.09 142	91.31 175	96.77 156	98.36 147

xiu_mvs_v1_base_debu		95.01 92	94.76 90	95.75 133	96.58 186	91.71 109	96.25 239	97.35 182	92.99 107	96.70 66	96.63 174	82.67 191	99.44 106	96.22 53	97.46 135	96.11 237

xiu_mvs_v1_base		95.01 92	94.76 90	95.75 133	96.58 186	91.71 109	96.25 239	97.35 182	92.99 107	96.70 66	96.63 174	82.67 191	99.44 106	96.22 53	97.46 135	96.11 237

xiu_mvs_v1_base_debi		95.01 92	94.76 90	95.75 133	96.58 186	91.71 109	96.25 239	97.35 182	92.99 107	96.70 66	96.63 174	82.67 191	99.44 106	96.22 53	97.46 135	96.11 237

PAPM_NR		95.01 92	94.59 95	96.26 106	98.89 51	90.68 159	97.24 153	97.73 126	91.80 145	92.93 171	96.62 177	89.13 87	99.14 135	89.21 216	97.78 129	98.97 93

lupinMVS		94.99 96	94.56 97	96.29 104	96.34 204	91.21 133	95.83 260	96.27 261	88.93 234	96.22 89	96.88 155	86.20 135	98.85 166	95.27 91	99.05 83	98.82 111

Effi-MVS+		94.93 97	94.45 104	96.36 98	96.61 183	91.47 122	96.41 223	97.41 175	91.02 171	94.50 132	95.92 208	87.53 114	98.78 171	93.89 123	96.81 155	98.84 110

IS-MVSNet		94.90 98	94.52 101	96.05 118	97.67 124	90.56 161	98.44 23	96.22 264	93.21 96	93.99 143	97.74 104	85.55 143	98.45 204	89.98 193	97.86 126	99.14 76

MVS_Test		94.89 99	94.62 94	95.68 139	96.83 170	89.55 191	96.70 199	97.17 193	91.17 166	95.60 112	96.11 203	87.87 108	98.76 175	93.01 144	97.17 150	98.72 116

PVSNet_Blended		94.87 100	94.56 97	95.81 130	98.27 83	89.46 197	95.47 277	98.36 24	88.84 237	94.36 134	96.09 204	88.02 104	99.58 77	93.44 131	98.18 119	98.40 143

jason		94.84 101	94.39 106	96.18 112	95.52 239	90.93 147	96.09 248	96.52 250	89.28 221	96.01 98	97.32 129	84.70 152	98.77 174	95.15 94	98.91 91	98.85 108
jason: jason.
API-MVS		94.84 101	94.49 102	95.90 126	97.90 114	92.00 102	97.80 89	97.48 156	89.19 224	94.81 126	96.71 160	88.84 91	99.17 131	88.91 223	98.76 95	96.53 221

test_yl		94.78 103	94.23 107	96.43 91	97.74 121	91.22 131	96.85 185	97.10 198	91.23 163	95.71 107	96.93 150	84.30 158	99.31 119	93.10 137	95.12 187	98.75 113

DCV-MVSNet		94.78 103	94.23 107	96.43 91	97.74 121	91.22 131	96.85 185	97.10 198	91.23 163	95.71 107	96.93 150	84.30 158	99.31 119	93.10 137	95.12 187	98.75 113

WTY-MVS		94.71 105	94.02 109	96.79 70	97.71 123	92.05 100	96.59 214	97.35 182	90.61 188	94.64 129	96.93 150	86.41 131	99.39 111	91.20 178	94.71 197	98.94 97

sss		94.51 106	93.80 113	96.64 72	97.07 153	91.97 103	96.32 234	98.06 82	88.94 233	94.50 132	96.78 157	84.60 153	99.27 122	91.90 159	96.02 169	98.68 120

test_cas_vis1_n_1920		94.48 107	94.55 100	94.28 214	96.78 173	86.45 280	97.63 112	97.64 138	93.32 94	97.68 38	98.36 50	73.75 317	99.08 144	96.73 39	99.05 83	97.31 201

CANet_DTU		94.37 108	93.65 117	96.55 78	96.46 198	92.13 98	96.21 243	96.67 241	94.38 58	93.53 154	97.03 147	79.34 251	99.71 46	90.76 183	98.45 109	97.82 177

AdaColmap		94.34 109	93.68 116	96.31 100	98.59 66	91.68 112	96.59 214	97.81 121	89.87 203	92.15 183	97.06 145	83.62 170	99.54 89	89.34 210	98.07 122	97.70 182

CNLPA		94.28 110	93.53 122	96.52 80	98.38 78	92.55 83	96.59 214	96.88 225	90.13 200	91.91 189	97.24 135	85.21 146	99.09 142	87.64 247	97.83 127	97.92 169

MAR-MVS		94.22 111	93.46 127	96.51 83	98.00 107	92.19 97	97.67 103	97.47 159	88.13 262	93.00 166	95.84 212	84.86 151	99.51 96	87.99 234	98.17 120	97.83 176
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		94.18 112	93.42 132	96.48 86	97.64 128	91.42 125	95.55 272	97.71 132	88.99 230	92.34 180	95.82 214	89.19 85	99.11 138	86.14 273	97.38 140	98.90 102

SDMVSNet		94.17 113	93.61 118	95.86 128	98.09 101	91.37 126	97.35 143	98.20 52	93.18 100	91.79 192	97.28 131	79.13 255	98.93 160	94.61 110	92.84 222	97.28 202

test_vis1_n_1920		94.17 113	94.58 96	92.91 276	97.42 140	82.02 337	97.83 84	97.85 116	94.68 46	98.10 29	98.49 38	70.15 337	99.32 117	97.91 15	98.82 92	97.40 196

h-mvs33		94.15 115	93.52 124	96.04 119	97.81 118	90.22 171	97.62 114	97.58 145	95.19 20	96.74 64	97.45 124	83.67 168	99.61 69	95.85 69	79.73 360	98.29 150

CHOSEN 1792x2688		94.15 115	93.51 125	96.06 117	98.27 83	89.38 200	95.18 291	98.48 21	85.60 313	93.76 149	97.11 142	83.15 178	99.61 69	91.33 174	98.72 96	99.19 71

Vis-MVSNet (Re-imp)		94.15 115	93.88 112	94.95 178	97.61 131	87.92 247	98.10 49	95.80 280	92.22 131	93.02 165	97.45 124	84.53 155	97.91 277	88.24 231	97.97 124	99.02 86

CDS-MVSNet		94.14 118	93.54 121	95.93 125	96.18 211	91.46 123	96.33 233	97.04 208	88.97 232	93.56 151	96.51 181	87.55 113	97.89 278	89.80 198	95.95 171	98.44 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	91.00 6	94.11 119	93.43 130	96.13 114	98.58 68	91.15 141	96.69 201	97.39 176	87.29 286	91.37 203	96.71 160	88.39 99	99.52 95	87.33 254	97.13 151	97.73 180
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs		94.09 120	93.70 115	95.27 159	95.70 231	92.03 101	98.10 49	98.68 13	93.36 93	90.39 224	96.70 162	87.63 112	97.94 270	92.25 151	90.50 267	95.84 245

PVSNet_BlendedMVS		94.06 121	93.92 111	94.47 202	98.27 83	89.46 197	96.73 195	98.36 24	90.17 197	94.36 134	95.24 244	88.02 104	99.58 77	93.44 131	90.72 263	94.36 331

nrg030		94.05 122	93.31 134	96.27 105	95.22 263	94.59 29	98.34 27	97.46 161	92.93 115	91.21 213	96.64 168	87.23 122	98.22 223	94.99 98	85.80 309	95.98 241

UGNet		94.04 123	93.28 135	96.31 100	96.85 167	91.19 136	97.88 78	97.68 133	94.40 56	93.00 166	96.18 196	73.39 319	99.61 69	91.72 165	98.46 108	98.13 159
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		94.01 124	93.46 127	95.64 140	96.16 213	90.45 165	96.71 198	96.89 224	89.27 222	93.46 156	96.92 153	87.29 120	97.94 270	88.70 227	95.74 176	98.53 126

114514_t		93.95 125	93.06 140	96.63 74	99.07 37	91.61 114	97.46 133	97.96 102	77.99 375	93.00 166	97.57 119	86.14 137	99.33 115	89.22 215	99.15 75	98.94 97

FC-MVSNet-test		93.94 126	93.57 119	95.04 169	95.48 241	91.45 124	98.12 48	98.71 11	93.37 91	90.23 227	96.70 162	87.66 110	97.85 280	91.49 171	90.39 268	95.83 246

mvsany_test1		93.93 127	93.98 110	93.78 242	94.94 279	86.80 271	94.62 301	92.55 366	88.77 243	96.85 60	98.49 38	88.98 88	98.08 243	95.03 96	95.62 180	96.46 226

GeoE		93.89 128	93.28 135	95.72 137	96.96 164	89.75 184	98.24 39	96.92 221	89.47 216	92.12 185	97.21 137	84.42 156	98.39 211	87.71 241	96.50 163	99.01 89

HY-MVS	89.66 9	93.87 129	92.95 143	96.63 74	97.10 152	92.49 85	95.64 270	96.64 242	89.05 228	93.00 166	95.79 218	85.77 141	99.45 105	89.16 219	94.35 199	97.96 167

XVG-OURS-SEG-HR		93.86 130	93.55 120	94.81 186	97.06 156	88.53 228	95.28 285	97.45 166	91.68 148	94.08 142	97.68 107	82.41 199	98.90 164	93.84 125	92.47 228	96.98 209

mvsmamba		93.83 131	93.46 127	94.93 181	94.88 284	90.85 150	98.55 14	95.49 297	94.24 61	91.29 210	96.97 149	83.04 182	98.14 231	95.56 86	91.17 253	95.78 251

VDD-MVS		93.82 132	93.08 139	96.02 121	97.88 115	89.96 180	97.72 98	95.85 278	92.43 127	95.86 102	98.44 44	68.42 346	99.39 111	96.31 49	94.85 191	98.71 118

mvs_anonymous		93.82 132	93.74 114	94.06 222	96.44 199	85.41 296	95.81 261	97.05 206	89.85 206	90.09 237	96.36 189	87.44 117	97.75 290	93.97 119	96.69 160	99.02 86

HQP_MVS		93.78 134	93.43 130	94.82 184	96.21 208	89.99 176	97.74 93	97.51 153	94.85 34	91.34 204	96.64 168	81.32 217	98.60 192	93.02 142	92.23 231	95.86 242

PS-MVSNAJss		93.74 135	93.51 125	94.44 203	93.91 320	89.28 207	97.75 92	97.56 149	92.50 126	89.94 241	96.54 180	88.65 95	98.18 228	93.83 126	90.90 260	95.86 242

XVG-OURS		93.72 136	93.35 133	94.80 189	97.07 153	88.61 223	94.79 298	97.46 161	91.97 143	93.99 143	97.86 95	81.74 212	98.88 165	92.64 148	92.67 227	96.92 213

HyFIR lowres test		93.66 137	92.92 144	95.87 127	98.24 87	89.88 181	94.58 303	98.49 19	85.06 323	93.78 148	95.78 219	82.86 187	98.67 185	91.77 164	95.71 178	99.07 85

iter_conf_final		93.60 138	93.11 138	95.04 169	97.13 150	91.30 128	97.92 73	95.65 290	92.98 112	91.60 196	96.64 168	79.28 253	98.13 232	95.34 90	91.49 245	95.70 259

LFMVS		93.60 138	92.63 158	96.52 80	98.13 100	91.27 130	97.94 71	93.39 357	90.57 191	96.29 86	98.31 60	69.00 342	99.16 132	94.18 116	95.87 173	99.12 80

F-COLMAP		93.58 140	92.98 142	95.37 157	98.40 75	88.98 216	97.18 161	97.29 187	87.75 275	90.49 221	97.10 143	85.21 146	99.50 99	86.70 264	96.72 159	97.63 184

ab-mvs		93.57 141	92.55 163	96.64 72	97.28 142	91.96 104	95.40 279	97.45 166	89.81 208	93.22 164	96.28 192	79.62 248	99.46 103	90.74 184	93.11 219	98.50 130

LS3D		93.57 141	92.61 161	96.47 87	97.59 134	91.61 114	97.67 103	97.72 128	85.17 321	90.29 226	98.34 54	84.60 153	99.73 42	83.85 306	98.27 115	98.06 166

FA-MVS(test-final)		93.52 143	92.92 144	95.31 158	96.77 175	88.54 227	94.82 297	96.21 266	89.61 211	94.20 138	95.25 243	83.24 175	99.14 135	90.01 192	96.16 168	98.25 151

Fast-Effi-MVS+		93.46 144	92.75 153	95.59 144	96.77 175	90.03 173	96.81 189	97.13 195	88.19 258	91.30 207	94.27 290	86.21 134	98.63 189	87.66 246	96.46 166	98.12 160

hse-mvs2		93.45 145	92.99 141	94.81 186	97.02 160	88.59 224	96.69 201	96.47 253	95.19 20	96.74 64	96.16 199	83.67 168	98.48 203	95.85 69	79.13 364	97.35 199

QAPM		93.45 145	92.27 173	96.98 69	96.77 175	92.62 80	98.39 26	98.12 67	84.50 331	88.27 288	97.77 102	82.39 200	99.81 29	85.40 286	98.81 93	98.51 129

UniMVSNet_NR-MVSNet		93.37 147	92.67 157	95.47 154	95.34 252	92.83 74	97.17 162	98.58 17	92.98 112	90.13 232	95.80 215	88.37 100	97.85 280	91.71 166	83.93 337	95.73 258

1112_ss		93.37 147	92.42 170	96.21 110	97.05 158	90.99 143	96.31 235	96.72 234	86.87 294	89.83 245	96.69 164	86.51 129	99.14 135	88.12 232	93.67 213	98.50 130

UniMVSNet (Re)		93.31 149	92.55 163	95.61 143	95.39 246	93.34 64	97.39 139	98.71 11	93.14 103	90.10 236	94.83 260	87.71 109	98.03 254	91.67 169	83.99 336	95.46 269

OPM-MVS		93.28 150	92.76 151	94.82 184	94.63 297	90.77 154	96.65 205	97.18 191	93.72 75	91.68 195	97.26 134	79.33 252	98.63 189	92.13 155	92.28 230	95.07 294
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet		93.24 151	92.48 168	95.51 149	95.70 231	92.39 87	97.86 79	98.66 16	92.30 130	92.09 187	95.37 238	80.49 230	98.40 207	93.95 120	85.86 308	95.75 256

test_fmvs1		93.21 152	93.53 122	92.25 295	96.55 191	81.20 344	97.40 138	96.96 214	90.68 181	96.80 61	98.04 79	69.25 341	98.40 207	97.58 21	98.50 104	97.16 206

MVSTER		93.20 153	92.81 150	94.37 207	96.56 189	89.59 189	97.06 168	97.12 196	91.24 162	91.30 207	95.96 206	82.02 206	98.05 250	93.48 130	90.55 265	95.47 268

test1111		93.19 154	92.82 149	94.30 213	97.58 136	84.56 311	98.21 43	89.02 384	93.53 84	94.58 130	98.21 67	72.69 320	99.05 151	93.06 140	98.48 107	99.28 65

ECVR-MVS		93.19 154	92.73 155	94.57 200	97.66 126	85.41 296	98.21 43	88.23 386	93.43 89	94.70 128	98.21 67	72.57 321	99.07 148	93.05 141	98.49 105	99.25 68

HQP-MVS		93.19 154	92.74 154	94.54 201	95.86 224	89.33 203	96.65 205	97.39 176	93.55 80	90.14 228	95.87 210	80.95 220	98.50 200	92.13 155	92.10 236	95.78 251

iter_conf05		93.18 157	92.63 158	94.83 183	96.64 181	90.69 157	97.60 115	95.53 296	92.52 125	91.58 197	96.64 168	76.35 293	98.13 232	95.43 88	91.42 248	95.68 261

CHOSEN 280x420		93.12 158	92.72 156	94.34 210	96.71 179	87.27 259	90.29 374	97.72 128	86.61 298	91.34 204	95.29 240	84.29 160	98.41 206	93.25 135	98.94 89	97.35 199

sd_testset		93.10 159	92.45 169	95.05 168	98.09 101	89.21 209	96.89 182	97.64 138	93.18 100	91.79 192	97.28 131	75.35 303	98.65 187	88.99 221	92.84 222	97.28 202

RRT_MVS		93.10 159	92.83 148	93.93 235	94.76 289	88.04 243	98.47 22	96.55 249	93.44 88	90.01 240	97.04 146	80.64 227	97.93 273	94.33 114	90.21 270	95.83 246

Effi-MVS+-dtu		93.08 161	93.21 137	92.68 286	96.02 222	83.25 326	97.14 165	96.72 234	93.85 72	91.20 214	93.44 323	83.08 180	98.30 218	91.69 168	95.73 177	96.50 223

test_djsdf		93.07 162	92.76 151	94.00 226	93.49 334	88.70 222	98.22 41	97.57 146	91.42 155	90.08 238	95.55 232	82.85 188	97.92 274	94.07 117	91.58 243	95.40 274

VDDNet		93.05 163	92.07 177	96.02 121	96.84 168	90.39 168	98.08 51	95.85 278	86.22 305	95.79 105	98.46 42	67.59 349	99.19 128	94.92 99	94.85 191	98.47 135

thisisatest0530		93.03 164	92.21 175	95.49 151	97.07 153	89.11 214	97.49 130	92.19 368	90.16 198	94.09 141	96.41 186	76.43 292	99.05 151	90.38 188	95.68 179	98.31 149

EI-MVSNet		93.03 164	92.88 146	93.48 256	95.77 229	86.98 268	96.44 219	97.12 196	90.66 184	91.30 207	97.64 114	86.56 127	98.05 250	89.91 195	90.55 265	95.41 271

CLD-MVS		92.98 166	92.53 165	94.32 211	96.12 218	89.20 210	95.28 285	97.47 159	92.66 122	89.90 242	95.62 228	80.58 228	98.40 207	92.73 147	92.40 229	95.38 276
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt0517		92.96 167	92.33 172	94.87 182	97.11 151	87.16 265	97.97 67	92.09 369	90.63 186	93.88 147	97.01 148	76.50 289	99.06 150	90.29 191	95.45 182	98.38 145

ACMM	89.79 8	92.96 167	92.50 167	94.35 208	96.30 206	88.71 221	97.58 117	97.36 181	91.40 157	90.53 220	96.65 167	79.77 244	98.75 176	91.24 177	91.64 241	95.59 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.94 169	92.56 162	94.10 220	96.16 213	88.26 235	97.65 106	97.46 161	91.29 158	90.12 234	97.16 139	79.05 257	98.73 178	92.25 151	91.89 239	95.31 281

BH-untuned		92.94 169	92.62 160	93.92 236	97.22 143	86.16 288	96.40 227	96.25 263	90.06 201	89.79 246	96.17 198	83.19 176	98.35 214	87.19 257	97.27 146	97.24 204

DU-MVS		92.90 171	92.04 178	95.49 151	94.95 277	92.83 74	97.16 163	98.24 47	93.02 106	90.13 232	95.71 222	83.47 171	97.85 280	91.71 166	83.93 337	95.78 251

PatchMatch-RL		92.90 171	92.02 180	95.56 145	98.19 95	90.80 152	95.27 287	97.18 191	87.96 264	91.86 191	95.68 225	80.44 231	98.99 156	84.01 302	97.54 134	96.89 214

PMMVS		92.86 173	92.34 171	94.42 205	94.92 280	86.73 274	94.53 305	96.38 257	84.78 328	94.27 136	95.12 249	83.13 179	98.40 207	91.47 172	96.49 164	98.12 160

OpenMVS	89.19 12	92.86 173	91.68 192	96.40 93	95.34 252	92.73 78	98.27 33	98.12 67	84.86 326	85.78 327	97.75 103	78.89 264	99.74 41	87.50 251	98.65 98	96.73 218

Test_1112_low_res		92.84 175	91.84 186	95.85 129	97.04 159	89.97 179	95.53 274	96.64 242	85.38 316	89.65 251	95.18 245	85.86 139	99.10 139	87.70 242	93.58 218	98.49 132

baseline1		92.82 176	91.90 184	95.55 147	97.20 145	90.77 154	97.19 160	94.58 334	92.20 133	92.36 178	96.34 190	84.16 162	98.21 224	89.20 217	83.90 340	97.68 183

1314		92.81 177	92.03 179	95.14 164	95.33 255	89.52 194	96.04 250	97.44 170	87.72 276	86.25 324	95.33 239	83.84 165	98.79 170	89.26 213	97.05 152	97.11 207

DP-MVS		92.76 178	91.51 200	96.52 80	98.77 53	90.99 143	97.38 141	96.08 270	82.38 351	89.29 263	97.87 93	83.77 166	99.69 52	81.37 328	96.69 160	98.89 105

test_fmvs1_n		92.73 179	92.88 146	92.29 293	96.08 221	81.05 345	97.98 61	97.08 201	90.72 179	96.79 62	98.18 70	63.07 368	98.45 204	97.62 20	98.42 110	97.36 197

BH-RMVSNet		92.72 180	91.97 182	94.97 176	97.16 147	87.99 245	96.15 246	95.60 291	90.62 187	91.87 190	97.15 141	78.41 270	98.57 196	83.16 308	97.60 133	98.36 147

ACMP	89.59 10	92.62 181	92.14 176	94.05 223	96.40 201	88.20 238	97.36 142	97.25 190	91.52 150	88.30 286	96.64 168	78.46 269	98.72 181	91.86 162	91.48 246	95.23 288
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re		92.50 182	92.52 166	92.44 288	96.82 172	81.89 338	96.92 180	93.71 353	92.41 128	84.30 340	94.60 271	85.08 148	97.03 333	91.51 170	97.36 141	98.40 143

TranMVSNet+NR-MVSNet		92.50 182	91.63 193	95.14 164	94.76 289	92.07 99	97.53 123	98.11 70	92.90 116	89.56 254	96.12 200	83.16 177	97.60 303	89.30 211	83.20 346	95.75 256

thres600view7		92.49 184	91.60 194	95.18 162	97.91 113	89.47 195	97.65 106	94.66 331	92.18 137	93.33 159	94.91 255	78.06 277	99.10 139	81.61 322	94.06 210	96.98 209

thres100view900		92.43 185	91.58 195	94.98 175	97.92 112	89.37 201	97.71 100	94.66 331	92.20 133	93.31 160	94.90 256	78.06 277	99.08 144	81.40 325	94.08 206	96.48 224

jajsoiax		92.42 186	91.89 185	94.03 225	93.33 340	88.50 229	97.73 95	97.53 151	92.00 142	88.85 273	96.50 182	75.62 301	98.11 238	93.88 124	91.56 244	95.48 265

thres400		92.42 186	91.52 198	95.12 166	97.85 116	89.29 205	97.41 134	94.88 326	92.19 135	93.27 162	94.46 279	78.17 273	99.08 144	81.40 325	94.08 206	96.98 209

tfpn200view9		92.38 188	91.52 198	94.95 178	97.85 116	89.29 205	97.41 134	94.88 326	92.19 135	93.27 162	94.46 279	78.17 273	99.08 144	81.40 325	94.08 206	96.48 224

test_vis1_n		92.37 189	92.26 174	92.72 283	94.75 291	82.64 329	98.02 56	96.80 231	91.18 165	97.77 37	97.93 88	58.02 376	98.29 219	97.63 19	98.21 117	97.23 205

bld_raw_dy_0_64		92.37 189	91.69 191	94.39 206	94.28 312	89.73 185	97.71 100	93.65 354	92.78 120	90.46 222	96.67 166	75.88 296	97.97 262	92.92 146	90.89 261	95.48 265

WR-MVS		92.34 191	91.53 197	94.77 191	95.13 270	90.83 151	96.40 227	97.98 100	91.88 144	89.29 263	95.54 233	82.50 196	97.80 285	89.79 199	85.27 317	95.69 260

NR-MVSNet		92.34 191	91.27 208	95.53 148	94.95 277	93.05 70	97.39 139	98.07 79	92.65 123	84.46 338	95.71 222	85.00 149	97.77 289	89.71 200	83.52 343	95.78 251

mvs_tets		92.31 193	91.76 187	93.94 233	93.41 337	88.29 233	97.63 112	97.53 151	92.04 140	88.76 276	96.45 184	74.62 309	98.09 242	93.91 122	91.48 246	95.45 270

TAPA-MVS	90.10 7	92.30 194	91.22 211	95.56 145	98.33 80	89.60 188	96.79 190	97.65 136	81.83 355	91.52 199	97.23 136	87.94 106	98.91 163	71.31 376	98.37 111	98.17 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0515		92.29 195	91.30 206	95.25 160	96.60 184	88.90 218	94.36 313	92.32 367	87.92 265	93.43 157	94.57 272	77.28 284	99.00 155	89.42 208	95.86 174	97.86 173

Fast-Effi-MVS+-dtu		92.29 195	91.99 181	93.21 267	95.27 259	85.52 294	97.03 169	96.63 245	92.09 138	89.11 269	95.14 247	80.33 234	98.08 243	87.54 250	94.74 196	96.03 240

IterMVS-LS		92.29 195	91.94 183	93.34 261	96.25 207	86.97 269	96.57 217	97.05 206	90.67 182	89.50 257	94.80 262	86.59 126	97.64 298	89.91 195	86.11 307	95.40 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet	86.66 18	92.24 198	91.74 190	93.73 243	97.77 120	83.69 323	92.88 355	96.72 234	87.91 266	93.00 166	94.86 258	78.51 268	99.05 151	86.53 265	97.45 139	98.47 135

VPNet		92.23 199	91.31 205	94.99 173	95.56 237	90.96 145	97.22 158	97.86 115	92.96 114	90.96 215	96.62 177	75.06 304	98.20 225	91.90 159	83.65 342	95.80 249

thres200		92.23 199	91.39 201	94.75 193	97.61 131	89.03 215	96.60 213	95.09 316	92.08 139	93.28 161	94.00 303	78.39 271	99.04 154	81.26 329	94.18 202	96.19 231

anonymousdsp		92.16 201	91.55 196	93.97 229	92.58 353	89.55 191	97.51 124	97.42 174	89.42 218	88.40 283	94.84 259	80.66 226	97.88 279	91.87 161	91.28 251	94.48 326

XXY-MVS		92.16 201	91.23 210	94.95 178	94.75 291	90.94 146	97.47 131	97.43 173	89.14 225	88.90 270	96.43 185	79.71 245	98.24 221	89.56 205	87.68 292	95.67 262

BH-w/o		92.14 203	91.75 188	93.31 262	96.99 163	85.73 291	95.67 266	95.69 286	88.73 244	89.26 265	94.82 261	82.97 185	98.07 247	85.26 288	96.32 167	96.13 236

Anonymous202405211		92.07 204	90.83 224	95.76 131	98.19 95	88.75 220	97.58 117	95.00 319	86.00 308	93.64 150	97.45 124	66.24 360	99.53 91	90.68 186	92.71 225	99.01 89

FE-MVS		92.05 205	91.05 215	95.08 167	96.83 170	87.93 246	93.91 331	95.70 284	86.30 302	94.15 140	94.97 251	76.59 288	99.21 126	84.10 300	96.86 153	98.09 164

WR-MVS_H		92.00 206	91.35 202	93.95 231	95.09 272	89.47 195	98.04 55	98.68 13	91.46 153	88.34 284	94.68 267	85.86 139	97.56 305	85.77 281	84.24 334	94.82 311

Anonymous20240529		91.98 207	90.73 228	95.73 136	98.14 99	89.40 199	97.99 60	97.72 128	79.63 369	93.54 153	97.41 127	69.94 339	99.56 85	91.04 180	91.11 255	98.22 153

PatchmatchNet		91.91 208	91.35 202	93.59 251	95.38 247	84.11 316	93.15 350	95.39 299	89.54 213	92.10 186	93.68 315	82.82 189	98.13 232	84.81 292	95.32 184	98.52 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CP-MVSNet		91.89 209	91.24 209	93.82 239	95.05 273	88.57 225	97.82 86	98.19 55	91.70 147	88.21 290	95.76 220	81.96 207	97.52 311	87.86 236	84.65 326	95.37 277

SCA		91.84 210	91.18 213	93.83 238	95.59 235	84.95 307	94.72 299	95.58 293	90.82 174	92.25 181	93.69 313	75.80 298	98.10 239	86.20 271	95.98 170	98.45 137

FMVSNet3		91.78 211	90.69 230	95.03 171	96.53 193	92.27 93	97.02 171	96.93 217	89.79 209	89.35 260	94.65 269	77.01 285	97.47 314	86.12 274	88.82 281	95.35 278

AUN-MVS		91.76 212	90.75 227	94.81 186	97.00 162	88.57 225	96.65 205	96.49 252	89.63 210	92.15 183	96.12 200	78.66 266	98.50 200	90.83 181	79.18 363	97.36 197

X-MVStestdata		91.71 213	89.67 272	97.81 28	99.38 14	94.03 48	98.59 12	98.20 52	94.85 34	96.59 74	32.69 398	91.70 48	99.80 30	95.66 75	99.40 50	99.62 18

MVS		91.71 213	90.44 237	95.51 149	95.20 265	91.59 116	96.04 250	97.45 166	73.44 383	87.36 307	95.60 229	85.42 144	99.10 139	85.97 278	97.46 135	95.83 246

EPNet_dtu		91.71 213	91.28 207	92.99 273	93.76 325	83.71 322	96.69 201	95.28 306	93.15 102	87.02 314	95.95 207	83.37 174	97.38 322	79.46 340	96.84 154	97.88 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline2		91.63 216	90.86 220	93.94 233	94.33 308	86.32 282	95.92 257	91.64 373	89.37 219	86.94 317	94.69 266	81.62 214	98.69 183	88.64 228	94.57 198	96.81 216

test2506		91.60 217	90.78 225	94.04 224	97.66 126	83.81 319	98.27 33	75.53 401	93.43 89	95.23 119	98.21 67	67.21 352	99.07 148	93.01 144	98.49 105	99.25 68

miper_ehance_all_eth		91.59 218	91.13 214	92.97 274	95.55 238	86.57 279	94.47 307	96.88 225	87.77 273	88.88 272	94.01 302	86.22 133	97.54 307	89.49 206	86.93 299	94.79 316

v2v482		91.59 218	90.85 222	93.80 240	93.87 322	88.17 240	96.94 179	96.88 225	89.54 213	89.53 255	94.90 256	81.70 213	98.02 255	89.25 214	85.04 323	95.20 289

V42		91.58 220	90.87 219	93.73 243	94.05 317	88.50 229	97.32 147	96.97 213	88.80 242	89.71 247	94.33 285	82.54 195	98.05 250	89.01 220	85.07 321	94.64 324

PCF-MVS	89.48 11	91.56 221	89.95 260	96.36 98	96.60 184	92.52 84	92.51 360	97.26 188	79.41 370	88.90 270	96.56 179	84.04 164	99.55 87	77.01 354	97.30 145	97.01 208
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PS-CasMVS		91.55 222	90.84 223	93.69 247	94.96 276	88.28 234	97.84 83	98.24 47	91.46 153	88.04 294	95.80 215	79.67 246	97.48 313	87.02 261	84.54 331	95.31 281

miper_enhance_ethall		91.54 223	91.01 216	93.15 268	95.35 251	87.07 267	93.97 326	96.90 222	86.79 295	89.17 267	93.43 325	86.55 128	97.64 298	89.97 194	86.93 299	94.74 320

PAPM		91.52 224	90.30 243	95.20 161	95.30 258	89.83 182	93.38 346	96.85 228	86.26 304	88.59 279	95.80 215	84.88 150	98.15 230	75.67 359	95.93 172	97.63 184

ET-MVSNet_ETH3D		91.49 225	90.11 253	95.63 141	96.40 201	91.57 118	95.34 281	93.48 356	90.60 190	75.58 377	95.49 235	80.08 238	96.79 342	94.25 115	89.76 274	98.52 127

TR-MVS		91.48 226	90.59 233	94.16 218	96.40 201	87.33 256	95.67 266	95.34 305	87.68 277	91.46 201	95.52 234	76.77 287	98.35 214	82.85 313	93.61 216	96.79 217

tpmrst		91.44 227	91.32 204	91.79 307	95.15 268	79.20 367	93.42 345	95.37 301	88.55 249	93.49 155	93.67 316	82.49 197	98.27 220	90.41 187	89.34 278	97.90 170

test-LLR		91.42 228	91.19 212	92.12 297	94.59 298	80.66 348	94.29 318	92.98 359	91.11 168	90.76 218	92.37 338	79.02 259	98.07 247	88.81 224	96.74 157	97.63 184

MSDG		91.42 228	90.24 247	94.96 177	97.15 149	88.91 217	93.69 338	96.32 259	85.72 312	86.93 318	96.47 183	80.24 235	98.98 157	80.57 331	95.05 190	96.98 209

c3_l		91.38 230	90.89 218	92.88 278	95.58 236	86.30 283	94.68 300	96.84 229	88.17 259	88.83 275	94.23 293	85.65 142	97.47 314	89.36 209	84.63 327	94.89 306

GA-MVS		91.38 230	90.31 242	94.59 195	94.65 296	87.62 254	94.34 314	96.19 267	90.73 178	90.35 225	93.83 307	71.84 324	97.96 267	87.22 256	93.61 216	98.21 154

v1144		91.37 232	90.60 232	93.68 248	93.89 321	88.23 237	96.84 187	97.03 210	88.37 254	89.69 249	94.39 281	82.04 205	97.98 259	87.80 238	85.37 314	94.84 308

GBi-Net		91.35 233	90.27 245	94.59 195	96.51 194	91.18 137	97.50 125	96.93 217	88.82 239	89.35 260	94.51 274	73.87 313	97.29 326	86.12 274	88.82 281	95.31 281

test1		91.35 233	90.27 245	94.59 195	96.51 194	91.18 137	97.50 125	96.93 217	88.82 239	89.35 260	94.51 274	73.87 313	97.29 326	86.12 274	88.82 281	95.31 281

UniMVSNet_ETH3D		91.34 235	90.22 250	94.68 194	94.86 285	87.86 250	97.23 157	97.46 161	87.99 263	89.90 242	96.92 153	66.35 358	98.23 222	90.30 190	90.99 258	97.96 167

FMVSNet2		91.31 236	90.08 254	94.99 173	96.51 194	92.21 94	97.41 134	96.95 215	88.82 239	88.62 278	94.75 264	73.87 313	97.42 319	85.20 289	88.55 286	95.35 278

D2MVS		91.30 237	90.95 217	92.35 290	94.71 294	85.52 294	96.18 245	98.21 51	88.89 235	86.60 321	93.82 309	79.92 242	97.95 269	89.29 212	90.95 259	93.56 344

v8		91.29 238	90.53 236	93.57 253	94.15 313	88.12 242	97.34 144	97.06 205	88.99 230	88.32 285	94.26 292	83.08 180	98.01 256	87.62 248	83.92 339	94.57 325

CVMVSNet		91.23 239	91.75 188	89.67 342	95.77 229	74.69 377	96.44 219	94.88 326	85.81 310	92.18 182	97.64 114	79.07 256	95.58 361	88.06 233	95.86 174	98.74 115

cl22		91.21 240	90.56 235	93.14 269	96.09 220	86.80 271	94.41 311	96.58 248	87.80 271	88.58 280	93.99 304	80.85 225	97.62 301	89.87 197	86.93 299	94.99 297

PEN-MVS		91.20 241	90.44 237	93.48 256	94.49 302	87.91 249	97.76 91	98.18 57	91.29 158	87.78 298	95.74 221	80.35 233	97.33 324	85.46 285	82.96 347	95.19 292

Baseline_NR-MVSNet		91.20 241	90.62 231	92.95 275	93.83 323	88.03 244	97.01 174	95.12 315	88.42 253	89.70 248	95.13 248	83.47 171	97.44 317	89.66 203	83.24 345	93.37 348

cascas		91.20 241	90.08 254	94.58 199	94.97 275	89.16 213	93.65 340	97.59 144	79.90 368	89.40 258	92.92 330	75.36 302	98.36 213	92.14 154	94.75 195	96.23 228

CostFormer		91.18 244	90.70 229	92.62 287	94.84 286	81.76 339	94.09 324	94.43 336	84.15 334	92.72 173	93.77 311	79.43 250	98.20 225	90.70 185	92.18 234	97.90 170

tt0805		91.09 245	90.07 257	94.16 218	95.61 234	88.31 232	97.56 119	96.51 251	89.56 212	89.17 267	95.64 227	67.08 356	98.38 212	91.07 179	88.44 287	95.80 249

v1192		91.07 246	90.23 248	93.58 252	93.70 326	87.82 251	96.73 195	97.07 203	87.77 273	89.58 252	94.32 287	80.90 224	97.97 262	86.52 266	85.48 312	94.95 298

v144192		91.06 247	90.28 244	93.39 259	93.66 329	87.23 262	96.83 188	97.07 203	87.43 282	89.69 249	94.28 289	81.48 215	98.00 257	87.18 258	84.92 325	94.93 302

v10		91.04 248	90.23 248	93.49 255	94.12 314	88.16 241	97.32 147	97.08 201	88.26 257	88.29 287	94.22 295	82.17 204	97.97 262	86.45 268	84.12 335	94.33 332

eth_miper_zixun_eth		91.02 249	90.59 233	92.34 292	95.33 255	84.35 312	94.10 323	96.90 222	88.56 248	88.84 274	94.33 285	84.08 163	97.60 303	88.77 226	84.37 333	95.06 295

v148		90.99 250	90.38 239	92.81 281	93.83 323	85.80 290	96.78 192	96.68 239	89.45 217	88.75 277	93.93 306	82.96 186	97.82 284	87.83 237	83.25 344	94.80 314

LTVRE_ROB	88.41 13	90.99 250	89.92 262	94.19 216	96.18 211	89.55 191	96.31 235	97.09 200	87.88 267	85.67 328	95.91 209	78.79 265	98.57 196	81.50 323	89.98 271	94.44 329
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		90.97 252	90.33 240	92.88 278	95.36 250	86.19 287	94.46 309	96.63 245	87.82 269	88.18 291	94.23 293	82.99 183	97.53 309	87.72 239	85.57 311	94.93 302

cl____		90.96 253	90.32 241	92.89 277	95.37 249	86.21 286	94.46 309	96.64 242	87.82 269	88.15 292	94.18 296	82.98 184	97.54 307	87.70 242	85.59 310	94.92 304

pmmvs4		90.93 254	89.85 264	94.17 217	93.34 339	90.79 153	94.60 302	96.02 271	84.62 329	87.45 303	95.15 246	81.88 210	97.45 316	87.70 242	87.87 291	94.27 336

XVG-ACMP-BASELINE		90.93 254	90.21 251	93.09 270	94.31 310	85.89 289	95.33 282	97.26 188	91.06 170	89.38 259	95.44 237	68.61 344	98.60 192	89.46 207	91.05 256	94.79 316

v1921920		90.85 256	90.03 259	93.29 263	93.55 330	86.96 270	96.74 194	97.04 208	87.36 284	89.52 256	94.34 284	80.23 236	97.97 262	86.27 269	85.21 318	94.94 300

CR-MVSNet		90.82 257	89.77 268	93.95 231	94.45 304	87.19 263	90.23 375	95.68 288	86.89 293	92.40 175	92.36 341	80.91 222	97.05 332	81.09 330	93.95 211	97.60 189

v7n		90.76 258	89.86 263	93.45 258	93.54 331	87.60 255	97.70 102	97.37 179	88.85 236	87.65 300	94.08 301	81.08 219	98.10 239	84.68 294	83.79 341	94.66 323

RPSCF		90.75 259	90.86 220	90.42 334	96.84 168	76.29 375	95.61 271	96.34 258	83.89 337	91.38 202	97.87 93	76.45 290	98.78 171	87.16 259	92.23 231	96.20 230

MVP-Stereo		90.74 260	90.08 254	92.71 284	93.19 342	88.20 238	95.86 259	96.27 261	86.07 307	84.86 336	94.76 263	77.84 280	97.75 290	83.88 305	98.01 123	92.17 366
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs1		90.72 261	89.65 274	93.96 230	94.29 311	89.63 186	97.79 90	96.82 230	89.07 226	86.12 326	95.48 236	78.61 267	97.78 287	86.97 262	81.67 352	94.46 327

v1240		90.70 262	89.85 264	93.23 265	93.51 333	86.80 271	96.61 211	97.02 211	87.16 289	89.58 252	94.31 288	79.55 249	97.98 259	85.52 284	85.44 313	94.90 305

EPMVS		90.70 262	89.81 266	93.37 260	94.73 293	84.21 314	93.67 339	88.02 387	89.50 215	92.38 177	93.49 321	77.82 281	97.78 287	86.03 277	92.68 226	98.11 163

Anonymous20231211		90.63 264	89.42 278	94.27 215	98.24 87	89.19 212	98.05 54	97.89 107	79.95 367	88.25 289	94.96 252	72.56 322	98.13 232	89.70 201	85.14 319	95.49 264

DTE-MVSNet		90.56 265	89.75 270	93.01 272	93.95 318	87.25 260	97.64 110	97.65 136	90.74 177	87.12 310	95.68 225	79.97 241	97.00 336	83.33 307	81.66 353	94.78 318

ACMH	87.59 16	90.53 266	89.42 278	93.87 237	96.21 208	87.92 247	97.24 153	96.94 216	88.45 252	83.91 348	96.27 193	71.92 323	98.62 191	84.43 297	89.43 277	95.05 296
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		90.51 267	90.19 252	91.44 316	93.41 337	81.25 342	96.98 176	96.28 260	91.68 148	86.55 322	96.30 191	74.20 312	97.98 259	88.96 222	87.40 297	95.09 293

miper_lstm_enhance		90.50 268	90.06 258	91.83 304	95.33 255	83.74 320	93.86 332	96.70 238	87.56 280	87.79 297	93.81 310	83.45 173	96.92 338	87.39 252	84.62 328	94.82 311

COLMAP_ROB	87.81 15	90.40 269	89.28 281	93.79 241	97.95 109	87.13 266	96.92 180	95.89 277	82.83 348	86.88 320	97.18 138	73.77 316	99.29 121	78.44 345	93.62 215	94.95 298
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing222		90.31 270	88.96 286	94.35 208	96.54 192	87.29 257	95.50 275	93.84 351	90.97 172	91.75 194	92.96 329	62.18 372	98.00 257	82.86 311	94.08 206	97.76 179

IterMVS-SCA-FT		90.31 270	89.81 266	91.82 305	95.52 239	84.20 315	94.30 317	96.15 268	90.61 188	87.39 306	94.27 290	75.80 298	96.44 345	87.34 253	86.88 303	94.82 311

MS-PatchMatch		90.27 272	89.77 268	91.78 308	94.33 308	84.72 310	95.55 272	96.73 233	86.17 306	86.36 323	95.28 242	71.28 328	97.80 285	84.09 301	98.14 121	92.81 354

tpm		90.25 273	89.74 271	91.76 310	93.92 319	79.73 361	93.98 325	93.54 355	88.28 256	91.99 188	93.25 326	77.51 283	97.44 317	87.30 255	87.94 290	98.12 160

AllTest		90.23 274	88.98 285	93.98 227	97.94 110	86.64 275	96.51 218	95.54 294	85.38 316	85.49 330	96.77 158	70.28 334	99.15 133	80.02 335	92.87 220	96.15 234

dmvs_re		90.21 275	89.50 277	92.35 290	95.47 244	85.15 302	95.70 265	94.37 339	90.94 173	88.42 282	93.57 319	74.63 308	95.67 358	82.80 314	89.57 276	96.22 229

ACMH+	87.92 14	90.20 276	89.18 283	93.25 264	96.48 197	86.45 280	96.99 175	96.68 239	88.83 238	84.79 337	96.22 195	70.16 336	98.53 198	84.42 298	88.04 289	94.77 319

test-mter		90.19 277	89.54 276	92.12 297	94.59 298	80.66 348	94.29 318	92.98 359	87.68 277	90.76 218	92.37 338	67.67 348	98.07 247	88.81 224	96.74 157	97.63 184

IterMVS		90.15 278	89.67 272	91.61 312	95.48 241	83.72 321	94.33 315	96.12 269	89.99 202	87.31 309	94.15 298	75.78 300	96.27 348	86.97 262	86.89 302	94.83 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		90.06 279	89.42 278	91.97 300	94.41 306	80.62 350	94.29 318	91.97 371	87.28 287	90.44 223	92.47 337	68.79 343	97.67 295	88.50 230	96.60 162	97.61 188

tpm2		89.96 280	89.21 282	92.23 296	94.91 282	81.25 342	93.78 334	94.42 337	80.62 365	91.56 198	93.44 323	76.44 291	97.94 270	85.60 283	92.08 238	97.49 193

IB-MVS	87.33 17	89.91 281	88.28 295	94.79 190	95.26 262	87.70 253	95.12 293	93.95 348	89.35 220	87.03 313	92.49 336	70.74 332	99.19 128	89.18 218	81.37 354	97.49 193
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		89.89 282	88.68 290	93.53 254	95.86 224	84.89 308	90.93 370	95.07 317	83.23 346	91.28 211	91.81 350	79.01 261	97.85 280	79.52 337	91.39 249	97.84 174

WB-MVSnew		89.88 283	89.56 275	90.82 326	94.57 301	83.06 327	95.65 269	92.85 361	87.86 268	90.83 217	94.10 299	79.66 247	96.88 339	76.34 355	94.19 201	92.54 359

FMVSNet1		89.88 283	88.31 294	94.59 195	95.41 245	91.18 137	97.50 125	96.93 217	86.62 297	87.41 305	94.51 274	65.94 362	97.29 326	83.04 310	87.43 295	95.31 281

pmmvs5		89.86 285	88.87 288	92.82 280	92.86 346	86.23 285	96.26 238	95.39 299	84.24 333	87.12 310	94.51 274	74.27 311	97.36 323	87.61 249	87.57 293	94.86 307

tpmvs		89.83 286	89.15 284	91.89 302	94.92 280	80.30 355	93.11 351	95.46 298	86.28 303	88.08 293	92.65 332	80.44 231	98.52 199	81.47 324	89.92 272	96.84 215

test_fmvs2		89.77 287	89.93 261	89.31 346	93.68 328	76.37 374	97.64 110	95.90 275	89.84 207	91.49 200	96.26 194	58.77 375	97.10 330	94.65 108	91.13 254	94.46 327

tfpnnormal		89.70 288	88.40 293	93.60 250	95.15 268	90.10 172	97.56 119	98.16 61	87.28 287	86.16 325	94.63 270	77.57 282	98.05 250	74.48 363	84.59 329	92.65 357

ADS-MVSNet2		89.45 289	88.59 291	92.03 299	95.86 224	82.26 335	90.93 370	94.32 342	83.23 346	91.28 211	91.81 350	79.01 261	95.99 350	79.52 337	91.39 249	97.84 174

Patchmatch-test		89.42 290	87.99 297	93.70 246	95.27 259	85.11 303	88.98 381	94.37 339	81.11 359	87.10 312	93.69 313	82.28 201	97.50 312	74.37 365	94.76 194	98.48 134

test0.0.03 1		89.37 291	88.70 289	91.41 317	92.47 355	85.63 292	95.22 290	92.70 364	91.11 168	86.91 319	93.65 317	79.02 259	93.19 381	78.00 347	89.18 279	95.41 271

SixPastTwentyTwo		89.15 292	88.54 292	90.98 323	93.49 334	80.28 356	96.70 199	94.70 330	90.78 175	84.15 343	95.57 230	71.78 325	97.71 293	84.63 295	85.07 321	94.94 300

RPMNet		88.98 293	87.05 307	94.77 191	94.45 304	87.19 263	90.23 375	98.03 91	77.87 377	92.40 175	87.55 380	80.17 237	99.51 96	68.84 381	93.95 211	97.60 189

TransMVSNet (Re)		88.94 294	87.56 300	93.08 271	94.35 307	88.45 231	97.73 95	95.23 310	87.47 281	84.26 341	95.29 240	79.86 243	97.33 324	79.44 341	74.44 375	93.45 347

USDC		88.94 294	87.83 299	92.27 294	94.66 295	84.96 306	93.86 332	95.90 275	87.34 285	83.40 350	95.56 231	67.43 350	98.19 227	82.64 318	89.67 275	93.66 343

dp		88.90 296	88.26 296	90.81 327	94.58 300	76.62 373	92.85 356	94.93 323	85.12 322	90.07 239	93.07 327	75.81 297	98.12 237	80.53 332	87.42 296	97.71 181

PatchT		88.87 297	87.42 301	93.22 266	94.08 316	85.10 304	89.51 379	94.64 333	81.92 354	92.36 178	88.15 376	80.05 239	97.01 335	72.43 372	93.65 214	97.54 192

our_test_3		88.78 298	87.98 298	91.20 321	92.45 356	82.53 331	93.61 342	95.69 286	85.77 311	84.88 335	93.71 312	79.99 240	96.78 343	79.47 339	86.24 304	94.28 335

EU-MVSNet		88.72 299	88.90 287	88.20 350	93.15 343	74.21 378	96.63 210	94.22 343	85.18 320	87.32 308	95.97 205	76.16 294	94.98 366	85.27 287	86.17 305	95.41 271

Patchmtry		88.64 300	87.25 303	92.78 282	94.09 315	86.64 275	89.82 378	95.68 288	80.81 363	87.63 301	92.36 341	80.91 222	97.03 333	78.86 343	85.12 320	94.67 322

MIMVSNet		88.50 301	86.76 311	93.72 245	94.84 286	87.77 252	91.39 365	94.05 345	86.41 301	87.99 295	92.59 335	63.27 367	95.82 355	77.44 348	92.84 222	97.57 191

tpm cat1		88.36 302	87.21 305	91.81 306	95.13 270	80.55 351	92.58 359	95.70 284	74.97 380	87.45 303	91.96 348	78.01 279	98.17 229	80.39 333	88.74 284	96.72 219

ppachtmachnet_test		88.35 303	87.29 302	91.53 313	92.45 356	83.57 324	93.75 335	95.97 272	84.28 332	85.32 333	94.18 296	79.00 263	96.93 337	75.71 358	84.99 324	94.10 337

JIA-IIPM		88.26 304	87.04 308	91.91 301	93.52 332	81.42 341	89.38 380	94.38 338	80.84 362	90.93 216	80.74 387	79.22 254	97.92 274	82.76 315	91.62 242	96.38 227

testgi		87.97 305	87.21 305	90.24 336	92.86 346	80.76 346	96.67 204	94.97 321	91.74 146	85.52 329	95.83 213	62.66 370	94.47 370	76.25 356	88.36 288	95.48 265

LF4IMVS		87.94 306	87.25 303	89.98 339	92.38 358	80.05 359	94.38 312	95.25 309	87.59 279	84.34 339	94.74 265	64.31 365	97.66 297	84.83 291	87.45 294	92.23 363

gg-mvs-nofinetune		87.82 307	85.61 319	94.44 203	94.46 303	89.27 208	91.21 369	84.61 395	80.88 361	89.89 244	74.98 389	71.50 326	97.53 309	85.75 282	97.21 148	96.51 222

pmmvs6		87.81 308	86.19 315	92.69 285	91.32 363	86.30 283	97.34 144	96.41 256	80.59 366	84.05 347	94.37 283	67.37 351	97.67 295	84.75 293	79.51 362	94.09 339

testing3		87.67 309	86.88 310	90.05 338	96.14 216	80.71 347	97.10 167	92.85 361	90.15 199	87.54 302	94.55 273	55.70 381	94.10 373	73.77 368	94.10 205	95.35 278

K. test v3		87.64 310	86.75 312	90.32 335	93.02 345	79.48 365	96.61 211	92.08 370	90.66 184	80.25 366	94.09 300	67.21 352	96.65 344	85.96 279	80.83 356	94.83 309

Patchmatch-RL test		87.38 311	86.24 314	90.81 327	88.74 379	78.40 371	88.12 385	93.17 358	87.11 290	82.17 357	89.29 368	81.95 208	95.60 360	88.64 228	77.02 368	98.41 142

FMVSNet5		87.29 312	85.79 318	91.78 308	94.80 288	87.28 258	95.49 276	95.28 306	84.09 335	83.85 349	91.82 349	62.95 369	94.17 372	78.48 344	85.34 316	93.91 341

myMVS_eth3d		87.18 313	86.38 313	89.58 343	95.16 266	79.53 362	95.00 294	93.93 349	88.55 249	86.96 315	91.99 346	56.23 380	94.00 374	75.47 361	94.11 203	95.20 289

Syy-MVS		87.13 314	87.02 309	87.47 353	95.16 266	73.21 381	95.00 294	93.93 349	88.55 249	86.96 315	91.99 346	75.90 295	94.00 374	61.59 387	94.11 203	95.20 289

Anonymous20231206		87.09 315	86.14 316	89.93 340	91.22 364	80.35 353	96.11 247	95.35 302	83.57 343	84.16 342	93.02 328	73.54 318	95.61 359	72.16 373	86.14 306	93.84 342

EG-PatchMatch MVS		87.02 316	85.44 320	91.76 310	92.67 350	85.00 305	96.08 249	96.45 254	83.41 345	79.52 368	93.49 321	57.10 378	97.72 292	79.34 342	90.87 262	92.56 358

TinyColmap		86.82 317	85.35 323	91.21 320	94.91 282	82.99 328	93.94 328	94.02 347	83.58 342	81.56 358	94.68 267	62.34 371	98.13 232	75.78 357	87.35 298	92.52 360

TDRefinement		86.53 318	84.76 329	91.85 303	82.23 391	84.25 313	96.38 229	95.35 302	84.97 325	84.09 345	94.94 253	65.76 363	98.34 217	84.60 296	74.52 374	92.97 351

test_0402		86.46 319	84.79 328	91.45 315	95.02 274	85.55 293	96.29 237	94.89 325	80.90 360	82.21 356	93.97 305	68.21 347	97.29 326	62.98 385	88.68 285	91.51 370

Anonymous20240521		86.42 320	85.44 320	89.34 345	90.33 368	79.79 360	96.73 195	95.92 273	83.71 341	83.25 351	91.36 354	63.92 366	96.01 349	78.39 346	85.36 315	92.22 364

DSMNet-mixed		86.34 321	86.12 317	87.00 357	89.88 372	70.43 383	94.93 296	90.08 381	77.97 376	85.42 332	92.78 331	74.44 310	93.96 376	74.43 364	95.14 186	96.62 220

CL-MVSNet_self_test		86.31 322	85.15 324	89.80 341	88.83 378	81.74 340	93.93 329	96.22 264	86.67 296	85.03 334	90.80 357	78.09 276	94.50 368	74.92 362	71.86 380	93.15 350

pmmvs-eth3d		86.22 323	84.45 330	91.53 313	88.34 380	87.25 260	94.47 307	95.01 318	83.47 344	79.51 369	89.61 366	69.75 340	95.71 356	83.13 309	76.73 371	91.64 367

test_vis1_rt		86.16 324	85.06 325	89.46 344	93.47 336	80.46 352	96.41 223	86.61 392	85.22 319	79.15 370	88.64 371	52.41 384	97.06 331	93.08 139	90.57 264	90.87 375

test20.03		86.14 325	85.40 322	88.35 348	90.12 369	80.06 358	95.90 258	95.20 311	88.59 245	81.29 359	93.62 318	71.43 327	92.65 382	71.26 377	81.17 355	92.34 362

UnsupCasMVSNet_eth		85.99 326	84.45 330	90.62 331	89.97 371	82.40 334	93.62 341	97.37 179	89.86 204	78.59 372	92.37 338	65.25 364	95.35 365	82.27 320	70.75 381	94.10 337

KD-MVS_self_test		85.95 327	84.95 326	88.96 347	89.55 375	79.11 368	95.13 292	96.42 255	85.91 309	84.07 346	90.48 358	70.03 338	94.82 367	80.04 334	72.94 378	92.94 352

YYNet1		85.87 328	84.23 332	90.78 330	92.38 358	82.46 333	93.17 348	95.14 314	82.12 353	67.69 383	92.36 341	78.16 275	95.50 363	77.31 350	79.73 360	94.39 330

MDA-MVSNet_test_wron		85.87 328	84.23 332	90.80 329	92.38 358	82.57 330	93.17 348	95.15 313	82.15 352	67.65 384	92.33 344	78.20 272	95.51 362	77.33 349	79.74 359	94.31 334

CMPMVS	62.92 21	85.62 330	84.92 327	87.74 352	89.14 376	73.12 382	94.17 321	96.80 231	73.98 381	73.65 380	94.93 254	66.36 357	97.61 302	83.95 304	91.28 251	92.48 361
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	82.17 19	85.46 331	83.64 334	90.92 324	95.27 259	79.49 364	90.55 373	95.60 291	83.76 340	83.00 354	89.95 363	71.09 329	97.97 262	82.75 316	60.79 393	95.31 281

MDA-MVSNet-bldmvs		85.00 332	82.95 337	91.17 322	93.13 344	83.33 325	94.56 304	95.00 319	84.57 330	65.13 388	92.65 332	70.45 333	95.85 353	73.57 369	77.49 367	94.33 332

MIMVSNet1		84.93 333	83.05 335	90.56 332	89.56 374	84.84 309	95.40 279	95.35 302	83.91 336	80.38 364	92.21 345	57.23 377	93.34 380	70.69 379	82.75 350	93.50 345

KD-MVS_2432*1600		84.81 334	82.64 338	91.31 318	91.07 365	85.34 300	91.22 367	95.75 282	85.56 314	83.09 352	90.21 361	67.21 352	95.89 351	77.18 352	62.48 391	92.69 355

miper_refine_blended		84.81 334	82.64 338	91.31 318	91.07 365	85.34 300	91.22 367	95.75 282	85.56 314	83.09 352	90.21 361	67.21 352	95.89 351	77.18 352	62.48 391	92.69 355

OpenMVS_ROB	81.14 20	84.42 336	82.28 342	90.83 325	90.06 370	84.05 318	95.73 264	94.04 346	73.89 382	80.17 367	91.53 353	59.15 374	97.64 298	66.92 383	89.05 280	90.80 376

mvsany_test3		83.59 337	82.44 341	87.03 356	83.80 387	73.82 379	93.70 336	90.92 379	86.42 300	82.51 355	90.26 360	46.76 387	95.71 356	90.82 182	76.76 370	91.57 369

PM-MVS		83.48 338	81.86 344	88.31 349	87.83 382	77.59 372	93.43 344	91.75 372	86.91 292	80.63 362	89.91 364	44.42 388	95.84 354	85.17 290	76.73 371	91.50 371

test_fmvs3		83.21 339	83.02 336	83.78 362	86.77 384	68.34 388	96.76 193	94.91 324	86.49 299	84.14 344	89.48 367	36.04 392	91.73 384	91.86 162	80.77 357	91.26 374

new-patchmatchnet		83.18 340	81.87 343	87.11 355	86.88 383	75.99 376	93.70 336	95.18 312	85.02 324	77.30 375	88.40 373	65.99 361	93.88 377	74.19 367	70.18 382	91.47 372

new_pmnet		82.89 341	81.12 346	88.18 351	89.63 373	80.18 357	91.77 364	92.57 365	76.79 379	75.56 378	88.23 375	61.22 373	94.48 369	71.43 375	82.92 348	89.87 379

MVS-HIRNet		82.47 342	81.21 345	86.26 359	95.38 247	69.21 386	88.96 382	89.49 382	66.28 386	80.79 361	74.08 391	68.48 345	97.39 321	71.93 374	95.47 181	92.18 365

UnsupCasMVSNet_bld		82.13 343	79.46 348	90.14 337	88.00 381	82.47 332	90.89 372	96.62 247	78.94 372	75.61 376	84.40 385	56.63 379	96.31 347	77.30 351	66.77 388	91.63 368

dmvs_testset		81.38 344	82.60 340	77.73 368	91.74 362	51.49 401	93.03 353	84.21 396	89.07 226	78.28 373	91.25 355	76.97 286	88.53 391	56.57 391	82.24 351	93.16 349

test_f		80.57 345	79.62 347	83.41 363	83.38 389	67.80 390	93.57 343	93.72 352	80.80 364	77.91 374	87.63 379	33.40 393	92.08 383	87.14 260	79.04 365	90.34 378

pmmvs3		79.97 346	77.50 351	87.39 354	82.80 390	79.38 366	92.70 358	90.75 380	70.69 384	78.66 371	87.47 381	51.34 385	93.40 379	73.39 370	69.65 383	89.38 380

APD_test1		79.31 347	77.70 350	84.14 361	89.11 377	69.07 387	92.36 363	91.50 374	69.07 385	73.87 379	92.63 334	39.93 390	94.32 371	70.54 380	80.25 358	89.02 381

N_pmnet		78.73 348	78.71 349	78.79 367	92.80 348	46.50 404	94.14 322	43.71 406	78.61 373	80.83 360	91.66 352	74.94 306	96.36 346	67.24 382	84.45 332	93.50 345

WB-MVS		76.77 349	76.63 352	77.18 369	85.32 385	56.82 399	94.53 305	89.39 383	82.66 350	71.35 381	89.18 369	75.03 305	88.88 389	35.42 397	66.79 387	85.84 383

SSC-MVS		76.05 350	75.83 353	76.72 373	84.77 386	56.22 400	94.32 316	88.96 385	81.82 356	70.52 382	88.91 370	74.79 307	88.71 390	33.69 398	64.71 389	85.23 384

test_vis3_rt		72.73 351	70.55 354	79.27 366	80.02 392	68.13 389	93.92 330	74.30 403	76.90 378	58.99 392	73.58 392	20.29 401	95.37 364	84.16 299	72.80 379	74.31 391

LCM-MVSNet		72.55 352	69.39 356	82.03 364	70.81 401	65.42 393	90.12 377	94.36 341	55.02 392	65.88 386	81.72 386	24.16 400	89.96 385	74.32 366	68.10 386	90.71 377

FPMVS		71.27 353	69.85 355	75.50 374	74.64 396	59.03 397	91.30 366	91.50 374	58.80 389	57.92 393	88.28 374	29.98 396	85.53 394	53.43 392	82.84 349	81.95 387

PMMVS2		70.19 354	66.92 357	80.01 365	76.35 395	65.67 392	86.22 386	87.58 389	64.83 388	62.38 389	80.29 388	26.78 398	88.49 392	63.79 384	54.07 394	85.88 382

testf1		69.31 355	66.76 358	76.94 371	78.61 393	61.93 395	88.27 383	86.11 393	55.62 390	59.69 390	85.31 383	20.19 402	89.32 386	57.62 388	69.44 384	79.58 388

APD_test2		69.31 355	66.76 358	76.94 371	78.61 393	61.93 395	88.27 383	86.11 393	55.62 390	59.69 390	85.31 383	20.19 402	89.32 386	57.62 388	69.44 384	79.58 388

EGC-MVSNET		68.77 357	63.01 362	86.07 360	92.49 354	82.24 336	93.96 327	90.96 378	0.71 403	2.62 404	90.89 356	53.66 382	93.46 378	57.25 390	84.55 330	82.51 386

Gipuma		67.86 358	65.41 360	75.18 375	92.66 351	73.45 380	66.50 394	94.52 335	53.33 393	57.80 394	66.07 394	30.81 394	89.20 388	48.15 394	78.88 366	62.90 394
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		66.11 359	64.89 361	69.79 377	72.62 399	35.23 408	65.19 395	92.83 363	20.35 398	65.20 387	88.08 377	43.14 389	82.70 395	73.12 371	63.46 390	91.45 373

ANet_high		63.94 360	59.58 363	77.02 370	61.24 403	66.06 391	85.66 388	87.93 388	78.53 374	42.94 396	71.04 393	25.42 399	80.71 396	52.60 393	30.83 397	84.28 385

PMVS	53.92 22	58.58 361	55.40 364	68.12 378	51.00 404	48.64 402	78.86 391	87.10 391	46.77 394	35.84 400	74.28 390	8.76 404	86.34 393	42.07 395	73.91 376	69.38 392
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 362	52.56 366	55.43 380	74.43 397	47.13 403	83.63 390	76.30 400	42.23 395	42.59 397	62.22 396	28.57 397	74.40 398	31.53 399	31.51 396	44.78 395

MVE	50.73 23	53.25 363	48.81 368	66.58 379	65.34 402	57.50 398	72.49 393	70.94 404	40.15 397	39.28 399	63.51 395	6.89 406	73.48 400	38.29 396	42.38 395	68.76 393
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		52.08 364	51.31 367	54.39 381	72.62 399	45.39 405	83.84 389	75.51 402	41.13 396	40.77 398	59.65 397	30.08 395	73.60 399	28.31 400	29.90 398	44.18 396

tmp_tt		51.94 365	53.82 365	46.29 382	33.73 405	45.30 406	78.32 392	67.24 405	18.02 399	50.93 395	87.05 382	52.99 383	53.11 401	70.76 378	25.29 399	40.46 397

wuyk23d		25.11 366	24.57 370	26.74 383	73.98 398	39.89 407	57.88 396	9.80 407	12.27 400	10.39 401	6.97 403	7.03 405	36.44 402	25.43 401	17.39 400	3.89 400

cdsmvs_eth3d_5k		23.24 367	30.99 369	0.00 386	0.00 408	0.00 411	0.00 397	97.63 140	0.00 404	0.00 405	96.88 155	84.38 157	0.00 405	0.00 404	0.00 403	0.00 401

testmvs		13.36 368	16.33 371	4.48 385	5.04 406	2.26 410	93.18 347	3.28 408	2.70 401	8.24 402	21.66 399	2.29 408	2.19 403	7.58 402	2.96 401	9.00 399

test123		13.04 369	15.66 372	5.18 384	4.51 407	3.45 409	92.50 361	1.81 409	2.50 402	7.58 403	20.15 400	3.67 407	2.18 404	7.13 403	1.07 402	9.90 398

ab-mvs-re		8.06 370	10.74 373	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	96.69 164	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		7.39 371	9.85 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	88.65 95	0.00 405	0.00 404	0.00 403	0.00 401

test_blank		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet_test		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

MM				98.23 11		95.03 25	98.07 52	95.76 281	97.78 1	97.52 40	98.80 22	88.09 102	99.86 8	99.44 1	99.37 56	99.80 1

WAC-MVS						79.53 362								75.56 360

FOURS1					99.55 1	93.34 64	99.29 1	98.35 27	94.98 29	98.49 23

MSC_two_6792asdad				98.86 1	98.67 58	96.94 1		97.93 105					99.86 8	97.68 16	99.67 6	99.77 2

PC_three_1452									90.77 176	98.89 14	98.28 65	96.24 1	98.35 214	95.76 73	99.58 21	99.59 22

No_MVS				98.86 1	98.67 58	96.94 1		97.93 105					99.86 8	97.68 16	99.67 6	99.77 2

test_one_0601					99.32 22	95.20 20		98.25 45	95.13 23	98.48 24	98.87 15	95.16 7

eth-test2					0.00 408

eth-test					0.00 408

ZD-MVS					99.05 39	94.59 29		98.08 74	89.22 223	97.03 57	98.10 73	92.52 35	99.65 58	94.58 111	99.31 60

RE-MVS-def			96.72 42		99.02 42	92.34 89	97.98 61	98.03 91	93.52 85	97.43 45	98.51 36	90.71 70		96.05 61	99.26 64	99.43 51

IU-MVS					99.42 7	95.39 11		97.94 104	90.40 195	98.94 8				97.41 29	99.66 10	99.74 8

OPU-MVS				98.55 3	98.82 52	96.86 3	98.25 36				98.26 66	96.04 2	99.24 124	95.36 89	99.59 17	99.56 29

test_241102_TWO								98.27 39	95.13 23	98.93 9	98.89 13	94.99 11	99.85 18	97.52 22	99.65 12	99.74 8

test_241102_ONE					99.42 7	95.30 17		98.27 39	95.09 26	99.19 4	98.81 21	95.54 5	99.65 58

9.14			96.75 40		98.93 47		97.73 95	98.23 50	91.28 161	97.88 35	98.44 44	93.00 26	99.65 58	95.76 73	99.47 38

save fliter					98.91 49	94.28 36	97.02 171	98.02 94	95.35 16

test_0728_THIRD									94.78 41	98.73 18	98.87 15	95.87 4	99.84 23	97.45 26	99.72 2	99.77 2

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 43	98.28 36					99.86 8	97.52 22	99.67 6	99.75 6

test0726					99.45 3	95.36 13	98.31 29	98.29 34	94.92 32	98.99 7	98.92 10	95.08 8

GSMVS																98.45 137

test_part2					99.28 25	95.74 8				98.10 29

sam_mvs1												82.76 190				98.45 137

sam_mvs												81.94 209

ambc				86.56 358	83.60 388	70.00 385	85.69 387	94.97 321		80.60 363	88.45 372	37.42 391	96.84 341	82.69 317	75.44 373	92.86 353

MTGPA								98.08 74

test_post1							92.81 357				16.58 402	80.53 229	97.68 294	86.20 271

test_post											17.58 401	81.76 211	98.08 243

patchmatchnet-post											90.45 359	82.65 194	98.10 239

GG-mvs-BLEND				93.62 249	93.69 327	89.20 210	92.39 362	83.33 397		87.98 296	89.84 365	71.00 330	96.87 340	82.08 321	95.40 183	94.80 314

MTMP							97.86 79	82.03 398

gm-plane-assit					93.22 341	78.89 370			84.82 327		93.52 320		98.64 188	87.72 239

test9_res														94.81 103	99.38 53	99.45 47

TEST9					98.70 56	94.19 40	96.41 223	98.02 94	88.17 259	96.03 95	97.56 121	92.74 30	99.59 74

test_8					98.67 58	94.06 47	96.37 230	98.01 97	88.58 246	95.98 99	97.55 123	92.73 31	99.58 77

agg_prior2														93.94 121	99.38 53	99.50 40

agg_prior					98.67 58	93.79 52		98.00 98		95.68 109			99.57 84

TestCases				93.98 227	97.94 110	86.64 275		95.54 294	85.38 316	85.49 330	96.77 158	70.28 334	99.15 133	80.02 335	92.87 220	96.15 234

test_prior4						93.66 55	96.42 222

test_prior2							96.35 231		92.80 119	96.03 95	97.59 118	92.01 43		95.01 97	99.38 53

test_prior				97.23 58	98.67 58	92.99 71		98.00 98					99.41 109			99.29 63

旧先验2							95.94 256		81.66 357	97.34 48			98.82 168	92.26 149

新几何2							95.79 262

新几何1				97.32 51	98.60 65	93.59 56		97.75 123	81.58 358	95.75 106	97.85 96	90.04 77	99.67 56	86.50 267	99.13 77	98.69 119

旧先验1					98.38 78	93.38 61		97.75 123			98.09 75	92.30 41			99.01 86	99.16 73

无先验							95.79 262	97.87 111	83.87 339				99.65 58	87.68 245		98.89 105

原ACMM2							95.67 266

原ACMM1				96.38 96	98.59 66	91.09 142		97.89 107	87.41 283	95.22 120	97.68 107	90.25 74	99.54 89	87.95 235	99.12 79	98.49 132

test222					98.24 87	92.21 94	95.33 282	97.60 142	79.22 371	95.25 118	97.84 98	88.80 92			99.15 75	98.72 116

testdata2													99.67 56	85.96 279

segment_acmp												92.89 27

testdata				95.46 155	98.18 97	88.90 218		97.66 134	82.73 349	97.03 57	98.07 76	90.06 76	98.85 166	89.67 202	98.98 87	98.64 122

testdata1							95.26 289		93.10 105

test12				97.65 41	98.46 70	94.26 37		97.66 134		95.52 116		90.89 67	99.46 103		99.25 66	99.22 70

plane_prior7					96.21 208	89.98 178

plane_prior6					96.10 219	90.00 174						81.32 217

plane_prior5								97.51 153					98.60 192	93.02 142	92.23 231	95.86 242

plane_prior4											96.64 168

plane_prior3						90.00 174			94.46 54	91.34 204

plane_prior2							97.74 93		94.85 34

plane_prior1					96.14 216

plane_prior						89.99 176	97.24 153		94.06 65						92.16 235

n2								0.00 410

nn								0.00 410

door-mid								91.06 377

lessismore_v0				90.45 333	91.96 361	79.09 369		87.19 390		80.32 365	94.39 281	66.31 359	97.55 306	84.00 303	76.84 369	94.70 321

LGP-MVS_train				94.10 220	96.16 213	88.26 235		97.46 161	91.29 158	90.12 234	97.16 139	79.05 257	98.73 178	92.25 151	91.89 239	95.31 281

test11								97.88 109

door								91.13 376

HQP5-MVS						89.33 203

HQP-NCC					95.86 224		96.65 205		93.55 80	90.14 228

ACMP_Plane					95.86 224		96.65 205		93.55 80	90.14 228

BP-MVS														92.13 155

HQP4-MVS										90.14 228			98.50 200			95.78 251

HQP3-MVS								97.39 176							92.10 236

HQP2-MVS												80.95 220

NP-MVS					95.99 223	89.81 183					95.87 210

MDTV_nov1_ep13_2view						70.35 384	93.10 352		83.88 338	93.55 152		82.47 198		86.25 270		98.38 145

MDTV_nov1_ep13			90.76 226		95.22 263	80.33 354	93.03 353	95.28 306	88.14 261	92.84 172	93.83 307	81.34 216	98.08 243	82.86 311	94.34 200

ACMMP++_ref															90.30 269

ACMMP++															91.02 257

Test By Simon												88.73 94

ITE_SJBPF				92.43 289	95.34 252	85.37 299		95.92 273	91.47 152	87.75 299	96.39 188	71.00 330	97.96 267	82.36 319	89.86 273	93.97 340

DeepMVS_CX				74.68 376	90.84 367	64.34 394		81.61 399	65.34 387	67.47 385	88.01 378	48.60 386	80.13 397	62.33 386	73.68 377	79.58 388

High-resolution multi-view benchmark