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 30	97.78 51	86.00 49	98.29 1	97.49 6	90.75 19	97.62 5	98.06 11	92.59 2	99.61 4	95.64 19	99.02 12	98.86 11

SED-MVS		95.91 2	96.28 2	94.80 32	98.77 5	85.99 51	97.13 14	97.44 15	90.31 28	97.71 1	98.07 9	92.31 4	99.58 10	95.66 17	99.13 3	98.84 14

DVP-MVS		95.67 3	96.02 3	94.64 38	98.78 3	85.93 54	97.09 16	96.73 79	90.27 31	97.04 11	98.05 13	91.47 8	99.55 16	95.62 21	99.08 7	98.45 36
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 4	95.67 4	95.25 10	98.36 25	87.28 17	95.56 94	97.51 5	89.13 63	97.14 9	97.91 18	91.64 7	99.62 2	94.61 27	99.17 2	98.86 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 28	86.29 45	97.46 6	97.40 20	89.03 67	96.20 19	98.10 7	89.39 16	99.34 34	95.88 16	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 6	95.56 6	94.98 19	98.49 17	86.52 35	96.91 25	97.47 11	91.73 10	96.10 20	96.69 66	89.90 12	99.30 40	94.70 25	98.04 67	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 7	95.37 7	95.50 8	98.11 36	88.51 7	95.29 105	96.96 52	92.09 6	95.32 31	97.08 49	89.49 15	99.33 37	95.10 24	98.85 19	98.66 20

SMA-MVS		95.20 8	95.07 11	95.59 6	98.14 35	88.48 8	96.26 47	97.28 31	85.90 155	97.67 3	98.10 7	88.41 20	99.56 12	94.66 26	99.19 1	98.71 19
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 8	95.32 9	94.85 25	96.99 72	86.33 41	97.33 7	97.30 29	91.38 12	95.39 30	97.46 30	88.98 19	99.40 30	94.12 31	98.89 18	98.82 16
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 10	94.91 13	95.83 4	98.25 29	89.65 4	95.92 73	96.96 52	91.75 9	94.02 46	96.83 61	88.12 24	99.55 16	93.41 42	98.94 16	98.28 50

MM		95.10 11	94.91 13	95.68 5	96.09 101	88.34 9	96.68 33	94.37 234	95.08 1	94.68 36	97.72 24	82.94 83	99.64 1	97.85 1	98.76 28	99.06 7

SF-MVS		94.97 12	94.90 15	95.20 12	97.84 47	87.76 10	96.65 35	97.48 10	87.76 111	95.71 27	97.70 25	88.28 23	99.35 33	93.89 35	98.78 25	98.48 30

SD-MVS		94.96 13	95.33 8	93.88 57	97.25 69	86.69 27	96.19 50	97.11 43	90.42 27	96.95 13	97.27 38	89.53 14	96.91 252	94.38 29	98.85 19	98.03 70
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 14	94.94 12	94.58 41	98.25 29	86.33 41	96.11 60	96.62 88	88.14 98	96.10 20	96.96 55	89.09 18	98.94 78	94.48 28	98.68 38	98.48 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
NCCC		94.81 15	94.69 18	95.17 14	97.83 48	87.46 16	95.66 87	96.93 56	92.34 4	93.94 47	96.58 76	87.74 27	99.44 29	92.83 50	98.40 53	98.62 21

ACMMP_NAP		94.74 16	94.56 19	95.28 9	98.02 41	87.70 11	95.68 84	97.34 23	88.28 91	95.30 32	97.67 26	85.90 47	99.54 20	93.91 34	98.95 15	98.60 23

test_fmvsm_n_1920		94.71 17	95.11 10	93.50 69	95.79 115	84.62 78	96.15 55	97.64 2	89.85 40	97.19 8	97.89 19	86.28 43	98.71 97	97.11 7	98.08 66	97.17 108

test_fmvsmconf_n		94.60 18	94.81 16	93.98 53	94.62 171	84.96 72	96.15 55	97.35 22	89.37 54	96.03 23	98.11 5	86.36 41	99.01 63	97.45 3	97.83 74	97.96 73

MVS_0304		94.60 18	94.38 25	95.23 11	95.41 130	87.49 15	96.53 38	92.75 277	93.82 2	93.07 65	97.84 22	83.66 74	99.59 8	97.61 2	98.76 28	98.61 22

HFP-MVS		94.52 20	94.40 23	94.86 24	98.61 10	86.81 24	96.94 20	97.34 23	88.63 80	93.65 51	97.21 42	86.10 45	99.49 26	92.35 61	98.77 27	98.30 47

ZNCC-MVS		94.47 21	94.28 30	95.03 16	98.52 15	86.96 19	96.85 28	97.32 27	88.24 92	93.15 61	97.04 52	86.17 44	99.62 2	92.40 59	98.81 22	98.52 26

XVS		94.45 22	94.32 26	94.85 25	98.54 13	86.60 33	96.93 22	97.19 35	90.66 24	92.85 69	97.16 47	85.02 59	99.49 26	91.99 74	98.56 49	98.47 33

MCST-MVS		94.45 22	94.20 35	95.19 13	98.46 19	87.50 14	95.00 124	97.12 41	87.13 122	92.51 83	96.30 83	89.24 17	99.34 34	93.46 39	98.62 45	98.73 17

region2R		94.43 24	94.27 32	94.92 20	98.65 8	86.67 29	96.92 24	97.23 34	88.60 82	93.58 53	97.27 38	85.22 54	99.54 20	92.21 64	98.74 31	98.56 25

ACMMPR		94.43 24	94.28 30	94.91 21	98.63 9	86.69 27	96.94 20	97.32 27	88.63 80	93.53 56	97.26 40	85.04 58	99.54 20	92.35 61	98.78 25	98.50 27

MTAPA		94.42 26	94.22 33	95.00 18	98.42 21	86.95 20	94.36 169	96.97 50	91.07 13	93.14 62	97.56 27	84.30 67	99.56 12	93.43 40	98.75 30	98.47 33

CP-MVS		94.34 27	94.21 34	94.74 36	98.39 23	86.64 31	97.60 4	97.24 32	88.53 84	92.73 77	97.23 41	85.20 55	99.32 38	92.15 67	98.83 21	98.25 55

fmvsm_l_conf0.5_n		94.29 28	94.46 21	93.79 63	95.28 134	85.43 66	95.68 84	96.43 97	86.56 137	96.84 14	97.81 23	87.56 32	98.77 92	97.14 6	96.82 97	97.16 112

MP-MVS		94.25 29	94.07 39	94.77 34	98.47 18	86.31 43	96.71 31	96.98 49	89.04 66	91.98 94	97.19 44	85.43 52	99.56 12	92.06 73	98.79 23	98.44 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 30	94.07 39	94.75 35	98.06 39	86.90 22	95.88 74	96.94 55	85.68 161	95.05 34	97.18 45	87.31 35	99.07 53	91.90 80	98.61 47	98.28 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 31	94.17 37	94.43 46	98.21 32	85.78 61	96.40 41	96.90 59	88.20 96	94.33 40	97.40 33	84.75 64	99.03 58	93.35 43	97.99 68	98.48 30

GST-MVS		94.21 32	93.97 43	94.90 23	98.41 22	86.82 23	96.54 37	97.19 35	88.24 92	93.26 58	96.83 61	85.48 51	99.59 8	91.43 87	98.40 53	98.30 47

MP-MVS-pluss		94.21 32	94.00 42	94.85 25	98.17 33	86.65 30	94.82 135	97.17 39	86.26 145	92.83 71	97.87 20	85.57 50	99.56 12	94.37 30	98.92 17	98.34 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_l_conf0.5_n_a		94.20 34	94.40 23	93.60 67	95.29 133	84.98 71	95.61 91	96.28 108	86.31 143	96.75 16	97.86 21	87.40 33	98.74 95	97.07 8	97.02 90	97.07 114

test_fmvsmconf0.1_n		94.20 34	94.31 28	93.88 57	92.46 255	84.80 75	96.18 52	96.82 68	89.29 57	95.68 28	98.11 5	85.10 56	98.99 70	97.38 4	97.75 78	97.86 80

DeepPCF-MVS	89.96 1	94.20 34	94.77 17	92.49 114	96.52 87	80.00 219	94.00 193	97.08 44	90.05 35	95.65 29	97.29 37	89.66 13	98.97 75	93.95 33	98.71 33	98.50 27

CS-MVS		94.12 37	94.44 22	93.17 76	96.55 84	83.08 129	97.63 3	96.95 54	91.71 11	93.50 57	96.21 86	85.61 48	98.24 138	93.64 37	98.17 59	98.19 58

DeepC-MVS_fast	89.43 2	94.04 38	93.79 46	94.80 32	97.48 61	86.78 25	95.65 89	96.89 60	89.40 53	92.81 72	96.97 54	85.37 53	99.24 43	90.87 97	98.69 36	98.38 41
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		94.02 39	94.29 29	93.24 73	96.69 78	83.24 119	97.49 5	96.92 57	92.14 5	92.90 67	95.77 108	85.02 59	98.33 133	93.03 47	98.62 45	98.13 62

HPM-MVS		94.02 39	93.88 44	94.43 46	98.39 23	85.78 61	97.25 10	97.07 45	86.90 130	92.62 80	96.80 65	84.85 63	99.17 47	92.43 57	98.65 43	98.33 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 41	93.78 47	94.63 39	98.50 16	85.90 58	96.87 26	96.91 58	88.70 78	91.83 103	97.17 46	83.96 71	99.55 16	91.44 86	98.64 44	98.43 38

PGM-MVS		93.96 42	93.72 50	94.68 37	98.43 20	86.22 46	95.30 103	97.78 1	87.45 118	93.26 58	97.33 36	84.62 65	99.51 24	90.75 99	98.57 48	98.32 46

PHI-MVS		93.89 43	93.65 54	94.62 40	96.84 75	86.43 38	96.69 32	97.49 6	85.15 175	93.56 55	96.28 84	85.60 49	99.31 39	92.45 56	98.79 23	98.12 64

SR-MVS-dyc-post		93.82 44	93.82 45	93.82 60	97.92 43	84.57 80	96.28 45	96.76 75	87.46 116	93.75 49	97.43 31	84.24 68	99.01 63	92.73 51	97.80 75	97.88 78

APD-MVS_3200maxsize		93.78 45	93.77 48	93.80 62	97.92 43	84.19 94	96.30 43	96.87 62	86.96 126	93.92 48	97.47 29	83.88 72	98.96 77	92.71 54	97.87 72	98.26 54

fmvsm_s_conf0.5_n		93.76 46	94.06 41	92.86 94	95.62 123	83.17 122	96.14 57	96.12 123	88.13 99	95.82 26	98.04 16	83.43 75	98.48 112	96.97 9	96.23 108	96.92 127

patch_mono-2		93.74 47	94.32 26	92.01 132	97.54 57	78.37 257	93.40 217	97.19 35	88.02 101	94.99 35	97.21 42	88.35 21	98.44 122	94.07 32	98.09 64	99.23 1

MSLP-MVS++		93.72 48	94.08 38	92.65 106	97.31 65	83.43 114	95.79 79	97.33 25	90.03 36	93.58 53	96.96 55	84.87 62	97.76 176	92.19 66	98.66 41	96.76 134

TSAR-MVS + GP.		93.66 49	93.41 56	94.41 48	96.59 82	86.78 25	94.40 162	93.93 250	89.77 45	94.21 41	95.59 115	87.35 34	98.61 104	92.72 53	96.15 110	97.83 83

fmvsm_s_conf0.5_n_a		93.57 50	93.76 49	93.00 86	95.02 147	83.67 106	96.19 50	96.10 125	87.27 120	95.98 24	98.05 13	83.07 82	98.45 120	96.68 11	95.51 117	96.88 130

CANet		93.54 51	93.20 61	94.55 42	95.65 121	85.73 63	94.94 127	96.69 84	91.89 8	90.69 119	95.88 102	81.99 102	99.54 20	93.14 46	97.95 70	98.39 39

dcpmvs_2		93.49 52	94.19 36	91.38 166	97.69 54	76.78 289	94.25 172	96.29 105	88.33 88	94.46 38	96.88 58	88.07 25	98.64 100	93.62 38	98.09 64	98.73 17

fmvsm_s_conf0.1_n		93.46 53	93.66 53	92.85 95	93.75 216	83.13 124	96.02 67	95.74 154	87.68 113	95.89 25	98.17 2	82.78 86	98.46 116	96.71 10	96.17 109	96.98 123

MVS_111021_HR		93.45 54	93.31 57	93.84 59	96.99 72	84.84 73	93.24 229	97.24 32	88.76 75	91.60 108	95.85 103	86.07 46	98.66 98	91.91 78	98.16 60	98.03 70

test_fmvsmvis_n_1920		93.44 55	93.55 55	93.10 79	93.67 220	84.26 93	95.83 77	96.14 120	89.00 70	92.43 86	97.50 28	83.37 78	98.72 96	96.61 12	97.44 82	96.32 149

train_agg		93.44 55	93.08 62	94.52 43	97.53 58	86.49 36	94.07 185	96.78 72	81.86 252	92.77 74	96.20 87	87.63 29	99.12 51	92.14 68	98.69 36	97.94 74

EC-MVSNet		93.44 55	93.71 51	92.63 107	95.21 139	82.43 149	97.27 9	96.71 82	90.57 26	92.88 68	95.80 106	83.16 79	98.16 144	93.68 36	98.14 61	97.31 101

DELS-MVS		93.43 58	93.25 59	93.97 54	95.42 129	85.04 70	93.06 236	97.13 40	90.74 21	91.84 101	95.09 133	86.32 42	99.21 45	91.22 88	98.45 51	97.65 89
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 59	93.22 60	93.94 56	98.36 25	84.83 74	97.15 13	96.80 71	85.77 158	92.47 85	97.13 48	82.38 90	99.07 53	90.51 103	98.40 53	97.92 77

DeepC-MVS	88.79 3	93.31 60	92.99 65	94.26 51	96.07 103	85.83 59	94.89 130	96.99 48	89.02 69	89.56 133	97.37 35	82.51 89	99.38 31	92.20 65	98.30 56	97.57 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		93.27 61	92.75 69	94.85 25	95.70 120	87.66 12	96.33 42	96.41 99	90.00 37	94.09 44	94.60 154	82.33 92	98.62 103	92.40 59	92.86 174	98.27 52

ACMMP		93.24 62	92.88 67	94.30 50	98.09 38	85.33 68	96.86 27	97.45 14	88.33 88	90.15 128	97.03 53	81.44 105	99.51 24	90.85 98	95.74 113	98.04 69
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 63	93.05 63	93.76 64	98.04 40	84.07 96	96.22 49	97.37 21	84.15 195	90.05 129	95.66 112	87.77 26	99.15 50	89.91 108	98.27 57	98.07 66

fmvsm_s_conf0.1_n_a		93.19 64	93.26 58	92.97 88	92.49 253	83.62 109	96.02 67	95.72 157	86.78 132	96.04 22	98.19 1	82.30 93	98.43 125	96.38 13	95.42 123	96.86 131

test_fmvsmconf0.01_n		93.19 64	93.02 64	93.71 65	89.25 348	84.42 91	96.06 64	96.29 105	89.06 64	94.68 36	98.13 3	79.22 128	98.98 74	97.22 5	97.24 85	97.74 87

alignmvs		93.08 66	92.50 73	94.81 31	95.62 123	87.61 13	95.99 69	96.07 128	89.77 45	94.12 43	94.87 139	80.56 111	98.66 98	92.42 58	93.10 170	98.15 61

EI-MVSNet-Vis-set		93.01 67	92.92 66	93.29 71	95.01 148	83.51 113	94.48 154	95.77 151	90.87 15	92.52 82	96.67 68	84.50 66	99.00 68	91.99 74	94.44 145	97.36 100

casdiffmvs_mvg		92.96 68	92.83 68	93.35 70	94.59 172	83.40 116	95.00 124	96.34 103	90.30 30	92.05 92	96.05 95	83.43 75	98.15 145	92.07 70	95.67 114	98.49 29
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 69	92.54 72	93.68 66	96.10 100	84.71 77	95.66 87	96.39 100	91.92 7	93.22 60	96.49 79	83.16 79	98.87 82	84.47 174	95.47 120	97.45 99

CDPH-MVS		92.83 69	92.30 75	94.44 44	97.79 49	86.11 48	94.06 187	96.66 85	80.09 281	92.77 74	96.63 73	86.62 38	99.04 57	87.40 136	98.66 41	98.17 60

ETV-MVS		92.74 71	92.66 70	92.97 88	95.20 140	84.04 98	95.07 120	96.51 94	90.73 22	92.96 66	91.19 273	84.06 69	98.34 131	91.72 82	96.54 102	96.54 145

EI-MVSNet-UG-set		92.74 71	92.62 71	93.12 78	94.86 159	83.20 121	94.40 162	95.74 154	90.71 23	92.05 92	96.60 75	84.00 70	98.99 70	91.55 84	93.63 155	97.17 108

DPM-MVS		92.58 73	91.74 81	95.08 15	96.19 95	89.31 5	92.66 248	96.56 93	83.44 213	91.68 107	95.04 134	86.60 40	98.99 70	85.60 160	97.92 71	96.93 126

casdiffmvs		92.51 74	92.43 74	92.74 101	94.41 185	81.98 159	94.54 152	96.23 114	89.57 49	91.96 96	96.17 91	82.58 88	98.01 163	90.95 95	95.45 122	98.23 56
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_111021_LR		92.47 75	92.29 76	92.98 87	95.99 109	84.43 89	93.08 234	96.09 126	88.20 96	91.12 115	95.72 111	81.33 107	97.76 176	91.74 81	97.37 84	96.75 135

3Dnovator+	87.14 4	92.42 76	91.37 84	95.55 7	95.63 122	88.73 6	97.07 18	96.77 74	90.84 16	84.02 265	96.62 74	75.95 163	99.34 34	87.77 131	97.68 79	98.59 24

baseline		92.39 77	92.29 76	92.69 105	94.46 181	81.77 164	94.14 178	96.27 109	89.22 59	91.88 99	96.00 96	82.35 91	97.99 165	91.05 90	95.27 128	98.30 47

VNet		92.24 78	91.91 79	93.24 73	96.59 82	83.43 114	94.84 134	96.44 96	89.19 61	94.08 45	95.90 101	77.85 147	98.17 143	88.90 118	93.38 164	98.13 62

CPTT-MVS		91.99 79	91.80 80	92.55 111	98.24 31	81.98 159	96.76 30	96.49 95	81.89 251	90.24 124	96.44 81	78.59 136	98.61 104	89.68 109	97.85 73	97.06 115

EIA-MVS		91.95 80	91.94 78	91.98 136	95.16 141	80.01 218	95.36 98	96.73 79	88.44 85	89.34 137	92.16 239	83.82 73	98.45 120	89.35 112	97.06 88	97.48 97

DP-MVS Recon		91.95 80	91.28 86	93.96 55	98.33 27	85.92 56	94.66 146	96.66 85	82.69 233	90.03 130	95.82 105	82.30 93	99.03 58	84.57 172	96.48 105	96.91 128

EPNet		91.79 82	91.02 92	94.10 52	90.10 336	85.25 69	96.03 66	92.05 298	92.83 3	87.39 173	95.78 107	79.39 126	99.01 63	88.13 127	97.48 81	98.05 68
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		91.77 83	91.70 82	92.00 135	97.08 71	80.03 217	93.60 211	95.18 194	87.85 109	90.89 117	96.47 80	82.06 100	98.36 128	85.07 164	97.04 89	97.62 90

Vis-MVSNet		91.75 84	91.23 87	93.29 71	95.32 132	83.78 103	96.14 57	95.98 134	89.89 38	90.45 121	96.58 76	75.09 175	98.31 136	84.75 170	96.90 93	97.78 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 85	90.82 96	94.44 44	94.59 172	86.37 40	97.18 12	97.02 47	89.20 60	84.31 261	96.66 69	73.74 199	99.17 47	86.74 146	97.96 69	97.79 85

EPP-MVSNet		91.70 86	91.56 83	92.13 131	95.88 112	80.50 202	97.33 7	95.25 190	86.15 150	89.76 132	95.60 114	83.42 77	98.32 135	87.37 138	93.25 167	97.56 95

MVSFormer		91.68 87	91.30 85	92.80 97	93.86 210	83.88 101	95.96 71	95.90 142	84.66 189	91.76 104	94.91 137	77.92 144	97.30 222	89.64 110	97.11 86	97.24 104

Effi-MVS+		91.59 88	91.11 89	93.01 85	94.35 190	83.39 117	94.60 148	95.10 198	87.10 123	90.57 120	93.10 211	81.43 106	98.07 159	89.29 114	94.48 143	97.59 93

IS-MVSNet		91.43 89	91.09 91	92.46 115	95.87 114	81.38 177	96.95 19	93.69 260	89.72 47	89.50 135	95.98 98	78.57 137	97.77 175	83.02 192	96.50 104	98.22 57

PVSNet_Blended_VisFu		91.38 90	90.91 94	92.80 97	96.39 90	83.17 122	94.87 132	96.66 85	83.29 218	89.27 138	94.46 158	80.29 113	99.17 47	87.57 134	95.37 124	96.05 166

diffmvs		91.37 91	91.23 87	91.77 152	93.09 235	80.27 206	92.36 257	95.52 173	87.03 125	91.40 112	94.93 136	80.08 115	97.44 206	92.13 69	94.56 140	97.61 91
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 92	91.11 89	91.93 140	94.37 186	80.14 210	93.46 216	95.80 149	86.46 140	91.35 113	93.77 190	82.21 96	98.09 156	87.57 134	94.95 131	97.55 96

OMC-MVS		91.23 93	90.62 98	93.08 81	96.27 93	84.07 96	93.52 213	95.93 138	86.95 127	89.51 134	96.13 93	78.50 138	98.35 130	85.84 158	92.90 173	96.83 133

PAPM_NR		91.22 94	90.78 97	92.52 113	97.60 56	81.46 174	94.37 168	96.24 113	86.39 142	87.41 170	94.80 145	82.06 100	98.48 112	82.80 198	95.37 124	97.61 91

PS-MVSNAJ		91.18 95	90.92 93	91.96 138	95.26 137	82.60 148	92.09 269	95.70 158	86.27 144	91.84 101	92.46 229	79.70 121	98.99 70	89.08 116	95.86 112	94.29 240

xiu_mvs_v2_base		91.13 96	90.89 95	91.86 146	94.97 151	82.42 150	92.24 263	95.64 165	86.11 154	91.74 106	93.14 209	79.67 124	98.89 81	89.06 117	95.46 121	94.28 241

nrg030		91.08 97	90.39 99	93.17 76	93.07 236	86.91 21	96.41 39	96.26 110	88.30 90	88.37 153	94.85 142	82.19 97	97.64 187	91.09 89	82.95 296	94.96 205

lupinMVS		90.92 98	90.21 102	93.03 84	93.86 210	83.88 101	92.81 245	93.86 254	79.84 284	91.76 104	94.29 164	77.92 144	98.04 161	90.48 106	97.11 86	97.17 108

h-mvs33		90.80 99	90.15 105	92.75 100	96.01 105	82.66 145	95.43 97	95.53 172	89.80 41	93.08 63	95.64 113	75.77 164	99.00 68	92.07 70	78.05 351	96.60 140

jason		90.80 99	90.10 106	92.90 92	93.04 239	83.53 112	93.08 234	94.15 243	80.22 278	91.41 111	94.91 137	76.87 151	97.93 170	90.28 107	96.90 93	97.24 104
jason: jason.
VDD-MVS		90.74 101	89.92 113	93.20 75	96.27 93	83.02 131	95.73 81	93.86 254	88.42 87	92.53 81	96.84 60	62.09 316	98.64 100	90.95 95	92.62 177	97.93 76

PVSNet_Blended		90.73 102	90.32 101	91.98 136	96.12 97	81.25 179	92.55 252	96.83 66	82.04 245	89.10 140	92.56 227	81.04 109	98.85 86	86.72 148	95.91 111	95.84 173

test_yl		90.69 103	90.02 111	92.71 102	95.72 118	82.41 152	94.11 180	95.12 196	85.63 162	91.49 109	94.70 148	74.75 179	98.42 126	86.13 153	92.53 179	97.31 101

DCV-MVSNet		90.69 103	90.02 111	92.71 102	95.72 118	82.41 152	94.11 180	95.12 196	85.63 162	91.49 109	94.70 148	74.75 179	98.42 126	86.13 153	92.53 179	97.31 101

API-MVS		90.66 105	90.07 107	92.45 116	96.36 91	84.57 80	96.06 64	95.22 193	82.39 236	89.13 139	94.27 167	80.32 112	98.46 116	80.16 248	96.71 99	94.33 238

xiu_mvs_v1_base_debu		90.64 106	90.05 108	92.40 118	93.97 207	84.46 86	93.32 220	95.46 175	85.17 172	92.25 87	94.03 171	70.59 235	98.57 108	90.97 92	94.67 135	94.18 242

xiu_mvs_v1_base		90.64 106	90.05 108	92.40 118	93.97 207	84.46 86	93.32 220	95.46 175	85.17 172	92.25 87	94.03 171	70.59 235	98.57 108	90.97 92	94.67 135	94.18 242

xiu_mvs_v1_base_debi		90.64 106	90.05 108	92.40 118	93.97 207	84.46 86	93.32 220	95.46 175	85.17 172	92.25 87	94.03 171	70.59 235	98.57 108	90.97 92	94.67 135	94.18 242

HQP_MVS		90.60 109	90.19 103	91.82 149	94.70 167	82.73 141	95.85 75	96.22 115	90.81 17	86.91 181	94.86 140	74.23 187	98.12 146	88.15 125	89.99 208	94.63 217

FIs		90.51 110	90.35 100	90.99 187	93.99 206	80.98 188	95.73 81	97.54 4	89.15 62	86.72 188	94.68 150	81.83 104	97.24 230	85.18 163	88.31 244	94.76 215

MAR-MVS		90.30 111	89.37 124	93.07 83	96.61 81	84.48 85	95.68 84	95.67 160	82.36 238	87.85 161	92.85 216	76.63 157	98.80 90	80.01 249	96.68 100	95.91 169
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		90.27 112	90.18 104	90.53 199	93.71 217	79.85 224	95.77 80	97.59 3	89.31 56	86.27 199	94.67 151	81.93 103	97.01 246	84.26 176	88.09 247	94.71 216

CANet_DTU		90.26 113	89.41 123	92.81 96	93.46 226	83.01 132	93.48 214	94.47 229	89.43 52	87.76 165	94.23 168	70.54 239	99.03 58	84.97 165	96.39 106	96.38 148

SDMVSNet		90.19 114	89.61 117	91.93 140	96.00 106	83.09 128	92.89 241	95.98 134	88.73 76	86.85 185	95.20 128	72.09 219	97.08 240	88.90 118	89.85 214	95.63 183

OPM-MVS		90.12 115	89.56 118	91.82 149	93.14 233	83.90 100	94.16 177	95.74 154	88.96 71	87.86 160	95.43 119	72.48 215	97.91 171	88.10 129	90.18 207	93.65 275
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 116	89.13 130	92.95 90	96.71 77	82.32 154	96.08 61	89.91 351	86.79 131	92.15 91	96.81 63	62.60 314	98.34 131	87.18 140	93.90 151	98.19 58

GeoE		90.05 117	89.43 122	91.90 145	95.16 141	80.37 205	95.80 78	94.65 226	83.90 200	87.55 169	94.75 147	78.18 142	97.62 189	81.28 228	93.63 155	97.71 88

PAPR		90.02 118	89.27 129	92.29 126	95.78 116	80.95 190	92.68 247	96.22 115	81.91 249	86.66 189	93.75 192	82.23 95	98.44 122	79.40 260	94.79 133	97.48 97

PVSNet_BlendedMVS		89.98 119	89.70 115	90.82 192	96.12 97	81.25 179	93.92 198	96.83 66	83.49 212	89.10 140	92.26 237	81.04 109	98.85 86	86.72 148	87.86 251	92.35 321

PS-MVSNAJss		89.97 120	89.62 116	91.02 184	91.90 273	80.85 193	95.26 108	95.98 134	86.26 145	86.21 201	94.29 164	79.70 121	97.65 184	88.87 120	88.10 245	94.57 222

mvsmamba		89.96 121	89.50 119	91.33 169	92.90 246	81.82 162	96.68 33	92.37 286	89.03 67	87.00 177	94.85 142	73.05 207	97.65 184	91.03 91	88.63 235	94.51 227

XVG-OURS-SEG-HR		89.95 122	89.45 120	91.47 163	94.00 205	81.21 182	91.87 273	96.06 130	85.78 157	88.55 149	95.73 110	74.67 183	97.27 226	88.71 121	89.64 219	95.91 169

UGNet		89.95 122	88.95 135	92.95 90	94.51 178	83.31 118	95.70 83	95.23 191	89.37 54	87.58 167	93.94 180	64.00 304	98.78 91	83.92 181	96.31 107	96.74 136
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		89.92 124	89.29 127	91.81 151	93.39 228	83.72 104	94.43 160	97.12 41	89.80 41	86.46 192	93.32 200	83.16 79	97.23 231	84.92 166	81.02 325	94.49 232

AdaColmap		89.89 125	89.07 131	92.37 121	97.41 62	83.03 130	94.42 161	95.92 139	82.81 230	86.34 198	94.65 152	73.89 195	99.02 61	80.69 239	95.51 117	95.05 200

iter_conf05_11		89.88 126	89.04 133	92.41 117	95.12 144	81.63 167	92.87 243	92.45 284	86.21 148	92.48 84	93.95 179	59.05 342	98.60 106	90.50 104	98.72 32	96.99 121

hse-mvs2		89.88 126	89.34 125	91.51 160	94.83 161	81.12 185	93.94 196	93.91 253	89.80 41	93.08 63	93.60 194	75.77 164	97.66 183	92.07 70	77.07 358	95.74 178

UniMVSNet (Re)		89.80 128	89.07 131	92.01 132	93.60 222	84.52 83	94.78 138	97.47 11	89.26 58	86.44 195	92.32 234	82.10 98	97.39 218	84.81 169	80.84 329	94.12 246

HQP-MVS		89.80 128	89.28 128	91.34 168	94.17 195	81.56 168	94.39 164	96.04 131	88.81 72	85.43 226	93.97 178	73.83 197	97.96 167	87.11 143	89.77 217	94.50 230

FA-MVS(test-final)		89.66 130	88.91 137	91.93 140	94.57 175	80.27 206	91.36 285	94.74 222	84.87 181	89.82 131	92.61 226	74.72 182	98.47 115	83.97 180	93.53 158	97.04 117

VPA-MVSNet		89.62 131	88.96 134	91.60 157	93.86 210	82.89 136	95.46 96	97.33 25	87.91 104	88.43 152	93.31 201	74.17 190	97.40 215	87.32 139	82.86 301	94.52 225

WTY-MVS		89.60 132	88.92 136	91.67 155	95.47 128	81.15 184	92.38 256	94.78 220	83.11 222	89.06 143	94.32 162	78.67 135	96.61 265	81.57 225	90.89 198	97.24 104

Vis-MVSNet (Re-imp)		89.59 133	89.44 121	90.03 225	95.74 117	75.85 303	95.61 91	90.80 335	87.66 115	87.83 162	95.40 120	76.79 153	96.46 278	78.37 265	96.73 98	97.80 84

VDDNet		89.56 134	88.49 151	92.76 99	95.07 146	82.09 156	96.30 43	93.19 267	81.05 273	91.88 99	96.86 59	61.16 329	98.33 133	88.43 124	92.49 181	97.84 82

114514_t		89.51 135	88.50 149	92.54 112	98.11 36	81.99 158	95.16 116	96.36 102	70.19 376	85.81 207	95.25 124	76.70 155	98.63 102	82.07 213	96.86 96	97.00 120

QAPM		89.51 135	88.15 160	93.59 68	94.92 155	84.58 79	96.82 29	96.70 83	78.43 307	83.41 280	96.19 90	73.18 206	99.30 40	77.11 281	96.54 102	96.89 129

CLD-MVS		89.47 137	88.90 138	91.18 174	94.22 194	82.07 157	92.13 267	96.09 126	87.90 105	85.37 232	92.45 230	74.38 185	97.56 192	87.15 141	90.43 203	93.93 255
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		89.45 138	88.90 138	91.12 176	94.47 179	81.49 172	95.30 103	96.14 120	86.73 134	85.45 223	95.16 130	69.89 245	98.10 148	87.70 132	89.23 227	93.77 269

CDS-MVSNet		89.45 138	88.51 148	92.29 126	93.62 221	83.61 111	93.01 237	94.68 225	81.95 247	87.82 163	93.24 205	78.69 134	96.99 247	80.34 245	93.23 168	96.28 152
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		89.41 140	88.64 143	91.71 154	94.74 163	80.81 194	93.54 212	95.10 198	83.11 222	86.82 187	90.67 292	79.74 120	97.75 179	80.51 243	93.55 157	96.57 143

ab-mvs		89.41 140	88.35 153	92.60 108	95.15 143	82.65 146	92.20 265	95.60 167	83.97 199	88.55 149	93.70 193	74.16 191	98.21 142	82.46 203	89.37 223	96.94 125

XVG-OURS		89.40 142	88.70 142	91.52 159	94.06 199	81.46 174	91.27 289	96.07 128	86.14 151	88.89 145	95.77 108	68.73 266	97.26 228	87.39 137	89.96 210	95.83 174

test_vis1_n_1920		89.39 143	89.84 114	88.04 288	92.97 243	72.64 337	94.71 143	96.03 133	86.18 149	91.94 98	96.56 78	61.63 319	95.74 313	93.42 41	95.11 130	95.74 178

mvs_anonymous		89.37 144	89.32 126	89.51 251	93.47 225	74.22 320	91.65 280	94.83 216	82.91 228	85.45 223	93.79 188	81.23 108	96.36 285	86.47 150	94.09 148	97.94 74

DU-MVS		89.34 145	88.50 149	91.85 148	93.04 239	83.72 104	94.47 157	96.59 90	89.50 50	86.46 192	93.29 203	77.25 149	97.23 231	84.92 166	81.02 325	94.59 220

TAMVS		89.21 146	88.29 157	91.96 138	93.71 217	82.62 147	93.30 224	94.19 241	82.22 240	87.78 164	93.94 180	78.83 131	96.95 249	77.70 274	92.98 172	96.32 149

ACMM	84.12 9	89.14 147	88.48 152	91.12 176	94.65 170	81.22 181	95.31 101	96.12 123	85.31 170	85.92 206	94.34 160	70.19 243	98.06 160	85.65 159	88.86 233	94.08 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 148	88.64 143	90.48 205	95.53 127	74.97 311	96.08 61	84.89 379	88.13 99	90.16 127	96.65 70	63.29 310	98.10 148	86.14 151	96.90 93	98.39 39

EI-MVSNet		89.10 148	88.86 140	89.80 238	91.84 275	78.30 259	93.70 208	95.01 201	85.73 159	87.15 175	95.28 122	79.87 118	97.21 233	83.81 183	87.36 259	93.88 258

ECVR-MVS		89.09 150	88.53 147	90.77 194	95.62 123	75.89 302	96.16 53	84.22 381	87.89 107	90.20 125	96.65 70	63.19 312	98.10 148	85.90 156	96.94 91	98.33 43

RRT_MVS		89.09 150	88.62 146	90.49 203	92.85 247	79.65 228	96.41 39	94.41 232	88.22 94	85.50 219	94.77 146	69.36 253	97.31 221	89.33 113	86.73 266	94.51 227

CNLPA		89.07 152	87.98 164	92.34 122	96.87 74	84.78 76	94.08 184	93.24 265	81.41 264	84.46 251	95.13 132	75.57 171	96.62 262	77.21 279	93.84 153	95.61 185

PLC	84.53 7	89.06 153	88.03 163	92.15 130	97.27 68	82.69 144	94.29 170	95.44 180	79.71 286	84.01 266	94.18 169	76.68 156	98.75 93	77.28 278	93.41 163	95.02 201
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 154	88.64 143	90.21 216	90.74 322	79.28 240	95.96 71	95.90 142	84.66 189	85.33 234	92.94 215	74.02 193	97.30 222	89.64 110	88.53 237	94.05 252

HY-MVS	83.01 12	89.03 154	87.94 166	92.29 126	94.86 159	82.77 137	92.08 270	94.49 228	81.52 263	86.93 179	92.79 222	78.32 141	98.23 139	79.93 250	90.55 201	95.88 171

ACMP	84.23 8	89.01 156	88.35 153	90.99 187	94.73 164	81.27 178	95.07 120	95.89 144	86.48 138	83.67 273	94.30 163	69.33 254	97.99 165	87.10 145	88.55 236	93.72 273
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 157	88.26 159	90.94 190	94.05 200	80.78 195	91.71 277	95.38 184	81.55 262	88.63 148	93.91 184	75.04 176	95.47 324	82.47 202	91.61 186	96.57 143

bld_raw_dy_0_64		88.86 158	87.75 170	92.21 129	95.12 144	81.19 183	95.56 94	91.29 321	85.30 171	89.10 140	94.38 159	59.04 343	98.44 122	90.50 104	89.43 221	96.99 121

iter_conf05		88.85 159	88.08 162	91.17 175	94.27 192	81.64 166	95.18 113	92.15 295	86.23 147	87.28 174	94.07 170	63.89 307	97.55 193	90.63 100	89.00 231	94.32 239

TranMVSNet+NR-MVSNet		88.84 160	87.95 165	91.49 161	92.68 251	83.01 132	94.92 129	96.31 104	89.88 39	85.53 216	93.85 187	76.63 157	96.96 248	81.91 217	79.87 342	94.50 230

CHOSEN 1792x2688		88.84 160	87.69 171	92.30 125	96.14 96	81.42 176	90.01 317	95.86 146	74.52 346	87.41 170	93.94 180	75.46 172	98.36 128	80.36 244	95.53 116	97.12 113

MVSTER		88.84 160	88.29 157	90.51 202	92.95 244	80.44 203	93.73 205	95.01 201	84.66 189	87.15 175	93.12 210	72.79 211	97.21 233	87.86 130	87.36 259	93.87 259

test_cas_vis1_n_1920		88.83 163	88.85 141	88.78 267	91.15 303	76.72 290	93.85 201	94.93 208	83.23 221	92.81 72	96.00 96	61.17 328	94.45 334	91.67 83	94.84 132	95.17 197

OpenMVS	83.78 11	88.74 164	87.29 181	93.08 81	92.70 250	85.39 67	96.57 36	96.43 97	78.74 302	80.85 310	96.07 94	69.64 249	99.01 63	78.01 272	96.65 101	94.83 212

thisisatest0530		88.67 165	87.61 173	91.86 146	94.87 158	80.07 213	94.63 147	89.90 352	84.00 198	88.46 151	93.78 189	66.88 281	98.46 116	83.30 188	92.65 176	97.06 115

Effi-MVS+-dtu		88.65 166	88.35 153	89.54 248	93.33 229	76.39 296	94.47 157	94.36 235	87.70 112	85.43 226	89.56 318	73.45 202	97.26 228	85.57 161	91.28 190	94.97 202

tttt0517		88.61 167	87.78 169	91.11 179	94.96 152	77.81 272	95.35 99	89.69 355	85.09 177	88.05 158	94.59 155	66.93 279	98.48 112	83.27 189	92.13 184	97.03 118

BH-untuned		88.60 168	88.13 161	90.01 228	95.24 138	78.50 253	93.29 225	94.15 243	84.75 186	84.46 251	93.40 197	75.76 166	97.40 215	77.59 275	94.52 142	94.12 246

sd_testset		88.59 169	87.85 168	90.83 191	96.00 106	80.42 204	92.35 258	94.71 223	88.73 76	86.85 185	95.20 128	67.31 273	96.43 280	79.64 254	89.85 214	95.63 183

NR-MVSNet		88.58 170	87.47 177	91.93 140	93.04 239	84.16 95	94.77 139	96.25 112	89.05 65	80.04 323	93.29 203	79.02 130	97.05 244	81.71 224	80.05 339	94.59 220

1112_ss		88.42 171	87.33 180	91.72 153	94.92 155	80.98 188	92.97 239	94.54 227	78.16 313	83.82 269	93.88 185	78.78 133	97.91 171	79.45 256	89.41 222	96.26 153

WR-MVS		88.38 172	87.67 172	90.52 201	93.30 230	80.18 208	93.26 227	95.96 137	88.57 83	85.47 222	92.81 220	76.12 159	96.91 252	81.24 229	82.29 305	94.47 235

BH-RMVSNet		88.37 173	87.48 176	91.02 184	95.28 134	79.45 232	92.89 241	93.07 269	85.45 167	86.91 181	94.84 144	70.35 240	97.76 176	73.97 308	94.59 139	95.85 172

IterMVS-LS		88.36 174	87.91 167	89.70 242	93.80 213	78.29 260	93.73 205	95.08 200	85.73 159	84.75 243	91.90 253	79.88 117	96.92 251	83.83 182	82.51 302	93.89 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 175	86.13 221	94.85 25	98.54 13	86.60 33	96.93 22	97.19 35	90.66 24	92.85 69	23.41 403	85.02 59	99.49 26	91.99 74	98.56 49	98.47 33

LCM-MVSNet-Re		88.30 176	88.32 156	88.27 281	94.71 166	72.41 342	93.15 230	90.98 329	87.77 110	79.25 332	91.96 251	78.35 140	95.75 312	83.04 191	95.62 115	96.65 139

jajsoiax		88.24 177	87.50 175	90.48 205	90.89 316	80.14 210	95.31 101	95.65 164	84.97 179	84.24 262	94.02 174	65.31 297	97.42 208	88.56 122	88.52 238	93.89 256

VPNet		88.20 178	87.47 177	90.39 210	93.56 223	79.46 231	94.04 188	95.54 171	88.67 79	86.96 178	94.58 156	69.33 254	97.15 235	84.05 179	80.53 334	94.56 223

TAPA-MVS	84.62 6	88.16 179	87.01 189	91.62 156	96.64 80	80.65 197	94.39 164	96.21 118	76.38 326	86.19 202	95.44 117	79.75 119	98.08 158	62.75 370	95.29 126	96.13 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 180	87.28 182	90.57 197	94.96 152	80.07 213	94.27 171	91.29 321	86.74 133	87.41 170	94.00 176	76.77 154	96.20 291	80.77 237	79.31 347	95.44 187

Anonymous20240529		88.09 181	86.59 204	92.58 110	96.53 86	81.92 161	95.99 69	95.84 147	74.11 350	89.06 143	95.21 127	61.44 322	98.81 89	83.67 186	87.47 256	97.01 119

HyFIR lowres test		88.09 181	86.81 193	91.93 140	96.00 106	80.63 198	90.01 317	95.79 150	73.42 357	87.68 166	92.10 245	73.86 196	97.96 167	80.75 238	91.70 185	97.19 107

mvs_tets		88.06 183	87.28 182	90.38 212	90.94 312	79.88 222	95.22 110	95.66 162	85.10 176	84.21 263	93.94 180	63.53 308	97.40 215	88.50 123	88.40 242	93.87 259

F-COLMAP		87.95 184	86.80 194	91.40 165	96.35 92	80.88 192	94.73 141	95.45 178	79.65 287	82.04 297	94.61 153	71.13 226	98.50 111	76.24 290	91.05 196	94.80 214

LS3D		87.89 185	86.32 214	92.59 109	96.07 103	82.92 135	95.23 109	94.92 209	75.66 333	82.89 287	95.98 98	72.48 215	99.21 45	68.43 342	95.23 129	95.64 182

anonymousdsp		87.84 186	87.09 185	90.12 221	89.13 349	80.54 201	94.67 145	95.55 169	82.05 243	83.82 269	92.12 242	71.47 224	97.15 235	87.15 141	87.80 254	92.67 309

v2v482		87.84 186	87.06 186	90.17 217	90.99 308	79.23 243	94.00 193	95.13 195	84.87 181	85.53 216	92.07 248	74.45 184	97.45 203	84.71 171	81.75 313	93.85 262

WR-MVS_H		87.80 188	87.37 179	89.10 260	93.23 231	78.12 263	95.61 91	97.30 29	87.90 105	83.72 271	92.01 250	79.65 125	96.01 299	76.36 287	80.54 333	93.16 294

AUN-MVS		87.78 189	86.54 206	91.48 162	94.82 162	81.05 186	93.91 200	93.93 250	83.00 225	86.93 179	93.53 195	69.50 251	97.67 181	86.14 151	77.12 357	95.73 180

PCF-MVS	84.11 10	87.74 190	86.08 225	92.70 104	94.02 201	84.43 89	89.27 329	95.87 145	73.62 355	84.43 253	94.33 161	78.48 139	98.86 84	70.27 328	94.45 144	94.81 213
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 191	86.13 221	92.31 124	96.66 79	80.74 196	94.87 132	91.49 316	80.47 277	89.46 136	95.44 117	54.72 362	98.23 139	82.19 209	89.89 212	97.97 72

V42		87.68 191	86.86 191	90.15 219	90.58 327	80.14 210	94.24 174	95.28 189	83.66 206	85.67 211	91.33 268	74.73 181	97.41 213	84.43 175	81.83 311	92.89 304

thres600view7		87.65 193	86.67 199	90.59 196	96.08 102	78.72 246	94.88 131	91.58 312	87.06 124	88.08 156	92.30 235	68.91 263	98.10 148	70.05 335	91.10 191	94.96 205

XXY-MVS		87.65 193	86.85 192	90.03 225	92.14 263	80.60 200	93.76 204	95.23 191	82.94 227	84.60 246	94.02 174	74.27 186	95.49 323	81.04 231	83.68 289	94.01 254

Test_1112_low_res		87.65 193	86.51 207	91.08 180	94.94 154	79.28 240	91.77 275	94.30 237	76.04 331	83.51 278	92.37 232	77.86 146	97.73 180	78.69 264	89.13 229	96.22 154

thres100view900		87.63 196	86.71 197	90.38 212	96.12 97	78.55 250	95.03 123	91.58 312	87.15 121	88.06 157	92.29 236	68.91 263	98.10 148	70.13 332	91.10 191	94.48 233

CP-MVSNet		87.63 196	87.26 184	88.74 271	93.12 234	76.59 293	95.29 105	96.58 91	88.43 86	83.49 279	92.98 214	75.28 173	95.83 307	78.97 262	81.15 321	93.79 264

thres400		87.62 198	86.64 200	90.57 197	95.99 109	78.64 248	94.58 149	91.98 302	86.94 128	88.09 154	91.77 255	69.18 259	98.10 148	70.13 332	91.10 191	94.96 205

v1144		87.61 199	86.79 195	90.06 224	91.01 307	79.34 236	93.95 195	95.42 183	83.36 217	85.66 212	91.31 271	74.98 177	97.42 208	83.37 187	82.06 307	93.42 284

tfpn200view9		87.58 200	86.64 200	90.41 209	95.99 109	78.64 248	94.58 149	91.98 302	86.94 128	88.09 154	91.77 255	69.18 259	98.10 148	70.13 332	91.10 191	94.48 233

BH-w/o		87.57 201	87.05 187	89.12 259	94.90 157	77.90 268	92.41 254	93.51 262	82.89 229	83.70 272	91.34 267	75.75 167	97.07 242	75.49 294	93.49 160	92.39 319

UniMVSNet_ETH3D		87.53 202	86.37 211	91.00 186	92.44 256	78.96 245	94.74 140	95.61 166	84.07 197	85.36 233	94.52 157	59.78 337	97.34 220	82.93 193	87.88 250	96.71 137

ET-MVSNet_ETH3D		87.51 203	85.91 233	92.32 123	93.70 219	83.93 99	92.33 260	90.94 331	84.16 194	72.09 372	92.52 228	69.90 244	95.85 306	89.20 115	88.36 243	97.17 108

1314		87.51 203	86.57 205	90.34 214	92.42 257	79.74 226	92.63 249	95.35 188	78.35 308	80.14 320	91.62 262	74.05 192	97.15 235	81.05 230	93.53 158	94.12 246

v8		87.50 205	86.71 197	89.89 232	91.37 293	79.40 233	94.50 153	95.38 184	84.81 184	83.60 276	91.33 268	76.05 160	97.42 208	82.84 196	80.51 336	92.84 306

Fast-Effi-MVS+-dtu		87.44 206	86.72 196	89.63 246	92.04 267	77.68 278	94.03 189	93.94 249	85.81 156	82.42 291	91.32 270	70.33 241	97.06 243	80.33 246	90.23 206	94.14 245

MVS		87.44 206	86.10 224	91.44 164	92.61 252	83.62 109	92.63 249	95.66 162	67.26 380	81.47 302	92.15 240	77.95 143	98.22 141	79.71 252	95.48 119	92.47 315

FE-MVS		87.40 208	86.02 227	91.57 158	94.56 176	79.69 227	90.27 306	93.72 259	80.57 276	88.80 146	91.62 262	65.32 296	98.59 107	74.97 302	94.33 147	96.44 146

FMVSNet3		87.40 208	86.11 223	91.30 170	93.79 215	83.64 108	94.20 176	94.81 218	83.89 201	84.37 254	91.87 254	68.45 269	96.56 270	78.23 269	85.36 274	93.70 274

test_fmvs1		87.34 210	87.56 174	86.68 321	90.59 326	71.80 346	94.01 191	94.04 248	78.30 309	91.97 95	95.22 125	56.28 354	93.71 349	92.89 49	94.71 134	94.52 225

thisisatest0515		87.33 211	85.99 228	91.37 167	93.49 224	79.55 229	90.63 302	89.56 358	80.17 279	87.56 168	90.86 284	67.07 278	98.28 137	81.50 226	93.02 171	96.29 151

PS-CasMVS		87.32 212	86.88 190	88.63 274	92.99 242	76.33 298	95.33 100	96.61 89	88.22 94	83.30 284	93.07 212	73.03 209	95.79 311	78.36 266	81.00 327	93.75 271

GBi-Net		87.26 213	85.98 229	91.08 180	94.01 202	83.10 125	95.14 117	94.94 204	83.57 208	84.37 254	91.64 258	66.59 286	96.34 286	78.23 269	85.36 274	93.79 264

test1		87.26 213	85.98 229	91.08 180	94.01 202	83.10 125	95.14 117	94.94 204	83.57 208	84.37 254	91.64 258	66.59 286	96.34 286	78.23 269	85.36 274	93.79 264

v1192		87.25 215	86.33 213	90.00 229	90.76 321	79.04 244	93.80 202	95.48 174	82.57 234	85.48 221	91.18 275	73.38 205	97.42 208	82.30 206	82.06 307	93.53 278

v10		87.25 215	86.38 210	89.85 233	91.19 299	79.50 230	94.48 154	95.45 178	83.79 204	83.62 275	91.19 273	75.13 174	97.42 208	81.94 216	80.60 331	92.63 311

DP-MVS		87.25 215	85.36 249	92.90 92	97.65 55	83.24 119	94.81 136	92.00 300	74.99 341	81.92 299	95.00 135	72.66 212	99.05 55	66.92 354	92.33 182	96.40 147

miper_ehance_all_eth		87.22 218	86.62 203	89.02 263	92.13 264	77.40 282	90.91 298	94.81 218	81.28 267	84.32 259	90.08 307	79.26 127	96.62 262	83.81 183	82.94 297	93.04 299

test2506		87.21 219	86.28 216	90.02 227	95.62 123	73.64 325	96.25 48	71.38 403	87.89 107	90.45 121	96.65 70	55.29 360	98.09 156	86.03 155	96.94 91	98.33 43

thres200		87.21 219	86.24 218	90.12 221	95.36 131	78.53 251	93.26 227	92.10 296	86.42 141	88.00 159	91.11 279	69.24 258	98.00 164	69.58 336	91.04 197	93.83 263

v144192		87.19 221	86.35 212	89.74 239	90.64 325	78.24 261	93.92 198	95.43 181	81.93 248	85.51 218	91.05 281	74.21 189	97.45 203	82.86 195	81.56 315	93.53 278

FMVSNet2		87.19 221	85.82 235	91.30 170	94.01 202	83.67 106	94.79 137	94.94 204	83.57 208	83.88 268	92.05 249	66.59 286	96.51 273	77.56 276	85.01 277	93.73 272

c3_l		87.14 223	86.50 208	89.04 262	92.20 261	77.26 283	91.22 292	94.70 224	82.01 246	84.34 258	90.43 296	78.81 132	96.61 265	83.70 185	81.09 322	93.25 289

testing91		87.11 224	86.18 219	89.92 231	94.43 184	75.38 310	91.53 282	92.27 291	86.48 138	86.50 190	90.24 299	61.19 327	97.53 195	82.10 211	90.88 199	96.84 132

Baseline_NR-MVSNet		87.07 225	86.63 202	88.40 277	91.44 288	77.87 270	94.23 175	92.57 282	84.12 196	85.74 210	92.08 246	77.25 149	96.04 296	82.29 207	79.94 340	91.30 342

v148		87.04 226	86.32 214	89.21 256	90.94 312	77.26 283	93.71 207	94.43 230	84.84 183	84.36 257	90.80 288	76.04 161	97.05 244	82.12 210	79.60 344	93.31 286

test_fmvs1_n		87.03 227	87.04 188	86.97 313	89.74 344	71.86 344	94.55 151	94.43 230	78.47 305	91.95 97	95.50 116	51.16 373	93.81 347	93.02 48	94.56 140	95.26 194

v1921920		86.97 228	86.06 226	89.69 243	90.53 330	78.11 264	93.80 202	95.43 181	81.90 250	85.33 234	91.05 281	72.66 212	97.41 213	82.05 214	81.80 312	93.53 278

tt0805		86.92 229	85.74 241	90.48 205	92.22 260	79.98 220	95.63 90	94.88 212	83.83 203	84.74 244	92.80 221	57.61 349	97.67 181	85.48 162	84.42 281	93.79 264

miper_enhance_ethall		86.90 230	86.18 219	89.06 261	91.66 284	77.58 280	90.22 312	94.82 217	79.16 293	84.48 250	89.10 323	79.19 129	96.66 260	84.06 178	82.94 297	92.94 302

v7n		86.81 231	85.76 239	89.95 230	90.72 323	79.25 242	95.07 120	95.92 139	84.45 192	82.29 292	90.86 284	72.60 214	97.53 195	79.42 259	80.52 335	93.08 298

PEN-MVS		86.80 232	86.27 217	88.40 277	92.32 259	75.71 305	95.18 113	96.38 101	87.97 102	82.82 288	93.15 208	73.39 204	95.92 302	76.15 291	79.03 349	93.59 276

cl22		86.78 233	85.98 229	89.18 258	92.34 258	77.62 279	90.84 299	94.13 245	81.33 266	83.97 267	90.15 304	73.96 194	96.60 267	84.19 177	82.94 297	93.33 285

v1240		86.78 233	85.85 234	89.56 247	90.45 331	77.79 274	93.61 210	95.37 186	81.65 258	85.43 226	91.15 277	71.50 223	97.43 207	81.47 227	82.05 309	93.47 282

TR-MVS		86.78 233	85.76 239	89.82 235	94.37 186	78.41 255	92.47 253	92.83 274	81.11 272	86.36 196	92.40 231	68.73 266	97.48 199	73.75 311	89.85 214	93.57 277

PatchMatch-RL		86.77 236	85.54 243	90.47 208	95.88 112	82.71 143	90.54 303	92.31 289	79.82 285	84.32 259	91.57 266	68.77 265	96.39 282	73.16 313	93.48 162	92.32 322

testing99		86.72 237	85.73 242	89.69 243	94.23 193	74.91 313	91.35 286	90.97 330	86.14 151	86.36 196	90.22 300	59.41 339	97.48 199	82.24 208	90.66 200	96.69 138

PAPM		86.68 238	85.39 247	90.53 199	93.05 238	79.33 239	89.79 320	94.77 221	78.82 299	81.95 298	93.24 205	76.81 152	97.30 222	66.94 352	93.16 169	94.95 208

pm-mvs1		86.61 239	85.54 243	89.82 235	91.44 288	80.18 208	95.28 107	94.85 214	83.84 202	81.66 300	92.62 225	72.45 217	96.48 275	79.67 253	78.06 350	92.82 307

GA-MVS		86.61 239	85.27 252	90.66 195	91.33 296	78.71 247	90.40 305	93.81 257	85.34 169	85.12 236	89.57 317	61.25 324	97.11 239	80.99 234	89.59 220	96.15 156

Anonymous20231211		86.59 241	85.13 254	90.98 189	96.52 87	81.50 170	96.14 57	96.16 119	73.78 353	83.65 274	92.15 240	63.26 311	97.37 219	82.82 197	81.74 314	94.06 251

test_vis1_n		86.56 242	86.49 209	86.78 320	88.51 354	72.69 334	94.68 144	93.78 258	79.55 288	90.70 118	95.31 121	48.75 378	93.28 355	93.15 45	93.99 149	94.38 237

DIV-MVS_self_test		86.53 243	85.78 236	88.75 269	92.02 269	76.45 295	90.74 300	94.30 237	81.83 254	83.34 282	90.82 287	75.75 167	96.57 268	81.73 223	81.52 317	93.24 290

cl____		86.52 244	85.78 236	88.75 269	92.03 268	76.46 294	90.74 300	94.30 237	81.83 254	83.34 282	90.78 289	75.74 169	96.57 268	81.74 222	81.54 316	93.22 291

eth_miper_zixun_eth		86.50 245	85.77 238	88.68 272	91.94 270	75.81 304	90.47 304	94.89 210	82.05 243	84.05 264	90.46 295	75.96 162	96.77 256	82.76 199	79.36 346	93.46 283

baseline2		86.50 245	85.39 247	89.84 234	91.12 304	76.70 291	91.88 272	88.58 361	82.35 239	79.95 324	90.95 283	73.42 203	97.63 188	80.27 247	89.95 211	95.19 196

EPNet_dtu		86.49 247	85.94 232	88.14 286	90.24 334	72.82 332	94.11 180	92.20 293	86.66 136	79.42 331	92.36 233	73.52 200	95.81 309	71.26 320	93.66 154	95.80 176
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		86.44 248	85.35 250	89.69 243	94.29 191	75.40 309	91.30 287	90.53 338	84.76 185	85.06 237	90.13 305	58.95 345	97.45 203	82.08 212	91.09 195	96.21 155

cascas		86.43 249	84.98 257	90.80 193	92.10 266	80.92 191	90.24 310	95.91 141	73.10 360	83.57 277	88.39 335	65.15 298	97.46 202	84.90 168	91.43 188	94.03 253

SCA		86.32 250	85.18 253	89.73 241	92.15 262	76.60 292	91.12 293	91.69 309	83.53 211	85.50 219	88.81 328	66.79 282	96.48 275	76.65 284	90.35 205	96.12 159

LTVRE_ROB	82.13 13	86.26 251	84.90 260	90.34 214	94.44 183	81.50 170	92.31 262	94.89 210	83.03 224	79.63 329	92.67 223	69.69 248	97.79 174	71.20 321	86.26 269	91.72 332
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		86.11 252	85.48 245	87.98 289	91.65 285	74.92 312	94.93 128	95.75 153	87.36 119	82.26 293	93.04 213	72.85 210	95.82 308	74.04 307	77.46 355	93.20 292

XVG-ACMP-BASELINE		86.00 253	84.84 262	89.45 252	91.20 298	78.00 265	91.70 278	95.55 169	85.05 178	82.97 286	92.25 238	54.49 363	97.48 199	82.93 193	87.45 258	92.89 304

MVP-Stereo		85.97 254	84.86 261	89.32 254	90.92 314	82.19 155	92.11 268	94.19 241	78.76 301	78.77 337	91.63 261	68.38 270	96.56 270	75.01 301	93.95 150	89.20 367
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 255	85.09 255	88.35 279	90.79 319	77.42 281	91.83 274	95.70 158	80.77 275	80.08 322	90.02 308	66.74 284	96.37 283	81.88 218	87.97 249	91.26 343

test-LLR		85.87 256	85.41 246	87.25 305	90.95 310	71.67 348	89.55 323	89.88 353	83.41 214	84.54 248	87.95 342	67.25 275	95.11 329	81.82 219	93.37 165	94.97 202

FMVSNet1		85.85 257	84.11 271	91.08 180	92.81 248	83.10 125	95.14 117	94.94 204	81.64 259	82.68 289	91.64 258	59.01 344	96.34 286	75.37 296	83.78 286	93.79 264

PatchmatchNet		85.85 257	84.70 264	89.29 255	91.76 279	75.54 306	88.49 341	91.30 320	81.63 260	85.05 238	88.70 332	71.71 220	96.24 290	74.61 305	89.05 230	96.08 162
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		85.77 259	84.94 259	88.26 282	91.16 302	72.58 340	89.47 327	91.04 328	76.26 329	86.45 194	89.97 310	70.74 233	96.86 255	82.35 205	87.07 264	95.34 193

PMMVS		85.71 260	84.96 258	87.95 290	88.90 352	77.09 285	88.68 339	90.06 347	72.32 367	86.47 191	90.76 290	72.15 218	94.40 336	81.78 221	93.49 160	92.36 320

PVSNet	78.82 18	85.55 261	84.65 265	88.23 284	94.72 165	71.93 343	87.12 358	92.75 277	78.80 300	84.95 240	90.53 294	64.43 302	96.71 259	74.74 303	93.86 152	96.06 165

IterMVS-SCA-FT		85.45 262	84.53 268	88.18 285	91.71 281	76.87 288	90.19 313	92.65 281	85.40 168	81.44 303	90.54 293	66.79 282	95.00 332	81.04 231	81.05 323	92.66 310

pmmvs4		85.43 263	83.86 276	90.16 218	90.02 339	82.97 134	90.27 306	92.67 280	75.93 332	80.73 311	91.74 257	71.05 227	95.73 314	78.85 263	83.46 293	91.78 331

mvsany_test1		85.42 264	85.30 251	85.77 331	87.95 365	75.41 308	87.61 355	80.97 389	76.82 323	88.68 147	95.83 104	77.44 148	90.82 377	85.90 156	86.51 267	91.08 350

ACMH	80.38 17	85.36 265	83.68 278	90.39 210	94.45 182	80.63 198	94.73 141	94.85 214	82.09 242	77.24 345	92.65 224	60.01 335	97.58 190	72.25 317	84.87 278	92.96 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 266	84.64 266	87.49 299	90.77 320	72.59 339	94.01 191	94.40 233	84.72 187	79.62 330	93.17 207	61.91 318	96.72 257	81.99 215	81.16 319	93.16 294

CR-MVSNet		85.35 266	83.76 277	90.12 221	90.58 327	79.34 236	85.24 371	91.96 304	78.27 310	85.55 214	87.87 345	71.03 228	95.61 316	73.96 309	89.36 224	95.40 189

tpmrst		85.35 266	84.99 256	86.43 323	90.88 317	67.88 370	88.71 338	91.43 318	80.13 280	86.08 204	88.80 330	73.05 207	96.02 298	82.48 201	83.40 295	95.40 189

miper_lstm_enhance		85.27 269	84.59 267	87.31 302	91.28 297	74.63 315	87.69 352	94.09 247	81.20 271	81.36 305	89.85 313	74.97 178	94.30 339	81.03 233	79.84 343	93.01 300

IB-MVS	80.51 15	85.24 270	83.26 284	91.19 173	92.13 264	79.86 223	91.75 276	91.29 321	83.28 219	80.66 313	88.49 334	61.28 323	98.46 116	80.99 234	79.46 345	95.25 195
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		85.15 271	83.99 274	88.65 273	92.47 254	78.40 256	79.68 391	92.76 276	74.90 343	81.41 304	89.59 316	69.85 247	95.51 320	79.92 251	95.29 126	92.03 327

RPSCF		85.07 272	84.27 269	87.48 300	92.91 245	70.62 359	91.69 279	92.46 283	76.20 330	82.67 290	95.22 125	63.94 305	97.29 225	77.51 277	85.80 271	94.53 224

MS-PatchMatch		85.05 273	84.16 270	87.73 293	91.42 291	78.51 252	91.25 290	93.53 261	77.50 316	80.15 319	91.58 264	61.99 317	95.51 320	75.69 293	94.35 146	89.16 368

ACMH+	81.04 14	85.05 273	83.46 281	89.82 235	94.66 169	79.37 234	94.44 159	94.12 246	82.19 241	78.04 340	92.82 219	58.23 347	97.54 194	73.77 310	82.90 300	92.54 312

IterMVS		84.88 275	83.98 275	87.60 295	91.44 288	76.03 300	90.18 314	92.41 285	83.24 220	81.06 309	90.42 297	66.60 285	94.28 340	79.46 255	80.98 328	92.48 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 276	83.09 287	90.14 220	93.80 213	80.05 215	89.18 332	93.09 268	78.89 297	78.19 338	91.91 252	65.86 295	97.27 226	68.47 341	88.45 240	93.11 296

testing222		84.84 277	83.32 282	89.43 253	94.15 198	75.94 301	91.09 294	89.41 359	84.90 180	85.78 208	89.44 319	52.70 370	96.28 289	70.80 327	91.57 187	96.07 163

tpm		84.73 278	84.02 273	86.87 318	90.33 332	68.90 366	89.06 334	89.94 350	80.85 274	85.75 209	89.86 312	68.54 268	95.97 300	77.76 273	84.05 285	95.75 177

tfpnnormal		84.72 279	83.23 285	89.20 257	92.79 249	80.05 215	94.48 154	95.81 148	82.38 237	81.08 308	91.21 272	69.01 262	96.95 249	61.69 372	80.59 332	90.58 357

CVMVSNet		84.69 280	84.79 263	84.37 344	91.84 275	64.92 380	93.70 208	91.47 317	66.19 382	86.16 203	95.28 122	67.18 277	93.33 354	80.89 236	90.42 204	94.88 210

test-mter		84.54 281	83.64 279	87.25 305	90.95 310	71.67 348	89.55 323	89.88 353	79.17 292	84.54 248	87.95 342	55.56 356	95.11 329	81.82 219	93.37 165	94.97 202

ETVMVS		84.43 282	82.92 291	88.97 265	94.37 186	74.67 314	91.23 291	88.35 363	83.37 216	86.06 205	89.04 324	55.38 358	95.67 315	67.12 350	91.34 189	96.58 142

TransMVSNet (Re)		84.43 282	83.06 289	88.54 275	91.72 280	78.44 254	95.18 113	92.82 275	82.73 232	79.67 328	92.12 242	73.49 201	95.96 301	71.10 325	68.73 380	91.21 344

pmmvs5		84.21 284	82.84 294	88.34 280	88.95 351	76.94 287	92.41 254	91.91 306	75.63 334	80.28 317	91.18 275	64.59 301	95.57 317	77.09 282	83.47 292	92.53 313

dmvs_re		84.20 285	83.22 286	87.14 311	91.83 277	77.81 272	90.04 316	90.19 343	84.70 188	81.49 301	89.17 322	64.37 303	91.13 375	71.58 319	85.65 273	92.46 316

tpm2		84.08 286	82.94 290	87.48 300	91.39 292	71.27 350	89.23 331	90.37 340	71.95 369	84.64 245	89.33 320	67.30 274	96.55 272	75.17 298	87.09 263	94.63 217

test_fmvs2		83.98 287	84.03 272	83.83 349	87.16 368	67.53 373	93.93 197	92.89 272	77.62 315	86.89 184	93.53 195	47.18 382	92.02 367	90.54 101	86.51 267	91.93 329

COLMAP_ROB	80.39 16	83.96 288	82.04 297	89.74 239	95.28 134	79.75 225	94.25 172	92.28 290	75.17 339	78.02 341	93.77 190	58.60 346	97.84 173	65.06 362	85.92 270	91.63 334
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 289	81.53 300	91.21 172	90.58 327	79.34 236	85.24 371	96.76 75	71.44 371	85.55 214	82.97 378	70.87 231	98.91 80	61.01 374	89.36 224	95.40 189

SixPastTwentyTwo		83.91 290	82.90 292	86.92 315	90.99 308	70.67 358	93.48 214	91.99 301	85.54 165	77.62 344	92.11 244	60.59 331	96.87 254	76.05 292	77.75 352	93.20 292

EPMVS		83.90 291	82.70 295	87.51 297	90.23 335	72.67 335	88.62 340	81.96 387	81.37 265	85.01 239	88.34 336	66.31 289	94.45 334	75.30 297	87.12 262	95.43 188

WB-MVSnew		83.77 292	83.28 283	85.26 338	91.48 287	71.03 354	91.89 271	87.98 364	78.91 295	84.78 242	90.22 300	69.11 261	94.02 343	64.70 363	90.44 202	90.71 352

TESTMET0.1,1		83.74 293	82.85 293	86.42 324	89.96 340	71.21 352	89.55 323	87.88 365	77.41 317	83.37 281	87.31 350	56.71 352	93.65 351	80.62 241	92.85 175	94.40 236

UWE-MVS		83.69 294	83.09 287	85.48 333	93.06 237	65.27 379	90.92 297	86.14 372	79.90 283	86.26 200	90.72 291	57.17 351	95.81 309	71.03 326	92.62 177	95.35 192

pmmvs6		83.42 295	81.60 299	88.87 266	88.01 363	77.87 270	94.96 126	94.24 240	74.67 345	78.80 336	91.09 280	60.17 334	96.49 274	77.06 283	75.40 364	92.23 324

AllTest		83.42 295	81.39 301	89.52 249	95.01 148	77.79 274	93.12 231	90.89 333	77.41 317	76.12 353	93.34 198	54.08 365	97.51 197	68.31 343	84.27 283	93.26 287

tpmvs		83.35 297	82.07 296	87.20 309	91.07 306	71.00 356	88.31 344	91.70 308	78.91 295	80.49 316	87.18 354	69.30 257	97.08 240	68.12 346	83.56 291	93.51 281

USDC		82.76 298	81.26 303	87.26 304	91.17 300	74.55 316	89.27 329	93.39 264	78.26 311	75.30 358	92.08 246	54.43 364	96.63 261	71.64 318	85.79 272	90.61 354

Patchmtry		82.71 299	80.93 305	88.06 287	90.05 338	76.37 297	84.74 376	91.96 304	72.28 368	81.32 306	87.87 345	71.03 228	95.50 322	68.97 338	80.15 338	92.32 322

PatchT		82.68 300	81.27 302	86.89 317	90.09 337	70.94 357	84.06 378	90.15 344	74.91 342	85.63 213	83.57 373	69.37 252	94.87 333	65.19 359	88.50 239	94.84 211

MIMVSNet		82.59 301	80.53 306	88.76 268	91.51 286	78.32 258	86.57 362	90.13 345	79.32 289	80.70 312	88.69 333	52.98 369	93.07 359	66.03 357	88.86 233	94.90 209

test0.0.03 1		82.41 302	81.69 298	84.59 342	88.23 360	72.89 331	90.24 310	87.83 366	83.41 214	79.86 326	89.78 314	67.25 275	88.99 385	65.18 360	83.42 294	91.90 330

EG-PatchMatch MVS		82.37 303	80.34 309	88.46 276	90.27 333	79.35 235	92.80 246	94.33 236	77.14 321	73.26 369	90.18 303	47.47 381	96.72 257	70.25 329	87.32 261	89.30 365

tpm cat1		81.96 304	80.27 310	87.01 312	91.09 305	71.02 355	87.38 356	91.53 315	66.25 381	80.17 318	86.35 360	68.22 271	96.15 294	69.16 337	82.29 305	93.86 261

our_test_3		81.93 305	80.46 308	86.33 325	88.46 357	73.48 327	88.46 342	91.11 324	76.46 324	76.69 349	88.25 338	66.89 280	94.36 337	68.75 339	79.08 348	91.14 346

ppachtmachnet_test		81.84 306	80.07 314	87.15 310	88.46 357	74.43 319	89.04 335	92.16 294	75.33 337	77.75 342	88.99 325	66.20 291	95.37 325	65.12 361	77.60 353	91.65 333

gg-mvs-nofinetune		81.77 307	79.37 322	88.99 264	90.85 318	77.73 277	86.29 363	79.63 392	74.88 344	83.19 285	69.05 393	60.34 332	96.11 295	75.46 295	94.64 138	93.11 296

CL-MVSNet_self_test		81.74 308	80.53 306	85.36 335	85.96 374	72.45 341	90.25 308	93.07 269	81.24 269	79.85 327	87.29 351	70.93 230	92.52 362	66.95 351	69.23 376	91.11 348

Patchmatch-RL test		81.67 309	79.96 315	86.81 319	85.42 379	71.23 351	82.17 385	87.50 369	78.47 305	77.19 346	82.50 380	70.81 232	93.48 352	82.66 200	72.89 368	95.71 181

ADS-MVSNet2		81.66 310	79.71 319	87.50 298	91.35 294	74.19 321	83.33 381	88.48 362	72.90 362	82.24 294	85.77 364	64.98 299	93.20 357	64.57 364	83.74 287	95.12 198

K. test v3		81.59 311	80.15 313	85.91 330	89.89 342	69.42 365	92.57 251	87.71 367	85.56 164	73.44 368	89.71 315	55.58 355	95.52 319	77.17 280	69.76 374	92.78 308

ADS-MVSNet		81.56 312	79.78 316	86.90 316	91.35 294	71.82 345	83.33 381	89.16 360	72.90 362	82.24 294	85.77 364	64.98 299	93.76 348	64.57 364	83.74 287	95.12 198

FMVSNet5		81.52 313	79.60 320	87.27 303	91.17 300	77.95 266	91.49 283	92.26 292	76.87 322	76.16 352	87.91 344	51.67 371	92.34 364	67.74 347	81.16 319	91.52 337

dp		81.47 314	80.23 311	85.17 339	89.92 341	65.49 377	86.74 360	90.10 346	76.30 328	81.10 307	87.12 355	62.81 313	95.92 302	68.13 345	79.88 341	94.09 249

Patchmatch-test		81.37 315	79.30 323	87.58 296	90.92 314	74.16 322	80.99 387	87.68 368	70.52 375	76.63 350	88.81 328	71.21 225	92.76 361	60.01 378	86.93 265	95.83 174

EU-MVSNet		81.32 316	80.95 304	82.42 356	88.50 356	63.67 384	93.32 220	91.33 319	64.02 385	80.57 315	92.83 218	61.21 326	92.27 365	76.34 288	80.38 337	91.32 341

test_0402		81.30 317	79.17 327	87.67 294	93.19 232	78.17 262	92.98 238	91.71 307	75.25 338	76.02 355	90.31 298	59.23 340	96.37 283	50.22 389	83.63 290	88.47 374

JIA-IIPM		81.04 318	78.98 330	87.25 305	88.64 353	73.48 327	81.75 386	89.61 357	73.19 359	82.05 296	73.71 390	66.07 294	95.87 305	71.18 323	84.60 280	92.41 318

Anonymous20231206		81.03 319	79.77 318	84.82 341	87.85 366	70.26 361	91.42 284	92.08 297	73.67 354	77.75 342	89.25 321	62.43 315	93.08 358	61.50 373	82.00 310	91.12 347

pmmvs-eth3d		80.97 320	78.72 332	87.74 292	84.99 381	79.97 221	90.11 315	91.65 310	75.36 336	73.51 367	86.03 361	59.45 338	93.96 346	75.17 298	72.21 369	89.29 366

testgi		80.94 321	80.20 312	83.18 350	87.96 364	66.29 374	91.28 288	90.70 337	83.70 205	78.12 339	92.84 217	51.37 372	90.82 377	63.34 367	82.46 303	92.43 317

CMPMVS	59.16 21	80.52 322	79.20 326	84.48 343	83.98 382	67.63 372	89.95 319	93.84 256	64.79 384	66.81 383	91.14 278	57.93 348	95.17 327	76.25 289	88.10 245	90.65 353
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		80.46 323	79.59 321	83.06 352	93.44 227	64.64 381	93.33 219	85.47 376	84.34 193	79.93 325	90.84 286	44.35 386	92.39 363	57.06 384	87.56 255	92.16 326

Anonymous20240521		80.44 324	79.21 325	84.11 347	85.75 377	67.89 369	92.86 244	93.23 266	75.61 335	75.59 357	87.47 349	50.03 374	94.33 338	71.14 324	81.21 318	90.12 359

LF4IMVS		80.37 325	79.07 329	84.27 346	86.64 370	69.87 364	89.39 328	91.05 327	76.38 326	74.97 360	90.00 309	47.85 380	94.25 341	74.55 306	80.82 330	88.69 372

KD-MVS_self_test		80.20 326	79.24 324	83.07 351	85.64 378	65.29 378	91.01 296	93.93 250	78.71 303	76.32 351	86.40 359	59.20 341	92.93 360	72.59 315	69.35 375	91.00 351

Syy-MVS		80.07 327	79.78 316	80.94 359	91.92 271	59.93 390	89.75 321	87.40 370	81.72 256	78.82 334	87.20 352	66.29 290	91.29 373	47.06 391	87.84 252	91.60 335

UnsupCasMVSNet_eth		80.07 327	78.27 333	85.46 334	85.24 380	72.63 338	88.45 343	94.87 213	82.99 226	71.64 375	88.07 341	56.34 353	91.75 370	73.48 312	63.36 387	92.01 328

test20.03		79.95 329	79.08 328	82.55 354	85.79 376	67.74 371	91.09 294	91.08 325	81.23 270	74.48 364	89.96 311	61.63 319	90.15 379	60.08 376	76.38 360	89.76 360

TDRefinement		79.81 330	77.34 335	87.22 308	79.24 393	75.48 307	93.12 231	92.03 299	76.45 325	75.01 359	91.58 264	49.19 377	96.44 279	70.22 331	69.18 377	89.75 361

TinyColmap		79.76 331	77.69 334	85.97 327	91.71 281	73.12 329	89.55 323	90.36 341	75.03 340	72.03 373	90.19 302	46.22 383	96.19 293	63.11 368	81.03 324	88.59 373

myMVS_eth3d		79.67 332	78.79 331	82.32 357	91.92 271	64.08 382	89.75 321	87.40 370	81.72 256	78.82 334	87.20 352	45.33 384	91.29 373	59.09 380	87.84 252	91.60 335

OpenMVS_ROB	74.94 19	79.51 333	77.03 340	86.93 314	87.00 369	76.23 299	92.33 260	90.74 336	68.93 378	74.52 363	88.23 339	49.58 376	96.62 262	57.64 382	84.29 282	87.94 377

MIMVSNet1		79.38 334	77.28 336	85.69 332	86.35 371	73.67 324	91.61 281	92.75 277	78.11 314	72.64 371	88.12 340	48.16 379	91.97 369	60.32 375	77.49 354	91.43 340

YYNet1		79.22 335	77.20 337	85.28 337	88.20 362	72.66 336	85.87 365	90.05 349	74.33 348	62.70 385	87.61 347	66.09 293	92.03 366	66.94 352	72.97 367	91.15 345

MDA-MVSNet_test_wron		79.21 336	77.19 338	85.29 336	88.22 361	72.77 333	85.87 365	90.06 347	74.34 347	62.62 387	87.56 348	66.14 292	91.99 368	66.90 355	73.01 366	91.10 349

MDA-MVSNet-bldmvs		78.85 337	76.31 342	86.46 322	89.76 343	73.88 323	88.79 337	90.42 339	79.16 293	59.18 389	88.33 337	60.20 333	94.04 342	62.00 371	68.96 378	91.48 339

KD-MVS_2432*1600		78.50 338	76.02 345	85.93 328	86.22 372	74.47 317	84.80 374	92.33 287	79.29 290	76.98 347	85.92 362	53.81 367	93.97 344	67.39 348	57.42 392	89.36 363

miper_refine_blended		78.50 338	76.02 345	85.93 328	86.22 372	74.47 317	84.80 374	92.33 287	79.29 290	76.98 347	85.92 362	53.81 367	93.97 344	67.39 348	57.42 392	89.36 363

PM-MVS		78.11 340	76.12 344	84.09 348	83.54 384	70.08 362	88.97 336	85.27 378	79.93 282	74.73 362	86.43 358	34.70 392	93.48 352	79.43 258	72.06 370	88.72 371

test_vis1_rt		77.96 341	76.46 341	82.48 355	85.89 375	71.74 347	90.25 308	78.89 393	71.03 374	71.30 376	81.35 382	42.49 388	91.05 376	84.55 173	82.37 304	84.65 380

test_fmvs3		77.67 342	77.16 339	79.22 362	79.52 392	61.14 388	92.34 259	91.64 311	73.98 351	78.86 333	86.59 356	27.38 396	87.03 387	88.12 128	75.97 362	89.50 362

PVSNet_0	73.20 20	77.22 343	74.83 349	84.37 344	90.70 324	71.10 353	83.09 383	89.67 356	72.81 364	73.93 366	83.13 375	60.79 330	93.70 350	68.54 340	50.84 396	88.30 375

DSMNet-mixed		76.94 344	76.29 343	78.89 363	83.10 385	56.11 399	87.78 349	79.77 391	60.65 388	75.64 356	88.71 331	61.56 321	88.34 386	60.07 377	89.29 226	92.21 325

new-patchmatchnet		76.41 345	75.17 348	80.13 360	82.65 387	59.61 391	87.66 353	91.08 325	78.23 312	69.85 379	83.22 374	54.76 361	91.63 372	64.14 366	64.89 385	89.16 368

UnsupCasMVSNet_bld		76.23 346	73.27 350	85.09 340	83.79 383	72.92 330	85.65 368	93.47 263	71.52 370	68.84 381	79.08 385	49.77 375	93.21 356	66.81 356	60.52 389	89.13 370

mvsany_test3		74.95 347	73.26 351	80.02 361	74.61 395	63.16 386	85.53 369	78.42 394	74.16 349	74.89 361	86.46 357	36.02 391	89.09 384	82.39 204	66.91 381	87.82 378

dmvs_testset		74.57 348	75.81 347	70.86 373	87.72 367	40.47 406	87.05 359	77.90 398	82.75 231	71.15 377	85.47 366	67.98 272	84.12 395	45.26 392	76.98 359	88.00 376

MVS-HIRNet		73.70 349	72.20 352	78.18 366	91.81 278	56.42 398	82.94 384	82.58 385	55.24 390	68.88 380	66.48 394	55.32 359	95.13 328	58.12 381	88.42 241	83.01 383

new_pmnet		72.15 350	70.13 354	78.20 365	82.95 386	65.68 375	83.91 379	82.40 386	62.94 387	64.47 384	79.82 384	42.85 387	86.26 391	57.41 383	74.44 365	82.65 385

test_f		71.95 351	70.87 353	75.21 369	74.21 397	59.37 392	85.07 373	85.82 374	65.25 383	70.42 378	83.13 375	23.62 397	82.93 397	78.32 267	71.94 371	83.33 382

pmmvs3		71.81 352	68.71 355	81.11 358	75.86 394	70.42 360	86.74 360	83.66 382	58.95 389	68.64 382	80.89 383	36.93 390	89.52 382	63.10 369	63.59 386	83.39 381

APD_test1		69.04 353	66.26 359	77.36 368	80.51 390	62.79 387	85.46 370	83.51 383	54.11 392	59.14 390	84.79 369	23.40 399	89.61 381	55.22 385	70.24 373	79.68 389

N_pmnet		68.89 354	68.44 356	70.23 374	89.07 350	28.79 411	88.06 345	19.50 411	69.47 377	71.86 374	84.93 367	61.24 325	91.75 370	54.70 386	77.15 356	90.15 358

WB-MVS		67.92 355	67.49 357	69.21 377	81.09 388	41.17 405	88.03 346	78.00 397	73.50 356	62.63 386	83.11 377	63.94 305	86.52 389	25.66 402	51.45 395	79.94 388

SSC-MVS		67.06 356	66.56 358	68.56 379	80.54 389	40.06 407	87.77 350	77.37 400	72.38 366	61.75 388	82.66 379	63.37 309	86.45 390	24.48 403	48.69 398	79.16 390

LCM-MVSNet		66.00 357	62.16 362	77.51 367	64.51 405	58.29 393	83.87 380	90.90 332	48.17 394	54.69 391	73.31 391	16.83 405	86.75 388	65.47 358	61.67 388	87.48 379

test_vis3_rt		65.12 358	62.60 360	72.69 371	71.44 398	60.71 389	87.17 357	65.55 404	63.80 386	53.22 392	65.65 396	14.54 406	89.44 383	76.65 284	65.38 383	67.91 395

FPMVS		64.63 359	62.55 361	70.88 372	70.80 399	56.71 394	84.42 377	84.42 380	51.78 393	49.57 393	81.61 381	23.49 398	81.48 398	40.61 398	76.25 361	74.46 391

EGC-MVSNET		61.97 360	56.37 364	78.77 364	89.63 346	73.50 326	89.12 333	82.79 384	0.21 408	1.24 409	84.80 368	39.48 389	90.04 380	44.13 393	75.94 363	72.79 392

PMMVS2		59.60 361	56.40 363	69.21 377	68.83 402	46.58 403	73.02 396	77.48 399	55.07 391	49.21 394	72.95 392	17.43 404	80.04 399	49.32 390	44.33 399	80.99 387

testf1		59.54 362	56.11 365	69.85 375	69.28 400	56.61 396	80.37 389	76.55 401	42.58 397	45.68 396	75.61 386	11.26 407	84.18 393	43.20 395	60.44 390	68.75 393

APD_test2		59.54 362	56.11 365	69.85 375	69.28 400	56.61 396	80.37 389	76.55 401	42.58 397	45.68 396	75.61 386	11.26 407	84.18 393	43.20 395	60.44 390	68.75 393

ANet_high		58.88 364	54.22 368	72.86 370	56.50 408	56.67 395	80.75 388	86.00 373	73.09 361	37.39 400	64.63 397	22.17 400	79.49 400	43.51 394	23.96 402	82.43 386

Gipuma		57.99 365	54.91 367	67.24 380	88.51 354	65.59 376	52.21 399	90.33 342	43.58 396	42.84 399	51.18 400	20.29 402	85.07 392	34.77 399	70.45 372	51.05 399
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 366	48.46 370	63.48 381	45.72 410	46.20 404	73.41 395	78.31 395	41.03 399	30.06 402	65.68 395	6.05 409	83.43 396	30.04 400	65.86 382	60.80 396
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 367	48.47 369	56.66 383	52.26 409	18.98 413	41.51 401	81.40 388	10.10 403	44.59 398	75.01 389	28.51 394	68.16 401	53.54 387	49.31 397	82.83 384

MVE	39.65 23	43.39 368	38.59 374	57.77 382	56.52 407	48.77 402	55.38 398	58.64 408	29.33 402	28.96 403	52.65 399	4.68 410	64.62 404	28.11 401	33.07 400	59.93 397
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 369	42.29 371	46.03 385	65.58 404	37.41 408	73.51 394	64.62 405	33.99 400	28.47 404	47.87 401	19.90 403	67.91 402	22.23 404	24.45 401	32.77 400

EMVS		42.07 370	41.12 372	44.92 386	63.45 406	35.56 410	73.65 393	63.48 406	33.05 401	26.88 405	45.45 402	21.27 401	67.14 403	19.80 405	23.02 403	32.06 401

tmp_tt		35.64 371	39.24 373	24.84 387	14.87 411	23.90 412	62.71 397	51.51 410	6.58 405	36.66 401	62.08 398	44.37 385	30.34 407	52.40 388	22.00 404	20.27 402

cdsmvs_eth3d_5k		22.14 372	29.52 375	0.00 391	0.00 414	0.00 416	0.00 402	95.76 152	0.00 409	0.00 410	94.29 164	75.66 170	0.00 410	0.00 409	0.00 408	0.00 406

wuyk23d		21.27 373	20.48 376	23.63 388	68.59 403	36.41 409	49.57 400	6.85 412	9.37 404	7.89 406	4.46 408	4.03 411	31.37 406	17.47 406	16.07 405	3.12 403

testmvs		8.92 374	11.52 377	1.12 390	1.06 412	0.46 415	86.02 364	0.65 413	0.62 406	2.74 407	9.52 406	0.31 413	0.45 409	2.38 407	0.39 406	2.46 405

test123		8.76 375	11.22 378	1.39 389	0.85 413	0.97 414	85.76 367	0.35 414	0.54 407	2.45 408	8.14 407	0.60 412	0.48 408	2.16 408	0.17 407	2.71 404

ab-mvs-re		7.82 376	10.43 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	93.88 185	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		6.64 377	8.86 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	79.70 121	0.00 410	0.00 409	0.00 408	0.00 406

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						64.08 382								59.14 379

FOURS1					98.86 1	85.54 65	98.29 1	97.49 6	89.79 44	96.29 18

MSC_two_6792asdad				96.52 1	97.78 51	90.86 1		96.85 63					99.61 4	96.03 14	99.06 9	99.07 5

PC_three_1452									82.47 235	97.09 10	97.07 51	92.72 1	98.04 161	92.70 55	99.02 12	98.86 11

No_MVS				96.52 1	97.78 51	90.86 1		96.85 63					99.61 4	96.03 14	99.06 9	99.07 5

test_one_0601					98.58 11	85.83 59		97.44 15	91.05 14	96.78 15	98.06 11	91.45 11

eth-test2					0.00 414

eth-test					0.00 414

ZD-MVS					98.15 34	86.62 32		97.07 45	83.63 207	94.19 42	96.91 57	87.57 31	99.26 42	91.99 74	98.44 52

RE-MVS-def			93.68 52		97.92 43	84.57 80	96.28 45	96.76 75	87.46 116	93.75 49	97.43 31	82.94 83		92.73 51	97.80 75	97.88 78

IU-MVS					98.77 5	86.00 49		96.84 65	81.26 268	97.26 7				95.50 23	99.13 3	99.03 8

OPU-MVS				96.21 3	98.00 42	90.85 3	97.13 14				97.08 49	92.59 2	98.94 78	92.25 63	98.99 14	98.84 14

test_241102_TWO								97.44 15	90.31 28	97.62 5	98.07 9	91.46 10	99.58 10	95.66 17	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 51		97.44 15	90.26 33	97.71 1	97.96 17	92.31 4	99.38 31

9.14			94.47 20		97.79 49		96.08 61	97.44 15	86.13 153	95.10 33	97.40 33	88.34 22	99.22 44	93.25 44	98.70 35

save fliter					97.85 46	85.63 64	95.21 111	96.82 68	89.44 51

test_0728_THIRD									90.75 19	97.04 11	98.05 13	92.09 6	99.55 16	95.64 19	99.13 3	99.13 2

test_0728_SECOND				95.01 17	98.79 2	86.43 38	97.09 16	97.49 6					99.61 4	95.62 21	99.08 7	98.99 9

test0726					98.78 3	85.93 54	97.19 11	97.47 11	90.27 31	97.64 4	98.13 3	91.47 8

GSMVS																96.12 159

test_part2					98.55 12	87.22 18				96.40 17

sam_mvs1												71.70 221				96.12 159

sam_mvs												70.60 234

ambc				83.06 352	79.99 391	63.51 385	77.47 392	92.86 273		74.34 365	84.45 370	28.74 393	95.06 331	73.06 314	68.89 379	90.61 354

MTGPA								96.97 50

test_post1							88.00 347				9.81 405	69.31 256	95.53 318	76.65 284

test_post											10.29 404	70.57 238	95.91 304

patchmatchnet-post											83.76 372	71.53 222	96.48 275

GG-mvs-BLEND				87.94 291	89.73 345	77.91 267	87.80 348	78.23 396		80.58 314	83.86 371	59.88 336	95.33 326	71.20 321	92.22 183	90.60 356

MTMP							96.16 53	60.64 407

gm-plane-assit					89.60 347	68.00 368			77.28 320		88.99 325		97.57 191	79.44 257

test9_res														91.91 78	98.71 33	98.07 66

TEST9					97.53 58	86.49 36	94.07 185	96.78 72	81.61 261	92.77 74	96.20 87	87.71 28	99.12 51

test_8					97.49 60	86.30 44	94.02 190	96.76 75	81.86 252	92.70 78	96.20 87	87.63 29	99.02 61

agg_prior2														90.54 101	98.68 38	98.27 52

agg_prior					97.38 63	85.92 56		96.72 81		92.16 90			98.97 75

TestCases				89.52 249	95.01 148	77.79 274		90.89 333	77.41 317	76.12 353	93.34 198	54.08 365	97.51 197	68.31 343	84.27 283	93.26 287

test_prior4						85.96 53	94.11 180

test_prior2							94.12 179		87.67 114	92.63 79	96.39 82	86.62 38		91.50 85	98.67 40

test_prior				93.82 60	97.29 67	84.49 84		96.88 61					98.87 82			98.11 65

旧先验2							93.36 218		71.25 372	94.37 39			97.13 238	86.74 146

新几何2							93.11 233

新几何1				93.10 79	97.30 66	84.35 92		95.56 168	71.09 373	91.26 114	96.24 85	82.87 85	98.86 84	79.19 261	98.10 63	96.07 163

旧先验1					96.79 76	81.81 163		95.67 160			96.81 63	86.69 37			97.66 80	96.97 124

无先验							93.28 226	96.26 110	73.95 352				99.05 55	80.56 242		96.59 141

原ACMM2							92.94 240

原ACMM1				92.01 132	97.34 64	81.05 186		96.81 70	78.89 297	90.45 121	95.92 100	82.65 87	98.84 88	80.68 240	98.26 58	96.14 157

test222					96.55 84	81.70 165	92.22 264	95.01 201	68.36 379	90.20 125	96.14 92	80.26 114			97.80 75	96.05 166

testdata2													98.75 93	78.30 268

segment_acmp												87.16 36

testdata				90.49 203	96.40 89	77.89 269		95.37 186	72.51 365	93.63 52	96.69 66	82.08 99	97.65 184	83.08 190	97.39 83	95.94 168

testdata1							92.15 266		87.94 103

test12				94.34 49	97.13 70	86.15 47		96.29 105		91.04 116		85.08 57	99.01 63		98.13 62	97.86 80

plane_prior7					94.70 167	82.74 140

plane_prior6					94.52 177	82.75 138						74.23 187

plane_prior5								96.22 115					98.12 146	88.15 125	89.99 208	94.63 217

plane_prior4											94.86 140

plane_prior3						82.75 138			90.26 33	86.91 181

plane_prior2							95.85 75		90.81 17

plane_prior1					94.59 172

plane_prior						82.73 141	95.21 111		89.66 48						89.88 213

n2								0.00 415

nn								0.00 415

door-mid								85.49 375

lessismore_v0				86.04 326	88.46 357	68.78 367		80.59 390		73.01 370	90.11 306	55.39 357	96.43 280	75.06 300	65.06 384	92.90 303

LGP-MVS_train				91.12 176	94.47 179	81.49 172		96.14 120	86.73 134	85.45 223	95.16 130	69.89 245	98.10 148	87.70 132	89.23 227	93.77 269

test11								96.57 92

door								85.33 377

HQP5-MVS						81.56 168

HQP-NCC					94.17 195		94.39 164		88.81 72	85.43 226

ACMP_Plane					94.17 195		94.39 164		88.81 72	85.43 226

BP-MVS														87.11 143

HQP4-MVS										85.43 226			97.96 167			94.51 227

HQP3-MVS								96.04 131							89.77 217

HQP2-MVS												73.83 197

NP-MVS					94.37 186	82.42 150					93.98 177

MDTV_nov1_ep13_2view						55.91 400	87.62 354		73.32 358	84.59 247		70.33 241		74.65 304		95.50 186

MDTV_nov1_ep13			83.56 280		91.69 283	69.93 363	87.75 351	91.54 314	78.60 304	84.86 241	88.90 327	69.54 250	96.03 297	70.25 329	88.93 232

ACMMP++_ref															87.47 256

ACMMP++															88.01 248

Test By Simon												80.02 116

ITE_SJBPF				88.24 283	91.88 274	77.05 286		92.92 271	85.54 165	80.13 321	93.30 202	57.29 350	96.20 291	72.46 316	84.71 279	91.49 338

DeepMVS_CX				56.31 384	74.23 396	51.81 401		56.67 409	44.85 395	48.54 395	75.16 388	27.87 395	58.74 405	40.92 397	52.22 394	58.39 398

High-resolution multi-view benchmark