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.

MSC_two_6792asdad				96.52 1	97.78 56	90.86 1		96.85 74					99.61 4	96.03 24	99.06 9	99.07 5

No_MVS				96.52 1	97.78 56	90.86 1		96.85 74					99.61 4	96.03 24	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 44	90.85 3	97.13 15				97.08 63	92.59 2	98.94 86	92.25 85	98.99 14	98.84 15

HPM-MVS++		95.14 10	94.91 21	95.83 4	98.25 31	89.65 4	95.92 81	96.96 62	91.75 12	94.02 64	96.83 75	88.12 24	99.55 16	93.41 59	98.94 16	98.28 57

MM		95.10 11	94.91 21	95.68 5	96.09 111	88.34 9	96.68 34	94.37 261	95.08 1	94.68 50	97.72 36	82.94 95	99.64 1	97.85 4	98.76 29	99.06 7

SMA-MVS		95.20 8	95.07 15	95.59 6	98.14 37	88.48 8	96.26 49	97.28 35	85.90 182	97.67 3	98.10 12	88.41 20	99.56 12	94.66 43	99.19 1	98.71 21
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
3Dnovator+	87.14 4	92.42 97	91.37 107	95.55 7	95.63 136	88.73 6	97.07 19	96.77 85	90.84 23	84.02 298	96.62 88	75.95 184	99.34 38	87.77 158	97.68 90	98.59 25

CNVR-MVS		95.40 7	95.37 9	95.50 8	98.11 38	88.51 7	95.29 122	96.96 62	92.09 9	95.32 42	97.08 63	89.49 15	99.33 41	95.10 38	98.85 20	98.66 22

MVS_0304		94.18 44	93.80 58	95.34 9	94.91 174	87.62 14	95.97 76	93.01 304	92.58 6	94.22 55	97.20 57	80.56 127	99.59 8	97.04 17	98.68 37	98.81 18

ACMMP_NAP		94.74 22	94.56 27	95.28 10	98.02 43	87.70 11	95.68 99	97.34 26	88.28 115	95.30 43	97.67 38	85.90 51	99.54 20	93.91 51	98.95 15	98.60 24

DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 27	87.28 18	95.56 111	97.51 7	89.13 84	97.14 13	97.91 29	91.64 7	99.62 2	94.61 44	99.17 2	98.86 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		94.97 14	94.90 23	95.20 12	97.84 52	87.76 10	96.65 35	97.48 12	87.76 137	95.71 37	97.70 37	88.28 23	99.35 37	93.89 52	98.78 26	98.48 31

MCST-MVS		94.45 29	94.20 45	95.19 13	98.46 19	87.50 16	95.00 145	97.12 50	87.13 150	92.51 105	96.30 97	89.24 17	99.34 38	93.46 56	98.62 46	98.73 19

NCCC		94.81 19	94.69 26	95.17 14	97.83 53	87.46 17	95.66 102	96.93 66	92.34 7	93.94 65	96.58 90	87.74 27	99.44 29	92.83 68	98.40 54	98.62 23

DPM-MVS		92.58 93	91.74 103	95.08 15	96.19 102	89.31 5	92.66 282	96.56 105	83.44 245	91.68 129	95.04 158	86.60 43	98.99 76	85.60 190	97.92 79	96.93 154

ZNCC-MVS		94.47 28	94.28 39	95.03 16	98.52 15	86.96 20	96.85 29	97.32 30	88.24 116	93.15 80	97.04 66	86.17 48	99.62 2	92.40 79	98.81 23	98.52 27

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 17	97.49 8					99.61 4	95.62 31	99.08 7	98.99 9

MTAPA		94.42 33	94.22 42	95.00 18	98.42 21	86.95 21	94.36 195	96.97 59	91.07 19	93.14 81	97.56 40	84.30 76	99.56 12	93.43 57	98.75 30	98.47 34

MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 26	97.47 13	91.73 13	96.10 30	96.69 80	89.90 12	99.30 44	94.70 42	98.04 74	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
region2R		94.43 31	94.27 41	94.92 20	98.65 8	86.67 30	96.92 25	97.23 38	88.60 106	93.58 72	97.27 51	85.22 59	99.54 20	92.21 86	98.74 31	98.56 26

APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 30	86.29 46	97.46 7	97.40 22	89.03 89	96.20 29	98.10 12	89.39 16	99.34 38	95.88 26	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		94.43 31	94.28 39	94.91 21	98.63 9	86.69 28	96.94 21	97.32 30	88.63 103	93.53 75	97.26 53	85.04 63	99.54 20	92.35 82	98.78 26	98.50 28

GST-MVS		94.21 39	93.97 54	94.90 23	98.41 22	86.82 24	96.54 37	97.19 39	88.24 116	93.26 77	96.83 75	85.48 56	99.59 8	91.43 112	98.40 54	98.30 51

HFP-MVS		94.52 26	94.40 32	94.86 24	98.61 10	86.81 25	96.94 21	97.34 26	88.63 103	93.65 70	97.21 55	86.10 49	99.49 26	92.35 82	98.77 28	98.30 51

sasdasda		93.27 76	92.75 86	94.85 25	95.70 131	87.66 12	96.33 40	96.41 115	90.00 48	94.09 60	94.60 180	82.33 104	98.62 124	92.40 79	92.86 202	98.27 59

MP-MVS-pluss		94.21 39	94.00 53	94.85 25	98.17 35	86.65 31	94.82 158	97.17 44	86.26 174	92.83 90	97.87 31	85.57 55	99.56 12	94.37 47	98.92 17	98.34 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 76	92.75 86	94.85 25	95.70 131	87.66 12	96.33 40	96.41 115	90.00 48	94.09 60	94.60 180	82.33 104	98.62 124	92.40 79	92.86 202	98.27 59

XVS		94.45 29	94.32 35	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 31	92.85 88	97.16 61	85.02 64	99.49 26	91.99 97	98.56 50	98.47 34

X-MVStestdata		88.31 202	86.13 250	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 31	92.85 88	23.41 447	85.02 64	99.49 26	91.99 97	98.56 50	98.47 34

SteuartSystems-ACMMP		95.20 8	95.32 11	94.85 25	96.99 77	86.33 42	97.33 8	97.30 32	91.38 17	95.39 41	97.46 43	88.98 19	99.40 30	94.12 48	98.89 18	98.82 17
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS++		95.98 1	96.36 1	94.82 31	97.78 56	86.00 52	98.29 1	97.49 8	90.75 26	97.62 6	98.06 20	92.59 2	99.61 4	95.64 29	99.02 12	98.86 12

alignmvs		93.08 84	92.50 92	94.81 32	95.62 137	87.61 15	95.99 74	96.07 151	89.77 61	94.12 59	94.87 164	80.56 127	98.66 117	92.42 78	93.10 198	98.15 71

SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 54	97.13 15	97.44 17	90.31 38	97.71 1	98.07 18	92.31 4	99.58 10	95.66 27	99.13 3	98.84 15

DeepC-MVS_fast	89.43 2	94.04 47	93.79 59	94.80 33	97.48 66	86.78 26	95.65 104	96.89 71	89.40 72	92.81 91	96.97 68	85.37 58	99.24 47	90.87 121	98.69 35	98.38 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		94.25 36	94.07 50	94.77 35	98.47 18	86.31 44	96.71 32	96.98 58	89.04 87	91.98 116	97.19 58	85.43 57	99.56 12	92.06 95	98.79 24	98.44 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 37	94.07 50	94.75 36	98.06 41	86.90 23	95.88 83	96.94 65	85.68 189	95.05 48	97.18 59	87.31 35	99.07 59	91.90 103	98.61 48	98.28 57
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 34	94.21 44	94.74 37	98.39 25	86.64 32	97.60 5	97.24 36	88.53 108	92.73 96	97.23 54	85.20 60	99.32 42	92.15 89	98.83 22	98.25 64

PGM-MVS		93.96 52	93.72 64	94.68 38	98.43 20	86.22 47	95.30 120	97.78 1	87.45 144	93.26 77	97.33 49	84.62 73	99.51 24	90.75 123	98.57 49	98.32 50

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 57	97.09 17	96.73 91	90.27 42	97.04 17	98.05 22	91.47 8	99.55 16	95.62 31	99.08 7	98.45 37
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
mPP-MVS		93.99 50	93.78 60	94.63 40	98.50 16	85.90 62	96.87 27	96.91 69	88.70 101	91.83 125	97.17 60	83.96 80	99.55 16	91.44 111	98.64 45	98.43 39

PHI-MVS		93.89 54	93.65 68	94.62 41	96.84 80	86.43 39	96.69 33	97.49 8	85.15 204	93.56 74	96.28 98	85.60 54	99.31 43	92.45 76	98.79 24	98.12 75

TSAR-MVS + MP.		94.85 16	94.94 19	94.58 42	98.25 31	86.33 42	96.11 62	96.62 100	88.14 121	96.10 30	96.96 69	89.09 18	98.94 86	94.48 45	98.68 37	98.48 31
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		93.54 63	93.20 77	94.55 43	95.65 134	85.73 67	94.94 148	96.69 96	91.89 11	90.69 144	95.88 121	81.99 116	99.54 20	93.14 63	97.95 78	98.39 41

train_agg		93.44 69	93.08 79	94.52 44	97.53 63	86.49 37	94.07 213	96.78 83	81.86 286	92.77 93	96.20 101	87.63 29	99.12 57	92.14 90	98.69 35	97.94 87

CDPH-MVS		92.83 88	92.30 95	94.44 45	97.79 54	86.11 51	94.06 215	96.66 97	80.09 317	92.77 93	96.63 87	86.62 41	99.04 63	87.40 163	98.66 41	98.17 69

3Dnovator	86.66 5	91.73 108	90.82 121	94.44 45	94.59 194	86.37 41	97.18 13	97.02 56	89.20 81	84.31 293	96.66 83	73.74 221	99.17 51	86.74 173	97.96 77	97.79 100

SR-MVS		94.23 38	94.17 48	94.43 47	98.21 34	85.78 65	96.40 39	96.90 70	88.20 119	94.33 54	97.40 46	84.75 72	99.03 64	93.35 60	97.99 76	98.48 31

HPM-MVS		94.02 48	93.88 55	94.43 47	98.39 25	85.78 65	97.25 11	97.07 54	86.90 158	92.62 102	96.80 79	84.85 70	99.17 51	92.43 77	98.65 44	98.33 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 61	93.41 72	94.41 49	96.59 87	86.78 26	94.40 187	93.93 279	89.77 61	94.21 56	95.59 135	87.35 34	98.61 126	92.72 71	96.15 128	97.83 98

reproduce-ours		94.82 17	94.97 17	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 54	95.23 45	98.10 12	87.09 37	99.37 33	95.30 35	98.25 62	98.30 51

our_new_method		94.82 17	94.97 17	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 54	95.23 45	98.10 12	87.09 37	99.37 33	95.30 35	98.25 62	98.30 51

NormalMVS		93.46 66	93.16 78	94.37 52	98.40 23	86.20 48	96.30 42	96.27 128	91.65 15	92.68 98	96.13 107	77.97 160	98.84 99	90.75 123	98.26 59	98.07 77

test12				94.34 53	97.13 75	86.15 50		96.29 124		91.04 140		85.08 62	99.01 69		98.13 69	97.86 95

SymmetryMVS		92.81 90	92.31 94	94.32 54	96.15 103	86.20 48	96.30 42	94.43 257	91.65 15	92.68 98	96.13 107	77.97 160	98.84 99	90.75 123	94.72 158	97.92 90

ACMMP		93.24 78	92.88 84	94.30 55	98.09 40	85.33 74	96.86 28	97.45 16	88.33 112	90.15 156	97.03 67	81.44 121	99.51 24	90.85 122	95.74 135	98.04 82
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
reproduce_model		94.76 21	94.92 20	94.29 56	97.92 45	85.18 76	95.95 79	97.19 39	89.67 64	95.27 44	98.16 5	86.53 44	99.36 36	95.42 34	98.15 67	98.33 46

DeepC-MVS	88.79 3	93.31 75	92.99 82	94.26 57	96.07 113	85.83 63	94.89 151	96.99 57	89.02 90	89.56 161	97.37 48	82.51 101	99.38 31	92.20 87	98.30 57	97.57 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		93.03 85	92.63 89	94.23 58	95.62 137	85.92 59	96.08 64	96.33 122	89.86 52	93.89 67	94.66 177	82.11 111	98.50 132	92.33 84	92.82 205	98.27 59

fmvsm_l_conf0.5_n_3		94.80 20	95.01 16	94.15 59	95.64 135	85.08 77	96.09 63	97.36 24	90.98 21	97.09 15	98.12 9	84.98 68	98.94 86	97.07 14	97.80 85	98.43 39

EPNet		91.79 105	91.02 116	94.10 60	90.10 373	85.25 75	96.03 71	92.05 331	92.83 5	87.39 204	95.78 127	79.39 143	99.01 69	88.13 153	97.48 93	98.05 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture		95.10 11	95.46 8	94.01 61	98.40 23	84.36 102	97.70 3	97.78 1	91.19 18	96.22 28	98.08 17	86.64 40	99.37 33	94.91 40	98.26 59	98.29 56

test_fmvsmconf_n		94.60 24	94.81 24	93.98 62	94.62 192	84.96 80	96.15 57	97.35 25	89.37 73	96.03 33	98.11 10	86.36 45	99.01 69	97.45 9	97.83 83	97.96 86

DELS-MVS		93.43 73	93.25 75	93.97 63	95.42 145	85.04 78	93.06 269	97.13 49	90.74 28	91.84 123	95.09 157	86.32 46	99.21 49	91.22 113	98.45 52	97.65 109
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
DP-MVS Recon		91.95 102	91.28 110	93.96 64	98.33 29	85.92 59	94.66 170	96.66 97	82.69 265	90.03 158	95.82 125	82.30 106	99.03 64	84.57 202	96.48 121	96.91 156

HPM-MVS_fast		93.40 74	93.22 76	93.94 65	98.36 27	84.83 82	97.15 14	96.80 82	85.77 186	92.47 106	97.13 62	82.38 102	99.07 59	90.51 128	98.40 54	97.92 90

test_fmvsmconf0.1_n		94.20 41	94.31 37	93.88 66	92.46 289	84.80 83	96.18 54	96.82 79	89.29 78	95.68 38	98.11 10	85.10 61	98.99 76	97.38 10	97.75 89	97.86 95

SD-MVS		94.96 15	95.33 10	93.88 66	97.25 74	86.69 28	96.19 52	97.11 52	90.42 34	96.95 19	97.27 51	89.53 14	96.91 281	94.38 46	98.85 20	98.03 83
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
MVS_111021_HR		93.45 68	93.31 73	93.84 68	96.99 77	84.84 81	93.24 261	97.24 36	88.76 98	91.60 130	95.85 123	86.07 50	98.66 117	91.91 101	98.16 66	98.03 83

SR-MVS-dyc-post		93.82 56	93.82 57	93.82 69	97.92 45	84.57 89	96.28 46	96.76 86	87.46 142	93.75 68	97.43 44	84.24 77	99.01 69	92.73 69	97.80 85	97.88 93

test_prior				93.82 69	97.29 72	84.49 93		96.88 72					98.87 93			98.11 76

APD-MVS_3200maxsize		93.78 57	93.77 61	93.80 71	97.92 45	84.19 106	96.30 42	96.87 73	86.96 154	93.92 66	97.47 42	83.88 81	98.96 83	92.71 72	97.87 81	98.26 63

fmvsm_l_conf0.5_n		94.29 35	94.46 30	93.79 72	95.28 150	85.43 72	95.68 99	96.43 113	86.56 166	96.84 21	97.81 34	87.56 32	98.77 108	97.14 12	96.82 111	97.16 136

CSCG		93.23 79	93.05 80	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 227	90.05 157	95.66 132	87.77 26	99.15 55	89.91 133	98.27 58	98.07 77

GDP-MVS		92.04 100	91.46 106	93.75 74	94.55 200	84.69 86	95.60 110	96.56 105	87.83 134	93.07 84	95.89 120	73.44 225	98.65 119	90.22 131	96.03 130	97.91 92

BP-MVS1		92.48 95	92.07 98	93.72 75	94.50 203	84.39 101	95.90 82	94.30 264	90.39 35	92.67 100	95.94 117	74.46 205	98.65 119	93.14 63	97.35 97	98.13 72

test_fmvsmconf0.01_n		93.19 80	93.02 81	93.71 76	89.25 386	84.42 100	96.06 68	96.29 124	89.06 85	94.68 50	98.13 6	79.22 145	98.98 80	97.22 11	97.24 99	97.74 103

UA-Net		92.83 88	92.54 91	93.68 77	96.10 110	84.71 85	95.66 102	96.39 117	91.92 10	93.22 79	96.49 93	83.16 90	98.87 93	84.47 204	95.47 142	97.45 120

fmvsm_l_conf0.5_n_a		94.20 41	94.40 32	93.60 78	95.29 149	84.98 79	95.61 107	96.28 127	86.31 172	96.75 23	97.86 32	87.40 33	98.74 112	97.07 14	97.02 104	97.07 141

QAPM		89.51 164	88.15 188	93.59 79	94.92 172	84.58 88	96.82 30	96.70 95	78.43 344	83.41 314	96.19 104	73.18 230	99.30 44	77.11 313	96.54 118	96.89 157

test_fmvsm_n_1920		94.71 23	95.11 14	93.50 80	95.79 126	84.62 87	96.15 57	97.64 3	89.85 53	97.19 12	97.89 30	86.28 47	98.71 115	97.11 13	98.08 73	97.17 132

fmvsm_s_conf0.5_n_9		94.99 13	95.50 7	93.44 81	96.51 95	82.25 173	95.76 94	96.92 67	93.37 3	97.63 5	98.43 1	84.82 71	99.16 54	98.15 1	97.92 79	98.90 11

KinetiMVS		91.82 104	91.30 108	93.39 82	94.72 185	83.36 133	95.45 113	96.37 119	90.33 37	92.17 111	96.03 112	72.32 242	98.75 109	87.94 156	96.34 123	98.07 77

casdiffmvs_mvg		92.96 87	92.83 85	93.35 83	94.59 194	83.40 131	95.00 145	96.34 121	90.30 40	92.05 114	96.05 111	83.43 84	98.15 168	92.07 92	95.67 136	98.49 30
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		93.96 52	94.18 47	93.30 84	94.79 181	83.81 117	95.77 92	96.74 90	88.02 124	96.23 27	97.84 33	83.36 88	98.83 102	97.49 7	97.34 98	97.25 127

EI-MVSNet-Vis-set		93.01 86	92.92 83	93.29 85	95.01 164	83.51 128	94.48 179	95.77 176	90.87 22	92.52 104	96.67 82	84.50 74	99.00 74	91.99 97	94.44 170	97.36 122

Vis-MVSNet		91.75 107	91.23 111	93.29 85	95.32 148	83.78 118	96.14 59	95.98 158	89.89 50	90.45 148	96.58 90	75.09 196	98.31 159	84.75 200	96.90 107	97.78 101
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 51	94.22 42	93.26 87	96.13 105	83.29 135	96.27 48	96.52 108	89.82 54	95.56 40	95.51 137	84.50 74	98.79 106	94.83 41	98.86 19	97.72 105

SPE-MVS-test		94.02 48	94.29 38	93.24 88	96.69 83	83.24 136	97.49 6	96.92 67	92.14 8	92.90 86	95.77 128	85.02 64	98.33 156	93.03 65	98.62 46	98.13 72

VNet		92.24 99	91.91 100	93.24 88	96.59 87	83.43 129	94.84 157	96.44 112	89.19 82	94.08 63	95.90 119	77.85 166	98.17 166	88.90 144	93.38 191	98.13 72

VDD-MVS		90.74 127	89.92 140	93.20 90	96.27 100	83.02 149	95.73 96	93.86 283	88.42 111	92.53 103	96.84 74	62.09 347	98.64 121	90.95 119	92.62 208	97.93 89

Elysia		90.12 144	89.10 159	93.18 91	93.16 264	84.05 110	95.22 129	96.27 128	85.16 202	90.59 145	94.68 173	64.64 330	98.37 149	86.38 179	95.77 133	97.12 138

StellarMVS		90.12 144	89.10 159	93.18 91	93.16 264	84.05 110	95.22 129	96.27 128	85.16 202	90.59 145	94.68 173	64.64 330	98.37 149	86.38 179	95.77 133	97.12 138

CS-MVS		94.12 45	94.44 31	93.17 93	96.55 90	83.08 146	97.63 4	96.95 64	91.71 14	93.50 76	96.21 100	85.61 53	98.24 161	93.64 54	98.17 65	98.19 67

nrg030		91.08 121	90.39 125	93.17 93	93.07 270	86.91 22	96.41 38	96.26 132	88.30 114	88.37 182	94.85 167	82.19 110	97.64 212	91.09 114	82.95 329	94.96 238

MVSMamba_PlusPlus		93.44 69	93.54 70	93.14 95	96.58 89	83.05 147	96.06 68	96.50 110	84.42 224	94.09 60	95.56 136	85.01 67	98.69 116	94.96 39	98.66 41	97.67 108

EI-MVSNet-UG-set		92.74 91	92.62 90	93.12 96	94.86 177	83.20 138	94.40 187	95.74 179	90.71 30	92.05 114	96.60 89	84.00 79	98.99 76	91.55 109	93.63 182	97.17 132

test_fmvsmvis_n_1920		93.44 69	93.55 69	93.10 97	93.67 250	84.26 104	95.83 88	96.14 142	89.00 91	92.43 107	97.50 41	83.37 87	98.72 113	96.61 21	97.44 94	96.32 179

新几何1				93.10 97	97.30 71	84.35 103		95.56 193	71.09 410	91.26 137	96.24 99	82.87 97	98.86 95	79.19 292	98.10 70	96.07 195

OMC-MVS		91.23 116	90.62 124	93.08 99	96.27 100	84.07 108	93.52 243	95.93 162	86.95 155	89.51 162	96.13 107	78.50 155	98.35 153	85.84 188	92.90 201	96.83 161

OpenMVS	83.78 11	88.74 190	87.29 208	93.08 99	92.70 284	85.39 73	96.57 36	96.43 113	78.74 339	80.85 346	96.07 110	69.64 277	99.01 69	78.01 304	96.65 116	94.83 246

MAR-MVS		90.30 140	89.37 152	93.07 101	96.61 86	84.48 94	95.68 99	95.67 185	82.36 270	87.85 191	92.85 242	76.63 177	98.80 104	80.01 280	96.68 115	95.91 201
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
lupinMVS		90.92 122	90.21 129	93.03 102	93.86 239	83.88 115	92.81 279	93.86 283	79.84 320	91.76 126	94.29 190	77.92 163	98.04 184	90.48 129	97.11 100	97.17 132

Effi-MVS+		91.59 111	91.11 113	93.01 103	94.35 216	83.39 132	94.60 172	95.10 224	87.10 151	90.57 147	93.10 237	81.43 122	98.07 182	89.29 139	94.48 168	97.59 113

fmvsm_s_conf0.5_n_a		93.57 62	93.76 62	93.00 104	95.02 163	83.67 121	96.19 52	96.10 148	87.27 147	95.98 34	98.05 22	83.07 94	98.45 142	96.68 20	95.51 139	96.88 158

MVS_111021_LR		92.47 96	92.29 96	92.98 105	95.99 119	84.43 98	93.08 267	96.09 149	88.20 119	91.12 139	95.72 131	81.33 123	97.76 201	91.74 105	97.37 96	96.75 163

fmvsm_s_conf0.1_n_a		93.19 80	93.26 74	92.97 106	92.49 287	83.62 124	96.02 72	95.72 182	86.78 160	96.04 32	98.19 3	82.30 106	98.43 146	96.38 22	95.42 145	96.86 159

ETV-MVS		92.74 91	92.66 88	92.97 106	95.20 156	84.04 112	95.07 141	96.51 109	90.73 29	92.96 85	91.19 303	84.06 78	98.34 154	91.72 106	96.54 118	96.54 174

LFMVS		90.08 147	89.13 158	92.95 108	96.71 82	82.32 172	96.08 64	89.91 387	86.79 159	92.15 113	96.81 77	62.60 345	98.34 154	87.18 167	93.90 177	98.19 67

UGNet		89.95 152	88.95 164	92.95 108	94.51 202	83.31 134	95.70 98	95.23 217	89.37 73	87.58 198	93.94 206	64.00 335	98.78 107	83.92 211	96.31 124	96.74 164
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
jason		90.80 125	90.10 133	92.90 110	93.04 273	83.53 127	93.08 267	94.15 272	80.22 314	91.41 134	94.91 161	76.87 171	97.93 194	90.28 130	96.90 107	97.24 128
jason: jason.
DP-MVS		87.25 242	85.36 279	92.90 110	97.65 60	83.24 136	94.81 159	92.00 333	74.99 378	81.92 335	95.00 159	72.66 235	99.05 61	66.92 393	92.33 213	96.40 176

fmvsm_s_conf0.5_n_8		94.56 25	95.12 13	92.87 112	95.96 122	81.32 196	95.76 94	97.57 5	93.48 2	97.53 8	98.32 2	81.78 120	99.13 56	97.91 2	97.81 84	98.16 70

fmvsm_s_conf0.5_n		93.76 58	94.06 52	92.86 113	95.62 137	83.17 139	96.14 59	96.12 146	88.13 122	95.82 36	98.04 25	83.43 84	98.48 134	96.97 18	96.23 125	96.92 155

fmvsm_s_conf0.1_n		93.46 66	93.66 67	92.85 114	93.75 246	83.13 141	96.02 72	95.74 179	87.68 139	95.89 35	98.17 4	82.78 98	98.46 138	96.71 19	96.17 127	96.98 150

CANet_DTU		90.26 142	89.41 151	92.81 115	93.46 257	83.01 150	93.48 244	94.47 256	89.43 71	87.76 196	94.23 195	70.54 265	99.03 64	84.97 195	96.39 122	96.38 177

MVSFormer		91.68 110	91.30 108	92.80 116	93.86 239	83.88 115	95.96 77	95.90 166	84.66 220	91.76 126	94.91 161	77.92 163	97.30 248	89.64 135	97.11 100	97.24 128

PVSNet_Blended_VisFu		91.38 113	90.91 118	92.80 116	96.39 97	83.17 139	94.87 153	96.66 97	83.29 250	89.27 168	94.46 185	80.29 130	99.17 51	87.57 161	95.37 146	96.05 198

LuminaMVS		90.55 137	89.81 142	92.77 118	92.78 282	84.21 105	94.09 211	94.17 271	85.82 183	91.54 131	94.14 197	69.93 271	97.92 195	91.62 108	94.21 173	96.18 187

fmvsm_s_conf0.5_n_6		94.11 46	94.56 27	92.76 119	94.98 167	81.96 180	95.79 90	97.29 34	89.31 76	97.52 9	97.61 39	83.25 89	98.88 92	97.05 16	98.22 64	97.43 121

VDDNet		89.56 163	88.49 179	92.76 119	95.07 162	82.09 175	96.30 42	93.19 299	81.05 308	91.88 121	96.86 73	61.16 363	98.33 156	88.43 150	92.49 212	97.84 97

h-mvs33		90.80 125	90.15 132	92.75 121	96.01 115	82.66 163	95.43 114	95.53 197	89.80 57	93.08 82	95.64 133	75.77 185	99.00 74	92.07 92	78.05 386	96.60 169

casdiffmvs		92.51 94	92.43 93	92.74 122	94.41 211	81.98 178	94.54 176	96.23 136	89.57 67	91.96 118	96.17 105	82.58 100	98.01 186	90.95 119	95.45 144	98.23 65
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl		90.69 129	90.02 138	92.71 123	95.72 129	82.41 170	94.11 207	95.12 222	85.63 190	91.49 132	94.70 171	74.75 200	98.42 147	86.13 183	92.53 210	97.31 123

DCV-MVSNet		90.69 129	90.02 138	92.71 123	95.72 129	82.41 170	94.11 207	95.12 222	85.63 190	91.49 132	94.70 171	74.75 200	98.42 147	86.13 183	92.53 210	97.31 123

PCF-MVS	84.11 10	87.74 217	86.08 254	92.70 125	94.02 229	84.43 98	89.27 368	95.87 170	73.62 392	84.43 285	94.33 187	78.48 156	98.86 95	70.27 367	94.45 169	94.81 247
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline		92.39 98	92.29 96	92.69 126	94.46 206	81.77 183	94.14 204	96.27 128	89.22 80	91.88 121	96.00 113	82.35 103	97.99 188	91.05 115	95.27 150	98.30 51

MSLP-MVS++		93.72 60	94.08 49	92.65 127	97.31 70	83.43 129	95.79 90	97.33 28	90.03 47	93.58 72	96.96 69	84.87 69	97.76 201	92.19 88	98.66 41	96.76 162

EC-MVSNet		93.44 69	93.71 65	92.63 128	95.21 155	82.43 167	97.27 10	96.71 94	90.57 33	92.88 87	95.80 126	83.16 90	98.16 167	93.68 53	98.14 68	97.31 123

ab-mvs		89.41 169	88.35 181	92.60 129	95.15 160	82.65 164	92.20 299	95.60 192	83.97 231	88.55 178	93.70 219	74.16 213	98.21 165	82.46 233	89.37 256	96.94 153

LS3D		87.89 212	86.32 243	92.59 130	96.07 113	82.92 153	95.23 127	94.92 236	75.66 370	82.89 321	95.98 115	72.48 239	99.21 49	68.43 381	95.23 151	95.64 214

Anonymous20240529		88.09 208	86.59 232	92.58 131	96.53 92	81.92 181	95.99 74	95.84 172	74.11 387	89.06 172	95.21 151	61.44 355	98.81 103	83.67 216	87.47 287	97.01 148

fmvsm_s_conf0.5_n_3		94.49 27	95.13 12	92.56 132	95.49 143	81.10 206	95.93 80	97.16 45	92.96 4	97.39 10	98.13 6	83.63 83	98.80 104	97.89 3	97.61 92	97.78 101

CPTT-MVS		91.99 101	91.80 101	92.55 133	98.24 33	81.98 178	96.76 31	96.49 111	81.89 285	90.24 151	96.44 95	78.59 153	98.61 126	89.68 134	97.85 82	97.06 142

114514_t		89.51 164	88.50 177	92.54 134	98.11 38	81.99 177	95.16 137	96.36 120	70.19 414	85.81 238	95.25 147	76.70 175	98.63 123	82.07 243	96.86 110	97.00 149

PAPM_NR		91.22 117	90.78 122	92.52 135	97.60 61	81.46 192	94.37 193	96.24 135	86.39 171	87.41 201	94.80 169	82.06 114	98.48 134	82.80 228	95.37 146	97.61 111

DeepPCF-MVS	89.96 1	94.20 41	94.77 25	92.49 136	96.52 93	80.00 242	94.00 221	97.08 53	90.05 46	95.65 39	97.29 50	89.66 13	98.97 81	93.95 50	98.71 32	98.50 28

IS-MVSNet		91.43 112	91.09 115	92.46 137	95.87 125	81.38 195	96.95 20	93.69 291	89.72 63	89.50 164	95.98 115	78.57 154	97.77 200	83.02 222	96.50 120	98.22 66

API-MVS		90.66 132	90.07 134	92.45 138	96.36 98	84.57 89	96.06 68	95.22 219	82.39 268	89.13 169	94.27 193	80.32 129	98.46 138	80.16 279	96.71 114	94.33 270

xiu_mvs_v1_base_debu		90.64 133	90.05 135	92.40 139	93.97 235	84.46 95	93.32 252	95.46 201	85.17 199	92.25 108	94.03 198	70.59 261	98.57 129	90.97 116	94.67 160	94.18 273

xiu_mvs_v1_base		90.64 133	90.05 135	92.40 139	93.97 235	84.46 95	93.32 252	95.46 201	85.17 199	92.25 108	94.03 198	70.59 261	98.57 129	90.97 116	94.67 160	94.18 273

xiu_mvs_v1_base_debi		90.64 133	90.05 135	92.40 139	93.97 235	84.46 95	93.32 252	95.46 201	85.17 199	92.25 108	94.03 198	70.59 261	98.57 129	90.97 116	94.67 160	94.18 273

fmvsm_s_conf0.5_n_2		93.47 65	93.83 56	92.39 142	95.36 146	81.19 202	95.20 134	96.56 105	90.37 36	97.13 14	98.03 26	77.47 167	98.96 83	97.79 5	96.58 117	97.03 145

fmvsm_s_conf0.1_n_2		93.16 82	93.42 71	92.37 143	94.62 192	81.13 204	95.23 127	95.89 168	90.30 40	96.74 24	98.02 27	76.14 179	98.95 85	97.64 6	96.21 126	97.03 145

AdaColmap		89.89 155	89.07 161	92.37 143	97.41 67	83.03 148	94.42 186	95.92 163	82.81 262	86.34 227	94.65 178	73.89 217	99.02 67	80.69 270	95.51 139	95.05 233

CNLPA		89.07 180	87.98 192	92.34 145	96.87 79	84.78 84	94.08 212	93.24 297	81.41 299	84.46 283	95.13 156	75.57 192	96.62 294	77.21 311	93.84 179	95.61 217

fmvsm_s_conf0.5_n_4		93.86 55	94.37 34	92.33 146	95.13 161	80.95 211	95.64 105	96.97 59	89.60 66	96.85 20	97.77 35	83.08 93	98.92 89	97.49 7	96.78 112	97.13 137

ET-MVSNet_ETH3D		87.51 230	85.91 262	92.32 147	93.70 249	83.93 113	92.33 294	90.94 365	84.16 226	72.09 413	92.52 255	69.90 272	95.85 341	89.20 140	88.36 274	97.17 132

Anonymous202405211		87.68 218	86.13 250	92.31 148	96.66 84	80.74 218	94.87 153	91.49 350	80.47 313	89.46 165	95.44 139	54.72 399	98.23 162	82.19 239	89.89 246	97.97 85

CHOSEN 1792x2688		88.84 186	87.69 198	92.30 149	96.14 104	81.42 194	90.01 355	95.86 171	74.52 383	87.41 201	93.94 206	75.46 193	98.36 151	80.36 275	95.53 138	97.12 138

HY-MVS	83.01 12	89.03 182	87.94 194	92.29 150	94.86 177	82.77 155	92.08 304	94.49 255	81.52 298	86.93 208	92.79 248	78.32 158	98.23 162	79.93 281	90.55 234	95.88 203

CDS-MVSNet		89.45 167	88.51 176	92.29 150	93.62 252	83.61 126	93.01 270	94.68 252	81.95 280	87.82 194	93.24 231	78.69 151	96.99 275	80.34 276	93.23 196	96.28 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 149	89.27 157	92.29 150	95.78 127	80.95 211	92.68 281	96.22 137	81.91 282	86.66 218	93.75 218	82.23 108	98.44 144	79.40 291	94.79 157	97.48 118

mvsmamba		90.33 139	89.69 144	92.25 153	95.17 157	81.64 185	95.27 125	93.36 296	84.88 211	89.51 162	94.27 193	69.29 286	97.42 234	89.34 138	96.12 129	97.68 107

PLC	84.53 7	89.06 181	88.03 190	92.15 154	97.27 73	82.69 162	94.29 196	95.44 206	79.71 322	84.01 299	94.18 196	76.68 176	98.75 109	77.28 310	93.41 190	95.02 234
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 109	91.56 105	92.13 155	95.88 123	80.50 225	97.33 8	95.25 216	86.15 177	89.76 160	95.60 134	83.42 86	98.32 158	87.37 165	93.25 195	97.56 115

patch_mono-2		93.74 59	94.32 35	92.01 156	97.54 62	78.37 284	93.40 248	97.19 39	88.02 124	94.99 49	97.21 55	88.35 21	98.44 144	94.07 49	98.09 71	99.23 1

原ACMM1				92.01 156	97.34 69	81.05 207		96.81 81	78.89 333	90.45 148	95.92 118	82.65 99	98.84 99	80.68 271	98.26 59	96.14 189

UniMVSNet (Re)		89.80 157	89.07 161	92.01 156	93.60 253	84.52 92	94.78 161	97.47 13	89.26 79	86.44 224	92.32 261	82.10 112	97.39 245	84.81 199	80.84 363	94.12 277

MG-MVS		91.77 106	91.70 104	92.00 159	97.08 76	80.03 240	93.60 241	95.18 220	87.85 133	90.89 142	96.47 94	82.06 114	98.36 151	85.07 194	97.04 103	97.62 110

EIA-MVS		91.95 102	91.94 99	91.98 160	95.16 158	80.01 241	95.36 115	96.73 91	88.44 109	89.34 166	92.16 266	83.82 82	98.45 142	89.35 137	97.06 102	97.48 118

PVSNet_Blended		90.73 128	90.32 127	91.98 160	96.12 106	81.25 198	92.55 286	96.83 77	82.04 278	89.10 170	92.56 254	81.04 125	98.85 97	86.72 175	95.91 131	95.84 205

guyue		91.12 120	90.84 120	91.96 162	94.59 194	80.57 223	94.87 153	93.71 290	88.96 92	91.14 138	95.22 148	73.22 229	97.76 201	92.01 96	93.81 180	97.54 117

PS-MVSNAJ		91.18 118	90.92 117	91.96 162	95.26 153	82.60 166	92.09 303	95.70 183	86.27 173	91.84 123	92.46 256	79.70 138	98.99 76	89.08 141	95.86 132	94.29 271

TAMVS		89.21 175	88.29 185	91.96 162	93.71 247	82.62 165	93.30 256	94.19 269	82.22 273	87.78 195	93.94 206	78.83 148	96.95 278	77.70 306	92.98 200	96.32 179

SDMVSNet		90.19 143	89.61 146	91.93 165	96.00 116	83.09 145	92.89 276	95.98 158	88.73 99	86.85 214	95.20 152	72.09 244	97.08 267	88.90 144	89.85 248	95.63 215

FA-MVS(test-final)		89.66 159	88.91 166	91.93 165	94.57 198	80.27 229	91.36 319	94.74 249	84.87 212	89.82 159	92.61 253	74.72 203	98.47 137	83.97 210	93.53 185	97.04 144

MVS_Test		91.31 115	91.11 113	91.93 165	94.37 212	80.14 233	93.46 246	95.80 174	86.46 169	91.35 136	93.77 216	82.21 109	98.09 179	87.57 161	94.95 154	97.55 116

NR-MVSNet		88.58 196	87.47 204	91.93 165	93.04 273	84.16 107	94.77 162	96.25 134	89.05 86	80.04 360	93.29 229	79.02 147	97.05 272	81.71 254	80.05 373	94.59 254

HyFIR lowres test		88.09 208	86.81 220	91.93 165	96.00 116	80.63 220	90.01 355	95.79 175	73.42 394	87.68 197	92.10 272	73.86 218	97.96 190	80.75 269	91.70 217	97.19 131

GeoE		90.05 148	89.43 150	91.90 170	95.16 158	80.37 228	95.80 89	94.65 253	83.90 232	87.55 200	94.75 170	78.18 159	97.62 214	81.28 259	93.63 182	97.71 106

thisisatest0530		88.67 191	87.61 200	91.86 171	94.87 176	80.07 236	94.63 171	89.90 388	84.00 230	88.46 180	93.78 215	66.88 310	98.46 138	83.30 218	92.65 207	97.06 142

xiu_mvs_v2_base		91.13 119	90.89 119	91.86 171	94.97 168	82.42 168	92.24 297	95.64 190	86.11 181	91.74 128	93.14 235	79.67 141	98.89 91	89.06 142	95.46 143	94.28 272

DU-MVS		89.34 174	88.50 177	91.85 173	93.04 273	83.72 119	94.47 182	96.59 102	89.50 68	86.46 221	93.29 229	77.25 169	97.23 257	84.92 196	81.02 359	94.59 254

AstraMVS		90.69 129	90.30 128	91.84 174	93.81 242	79.85 247	94.76 163	92.39 319	88.96 92	91.01 141	95.87 122	70.69 259	97.94 193	92.49 75	92.70 206	97.73 104

OPM-MVS		90.12 144	89.56 147	91.82 175	93.14 266	83.90 114	94.16 203	95.74 179	88.96 92	87.86 190	95.43 141	72.48 239	97.91 196	88.10 155	90.18 241	93.65 308
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 136	90.19 130	91.82 175	94.70 188	82.73 159	95.85 86	96.22 137	90.81 24	86.91 210	94.86 165	74.23 209	98.12 169	88.15 151	89.99 242	94.63 251

UniMVSNet_NR-MVSNet		89.92 154	89.29 155	91.81 177	93.39 259	83.72 119	94.43 185	97.12 50	89.80 57	86.46 221	93.32 226	83.16 90	97.23 257	84.92 196	81.02 359	94.49 264

diffmvs		91.37 114	91.23 111	91.77 178	93.09 269	80.27 229	92.36 291	95.52 198	87.03 153	91.40 135	94.93 160	80.08 132	97.44 232	92.13 91	94.56 165	97.61 111
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
1112_ss		88.42 197	87.33 207	91.72 179	94.92 172	80.98 209	92.97 273	94.54 254	78.16 350	83.82 302	93.88 211	78.78 150	97.91 196	79.45 287	89.41 255	96.26 183

Fast-Effi-MVS+		89.41 169	88.64 172	91.71 180	94.74 182	80.81 216	93.54 242	95.10 224	83.11 254	86.82 216	90.67 326	79.74 137	97.75 205	80.51 274	93.55 184	96.57 172

WTY-MVS		89.60 161	88.92 165	91.67 181	95.47 144	81.15 203	92.38 290	94.78 247	83.11 254	89.06 172	94.32 188	78.67 152	96.61 297	81.57 255	90.89 230	97.24 128

TAPA-MVS	84.62 6	88.16 206	87.01 216	91.62 182	96.64 85	80.65 219	94.39 189	96.21 140	76.38 363	86.19 231	95.44 139	79.75 136	98.08 181	62.75 410	95.29 148	96.13 190
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 160	88.96 163	91.60 183	93.86 239	82.89 154	95.46 112	97.33 28	87.91 128	88.43 181	93.31 227	74.17 212	97.40 242	87.32 166	82.86 334	94.52 259

FE-MVS		87.40 235	86.02 256	91.57 184	94.56 199	79.69 251	90.27 342	93.72 289	80.57 311	88.80 175	91.62 292	65.32 325	98.59 128	74.97 336	94.33 172	96.44 175

XVG-OURS		89.40 171	88.70 171	91.52 185	94.06 227	81.46 192	91.27 323	96.07 151	86.14 178	88.89 174	95.77 128	68.73 295	97.26 254	87.39 164	89.96 244	95.83 206

hse-mvs2		89.88 156	89.34 153	91.51 186	94.83 179	81.12 205	93.94 224	93.91 282	89.80 57	93.08 82	93.60 220	75.77 185	97.66 209	92.07 92	77.07 393	95.74 210

TranMVSNet+NR-MVSNet		88.84 186	87.95 193	91.49 187	92.68 285	83.01 150	94.92 150	96.31 123	89.88 51	85.53 247	93.85 213	76.63 177	96.96 277	81.91 247	79.87 376	94.50 262

AUN-MVS		87.78 216	86.54 235	91.48 188	94.82 180	81.05 207	93.91 228	93.93 279	83.00 257	86.93 208	93.53 221	69.50 280	97.67 207	86.14 181	77.12 392	95.73 212

XVG-OURS-SEG-HR		89.95 152	89.45 148	91.47 189	94.00 233	81.21 201	91.87 307	96.06 153	85.78 185	88.55 178	95.73 130	74.67 204	97.27 252	88.71 147	89.64 253	95.91 201

MVS		87.44 233	86.10 253	91.44 190	92.61 286	83.62 124	92.63 283	95.66 187	67.26 420	81.47 338	92.15 267	77.95 162	98.22 164	79.71 283	95.48 141	92.47 351

F-COLMAP		87.95 211	86.80 221	91.40 191	96.35 99	80.88 214	94.73 165	95.45 204	79.65 323	82.04 333	94.61 179	71.13 251	98.50 132	76.24 323	91.05 228	94.80 248

dcpmvs_2		93.49 64	94.19 46	91.38 192	97.69 59	76.78 318	94.25 198	96.29 124	88.33 112	94.46 52	96.88 72	88.07 25	98.64 121	93.62 55	98.09 71	98.73 19

thisisatest0515		87.33 238	85.99 257	91.37 193	93.49 255	79.55 252	90.63 337	89.56 396	80.17 315	87.56 199	90.86 316	67.07 307	98.28 160	81.50 256	93.02 199	96.29 181

HQP-MVS		89.80 157	89.28 156	91.34 194	94.17 222	81.56 186	94.39 189	96.04 154	88.81 95	85.43 256	93.97 205	73.83 219	97.96 190	87.11 170	89.77 251	94.50 262

fmvsm_s_conf0.5_n_7		93.15 83	93.76 62	91.31 195	94.42 210	79.48 254	94.52 177	97.14 48	89.33 75	94.17 58	98.09 16	81.83 118	97.49 224	96.33 23	98.02 75	96.95 152

RRT-MVS		90.85 124	90.70 123	91.30 196	94.25 218	76.83 317	94.85 156	96.13 145	89.04 87	90.23 152	94.88 163	70.15 270	98.72 113	91.86 104	94.88 155	98.34 44

FMVSNet3		87.40 235	86.11 252	91.30 196	93.79 245	83.64 123	94.20 202	94.81 245	83.89 233	84.37 286	91.87 283	68.45 298	96.56 302	78.23 301	85.36 304	93.70 307

FMVSNet2		87.19 248	85.82 265	91.30 196	94.01 230	83.67 121	94.79 160	94.94 231	83.57 240	83.88 301	92.05 276	66.59 315	96.51 306	77.56 308	85.01 307	93.73 305

RPMNet		83.95 324	81.53 335	91.21 199	90.58 363	79.34 260	85.24 413	96.76 86	71.44 408	85.55 245	82.97 420	70.87 256	98.91 90	61.01 414	89.36 257	95.40 221

IB-MVS	80.51 15	85.24 302	83.26 319	91.19 200	92.13 298	79.86 246	91.75 310	91.29 355	83.28 251	80.66 350	88.49 373	61.28 357	98.46 138	80.99 265	79.46 380	95.25 227
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
CLD-MVS		89.47 166	88.90 167	91.18 201	94.22 220	82.07 176	92.13 301	96.09 149	87.90 129	85.37 262	92.45 257	74.38 207	97.56 218	87.15 168	90.43 236	93.93 286
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		89.45 167	88.90 167	91.12 202	94.47 204	81.49 190	95.30 120	96.14 142	86.73 162	85.45 253	95.16 154	69.89 273	98.10 171	87.70 159	89.23 260	93.77 301

LGP-MVS_train				91.12 202	94.47 204	81.49 190		96.14 142	86.73 162	85.45 253	95.16 154	69.89 273	98.10 171	87.70 159	89.23 260	93.77 301

ACMM	84.12 9	89.14 176	88.48 180	91.12 202	94.65 191	81.22 200	95.31 118	96.12 146	85.31 198	85.92 236	94.34 186	70.19 269	98.06 183	85.65 189	88.86 265	94.08 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 193	87.78 197	91.11 205	94.96 169	77.81 300	95.35 116	89.69 391	85.09 206	88.05 188	94.59 182	66.93 308	98.48 134	83.27 219	92.13 215	97.03 145

GBi-Net		87.26 240	85.98 258	91.08 206	94.01 230	83.10 142	95.14 138	94.94 231	83.57 240	84.37 286	91.64 288	66.59 315	96.34 319	78.23 301	85.36 304	93.79 296

test1		87.26 240	85.98 258	91.08 206	94.01 230	83.10 142	95.14 138	94.94 231	83.57 240	84.37 286	91.64 288	66.59 315	96.34 319	78.23 301	85.36 304	93.79 296

FMVSNet1		85.85 287	84.11 306	91.08 206	92.81 280	83.10 142	95.14 138	94.94 231	81.64 293	82.68 323	91.64 288	59.01 379	96.34 319	75.37 330	83.78 318	93.79 296

Test_1112_low_res		87.65 220	86.51 236	91.08 206	94.94 171	79.28 264	91.77 309	94.30 264	76.04 368	83.51 312	92.37 259	77.86 165	97.73 206	78.69 296	89.13 262	96.22 184

PS-MVSNAJss		89.97 151	89.62 145	91.02 210	91.90 307	80.85 215	95.26 126	95.98 158	86.26 174	86.21 230	94.29 190	79.70 138	97.65 210	88.87 146	88.10 276	94.57 256

BH-RMVSNet		88.37 200	87.48 203	91.02 210	95.28 150	79.45 256	92.89 276	93.07 302	85.45 195	86.91 210	94.84 168	70.35 266	97.76 201	73.97 344	94.59 164	95.85 204

UniMVSNet_ETH3D		87.53 229	86.37 240	91.00 212	92.44 290	78.96 269	94.74 164	95.61 191	84.07 229	85.36 263	94.52 184	59.78 371	97.34 247	82.93 223	87.88 281	96.71 165

FIs		90.51 138	90.35 126	90.99 213	93.99 234	80.98 209	95.73 96	97.54 6	89.15 83	86.72 217	94.68 173	81.83 118	97.24 256	85.18 193	88.31 275	94.76 249

ACMP	84.23 8	89.01 184	88.35 181	90.99 213	94.73 183	81.27 197	95.07 141	95.89 168	86.48 167	83.67 307	94.30 189	69.33 282	97.99 188	87.10 172	88.55 267	93.72 306
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 270	85.13 285	90.98 215	96.52 93	81.50 188	96.14 59	96.16 141	73.78 390	83.65 308	92.15 267	63.26 341	97.37 246	82.82 227	81.74 348	94.06 282

sss		88.93 185	88.26 187	90.94 216	94.05 228	80.78 217	91.71 311	95.38 210	81.55 297	88.63 177	93.91 210	75.04 197	95.47 360	82.47 232	91.61 218	96.57 172

sd_testset		88.59 195	87.85 196	90.83 217	96.00 116	80.42 227	92.35 292	94.71 250	88.73 99	86.85 214	95.20 152	67.31 302	96.43 313	79.64 285	89.85 248	95.63 215

PVSNet_BlendedMVS		89.98 150	89.70 143	90.82 218	96.12 106	81.25 198	93.92 226	96.83 77	83.49 244	89.10 170	92.26 264	81.04 125	98.85 97	86.72 175	87.86 282	92.35 357

cascas		86.43 278	84.98 288	90.80 219	92.10 300	80.92 213	90.24 346	95.91 165	73.10 397	83.57 311	88.39 374	65.15 327	97.46 228	84.90 198	91.43 220	94.03 284

ECVR-MVS		89.09 179	88.53 175	90.77 220	95.62 137	75.89 331	96.16 55	84.22 422	87.89 131	90.20 153	96.65 84	63.19 342	98.10 171	85.90 186	96.94 105	98.33 46

GA-MVS		86.61 268	85.27 282	90.66 221	91.33 330	78.71 273	90.40 341	93.81 286	85.34 197	85.12 266	89.57 355	61.25 358	97.11 266	80.99 265	89.59 254	96.15 188

thres600view7		87.65 220	86.67 227	90.59 222	96.08 112	78.72 271	94.88 152	91.58 346	87.06 152	88.08 186	92.30 262	68.91 292	98.10 171	70.05 374	91.10 223	94.96 238

thres400		87.62 225	86.64 228	90.57 223	95.99 119	78.64 274	94.58 173	91.98 335	86.94 156	88.09 184	91.77 284	69.18 288	98.10 171	70.13 371	91.10 223	94.96 238

baseline1		88.10 207	87.28 209	90.57 223	94.96 169	80.07 236	94.27 197	91.29 355	86.74 161	87.41 201	94.00 203	76.77 174	96.20 324	80.77 268	79.31 382	95.44 219

FC-MVSNet-test		90.27 141	90.18 131	90.53 225	93.71 247	79.85 247	95.77 92	97.59 4	89.31 76	86.27 228	94.67 176	81.93 117	97.01 274	84.26 206	88.09 278	94.71 250

PAPM		86.68 267	85.39 277	90.53 225	93.05 272	79.33 263	89.79 358	94.77 248	78.82 336	81.95 334	93.24 231	76.81 172	97.30 248	66.94 391	93.16 197	94.95 242

WR-MVS		88.38 199	87.67 199	90.52 227	93.30 261	80.18 231	93.26 259	95.96 161	88.57 107	85.47 252	92.81 246	76.12 180	96.91 281	81.24 260	82.29 339	94.47 267

MVSTER		88.84 186	88.29 185	90.51 228	92.95 278	80.44 226	93.73 235	95.01 228	84.66 220	87.15 205	93.12 236	72.79 234	97.21 259	87.86 157	87.36 290	93.87 291

testdata				90.49 229	96.40 96	77.89 297		95.37 212	72.51 402	93.63 71	96.69 80	82.08 113	97.65 210	83.08 220	97.39 95	95.94 200

test1111		89.10 177	88.64 172	90.48 230	95.53 142	74.97 341	96.08 64	84.89 420	88.13 122	90.16 155	96.65 84	63.29 340	98.10 171	86.14 181	96.90 107	98.39 41

tt0805		86.92 256	85.74 271	90.48 230	92.22 294	79.98 243	95.63 106	94.88 239	83.83 235	84.74 275	92.80 247	57.61 385	97.67 207	85.48 192	84.42 311	93.79 296

jajsoiax		88.24 204	87.50 202	90.48 230	90.89 351	80.14 233	95.31 118	95.65 189	84.97 209	84.24 294	94.02 201	65.31 326	97.42 234	88.56 148	88.52 269	93.89 287

PatchMatch-RL		86.77 264	85.54 273	90.47 233	95.88 123	82.71 161	90.54 339	92.31 323	79.82 321	84.32 291	91.57 296	68.77 294	96.39 315	73.16 350	93.48 189	92.32 358

tfpn200view9		87.58 227	86.64 228	90.41 234	95.99 119	78.64 274	94.58 173	91.98 335	86.94 156	88.09 184	91.77 284	69.18 288	98.10 171	70.13 371	91.10 223	94.48 265

VPNet		88.20 205	87.47 204	90.39 235	93.56 254	79.46 255	94.04 216	95.54 196	88.67 102	86.96 207	94.58 183	69.33 282	97.15 261	84.05 209	80.53 368	94.56 257

ACMH	80.38 17	85.36 297	83.68 313	90.39 235	94.45 207	80.63 220	94.73 165	94.85 241	82.09 275	77.24 383	92.65 251	60.01 369	97.58 216	72.25 355	84.87 308	92.96 336
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 223	86.71 224	90.38 237	96.12 106	78.55 277	95.03 144	91.58 346	87.15 149	88.06 187	92.29 263	68.91 292	98.10 171	70.13 371	91.10 223	94.48 265

mvs_tets		88.06 210	87.28 209	90.38 237	90.94 347	79.88 245	95.22 129	95.66 187	85.10 205	84.21 295	93.94 206	63.53 338	97.40 242	88.50 149	88.40 273	93.87 291

1314		87.51 230	86.57 233	90.34 239	92.42 291	79.74 250	92.63 283	95.35 214	78.35 345	80.14 357	91.62 292	74.05 214	97.15 261	81.05 261	93.53 185	94.12 277

LTVRE_ROB	82.13 13	86.26 281	84.90 291	90.34 239	94.44 208	81.50 188	92.31 296	94.89 237	83.03 256	79.63 366	92.67 250	69.69 276	97.79 199	71.20 360	86.26 299	91.72 368
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
test_djsdf		89.03 182	88.64 172	90.21 241	90.74 358	79.28 264	95.96 77	95.90 166	84.66 220	85.33 264	92.94 241	74.02 215	97.30 248	89.64 135	88.53 268	94.05 283

v2v482		87.84 213	87.06 213	90.17 242	90.99 343	79.23 267	94.00 221	95.13 221	84.87 212	85.53 247	92.07 275	74.45 206	97.45 229	84.71 201	81.75 347	93.85 294

pmmvs4		85.43 295	83.86 311	90.16 243	90.02 376	82.97 152	90.27 342	92.67 314	75.93 369	80.73 348	91.74 286	71.05 252	95.73 349	78.85 295	83.46 325	91.78 367

V42		87.68 218	86.86 218	90.15 244	90.58 363	80.14 233	94.24 200	95.28 215	83.66 238	85.67 242	91.33 298	74.73 202	97.41 240	84.43 205	81.83 345	92.89 339

MSDG		84.86 310	83.09 322	90.14 245	93.80 243	80.05 238	89.18 371	93.09 301	78.89 333	78.19 375	91.91 281	65.86 324	97.27 252	68.47 380	88.45 271	93.11 331

sc_t1		81.53 348	78.67 369	90.12 246	90.78 355	78.64 274	93.91 228	90.20 378	68.42 417	80.82 347	89.88 348	46.48 422	96.76 286	76.03 326	71.47 407	94.96 238

anonymousdsp		87.84 213	87.09 212	90.12 246	89.13 387	80.54 224	94.67 169	95.55 194	82.05 276	83.82 302	92.12 269	71.47 249	97.15 261	87.15 168	87.80 285	92.67 345

thres200		87.21 246	86.24 247	90.12 246	95.36 146	78.53 278	93.26 259	92.10 329	86.42 170	88.00 189	91.11 309	69.24 287	98.00 187	69.58 375	91.04 229	93.83 295

CR-MVSNet		85.35 298	83.76 312	90.12 246	90.58 363	79.34 260	85.24 413	91.96 337	78.27 347	85.55 245	87.87 384	71.03 253	95.61 352	73.96 345	89.36 257	95.40 221

v1144		87.61 226	86.79 222	90.06 250	91.01 342	79.34 260	93.95 223	95.42 209	83.36 249	85.66 243	91.31 301	74.98 198	97.42 234	83.37 217	82.06 341	93.42 317

XXY-MVS		87.65 220	86.85 219	90.03 251	92.14 297	80.60 222	93.76 234	95.23 217	82.94 259	84.60 277	94.02 201	74.27 208	95.49 359	81.04 262	83.68 321	94.01 285

Vis-MVSNet (Re-imp)		89.59 162	89.44 149	90.03 251	95.74 128	75.85 332	95.61 107	90.80 369	87.66 141	87.83 193	95.40 142	76.79 173	96.46 311	78.37 297	96.73 113	97.80 99

test2506		87.21 246	86.28 245	90.02 253	95.62 137	73.64 357	96.25 50	71.38 445	87.89 131	90.45 148	96.65 84	55.29 396	98.09 179	86.03 185	96.94 105	98.33 46

BH-untuned		88.60 194	88.13 189	90.01 254	95.24 154	78.50 280	93.29 257	94.15 272	84.75 217	84.46 283	93.40 223	75.76 187	97.40 242	77.59 307	94.52 167	94.12 277

v1192		87.25 242	86.33 242	90.00 255	90.76 357	79.04 268	93.80 232	95.48 199	82.57 266	85.48 251	91.18 305	73.38 228	97.42 234	82.30 236	82.06 341	93.53 311

v7n		86.81 259	85.76 269	89.95 256	90.72 359	79.25 266	95.07 141	95.92 163	84.45 223	82.29 327	90.86 316	72.60 238	97.53 220	79.42 290	80.52 369	93.08 333

testing91		87.11 251	86.18 248	89.92 257	94.43 209	75.38 340	91.53 316	92.27 325	86.48 167	86.50 219	90.24 334	61.19 361	97.53 220	82.10 241	90.88 231	96.84 160

v8		87.50 232	86.71 224	89.89 258	91.37 327	79.40 257	94.50 178	95.38 210	84.81 215	83.60 310	91.33 298	76.05 181	97.42 234	82.84 226	80.51 370	92.84 341

v10		87.25 242	86.38 239	89.85 259	91.19 333	79.50 253	94.48 179	95.45 204	83.79 236	83.62 309	91.19 303	75.13 195	97.42 234	81.94 246	80.60 365	92.63 347

baseline2		86.50 274	85.39 277	89.84 260	91.12 338	76.70 320	91.88 306	88.58 400	82.35 271	79.95 361	90.95 314	73.42 226	97.63 213	80.27 278	89.95 245	95.19 228

pm-mvs1		86.61 268	85.54 273	89.82 261	91.44 322	80.18 231	95.28 124	94.85 241	83.84 234	81.66 336	92.62 252	72.45 241	96.48 308	79.67 284	78.06 385	92.82 342

TR-MVS		86.78 261	85.76 269	89.82 261	94.37 212	78.41 282	92.47 287	92.83 308	81.11 307	86.36 225	92.40 258	68.73 295	97.48 225	73.75 348	89.85 248	93.57 310

ACMH+	81.04 14	85.05 305	83.46 316	89.82 261	94.66 190	79.37 258	94.44 184	94.12 275	82.19 274	78.04 377	92.82 245	58.23 382	97.54 219	73.77 347	82.90 333	92.54 348

EI-MVSNet		89.10 177	88.86 169	89.80 264	91.84 309	78.30 286	93.70 238	95.01 228	85.73 187	87.15 205	95.28 145	79.87 135	97.21 259	83.81 213	87.36 290	93.88 290

v144192		87.19 248	86.35 241	89.74 265	90.64 361	78.24 288	93.92 226	95.43 207	81.93 281	85.51 249	91.05 312	74.21 211	97.45 229	82.86 225	81.56 349	93.53 311

COLMAP_ROB	80.39 16	83.96 323	82.04 332	89.74 265	95.28 150	79.75 249	94.25 198	92.28 324	75.17 376	78.02 378	93.77 216	58.60 381	97.84 198	65.06 402	85.92 300	91.63 370
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 280	85.18 284	89.73 267	92.15 296	76.60 321	91.12 327	91.69 342	83.53 243	85.50 250	88.81 367	66.79 311	96.48 308	76.65 316	90.35 238	96.12 191

IterMVS-LS		88.36 201	87.91 195	89.70 268	93.80 243	78.29 287	93.73 235	95.08 226	85.73 187	84.75 274	91.90 282	79.88 134	96.92 280	83.83 212	82.51 335	93.89 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 277	85.35 280	89.69 269	94.29 217	75.40 339	91.30 321	90.53 373	84.76 216	85.06 268	90.13 340	58.95 380	97.45 229	82.08 242	91.09 227	96.21 186

testing99		86.72 265	85.73 272	89.69 269	94.23 219	74.91 343	91.35 320	90.97 363	86.14 178	86.36 225	90.22 335	59.41 374	97.48 225	82.24 238	90.66 233	96.69 167

v1921920		86.97 255	86.06 255	89.69 269	90.53 366	78.11 291	93.80 232	95.43 207	81.90 283	85.33 264	91.05 312	72.66 235	97.41 240	82.05 244	81.80 346	93.53 311

VortexMVS		88.42 197	88.01 191	89.63 272	93.89 238	78.82 270	93.82 231	95.47 200	86.67 164	84.53 281	91.99 278	72.62 237	96.65 292	89.02 143	84.09 315	93.41 318

Fast-Effi-MVS+-dtu		87.44 233	86.72 223	89.63 272	92.04 301	77.68 306	94.03 217	93.94 278	85.81 184	82.42 326	91.32 300	70.33 267	97.06 270	80.33 277	90.23 240	94.14 276

v1240		86.78 261	85.85 264	89.56 274	90.45 368	77.79 302	93.61 240	95.37 212	81.65 292	85.43 256	91.15 307	71.50 248	97.43 233	81.47 257	82.05 343	93.47 315

Effi-MVS+-dtu		88.65 192	88.35 181	89.54 275	93.33 260	76.39 325	94.47 182	94.36 262	87.70 138	85.43 256	89.56 356	73.45 224	97.26 254	85.57 191	91.28 222	94.97 235

AllTest		83.42 330	81.39 336	89.52 276	95.01 164	77.79 302	93.12 263	90.89 367	77.41 354	76.12 392	93.34 224	54.08 402	97.51 222	68.31 382	84.27 313	93.26 321

TestCases				89.52 276	95.01 164	77.79 302		90.89 367	77.41 354	76.12 392	93.34 224	54.08 402	97.51 222	68.31 382	84.27 313	93.26 321

mvs_anonymous		89.37 173	89.32 154	89.51 278	93.47 256	74.22 350	91.65 314	94.83 243	82.91 260	85.45 253	93.79 214	81.23 124	96.36 318	86.47 177	94.09 174	97.94 87

XVG-ACMP-BASELINE		86.00 283	84.84 293	89.45 279	91.20 332	78.00 293	91.70 312	95.55 194	85.05 207	82.97 320	92.25 265	54.49 400	97.48 225	82.93 223	87.45 289	92.89 339

testing222		84.84 311	83.32 317	89.43 280	94.15 225	75.94 330	91.09 328	89.41 398	84.90 210	85.78 239	89.44 357	52.70 407	96.28 322	70.80 366	91.57 219	96.07 195

MVP-Stereo		85.97 284	84.86 292	89.32 281	90.92 349	82.19 174	92.11 302	94.19 269	78.76 338	78.77 374	91.63 291	68.38 299	96.56 302	75.01 335	93.95 176	89.20 408
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 287	84.70 295	89.29 282	91.76 313	75.54 336	88.49 380	91.30 354	81.63 294	85.05 269	88.70 371	71.71 245	96.24 323	74.61 340	89.05 263	96.08 194
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 253	86.32 243	89.21 283	90.94 347	77.26 311	93.71 237	94.43 257	84.84 214	84.36 289	90.80 320	76.04 182	97.05 272	82.12 240	79.60 379	93.31 320

tfpnnormal		84.72 313	83.23 320	89.20 284	92.79 281	80.05 238	94.48 179	95.81 173	82.38 269	81.08 344	91.21 302	69.01 291	96.95 278	61.69 412	80.59 366	90.58 395

cl22		86.78 261	85.98 258	89.18 285	92.34 292	77.62 307	90.84 333	94.13 274	81.33 301	83.97 300	90.15 339	73.96 216	96.60 299	84.19 207	82.94 330	93.33 319

BH-w/o		87.57 228	87.05 214	89.12 286	94.90 175	77.90 296	92.41 288	93.51 293	82.89 261	83.70 306	91.34 297	75.75 188	97.07 269	75.49 328	93.49 187	92.39 355

WR-MVS_H		87.80 215	87.37 206	89.10 287	93.23 262	78.12 290	95.61 107	97.30 32	87.90 129	83.72 305	92.01 277	79.65 142	96.01 333	76.36 320	80.54 367	93.16 329

miper_enhance_ethall		86.90 257	86.18 248	89.06 288	91.66 318	77.58 308	90.22 348	94.82 244	79.16 329	84.48 282	89.10 361	79.19 146	96.66 291	84.06 208	82.94 330	92.94 337

c3_l		87.14 250	86.50 237	89.04 289	92.20 295	77.26 311	91.22 326	94.70 251	82.01 279	84.34 290	90.43 331	78.81 149	96.61 297	83.70 215	81.09 356	93.25 323

miper_ehance_all_eth		87.22 245	86.62 231	89.02 290	92.13 298	77.40 310	90.91 332	94.81 245	81.28 302	84.32 291	90.08 342	79.26 144	96.62 294	83.81 213	82.94 330	93.04 334

gg-mvs-nofinetune		81.77 342	79.37 357	88.99 291	90.85 353	77.73 305	86.29 405	79.63 433	74.88 381	83.19 319	69.05 436	60.34 366	96.11 328	75.46 329	94.64 163	93.11 331

ETVMVS		84.43 317	82.92 326	88.97 292	94.37 212	74.67 344	91.23 325	88.35 402	83.37 248	86.06 234	89.04 362	55.38 394	95.67 351	67.12 389	91.34 221	96.58 171

pmmvs6		83.42 330	81.60 334	88.87 293	88.01 402	77.87 298	94.96 147	94.24 268	74.67 382	78.80 373	91.09 310	60.17 368	96.49 307	77.06 315	75.40 399	92.23 360

test_cas_vis1_n_1920		88.83 189	88.85 170	88.78 294	91.15 337	76.72 319	93.85 230	94.93 235	83.23 253	92.81 91	96.00 113	61.17 362	94.45 373	91.67 107	94.84 156	95.17 229

MIMVSNet		82.59 336	80.53 341	88.76 295	91.51 320	78.32 285	86.57 404	90.13 381	79.32 325	80.70 349	88.69 372	52.98 406	93.07 398	66.03 397	88.86 265	94.90 243

cl____		86.52 273	85.78 266	88.75 296	92.03 302	76.46 323	90.74 334	94.30 264	81.83 288	83.34 316	90.78 321	75.74 190	96.57 300	81.74 252	81.54 350	93.22 325

DIV-MVS_self_test		86.53 272	85.78 266	88.75 296	92.02 303	76.45 324	90.74 334	94.30 264	81.83 288	83.34 316	90.82 319	75.75 188	96.57 300	81.73 253	81.52 351	93.24 324

CP-MVSNet		87.63 223	87.26 211	88.74 298	93.12 267	76.59 322	95.29 122	96.58 103	88.43 110	83.49 313	92.98 240	75.28 194	95.83 342	78.97 293	81.15 355	93.79 296

eth_miper_zixun_eth		86.50 274	85.77 268	88.68 299	91.94 304	75.81 333	90.47 340	94.89 237	82.05 276	84.05 297	90.46 330	75.96 183	96.77 285	82.76 229	79.36 381	93.46 316

CHOSEN 280x420		85.15 303	83.99 309	88.65 300	92.47 288	78.40 283	79.68 435	92.76 311	74.90 380	81.41 340	89.59 354	69.85 275	95.51 356	79.92 282	95.29 148	92.03 363

PS-CasMVS		87.32 239	86.88 217	88.63 301	92.99 276	76.33 327	95.33 117	96.61 101	88.22 118	83.30 318	93.07 238	73.03 232	95.79 346	78.36 298	81.00 361	93.75 303

TransMVSNet (Re)		84.43 317	83.06 324	88.54 302	91.72 314	78.44 281	95.18 135	92.82 310	82.73 264	79.67 365	92.12 269	73.49 223	95.96 335	71.10 364	68.73 417	91.21 382

tt0320-xc		79.63 371	76.66 380	88.52 303	91.03 341	78.72 271	93.00 271	89.53 397	66.37 421	76.11 394	87.11 395	46.36 424	95.32 364	72.78 352	67.67 418	91.51 374

EG-PatchMatch MVS		82.37 338	80.34 344	88.46 304	90.27 370	79.35 259	92.80 280	94.33 263	77.14 358	73.26 410	90.18 338	47.47 419	96.72 287	70.25 368	87.32 292	89.30 405

PEN-MVS		86.80 260	86.27 246	88.40 305	92.32 293	75.71 335	95.18 135	96.38 118	87.97 126	82.82 322	93.15 234	73.39 227	95.92 337	76.15 324	79.03 384	93.59 309

Baseline_NR-MVSNet		87.07 252	86.63 230	88.40 305	91.44 322	77.87 298	94.23 201	92.57 316	84.12 228	85.74 241	92.08 273	77.25 169	96.04 329	82.29 237	79.94 374	91.30 380

UBG		85.51 293	84.57 299	88.35 307	94.21 221	71.78 381	90.07 353	89.66 393	82.28 272	85.91 237	89.01 363	61.30 356	97.06 270	76.58 319	92.06 216	96.22 184

D2MVS		85.90 285	85.09 286	88.35 307	90.79 354	77.42 309	91.83 308	95.70 183	80.77 310	80.08 359	90.02 344	66.74 313	96.37 316	81.88 248	87.97 280	91.26 381

pmmvs5		84.21 319	82.84 329	88.34 309	88.95 389	76.94 315	92.41 288	91.91 339	75.63 371	80.28 354	91.18 305	64.59 332	95.57 353	77.09 314	83.47 324	92.53 349

mamv4		90.92 122	91.78 102	88.33 310	95.67 133	70.75 394	92.92 275	96.02 157	81.90 283	88.11 183	95.34 143	85.88 52	96.97 276	95.22 37	95.01 153	97.26 126

tt0320		80.13 364	77.41 373	88.29 311	90.50 367	78.02 292	93.10 266	90.71 371	66.06 424	76.75 387	86.97 396	49.56 414	95.40 361	71.65 356	71.41 408	91.46 377

LCM-MVSNet-Re		88.30 203	88.32 184	88.27 312	94.71 187	72.41 376	93.15 262	90.98 362	87.77 136	79.25 369	91.96 279	78.35 157	95.75 347	83.04 221	95.62 137	96.65 168

CostFormer		85.77 290	84.94 290	88.26 313	91.16 336	72.58 374	89.47 366	91.04 361	76.26 366	86.45 223	89.97 346	70.74 258	96.86 284	82.35 235	87.07 295	95.34 225

ITE_SJBPF				88.24 314	91.88 308	77.05 314		92.92 305	85.54 193	80.13 358	93.30 228	57.29 386	96.20 324	72.46 354	84.71 309	91.49 375

PVSNet	78.82 18	85.55 292	84.65 296	88.23 315	94.72 185	71.93 377	87.12 400	92.75 312	78.80 337	84.95 271	90.53 328	64.43 333	96.71 289	74.74 338	93.86 178	96.06 197

IterMVS-SCA-FT		85.45 294	84.53 300	88.18 316	91.71 315	76.87 316	90.19 350	92.65 315	85.40 196	81.44 339	90.54 327	66.79 311	95.00 370	81.04 262	81.05 357	92.66 346

EPNet_dtu		86.49 276	85.94 261	88.14 317	90.24 371	72.82 366	94.11 207	92.20 327	86.66 165	79.42 368	92.36 260	73.52 222	95.81 344	71.26 359	93.66 181	95.80 208
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 334	80.93 340	88.06 318	90.05 375	76.37 326	84.74 418	91.96 337	72.28 405	81.32 342	87.87 384	71.03 253	95.50 358	68.97 377	80.15 372	92.32 358

test_vis1_n_1920		89.39 172	89.84 141	88.04 319	92.97 277	72.64 371	94.71 167	96.03 156	86.18 176	91.94 120	96.56 92	61.63 351	95.74 348	93.42 58	95.11 152	95.74 210

DTE-MVSNet		86.11 282	85.48 275	87.98 320	91.65 319	74.92 342	94.93 149	95.75 178	87.36 146	82.26 328	93.04 239	72.85 233	95.82 343	74.04 343	77.46 390	93.20 327

PMMVS		85.71 291	84.96 289	87.95 321	88.90 390	77.09 313	88.68 378	90.06 383	72.32 404	86.47 220	90.76 322	72.15 243	94.40 375	81.78 251	93.49 187	92.36 356

GG-mvs-BLEND				87.94 322	89.73 382	77.91 295	87.80 389	78.23 438		80.58 351	83.86 413	59.88 370	95.33 363	71.20 360	92.22 214	90.60 394

MonoMVSNet		86.89 258	86.55 234	87.92 323	89.46 385	73.75 354	94.12 205	93.10 300	87.82 135	85.10 267	90.76 322	69.59 278	94.94 371	86.47 177	82.50 336	95.07 232

reproduce_monomvs		86.37 279	85.87 263	87.87 324	93.66 251	73.71 355	93.44 247	95.02 227	88.61 105	82.64 325	91.94 280	57.88 384	96.68 290	89.96 132	79.71 378	93.22 325

pmmvs-eth3d		80.97 357	78.72 368	87.74 325	84.99 420	79.97 244	90.11 352	91.65 344	75.36 373	73.51 408	86.03 403	59.45 373	93.96 385	75.17 332	72.21 404	89.29 407

MS-PatchMatch		85.05 305	84.16 304	87.73 326	91.42 325	78.51 279	91.25 324	93.53 292	77.50 353	80.15 356	91.58 294	61.99 348	95.51 356	75.69 327	94.35 171	89.16 409

mmtdpeth		85.04 307	84.15 305	87.72 327	93.11 268	75.74 334	94.37 193	92.83 308	84.98 208	89.31 167	86.41 400	61.61 353	97.14 264	92.63 74	62.11 428	90.29 396

test_0402		81.30 353	79.17 362	87.67 328	93.19 263	78.17 289	92.98 272	91.71 340	75.25 375	76.02 395	90.31 333	59.23 375	96.37 316	50.22 431	83.63 322	88.47 416

IterMVS		84.88 309	83.98 310	87.60 329	91.44 322	76.03 329	90.18 351	92.41 318	83.24 252	81.06 345	90.42 332	66.60 314	94.28 379	79.46 286	80.98 362	92.48 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 351	79.30 358	87.58 330	90.92 349	74.16 352	80.99 430	87.68 407	70.52 412	76.63 389	88.81 367	71.21 250	92.76 400	60.01 418	86.93 296	95.83 206

EPMVS		83.90 326	82.70 330	87.51 331	90.23 372	72.67 369	88.62 379	81.96 428	81.37 300	85.01 270	88.34 375	66.31 318	94.45 373	75.30 331	87.12 293	95.43 220

ADS-MVSNet2		81.66 345	79.71 354	87.50 332	91.35 328	74.19 351	83.33 423	88.48 401	72.90 399	82.24 329	85.77 406	64.98 328	93.20 396	64.57 404	83.74 319	95.12 230

OurMVSNet-221017-0		85.35 298	84.64 297	87.49 333	90.77 356	72.59 373	94.01 219	94.40 260	84.72 218	79.62 367	93.17 233	61.91 349	96.72 287	81.99 245	81.16 353	93.16 329

tpm2		84.08 321	82.94 325	87.48 334	91.39 326	71.27 386	89.23 370	90.37 375	71.95 406	84.64 276	89.33 358	67.30 303	96.55 304	75.17 332	87.09 294	94.63 251

RPSCF		85.07 304	84.27 301	87.48 334	92.91 279	70.62 396	91.69 313	92.46 317	76.20 367	82.67 324	95.22 148	63.94 336	97.29 251	77.51 309	85.80 301	94.53 258

myMVS_eth3d28		85.80 289	85.26 283	87.42 336	94.73 183	69.92 401	90.60 338	90.95 364	87.21 148	86.06 234	90.04 343	59.47 372	96.02 331	74.89 337	93.35 194	96.33 178

WBMVS		84.97 308	84.18 303	87.34 337	94.14 226	71.62 385	90.20 349	92.35 320	81.61 295	84.06 296	90.76 322	61.82 350	96.52 305	78.93 294	83.81 317	93.89 287

miper_lstm_enhance		85.27 301	84.59 298	87.31 338	91.28 331	74.63 345	87.69 394	94.09 276	81.20 306	81.36 341	89.85 350	74.97 199	94.30 378	81.03 264	79.84 377	93.01 335

FMVSNet5		81.52 349	79.60 355	87.27 339	91.17 334	77.95 294	91.49 317	92.26 326	76.87 359	76.16 391	87.91 383	51.67 408	92.34 403	67.74 386	81.16 353	91.52 373

USDC		82.76 333	81.26 338	87.26 340	91.17 334	74.55 346	89.27 368	93.39 295	78.26 348	75.30 399	92.08 273	54.43 401	96.63 293	71.64 357	85.79 302	90.61 392

test-LLR		85.87 286	85.41 276	87.25 341	90.95 345	71.67 383	89.55 362	89.88 389	83.41 246	84.54 279	87.95 381	67.25 304	95.11 367	81.82 249	93.37 192	94.97 235

test-mter		84.54 316	83.64 314	87.25 341	90.95 345	71.67 383	89.55 362	89.88 389	79.17 328	84.54 279	87.95 381	55.56 392	95.11 367	81.82 249	93.37 192	94.97 235

JIA-IIPM		81.04 354	78.98 366	87.25 341	88.64 391	73.48 359	81.75 429	89.61 395	73.19 396	82.05 332	73.71 432	66.07 323	95.87 340	71.18 362	84.60 310	92.41 354

TDRefinement		79.81 368	77.34 374	87.22 344	79.24 435	75.48 337	93.12 263	92.03 332	76.45 362	75.01 400	91.58 294	49.19 415	96.44 312	70.22 370	69.18 414	89.75 401

tpmvs		83.35 332	82.07 331	87.20 345	91.07 340	71.00 392	88.31 383	91.70 341	78.91 331	80.49 353	87.18 393	69.30 285	97.08 267	68.12 385	83.56 323	93.51 314

ppachtmachnet_test		81.84 341	80.07 349	87.15 346	88.46 395	74.43 349	89.04 374	92.16 328	75.33 374	77.75 380	88.99 364	66.20 320	95.37 362	65.12 401	77.60 388	91.65 369

dmvs_re		84.20 320	83.22 321	87.14 347	91.83 311	77.81 300	90.04 354	90.19 379	84.70 219	81.49 337	89.17 360	64.37 334	91.13 414	71.58 358	85.65 303	92.46 352

tpm cat1		81.96 339	80.27 345	87.01 348	91.09 339	71.02 391	87.38 398	91.53 349	66.25 422	80.17 355	86.35 402	68.22 300	96.15 327	69.16 376	82.29 339	93.86 293

test_fmvs1_n		87.03 254	87.04 215	86.97 349	89.74 381	71.86 378	94.55 175	94.43 257	78.47 342	91.95 119	95.50 138	51.16 410	93.81 386	93.02 66	94.56 165	95.26 226

OpenMVS_ROB	74.94 19	79.51 372	77.03 379	86.93 350	87.00 408	76.23 328	92.33 294	90.74 370	68.93 416	74.52 404	88.23 378	49.58 413	96.62 294	57.64 423	84.29 312	87.94 419

SixPastTwentyTwo		83.91 325	82.90 327	86.92 351	90.99 343	70.67 395	93.48 244	91.99 334	85.54 193	77.62 382	92.11 271	60.59 365	96.87 283	76.05 325	77.75 387	93.20 327

ADS-MVSNet		81.56 347	79.78 351	86.90 352	91.35 328	71.82 379	83.33 423	89.16 399	72.90 399	82.24 329	85.77 406	64.98 328	93.76 387	64.57 404	83.74 319	95.12 230

PatchT		82.68 335	81.27 337	86.89 353	90.09 374	70.94 393	84.06 420	90.15 380	74.91 379	85.63 244	83.57 415	69.37 281	94.87 372	65.19 399	88.50 270	94.84 245

tpm		84.73 312	84.02 308	86.87 354	90.33 369	68.90 404	89.06 373	89.94 386	80.85 309	85.75 240	89.86 349	68.54 297	95.97 334	77.76 305	84.05 316	95.75 209

Patchmatch-RL test		81.67 344	79.96 350	86.81 355	85.42 418	71.23 387	82.17 428	87.50 408	78.47 342	77.19 384	82.50 422	70.81 257	93.48 391	82.66 230	72.89 403	95.71 213

test_vis1_n		86.56 271	86.49 238	86.78 356	88.51 392	72.69 368	94.68 168	93.78 288	79.55 324	90.70 143	95.31 144	48.75 416	93.28 394	93.15 62	93.99 175	94.38 269

testing3-2		86.72 265	86.71 224	86.74 357	96.11 109	65.92 415	93.39 249	89.65 394	89.46 69	87.84 192	92.79 248	59.17 377	97.60 215	81.31 258	90.72 232	96.70 166

test_fmvs1		87.34 237	87.56 201	86.68 358	90.59 362	71.80 380	94.01 219	94.04 277	78.30 346	91.97 117	95.22 148	56.28 390	93.71 388	92.89 67	94.71 159	94.52 259

MDA-MVSNet-bldmvs		78.85 377	76.31 382	86.46 359	89.76 380	73.88 353	88.79 376	90.42 374	79.16 329	59.18 432	88.33 376	60.20 367	94.04 381	62.00 411	68.96 415	91.48 376

mvs5depth		80.98 356	79.15 363	86.45 360	84.57 421	73.29 361	87.79 390	91.67 343	80.52 312	82.20 331	89.72 352	55.14 397	95.93 336	73.93 346	66.83 420	90.12 398

tpmrst		85.35 298	84.99 287	86.43 361	90.88 352	67.88 409	88.71 377	91.43 352	80.13 316	86.08 233	88.80 369	73.05 231	96.02 331	82.48 231	83.40 327	95.40 221

TESTMET0.1,1		83.74 328	82.85 328	86.42 362	89.96 377	71.21 388	89.55 362	87.88 404	77.41 354	83.37 315	87.31 389	56.71 388	93.65 390	80.62 272	92.85 204	94.40 268

our_test_3		81.93 340	80.46 343	86.33 363	88.46 395	73.48 359	88.46 381	91.11 357	76.46 361	76.69 388	88.25 377	66.89 309	94.36 376	68.75 378	79.08 383	91.14 384

lessismore_v0				86.04 364	88.46 395	68.78 405		80.59 431		73.01 411	90.11 341	55.39 393	96.43 313	75.06 334	65.06 423	92.90 338

TinyColmap		79.76 369	77.69 372	85.97 365	91.71 315	73.12 362	89.55 362	90.36 376	75.03 377	72.03 414	90.19 337	46.22 425	96.19 326	63.11 408	81.03 358	88.59 415

KD-MVS_2432*1600		78.50 378	76.02 385	85.93 366	86.22 411	74.47 347	84.80 416	92.33 321	79.29 326	76.98 385	85.92 404	53.81 404	93.97 383	67.39 387	57.42 433	89.36 403

miper_refine_blended		78.50 378	76.02 385	85.93 366	86.22 411	74.47 347	84.80 416	92.33 321	79.29 326	76.98 385	85.92 404	53.81 404	93.97 383	67.39 387	57.42 433	89.36 403

K. test v3		81.59 346	80.15 348	85.91 368	89.89 379	69.42 403	92.57 285	87.71 406	85.56 192	73.44 409	89.71 353	55.58 391	95.52 355	77.17 312	69.76 411	92.78 343

SSC-MVS3.2		84.60 315	84.19 302	85.85 369	92.74 283	68.07 406	88.15 385	93.81 286	87.42 145	83.76 304	91.07 311	62.91 343	95.73 349	74.56 341	83.24 328	93.75 303

mvsany_test1		85.42 296	85.30 281	85.77 370	87.95 404	75.41 338	87.61 397	80.97 430	76.82 360	88.68 176	95.83 124	77.44 168	90.82 416	85.90 186	86.51 297	91.08 388

MIMVSNet1		79.38 373	77.28 375	85.69 371	86.35 410	73.67 356	91.61 315	92.75 312	78.11 351	72.64 412	88.12 379	48.16 417	91.97 408	60.32 415	77.49 389	91.43 378

UWE-MVS		83.69 329	83.09 322	85.48 372	93.06 271	65.27 420	90.92 331	86.14 412	79.90 319	86.26 229	90.72 325	57.17 387	95.81 344	71.03 365	92.62 208	95.35 224

UnsupCasMVSNet_eth		80.07 365	78.27 371	85.46 373	85.24 419	72.63 372	88.45 382	94.87 240	82.99 258	71.64 416	88.07 380	56.34 389	91.75 409	73.48 349	63.36 426	92.01 364

CL-MVSNet_self_test		81.74 343	80.53 341	85.36 374	85.96 413	72.45 375	90.25 344	93.07 302	81.24 304	79.85 364	87.29 390	70.93 255	92.52 401	66.95 390	69.23 413	91.11 386

MDA-MVSNet_test_wron		79.21 375	77.19 377	85.29 375	88.22 399	72.77 367	85.87 407	90.06 383	74.34 384	62.62 429	87.56 387	66.14 321	91.99 407	66.90 394	73.01 401	91.10 387

YYNet1		79.22 374	77.20 376	85.28 376	88.20 400	72.66 370	85.87 407	90.05 385	74.33 385	62.70 427	87.61 386	66.09 322	92.03 405	66.94 391	72.97 402	91.15 383

WB-MVSnew		83.77 327	83.28 318	85.26 377	91.48 321	71.03 390	91.89 305	87.98 403	78.91 331	84.78 273	90.22 335	69.11 290	94.02 382	64.70 403	90.44 235	90.71 390

dp		81.47 350	80.23 346	85.17 378	89.92 378	65.49 418	86.74 402	90.10 382	76.30 365	81.10 343	87.12 394	62.81 344	95.92 337	68.13 384	79.88 375	94.09 280

UnsupCasMVSNet_bld		76.23 387	73.27 391	85.09 379	83.79 423	72.92 364	85.65 410	93.47 294	71.52 407	68.84 422	79.08 427	49.77 412	93.21 395	66.81 395	60.52 430	89.13 411

Anonymous20231206		81.03 355	79.77 353	84.82 380	87.85 405	70.26 398	91.42 318	92.08 330	73.67 391	77.75 380	89.25 359	62.43 346	93.08 397	61.50 413	82.00 344	91.12 385

test0.0.03 1		82.41 337	81.69 333	84.59 381	88.23 398	72.89 365	90.24 346	87.83 405	83.41 246	79.86 363	89.78 351	67.25 304	88.99 426	65.18 400	83.42 326	91.90 366

CMPMVS	59.16 21	80.52 359	79.20 361	84.48 382	83.98 422	67.63 412	89.95 357	93.84 285	64.79 426	66.81 424	91.14 308	57.93 383	95.17 365	76.25 322	88.10 276	90.65 391
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 314	84.79 294	84.37 383	91.84 309	64.92 421	93.70 238	91.47 351	66.19 423	86.16 232	95.28 145	67.18 306	93.33 393	80.89 267	90.42 237	94.88 244

PVSNet_0	73.20 20	77.22 383	74.83 389	84.37 383	90.70 360	71.10 389	83.09 425	89.67 392	72.81 401	73.93 407	83.13 417	60.79 364	93.70 389	68.54 379	50.84 438	88.30 417

LF4IMVS		80.37 362	79.07 365	84.27 385	86.64 409	69.87 402	89.39 367	91.05 360	76.38 363	74.97 401	90.00 345	47.85 418	94.25 380	74.55 342	80.82 364	88.69 414

Anonymous20240521		80.44 361	79.21 360	84.11 386	85.75 416	67.89 408	92.86 278	93.23 298	75.61 372	75.59 398	87.47 388	50.03 411	94.33 377	71.14 363	81.21 352	90.12 398

PM-MVS		78.11 380	76.12 384	84.09 387	83.54 424	70.08 399	88.97 375	85.27 419	79.93 318	74.73 403	86.43 399	34.70 436	93.48 391	79.43 289	72.06 405	88.72 413

test_fmvs2		83.98 322	84.03 307	83.83 388	87.16 407	67.53 413	93.93 225	92.89 306	77.62 352	86.89 213	93.53 221	47.18 420	92.02 406	90.54 126	86.51 297	91.93 365

testgi		80.94 358	80.20 347	83.18 389	87.96 403	66.29 414	91.28 322	90.70 372	83.70 237	78.12 376	92.84 243	51.37 409	90.82 416	63.34 407	82.46 337	92.43 353

KD-MVS_self_test		80.20 363	79.24 359	83.07 390	85.64 417	65.29 419	91.01 330	93.93 279	78.71 340	76.32 390	86.40 401	59.20 376	92.93 399	72.59 353	69.35 412	91.00 389

testing3		80.46 360	79.59 356	83.06 391	93.44 258	64.64 422	93.33 251	85.47 417	84.34 225	79.93 362	90.84 318	44.35 428	92.39 402	57.06 425	87.56 286	92.16 362

ambc				83.06 391	79.99 433	63.51 426	77.47 436	92.86 307		74.34 406	84.45 412	28.74 437	95.06 369	73.06 351	68.89 416	90.61 392

test20.03		79.95 367	79.08 364	82.55 393	85.79 415	67.74 411	91.09 328	91.08 358	81.23 305	74.48 405	89.96 347	61.63 351	90.15 418	60.08 416	76.38 395	89.76 400

MVStest1		72.91 391	69.70 396	82.54 394	78.14 436	73.05 363	88.21 384	86.21 411	60.69 430	64.70 425	90.53 328	46.44 423	85.70 433	58.78 421	53.62 435	88.87 412

test_vis1_rt		77.96 381	76.46 381	82.48 395	85.89 414	71.74 382	90.25 344	78.89 434	71.03 411	71.30 417	81.35 424	42.49 430	91.05 415	84.55 203	82.37 338	84.65 422

EU-MVSNet		81.32 352	80.95 339	82.42 396	88.50 394	63.67 425	93.32 252	91.33 353	64.02 427	80.57 352	92.83 244	61.21 360	92.27 404	76.34 321	80.38 371	91.32 379

myMVS_eth3d		79.67 370	78.79 367	82.32 397	91.92 305	64.08 423	89.75 360	87.40 409	81.72 290	78.82 371	87.20 391	45.33 426	91.29 412	59.09 420	87.84 283	91.60 371

ttmdpeth		76.55 385	74.64 390	82.29 398	82.25 429	67.81 410	89.76 359	85.69 415	70.35 413	75.76 396	91.69 287	46.88 421	89.77 420	66.16 396	63.23 427	89.30 405

pmmvs3		71.81 394	68.71 397	81.11 399	75.86 438	70.42 397	86.74 402	83.66 423	58.95 433	68.64 423	80.89 425	36.93 434	89.52 422	63.10 409	63.59 425	83.39 423

Syy-MVS		80.07 365	79.78 351	80.94 400	91.92 305	59.93 432	89.75 360	87.40 409	81.72 290	78.82 371	87.20 391	66.29 319	91.29 412	47.06 433	87.84 283	91.60 371

UWE-MVS-28		78.98 376	78.38 370	80.80 401	88.18 401	60.66 431	90.65 336	78.51 435	78.84 335	77.93 379	90.93 315	59.08 378	89.02 425	50.96 430	90.33 239	92.72 344

new-patchmatchnet		76.41 386	75.17 388	80.13 402	82.65 428	59.61 433	87.66 395	91.08 358	78.23 349	69.85 420	83.22 416	54.76 398	91.63 411	64.14 406	64.89 424	89.16 409

mvsany_test3		74.95 388	73.26 392	80.02 403	74.61 439	63.16 427	85.53 411	78.42 436	74.16 386	74.89 402	86.46 398	36.02 435	89.09 424	82.39 234	66.91 419	87.82 420

test_fmvs3		77.67 382	77.16 378	79.22 404	79.52 434	61.14 429	92.34 293	91.64 345	73.98 388	78.86 370	86.59 397	27.38 440	87.03 428	88.12 154	75.97 397	89.50 402

DSMNet-mixed		76.94 384	76.29 383	78.89 405	83.10 426	56.11 441	87.78 391	79.77 432	60.65 431	75.64 397	88.71 370	61.56 354	88.34 427	60.07 417	89.29 259	92.21 361

EGC-MVSNET		61.97 402	56.37 407	78.77 406	89.63 383	73.50 358	89.12 372	82.79 425	0.21 452	1.24 453	84.80 410	39.48 431	90.04 419	44.13 435	75.94 398	72.79 434

new_pmnet		72.15 392	70.13 395	78.20 407	82.95 427	65.68 416	83.91 421	82.40 427	62.94 429	64.47 426	79.82 426	42.85 429	86.26 432	57.41 424	74.44 400	82.65 427

MVS-HIRNet		73.70 390	72.20 393	78.18 408	91.81 312	56.42 440	82.94 426	82.58 426	55.24 434	68.88 421	66.48 437	55.32 395	95.13 366	58.12 422	88.42 272	83.01 425

LCM-MVSNet		66.00 399	62.16 404	77.51 409	64.51 449	58.29 435	83.87 422	90.90 366	48.17 438	54.69 435	73.31 433	16.83 449	86.75 429	65.47 398	61.67 429	87.48 421

APD_test1		69.04 395	66.26 401	77.36 410	80.51 432	62.79 428	85.46 412	83.51 424	54.11 436	59.14 433	84.79 411	23.40 443	89.61 421	55.22 426	70.24 410	79.68 431

test_f		71.95 393	70.87 394	75.21 411	74.21 441	59.37 434	85.07 415	85.82 414	65.25 425	70.42 419	83.13 417	23.62 441	82.93 439	78.32 299	71.94 406	83.33 424

ANet_high		58.88 406	54.22 411	72.86 412	56.50 452	56.67 437	80.75 431	86.00 413	73.09 398	37.39 444	64.63 440	22.17 444	79.49 442	43.51 436	23.96 446	82.43 428

test_vis3_rt		65.12 400	62.60 402	72.69 413	71.44 442	60.71 430	87.17 399	65.55 446	63.80 428	53.22 436	65.65 439	14.54 450	89.44 423	76.65 316	65.38 422	67.91 437

FPMVS		64.63 401	62.55 403	70.88 414	70.80 443	56.71 436	84.42 419	84.42 421	51.78 437	49.57 437	81.61 423	23.49 442	81.48 440	40.61 440	76.25 396	74.46 433

dmvs_testset		74.57 389	75.81 387	70.86 415	87.72 406	40.47 450	87.05 401	77.90 440	82.75 263	71.15 418	85.47 408	67.98 301	84.12 437	45.26 434	76.98 394	88.00 418

N_pmnet		68.89 396	68.44 398	70.23 416	89.07 388	28.79 455	88.06 386	19.50 455	69.47 415	71.86 415	84.93 409	61.24 359	91.75 409	54.70 427	77.15 391	90.15 397

testf1		59.54 404	56.11 408	69.85 417	69.28 444	56.61 438	80.37 432	76.55 443	42.58 441	45.68 440	75.61 428	11.26 451	84.18 435	43.20 437	60.44 431	68.75 435

APD_test2		59.54 404	56.11 408	69.85 417	69.28 444	56.61 438	80.37 432	76.55 443	42.58 441	45.68 440	75.61 428	11.26 451	84.18 435	43.20 437	60.44 431	68.75 435

WB-MVS		67.92 397	67.49 399	69.21 419	81.09 430	41.17 449	88.03 387	78.00 439	73.50 393	62.63 428	83.11 419	63.94 336	86.52 430	25.66 445	51.45 437	79.94 430

PMMVS2		59.60 403	56.40 406	69.21 419	68.83 446	46.58 445	73.02 440	77.48 441	55.07 435	49.21 438	72.95 434	17.43 448	80.04 441	49.32 432	44.33 441	80.99 429

SSC-MVS		67.06 398	66.56 400	68.56 421	80.54 431	40.06 451	87.77 392	77.37 442	72.38 403	61.75 430	82.66 421	63.37 339	86.45 431	24.48 446	48.69 440	79.16 432

Gipuma		57.99 408	54.91 410	67.24 422	88.51 392	65.59 417	52.21 443	90.33 377	43.58 440	42.84 443	51.18 444	20.29 446	85.07 434	34.77 441	70.45 409	51.05 443
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 410	48.46 414	63.48 423	45.72 454	46.20 446	73.41 439	78.31 437	41.03 443	30.06 446	65.68 438	6.05 453	83.43 438	30.04 443	65.86 421	60.80 438
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 407	58.24 405	60.56 424	83.13 425	45.09 448	82.32 427	48.22 454	67.61 419	61.70 431	69.15 435	38.75 432	76.05 443	32.01 442	41.31 442	60.55 439

MVE	39.65 23	43.39 412	38.59 418	57.77 425	56.52 451	48.77 444	55.38 442	58.64 450	29.33 446	28.96 447	52.65 443	4.68 454	64.62 447	28.11 444	33.07 444	59.93 440
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 411	48.47 413	56.66 426	52.26 453	18.98 457	41.51 445	81.40 429	10.10 447	44.59 442	75.01 431	28.51 438	68.16 444	53.54 428	49.31 439	82.83 426

DeepMVS_CX				56.31 427	74.23 440	51.81 443		56.67 451	44.85 439	48.54 439	75.16 430	27.87 439	58.74 449	40.92 439	52.22 436	58.39 441

kuosan		53.51 409	53.30 412	54.13 428	76.06 437	45.36 447	80.11 434	48.36 453	59.63 432	54.84 434	63.43 441	37.41 433	62.07 448	20.73 448	39.10 443	54.96 442

E-PMN		43.23 413	42.29 415	46.03 429	65.58 448	37.41 452	73.51 438	64.62 447	33.99 444	28.47 448	47.87 445	19.90 447	67.91 445	22.23 447	24.45 445	32.77 444

EMVS		42.07 414	41.12 416	44.92 430	63.45 450	35.56 454	73.65 437	63.48 448	33.05 445	26.88 449	45.45 446	21.27 445	67.14 446	19.80 449	23.02 447	32.06 445

tmp_tt		35.64 415	39.24 417	24.84 431	14.87 455	23.90 456	62.71 441	51.51 452	6.58 449	36.66 445	62.08 442	44.37 427	30.34 451	52.40 429	22.00 448	20.27 446

wuyk23d		21.27 417	20.48 420	23.63 432	68.59 447	36.41 453	49.57 444	6.85 456	9.37 448	7.89 450	4.46 452	4.03 455	31.37 450	17.47 450	16.07 449	3.12 447

test123		8.76 419	11.22 422	1.39 433	0.85 457	0.97 458	85.76 409	0.35 458	0.54 451	2.45 452	8.14 451	0.60 456	0.48 452	2.16 452	0.17 451	2.71 448

testmvs		8.92 418	11.52 421	1.12 434	1.06 456	0.46 459	86.02 406	0.65 457	0.62 450	2.74 451	9.52 450	0.31 457	0.45 453	2.38 451	0.39 450	2.46 449

mmdepth		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

monomultidepth		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

test_blank		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uanet_test		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

DCPMVS		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

cdsmvs_eth3d_5k		22.14 416	29.52 419	0.00 435	0.00 458	0.00 460	0.00 446	95.76 177	0.00 453	0.00 454	94.29 190	75.66 191	0.00 454	0.00 453	0.00 452	0.00 450

pcd_1.5k_mvsjas		6.64 421	8.86 424	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	79.70 138	0.00 454	0.00 453	0.00 452	0.00 450

sosnet-low-res		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

sosnet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uncertanet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

Regformer		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

ab-mvs-re		7.82 420	10.43 423	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	93.88 211	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uanet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

WAC-MVS						64.08 423								59.14 419

FOURS1					98.86 1	85.54 69	98.29 1	97.49 8	89.79 60	96.29 26

PC_three_1452									82.47 267	97.09 15	97.07 65	92.72 1	98.04 184	92.70 73	99.02 12	98.86 12

test_one_0601					98.58 11	85.83 63		97.44 17	91.05 20	96.78 22	98.06 20	91.45 11

eth-test2					0.00 458

eth-test					0.00 458

ZD-MVS					98.15 36	86.62 33		97.07 54	83.63 239	94.19 57	96.91 71	87.57 31	99.26 46	91.99 97	98.44 53

RE-MVS-def			93.68 66		97.92 45	84.57 89	96.28 46	96.76 86	87.46 142	93.75 68	97.43 44	82.94 95		92.73 69	97.80 85	97.88 93

IU-MVS					98.77 5	86.00 52		96.84 76	81.26 303	97.26 11				95.50 33	99.13 3	99.03 8

test_241102_TWO								97.44 17	90.31 38	97.62 6	98.07 18	91.46 10	99.58 10	95.66 27	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 54		97.44 17	90.26 44	97.71 1	97.96 28	92.31 4	99.38 31

9.14			94.47 29		97.79 54		96.08 64	97.44 17	86.13 180	95.10 47	97.40 46	88.34 22	99.22 48	93.25 61	98.70 34

save fliter					97.85 51	85.63 68	95.21 132	96.82 79	89.44 70

test_0728_THIRD									90.75 26	97.04 17	98.05 22	92.09 6	99.55 16	95.64 29	99.13 3	99.13 2

test0726					98.78 3	85.93 57	97.19 12	97.47 13	90.27 42	97.64 4	98.13 6	91.47 8

GSMVS																96.12 191

test_part2					98.55 12	87.22 19				96.40 25

sam_mvs1												71.70 246				96.12 191

sam_mvs												70.60 260

MTGPA								96.97 59

test_post1							88.00 388				9.81 449	69.31 284	95.53 354	76.65 316

test_post											10.29 448	70.57 264	95.91 339

patchmatchnet-post											83.76 414	71.53 247	96.48 308

MTMP							96.16 55	60.64 449

gm-plane-assit					89.60 384	68.00 407			77.28 357		88.99 364		97.57 217	79.44 288

test9_res														91.91 101	98.71 32	98.07 77

TEST9					97.53 63	86.49 37	94.07 213	96.78 83	81.61 295	92.77 93	96.20 101	87.71 28	99.12 57

test_8					97.49 65	86.30 45	94.02 218	96.76 86	81.86 286	92.70 97	96.20 101	87.63 29	99.02 67

agg_prior2														90.54 126	98.68 37	98.27 59

agg_prior					97.38 68	85.92 59		96.72 93		92.16 112			98.97 81

test_prior4						85.96 56	94.11 207

test_prior2							94.12 205		87.67 140	92.63 101	96.39 96	86.62 41		91.50 110	98.67 40

旧先验2							93.36 250		71.25 409	94.37 53			97.13 265	86.74 173

新几何2							93.11 265

旧先验1					96.79 81	81.81 182		95.67 185			96.81 77	86.69 39			97.66 91	96.97 151

无先验							93.28 258	96.26 132	73.95 389				99.05 61	80.56 273		96.59 170

原ACMM2							92.94 274

test222					96.55 90	81.70 184	92.22 298	95.01 228	68.36 418	90.20 153	96.14 106	80.26 131			97.80 85	96.05 198

testdata2													98.75 109	78.30 300

segment_acmp												87.16 36

testdata1							92.15 300		87.94 127

plane_prior7					94.70 188	82.74 158

plane_prior6					94.52 201	82.75 156						74.23 209

plane_prior5								96.22 137					98.12 169	88.15 151	89.99 242	94.63 251

plane_prior4											94.86 165

plane_prior3						82.75 156			90.26 44	86.91 210

plane_prior2							95.85 86		90.81 24

plane_prior1					94.59 194

plane_prior						82.73 159	95.21 132		89.66 65						89.88 247

n2								0.00 459

nn								0.00 459

door-mid								85.49 416

test11								96.57 104

door								85.33 418

HQP5-MVS						81.56 186

HQP-NCC					94.17 222		94.39 189		88.81 95	85.43 256

ACMP_Plane					94.17 222		94.39 189		88.81 95	85.43 256

BP-MVS														87.11 170

HQP4-MVS										85.43 256			97.96 190			94.51 261

HQP3-MVS								96.04 154							89.77 251

HQP2-MVS												73.83 219

NP-MVS					94.37 212	82.42 168					93.98 204

MDTV_nov1_ep13_2view						55.91 442	87.62 396		73.32 395	84.59 278		70.33 267		74.65 339		95.50 218

MDTV_nov1_ep13			83.56 315		91.69 317	69.93 400	87.75 393	91.54 348	78.60 341	84.86 272	88.90 366	69.54 279	96.03 330	70.25 368	88.93 264

ACMMP++_ref															87.47 287

ACMMP++															88.01 279

Test By Simon												80.02 133

High-resolution multi-view benchmark