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	93.01 305	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	83.44 246	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	23.41 448	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	81.86 287	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	80.09 318	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	73.74 222	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	90.10 374	85.25 75	96.03 71	92.05 332	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	82.69 266	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	92.46 290	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	73.44 226	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	89.25 387	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	78.43 345	83.41 314	96.19 104	73.18 231	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	72.32 243	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	62.09 348	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	64.64 331	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	64.64 331	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	82.95 330	94.96 239

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	71.09 411	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	92.70 285	85.39 73	96.57 36	96.43 113	78.74 340	80.85 346	96.07 110	69.64 278	99.01 69	78.01 304	96.65 116	94.83 247

MAR-MVS		90.30 140	89.37 152	93.07 101	96.61 86	84.48 94	95.68 99	95.67 185	82.36 271	87.85 191	92.85 243	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	79.84 321	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	93.10 238	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	92.49 288	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	91.19 304	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	89.91 388	86.79 159	92.15 113	96.81 77	62.60 346	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	64.00 336	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	80.22 315	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	92.00 334	74.99 379	81.92 335	95.00 159	72.66 236	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	70.54 266	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	83.29 251	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	92.78 283	84.21 105	94.09 211	94.17 271	85.82 183	91.54 131	94.14 197	69.93 272	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	93.19 300	81.05 309	91.88 121	96.86 73	61.16 364	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	78.05 387	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	89.27 369	95.87 170	73.62 393	84.43 285	94.33 187	78.48 156	98.86 95	70.27 367	94.45 169	94.81 248
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	93.70 220	74.16 213	98.21 165	82.46 233	89.37 257	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	75.66 371	82.89 321	95.98 115	72.48 240	99.21 49	68.43 381	95.23 151	95.64 215

Anonymous20240529		88.09 208	86.59 232	92.58 131	96.53 92	81.92 181	95.99 74	95.84 172	74.11 388	89.06 172	95.21 151	61.44 356	98.81 103	83.67 216	87.47 288	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	81.89 286	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	70.19 415	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	82.39 269	89.13 169	94.27 193	80.32 129	98.46 138	80.16 279	96.71 114	94.33 271

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	70.59 262	98.57 129	90.97 116	94.67 160	94.18 274

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	70.59 262	98.57 129	90.97 116	94.67 160	94.18 274

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	70.59 262	98.57 129	90.97 116	94.67 160	94.18 274

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	82.81 263	86.34 227	94.65 178	73.89 218	99.02 67	80.69 270	95.51 139	95.05 234

CNLPA		89.07 180	87.98 192	92.34 145	96.87 79	84.78 84	94.08 212	93.24 297	81.41 300	84.46 283	95.13 156	75.57 192	96.62 294	77.21 311	93.84 179	95.61 218

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	90.94 366	84.16 226	72.09 414	92.52 256	69.90 273	95.85 341	89.20 140	88.36 275	97.17 132

Anonymous202405211		87.68 218	86.13 250	92.31 148	96.66 84	80.74 218	94.87 153	91.49 351	80.47 314	89.46 165	95.44 139	54.72 400	98.23 162	82.19 239	89.89 247	97.97 85

CHOSEN 1792x2688		88.84 186	87.69 198	92.30 149	96.14 104	81.42 194	90.01 356	95.86 171	74.52 384	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	81.52 299	86.93 208	92.79 249	78.32 158	98.23 162	79.93 281	90.55 234	95.88 204

CDS-MVSNet		89.45 167	88.51 176	92.29 150	93.62 252	83.61 126	93.01 270	94.68 252	81.95 281	87.82 194	93.24 232	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	81.91 283	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	69.29 287	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	79.71 323	84.01 299	94.18 196	76.68 176	98.75 109	77.28 310	93.41 190	95.02 235
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	78.89 334	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	92.32 262	82.10 112	97.39 245	84.81 199	80.84 364	94.12 278

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	92.16 267	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	82.04 279	89.10 170	92.56 255	81.04 125	98.85 97	86.72 175	95.91 131	95.84 206

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	73.22 230	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	92.46 257	79.70 138	98.99 76	89.08 141	95.86 132	94.29 272

TAMVS		89.21 175	88.29 185	91.96 162	93.71 247	82.62 165	93.30 256	94.19 269	82.22 274	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	72.09 245	97.08 267	88.90 144	89.85 249	95.63 216

FA-MVS(test-final)		89.66 159	88.91 166	91.93 165	94.57 198	80.27 229	91.36 320	94.74 249	84.87 212	89.82 159	92.61 254	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	93.29 230	79.02 147	97.05 272	81.71 254	80.05 374	94.59 255

HyFIR lowres test		88.09 208	86.81 220	91.93 165	96.00 116	80.63 220	90.01 356	95.79 175	73.42 395	87.68 197	92.10 273	73.86 219	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	89.90 389	84.00 230	88.46 180	93.78 215	66.88 311	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	93.14 236	79.67 141	98.89 91	89.06 142	95.46 143	94.28 273

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	93.29 230	77.25 169	97.23 257	84.92 196	81.02 360	94.59 255

AstraMVS		90.69 129	90.30 128	91.84 174	93.81 242	79.85 247	94.76 163	92.39 320	88.96 92	91.01 141	95.87 122	70.69 260	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	72.48 240	97.91 196	88.10 155	90.18 241	93.65 309
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	89.99 243	94.63 252

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	93.32 227	83.16 90	97.23 257	84.92 196	81.02 360	94.49 265

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	78.16 351	83.82 302	93.88 211	78.78 150	97.91 196	79.45 287	89.41 256	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	83.11 255	86.82 216	90.67 327	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	83.11 255	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	76.38 364	86.19 231	95.44 139	79.75 136	98.08 181	62.75 411	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	93.31 228	74.17 212	97.40 242	87.32 166	82.86 335	94.52 260

FE-MVS		87.40 235	86.02 256	91.57 184	94.56 199	79.69 251	90.27 343	93.72 289	80.57 312	88.80 175	91.62 293	65.32 326	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	91.27 324	96.07 151	86.14 178	88.89 174	95.77 128	68.73 296	97.26 254	87.39 164	89.96 245	95.83 207

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	93.60 221	75.77 185	97.66 209	92.07 92	77.07 394	95.74 211

TranMVSNet+NR-MVSNet		88.84 186	87.95 193	91.49 187	92.68 286	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	79.87 377	94.50 263

AUN-MVS		87.78 216	86.54 235	91.48 188	94.82 180	81.05 207	93.91 228	93.93 279	83.00 258	86.93 208	93.53 222	69.50 281	97.67 207	86.14 181	77.12 393	95.73 213

XVG-OURS-SEG-HR		89.95 152	89.45 148	91.47 189	94.00 233	81.21 201	91.87 308	96.06 153	85.78 185	88.55 178	95.73 130	74.67 204	97.27 252	88.71 147	89.64 254	95.91 201

MVS		87.44 233	86.10 253	91.44 190	92.61 287	83.62 124	92.63 283	95.66 187	67.26 421	81.47 338	92.15 268	77.95 162	98.22 164	79.71 283	95.48 141	92.47 352

F-COLMAP		87.95 211	86.80 221	91.40 191	96.35 99	80.88 214	94.73 165	95.45 204	79.65 324	82.04 333	94.61 179	71.13 252	98.50 132	76.24 323	91.05 228	94.80 249

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	90.63 338	89.56 397	80.17 316	87.56 199	90.86 317	67.07 308	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	73.83 220	97.96 190	87.11 170	89.77 252	94.50 263

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	70.15 271	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	91.87 284	68.45 299	96.56 302	78.23 301	85.36 305	93.70 308

FMVSNet2		87.19 248	85.82 265	91.30 196	94.01 230	83.67 121	94.79 160	94.94 231	83.57 241	83.88 301	92.05 277	66.59 316	96.51 306	77.56 308	85.01 308	93.73 306

RPMNet		83.95 325	81.53 336	91.21 199	90.58 364	79.34 260	85.24 414	96.76 86	71.44 409	85.55 245	82.97 421	70.87 257	98.91 90	61.01 415	89.36 258	95.40 222

IB-MVS	80.51 15	85.24 302	83.26 320	91.19 200	92.13 299	79.86 246	91.75 311	91.29 356	83.28 252	80.66 350	88.49 374	61.28 358	98.46 138	80.99 265	79.46 381	95.25 228
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	92.45 258	74.38 207	97.56 218	87.15 168	90.43 236	93.93 287
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	69.89 274	98.10 171	87.70 159	89.23 261	93.77 302

LGP-MVS_train				91.12 202	94.47 204	81.49 190		96.14 142	86.73 162	85.45 253	95.16 154	69.89 274	98.10 171	87.70 159	89.23 261	93.77 302

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	70.19 270	98.06 183	85.65 189	88.86 266	94.08 282
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	89.69 392	85.09 206	88.05 188	94.59 182	66.93 309	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	83.57 241	84.37 286	91.64 289	66.59 316	96.34 319	78.23 301	85.36 305	93.79 297

test1		87.26 240	85.98 258	91.08 206	94.01 230	83.10 142	95.14 138	94.94 231	83.57 241	84.37 286	91.64 289	66.59 316	96.34 319	78.23 301	85.36 305	93.79 297

FMVSNet1		85.85 287	84.11 307	91.08 206	92.81 281	83.10 142	95.14 138	94.94 231	81.64 294	82.68 323	91.64 289	59.01 380	96.34 319	75.37 330	83.78 319	93.79 297

Test_1112_low_res		87.65 220	86.51 236	91.08 206	94.94 171	79.28 264	91.77 310	94.30 264	76.04 369	83.51 312	92.37 260	77.86 165	97.73 206	78.69 296	89.13 263	96.22 184

PS-MVSNAJss		89.97 151	89.62 145	91.02 210	91.90 308	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	88.10 277	94.57 257

BH-RMVSNet		88.37 200	87.48 203	91.02 210	95.28 150	79.45 256	92.89 276	93.07 303	85.45 195	86.91 210	94.84 168	70.35 267	97.76 201	73.97 344	94.59 164	95.85 205

UniMVSNet_ETH3D		87.53 229	86.37 240	91.00 212	92.44 291	78.96 269	94.74 164	95.61 191	84.07 229	85.36 263	94.52 184	59.78 372	97.34 247	82.93 223	87.88 282	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	88.31 276	94.76 250

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	69.33 283	97.99 188	87.10 172	88.55 268	93.72 307
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	73.78 391	83.65 308	92.15 268	63.26 342	97.37 246	82.82 227	81.74 349	94.06 283

sss		88.93 185	88.26 187	90.94 216	94.05 228	80.78 217	91.71 312	95.38 210	81.55 298	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	67.31 303	96.43 313	79.64 285	89.85 249	95.63 216

PVSNet_BlendedMVS		89.98 150	89.70 143	90.82 218	96.12 106	81.25 198	93.92 226	96.83 77	83.49 245	89.10 170	92.26 265	81.04 125	98.85 97	86.72 175	87.86 283	92.35 358

cascas		86.43 278	84.98 288	90.80 219	92.10 301	80.92 213	90.24 347	95.91 165	73.10 398	83.57 311	88.39 375	65.15 328	97.46 228	84.90 198	91.43 220	94.03 285

ECVR-MVS		89.09 179	88.53 175	90.77 220	95.62 137	75.89 331	96.16 55	84.22 423	87.89 131	90.20 153	96.65 84	63.19 343	98.10 171	85.90 186	96.94 105	98.33 46

GA-MVS		86.61 268	85.27 282	90.66 221	91.33 331	78.71 273	90.40 342	93.81 286	85.34 197	85.12 266	89.57 356	61.25 359	97.11 266	80.99 265	89.59 255	96.15 188

thres600view7		87.65 220	86.67 227	90.59 222	96.08 112	78.72 271	94.88 152	91.58 347	87.06 152	88.08 186	92.30 263	68.91 293	98.10 171	70.05 374	91.10 223	94.96 239

thres400		87.62 225	86.64 228	90.57 223	95.99 119	78.64 274	94.58 173	91.98 336	86.94 156	88.09 184	91.77 285	69.18 289	98.10 171	70.13 371	91.10 223	94.96 239

baseline1		88.10 207	87.28 209	90.57 223	94.96 169	80.07 236	94.27 197	91.29 356	86.74 161	87.41 201	94.00 203	76.77 174	96.20 324	80.77 268	79.31 383	95.44 220

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	88.09 279	94.71 251

PAPM		86.68 267	85.39 277	90.53 225	93.05 272	79.33 263	89.79 359	94.77 248	78.82 337	81.95 334	93.24 232	76.81 172	97.30 248	66.94 391	93.16 197	94.95 243

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	92.81 247	76.12 180	96.91 281	81.24 260	82.29 340	94.47 268

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	93.12 237	72.79 235	97.21 259	87.86 157	87.36 291	93.87 292

testdata				90.49 229	96.40 96	77.89 297		95.37 212	72.51 403	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	84.89 421	88.13 122	90.16 155	96.65 84	63.29 341	98.10 171	86.14 181	96.90 107	98.39 41

tt0805		86.92 256	85.74 271	90.48 230	92.22 295	79.98 243	95.63 106	94.88 239	83.83 235	84.74 275	92.80 248	57.61 386	97.67 207	85.48 192	84.42 312	93.79 297

jajsoiax		88.24 204	87.50 202	90.48 230	90.89 352	80.14 233	95.31 118	95.65 189	84.97 209	84.24 294	94.02 201	65.31 327	97.42 234	88.56 148	88.52 270	93.89 288

PatchMatch-RL		86.77 264	85.54 273	90.47 233	95.88 123	82.71 161	90.54 340	92.31 324	79.82 322	84.32 291	91.57 297	68.77 295	96.39 315	73.16 350	93.48 189	92.32 359

tfpn200view9		87.58 227	86.64 228	90.41 234	95.99 119	78.64 274	94.58 173	91.98 336	86.94 156	88.09 184	91.77 285	69.18 289	98.10 171	70.13 371	91.10 223	94.48 266

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	69.33 283	97.15 261	84.05 209	80.53 369	94.56 258

ACMH	80.38 17	85.36 297	83.68 314	90.39 235	94.45 207	80.63 220	94.73 165	94.85 241	82.09 276	77.24 384	92.65 252	60.01 370	97.58 216	72.25 355	84.87 309	92.96 337
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	91.58 347	87.15 149	88.06 187	92.29 264	68.91 293	98.10 171	70.13 371	91.10 223	94.48 266

mvs_tets		88.06 210	87.28 209	90.38 237	90.94 348	79.88 245	95.22 129	95.66 187	85.10 205	84.21 295	93.94 206	63.53 339	97.40 242	88.50 149	88.40 274	93.87 292

1314		87.51 230	86.57 233	90.34 239	92.42 292	79.74 250	92.63 283	95.35 214	78.35 346	80.14 357	91.62 293	74.05 214	97.15 261	81.05 261	93.53 185	94.12 278

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	83.03 257	79.63 366	92.67 251	69.69 277	97.79 199	71.20 360	86.26 300	91.72 369
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	90.74 359	79.28 264	95.96 77	95.90 166	84.66 220	85.33 264	92.94 242	74.02 215	97.30 248	89.64 135	88.53 269	94.05 284

v2v482		87.84 213	87.06 213	90.17 242	90.99 344	79.23 267	94.00 221	95.13 221	84.87 212	85.53 247	92.07 276	74.45 206	97.45 229	84.71 201	81.75 348	93.85 295

pmmvs4		85.43 295	83.86 312	90.16 243	90.02 377	82.97 152	90.27 343	92.67 315	75.93 370	80.73 348	91.74 287	71.05 253	95.73 349	78.85 295	83.46 326	91.78 368

V42		87.68 218	86.86 218	90.15 244	90.58 364	80.14 233	94.24 200	95.28 215	83.66 239	85.67 242	91.33 299	74.73 202	97.41 240	84.43 205	81.83 346	92.89 340

MSDG		84.86 310	83.09 323	90.14 245	93.80 243	80.05 238	89.18 372	93.09 302	78.89 334	78.19 376	91.91 282	65.86 325	97.27 252	68.47 380	88.45 272	93.11 332

sc_t1		81.53 349	78.67 370	90.12 246	90.78 356	78.64 274	93.91 228	90.20 379	68.42 418	80.82 347	89.88 349	46.48 423	96.76 286	76.03 326	71.47 408	94.96 239

anonymousdsp		87.84 213	87.09 212	90.12 246	89.13 388	80.54 224	94.67 169	95.55 194	82.05 277	83.82 302	92.12 270	71.47 250	97.15 261	87.15 168	87.80 286	92.67 346

thres200		87.21 246	86.24 247	90.12 246	95.36 146	78.53 278	93.26 259	92.10 330	86.42 170	88.00 189	91.11 310	69.24 288	98.00 187	69.58 375	91.04 229	93.83 296

CR-MVSNet		85.35 298	83.76 313	90.12 246	90.58 364	79.34 260	85.24 414	91.96 338	78.27 348	85.55 245	87.87 385	71.03 254	95.61 352	73.96 345	89.36 258	95.40 222

v1144		87.61 226	86.79 222	90.06 250	91.01 343	79.34 260	93.95 223	95.42 209	83.36 250	85.66 243	91.31 302	74.98 198	97.42 234	83.37 217	82.06 342	93.42 318

XXY-MVS		87.65 220	86.85 219	90.03 251	92.14 298	80.60 222	93.76 234	95.23 217	82.94 260	84.60 277	94.02 201	74.27 208	95.49 359	81.04 262	83.68 322	94.01 286

Vis-MVSNet (Re-imp)		89.59 162	89.44 149	90.03 251	95.74 128	75.85 332	95.61 107	90.80 370	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	71.38 446	87.89 131	90.45 148	96.65 84	55.29 397	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	93.40 224	75.76 187	97.40 242	77.59 307	94.52 167	94.12 278

v1192		87.25 242	86.33 242	90.00 255	90.76 358	79.04 268	93.80 232	95.48 199	82.57 267	85.48 251	91.18 306	73.38 229	97.42 234	82.30 236	82.06 342	93.53 312

v7n		86.81 259	85.76 269	89.95 256	90.72 360	79.25 266	95.07 141	95.92 163	84.45 223	82.29 327	90.86 317	72.60 239	97.53 220	79.42 290	80.52 370	93.08 334

testing91		87.11 251	86.18 248	89.92 257	94.43 209	75.38 340	91.53 317	92.27 326	86.48 167	86.50 219	90.24 335	61.19 362	97.53 220	82.10 241	90.88 231	96.84 160

v8		87.50 232	86.71 224	89.89 258	91.37 328	79.40 257	94.50 178	95.38 210	84.81 215	83.60 310	91.33 299	76.05 181	97.42 234	82.84 226	80.51 371	92.84 342

v10		87.25 242	86.38 239	89.85 259	91.19 334	79.50 253	94.48 179	95.45 204	83.79 237	83.62 309	91.19 304	75.13 195	97.42 234	81.94 246	80.60 366	92.63 348

baseline2		86.50 274	85.39 277	89.84 260	91.12 339	76.70 320	91.88 307	88.58 401	82.35 272	79.95 361	90.95 315	73.42 227	97.63 213	80.27 278	89.95 246	95.19 229

pm-mvs1		86.61 268	85.54 273	89.82 261	91.44 323	80.18 231	95.28 124	94.85 241	83.84 234	81.66 336	92.62 253	72.45 242	96.48 308	79.67 284	78.06 386	92.82 343

TR-MVS		86.78 261	85.76 269	89.82 261	94.37 212	78.41 282	92.47 287	92.83 309	81.11 308	86.36 225	92.40 259	68.73 296	97.48 225	73.75 348	89.85 249	93.57 311

ACMH+	81.04 14	85.05 305	83.46 317	89.82 261	94.66 190	79.37 258	94.44 184	94.12 275	82.19 275	78.04 378	92.82 246	58.23 383	97.54 219	73.77 347	82.90 334	92.54 349

EI-MVSNet		89.10 177	88.86 169	89.80 264	91.84 310	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	87.36 291	93.88 291

v144192		87.19 248	86.35 241	89.74 265	90.64 362	78.24 288	93.92 226	95.43 207	81.93 282	85.51 249	91.05 313	74.21 211	97.45 229	82.86 225	81.56 350	93.53 312

COLMAP_ROB	80.39 16	83.96 324	82.04 333	89.74 265	95.28 150	79.75 249	94.25 198	92.28 325	75.17 377	78.02 379	93.77 216	58.60 382	97.84 198	65.06 402	85.92 301	91.63 371
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	92.15 297	76.60 321	91.12 328	91.69 343	83.53 244	85.50 250	88.81 368	66.79 312	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	91.90 283	79.88 134	96.92 280	83.83 212	82.51 336	93.89 288
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	91.30 322	90.53 374	84.76 216	85.06 268	90.13 341	58.95 381	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	91.35 321	90.97 364	86.14 178	86.36 225	90.22 336	59.41 375	97.48 225	82.24 238	90.66 233	96.69 167

v1921920		86.97 255	86.06 255	89.69 269	90.53 367	78.11 291	93.80 232	95.43 207	81.90 284	85.33 264	91.05 313	72.66 236	97.41 240	82.05 244	81.80 347	93.53 312

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	91.99 279	72.62 238	96.65 292	89.02 143	84.09 316	93.41 319

Fast-Effi-MVS+-dtu		87.44 233	86.72 223	89.63 272	92.04 302	77.68 306	94.03 217	93.94 278	85.81 184	82.42 326	91.32 301	70.33 268	97.06 270	80.33 277	90.23 240	94.14 277

v1240		86.78 261	85.85 264	89.56 274	90.45 369	77.79 302	93.61 240	95.37 212	81.65 293	85.43 256	91.15 308	71.50 249	97.43 233	81.47 257	82.05 344	93.47 316

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	89.56 357	73.45 225	97.26 254	85.57 191	91.28 222	94.97 236

AllTest		83.42 331	81.39 337	89.52 276	95.01 164	77.79 302	93.12 263	90.89 368	77.41 355	76.12 393	93.34 225	54.08 403	97.51 222	68.31 382	84.27 314	93.26 322

TestCases				89.52 276	95.01 164	77.79 302		90.89 368	77.41 355	76.12 393	93.34 225	54.08 403	97.51 222	68.31 382	84.27 314	93.26 322

mvs_anonymous		89.37 173	89.32 154	89.51 278	93.47 256	74.22 350	91.65 315	94.83 243	82.91 261	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	91.20 333	78.00 293	91.70 313	95.55 194	85.05 207	82.97 320	92.25 266	54.49 401	97.48 225	82.93 223	87.45 290	92.89 340

testing222		84.84 311	83.32 318	89.43 280	94.15 225	75.94 330	91.09 329	89.41 399	84.90 210	85.78 239	89.44 358	52.70 408	96.28 322	70.80 366	91.57 219	96.07 195

MVP-Stereo		85.97 284	84.86 292	89.32 281	90.92 350	82.19 174	92.11 302	94.19 269	78.76 339	78.77 375	91.63 292	68.38 300	96.56 302	75.01 335	93.95 176	89.20 409
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	91.76 314	75.54 336	88.49 381	91.30 355	81.63 295	85.05 269	88.70 372	71.71 246	96.24 323	74.61 340	89.05 264	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	90.94 348	77.26 311	93.71 237	94.43 257	84.84 214	84.36 289	90.80 321	76.04 182	97.05 272	82.12 240	79.60 380	93.31 321

tfpnnormal		84.72 313	83.23 321	89.20 284	92.79 282	80.05 238	94.48 179	95.81 173	82.38 270	81.08 344	91.21 303	69.01 292	96.95 278	61.69 413	80.59 367	90.58 396

cl22		86.78 261	85.98 258	89.18 285	92.34 293	77.62 307	90.84 334	94.13 274	81.33 302	83.97 300	90.15 340	73.96 216	96.60 299	84.19 207	82.94 331	93.33 320

BH-w/o		87.57 228	87.05 214	89.12 286	94.90 175	77.90 296	92.41 288	93.51 293	82.89 262	83.70 306	91.34 298	75.75 188	97.07 269	75.49 328	93.49 187	92.39 356

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	92.01 278	79.65 142	96.01 333	76.36 320	80.54 368	93.16 330

miper_enhance_ethall		86.90 257	86.18 248	89.06 288	91.66 319	77.58 308	90.22 349	94.82 244	79.16 330	84.48 282	89.10 362	79.19 146	96.66 291	84.06 208	82.94 331	92.94 338

c3_l		87.14 250	86.50 237	89.04 289	92.20 296	77.26 311	91.22 327	94.70 251	82.01 280	84.34 290	90.43 332	78.81 149	96.61 297	83.70 215	81.09 357	93.25 324

miper_ehance_all_eth		87.22 245	86.62 231	89.02 290	92.13 299	77.40 310	90.91 333	94.81 245	81.28 303	84.32 291	90.08 343	79.26 144	96.62 294	83.81 213	82.94 331	93.04 335

gg-mvs-nofinetune		81.77 343	79.37 358	88.99 291	90.85 354	77.73 305	86.29 406	79.63 434	74.88 382	83.19 319	69.05 437	60.34 367	96.11 328	75.46 329	94.64 163	93.11 332

ETVMVS		84.43 318	82.92 327	88.97 292	94.37 212	74.67 344	91.23 326	88.35 403	83.37 249	86.06 234	89.04 363	55.38 395	95.67 351	67.12 389	91.34 221	96.58 171

pmmvs6		83.42 331	81.60 335	88.87 293	88.01 403	77.87 298	94.96 147	94.24 268	74.67 383	78.80 374	91.09 311	60.17 369	96.49 307	77.06 315	75.40 400	92.23 361

test_cas_vis1_n_1920		88.83 189	88.85 170	88.78 294	91.15 338	76.72 319	93.85 230	94.93 235	83.23 254	92.81 91	96.00 113	61.17 363	94.45 373	91.67 107	94.84 156	95.17 230

MIMVSNet		82.59 337	80.53 342	88.76 295	91.51 321	78.32 285	86.57 405	90.13 382	79.32 326	80.70 349	88.69 373	52.98 407	93.07 398	66.03 397	88.86 266	94.90 244

cl____		86.52 273	85.78 266	88.75 296	92.03 303	76.46 323	90.74 335	94.30 264	81.83 289	83.34 316	90.78 322	75.74 190	96.57 300	81.74 252	81.54 351	93.22 326

DIV-MVS_self_test		86.53 272	85.78 266	88.75 296	92.02 304	76.45 324	90.74 335	94.30 264	81.83 289	83.34 316	90.82 320	75.75 188	96.57 300	81.73 253	81.52 352	93.24 325

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	92.98 241	75.28 194	95.83 342	78.97 293	81.15 356	93.79 297

eth_miper_zixun_eth		86.50 274	85.77 268	88.68 299	91.94 305	75.81 333	90.47 341	94.89 237	82.05 277	84.05 297	90.46 331	75.96 183	96.77 285	82.76 229	79.36 382	93.46 317

CHOSEN 280x420		85.15 303	83.99 310	88.65 300	92.47 289	78.40 283	79.68 436	92.76 312	74.90 381	81.41 340	89.59 355	69.85 276	95.51 356	79.92 282	95.29 148	92.03 364

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	93.07 239	73.03 233	95.79 346	78.36 298	81.00 362	93.75 304

TransMVSNet (Re)		84.43 318	83.06 325	88.54 302	91.72 315	78.44 281	95.18 135	92.82 311	82.73 265	79.67 365	92.12 270	73.49 224	95.96 335	71.10 364	68.73 418	91.21 383

tt0320-xc		79.63 372	76.66 381	88.52 303	91.03 342	78.72 271	93.00 271	89.53 398	66.37 422	76.11 395	87.11 396	46.36 425	95.32 364	72.78 352	67.67 419	91.51 375

EG-PatchMatch MVS		82.37 339	80.34 345	88.46 304	90.27 371	79.35 259	92.80 280	94.33 263	77.14 359	73.26 411	90.18 339	47.47 420	96.72 287	70.25 368	87.32 293	89.30 406

PEN-MVS		86.80 260	86.27 246	88.40 305	92.32 294	75.71 335	95.18 135	96.38 118	87.97 126	82.82 322	93.15 235	73.39 228	95.92 337	76.15 324	79.03 385	93.59 310

Baseline_NR-MVSNet		87.07 252	86.63 230	88.40 305	91.44 323	77.87 298	94.23 201	92.57 317	84.12 228	85.74 241	92.08 274	77.25 169	96.04 329	82.29 237	79.94 375	91.30 381

UBG		85.51 293	84.57 300	88.35 307	94.21 221	71.78 381	90.07 354	89.66 394	82.28 273	85.91 237	89.01 364	61.30 357	97.06 270	76.58 319	92.06 216	96.22 184

D2MVS		85.90 285	85.09 286	88.35 307	90.79 355	77.42 309	91.83 309	95.70 183	80.77 311	80.08 359	90.02 345	66.74 314	96.37 316	81.88 248	87.97 281	91.26 382

pmmvs5		84.21 320	82.84 330	88.34 309	88.95 390	76.94 315	92.41 288	91.91 340	75.63 372	80.28 354	91.18 306	64.59 333	95.57 353	77.09 314	83.47 325	92.53 350

mamv4		90.92 122	91.78 102	88.33 310	95.67 133	70.75 394	92.92 275	96.02 157	81.90 284	88.11 183	95.34 143	85.88 52	96.97 276	95.22 37	95.01 153	97.26 126

tt0320		80.13 365	77.41 374	88.29 311	90.50 368	78.02 292	93.10 266	90.71 372	66.06 425	76.75 388	86.97 397	49.56 415	95.40 361	71.65 356	71.41 409	91.46 378

LCM-MVSNet-Re		88.30 203	88.32 184	88.27 312	94.71 187	72.41 376	93.15 262	90.98 363	87.77 136	79.25 369	91.96 280	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	91.16 337	72.58 374	89.47 367	91.04 362	76.26 367	86.45 223	89.97 347	70.74 259	96.86 284	82.35 235	87.07 296	95.34 226

ITE_SJBPF				88.24 314	91.88 309	77.05 314		92.92 306	85.54 193	80.13 358	93.30 229	57.29 387	96.20 324	72.46 354	84.71 310	91.49 376

PVSNet	78.82 18	85.55 292	84.65 296	88.23 315	94.72 185	71.93 377	87.12 401	92.75 313	78.80 338	84.95 271	90.53 329	64.43 334	96.71 289	74.74 338	93.86 178	96.06 197

IterMVS-SCA-FT		85.45 294	84.53 301	88.18 316	91.71 316	76.87 316	90.19 351	92.65 316	85.40 196	81.44 339	90.54 328	66.79 312	95.00 370	81.04 262	81.05 358	92.66 347

EPNet_dtu		86.49 276	85.94 261	88.14 317	90.24 372	72.82 366	94.11 207	92.20 328	86.66 165	79.42 368	92.36 261	73.52 223	95.81 344	71.26 359	93.66 181	95.80 209
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 335	80.93 341	88.06 318	90.05 376	76.37 326	84.74 419	91.96 338	72.28 406	81.32 342	87.87 385	71.03 254	95.50 358	68.97 377	80.15 373	92.32 359

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	61.63 352	95.74 348	93.42 58	95.11 152	95.74 211

DTE-MVSNet		86.11 282	85.48 275	87.98 320	91.65 320	74.92 342	94.93 149	95.75 178	87.36 146	82.26 328	93.04 240	72.85 234	95.82 343	74.04 343	77.46 391	93.20 328

PMMVS		85.71 291	84.96 289	87.95 321	88.90 391	77.09 313	88.68 379	90.06 384	72.32 405	86.47 220	90.76 323	72.15 244	94.40 375	81.78 251	93.49 187	92.36 357

GG-mvs-BLEND				87.94 322	89.73 383	77.91 295	87.80 390	78.23 439		80.58 351	83.86 414	59.88 371	95.33 363	71.20 360	92.22 214	90.60 395

MonoMVSNet		86.89 258	86.55 234	87.92 323	89.46 386	73.75 354	94.12 205	93.10 301	87.82 135	85.10 267	90.76 323	69.59 279	94.94 371	86.47 177	82.50 337	95.07 233

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	91.94 281	57.88 385	96.68 290	89.96 132	79.71 379	93.22 326

pmmvs-eth3d		80.97 358	78.72 369	87.74 325	84.99 421	79.97 244	90.11 353	91.65 345	75.36 374	73.51 409	86.03 404	59.45 374	93.96 385	75.17 332	72.21 405	89.29 408

MS-PatchMatch		85.05 305	84.16 305	87.73 326	91.42 326	78.51 279	91.25 325	93.53 292	77.50 354	80.15 356	91.58 295	61.99 349	95.51 356	75.69 327	94.35 171	89.16 410

mmtdpeth		85.04 307	84.15 306	87.72 327	93.11 268	75.74 334	94.37 193	92.83 309	84.98 208	89.31 167	86.41 401	61.61 354	97.14 264	92.63 74	62.11 429	90.29 397

test_0402		81.30 354	79.17 363	87.67 328	93.19 263	78.17 289	92.98 272	91.71 341	75.25 376	76.02 396	90.31 334	59.23 376	96.37 316	50.22 432	83.63 323	88.47 417

IterMVS		84.88 309	83.98 311	87.60 329	91.44 323	76.03 329	90.18 352	92.41 319	83.24 253	81.06 345	90.42 333	66.60 315	94.28 379	79.46 286	80.98 363	92.48 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 352	79.30 359	87.58 330	90.92 350	74.16 352	80.99 431	87.68 408	70.52 413	76.63 390	88.81 368	71.21 251	92.76 401	60.01 419	86.93 297	95.83 207

EPMVS		83.90 327	82.70 331	87.51 331	90.23 373	72.67 369	88.62 380	81.96 429	81.37 301	85.01 270	88.34 376	66.31 319	94.45 373	75.30 331	87.12 294	95.43 221

ADS-MVSNet2		81.66 346	79.71 355	87.50 332	91.35 329	74.19 351	83.33 424	88.48 402	72.90 400	82.24 329	85.77 407	64.98 329	93.20 396	64.57 404	83.74 320	95.12 231

OurMVSNet-221017-0		85.35 298	84.64 298	87.49 333	90.77 357	72.59 373	94.01 219	94.40 260	84.72 218	79.62 367	93.17 234	61.91 350	96.72 287	81.99 245	81.16 354	93.16 330

tpm2		84.08 322	82.94 326	87.48 334	91.39 327	71.27 386	89.23 371	90.37 376	71.95 407	84.64 276	89.33 359	67.30 304	96.55 304	75.17 332	87.09 295	94.63 252

RPSCF		85.07 304	84.27 302	87.48 334	92.91 280	70.62 396	91.69 314	92.46 318	76.20 368	82.67 324	95.22 148	63.94 337	97.29 251	77.51 309	85.80 302	94.53 259

myMVS_eth3d28		85.80 289	85.26 283	87.42 336	94.73 183	69.92 401	90.60 339	90.95 365	87.21 148	86.06 234	90.04 344	59.47 373	96.02 331	74.89 337	93.35 194	96.33 178

WBMVS		84.97 308	84.18 304	87.34 337	94.14 226	71.62 385	90.20 350	92.35 321	81.61 296	84.06 296	90.76 323	61.82 351	96.52 305	78.93 294	83.81 318	93.89 288

miper_lstm_enhance		85.27 301	84.59 299	87.31 338	91.28 332	74.63 345	87.69 395	94.09 276	81.20 307	81.36 341	89.85 351	74.97 199	94.30 378	81.03 264	79.84 378	93.01 336

FMVSNet5		81.52 350	79.60 356	87.27 339	91.17 335	77.95 294	91.49 318	92.26 327	76.87 360	76.16 392	87.91 384	51.67 409	92.34 404	67.74 386	81.16 354	91.52 374

USDC		82.76 334	81.26 339	87.26 340	91.17 335	74.55 346	89.27 369	93.39 295	78.26 349	75.30 400	92.08 274	54.43 402	96.63 293	71.64 357	85.79 303	90.61 393

test-LLR		85.87 286	85.41 276	87.25 341	90.95 346	71.67 383	89.55 363	89.88 390	83.41 247	84.54 279	87.95 382	67.25 305	95.11 367	81.82 249	93.37 192	94.97 236

test-mter		84.54 317	83.64 315	87.25 341	90.95 346	71.67 383	89.55 363	89.88 390	79.17 329	84.54 279	87.95 382	55.56 393	95.11 367	81.82 249	93.37 192	94.97 236

JIA-IIPM		81.04 355	78.98 367	87.25 341	88.64 392	73.48 359	81.75 430	89.61 396	73.19 397	82.05 332	73.71 433	66.07 324	95.87 340	71.18 362	84.60 311	92.41 355

TDRefinement		79.81 369	77.34 375	87.22 344	79.24 436	75.48 337	93.12 263	92.03 333	76.45 363	75.01 401	91.58 295	49.19 416	96.44 312	70.22 370	69.18 415	89.75 402

tpmvs		83.35 333	82.07 332	87.20 345	91.07 341	71.00 392	88.31 384	91.70 342	78.91 332	80.49 353	87.18 394	69.30 286	97.08 267	68.12 385	83.56 324	93.51 315

ppachtmachnet_test		81.84 342	80.07 350	87.15 346	88.46 396	74.43 349	89.04 375	92.16 329	75.33 375	77.75 381	88.99 365	66.20 321	95.37 362	65.12 401	77.60 389	91.65 370

dmvs_re		84.20 321	83.22 322	87.14 347	91.83 312	77.81 300	90.04 355	90.19 380	84.70 219	81.49 337	89.17 361	64.37 335	91.13 415	71.58 358	85.65 304	92.46 353

tpm cat1		81.96 340	80.27 346	87.01 348	91.09 340	71.02 391	87.38 399	91.53 350	66.25 423	80.17 355	86.35 403	68.22 301	96.15 327	69.16 376	82.29 340	93.86 294

test_fmvs1_n		87.03 254	87.04 215	86.97 349	89.74 382	71.86 378	94.55 175	94.43 257	78.47 343	91.95 119	95.50 138	51.16 411	93.81 386	93.02 66	94.56 165	95.26 227

OpenMVS_ROB	74.94 19	79.51 373	77.03 380	86.93 350	87.00 409	76.23 328	92.33 294	90.74 371	68.93 417	74.52 405	88.23 379	49.58 414	96.62 294	57.64 424	84.29 313	87.94 420

SixPastTwentyTwo		83.91 326	82.90 328	86.92 351	90.99 344	70.67 395	93.48 244	91.99 335	85.54 193	77.62 383	92.11 272	60.59 366	96.87 283	76.05 325	77.75 388	93.20 328

ADS-MVSNet		81.56 348	79.78 352	86.90 352	91.35 329	71.82 379	83.33 424	89.16 400	72.90 400	82.24 329	85.77 407	64.98 329	93.76 387	64.57 404	83.74 320	95.12 231

PatchT		82.68 336	81.27 338	86.89 353	90.09 375	70.94 393	84.06 421	90.15 381	74.91 380	85.63 244	83.57 416	69.37 282	94.87 372	65.19 399	88.50 271	94.84 246

tpm		84.73 312	84.02 309	86.87 354	90.33 370	68.90 404	89.06 374	89.94 387	80.85 310	85.75 240	89.86 350	68.54 298	95.97 334	77.76 305	84.05 317	95.75 210

Patchmatch-RL test		81.67 345	79.96 351	86.81 355	85.42 419	71.23 387	82.17 429	87.50 409	78.47 343	77.19 385	82.50 423	70.81 258	93.48 391	82.66 230	72.89 404	95.71 214

test_vis1_n		86.56 271	86.49 238	86.78 356	88.51 393	72.69 368	94.68 168	93.78 288	79.55 325	90.70 143	95.31 144	48.75 417	93.28 394	93.15 62	93.99 175	94.38 270

testing3-2		86.72 265	86.71 224	86.74 357	96.11 109	65.92 416	93.39 249	89.65 395	89.46 69	87.84 192	92.79 249	59.17 378	97.60 215	81.31 258	90.72 232	96.70 166

test_fmvs1		87.34 237	87.56 201	86.68 358	90.59 363	71.80 380	94.01 219	94.04 277	78.30 347	91.97 117	95.22 148	56.28 391	93.71 388	92.89 67	94.71 159	94.52 260

MDA-MVSNet-bldmvs		78.85 378	76.31 383	86.46 359	89.76 381	73.88 353	88.79 377	90.42 375	79.16 330	59.18 433	88.33 377	60.20 368	94.04 381	62.00 412	68.96 416	91.48 377

mvs5depth		80.98 357	79.15 364	86.45 360	84.57 422	73.29 361	87.79 391	91.67 344	80.52 313	82.20 331	89.72 353	55.14 398	95.93 336	73.93 346	66.83 421	90.12 399

tpmrst		85.35 298	84.99 287	86.43 361	90.88 353	67.88 409	88.71 378	91.43 353	80.13 317	86.08 233	88.80 370	73.05 232	96.02 331	82.48 231	83.40 328	95.40 222

TESTMET0.1,1		83.74 329	82.85 329	86.42 362	89.96 378	71.21 388	89.55 363	87.88 405	77.41 355	83.37 315	87.31 390	56.71 389	93.65 390	80.62 272	92.85 204	94.40 269

our_test_3		81.93 341	80.46 344	86.33 363	88.46 396	73.48 359	88.46 382	91.11 358	76.46 362	76.69 389	88.25 378	66.89 310	94.36 376	68.75 378	79.08 384	91.14 385

lessismore_v0				86.04 364	88.46 396	68.78 405		80.59 432		73.01 412	90.11 342	55.39 394	96.43 313	75.06 334	65.06 424	92.90 339

TinyColmap		79.76 370	77.69 373	85.97 365	91.71 316	73.12 362	89.55 363	90.36 377	75.03 378	72.03 415	90.19 338	46.22 426	96.19 326	63.11 408	81.03 359	88.59 416

KD-MVS_2432*1600		78.50 379	76.02 386	85.93 366	86.22 412	74.47 347	84.80 417	92.33 322	79.29 327	76.98 386	85.92 405	53.81 405	93.97 383	67.39 387	57.42 434	89.36 404

miper_refine_blended		78.50 379	76.02 386	85.93 366	86.22 412	74.47 347	84.80 417	92.33 322	79.29 327	76.98 386	85.92 405	53.81 405	93.97 383	67.39 387	57.42 434	89.36 404

K. test v3		81.59 347	80.15 349	85.91 368	89.89 380	69.42 403	92.57 285	87.71 407	85.56 192	73.44 410	89.71 354	55.58 392	95.52 355	77.17 312	69.76 412	92.78 344

SSC-MVS3.2		84.60 316	84.19 303	85.85 369	92.74 284	68.07 406	88.15 386	93.81 286	87.42 145	83.76 304	91.07 312	62.91 344	95.73 349	74.56 341	83.24 329	93.75 304

mvsany_test1		85.42 296	85.30 281	85.77 370	87.95 405	75.41 338	87.61 398	80.97 431	76.82 361	88.68 176	95.83 124	77.44 168	90.82 417	85.90 186	86.51 298	91.08 389

MIMVSNet1		79.38 374	77.28 376	85.69 371	86.35 411	73.67 356	91.61 316	92.75 313	78.11 352	72.64 413	88.12 380	48.16 418	91.97 409	60.32 416	77.49 390	91.43 379

UWE-MVS		83.69 330	83.09 323	85.48 372	93.06 271	65.27 421	90.92 332	86.14 413	79.90 320	86.26 229	90.72 326	57.17 388	95.81 344	71.03 365	92.62 208	95.35 225

UnsupCasMVSNet_eth		80.07 366	78.27 372	85.46 373	85.24 420	72.63 372	88.45 383	94.87 240	82.99 259	71.64 417	88.07 381	56.34 390	91.75 410	73.48 349	63.36 427	92.01 365

CL-MVSNet_self_test		81.74 344	80.53 342	85.36 374	85.96 414	72.45 375	90.25 345	93.07 303	81.24 305	79.85 364	87.29 391	70.93 256	92.52 402	66.95 390	69.23 414	91.11 387

MDA-MVSNet_test_wron		79.21 376	77.19 378	85.29 375	88.22 400	72.77 367	85.87 408	90.06 384	74.34 385	62.62 430	87.56 388	66.14 322	91.99 408	66.90 394	73.01 402	91.10 388

YYNet1		79.22 375	77.20 377	85.28 376	88.20 401	72.66 370	85.87 408	90.05 386	74.33 386	62.70 428	87.61 387	66.09 323	92.03 406	66.94 391	72.97 403	91.15 384

WB-MVSnew		83.77 328	83.28 319	85.26 377	91.48 322	71.03 390	91.89 306	87.98 404	78.91 332	84.78 273	90.22 336	69.11 291	94.02 382	64.70 403	90.44 235	90.71 391

dp		81.47 351	80.23 347	85.17 378	89.92 379	65.49 419	86.74 403	90.10 383	76.30 366	81.10 343	87.12 395	62.81 345	95.92 337	68.13 384	79.88 376	94.09 281

UnsupCasMVSNet_bld		76.23 388	73.27 392	85.09 379	83.79 424	72.92 364	85.65 411	93.47 294	71.52 408	68.84 423	79.08 428	49.77 413	93.21 395	66.81 395	60.52 431	89.13 412

SD_0403		84.71 314	84.65 296	84.92 380	92.95 278	65.95 415	92.07 305	93.23 298	83.82 236	79.03 370	93.73 219	73.90 217	92.91 400	63.02 410	90.05 242	95.89 203

Anonymous20231206		81.03 356	79.77 354	84.82 381	87.85 406	70.26 398	91.42 319	92.08 331	73.67 392	77.75 381	89.25 360	62.43 347	93.08 397	61.50 414	82.00 345	91.12 386

test0.0.03 1		82.41 338	81.69 334	84.59 382	88.23 399	72.89 365	90.24 347	87.83 406	83.41 247	79.86 363	89.78 352	67.25 305	88.99 427	65.18 400	83.42 327	91.90 367

CMPMVS	59.16 21	80.52 360	79.20 362	84.48 383	83.98 423	67.63 412	89.95 358	93.84 285	64.79 427	66.81 425	91.14 309	57.93 384	95.17 365	76.25 322	88.10 277	90.65 392
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 315	84.79 294	84.37 384	91.84 310	64.92 422	93.70 238	91.47 352	66.19 424	86.16 232	95.28 145	67.18 307	93.33 393	80.89 267	90.42 237	94.88 245

PVSNet_0	73.20 20	77.22 384	74.83 390	84.37 384	90.70 361	71.10 389	83.09 426	89.67 393	72.81 402	73.93 408	83.13 418	60.79 365	93.70 389	68.54 379	50.84 439	88.30 418

LF4IMVS		80.37 363	79.07 366	84.27 386	86.64 410	69.87 402	89.39 368	91.05 361	76.38 364	74.97 402	90.00 346	47.85 419	94.25 380	74.55 342	80.82 365	88.69 415

Anonymous20240521		80.44 362	79.21 361	84.11 387	85.75 417	67.89 408	92.86 278	93.23 298	75.61 373	75.59 399	87.47 389	50.03 412	94.33 377	71.14 363	81.21 353	90.12 399

PM-MVS		78.11 381	76.12 385	84.09 388	83.54 425	70.08 399	88.97 376	85.27 420	79.93 319	74.73 404	86.43 400	34.70 437	93.48 391	79.43 289	72.06 406	88.72 414

test_fmvs2		83.98 323	84.03 308	83.83 389	87.16 408	67.53 413	93.93 225	92.89 307	77.62 353	86.89 213	93.53 222	47.18 421	92.02 407	90.54 126	86.51 298	91.93 366

testgi		80.94 359	80.20 348	83.18 390	87.96 404	66.29 414	91.28 323	90.70 373	83.70 238	78.12 377	92.84 244	51.37 410	90.82 417	63.34 407	82.46 338	92.43 354

KD-MVS_self_test		80.20 364	79.24 360	83.07 391	85.64 418	65.29 420	91.01 331	93.93 279	78.71 341	76.32 391	86.40 402	59.20 377	92.93 399	72.59 353	69.35 413	91.00 390

testing3		80.46 361	79.59 357	83.06 392	93.44 258	64.64 423	93.33 251	85.47 418	84.34 225	79.93 362	90.84 319	44.35 429	92.39 403	57.06 426	87.56 287	92.16 363

ambc				83.06 392	79.99 434	63.51 427	77.47 437	92.86 308		74.34 407	84.45 413	28.74 438	95.06 369	73.06 351	68.89 417	90.61 393

test20.03		79.95 368	79.08 365	82.55 394	85.79 416	67.74 411	91.09 329	91.08 359	81.23 306	74.48 406	89.96 348	61.63 352	90.15 419	60.08 417	76.38 396	89.76 401

MVStest1		72.91 392	69.70 397	82.54 395	78.14 437	73.05 363	88.21 385	86.21 412	60.69 431	64.70 426	90.53 329	46.44 424	85.70 434	58.78 422	53.62 436	88.87 413

test_vis1_rt		77.96 382	76.46 382	82.48 396	85.89 415	71.74 382	90.25 345	78.89 435	71.03 412	71.30 418	81.35 425	42.49 431	91.05 416	84.55 203	82.37 339	84.65 423

EU-MVSNet		81.32 353	80.95 340	82.42 397	88.50 395	63.67 426	93.32 252	91.33 354	64.02 428	80.57 352	92.83 245	61.21 361	92.27 405	76.34 321	80.38 372	91.32 380

myMVS_eth3d		79.67 371	78.79 368	82.32 398	91.92 306	64.08 424	89.75 361	87.40 410	81.72 291	78.82 372	87.20 392	45.33 427	91.29 413	59.09 421	87.84 284	91.60 372

ttmdpeth		76.55 386	74.64 391	82.29 399	82.25 430	67.81 410	89.76 360	85.69 416	70.35 414	75.76 397	91.69 288	46.88 422	89.77 421	66.16 396	63.23 428	89.30 406

pmmvs3		71.81 395	68.71 398	81.11 400	75.86 439	70.42 397	86.74 403	83.66 424	58.95 434	68.64 424	80.89 426	36.93 435	89.52 423	63.10 409	63.59 426	83.39 424

Syy-MVS		80.07 366	79.78 352	80.94 401	91.92 306	59.93 433	89.75 361	87.40 410	81.72 291	78.82 372	87.20 392	66.29 320	91.29 413	47.06 434	87.84 284	91.60 372

UWE-MVS-28		78.98 377	78.38 371	80.80 402	88.18 402	60.66 432	90.65 337	78.51 436	78.84 336	77.93 380	90.93 316	59.08 379	89.02 426	50.96 431	90.33 239	92.72 345

new-patchmatchnet		76.41 387	75.17 389	80.13 403	82.65 429	59.61 434	87.66 396	91.08 359	78.23 350	69.85 421	83.22 417	54.76 399	91.63 412	64.14 406	64.89 425	89.16 410

mvsany_test3		74.95 389	73.26 393	80.02 404	74.61 440	63.16 428	85.53 412	78.42 437	74.16 387	74.89 403	86.46 399	36.02 436	89.09 425	82.39 234	66.91 420	87.82 421

test_fmvs3		77.67 383	77.16 379	79.22 405	79.52 435	61.14 430	92.34 293	91.64 346	73.98 389	78.86 371	86.59 398	27.38 441	87.03 429	88.12 154	75.97 398	89.50 403

DSMNet-mixed		76.94 385	76.29 384	78.89 406	83.10 427	56.11 442	87.78 392	79.77 433	60.65 432	75.64 398	88.71 371	61.56 355	88.34 428	60.07 418	89.29 260	92.21 362

EGC-MVSNET		61.97 403	56.37 408	78.77 407	89.63 384	73.50 358	89.12 373	82.79 426	0.21 453	1.24 454	84.80 411	39.48 432	90.04 420	44.13 436	75.94 399	72.79 435

new_pmnet		72.15 393	70.13 396	78.20 408	82.95 428	65.68 417	83.91 422	82.40 428	62.94 430	64.47 427	79.82 427	42.85 430	86.26 433	57.41 425	74.44 401	82.65 428

MVS-HIRNet		73.70 391	72.20 394	78.18 409	91.81 313	56.42 441	82.94 427	82.58 427	55.24 435	68.88 422	66.48 438	55.32 396	95.13 366	58.12 423	88.42 273	83.01 426

LCM-MVSNet		66.00 400	62.16 405	77.51 410	64.51 450	58.29 436	83.87 423	90.90 367	48.17 439	54.69 436	73.31 434	16.83 450	86.75 430	65.47 398	61.67 430	87.48 422

APD_test1		69.04 396	66.26 402	77.36 411	80.51 433	62.79 429	85.46 413	83.51 425	54.11 437	59.14 434	84.79 412	23.40 444	89.61 422	55.22 427	70.24 411	79.68 432

test_f		71.95 394	70.87 395	75.21 412	74.21 442	59.37 435	85.07 416	85.82 415	65.25 426	70.42 420	83.13 418	23.62 442	82.93 440	78.32 299	71.94 407	83.33 425

ANet_high		58.88 407	54.22 412	72.86 413	56.50 453	56.67 438	80.75 432	86.00 414	73.09 399	37.39 445	64.63 441	22.17 445	79.49 443	43.51 437	23.96 447	82.43 429

test_vis3_rt		65.12 401	62.60 403	72.69 414	71.44 443	60.71 431	87.17 400	65.55 447	63.80 429	53.22 437	65.65 440	14.54 451	89.44 424	76.65 316	65.38 423	67.91 438

FPMVS		64.63 402	62.55 404	70.88 415	70.80 444	56.71 437	84.42 420	84.42 422	51.78 438	49.57 438	81.61 424	23.49 443	81.48 441	40.61 441	76.25 397	74.46 434

dmvs_testset		74.57 390	75.81 388	70.86 416	87.72 407	40.47 451	87.05 402	77.90 441	82.75 264	71.15 419	85.47 409	67.98 302	84.12 438	45.26 435	76.98 395	88.00 419

N_pmnet		68.89 397	68.44 399	70.23 417	89.07 389	28.79 456	88.06 387	19.50 456	69.47 416	71.86 416	84.93 410	61.24 360	91.75 410	54.70 428	77.15 392	90.15 398

testf1		59.54 405	56.11 409	69.85 418	69.28 445	56.61 439	80.37 433	76.55 444	42.58 442	45.68 441	75.61 429	11.26 452	84.18 436	43.20 438	60.44 432	68.75 436

APD_test2		59.54 405	56.11 409	69.85 418	69.28 445	56.61 439	80.37 433	76.55 444	42.58 442	45.68 441	75.61 429	11.26 452	84.18 436	43.20 438	60.44 432	68.75 436

WB-MVS		67.92 398	67.49 400	69.21 420	81.09 431	41.17 450	88.03 388	78.00 440	73.50 394	62.63 429	83.11 420	63.94 337	86.52 431	25.66 446	51.45 438	79.94 431

PMMVS2		59.60 404	56.40 407	69.21 420	68.83 447	46.58 446	73.02 441	77.48 442	55.07 436	49.21 439	72.95 435	17.43 449	80.04 442	49.32 433	44.33 442	80.99 430

SSC-MVS		67.06 399	66.56 401	68.56 422	80.54 432	40.06 452	87.77 393	77.37 443	72.38 404	61.75 431	82.66 422	63.37 340	86.45 432	24.48 447	48.69 441	79.16 433

Gipuma		57.99 409	54.91 411	67.24 423	88.51 393	65.59 418	52.21 444	90.33 378	43.58 441	42.84 444	51.18 445	20.29 447	85.07 435	34.77 442	70.45 410	51.05 444
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 411	48.46 415	63.48 424	45.72 455	46.20 447	73.41 440	78.31 438	41.03 444	30.06 447	65.68 439	6.05 454	83.43 439	30.04 444	65.86 422	60.80 439
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 408	58.24 406	60.56 425	83.13 426	45.09 449	82.32 428	48.22 455	67.61 420	61.70 432	69.15 436	38.75 433	76.05 444	32.01 443	41.31 443	60.55 440

MVE	39.65 23	43.39 413	38.59 419	57.77 426	56.52 452	48.77 445	55.38 443	58.64 451	29.33 447	28.96 448	52.65 444	4.68 455	64.62 448	28.11 445	33.07 445	59.93 441
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 412	48.47 414	56.66 427	52.26 454	18.98 458	41.51 446	81.40 430	10.10 448	44.59 443	75.01 432	28.51 439	68.16 445	53.54 429	49.31 440	82.83 427

DeepMVS_CX				56.31 428	74.23 441	51.81 444		56.67 452	44.85 440	48.54 440	75.16 431	27.87 440	58.74 450	40.92 440	52.22 437	58.39 442

kuosan		53.51 410	53.30 413	54.13 429	76.06 438	45.36 448	80.11 435	48.36 454	59.63 433	54.84 435	63.43 442	37.41 434	62.07 449	20.73 449	39.10 444	54.96 443

E-PMN		43.23 414	42.29 416	46.03 430	65.58 449	37.41 453	73.51 439	64.62 448	33.99 445	28.47 449	47.87 446	19.90 448	67.91 446	22.23 448	24.45 446	32.77 445

EMVS		42.07 415	41.12 417	44.92 431	63.45 451	35.56 455	73.65 438	63.48 449	33.05 446	26.88 450	45.45 447	21.27 446	67.14 447	19.80 450	23.02 448	32.06 446

tmp_tt		35.64 416	39.24 418	24.84 432	14.87 456	23.90 457	62.71 442	51.51 453	6.58 450	36.66 446	62.08 443	44.37 428	30.34 452	52.40 430	22.00 449	20.27 447

wuyk23d		21.27 418	20.48 421	23.63 433	68.59 448	36.41 454	49.57 445	6.85 457	9.37 449	7.89 451	4.46 453	4.03 456	31.37 451	17.47 451	16.07 450	3.12 448

test123		8.76 420	11.22 423	1.39 434	0.85 458	0.97 459	85.76 410	0.35 459	0.54 452	2.45 453	8.14 452	0.60 457	0.48 453	2.16 453	0.17 452	2.71 449

testmvs		8.92 419	11.52 422	1.12 435	1.06 457	0.46 460	86.02 407	0.65 458	0.62 451	2.74 452	9.52 451	0.31 458	0.45 454	2.38 452	0.39 451	2.46 450

mmdepth		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

monomultidepth		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

test_blank		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

uanet_test		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

DCPMVS		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

cdsmvs_eth3d_5k		22.14 417	29.52 420	0.00 436	0.00 459	0.00 461	0.00 447	95.76 177	0.00 454	0.00 455	94.29 190	75.66 191	0.00 455	0.00 454	0.00 453	0.00 451

pcd_1.5k_mvsjas		6.64 422	8.86 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	79.70 138	0.00 455	0.00 454	0.00 453	0.00 451

sosnet-low-res		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

sosnet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

uncertanet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

Regformer		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

ab-mvs-re		7.82 421	10.43 424	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	93.88 211	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

uanet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	0.00 454	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

WAC-MVS						64.08 424								59.14 420

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

PC_three_1452									82.47 268	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					0.00 459

eth-test					0.00 459

ZD-MVS					98.15 36	86.62 33		97.07 54	83.63 240	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	81.26 304	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												71.70 247				96.12 191

sam_mvs												70.60 261

MTGPA								96.97 59

test_post1							88.00 389				9.81 450	69.31 285	95.53 354	76.65 316

test_post											10.29 449	70.57 265	95.91 339

patchmatchnet-post											83.76 415	71.53 248	96.48 308

MTMP							96.16 55	60.64 450

gm-plane-assit					89.60 385	68.00 407			77.28 358		88.99 365		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	81.61 296	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	81.86 287	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		71.25 410	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	73.95 390				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	68.36 419	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	89.99 243	94.63 252

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						89.88 248

n2								0.00 460

nn								0.00 460

door-mid								85.49 417

test11								96.57 104

door								85.33 419

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			94.51 262

HQP3-MVS								96.04 154							89.77 252

HQP2-MVS												73.83 220

NP-MVS					94.37 212	82.42 168					93.98 204

MDTV_nov1_ep13_2view						55.91 443	87.62 397		73.32 396	84.59 278		70.33 268		74.65 339		95.50 219

MDTV_nov1_ep13			83.56 316		91.69 318	69.93 400	87.75 394	91.54 349	78.60 342	84.86 272	88.90 367	69.54 280	96.03 330	70.25 368	88.93 265

ACMMP++_ref															87.47 288

ACMMP++															88.01 280

Test By Simon												80.02 133

High-resolution multi-view benchmark