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	94.37 263	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	84.02 300	96.62 88	75.95 186	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	94.91 175	87.62 14	95.97 76	93.01 307	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	92.66 284	96.56 105	83.44 248	91.68 129	95.04 159	86.60 43	98.99 76	85.60 190	97.92 79	96.93 156

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	94.60 181	82.33 104	98.62 124	92.40 79	92.86 203	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	94.60 181	82.33 104	98.62 124	92.40 79	92.86 203	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		88.31 204	86.13 252	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 31	92.85 88	23.41 450	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	94.87 165	80.56 127	98.66 117	92.42 78	93.10 199	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	85.15 206	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	94.07 214	96.78 83	81.86 289	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	94.06 216	96.66 97	80.09 320	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	94.59 195	86.37 41	97.18 13	97.02 56	89.20 81	84.31 295	96.66 83	73.74 224	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	93.93 281	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	77.97 161	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	94.43 259	91.65 15	92.68 98	96.13 107	77.97 161	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	89.56 162	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	94.66 178	82.11 111	98.50 132	92.33 84	92.82 206	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	90.10 376	85.25 75	96.03 71	92.05 334	92.83 5	87.39 206	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	94.62 193	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	93.06 270	97.13 49	90.74 28	91.84 123	95.09 158	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	82.69 268	90.03 158	95.82 125	82.30 106	99.03 64	84.57 204	96.48 121	96.91 158

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	92.46 292	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	96.91 283	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	93.24 262	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	97.16 137

CSCG		93.23 79	93.05 80	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 229	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	94.55 201	84.69 86	95.60 110	96.56 105	87.83 134	93.07 84	95.89 120	73.44 228	98.65 119	90.22 131	96.03 130	97.91 92

BP-MVS1		92.48 95	92.07 98	93.72 75	94.50 204	84.39 101	95.90 82	94.30 266	90.39 35	92.67 100	95.94 117	74.46 207	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	89.25 389	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	84.47 206	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	97.07 142

QAPM		89.51 166	88.15 190	93.59 79	94.92 173	84.58 88	96.82 30	96.70 95	78.43 347	83.41 316	96.19 104	73.18 233	99.30 44	77.11 315	96.54 118	96.89 159

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	97.17 133

fmvsm_s_conf0.5_n_9		94.99 13	95.50 7	93.44 81	96.51 95	82.25 174	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	94.72 186	83.36 133	95.45 113	96.37 119	90.33 37	92.17 111	96.03 112	72.32 245	98.75 109	87.94 156	96.34 123	98.07 77

casdiffmvs_mvg		92.96 87	92.83 85	93.35 83	94.59 195	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	94.79 182	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	97.25 128

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	75.09 198	98.31 159	84.75 201	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	77.85 167	98.17 166	88.90 144	93.38 192	98.13 72

VDD-MVS		90.74 127	89.92 141	93.20 90	96.27 100	83.02 150	95.73 96	93.86 285	88.42 111	92.53 103	96.84 74	62.09 350	98.64 121	90.95 119	92.62 210	97.93 89

Elysia		90.12 145	89.10 161	93.18 91	93.16 266	84.05 110	95.22 129	96.27 128	85.16 204	90.59 145	94.68 174	64.64 333	98.37 149	86.38 179	95.77 133	97.12 139

StellarMVS		90.12 145	89.10 161	93.18 91	93.16 266	84.05 110	95.22 129	96.27 128	85.16 204	90.59 145	94.68 174	64.64 333	98.37 149	86.38 179	95.77 133	97.12 139

CS-MVS		94.12 45	94.44 31	93.17 93	96.55 90	83.08 147	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	93.07 272	86.91 22	96.41 38	96.26 132	88.30 114	88.37 184	94.85 168	82.19 110	97.64 214	91.09 114	82.95 332	94.96 241

MVSMamba_PlusPlus		93.44 69	93.54 70	93.14 95	96.58 89	83.05 148	96.06 68	96.50 110	84.42 226	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	94.86 178	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	93.63 183	97.17 133

test_fmvsmvis_n_1920		93.44 69	93.55 69	93.10 97	93.67 252	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	96.32 181

新几何1				93.10 97	97.30 71	84.35 103		95.56 193	71.09 413	91.26 137	96.24 99	82.87 97	98.86 95	79.19 294	98.10 70	96.07 197

OMC-MVS		91.23 116	90.62 124	93.08 99	96.27 100	84.07 108	93.52 244	95.93 162	86.95 155	89.51 163	96.13 107	78.50 155	98.35 153	85.84 188	92.90 202	96.83 163

OpenMVS	83.78 11	88.74 192	87.29 210	93.08 99	92.70 287	85.39 73	96.57 36	96.43 113	78.74 342	80.85 348	96.07 110	69.64 280	99.01 69	78.01 306	96.65 116	94.83 249

MAR-MVS		90.30 141	89.37 154	93.07 101	96.61 86	84.48 94	95.68 99	95.67 185	82.36 273	87.85 193	92.85 245	76.63 178	98.80 104	80.01 282	96.68 115	95.91 203
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	93.86 240	83.88 115	92.81 281	93.86 285	79.84 323	91.76 126	94.29 192	77.92 164	98.04 184	90.48 129	97.11 100	97.17 133

Effi-MVS+		91.59 111	91.11 113	93.01 103	94.35 217	83.39 132	94.60 172	95.10 225	87.10 151	90.57 147	93.10 240	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	96.88 160

MVS_111021_LR		92.47 96	92.29 96	92.98 105	95.99 119	84.43 98	93.08 268	96.09 149	88.20 119	91.12 139	95.72 131	81.33 123	97.76 203	91.74 105	97.37 96	96.75 165

fmvsm_s_conf0.1_n_a		93.19 80	93.26 74	92.97 106	92.49 290	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	96.86 161

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	91.19 306	84.06 78	98.34 154	91.72 106	96.54 118	96.54 176

LFMVS		90.08 148	89.13 160	92.95 108	96.71 82	82.32 173	96.08 64	89.91 390	86.79 159	92.15 113	96.81 77	62.60 348	98.34 154	87.18 167	93.90 178	98.19 67

UGNet		89.95 154	88.95 166	92.95 108	94.51 203	83.31 134	95.70 98	95.23 218	89.37 73	87.58 200	93.94 208	64.00 338	98.78 107	83.92 213	96.31 124	96.74 166
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	93.04 275	83.53 127	93.08 268	94.15 274	80.22 317	91.41 134	94.91 162	76.87 172	97.93 195	90.28 130	96.90 107	97.24 129
jason: jason.
DP-MVS		87.25 244	85.36 281	92.90 110	97.65 60	83.24 136	94.81 159	92.00 336	74.99 381	81.92 337	95.00 160	72.66 238	99.05 61	66.92 395	92.33 215	96.40 178

fmvsm_s_conf0.5_n_8		94.56 25	95.12 13	92.87 112	95.96 122	81.32 197	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	96.92 157

fmvsm_s_conf0.1_n		93.46 66	93.66 67	92.85 114	93.75 247	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	96.98 151

CANet_DTU		90.26 143	89.41 153	92.81 115	93.46 259	83.01 151	93.48 245	94.47 258	89.43 71	87.76 198	94.23 197	70.54 268	99.03 64	84.97 196	96.39 122	96.38 179

MVSFormer		91.68 110	91.30 108	92.80 116	93.86 240	83.88 115	95.96 77	95.90 166	84.66 222	91.76 126	94.91 162	77.92 164	97.30 250	89.64 135	97.11 100	97.24 129

PVSNet_Blended_VisFu		91.38 113	90.91 118	92.80 116	96.39 97	83.17 139	94.87 153	96.66 97	83.29 253	89.27 169	94.46 187	80.29 130	99.17 51	87.57 161	95.37 146	96.05 200

LuminaMVS		90.55 138	89.81 143	92.77 118	92.78 285	84.21 105	94.09 212	94.17 273	85.82 183	91.54 131	94.14 199	69.93 274	97.92 196	91.62 108	94.21 173	96.18 189

fmvsm_s_conf0.5_n_6		94.11 46	94.56 27	92.76 119	94.98 168	81.96 181	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		89.56 165	88.49 181	92.76 119	95.07 162	82.09 176	96.30 42	93.19 302	81.05 311	91.88 121	96.86 73	61.16 366	98.33 156	88.43 150	92.49 214	97.84 97

h-mvs33		90.80 125	90.15 132	92.75 121	96.01 115	82.66 164	95.43 114	95.53 197	89.80 57	93.08 82	95.64 133	75.77 187	99.00 74	92.07 92	78.05 389	96.60 171

casdiffmvs		92.51 94	92.43 93	92.74 122	94.41 212	81.98 179	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		90.69 130	90.02 139	92.71 123	95.72 129	82.41 171	94.11 208	95.12 223	85.63 190	91.49 132	94.70 172	74.75 202	98.42 147	86.13 183	92.53 212	97.31 123

DCV-MVSNet		90.69 130	90.02 139	92.71 123	95.72 129	82.41 171	94.11 208	95.12 223	85.63 190	91.49 132	94.70 172	74.75 202	98.42 147	86.13 183	92.53 212	97.31 123

PCF-MVS	84.11 10	87.74 219	86.08 256	92.70 125	94.02 230	84.43 98	89.27 371	95.87 170	73.62 395	84.43 287	94.33 189	78.48 157	98.86 95	70.27 369	94.45 169	94.81 250
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mamba_0404		90.73 128	90.08 134	92.69 126	95.00 167	83.13 141	94.32 196	95.00 232	85.41 197	89.84 159	95.35 143	76.13 181	97.98 190	85.46 193	94.18 174	96.95 153

baseline		92.39 98	92.29 96	92.69 126	94.46 207	81.77 184	94.14 205	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	92.65 128	97.31 70	83.43 129	95.79 90	97.33 28	90.03 47	93.58 72	96.96 69	84.87 69	97.76 203	92.19 88	98.66 41	96.76 164

EC-MVSNet		93.44 69	93.71 65	92.63 129	95.21 155	82.43 168	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		89.41 171	88.35 183	92.60 130	95.15 160	82.65 165	92.20 301	95.60 192	83.97 233	88.55 180	93.70 222	74.16 215	98.21 165	82.46 235	89.37 259	96.94 155

LS3D		87.89 214	86.32 245	92.59 131	96.07 113	82.92 154	95.23 127	94.92 238	75.66 373	82.89 323	95.98 115	72.48 242	99.21 49	68.43 383	95.23 151	95.64 217

Anonymous20240529		88.09 210	86.59 234	92.58 132	96.53 92	81.92 182	95.99 74	95.84 172	74.11 390	89.06 173	95.21 152	61.44 358	98.81 103	83.67 218	87.47 290	97.01 149

fmvsm_s_conf0.5_n_3		94.49 27	95.13 12	92.56 133	95.49 143	81.10 207	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	92.55 134	98.24 33	81.98 179	96.76 31	96.49 111	81.89 288	90.24 151	96.44 95	78.59 153	98.61 126	89.68 134	97.85 82	97.06 143

114514_t		89.51 166	88.50 179	92.54 135	98.11 38	81.99 178	95.16 137	96.36 120	70.19 417	85.81 240	95.25 148	76.70 176	98.63 123	82.07 245	96.86 110	97.00 150

PAPM_NR		91.22 117	90.78 122	92.52 136	97.60 61	81.46 193	94.37 193	96.24 135	86.39 171	87.41 203	94.80 170	82.06 114	98.48 134	82.80 230	95.37 146	97.61 111

DeepPCF-MVS	89.96 1	94.20 41	94.77 25	92.49 137	96.52 93	80.00 243	94.00 222	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	92.46 138	95.87 125	81.38 196	96.95 20	93.69 293	89.72 63	89.50 165	95.98 115	78.57 154	97.77 202	83.02 224	96.50 120	98.22 66

API-MVS		90.66 133	90.07 135	92.45 139	96.36 98	84.57 89	96.06 68	95.22 220	82.39 271	89.13 170	94.27 195	80.32 129	98.46 138	80.16 281	96.71 114	94.33 273

xiu_mvs_v1_base_debu		90.64 134	90.05 136	92.40 140	93.97 236	84.46 95	93.32 253	95.46 201	85.17 201	92.25 108	94.03 200	70.59 264	98.57 129	90.97 116	94.67 160	94.18 276

xiu_mvs_v1_base		90.64 134	90.05 136	92.40 140	93.97 236	84.46 95	93.32 253	95.46 201	85.17 201	92.25 108	94.03 200	70.59 264	98.57 129	90.97 116	94.67 160	94.18 276

xiu_mvs_v1_base_debi		90.64 134	90.05 136	92.40 140	93.97 236	84.46 95	93.32 253	95.46 201	85.17 201	92.25 108	94.03 200	70.59 264	98.57 129	90.97 116	94.67 160	94.18 276

fmvsm_s_conf0.5_n_2		93.47 65	93.83 56	92.39 143	95.36 146	81.19 203	95.20 134	96.56 105	90.37 36	97.13 14	98.03 26	77.47 168	98.96 83	97.79 5	96.58 117	97.03 146

fmvsm_s_conf0.1_n_2		93.16 82	93.42 71	92.37 144	94.62 193	81.13 205	95.23 127	95.89 168	90.30 40	96.74 24	98.02 27	76.14 180	98.95 85	97.64 6	96.21 126	97.03 146

AdaColmap		89.89 157	89.07 163	92.37 144	97.41 67	83.03 149	94.42 186	95.92 163	82.81 265	86.34 229	94.65 179	73.89 220	99.02 67	80.69 272	95.51 139	95.05 236

CNLPA		89.07 182	87.98 194	92.34 146	96.87 79	84.78 84	94.08 213	93.24 299	81.41 302	84.46 285	95.13 157	75.57 194	96.62 296	77.21 313	93.84 180	95.61 220

fmvsm_s_conf0.5_n_4		93.86 55	94.37 34	92.33 147	95.13 161	80.95 212	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	97.13 138

ET-MVSNet_ETH3D		87.51 232	85.91 264	92.32 148	93.70 251	83.93 113	92.33 296	90.94 368	84.16 228	72.09 416	92.52 258	69.90 275	95.85 343	89.20 140	88.36 277	97.17 133

Anonymous202405211		87.68 220	86.13 252	92.31 149	96.66 84	80.74 219	94.87 153	91.49 353	80.47 316	89.46 166	95.44 139	54.72 402	98.23 162	82.19 241	89.89 249	97.97 85

CHOSEN 1792x2688		88.84 188	87.69 200	92.30 150	96.14 104	81.42 195	90.01 358	95.86 171	74.52 386	87.41 203	93.94 208	75.46 195	98.36 151	80.36 277	95.53 138	97.12 139

HY-MVS	83.01 12	89.03 184	87.94 196	92.29 151	94.86 178	82.77 156	92.08 306	94.49 257	81.52 301	86.93 210	92.79 251	78.32 159	98.23 162	79.93 283	90.55 236	95.88 206

CDS-MVSNet		89.45 169	88.51 178	92.29 151	93.62 254	83.61 126	93.01 271	94.68 254	81.95 283	87.82 196	93.24 234	78.69 151	96.99 277	80.34 278	93.23 197	96.28 184
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 150	89.27 159	92.29 151	95.78 127	80.95 212	92.68 283	96.22 137	81.91 285	86.66 220	93.75 220	82.23 108	98.44 144	79.40 293	94.79 157	97.48 118

mvsmamba		90.33 140	89.69 145	92.25 154	95.17 157	81.64 186	95.27 125	93.36 298	84.88 213	89.51 163	94.27 195	69.29 289	97.42 236	89.34 138	96.12 129	97.68 107

PLC	84.53 7	89.06 183	88.03 192	92.15 155	97.27 73	82.69 163	94.29 197	95.44 206	79.71 325	84.01 301	94.18 198	76.68 177	98.75 109	77.28 312	93.41 191	95.02 237
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	92.13 156	95.88 123	80.50 226	97.33 8	95.25 217	86.15 177	89.76 161	95.60 134	83.42 86	98.32 158	87.37 165	93.25 196	97.56 115

patch_mono-2		93.74 59	94.32 35	92.01 157	97.54 62	78.37 285	93.40 249	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				92.01 157	97.34 69	81.05 208		96.81 81	78.89 336	90.45 148	95.92 118	82.65 99	98.84 99	80.68 273	98.26 59	96.14 191

UniMVSNet (Re)		89.80 159	89.07 163	92.01 157	93.60 255	84.52 92	94.78 161	97.47 13	89.26 79	86.44 226	92.32 264	82.10 112	97.39 247	84.81 200	80.84 366	94.12 280

MG-MVS		91.77 106	91.70 104	92.00 160	97.08 76	80.03 241	93.60 242	95.18 221	87.85 133	90.89 142	96.47 94	82.06 114	98.36 151	85.07 195	97.04 103	97.62 110

EIA-MVS		91.95 102	91.94 99	91.98 161	95.16 158	80.01 242	95.36 115	96.73 91	88.44 109	89.34 167	92.16 269	83.82 82	98.45 142	89.35 137	97.06 102	97.48 118

PVSNet_Blended		90.73 128	90.32 127	91.98 161	96.12 106	81.25 199	92.55 288	96.83 77	82.04 281	89.10 171	92.56 257	81.04 125	98.85 97	86.72 175	95.91 131	95.84 208

guyue		91.12 120	90.84 120	91.96 163	94.59 195	80.57 224	94.87 153	93.71 292	88.96 92	91.14 138	95.22 149	73.22 232	97.76 203	92.01 96	93.81 181	97.54 117

PS-MVSNAJ		91.18 118	90.92 117	91.96 163	95.26 153	82.60 167	92.09 305	95.70 183	86.27 173	91.84 123	92.46 259	79.70 138	98.99 76	89.08 141	95.86 132	94.29 274

TAMVS		89.21 177	88.29 187	91.96 163	93.71 249	82.62 166	93.30 257	94.19 271	82.22 276	87.78 197	93.94 208	78.83 148	96.95 280	77.70 308	92.98 201	96.32 181

SDMVSNet		90.19 144	89.61 148	91.93 166	96.00 116	83.09 146	92.89 278	95.98 158	88.73 99	86.85 216	95.20 153	72.09 247	97.08 269	88.90 144	89.85 251	95.63 218

FA-MVS(test-final)		89.66 161	88.91 168	91.93 166	94.57 199	80.27 230	91.36 322	94.74 251	84.87 214	89.82 160	92.61 256	74.72 205	98.47 137	83.97 212	93.53 186	97.04 145

MVS_Test		91.31 115	91.11 113	91.93 166	94.37 213	80.14 234	93.46 247	95.80 174	86.46 169	91.35 136	93.77 218	82.21 109	98.09 179	87.57 161	94.95 154	97.55 116

NR-MVSNet		88.58 198	87.47 206	91.93 166	93.04 275	84.16 107	94.77 162	96.25 134	89.05 86	80.04 362	93.29 232	79.02 147	97.05 274	81.71 256	80.05 376	94.59 257

HyFIR lowres test		88.09 210	86.81 222	91.93 166	96.00 116	80.63 221	90.01 358	95.79 175	73.42 397	87.68 199	92.10 275	73.86 221	97.96 191	80.75 271	91.70 219	97.19 132

GeoE		90.05 149	89.43 152	91.90 171	95.16 158	80.37 229	95.80 89	94.65 255	83.90 234	87.55 202	94.75 171	78.18 160	97.62 216	81.28 261	93.63 183	97.71 106

thisisatest0530		88.67 193	87.61 202	91.86 172	94.87 177	80.07 237	94.63 171	89.90 391	84.00 232	88.46 182	93.78 217	66.88 313	98.46 138	83.30 220	92.65 208	97.06 143

xiu_mvs_v2_base		91.13 119	90.89 119	91.86 172	94.97 169	82.42 169	92.24 299	95.64 190	86.11 181	91.74 128	93.14 238	79.67 141	98.89 91	89.06 142	95.46 143	94.28 275

DU-MVS		89.34 176	88.50 179	91.85 174	93.04 275	83.72 119	94.47 182	96.59 102	89.50 68	86.46 223	93.29 232	77.25 170	97.23 259	84.92 197	81.02 362	94.59 257

AstraMVS		90.69 130	90.30 128	91.84 175	93.81 243	79.85 248	94.76 163	92.39 322	88.96 92	91.01 141	95.87 122	70.69 262	97.94 194	92.49 75	92.70 207	97.73 104

OPM-MVS		90.12 145	89.56 149	91.82 176	93.14 268	83.90 114	94.16 204	95.74 179	88.96 92	87.86 192	95.43 141	72.48 242	97.91 197	88.10 155	90.18 243	93.65 311
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 137	90.19 130	91.82 176	94.70 189	82.73 160	95.85 86	96.22 137	90.81 24	86.91 212	94.86 166	74.23 211	98.12 169	88.15 151	89.99 245	94.63 254

UniMVSNet_NR-MVSNet		89.92 156	89.29 157	91.81 178	93.39 261	83.72 119	94.43 185	97.12 50	89.80 57	86.46 223	93.32 229	83.16 90	97.23 259	84.92 197	81.02 362	94.49 267

diffmvs		91.37 114	91.23 111	91.77 179	93.09 271	80.27 230	92.36 293	95.52 198	87.03 153	91.40 135	94.93 161	80.08 132	97.44 234	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		88.42 199	87.33 209	91.72 180	94.92 173	80.98 210	92.97 275	94.54 256	78.16 353	83.82 304	93.88 213	78.78 150	97.91 197	79.45 289	89.41 258	96.26 185

Fast-Effi-MVS+		89.41 171	88.64 174	91.71 181	94.74 183	80.81 217	93.54 243	95.10 225	83.11 257	86.82 218	90.67 329	79.74 137	97.75 207	80.51 276	93.55 185	96.57 174

WTY-MVS		89.60 163	88.92 167	91.67 182	95.47 144	81.15 204	92.38 292	94.78 249	83.11 257	89.06 173	94.32 190	78.67 152	96.61 299	81.57 257	90.89 232	97.24 129

TAPA-MVS	84.62 6	88.16 208	87.01 218	91.62 183	96.64 85	80.65 220	94.39 189	96.21 140	76.38 366	86.19 233	95.44 139	79.75 136	98.08 181	62.75 413	95.29 148	96.13 192
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 162	88.96 165	91.60 184	93.86 240	82.89 155	95.46 112	97.33 28	87.91 128	88.43 183	93.31 230	74.17 214	97.40 244	87.32 166	82.86 337	94.52 262

FE-MVS		87.40 237	86.02 258	91.57 185	94.56 200	79.69 252	90.27 345	93.72 291	80.57 314	88.80 176	91.62 295	65.32 328	98.59 128	74.97 338	94.33 172	96.44 177

XVG-OURS		89.40 173	88.70 173	91.52 186	94.06 228	81.46 193	91.27 326	96.07 151	86.14 178	88.89 175	95.77 128	68.73 298	97.26 256	87.39 164	89.96 247	95.83 209

hse-mvs2		89.88 158	89.34 155	91.51 187	94.83 180	81.12 206	93.94 225	93.91 284	89.80 57	93.08 82	93.60 223	75.77 187	97.66 211	92.07 92	77.07 396	95.74 213

TranMVSNet+NR-MVSNet		88.84 188	87.95 195	91.49 188	92.68 288	83.01 151	94.92 150	96.31 123	89.88 51	85.53 249	93.85 215	76.63 178	96.96 279	81.91 249	79.87 379	94.50 265

AUN-MVS		87.78 218	86.54 237	91.48 189	94.82 181	81.05 208	93.91 229	93.93 281	83.00 260	86.93 210	93.53 224	69.50 283	97.67 209	86.14 181	77.12 395	95.73 215

XVG-OURS-SEG-HR		89.95 154	89.45 150	91.47 190	94.00 234	81.21 202	91.87 310	96.06 153	85.78 185	88.55 180	95.73 130	74.67 206	97.27 254	88.71 147	89.64 256	95.91 203

MVS		87.44 235	86.10 255	91.44 191	92.61 289	83.62 124	92.63 285	95.66 187	67.26 423	81.47 340	92.15 270	77.95 163	98.22 164	79.71 285	95.48 141	92.47 354

F-COLMAP		87.95 213	86.80 223	91.40 192	96.35 99	80.88 215	94.73 165	95.45 204	79.65 326	82.04 335	94.61 180	71.13 254	98.50 132	76.24 325	91.05 230	94.80 251

dcpmvs_2		93.49 64	94.19 46	91.38 193	97.69 59	76.78 320	94.25 199	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		87.33 240	85.99 259	91.37 194	93.49 257	79.55 253	90.63 340	89.56 399	80.17 318	87.56 201	90.86 319	67.07 310	98.28 160	81.50 258	93.02 200	96.29 183

HQP-MVS		89.80 159	89.28 158	91.34 195	94.17 223	81.56 187	94.39 189	96.04 154	88.81 95	85.43 258	93.97 207	73.83 222	97.96 191	87.11 170	89.77 254	94.50 265

fmvsm_s_conf0.5_n_7		93.15 83	93.76 62	91.31 196	94.42 211	79.48 255	94.52 177	97.14 48	89.33 75	94.17 58	98.09 16	81.83 118	97.49 226	96.33 23	98.02 75	96.95 153

RRT-MVS		90.85 124	90.70 123	91.30 197	94.25 219	76.83 319	94.85 156	96.13 145	89.04 87	90.23 152	94.88 164	70.15 273	98.72 113	91.86 104	94.88 155	98.34 44

FMVSNet3		87.40 237	86.11 254	91.30 197	93.79 246	83.64 123	94.20 203	94.81 247	83.89 235	84.37 288	91.87 286	68.45 301	96.56 304	78.23 303	85.36 307	93.70 310

FMVSNet2		87.19 250	85.82 267	91.30 197	94.01 231	83.67 121	94.79 160	94.94 233	83.57 243	83.88 303	92.05 279	66.59 318	96.51 308	77.56 310	85.01 310	93.73 308

RPMNet		83.95 327	81.53 338	91.21 200	90.58 366	79.34 261	85.24 416	96.76 86	71.44 411	85.55 247	82.97 423	70.87 259	98.91 90	61.01 417	89.36 260	95.40 224

IB-MVS	80.51 15	85.24 304	83.26 322	91.19 201	92.13 301	79.86 247	91.75 313	91.29 358	83.28 254	80.66 352	88.49 376	61.28 360	98.46 138	80.99 267	79.46 383	95.25 230
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		89.47 168	88.90 169	91.18 202	94.22 221	82.07 177	92.13 303	96.09 149	87.90 129	85.37 264	92.45 260	74.38 209	97.56 220	87.15 168	90.43 238	93.93 289
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		89.45 169	88.90 169	91.12 203	94.47 205	81.49 191	95.30 120	96.14 142	86.73 162	85.45 255	95.16 155	69.89 276	98.10 171	87.70 159	89.23 263	93.77 304

LGP-MVS_train				91.12 203	94.47 205	81.49 191		96.14 142	86.73 162	85.45 255	95.16 155	69.89 276	98.10 171	87.70 159	89.23 263	93.77 304

ACMM	84.12 9	89.14 178	88.48 182	91.12 203	94.65 192	81.22 201	95.31 118	96.12 146	85.31 200	85.92 238	94.34 188	70.19 272	98.06 183	85.65 189	88.86 268	94.08 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 195	87.78 199	91.11 206	94.96 170	77.81 301	95.35 116	89.69 394	85.09 208	88.05 190	94.59 183	66.93 311	98.48 134	83.27 221	92.13 217	97.03 146

GBi-Net		87.26 242	85.98 260	91.08 207	94.01 231	83.10 143	95.14 138	94.94 233	83.57 243	84.37 288	91.64 291	66.59 318	96.34 321	78.23 303	85.36 307	93.79 299

test1		87.26 242	85.98 260	91.08 207	94.01 231	83.10 143	95.14 138	94.94 233	83.57 243	84.37 288	91.64 291	66.59 318	96.34 321	78.23 303	85.36 307	93.79 299

FMVSNet1		85.85 289	84.11 309	91.08 207	92.81 283	83.10 143	95.14 138	94.94 233	81.64 296	82.68 325	91.64 291	59.01 382	96.34 321	75.37 332	83.78 321	93.79 299

Test_1112_low_res		87.65 222	86.51 238	91.08 207	94.94 172	79.28 265	91.77 312	94.30 266	76.04 371	83.51 314	92.37 262	77.86 166	97.73 208	78.69 298	89.13 265	96.22 186

PS-MVSNAJss		89.97 152	89.62 147	91.02 211	91.90 310	80.85 216	95.26 126	95.98 158	86.26 174	86.21 232	94.29 192	79.70 138	97.65 212	88.87 146	88.10 279	94.57 259

BH-RMVSNet		88.37 202	87.48 205	91.02 211	95.28 150	79.45 257	92.89 278	93.07 305	85.45 196	86.91 212	94.84 169	70.35 269	97.76 203	73.97 346	94.59 164	95.85 207

UniMVSNet_ETH3D		87.53 231	86.37 242	91.00 213	92.44 293	78.96 270	94.74 164	95.61 191	84.07 231	85.36 265	94.52 185	59.78 374	97.34 249	82.93 225	87.88 284	96.71 167

FIs		90.51 139	90.35 126	90.99 214	93.99 235	80.98 210	95.73 96	97.54 6	89.15 83	86.72 219	94.68 174	81.83 118	97.24 258	85.18 194	88.31 278	94.76 252

ACMP	84.23 8	89.01 186	88.35 183	90.99 214	94.73 184	81.27 198	95.07 141	95.89 168	86.48 167	83.67 309	94.30 191	69.33 285	97.99 188	87.10 172	88.55 270	93.72 309
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 272	85.13 287	90.98 216	96.52 93	81.50 189	96.14 59	96.16 141	73.78 393	83.65 310	92.15 270	63.26 344	97.37 248	82.82 229	81.74 351	94.06 285

icg_test_0403		89.97 152	89.64 146	90.96 217	93.72 248	77.75 306	93.00 272	95.34 215	85.53 195	88.77 177	94.49 186	78.49 156	97.84 199	84.75 201	92.65 208	97.28 126

sss		88.93 187	88.26 189	90.94 218	94.05 229	80.78 218	91.71 314	95.38 210	81.55 300	88.63 179	93.91 212	75.04 199	95.47 362	82.47 234	91.61 220	96.57 174

sd_testset		88.59 197	87.85 198	90.83 219	96.00 116	80.42 228	92.35 294	94.71 252	88.73 99	86.85 216	95.20 153	67.31 305	96.43 315	79.64 287	89.85 251	95.63 218

PVSNet_BlendedMVS		89.98 151	89.70 144	90.82 220	96.12 106	81.25 199	93.92 227	96.83 77	83.49 247	89.10 171	92.26 267	81.04 125	98.85 97	86.72 175	87.86 285	92.35 360

cascas		86.43 280	84.98 290	90.80 221	92.10 303	80.92 214	90.24 349	95.91 165	73.10 400	83.57 313	88.39 377	65.15 330	97.46 230	84.90 199	91.43 222	94.03 287

ECVR-MVS		89.09 181	88.53 177	90.77 222	95.62 137	75.89 333	96.16 55	84.22 425	87.89 131	90.20 153	96.65 84	63.19 345	98.10 171	85.90 186	96.94 105	98.33 46

GA-MVS		86.61 270	85.27 284	90.66 223	91.33 333	78.71 274	90.40 344	93.81 288	85.34 199	85.12 268	89.57 358	61.25 361	97.11 268	80.99 267	89.59 257	96.15 190

thres600view7		87.65 222	86.67 229	90.59 224	96.08 112	78.72 272	94.88 152	91.58 349	87.06 152	88.08 188	92.30 265	68.91 295	98.10 171	70.05 376	91.10 225	94.96 241

thres400		87.62 227	86.64 230	90.57 225	95.99 119	78.64 275	94.58 173	91.98 338	86.94 156	88.09 186	91.77 287	69.18 291	98.10 171	70.13 373	91.10 225	94.96 241

baseline1		88.10 209	87.28 211	90.57 225	94.96 170	80.07 237	94.27 198	91.29 358	86.74 161	87.41 203	94.00 205	76.77 175	96.20 326	80.77 270	79.31 385	95.44 222

FC-MVSNet-test		90.27 142	90.18 131	90.53 227	93.71 249	79.85 248	95.77 92	97.59 4	89.31 76	86.27 230	94.67 177	81.93 117	97.01 276	84.26 208	88.09 281	94.71 253

PAPM		86.68 269	85.39 279	90.53 227	93.05 274	79.33 264	89.79 361	94.77 250	78.82 339	81.95 336	93.24 234	76.81 173	97.30 250	66.94 393	93.16 198	94.95 245

WR-MVS		88.38 201	87.67 201	90.52 229	93.30 263	80.18 232	93.26 260	95.96 161	88.57 107	85.47 254	92.81 249	76.12 182	96.91 283	81.24 262	82.29 342	94.47 270

MVSTER		88.84 188	88.29 187	90.51 230	92.95 280	80.44 227	93.73 236	95.01 229	84.66 222	87.15 207	93.12 239	72.79 237	97.21 261	87.86 157	87.36 293	93.87 294

testdata				90.49 231	96.40 96	77.89 298		95.37 212	72.51 405	93.63 71	96.69 80	82.08 113	97.65 212	83.08 222	97.39 95	95.94 202

test1111		89.10 179	88.64 174	90.48 232	95.53 142	74.97 343	96.08 64	84.89 423	88.13 122	90.16 155	96.65 84	63.29 343	98.10 171	86.14 181	96.90 107	98.39 41

tt0805		86.92 258	85.74 273	90.48 232	92.22 297	79.98 244	95.63 106	94.88 241	83.83 237	84.74 277	92.80 250	57.61 388	97.67 209	85.48 192	84.42 314	93.79 299

jajsoiax		88.24 206	87.50 204	90.48 232	90.89 354	80.14 234	95.31 118	95.65 189	84.97 211	84.24 296	94.02 203	65.31 329	97.42 236	88.56 148	88.52 272	93.89 290

PatchMatch-RL		86.77 266	85.54 275	90.47 235	95.88 123	82.71 162	90.54 342	92.31 326	79.82 324	84.32 293	91.57 299	68.77 297	96.39 317	73.16 352	93.48 190	92.32 361

tfpn200view9		87.58 229	86.64 230	90.41 236	95.99 119	78.64 275	94.58 173	91.98 338	86.94 156	88.09 186	91.77 287	69.18 291	98.10 171	70.13 373	91.10 225	94.48 268

VPNet		88.20 207	87.47 206	90.39 237	93.56 256	79.46 256	94.04 217	95.54 196	88.67 102	86.96 209	94.58 184	69.33 285	97.15 263	84.05 211	80.53 371	94.56 260

ACMH	80.38 17	85.36 299	83.68 316	90.39 237	94.45 208	80.63 221	94.73 165	94.85 243	82.09 278	77.24 386	92.65 254	60.01 372	97.58 218	72.25 357	84.87 311	92.96 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 225	86.71 226	90.38 239	96.12 106	78.55 278	95.03 144	91.58 349	87.15 149	88.06 189	92.29 266	68.91 295	98.10 171	70.13 373	91.10 225	94.48 268

mvs_tets		88.06 212	87.28 211	90.38 239	90.94 350	79.88 246	95.22 129	95.66 187	85.10 207	84.21 297	93.94 208	63.53 341	97.40 244	88.50 149	88.40 276	93.87 294

1314		87.51 232	86.57 235	90.34 241	92.42 294	79.74 251	92.63 285	95.35 214	78.35 348	80.14 359	91.62 295	74.05 216	97.15 263	81.05 263	93.53 186	94.12 280

LTVRE_ROB	82.13 13	86.26 283	84.90 293	90.34 241	94.44 209	81.50 189	92.31 298	94.89 239	83.03 259	79.63 368	92.67 253	69.69 279	97.79 201	71.20 362	86.26 302	91.72 371
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		89.03 184	88.64 174	90.21 243	90.74 361	79.28 265	95.96 77	95.90 166	84.66 222	85.33 266	92.94 244	74.02 217	97.30 250	89.64 135	88.53 271	94.05 286

v2v482		87.84 215	87.06 215	90.17 244	90.99 346	79.23 268	94.00 222	95.13 222	84.87 214	85.53 249	92.07 278	74.45 208	97.45 231	84.71 203	81.75 350	93.85 297

pmmvs4		85.43 297	83.86 314	90.16 245	90.02 379	82.97 153	90.27 345	92.67 317	75.93 372	80.73 350	91.74 289	71.05 255	95.73 351	78.85 297	83.46 328	91.78 370

V42		87.68 220	86.86 220	90.15 246	90.58 366	80.14 234	94.24 201	95.28 216	83.66 241	85.67 244	91.33 301	74.73 204	97.41 242	84.43 207	81.83 348	92.89 342

MSDG		84.86 312	83.09 325	90.14 247	93.80 244	80.05 239	89.18 374	93.09 304	78.89 336	78.19 378	91.91 284	65.86 327	97.27 254	68.47 382	88.45 274	93.11 334

sc_t1		81.53 351	78.67 372	90.12 248	90.78 358	78.64 275	93.91 229	90.20 381	68.42 420	80.82 349	89.88 351	46.48 425	96.76 288	76.03 328	71.47 410	94.96 241

anonymousdsp		87.84 215	87.09 214	90.12 248	89.13 390	80.54 225	94.67 169	95.55 194	82.05 279	83.82 304	92.12 272	71.47 252	97.15 263	87.15 168	87.80 288	92.67 348

thres200		87.21 248	86.24 249	90.12 248	95.36 146	78.53 279	93.26 260	92.10 332	86.42 170	88.00 191	91.11 312	69.24 290	98.00 187	69.58 377	91.04 231	93.83 298

CR-MVSNet		85.35 300	83.76 315	90.12 248	90.58 366	79.34 261	85.24 416	91.96 340	78.27 350	85.55 247	87.87 387	71.03 256	95.61 354	73.96 347	89.36 260	95.40 224

v1144		87.61 228	86.79 224	90.06 252	91.01 345	79.34 261	93.95 224	95.42 209	83.36 252	85.66 245	91.31 304	74.98 200	97.42 236	83.37 219	82.06 344	93.42 320

XXY-MVS		87.65 222	86.85 221	90.03 253	92.14 300	80.60 223	93.76 235	95.23 218	82.94 262	84.60 279	94.02 203	74.27 210	95.49 361	81.04 264	83.68 324	94.01 288

Vis-MVSNet (Re-imp)		89.59 164	89.44 151	90.03 253	95.74 128	75.85 334	95.61 107	90.80 372	87.66 141	87.83 195	95.40 142	76.79 174	96.46 313	78.37 299	96.73 113	97.80 99

test2506		87.21 248	86.28 247	90.02 255	95.62 137	73.64 359	96.25 50	71.38 448	87.89 131	90.45 148	96.65 84	55.29 399	98.09 179	86.03 185	96.94 105	98.33 46

BH-untuned		88.60 196	88.13 191	90.01 256	95.24 154	78.50 281	93.29 258	94.15 274	84.75 219	84.46 285	93.40 226	75.76 189	97.40 244	77.59 309	94.52 167	94.12 280

v1192		87.25 244	86.33 244	90.00 257	90.76 360	79.04 269	93.80 233	95.48 199	82.57 269	85.48 253	91.18 308	73.38 231	97.42 236	82.30 238	82.06 344	93.53 314

v7n		86.81 261	85.76 271	89.95 258	90.72 362	79.25 267	95.07 141	95.92 163	84.45 225	82.29 329	90.86 319	72.60 241	97.53 222	79.42 292	80.52 372	93.08 336

testing91		87.11 253	86.18 250	89.92 259	94.43 210	75.38 342	91.53 319	92.27 328	86.48 167	86.50 221	90.24 337	61.19 364	97.53 222	82.10 243	90.88 233	96.84 162

v8		87.50 234	86.71 226	89.89 260	91.37 330	79.40 258	94.50 178	95.38 210	84.81 217	83.60 312	91.33 301	76.05 183	97.42 236	82.84 228	80.51 373	92.84 344

v10		87.25 244	86.38 241	89.85 261	91.19 336	79.50 254	94.48 179	95.45 204	83.79 239	83.62 311	91.19 306	75.13 197	97.42 236	81.94 248	80.60 368	92.63 350

baseline2		86.50 276	85.39 279	89.84 262	91.12 341	76.70 322	91.88 309	88.58 403	82.35 274	79.95 363	90.95 317	73.42 229	97.63 215	80.27 280	89.95 248	95.19 231

pm-mvs1		86.61 270	85.54 275	89.82 263	91.44 325	80.18 232	95.28 124	94.85 243	83.84 236	81.66 338	92.62 255	72.45 244	96.48 310	79.67 286	78.06 388	92.82 345

TR-MVS		86.78 263	85.76 271	89.82 263	94.37 213	78.41 283	92.47 289	92.83 311	81.11 310	86.36 227	92.40 261	68.73 298	97.48 227	73.75 350	89.85 251	93.57 313

ACMH+	81.04 14	85.05 307	83.46 319	89.82 263	94.66 191	79.37 259	94.44 184	94.12 277	82.19 277	78.04 380	92.82 248	58.23 385	97.54 221	73.77 349	82.90 336	92.54 351

EI-MVSNet		89.10 179	88.86 171	89.80 266	91.84 312	78.30 287	93.70 239	95.01 229	85.73 187	87.15 207	95.28 146	79.87 135	97.21 261	83.81 215	87.36 293	93.88 293

v144192		87.19 250	86.35 243	89.74 267	90.64 364	78.24 289	93.92 227	95.43 207	81.93 284	85.51 251	91.05 315	74.21 213	97.45 231	82.86 227	81.56 352	93.53 314

COLMAP_ROB	80.39 16	83.96 326	82.04 335	89.74 267	95.28 150	79.75 250	94.25 199	92.28 327	75.17 379	78.02 381	93.77 218	58.60 384	97.84 199	65.06 404	85.92 303	91.63 373
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 282	85.18 286	89.73 269	92.15 299	76.60 323	91.12 330	91.69 345	83.53 246	85.50 252	88.81 370	66.79 314	96.48 310	76.65 318	90.35 240	96.12 193

IterMVS-LS		88.36 203	87.91 197	89.70 270	93.80 244	78.29 288	93.73 236	95.08 227	85.73 187	84.75 276	91.90 285	79.88 134	96.92 282	83.83 214	82.51 338	93.89 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 279	85.35 282	89.69 271	94.29 218	75.40 341	91.30 324	90.53 376	84.76 218	85.06 270	90.13 343	58.95 383	97.45 231	82.08 244	91.09 229	96.21 188

testing99		86.72 267	85.73 274	89.69 271	94.23 220	74.91 345	91.35 323	90.97 366	86.14 178	86.36 227	90.22 338	59.41 377	97.48 227	82.24 240	90.66 235	96.69 169

v1921920		86.97 257	86.06 257	89.69 271	90.53 369	78.11 292	93.80 233	95.43 207	81.90 286	85.33 266	91.05 315	72.66 238	97.41 242	82.05 246	81.80 349	93.53 314

VortexMVS		88.42 199	88.01 193	89.63 274	93.89 239	78.82 271	93.82 232	95.47 200	86.67 164	84.53 283	91.99 281	72.62 240	96.65 294	89.02 143	84.09 318	93.41 321

Fast-Effi-MVS+-dtu		87.44 235	86.72 225	89.63 274	92.04 304	77.68 308	94.03 218	93.94 280	85.81 184	82.42 328	91.32 303	70.33 270	97.06 272	80.33 279	90.23 242	94.14 279

v1240		86.78 263	85.85 266	89.56 276	90.45 371	77.79 303	93.61 241	95.37 212	81.65 295	85.43 258	91.15 310	71.50 251	97.43 235	81.47 259	82.05 346	93.47 318

Effi-MVS+-dtu		88.65 194	88.35 183	89.54 277	93.33 262	76.39 327	94.47 182	94.36 264	87.70 138	85.43 258	89.56 359	73.45 227	97.26 256	85.57 191	91.28 224	94.97 238

AllTest		83.42 333	81.39 339	89.52 278	95.01 164	77.79 303	93.12 264	90.89 370	77.41 357	76.12 395	93.34 227	54.08 405	97.51 224	68.31 384	84.27 316	93.26 324

TestCases				89.52 278	95.01 164	77.79 303		90.89 370	77.41 357	76.12 395	93.34 227	54.08 405	97.51 224	68.31 384	84.27 316	93.26 324

mvs_anonymous		89.37 175	89.32 156	89.51 280	93.47 258	74.22 352	91.65 317	94.83 245	82.91 263	85.45 255	93.79 216	81.23 124	96.36 320	86.47 177	94.09 175	97.94 87

XVG-ACMP-BASELINE		86.00 285	84.84 295	89.45 281	91.20 335	78.00 294	91.70 315	95.55 194	85.05 209	82.97 322	92.25 268	54.49 403	97.48 227	82.93 225	87.45 292	92.89 342

testing222		84.84 313	83.32 320	89.43 282	94.15 226	75.94 332	91.09 331	89.41 401	84.90 212	85.78 241	89.44 360	52.70 410	96.28 324	70.80 368	91.57 221	96.07 197

MVP-Stereo		85.97 286	84.86 294	89.32 283	90.92 352	82.19 175	92.11 304	94.19 271	78.76 341	78.77 377	91.63 294	68.38 302	96.56 304	75.01 337	93.95 177	89.20 411
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 289	84.70 297	89.29 284	91.76 316	75.54 338	88.49 383	91.30 357	81.63 297	85.05 271	88.70 374	71.71 248	96.24 325	74.61 342	89.05 266	96.08 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 255	86.32 245	89.21 285	90.94 350	77.26 313	93.71 238	94.43 259	84.84 216	84.36 291	90.80 323	76.04 184	97.05 274	82.12 242	79.60 382	93.31 323

tfpnnormal		84.72 315	83.23 323	89.20 286	92.79 284	80.05 239	94.48 179	95.81 173	82.38 272	81.08 346	91.21 305	69.01 294	96.95 280	61.69 415	80.59 369	90.58 398

cl22		86.78 263	85.98 260	89.18 287	92.34 295	77.62 309	90.84 336	94.13 276	81.33 304	83.97 302	90.15 342	73.96 218	96.60 301	84.19 209	82.94 333	93.33 322

BH-w/o		87.57 230	87.05 216	89.12 288	94.90 176	77.90 297	92.41 290	93.51 295	82.89 264	83.70 308	91.34 300	75.75 190	97.07 271	75.49 330	93.49 188	92.39 358

WR-MVS_H		87.80 217	87.37 208	89.10 289	93.23 264	78.12 291	95.61 107	97.30 32	87.90 129	83.72 307	92.01 280	79.65 142	96.01 335	76.36 322	80.54 370	93.16 332

miper_enhance_ethall		86.90 259	86.18 250	89.06 290	91.66 321	77.58 310	90.22 351	94.82 246	79.16 332	84.48 284	89.10 364	79.19 146	96.66 293	84.06 210	82.94 333	92.94 340

c3_l		87.14 252	86.50 239	89.04 291	92.20 298	77.26 313	91.22 329	94.70 253	82.01 282	84.34 292	90.43 334	78.81 149	96.61 299	83.70 217	81.09 359	93.25 326

miper_ehance_all_eth		87.22 247	86.62 233	89.02 292	92.13 301	77.40 312	90.91 335	94.81 247	81.28 305	84.32 293	90.08 345	79.26 144	96.62 296	83.81 215	82.94 333	93.04 337

gg-mvs-nofinetune		81.77 345	79.37 360	88.99 293	90.85 356	77.73 307	86.29 408	79.63 436	74.88 384	83.19 321	69.05 439	60.34 369	96.11 330	75.46 331	94.64 163	93.11 334

ETVMVS		84.43 320	82.92 329	88.97 294	94.37 213	74.67 346	91.23 328	88.35 405	83.37 251	86.06 236	89.04 365	55.38 397	95.67 353	67.12 391	91.34 223	96.58 173

pmmvs6		83.42 333	81.60 337	88.87 295	88.01 405	77.87 299	94.96 147	94.24 270	74.67 385	78.80 376	91.09 313	60.17 371	96.49 309	77.06 317	75.40 402	92.23 363

test_cas_vis1_n_1920		88.83 191	88.85 172	88.78 296	91.15 340	76.72 321	93.85 231	94.93 237	83.23 256	92.81 91	96.00 113	61.17 365	94.45 375	91.67 107	94.84 156	95.17 232

MIMVSNet		82.59 339	80.53 344	88.76 297	91.51 323	78.32 286	86.57 407	90.13 384	79.32 328	80.70 351	88.69 375	52.98 409	93.07 400	66.03 399	88.86 268	94.90 246

cl____		86.52 275	85.78 268	88.75 298	92.03 305	76.46 325	90.74 337	94.30 266	81.83 291	83.34 318	90.78 324	75.74 192	96.57 302	81.74 254	81.54 353	93.22 328

DIV-MVS_self_test		86.53 274	85.78 268	88.75 298	92.02 306	76.45 326	90.74 337	94.30 266	81.83 291	83.34 318	90.82 322	75.75 190	96.57 302	81.73 255	81.52 354	93.24 327

CP-MVSNet		87.63 225	87.26 213	88.74 300	93.12 269	76.59 324	95.29 122	96.58 103	88.43 110	83.49 315	92.98 243	75.28 196	95.83 344	78.97 295	81.15 358	93.79 299

eth_miper_zixun_eth		86.50 276	85.77 270	88.68 301	91.94 307	75.81 335	90.47 343	94.89 239	82.05 279	84.05 299	90.46 333	75.96 185	96.77 287	82.76 231	79.36 384	93.46 319

CHOSEN 280x420		85.15 305	83.99 312	88.65 302	92.47 291	78.40 284	79.68 438	92.76 314	74.90 383	81.41 342	89.59 357	69.85 278	95.51 358	79.92 284	95.29 148	92.03 366

PS-CasMVS		87.32 241	86.88 219	88.63 303	92.99 278	76.33 329	95.33 117	96.61 101	88.22 118	83.30 320	93.07 241	73.03 235	95.79 348	78.36 300	81.00 364	93.75 306

TransMVSNet (Re)		84.43 320	83.06 327	88.54 304	91.72 317	78.44 282	95.18 135	92.82 313	82.73 267	79.67 367	92.12 272	73.49 226	95.96 337	71.10 366	68.73 420	91.21 385

tt0320-xc		79.63 374	76.66 383	88.52 305	91.03 344	78.72 272	93.00 272	89.53 400	66.37 424	76.11 397	87.11 398	46.36 427	95.32 366	72.78 354	67.67 421	91.51 377

EG-PatchMatch MVS		82.37 341	80.34 347	88.46 306	90.27 373	79.35 260	92.80 282	94.33 265	77.14 361	73.26 413	90.18 341	47.47 422	96.72 289	70.25 370	87.32 295	89.30 408

PEN-MVS		86.80 262	86.27 248	88.40 307	92.32 296	75.71 337	95.18 135	96.38 118	87.97 126	82.82 324	93.15 237	73.39 230	95.92 339	76.15 326	79.03 387	93.59 312

Baseline_NR-MVSNet		87.07 254	86.63 232	88.40 307	91.44 325	77.87 299	94.23 202	92.57 319	84.12 230	85.74 243	92.08 276	77.25 170	96.04 331	82.29 239	79.94 377	91.30 383

UBG		85.51 295	84.57 302	88.35 309	94.21 222	71.78 383	90.07 356	89.66 396	82.28 275	85.91 239	89.01 366	61.30 359	97.06 272	76.58 321	92.06 218	96.22 186

D2MVS		85.90 287	85.09 288	88.35 309	90.79 357	77.42 311	91.83 311	95.70 183	80.77 313	80.08 361	90.02 347	66.74 316	96.37 318	81.88 250	87.97 283	91.26 384

pmmvs5		84.21 322	82.84 332	88.34 311	88.95 392	76.94 317	92.41 290	91.91 342	75.63 374	80.28 356	91.18 308	64.59 335	95.57 355	77.09 316	83.47 327	92.53 352

mamv4		90.92 122	91.78 102	88.33 312	95.67 133	70.75 396	92.92 277	96.02 157	81.90 286	88.11 185	95.34 144	85.88 52	96.97 278	95.22 37	95.01 153	97.26 127

tt0320		80.13 367	77.41 376	88.29 313	90.50 370	78.02 293	93.10 267	90.71 374	66.06 427	76.75 390	86.97 399	49.56 417	95.40 363	71.65 358	71.41 411	91.46 380

LCM-MVSNet-Re		88.30 205	88.32 186	88.27 314	94.71 188	72.41 378	93.15 263	90.98 365	87.77 136	79.25 371	91.96 282	78.35 158	95.75 349	83.04 223	95.62 137	96.65 170

CostFormer		85.77 292	84.94 292	88.26 315	91.16 339	72.58 376	89.47 369	91.04 364	76.26 369	86.45 225	89.97 349	70.74 261	96.86 286	82.35 237	87.07 298	95.34 228

ITE_SJBPF				88.24 316	91.88 311	77.05 316		92.92 308	85.54 193	80.13 360	93.30 231	57.29 389	96.20 326	72.46 356	84.71 312	91.49 378

PVSNet	78.82 18	85.55 294	84.65 298	88.23 317	94.72 186	71.93 379	87.12 403	92.75 315	78.80 340	84.95 273	90.53 331	64.43 336	96.71 291	74.74 340	93.86 179	96.06 199

IterMVS-SCA-FT		85.45 296	84.53 303	88.18 318	91.71 318	76.87 318	90.19 353	92.65 318	85.40 198	81.44 341	90.54 330	66.79 314	95.00 372	81.04 264	81.05 360	92.66 349

EPNet_dtu		86.49 278	85.94 263	88.14 319	90.24 374	72.82 368	94.11 208	92.20 330	86.66 165	79.42 370	92.36 263	73.52 225	95.81 346	71.26 361	93.66 182	95.80 211
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 337	80.93 343	88.06 320	90.05 378	76.37 328	84.74 421	91.96 340	72.28 408	81.32 344	87.87 387	71.03 256	95.50 360	68.97 379	80.15 375	92.32 361

test_vis1_n_1920		89.39 174	89.84 142	88.04 321	92.97 279	72.64 373	94.71 167	96.03 156	86.18 176	91.94 120	96.56 92	61.63 354	95.74 350	93.42 58	95.11 152	95.74 213

DTE-MVSNet		86.11 284	85.48 277	87.98 322	91.65 322	74.92 344	94.93 149	95.75 178	87.36 146	82.26 330	93.04 242	72.85 236	95.82 345	74.04 345	77.46 393	93.20 330

PMMVS		85.71 293	84.96 291	87.95 323	88.90 393	77.09 315	88.68 381	90.06 386	72.32 407	86.47 222	90.76 325	72.15 246	94.40 377	81.78 253	93.49 188	92.36 359

GG-mvs-BLEND				87.94 324	89.73 385	77.91 296	87.80 392	78.23 441		80.58 353	83.86 416	59.88 373	95.33 365	71.20 362	92.22 216	90.60 397

MonoMVSNet		86.89 260	86.55 236	87.92 325	89.46 388	73.75 356	94.12 206	93.10 303	87.82 135	85.10 269	90.76 325	69.59 281	94.94 373	86.47 177	82.50 339	95.07 235

reproduce_monomvs		86.37 281	85.87 265	87.87 326	93.66 253	73.71 357	93.44 248	95.02 228	88.61 105	82.64 327	91.94 283	57.88 387	96.68 292	89.96 132	79.71 381	93.22 328

pmmvs-eth3d		80.97 360	78.72 371	87.74 327	84.99 423	79.97 245	90.11 355	91.65 347	75.36 376	73.51 411	86.03 406	59.45 376	93.96 387	75.17 334	72.21 407	89.29 410

MS-PatchMatch		85.05 307	84.16 307	87.73 328	91.42 328	78.51 280	91.25 327	93.53 294	77.50 356	80.15 358	91.58 297	61.99 351	95.51 358	75.69 329	94.35 171	89.16 412

mmtdpeth		85.04 309	84.15 308	87.72 329	93.11 270	75.74 336	94.37 193	92.83 311	84.98 210	89.31 168	86.41 403	61.61 356	97.14 266	92.63 74	62.11 431	90.29 399

test_0402		81.30 356	79.17 365	87.67 330	93.19 265	78.17 290	92.98 274	91.71 343	75.25 378	76.02 398	90.31 336	59.23 378	96.37 318	50.22 434	83.63 325	88.47 419

IterMVS		84.88 311	83.98 313	87.60 331	91.44 325	76.03 331	90.18 354	92.41 321	83.24 255	81.06 347	90.42 335	66.60 317	94.28 381	79.46 288	80.98 365	92.48 353
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 354	79.30 361	87.58 332	90.92 352	74.16 354	80.99 433	87.68 410	70.52 415	76.63 392	88.81 370	71.21 253	92.76 403	60.01 421	86.93 299	95.83 209

EPMVS		83.90 329	82.70 333	87.51 333	90.23 375	72.67 371	88.62 382	81.96 431	81.37 303	85.01 272	88.34 378	66.31 321	94.45 375	75.30 333	87.12 296	95.43 223

ADS-MVSNet2		81.66 348	79.71 357	87.50 334	91.35 331	74.19 353	83.33 426	88.48 404	72.90 402	82.24 331	85.77 409	64.98 331	93.20 398	64.57 406	83.74 322	95.12 233

OurMVSNet-221017-0		85.35 300	84.64 300	87.49 335	90.77 359	72.59 375	94.01 220	94.40 262	84.72 220	79.62 369	93.17 236	61.91 352	96.72 289	81.99 247	81.16 356	93.16 332

tpm2		84.08 324	82.94 328	87.48 336	91.39 329	71.27 388	89.23 373	90.37 378	71.95 409	84.64 278	89.33 361	67.30 306	96.55 306	75.17 334	87.09 297	94.63 254

RPSCF		85.07 306	84.27 304	87.48 336	92.91 282	70.62 398	91.69 316	92.46 320	76.20 370	82.67 326	95.22 149	63.94 339	97.29 253	77.51 311	85.80 304	94.53 261

myMVS_eth3d28		85.80 291	85.26 285	87.42 338	94.73 184	69.92 403	90.60 341	90.95 367	87.21 148	86.06 236	90.04 346	59.47 375	96.02 333	74.89 339	93.35 195	96.33 180

WBMVS		84.97 310	84.18 306	87.34 339	94.14 227	71.62 387	90.20 352	92.35 323	81.61 298	84.06 298	90.76 325	61.82 353	96.52 307	78.93 296	83.81 320	93.89 290

miper_lstm_enhance		85.27 303	84.59 301	87.31 340	91.28 334	74.63 347	87.69 397	94.09 278	81.20 309	81.36 343	89.85 353	74.97 201	94.30 380	81.03 266	79.84 380	93.01 338

FMVSNet5		81.52 352	79.60 358	87.27 341	91.17 337	77.95 295	91.49 320	92.26 329	76.87 362	76.16 394	87.91 386	51.67 411	92.34 406	67.74 388	81.16 356	91.52 376

USDC		82.76 336	81.26 341	87.26 342	91.17 337	74.55 348	89.27 371	93.39 297	78.26 351	75.30 402	92.08 276	54.43 404	96.63 295	71.64 359	85.79 305	90.61 395

test-LLR		85.87 288	85.41 278	87.25 343	90.95 348	71.67 385	89.55 365	89.88 392	83.41 249	84.54 281	87.95 384	67.25 307	95.11 369	81.82 251	93.37 193	94.97 238

test-mter		84.54 319	83.64 317	87.25 343	90.95 348	71.67 385	89.55 365	89.88 392	79.17 331	84.54 281	87.95 384	55.56 395	95.11 369	81.82 251	93.37 193	94.97 238

JIA-IIPM		81.04 357	78.98 369	87.25 343	88.64 394	73.48 361	81.75 432	89.61 398	73.19 399	82.05 334	73.71 435	66.07 326	95.87 342	71.18 364	84.60 313	92.41 357

TDRefinement		79.81 371	77.34 377	87.22 346	79.24 438	75.48 339	93.12 264	92.03 335	76.45 365	75.01 403	91.58 297	49.19 418	96.44 314	70.22 372	69.18 417	89.75 404

tpmvs		83.35 335	82.07 334	87.20 347	91.07 343	71.00 394	88.31 386	91.70 344	78.91 334	80.49 355	87.18 396	69.30 288	97.08 269	68.12 387	83.56 326	93.51 317

ppachtmachnet_test		81.84 344	80.07 352	87.15 348	88.46 398	74.43 351	89.04 377	92.16 331	75.33 377	77.75 383	88.99 367	66.20 323	95.37 364	65.12 403	77.60 391	91.65 372

dmvs_re		84.20 323	83.22 324	87.14 349	91.83 314	77.81 301	90.04 357	90.19 382	84.70 221	81.49 339	89.17 363	64.37 337	91.13 417	71.58 360	85.65 306	92.46 355

tpm cat1		81.96 342	80.27 348	87.01 350	91.09 342	71.02 393	87.38 401	91.53 352	66.25 425	80.17 357	86.35 405	68.22 303	96.15 329	69.16 378	82.29 342	93.86 296

test_fmvs1_n		87.03 256	87.04 217	86.97 351	89.74 384	71.86 380	94.55 175	94.43 259	78.47 345	91.95 119	95.50 138	51.16 413	93.81 388	93.02 66	94.56 165	95.26 229

OpenMVS_ROB	74.94 19	79.51 375	77.03 382	86.93 352	87.00 411	76.23 330	92.33 296	90.74 373	68.93 419	74.52 407	88.23 381	49.58 416	96.62 296	57.64 426	84.29 315	87.94 422

SixPastTwentyTwo		83.91 328	82.90 330	86.92 353	90.99 346	70.67 397	93.48 245	91.99 337	85.54 193	77.62 385	92.11 274	60.59 368	96.87 285	76.05 327	77.75 390	93.20 330

ADS-MVSNet		81.56 350	79.78 354	86.90 354	91.35 331	71.82 381	83.33 426	89.16 402	72.90 402	82.24 331	85.77 409	64.98 331	93.76 389	64.57 406	83.74 322	95.12 233

PatchT		82.68 338	81.27 340	86.89 355	90.09 377	70.94 395	84.06 423	90.15 383	74.91 382	85.63 246	83.57 418	69.37 284	94.87 374	65.19 401	88.50 273	94.84 248

tpm		84.73 314	84.02 311	86.87 356	90.33 372	68.90 406	89.06 376	89.94 389	80.85 312	85.75 242	89.86 352	68.54 300	95.97 336	77.76 307	84.05 319	95.75 212

Patchmatch-RL test		81.67 347	79.96 353	86.81 357	85.42 421	71.23 389	82.17 431	87.50 411	78.47 345	77.19 387	82.50 425	70.81 260	93.48 393	82.66 232	72.89 406	95.71 216

test_vis1_n		86.56 273	86.49 240	86.78 358	88.51 395	72.69 370	94.68 168	93.78 290	79.55 327	90.70 143	95.31 145	48.75 419	93.28 396	93.15 62	93.99 176	94.38 272

testing3-2		86.72 267	86.71 226	86.74 359	96.11 109	65.92 418	93.39 250	89.65 397	89.46 69	87.84 194	92.79 251	59.17 380	97.60 217	81.31 260	90.72 234	96.70 168

test_fmvs1		87.34 239	87.56 203	86.68 360	90.59 365	71.80 382	94.01 220	94.04 279	78.30 349	91.97 117	95.22 149	56.28 393	93.71 390	92.89 67	94.71 159	94.52 262

MDA-MVSNet-bldmvs		78.85 380	76.31 385	86.46 361	89.76 383	73.88 355	88.79 379	90.42 377	79.16 332	59.18 435	88.33 379	60.20 370	94.04 383	62.00 414	68.96 418	91.48 379

mvs5depth		80.98 359	79.15 366	86.45 362	84.57 424	73.29 363	87.79 393	91.67 346	80.52 315	82.20 333	89.72 355	55.14 400	95.93 338	73.93 348	66.83 423	90.12 401

tpmrst		85.35 300	84.99 289	86.43 363	90.88 355	67.88 411	88.71 380	91.43 355	80.13 319	86.08 235	88.80 372	73.05 234	96.02 333	82.48 233	83.40 330	95.40 224

TESTMET0.1,1		83.74 331	82.85 331	86.42 364	89.96 380	71.21 390	89.55 365	87.88 407	77.41 357	83.37 317	87.31 392	56.71 391	93.65 392	80.62 274	92.85 205	94.40 271

our_test_3		81.93 343	80.46 346	86.33 365	88.46 398	73.48 361	88.46 384	91.11 360	76.46 364	76.69 391	88.25 380	66.89 312	94.36 378	68.75 380	79.08 386	91.14 387

lessismore_v0				86.04 366	88.46 398	68.78 407		80.59 434		73.01 414	90.11 344	55.39 396	96.43 315	75.06 336	65.06 426	92.90 341

TinyColmap		79.76 372	77.69 375	85.97 367	91.71 318	73.12 364	89.55 365	90.36 379	75.03 380	72.03 417	90.19 340	46.22 428	96.19 328	63.11 410	81.03 361	88.59 418

KD-MVS_2432*1600		78.50 381	76.02 388	85.93 368	86.22 414	74.47 349	84.80 419	92.33 324	79.29 329	76.98 388	85.92 407	53.81 407	93.97 385	67.39 389	57.42 436	89.36 406

miper_refine_blended		78.50 381	76.02 388	85.93 368	86.22 414	74.47 349	84.80 419	92.33 324	79.29 329	76.98 388	85.92 407	53.81 407	93.97 385	67.39 389	57.42 436	89.36 406

K. test v3		81.59 349	80.15 351	85.91 370	89.89 382	69.42 405	92.57 287	87.71 409	85.56 192	73.44 412	89.71 356	55.58 394	95.52 357	77.17 314	69.76 414	92.78 346

SSC-MVS3.2		84.60 318	84.19 305	85.85 371	92.74 286	68.07 408	88.15 388	93.81 288	87.42 145	83.76 306	91.07 314	62.91 346	95.73 351	74.56 343	83.24 331	93.75 306

mvsany_test1		85.42 298	85.30 283	85.77 372	87.95 407	75.41 340	87.61 400	80.97 433	76.82 363	88.68 178	95.83 124	77.44 169	90.82 419	85.90 186	86.51 300	91.08 391

MIMVSNet1		79.38 376	77.28 378	85.69 373	86.35 413	73.67 358	91.61 318	92.75 315	78.11 354	72.64 415	88.12 382	48.16 420	91.97 411	60.32 418	77.49 392	91.43 381

UWE-MVS		83.69 332	83.09 325	85.48 374	93.06 273	65.27 423	90.92 334	86.14 415	79.90 322	86.26 231	90.72 328	57.17 390	95.81 346	71.03 367	92.62 210	95.35 227

UnsupCasMVSNet_eth		80.07 368	78.27 374	85.46 375	85.24 422	72.63 374	88.45 385	94.87 242	82.99 261	71.64 419	88.07 383	56.34 392	91.75 412	73.48 351	63.36 429	92.01 367

CL-MVSNet_self_test		81.74 346	80.53 344	85.36 376	85.96 416	72.45 377	90.25 347	93.07 305	81.24 307	79.85 366	87.29 393	70.93 258	92.52 404	66.95 392	69.23 416	91.11 389

MDA-MVSNet_test_wron		79.21 378	77.19 380	85.29 377	88.22 402	72.77 369	85.87 410	90.06 386	74.34 387	62.62 432	87.56 390	66.14 324	91.99 410	66.90 396	73.01 404	91.10 390

YYNet1		79.22 377	77.20 379	85.28 378	88.20 403	72.66 372	85.87 410	90.05 388	74.33 388	62.70 430	87.61 389	66.09 325	92.03 408	66.94 393	72.97 405	91.15 386

WB-MVSnew		83.77 330	83.28 321	85.26 379	91.48 324	71.03 392	91.89 308	87.98 406	78.91 334	84.78 275	90.22 338	69.11 293	94.02 384	64.70 405	90.44 237	90.71 393

dp		81.47 353	80.23 349	85.17 380	89.92 381	65.49 421	86.74 405	90.10 385	76.30 368	81.10 345	87.12 397	62.81 347	95.92 339	68.13 386	79.88 378	94.09 283

UnsupCasMVSNet_bld		76.23 390	73.27 394	85.09 381	83.79 426	72.92 366	85.65 413	93.47 296	71.52 410	68.84 425	79.08 430	49.77 415	93.21 397	66.81 397	60.52 433	89.13 414

SD_0403		84.71 316	84.65 298	84.92 382	92.95 280	65.95 417	92.07 307	93.23 300	83.82 238	79.03 372	93.73 221	73.90 219	92.91 402	63.02 412	90.05 244	95.89 205

Anonymous20231206		81.03 358	79.77 356	84.82 383	87.85 408	70.26 400	91.42 321	92.08 333	73.67 394	77.75 383	89.25 362	62.43 349	93.08 399	61.50 416	82.00 347	91.12 388

test0.0.03 1		82.41 340	81.69 336	84.59 384	88.23 401	72.89 367	90.24 349	87.83 408	83.41 249	79.86 365	89.78 354	67.25 307	88.99 429	65.18 402	83.42 329	91.90 369

CMPMVS	59.16 21	80.52 362	79.20 364	84.48 385	83.98 425	67.63 414	89.95 360	93.84 287	64.79 429	66.81 427	91.14 311	57.93 386	95.17 367	76.25 324	88.10 279	90.65 394
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 317	84.79 296	84.37 386	91.84 312	64.92 424	93.70 239	91.47 354	66.19 426	86.16 234	95.28 146	67.18 309	93.33 395	80.89 269	90.42 239	94.88 247

PVSNet_0	73.20 20	77.22 386	74.83 392	84.37 386	90.70 363	71.10 391	83.09 428	89.67 395	72.81 404	73.93 410	83.13 420	60.79 367	93.70 391	68.54 381	50.84 441	88.30 420

LF4IMVS		80.37 365	79.07 368	84.27 388	86.64 412	69.87 404	89.39 370	91.05 363	76.38 366	74.97 404	90.00 348	47.85 421	94.25 382	74.55 344	80.82 367	88.69 417

Anonymous20240521		80.44 364	79.21 363	84.11 389	85.75 419	67.89 410	92.86 280	93.23 300	75.61 375	75.59 401	87.47 391	50.03 414	94.33 379	71.14 365	81.21 355	90.12 401

PM-MVS		78.11 383	76.12 387	84.09 390	83.54 427	70.08 401	88.97 378	85.27 422	79.93 321	74.73 406	86.43 402	34.70 439	93.48 393	79.43 291	72.06 408	88.72 416

test_fmvs2		83.98 325	84.03 310	83.83 391	87.16 410	67.53 415	93.93 226	92.89 309	77.62 355	86.89 215	93.53 224	47.18 423	92.02 409	90.54 126	86.51 300	91.93 368

testgi		80.94 361	80.20 350	83.18 392	87.96 406	66.29 416	91.28 325	90.70 375	83.70 240	78.12 379	92.84 246	51.37 412	90.82 419	63.34 409	82.46 340	92.43 356

KD-MVS_self_test		80.20 366	79.24 362	83.07 393	85.64 420	65.29 422	91.01 333	93.93 281	78.71 343	76.32 393	86.40 404	59.20 379	92.93 401	72.59 355	69.35 415	91.00 392

testing3		80.46 363	79.59 359	83.06 394	93.44 260	64.64 425	93.33 252	85.47 420	84.34 227	79.93 364	90.84 321	44.35 431	92.39 405	57.06 428	87.56 289	92.16 365

ambc				83.06 394	79.99 436	63.51 429	77.47 439	92.86 310		74.34 409	84.45 415	28.74 440	95.06 371	73.06 353	68.89 419	90.61 395

test20.03		79.95 370	79.08 367	82.55 396	85.79 418	67.74 413	91.09 331	91.08 361	81.23 308	74.48 408	89.96 350	61.63 354	90.15 421	60.08 419	76.38 398	89.76 403

MVStest1		72.91 394	69.70 399	82.54 397	78.14 439	73.05 365	88.21 387	86.21 414	60.69 433	64.70 428	90.53 331	46.44 426	85.70 436	58.78 424	53.62 438	88.87 415

test_vis1_rt		77.96 384	76.46 384	82.48 398	85.89 417	71.74 384	90.25 347	78.89 437	71.03 414	71.30 420	81.35 427	42.49 433	91.05 418	84.55 205	82.37 341	84.65 425

EU-MVSNet		81.32 355	80.95 342	82.42 399	88.50 397	63.67 428	93.32 253	91.33 356	64.02 430	80.57 354	92.83 247	61.21 363	92.27 407	76.34 323	80.38 374	91.32 382

myMVS_eth3d		79.67 373	78.79 370	82.32 400	91.92 308	64.08 426	89.75 363	87.40 412	81.72 293	78.82 374	87.20 394	45.33 429	91.29 415	59.09 423	87.84 286	91.60 374

ttmdpeth		76.55 388	74.64 393	82.29 401	82.25 432	67.81 412	89.76 362	85.69 418	70.35 416	75.76 399	91.69 290	46.88 424	89.77 423	66.16 398	63.23 430	89.30 408

pmmvs3		71.81 397	68.71 400	81.11 402	75.86 441	70.42 399	86.74 405	83.66 426	58.95 436	68.64 426	80.89 428	36.93 437	89.52 425	63.10 411	63.59 428	83.39 426

Syy-MVS		80.07 368	79.78 354	80.94 403	91.92 308	59.93 435	89.75 363	87.40 412	81.72 293	78.82 374	87.20 394	66.29 322	91.29 415	47.06 436	87.84 286	91.60 374

UWE-MVS-28		78.98 379	78.38 373	80.80 404	88.18 404	60.66 434	90.65 339	78.51 438	78.84 338	77.93 382	90.93 318	59.08 381	89.02 428	50.96 433	90.33 241	92.72 347

new-patchmatchnet		76.41 389	75.17 391	80.13 405	82.65 431	59.61 436	87.66 398	91.08 361	78.23 352	69.85 423	83.22 419	54.76 401	91.63 414	64.14 408	64.89 427	89.16 412

mvsany_test3		74.95 391	73.26 395	80.02 406	74.61 442	63.16 430	85.53 414	78.42 439	74.16 389	74.89 405	86.46 401	36.02 438	89.09 427	82.39 236	66.91 422	87.82 423

test_fmvs3		77.67 385	77.16 381	79.22 407	79.52 437	61.14 432	92.34 295	91.64 348	73.98 391	78.86 373	86.59 400	27.38 443	87.03 431	88.12 154	75.97 400	89.50 405

DSMNet-mixed		76.94 387	76.29 386	78.89 408	83.10 429	56.11 444	87.78 394	79.77 435	60.65 434	75.64 400	88.71 373	61.56 357	88.34 430	60.07 420	89.29 262	92.21 364

EGC-MVSNET		61.97 405	56.37 410	78.77 409	89.63 386	73.50 360	89.12 375	82.79 428	0.21 455	1.24 456	84.80 413	39.48 434	90.04 422	44.13 438	75.94 401	72.79 437

new_pmnet		72.15 395	70.13 398	78.20 410	82.95 430	65.68 419	83.91 424	82.40 430	62.94 432	64.47 429	79.82 429	42.85 432	86.26 435	57.41 427	74.44 403	82.65 430

MVS-HIRNet		73.70 393	72.20 396	78.18 411	91.81 315	56.42 443	82.94 429	82.58 429	55.24 437	68.88 424	66.48 440	55.32 398	95.13 368	58.12 425	88.42 275	83.01 428

LCM-MVSNet		66.00 402	62.16 407	77.51 412	64.51 452	58.29 438	83.87 425	90.90 369	48.17 441	54.69 438	73.31 436	16.83 452	86.75 432	65.47 400	61.67 432	87.48 424

APD_test1		69.04 398	66.26 404	77.36 413	80.51 435	62.79 431	85.46 415	83.51 427	54.11 439	59.14 436	84.79 414	23.40 446	89.61 424	55.22 429	70.24 413	79.68 434

test_f		71.95 396	70.87 397	75.21 414	74.21 444	59.37 437	85.07 418	85.82 417	65.25 428	70.42 422	83.13 420	23.62 444	82.93 442	78.32 301	71.94 409	83.33 427

ANet_high		58.88 409	54.22 414	72.86 415	56.50 455	56.67 440	80.75 434	86.00 416	73.09 401	37.39 447	64.63 443	22.17 447	79.49 445	43.51 439	23.96 449	82.43 431

test_vis3_rt		65.12 403	62.60 405	72.69 416	71.44 445	60.71 433	87.17 402	65.55 449	63.80 431	53.22 439	65.65 442	14.54 453	89.44 426	76.65 318	65.38 425	67.91 440

FPMVS		64.63 404	62.55 406	70.88 417	70.80 446	56.71 439	84.42 422	84.42 424	51.78 440	49.57 440	81.61 426	23.49 445	81.48 443	40.61 443	76.25 399	74.46 436

dmvs_testset		74.57 392	75.81 390	70.86 418	87.72 409	40.47 453	87.05 404	77.90 443	82.75 266	71.15 421	85.47 411	67.98 304	84.12 440	45.26 437	76.98 397	88.00 421

N_pmnet		68.89 399	68.44 401	70.23 419	89.07 391	28.79 458	88.06 389	19.50 458	69.47 418	71.86 418	84.93 412	61.24 362	91.75 412	54.70 430	77.15 394	90.15 400

testf1		59.54 407	56.11 411	69.85 420	69.28 447	56.61 441	80.37 435	76.55 446	42.58 444	45.68 443	75.61 431	11.26 454	84.18 438	43.20 440	60.44 434	68.75 438

APD_test2		59.54 407	56.11 411	69.85 420	69.28 447	56.61 441	80.37 435	76.55 446	42.58 444	45.68 443	75.61 431	11.26 454	84.18 438	43.20 440	60.44 434	68.75 438

WB-MVS		67.92 400	67.49 402	69.21 422	81.09 433	41.17 452	88.03 390	78.00 442	73.50 396	62.63 431	83.11 422	63.94 339	86.52 433	25.66 448	51.45 440	79.94 433

PMMVS2		59.60 406	56.40 409	69.21 422	68.83 449	46.58 448	73.02 443	77.48 444	55.07 438	49.21 441	72.95 437	17.43 451	80.04 444	49.32 435	44.33 444	80.99 432

SSC-MVS		67.06 401	66.56 403	68.56 424	80.54 434	40.06 454	87.77 395	77.37 445	72.38 406	61.75 433	82.66 424	63.37 342	86.45 434	24.48 449	48.69 443	79.16 435

Gipuma		57.99 411	54.91 413	67.24 425	88.51 395	65.59 420	52.21 446	90.33 380	43.58 443	42.84 446	51.18 447	20.29 449	85.07 437	34.77 444	70.45 412	51.05 446
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 413	48.46 417	63.48 426	45.72 457	46.20 449	73.41 442	78.31 440	41.03 446	30.06 449	65.68 441	6.05 456	83.43 441	30.04 446	65.86 424	60.80 441
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 410	58.24 408	60.56 427	83.13 428	45.09 451	82.32 430	48.22 457	67.61 422	61.70 434	69.15 438	38.75 435	76.05 446	32.01 445	41.31 445	60.55 442

MVE	39.65 23	43.39 415	38.59 421	57.77 428	56.52 454	48.77 447	55.38 445	58.64 453	29.33 449	28.96 450	52.65 446	4.68 457	64.62 450	28.11 447	33.07 447	59.93 443
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 414	48.47 416	56.66 429	52.26 456	18.98 460	41.51 448	81.40 432	10.10 450	44.59 445	75.01 434	28.51 441	68.16 447	53.54 431	49.31 442	82.83 429

DeepMVS_CX				56.31 430	74.23 443	51.81 446		56.67 454	44.85 442	48.54 442	75.16 433	27.87 442	58.74 452	40.92 442	52.22 439	58.39 444

kuosan		53.51 412	53.30 415	54.13 431	76.06 440	45.36 450	80.11 437	48.36 456	59.63 435	54.84 437	63.43 444	37.41 436	62.07 451	20.73 451	39.10 446	54.96 445

E-PMN		43.23 416	42.29 418	46.03 432	65.58 451	37.41 455	73.51 441	64.62 450	33.99 447	28.47 451	47.87 448	19.90 450	67.91 448	22.23 450	24.45 448	32.77 447

EMVS		42.07 417	41.12 419	44.92 433	63.45 453	35.56 457	73.65 440	63.48 451	33.05 448	26.88 452	45.45 449	21.27 448	67.14 449	19.80 452	23.02 450	32.06 448

tmp_tt		35.64 418	39.24 420	24.84 434	14.87 458	23.90 459	62.71 444	51.51 455	6.58 452	36.66 448	62.08 445	44.37 430	30.34 454	52.40 432	22.00 451	20.27 449

wuyk23d		21.27 420	20.48 423	23.63 435	68.59 450	36.41 456	49.57 447	6.85 459	9.37 451	7.89 453	4.46 455	4.03 458	31.37 453	17.47 453	16.07 452	3.12 450

test123		8.76 422	11.22 425	1.39 436	0.85 460	0.97 461	85.76 412	0.35 461	0.54 454	2.45 455	8.14 454	0.60 459	0.48 455	2.16 455	0.17 454	2.71 451

testmvs		8.92 421	11.52 424	1.12 437	1.06 459	0.46 462	86.02 409	0.65 460	0.62 453	2.74 454	9.52 453	0.31 460	0.45 456	2.38 454	0.39 453	2.46 452

mmdepth		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

monomultidepth		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

test_blank		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uanet_test		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

DCPMVS		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

cdsmvs_eth3d_5k		22.14 419	29.52 422	0.00 438	0.00 461	0.00 463	0.00 449	95.76 177	0.00 456	0.00 457	94.29 192	75.66 193	0.00 457	0.00 456	0.00 455	0.00 453

pcd_1.5k_mvsjas		6.64 424	8.86 427	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	79.70 138	0.00 457	0.00 456	0.00 455	0.00 453

sosnet-low-res		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

sosnet		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uncertanet		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

Regformer		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

ab-mvs-re		7.82 423	10.43 426	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	93.88 213	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

uanet		0.00 425	0.00 428	0.00 438	0.00 461	0.00 463	0.00 449	0.00 462	0.00 456	0.00 457	0.00 456	0.00 461	0.00 457	0.00 456	0.00 455	0.00 453

WAC-MVS						64.08 426								59.14 422

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

PC_three_1452									82.47 270	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					0.00 461

eth-test					0.00 461

ZD-MVS					98.15 36	86.62 33		97.07 54	83.63 242	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	81.26 306	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																96.12 193

test_part2					98.55 12	87.22 19				96.40 25

sam_mvs1												71.70 249				96.12 193

sam_mvs												70.60 263

MTGPA								96.97 59

test_post1							88.00 391				9.81 452	69.31 287	95.53 356	76.65 318

test_post											10.29 451	70.57 267	95.91 341

patchmatchnet-post											83.76 417	71.53 250	96.48 310

MTMP							96.16 55	60.64 452

gm-plane-assit					89.60 387	68.00 409			77.28 360		88.99 367		97.57 219	79.44 290

test9_res														91.91 101	98.71 32	98.07 77

TEST9					97.53 63	86.49 37	94.07 214	96.78 83	81.61 298	92.77 93	96.20 101	87.71 28	99.12 57

test_8					97.49 65	86.30 45	94.02 219	96.76 86	81.86 289	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	94.11 208

test_prior2							94.12 206		87.67 140	92.63 101	96.39 96	86.62 41		91.50 110	98.67 40

旧先验2							93.36 251		71.25 412	94.37 53			97.13 267	86.74 173

新几何2							93.11 266

旧先验1					96.79 81	81.81 183		95.67 185			96.81 77	86.69 39			97.66 91	96.97 152

无先验							93.28 259	96.26 132	73.95 392				99.05 61	80.56 275		96.59 172

原ACMM2							92.94 276

test222					96.55 90	81.70 185	92.22 300	95.01 229	68.36 421	90.20 153	96.14 106	80.26 131			97.80 85	96.05 200

testdata2													98.75 109	78.30 302

segment_acmp												87.16 36

testdata1							92.15 302		87.94 127

plane_prior7					94.70 189	82.74 159

plane_prior6					94.52 202	82.75 157						74.23 211

plane_prior5								96.22 137					98.12 169	88.15 151	89.99 245	94.63 254

plane_prior4											94.86 166

plane_prior3						82.75 157			90.26 44	86.91 212

plane_prior2							95.85 86		90.81 24

plane_prior1					94.59 195

plane_prior						82.73 160	95.21 132		89.66 65						89.88 250

n2								0.00 462

nn								0.00 462

door-mid								85.49 419

test11								96.57 104

door								85.33 421

HQP5-MVS						81.56 187

HQP-NCC					94.17 223		94.39 189		88.81 95	85.43 258

ACMP_Plane					94.17 223		94.39 189		88.81 95	85.43 258

BP-MVS														87.11 170

HQP4-MVS										85.43 258			97.96 191			94.51 264

HQP3-MVS								96.04 154							89.77 254

HQP2-MVS												73.83 222

NP-MVS					94.37 213	82.42 169					93.98 206

MDTV_nov1_ep13_2view						55.91 445	87.62 399		73.32 398	84.59 280		70.33 270		74.65 341		95.50 221

MDTV_nov1_ep13			83.56 318		91.69 320	69.93 402	87.75 396	91.54 351	78.60 344	84.86 274	88.90 369	69.54 282	96.03 332	70.25 370	88.93 267

ACMMP++_ref															87.47 290

ACMMP++															88.01 282

Test By Simon												80.02 133

High-resolution multi-view benchmark