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 76					99.61 4	96.03 26	99.06 9	99.07 5

No_MVS				96.52 1	97.78 56	90.86 1		96.85 76					99.61 4	96.03 26	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 44	90.85 3	97.13 15				97.08 65	92.59 2	98.94 87	92.25 87	98.99 14	98.84 15

HPM-MVS++		95.14 10	94.91 22	95.83 4	98.25 31	89.65 4	95.92 82	96.96 64	91.75 12	94.02 66	96.83 77	88.12 24	99.55 16	93.41 61	98.94 16	98.28 57

MM		95.10 11	94.91 22	95.68 5	96.09 111	88.34 9	96.68 34	94.37 281	95.08 1	94.68 52	97.72 38	82.94 96	99.64 1	97.85 5	98.76 29	99.06 7

SMA-MVS		95.20 8	95.07 16	95.59 6	98.14 37	88.48 8	96.26 49	97.28 36	85.90 191	97.67 4	98.10 13	88.41 20	99.56 12	94.66 45	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 99	91.37 113	95.55 7	95.63 138	88.73 6	97.07 19	96.77 87	90.84 25	84.02 317	96.62 90	75.95 201	99.34 38	87.77 168	97.68 92	98.59 25

CNVR-MVS		95.40 7	95.37 9	95.50 8	98.11 38	88.51 7	95.29 124	96.96 64	92.09 9	95.32 44	97.08 65	89.49 15	99.33 41	95.10 40	98.85 20	98.66 22

MGCNet		94.18 46	93.80 60	95.34 9	94.91 177	87.62 14	95.97 77	93.01 325	92.58 6	94.22 57	97.20 59	80.56 131	99.59 8	97.04 19	98.68 37	98.81 18

ACMMP_NAP		94.74 22	94.56 29	95.28 10	98.02 43	87.70 11	95.68 100	97.34 26	88.28 117	95.30 45	97.67 40	85.90 51	99.54 20	93.91 53	98.95 15	98.60 24

DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 27	87.28 18	95.56 112	97.51 7	89.13 86	97.14 15	97.91 31	91.64 7	99.62 2	94.61 46	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 24	95.20 12	97.84 52	87.76 10	96.65 35	97.48 12	87.76 139	95.71 39	97.70 39	88.28 23	99.35 37	93.89 54	98.78 26	98.48 31

MCST-MVS		94.45 30	94.20 47	95.19 13	98.46 19	87.50 16	95.00 147	97.12 51	87.13 157	92.51 107	96.30 99	89.24 17	99.34 38	93.46 58	98.62 46	98.73 19

NCCC		94.81 19	94.69 28	95.17 14	97.83 53	87.46 17	95.66 103	96.93 68	92.34 7	93.94 67	96.58 92	87.74 27	99.44 29	92.83 70	98.40 54	98.62 23

DPM-MVS		92.58 95	91.74 105	95.08 15	96.19 102	89.31 5	92.66 296	96.56 107	83.44 265	91.68 132	95.04 172	86.60 43	98.99 77	85.60 202	97.92 80	96.93 171

ZNCC-MVS		94.47 29	94.28 41	95.03 16	98.52 15	86.96 20	96.85 29	97.32 30	88.24 118	93.15 82	97.04 68	86.17 48	99.62 2	92.40 81	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 33	99.08 7	98.99 9

MTAPA		94.42 35	94.22 44	95.00 18	98.42 21	86.95 21	94.36 197	96.97 61	91.07 21	93.14 83	97.56 42	84.30 77	99.56 12	93.43 59	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 32	96.69 82	89.90 12	99.30 44	94.70 44	98.04 75	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 32	94.27 43	94.92 20	98.65 8	86.67 30	96.92 25	97.23 39	88.60 108	93.58 74	97.27 53	85.22 60	99.54 20	92.21 89	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 91	96.20 31	98.10 13	89.39 16	99.34 38	95.88 28	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 32	94.28 41	94.91 21	98.63 9	86.69 28	96.94 21	97.32 30	88.63 105	93.53 77	97.26 55	85.04 64	99.54 20	92.35 84	98.78 26	98.50 28

GST-MVS		94.21 41	93.97 56	94.90 23	98.41 22	86.82 24	96.54 37	97.19 40	88.24 118	93.26 79	96.83 77	85.48 57	99.59 8	91.43 115	98.40 54	98.30 51

HFP-MVS		94.52 27	94.40 34	94.86 24	98.61 10	86.81 25	96.94 21	97.34 26	88.63 105	93.65 72	97.21 57	86.10 49	99.49 26	92.35 84	98.77 28	98.30 51

sasdasda		93.27 78	92.75 88	94.85 25	95.70 133	87.66 12	96.33 40	96.41 117	90.00 50	94.09 62	94.60 196	82.33 105	98.62 126	92.40 81	92.86 216	98.27 59

MP-MVS-pluss		94.21 41	94.00 55	94.85 25	98.17 35	86.65 31	94.82 160	97.17 45	86.26 183	92.83 92	97.87 33	85.57 56	99.56 12	94.37 49	98.92 17	98.34 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 78	92.75 88	94.85 25	95.70 133	87.66 12	96.33 40	96.41 117	90.00 50	94.09 62	94.60 196	82.33 105	98.62 126	92.40 81	92.86 216	98.27 59

XVS		94.45 30	94.32 37	94.85 25	98.54 13	86.60 34	96.93 23	97.19 40	90.66 33	92.85 90	97.16 63	85.02 65	99.49 26	91.99 100	98.56 50	98.47 34

X-MVStestdata		88.31 221	86.13 270	94.85 25	98.54 13	86.60 34	96.93 23	97.19 40	90.66 33	92.85 90	23.41 469	85.02 65	99.49 26	91.99 100	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 33	91.38 18	95.39 43	97.46 45	88.98 19	99.40 30	94.12 50	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 28	97.62 8	98.06 21	92.59 2	99.61 4	95.64 31	99.02 12	98.86 12

alignmvs		93.08 86	92.50 94	94.81 32	95.62 139	87.61 15	95.99 74	96.07 154	89.77 63	94.12 61	94.87 180	80.56 131	98.66 118	92.42 80	93.10 212	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 40	97.71 2	98.07 19	92.31 4	99.58 10	95.66 29	99.13 3	98.84 15

DeepC-MVS_fast	89.43 2	94.04 49	93.79 61	94.80 33	97.48 66	86.78 26	95.65 105	96.89 73	89.40 74	92.81 93	96.97 70	85.37 59	99.24 47	90.87 124	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 38	94.07 52	94.77 35	98.47 18	86.31 44	96.71 32	96.98 60	89.04 89	91.98 118	97.19 60	85.43 58	99.56 12	92.06 98	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 39	94.07 52	94.75 36	98.06 41	86.90 23	95.88 84	96.94 67	85.68 198	95.05 50	97.18 61	87.31 35	99.07 60	91.90 106	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 36	94.21 46	94.74 37	98.39 25	86.64 32	97.60 5	97.24 37	88.53 110	92.73 98	97.23 56	85.20 61	99.32 42	92.15 92	98.83 22	98.25 64

PGM-MVS		93.96 54	93.72 66	94.68 38	98.43 20	86.22 47	95.30 122	97.78 1	87.45 148	93.26 79	97.33 51	84.62 74	99.51 24	90.75 126	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 93	90.27 44	97.04 19	98.05 24	91.47 8	99.55 16	95.62 33	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 52	93.78 62	94.63 40	98.50 16	85.90 62	96.87 27	96.91 71	88.70 103	91.83 127	97.17 62	83.96 81	99.55 16	91.44 114	98.64 45	98.43 39

PHI-MVS		93.89 56	93.65 70	94.62 41	96.84 80	86.43 39	96.69 33	97.49 8	85.15 222	93.56 76	96.28 100	85.60 55	99.31 43	92.45 78	98.79 24	98.12 76

TSAR-MVS + MP.		94.85 16	94.94 20	94.58 42	98.25 31	86.33 42	96.11 62	96.62 102	88.14 123	96.10 32	96.96 71	89.09 18	98.94 87	94.48 47	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 65	93.20 79	94.55 43	95.65 136	85.73 67	94.94 150	96.69 98	91.89 11	90.69 150	95.88 124	81.99 117	99.54 20	93.14 65	97.95 79	98.39 41

train_agg		93.44 71	93.08 81	94.52 44	97.53 63	86.49 37	94.07 217	96.78 85	81.86 307	92.77 95	96.20 103	87.63 29	99.12 58	92.14 93	98.69 35	97.94 89

CDPH-MVS		92.83 90	92.30 97	94.44 45	97.79 54	86.11 51	94.06 219	96.66 99	80.09 338	92.77 95	96.63 89	86.62 41	99.04 64	87.40 175	98.66 41	98.17 69

3Dnovator	86.66 5	91.73 112	90.82 128	94.44 45	94.59 202	86.37 41	97.18 13	97.02 58	89.20 83	84.31 312	96.66 85	73.74 241	99.17 52	86.74 185	97.96 78	97.79 106

SR-MVS		94.23 40	94.17 50	94.43 47	98.21 34	85.78 65	96.40 39	96.90 72	88.20 121	94.33 56	97.40 48	84.75 73	99.03 65	93.35 62	97.99 77	98.48 31

HPM-MVS		94.02 50	93.88 57	94.43 47	98.39 25	85.78 65	97.25 11	97.07 56	86.90 167	92.62 104	96.80 81	84.85 71	99.17 52	92.43 79	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 63	93.41 74	94.41 49	96.59 87	86.78 26	94.40 189	93.93 299	89.77 63	94.21 58	95.59 143	87.35 34	98.61 128	92.72 73	96.15 130	97.83 103

reproduce-ours		94.82 17	94.97 18	94.38 50	97.91 49	85.46 70	95.86 85	97.15 47	89.82 56	95.23 47	98.10 13	87.09 37	99.37 33	95.30 37	98.25 63	98.30 51

our_new_method		94.82 17	94.97 18	94.38 50	97.91 49	85.46 70	95.86 85	97.15 47	89.82 56	95.23 47	98.10 13	87.09 37	99.37 33	95.30 37	98.25 63	98.30 51

NormalMVS		93.46 68	93.16 80	94.37 52	98.40 23	86.20 48	96.30 42	96.27 130	91.65 16	92.68 100	96.13 109	77.97 171	98.84 100	90.75 126	98.26 59	98.07 78

test12				94.34 53	97.13 75	86.15 50		96.29 126		91.04 146		85.08 63	99.01 70		98.13 70	97.86 98

SymmetryMVS		92.81 92	92.31 96	94.32 54	96.15 103	86.20 48	96.30 42	94.43 277	91.65 16	92.68 100	96.13 109	77.97 171	98.84 100	90.75 126	94.72 162	97.92 93

ACMMP		93.24 80	92.88 86	94.30 55	98.09 40	85.33 74	96.86 28	97.45 16	88.33 114	90.15 165	97.03 69	81.44 124	99.51 24	90.85 125	95.74 137	98.04 84
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 21	94.29 56	97.92 45	85.18 76	95.95 80	97.19 40	89.67 66	95.27 46	98.16 5	86.53 44	99.36 36	95.42 36	98.15 68	98.33 46

DeepC-MVS	88.79 3	93.31 77	92.99 84	94.26 57	96.07 113	85.83 63	94.89 153	96.99 59	89.02 92	89.56 174	97.37 50	82.51 102	99.38 31	92.20 90	98.30 57	97.57 121
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 87	92.63 91	94.23 58	95.62 139	85.92 59	96.08 64	96.33 124	89.86 54	93.89 69	94.66 193	82.11 112	98.50 134	92.33 86	92.82 219	98.27 59

fmvsm_l_conf0.5_n_3		94.80 20	95.01 17	94.15 59	95.64 137	85.08 77	96.09 63	97.36 24	90.98 23	97.09 17	98.12 9	84.98 69	98.94 87	97.07 16	97.80 87	98.43 39

EPNet		91.79 107	91.02 122	94.10 60	90.10 394	85.25 75	96.03 71	92.05 352	92.83 5	87.39 223	95.78 135	79.39 153	99.01 70	88.13 162	97.48 95	98.05 83
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 19	96.22 30	98.08 18	86.64 40	99.37 33	94.91 42	98.26 59	98.29 56

test_fmvsmconf_n		94.60 25	94.81 26	93.98 62	94.62 198	84.96 80	96.15 57	97.35 25	89.37 75	96.03 35	98.11 10	86.36 45	99.01 70	97.45 10	97.83 85	97.96 88

DELS-MVS		93.43 75	93.25 77	93.97 63	95.42 147	85.04 78	93.06 279	97.13 50	90.74 30	91.84 125	95.09 171	86.32 46	99.21 50	91.22 116	98.45 52	97.65 115
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		91.95 104	91.28 116	93.96 64	98.33 29	85.92 59	94.66 172	96.66 99	82.69 285	90.03 167	95.82 131	82.30 107	99.03 65	84.57 220	96.48 123	96.91 173

HPM-MVS_fast		93.40 76	93.22 78	93.94 65	98.36 27	84.83 82	97.15 14	96.80 84	85.77 195	92.47 108	97.13 64	82.38 103	99.07 60	90.51 131	98.40 54	97.92 93

test_fmvsmconf0.1_n		94.20 43	94.31 39	93.88 66	92.46 310	84.80 83	96.18 54	96.82 81	89.29 80	95.68 40	98.11 10	85.10 62	98.99 77	97.38 11	97.75 91	97.86 98

SD-MVS		94.96 15	95.33 10	93.88 66	97.25 74	86.69 28	96.19 52	97.11 54	90.42 36	96.95 21	97.27 53	89.53 14	96.91 298	94.38 48	98.85 20	98.03 85
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 70	93.31 75	93.84 68	96.99 77	84.84 81	93.24 270	97.24 37	88.76 100	91.60 133	95.85 128	86.07 50	98.66 118	91.91 104	98.16 67	98.03 85

SR-MVS-dyc-post		93.82 58	93.82 59	93.82 69	97.92 45	84.57 89	96.28 46	96.76 88	87.46 146	93.75 70	97.43 46	84.24 78	99.01 70	92.73 71	97.80 87	97.88 96

test_prior				93.82 69	97.29 72	84.49 93		96.88 74					98.87 94			98.11 77

APD-MVS_3200maxsize		93.78 59	93.77 63	93.80 71	97.92 45	84.19 106	96.30 42	96.87 75	86.96 163	93.92 68	97.47 44	83.88 82	98.96 84	92.71 74	97.87 83	98.26 63

fmvsm_l_conf0.5_n		94.29 37	94.46 32	93.79 72	95.28 152	85.43 72	95.68 100	96.43 115	86.56 175	96.84 23	97.81 36	87.56 32	98.77 109	97.14 14	96.82 113	97.16 152

CSCG		93.23 81	93.05 82	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 246	90.05 166	95.66 140	87.77 26	99.15 56	89.91 136	98.27 58	98.07 78

GDP-MVS		92.04 102	91.46 110	93.75 74	94.55 208	84.69 86	95.60 111	96.56 107	87.83 136	93.07 86	95.89 123	73.44 245	98.65 120	90.22 134	96.03 132	97.91 95

BP-MVS1		92.48 97	92.07 100	93.72 75	94.50 211	84.39 101	95.90 83	94.30 284	90.39 37	92.67 102	95.94 120	74.46 224	98.65 120	93.14 65	97.35 99	98.13 73

test_fmvsmconf0.01_n		93.19 82	93.02 83	93.71 76	89.25 407	84.42 100	96.06 68	96.29 126	89.06 87	94.68 52	98.13 6	79.22 155	98.98 81	97.22 13	97.24 101	97.74 109

UA-Net		92.83 90	92.54 93	93.68 77	96.10 110	84.71 85	95.66 103	96.39 119	91.92 10	93.22 81	96.49 95	83.16 91	98.87 94	84.47 222	95.47 144	97.45 127

fmvsm_l_conf0.5_n_a		94.20 43	94.40 34	93.60 78	95.29 151	84.98 79	95.61 108	96.28 129	86.31 181	96.75 25	97.86 34	87.40 33	98.74 113	97.07 16	97.02 106	97.07 157

QAPM		89.51 178	88.15 205	93.59 79	94.92 175	84.58 88	96.82 30	96.70 97	78.43 365	83.41 333	96.19 106	73.18 250	99.30 44	77.11 333	96.54 120	96.89 174

test_fmvsm_n_1920		94.71 23	95.11 15	93.50 80	95.79 127	84.62 87	96.15 57	97.64 3	89.85 55	97.19 14	97.89 32	86.28 47	98.71 116	97.11 15	98.08 74	97.17 146

fmvsm_s_conf0.5_n_9		94.99 13	95.50 7	93.44 81	96.51 95	82.25 179	95.76 95	96.92 69	93.37 3	97.63 7	98.43 1	84.82 72	99.16 55	98.15 1	97.92 80	98.90 11

KinetiMVS		91.82 106	91.30 114	93.39 82	94.72 191	83.36 133	95.45 115	96.37 121	90.33 39	92.17 113	96.03 115	72.32 262	98.75 110	87.94 165	96.34 125	98.07 78

casdiffmvs_mvg		92.96 89	92.83 87	93.35 83	94.59 202	83.40 131	95.00 147	96.34 123	90.30 42	92.05 116	96.05 113	83.43 85	98.15 170	92.07 95	95.67 138	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 54	94.18 49	93.30 84	94.79 184	83.81 117	95.77 93	96.74 92	88.02 126	96.23 29	97.84 35	83.36 89	98.83 103	97.49 8	97.34 100	97.25 139

EI-MVSNet-Vis-set		93.01 88	92.92 85	93.29 85	95.01 166	83.51 128	94.48 181	95.77 182	90.87 24	92.52 106	96.67 84	84.50 75	99.00 75	91.99 100	94.44 175	97.36 130

Vis-MVSNet		91.75 111	91.23 117	93.29 85	95.32 150	83.78 118	96.14 59	95.98 161	89.89 52	90.45 154	96.58 92	75.09 213	98.31 161	84.75 214	96.90 109	97.78 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 53	94.22 44	93.26 87	96.13 105	83.29 135	96.27 48	96.52 110	89.82 56	95.56 42	95.51 146	84.50 75	98.79 107	94.83 43	98.86 19	97.72 111

SPE-MVS-test		94.02 50	94.29 40	93.24 88	96.69 83	83.24 136	97.49 6	96.92 69	92.14 8	92.90 88	95.77 136	85.02 65	98.33 158	93.03 67	98.62 46	98.13 73

VNet		92.24 101	91.91 102	93.24 88	96.59 87	83.43 129	94.84 159	96.44 114	89.19 84	94.08 65	95.90 122	77.85 177	98.17 168	88.90 152	93.38 201	98.13 73

fmvsm_s_conf0.5_n_10		94.43 32	94.84 25	93.20 90	95.73 130	83.19 139	95.99 74	97.31 32	91.08 20	97.67 4	98.11 10	81.87 119	99.22 48	97.86 4	97.91 82	97.20 144

VDD-MVS		90.74 136	89.92 150	93.20 90	96.27 100	83.02 151	95.73 97	93.86 303	88.42 113	92.53 105	96.84 76	62.09 368	98.64 123	90.95 122	92.62 226	97.93 92

Elysia		90.12 155	89.10 173	93.18 92	93.16 280	84.05 110	95.22 131	96.27 130	85.16 220	90.59 151	94.68 189	64.64 351	98.37 151	86.38 191	95.77 135	97.12 154

StellarMVS		90.12 155	89.10 173	93.18 92	93.16 280	84.05 110	95.22 131	96.27 130	85.16 220	90.59 151	94.68 189	64.64 351	98.37 151	86.38 191	95.77 135	97.12 154

CS-MVS		94.12 47	94.44 33	93.17 94	96.55 90	83.08 148	97.63 4	96.95 66	91.71 14	93.50 78	96.21 102	85.61 54	98.24 163	93.64 56	98.17 66	98.19 67

nrg030		91.08 130	90.39 134	93.17 94	93.07 287	86.91 22	96.41 38	96.26 134	88.30 116	88.37 199	94.85 183	82.19 111	97.64 224	91.09 117	82.95 350	94.96 259

MVSMamba_PlusPlus		93.44 71	93.54 72	93.14 96	96.58 89	83.05 149	96.06 68	96.50 112	84.42 243	94.09 62	95.56 145	85.01 68	98.69 117	94.96 41	98.66 41	97.67 114

EI-MVSNet-UG-set		92.74 93	92.62 92	93.12 97	94.86 180	83.20 138	94.40 189	95.74 185	90.71 32	92.05 116	96.60 91	84.00 80	98.99 77	91.55 112	93.63 191	97.17 146

test_fmvsmvis_n_1920		93.44 71	93.55 71	93.10 98	93.67 266	84.26 104	95.83 89	96.14 145	89.00 93	92.43 109	97.50 43	83.37 88	98.72 114	96.61 23	97.44 96	96.32 199

新几何1				93.10 98	97.30 71	84.35 103		95.56 202	71.09 432	91.26 142	96.24 101	82.87 98	98.86 96	79.19 312	98.10 71	96.07 215

OMC-MVS		91.23 122	90.62 133	93.08 100	96.27 100	84.07 108	93.52 252	95.93 167	86.95 164	89.51 175	96.13 109	78.50 165	98.35 155	85.84 200	92.90 215	96.83 181

OpenMVS	83.78 11	88.74 208	87.29 227	93.08 100	92.70 305	85.39 73	96.57 36	96.43 115	78.74 360	80.85 365	96.07 112	69.64 297	99.01 70	78.01 324	96.65 118	94.83 267

MAR-MVS		90.30 151	89.37 166	93.07 102	96.61 86	84.48 94	95.68 100	95.67 193	82.36 290	87.85 210	92.85 263	76.63 190	98.80 105	80.01 300	96.68 117	95.91 221
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		90.92 131	90.21 138	93.03 103	93.86 251	83.88 115	92.81 291	93.86 303	79.84 341	91.76 129	94.29 210	77.92 174	98.04 187	90.48 132	97.11 102	97.17 146

Effi-MVS+		91.59 116	91.11 119	93.01 104	94.35 224	83.39 132	94.60 174	95.10 237	87.10 158	90.57 153	93.10 258	81.43 125	98.07 184	89.29 144	94.48 173	97.59 120

fmvsm_s_conf0.5_n_a		93.57 64	93.76 64	93.00 105	95.02 165	83.67 121	96.19 52	96.10 151	87.27 152	95.98 36	98.05 24	83.07 95	98.45 144	96.68 22	95.51 141	96.88 175

MVS_111021_LR		92.47 98	92.29 98	92.98 106	95.99 119	84.43 98	93.08 276	96.09 152	88.20 121	91.12 145	95.72 139	81.33 126	97.76 213	91.74 108	97.37 98	96.75 183

fmvsm_s_conf0.1_n_a		93.19 82	93.26 76	92.97 107	92.49 308	83.62 124	96.02 72	95.72 189	86.78 169	96.04 34	98.19 3	82.30 107	98.43 148	96.38 24	95.42 147	96.86 176

ETV-MVS		92.74 93	92.66 90	92.97 107	95.20 158	84.04 112	95.07 143	96.51 111	90.73 31	92.96 87	91.19 324	84.06 79	98.34 156	91.72 109	96.54 120	96.54 194

LFMVS		90.08 158	89.13 172	92.95 109	96.71 82	82.32 178	96.08 64	89.91 409	86.79 168	92.15 115	96.81 79	62.60 366	98.34 156	87.18 179	93.90 185	98.19 67

UGNet		89.95 165	88.95 181	92.95 109	94.51 210	83.31 134	95.70 99	95.23 230	89.37 75	87.58 217	93.94 226	64.00 356	98.78 108	83.92 229	96.31 126	96.74 184
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		90.80 134	90.10 142	92.90 111	93.04 290	83.53 127	93.08 276	94.15 292	80.22 335	91.41 139	94.91 177	76.87 184	97.93 203	90.28 133	96.90 109	97.24 140
jason: jason.
DP-MVS		87.25 262	85.36 299	92.90 111	97.65 60	83.24 136	94.81 161	92.00 354	74.99 400	81.92 354	95.00 173	72.66 255	99.05 62	66.92 413	92.33 231	96.40 196

fmvsm_s_conf0.5_n_8		94.56 26	95.12 14	92.87 113	95.96 122	81.32 204	95.76 95	97.57 5	93.48 2	97.53 10	98.32 2	81.78 122	99.13 57	97.91 2	97.81 86	98.16 70

fmvsm_s_conf0.5_n		93.76 60	94.06 54	92.86 114	95.62 139	83.17 140	96.14 59	96.12 149	88.13 124	95.82 38	98.04 27	83.43 85	98.48 136	96.97 20	96.23 127	96.92 172

fmvsm_s_conf0.1_n		93.46 68	93.66 69	92.85 115	93.75 258	83.13 142	96.02 72	95.74 185	87.68 142	95.89 37	98.17 4	82.78 99	98.46 140	96.71 21	96.17 129	96.98 166

CANet_DTU		90.26 153	89.41 165	92.81 116	93.46 273	83.01 152	93.48 253	94.47 276	89.43 73	87.76 215	94.23 215	70.54 285	99.03 65	84.97 209	96.39 124	96.38 197

MVSFormer		91.68 114	91.30 114	92.80 117	93.86 251	83.88 115	95.96 78	95.90 171	84.66 239	91.76 129	94.91 177	77.92 174	97.30 265	89.64 140	97.11 102	97.24 140

PVSNet_Blended_VisFu		91.38 119	90.91 125	92.80 117	96.39 97	83.17 140	94.87 155	96.66 99	83.29 270	89.27 181	94.46 205	80.29 134	99.17 52	87.57 172	95.37 148	96.05 218

fmvsm_l_conf0.5_n_9		94.65 24	95.28 12	92.77 119	95.95 123	81.83 189	95.53 113	97.12 51	91.68 15	97.89 1	98.06 21	85.71 53	98.65 120	97.32 12	98.26 59	97.83 103

LuminaMVS		90.55 147	89.81 152	92.77 119	92.78 303	84.21 105	94.09 215	94.17 291	85.82 192	91.54 134	94.14 217	69.93 291	97.92 204	91.62 111	94.21 180	96.18 207

fmvsm_s_conf0.5_n_6		94.11 48	94.56 29	92.76 121	94.98 170	81.96 187	95.79 91	97.29 35	89.31 78	97.52 11	97.61 41	83.25 90	98.88 93	97.05 18	98.22 65	97.43 129

VDDNet		89.56 177	88.49 196	92.76 121	95.07 164	82.09 182	96.30 42	93.19 320	81.05 329	91.88 123	96.86 75	61.16 384	98.33 158	88.43 159	92.49 230	97.84 102

viewdifsd2359ckpt09		91.18 125	90.65 132	92.75 123	94.61 201	82.36 177	94.32 198	95.74 185	84.72 236	89.66 173	95.15 169	79.69 148	98.04 187	87.70 169	94.27 179	97.85 101

h-mvs33		90.80 134	90.15 141	92.75 123	96.01 115	82.66 165	95.43 116	95.53 206	89.80 59	93.08 84	95.64 141	75.77 202	99.00 75	92.07 95	78.05 407	96.60 189

casdiffmvs		92.51 96	92.43 95	92.74 125	94.41 219	81.98 185	94.54 178	96.23 138	89.57 69	91.96 120	96.17 107	82.58 101	98.01 190	90.95 122	95.45 146	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		90.69 139	90.02 148	92.71 126	95.72 131	82.41 175	94.11 211	95.12 235	85.63 199	91.49 136	94.70 187	74.75 217	98.42 149	86.13 195	92.53 228	97.31 131

DCV-MVSNet		90.69 139	90.02 148	92.71 126	95.72 131	82.41 175	94.11 211	95.12 235	85.63 199	91.49 136	94.70 187	74.75 217	98.42 149	86.13 195	92.53 228	97.31 131

PCF-MVS	84.11 10	87.74 236	86.08 274	92.70 128	94.02 240	84.43 98	89.27 386	95.87 176	73.62 414	84.43 304	94.33 207	78.48 167	98.86 96	70.27 387	94.45 174	94.81 268
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		90.73 137	90.08 143	92.69 129	95.00 169	83.13 142	94.32 198	95.00 245	85.41 210	89.84 168	95.35 154	76.13 193	97.98 195	85.46 205	94.18 181	96.95 168

baseline		92.39 100	92.29 98	92.69 129	94.46 214	81.77 191	94.14 208	96.27 130	89.22 82	91.88 123	96.00 116	82.35 104	97.99 192	91.05 118	95.27 152	98.30 51

MSLP-MVS++		93.72 62	94.08 51	92.65 131	97.31 70	83.43 129	95.79 91	97.33 28	90.03 49	93.58 74	96.96 71	84.87 70	97.76 213	92.19 91	98.66 41	96.76 182

EC-MVSNet		93.44 71	93.71 67	92.63 132	95.21 157	82.43 172	97.27 10	96.71 96	90.57 35	92.88 89	95.80 132	83.16 91	98.16 169	93.68 55	98.14 69	97.31 131

ab-mvs		89.41 185	88.35 198	92.60 133	95.15 162	82.65 169	92.20 315	95.60 200	83.97 250	88.55 195	93.70 240	74.16 232	98.21 167	82.46 253	89.37 275	96.94 170

LS3D		87.89 231	86.32 263	92.59 134	96.07 113	82.92 155	95.23 129	94.92 252	75.66 392	82.89 340	95.98 118	72.48 259	99.21 50	68.43 401	95.23 153	95.64 235

Anonymous20240529		88.09 227	86.59 252	92.58 135	96.53 92	81.92 188	95.99 74	95.84 178	74.11 409	89.06 185	95.21 164	61.44 376	98.81 104	83.67 236	87.47 306	97.01 164

fmvsm_s_conf0.5_n_3		94.49 28	95.13 13	92.56 136	95.49 145	81.10 214	95.93 81	97.16 46	92.96 4	97.39 12	98.13 6	83.63 84	98.80 105	97.89 3	97.61 94	97.78 107

CPTT-MVS		91.99 103	91.80 103	92.55 137	98.24 33	81.98 185	96.76 31	96.49 113	81.89 306	90.24 158	96.44 97	78.59 163	98.61 128	89.68 139	97.85 84	97.06 158

viewdifsd2359ckpt13		91.20 124	90.75 130	92.54 138	94.30 226	82.13 181	94.03 221	95.89 173	85.60 201	90.20 160	95.36 153	79.69 148	97.90 207	87.85 167	93.86 186	97.61 117

114514_t		89.51 178	88.50 194	92.54 138	98.11 38	81.99 184	95.16 139	96.36 122	70.19 436	85.81 257	95.25 160	76.70 188	98.63 125	82.07 263	96.86 112	97.00 165

PAPM_NR		91.22 123	90.78 129	92.52 140	97.60 61	81.46 200	94.37 195	96.24 137	86.39 180	87.41 220	94.80 185	82.06 115	98.48 136	82.80 248	95.37 148	97.61 117

mamba_0408		89.06 198	87.92 212	92.50 141	94.76 185	82.66 165	79.84 455	94.64 270	85.18 215	88.96 187	95.00 173	76.00 198	97.98 195	83.74 233	93.15 209	96.85 177

DeepPCF-MVS	89.96 1	94.20 43	94.77 27	92.49 142	96.52 93	80.00 254	94.00 226	97.08 55	90.05 48	95.65 41	97.29 52	89.66 13	98.97 82	93.95 52	98.71 32	98.50 28

SSM_0407		90.47 149	89.80 153	92.46 143	94.76 185	82.66 165	93.98 228	95.00 245	85.41 210	88.96 187	95.35 154	76.13 193	97.88 208	85.46 205	93.15 209	96.85 177

IS-MVSNet		91.43 118	91.09 121	92.46 143	95.87 126	81.38 203	96.95 20	93.69 311	89.72 65	89.50 177	95.98 118	78.57 164	97.77 212	83.02 242	96.50 122	98.22 66

API-MVS		90.66 142	90.07 144	92.45 145	96.36 98	84.57 89	96.06 68	95.22 232	82.39 288	89.13 182	94.27 213	80.32 133	98.46 140	80.16 299	96.71 116	94.33 291

xiu_mvs_v1_base_debu		90.64 143	90.05 145	92.40 146	93.97 246	84.46 95	93.32 261	95.46 210	85.17 217	92.25 110	94.03 218	70.59 281	98.57 131	90.97 119	94.67 164	94.18 294

xiu_mvs_v1_base		90.64 143	90.05 145	92.40 146	93.97 246	84.46 95	93.32 261	95.46 210	85.17 217	92.25 110	94.03 218	70.59 281	98.57 131	90.97 119	94.67 164	94.18 294

xiu_mvs_v1_base_debi		90.64 143	90.05 145	92.40 146	93.97 246	84.46 95	93.32 261	95.46 210	85.17 217	92.25 110	94.03 218	70.59 281	98.57 131	90.97 119	94.67 164	94.18 294

fmvsm_s_conf0.5_n_2		93.47 67	93.83 58	92.39 149	95.36 148	81.19 210	95.20 136	96.56 107	90.37 38	97.13 16	98.03 28	77.47 180	98.96 84	97.79 6	96.58 119	97.03 161

viewmacassd2359aftdt		91.67 115	91.43 112	92.37 150	93.95 249	81.00 218	93.90 236	95.97 164	87.75 140	91.45 138	96.04 114	79.92 140	97.97 197	89.26 145	94.67 164	98.14 72

viewmanbaseed2359cas		91.78 109	91.58 108	92.37 150	94.32 225	81.07 215	93.76 241	95.96 165	87.26 153	91.50 135	95.88 124	80.92 130	97.97 197	89.70 138	94.92 158	98.07 78

fmvsm_s_conf0.1_n_2		93.16 84	93.42 73	92.37 150	94.62 198	81.13 212	95.23 129	95.89 173	90.30 42	96.74 26	98.02 29	76.14 192	98.95 86	97.64 7	96.21 128	97.03 161

AdaColmap		89.89 168	89.07 175	92.37 150	97.41 67	83.03 150	94.42 188	95.92 168	82.81 282	86.34 246	94.65 194	73.89 237	99.02 68	80.69 290	95.51 141	95.05 254

CNLPA		89.07 197	87.98 209	92.34 154	96.87 79	84.78 84	94.08 216	93.24 317	81.41 320	84.46 302	95.13 170	75.57 209	96.62 311	77.21 331	93.84 188	95.61 238

fmvsm_s_conf0.5_n_4		93.86 57	94.37 36	92.33 155	95.13 163	80.95 221	95.64 106	96.97 61	89.60 68	96.85 22	97.77 37	83.08 94	98.92 90	97.49 8	96.78 114	97.13 153

ET-MVSNet_ETH3D		87.51 250	85.91 282	92.32 156	93.70 265	83.93 113	92.33 309	90.94 386	84.16 245	72.09 434	92.52 276	69.90 292	95.85 358	89.20 146	88.36 293	97.17 146

Anonymous202405211		87.68 237	86.13 270	92.31 157	96.66 84	80.74 229	94.87 155	91.49 371	80.47 334	89.46 178	95.44 149	54.72 421	98.23 164	82.19 259	89.89 265	97.97 87

CHOSEN 1792x2688		88.84 204	87.69 217	92.30 158	96.14 104	81.42 202	90.01 373	95.86 177	74.52 405	87.41 220	93.94 226	75.46 210	98.36 153	80.36 295	95.53 140	97.12 154

viewcassd2359sk11		91.79 107	91.62 107	92.29 159	94.62 198	80.88 224	93.70 246	96.18 143	87.38 150	91.13 144	95.85 128	81.62 123	98.06 185	89.71 137	94.40 176	97.94 89

HY-MVS	83.01 12	89.03 200	87.94 211	92.29 159	94.86 180	82.77 157	92.08 320	94.49 275	81.52 319	86.93 227	92.79 269	78.32 169	98.23 164	79.93 301	90.55 252	95.88 224

CDS-MVSNet		89.45 181	88.51 193	92.29 159	93.62 268	83.61 126	93.01 280	94.68 268	81.95 300	87.82 213	93.24 252	78.69 161	96.99 292	80.34 296	93.23 206	96.28 202
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 161	89.27 171	92.29 159	95.78 128	80.95 221	92.68 295	96.22 139	81.91 302	86.66 237	93.75 238	82.23 109	98.44 146	79.40 311	94.79 161	97.48 125

mvsmamba		90.33 150	89.69 156	92.25 163	95.17 159	81.64 193	95.27 127	93.36 316	84.88 229	89.51 175	94.27 213	69.29 306	97.42 250	89.34 143	96.12 131	97.68 113

PLC	84.53 7	89.06 198	88.03 207	92.15 164	97.27 73	82.69 164	94.29 200	95.44 215	79.71 343	84.01 318	94.18 216	76.68 189	98.75 110	77.28 330	93.41 200	95.02 255
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 113	91.56 109	92.13 165	95.88 124	80.50 236	97.33 8	95.25 229	86.15 186	89.76 172	95.60 142	83.42 87	98.32 160	87.37 177	93.25 205	97.56 122

patch_mono-2		93.74 61	94.32 37	92.01 166	97.54 62	78.37 296	93.40 257	97.19 40	88.02 126	94.99 51	97.21 57	88.35 21	98.44 146	94.07 51	98.09 72	99.23 1

原ACMM1				92.01 166	97.34 69	81.05 216		96.81 83	78.89 354	90.45 154	95.92 121	82.65 100	98.84 100	80.68 291	98.26 59	96.14 209

UniMVSNet (Re)		89.80 171	89.07 175	92.01 166	93.60 269	84.52 92	94.78 163	97.47 13	89.26 81	86.44 243	92.32 282	82.10 113	97.39 261	84.81 213	80.84 384	94.12 298

MG-MVS		91.77 110	91.70 106	92.00 169	97.08 76	80.03 252	93.60 250	95.18 233	87.85 135	90.89 148	96.47 96	82.06 115	98.36 153	85.07 208	97.04 105	97.62 116

EIA-MVS		91.95 104	91.94 101	91.98 170	95.16 160	80.01 253	95.36 117	96.73 93	88.44 111	89.34 179	92.16 287	83.82 83	98.45 144	89.35 142	97.06 104	97.48 125

PVSNet_Blended		90.73 137	90.32 136	91.98 170	96.12 106	81.25 206	92.55 300	96.83 79	82.04 298	89.10 183	92.56 275	81.04 128	98.85 98	86.72 187	95.91 133	95.84 226

guyue		91.12 128	90.84 127	91.96 172	94.59 202	80.57 234	94.87 155	93.71 310	88.96 94	91.14 143	95.22 161	73.22 249	97.76 213	92.01 99	93.81 189	97.54 124

PS-MVSNAJ		91.18 125	90.92 124	91.96 172	95.26 155	82.60 171	92.09 319	95.70 191	86.27 182	91.84 125	92.46 277	79.70 145	98.99 77	89.08 147	95.86 134	94.29 292

TAMVS		89.21 191	88.29 202	91.96 172	93.71 263	82.62 170	93.30 265	94.19 289	82.22 293	87.78 214	93.94 226	78.83 158	96.95 295	77.70 326	92.98 214	96.32 199

SDMVSNet		90.19 154	89.61 159	91.93 175	96.00 116	83.09 147	92.89 288	95.98 161	88.73 101	86.85 233	95.20 165	72.09 264	97.08 284	88.90 152	89.85 267	95.63 236

FA-MVS(test-final)		89.66 173	88.91 183	91.93 175	94.57 206	80.27 240	91.36 336	94.74 265	84.87 230	89.82 169	92.61 274	74.72 220	98.47 139	83.97 228	93.53 195	97.04 160

MVS_Test		91.31 121	91.11 119	91.93 175	94.37 220	80.14 245	93.46 255	95.80 180	86.46 178	91.35 141	93.77 236	82.21 110	98.09 181	87.57 172	94.95 157	97.55 123

NR-MVSNet		88.58 214	87.47 223	91.93 175	93.04 290	84.16 107	94.77 164	96.25 136	89.05 88	80.04 379	93.29 250	79.02 157	97.05 289	81.71 274	80.05 394	94.59 275

HyFIR lowres test		88.09 227	86.81 240	91.93 175	96.00 116	80.63 231	90.01 373	95.79 181	73.42 416	87.68 216	92.10 293	73.86 238	97.96 199	80.75 289	91.70 235	97.19 145

GeoE		90.05 159	89.43 164	91.90 180	95.16 160	80.37 239	95.80 90	94.65 269	83.90 251	87.55 219	94.75 186	78.18 170	97.62 226	81.28 279	93.63 191	97.71 112

thisisatest0530		88.67 209	87.61 219	91.86 181	94.87 179	80.07 248	94.63 173	89.90 410	84.00 249	88.46 197	93.78 235	66.88 330	98.46 140	83.30 238	92.65 221	97.06 158

xiu_mvs_v2_base		91.13 127	90.89 126	91.86 181	94.97 171	82.42 173	92.24 312	95.64 198	86.11 190	91.74 131	93.14 256	79.67 150	98.89 92	89.06 148	95.46 145	94.28 293

DU-MVS		89.34 190	88.50 194	91.85 183	93.04 290	83.72 119	94.47 184	96.59 104	89.50 70	86.46 240	93.29 250	77.25 182	97.23 274	84.92 210	81.02 380	94.59 275

AstraMVS		90.69 139	90.30 137	91.84 184	93.81 254	79.85 259	94.76 165	92.39 340	88.96 94	91.01 147	95.87 127	70.69 279	97.94 202	92.49 77	92.70 220	97.73 110

OPM-MVS		90.12 155	89.56 160	91.82 185	93.14 282	83.90 114	94.16 207	95.74 185	88.96 94	87.86 209	95.43 151	72.48 259	97.91 205	88.10 164	90.18 259	93.65 329
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 146	90.19 139	91.82 185	94.70 194	82.73 161	95.85 87	96.22 139	90.81 26	86.91 229	94.86 181	74.23 228	98.12 171	88.15 160	89.99 261	94.63 272

UniMVSNet_NR-MVSNet		89.92 167	89.29 169	91.81 187	93.39 275	83.72 119	94.43 187	97.12 51	89.80 59	86.46 240	93.32 247	83.16 91	97.23 274	84.92 210	81.02 380	94.49 285

diffmvs		91.37 120	91.23 117	91.77 188	93.09 285	80.27 240	92.36 306	95.52 207	87.03 160	91.40 140	94.93 176	80.08 137	97.44 248	92.13 94	94.56 170	97.61 117
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		91.51 117	91.44 111	91.73 189	93.09 285	80.27 240	92.51 301	95.58 201	87.22 154	91.80 128	95.57 144	79.96 139	97.48 240	92.23 88	94.97 156	97.45 127

1112_ss		88.42 216	87.33 226	91.72 190	94.92 175	80.98 219	92.97 284	94.54 272	78.16 371	83.82 321	93.88 231	78.78 160	97.91 205	79.45 307	89.41 274	96.26 203

Fast-Effi-MVS+		89.41 185	88.64 189	91.71 191	94.74 188	80.81 227	93.54 251	95.10 237	83.11 274	86.82 235	90.67 347	79.74 144	97.75 217	80.51 294	93.55 193	96.57 192

WTY-MVS		89.60 175	88.92 182	91.67 192	95.47 146	81.15 211	92.38 305	94.78 263	83.11 274	89.06 185	94.32 208	78.67 162	96.61 314	81.57 275	90.89 248	97.24 140

TAPA-MVS	84.62 6	88.16 225	87.01 235	91.62 193	96.64 85	80.65 230	94.39 191	96.21 142	76.38 385	86.19 250	95.44 149	79.75 143	98.08 183	62.75 431	95.29 150	96.13 210
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 174	88.96 180	91.60 194	93.86 251	82.89 156	95.46 114	97.33 28	87.91 130	88.43 198	93.31 248	74.17 231	97.40 258	87.32 178	82.86 355	94.52 280

FE-MVS		87.40 255	86.02 276	91.57 195	94.56 207	79.69 263	90.27 360	93.72 309	80.57 332	88.80 191	91.62 313	65.32 346	98.59 130	74.97 356	94.33 178	96.44 195

XVG-OURS		89.40 187	88.70 188	91.52 196	94.06 238	81.46 200	91.27 340	96.07 154	86.14 187	88.89 190	95.77 136	68.73 315	97.26 271	87.39 176	89.96 263	95.83 227

hse-mvs2		89.88 169	89.34 167	91.51 197	94.83 182	81.12 213	93.94 230	93.91 302	89.80 59	93.08 84	93.60 241	75.77 202	97.66 221	92.07 95	77.07 414	95.74 231

TranMVSNet+NR-MVSNet		88.84 204	87.95 210	91.49 198	92.68 306	83.01 152	94.92 152	96.31 125	89.88 53	85.53 266	93.85 233	76.63 190	96.96 294	81.91 267	79.87 397	94.50 283

AUN-MVS		87.78 235	86.54 255	91.48 199	94.82 183	81.05 216	93.91 234	93.93 299	83.00 277	86.93 227	93.53 242	69.50 300	97.67 219	86.14 193	77.12 413	95.73 233

XVG-OURS-SEG-HR		89.95 165	89.45 162	91.47 200	94.00 244	81.21 209	91.87 324	96.06 156	85.78 194	88.55 195	95.73 138	74.67 221	97.27 269	88.71 156	89.64 272	95.91 221

MVS		87.44 253	86.10 273	91.44 201	92.61 307	83.62 124	92.63 297	95.66 195	67.26 442	81.47 357	92.15 288	77.95 173	98.22 166	79.71 303	95.48 143	92.47 372

viewdifsd2359ckpt07		91.11 129	91.02 122	91.41 202	94.21 231	78.37 296	92.91 287	95.71 190	87.50 145	90.32 157	95.88 124	80.27 135	97.99 192	88.78 155	93.55 193	97.86 98

F-COLMAP		87.95 230	86.80 241	91.40 203	96.35 99	80.88 224	94.73 167	95.45 213	79.65 344	82.04 352	94.61 195	71.13 271	98.50 134	76.24 343	91.05 246	94.80 269

dcpmvs_2		93.49 66	94.19 48	91.38 204	97.69 59	76.78 338	94.25 202	96.29 126	88.33 114	94.46 54	96.88 74	88.07 25	98.64 123	93.62 57	98.09 72	98.73 19

thisisatest0515		87.33 258	85.99 277	91.37 205	93.49 271	79.55 264	90.63 354	89.56 418	80.17 336	87.56 218	90.86 337	67.07 327	98.28 162	81.50 276	93.02 213	96.29 201

HQP-MVS		89.80 171	89.28 170	91.34 206	94.17 233	81.56 194	94.39 191	96.04 157	88.81 97	85.43 275	93.97 225	73.83 239	97.96 199	87.11 182	89.77 270	94.50 283

fmvsm_s_conf0.5_n_7		93.15 85	93.76 64	91.31 207	94.42 218	79.48 266	94.52 179	97.14 49	89.33 77	94.17 60	98.09 17	81.83 120	97.49 239	96.33 25	98.02 76	96.95 168

RRT-MVS		90.85 133	90.70 131	91.30 208	94.25 228	76.83 337	94.85 158	96.13 148	89.04 89	90.23 159	94.88 179	70.15 290	98.72 114	91.86 107	94.88 159	98.34 44

FMVSNet3		87.40 255	86.11 272	91.30 208	93.79 257	83.64 123	94.20 206	94.81 261	83.89 252	84.37 305	91.87 304	68.45 318	96.56 319	78.23 321	85.36 323	93.70 328

FMVSNet2		87.19 268	85.82 285	91.30 208	94.01 241	83.67 121	94.79 162	94.94 247	83.57 260	83.88 320	92.05 297	66.59 335	96.51 323	77.56 328	85.01 326	93.73 326

RPMNet		83.95 345	81.53 356	91.21 211	90.58 384	79.34 272	85.24 433	96.76 88	71.44 430	85.55 264	82.97 442	70.87 276	98.91 91	61.01 435	89.36 276	95.40 242

IB-MVS	80.51 15	85.24 322	83.26 340	91.19 212	92.13 319	79.86 258	91.75 327	91.29 376	83.28 271	80.66 369	88.49 394	61.28 378	98.46 140	80.99 285	79.46 401	95.25 248
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		89.47 180	88.90 184	91.18 213	94.22 230	82.07 183	92.13 317	96.09 152	87.90 131	85.37 281	92.45 278	74.38 226	97.56 231	87.15 180	90.43 254	93.93 307
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		89.45 181	88.90 184	91.12 214	94.47 212	81.49 198	95.30 122	96.14 145	86.73 171	85.45 272	95.16 167	69.89 293	98.10 173	87.70 169	89.23 279	93.77 322

LGP-MVS_train				91.12 214	94.47 212	81.49 198		96.14 145	86.73 171	85.45 272	95.16 167	69.89 293	98.10 173	87.70 169	89.23 279	93.77 322

ACMM	84.12 9	89.14 193	88.48 197	91.12 214	94.65 197	81.22 208	95.31 120	96.12 149	85.31 214	85.92 255	94.34 206	70.19 289	98.06 185	85.65 201	88.86 284	94.08 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 211	87.78 216	91.11 217	94.96 172	77.81 314	95.35 118	89.69 413	85.09 224	88.05 207	94.59 198	66.93 328	98.48 136	83.27 239	92.13 233	97.03 161

GBi-Net		87.26 260	85.98 278	91.08 218	94.01 241	83.10 144	95.14 140	94.94 247	83.57 260	84.37 305	91.64 309	66.59 335	96.34 336	78.23 321	85.36 323	93.79 317

test1		87.26 260	85.98 278	91.08 218	94.01 241	83.10 144	95.14 140	94.94 247	83.57 260	84.37 305	91.64 309	66.59 335	96.34 336	78.23 321	85.36 323	93.79 317

FMVSNet1		85.85 307	84.11 327	91.08 218	92.81 301	83.10 144	95.14 140	94.94 247	81.64 314	82.68 342	91.64 309	59.01 400	96.34 336	75.37 350	83.78 339	93.79 317

Test_1112_low_res		87.65 239	86.51 256	91.08 218	94.94 174	79.28 276	91.77 326	94.30 284	76.04 390	83.51 331	92.37 280	77.86 176	97.73 218	78.69 316	89.13 281	96.22 204

PS-MVSNAJss		89.97 163	89.62 158	91.02 222	91.90 328	80.85 226	95.26 128	95.98 161	86.26 183	86.21 249	94.29 210	79.70 145	97.65 222	88.87 154	88.10 295	94.57 277

BH-RMVSNet		88.37 219	87.48 222	91.02 222	95.28 152	79.45 268	92.89 288	93.07 323	85.45 209	86.91 229	94.84 184	70.35 286	97.76 213	73.97 364	94.59 169	95.85 225

UniMVSNet_ETH3D		87.53 249	86.37 260	91.00 224	92.44 311	78.96 281	94.74 166	95.61 199	84.07 248	85.36 282	94.52 200	59.78 392	97.34 263	82.93 243	87.88 300	96.71 185

FIs		90.51 148	90.35 135	90.99 225	93.99 245	80.98 219	95.73 97	97.54 6	89.15 85	86.72 236	94.68 189	81.83 120	97.24 273	85.18 207	88.31 294	94.76 270

ACMP	84.23 8	89.01 202	88.35 198	90.99 225	94.73 189	81.27 205	95.07 143	95.89 173	86.48 176	83.67 326	94.30 209	69.33 302	97.99 192	87.10 184	88.55 286	93.72 327
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 290	85.13 305	90.98 227	96.52 93	81.50 196	96.14 59	96.16 144	73.78 412	83.65 327	92.15 288	63.26 362	97.37 262	82.82 247	81.74 369	94.06 303

IMVS_0403		89.97 163	89.64 157	90.96 228	93.72 259	77.75 319	93.00 281	95.34 224	85.53 205	88.77 192	94.49 201	78.49 166	97.84 209	84.75 214	92.65 221	97.28 134

sss		88.93 203	88.26 204	90.94 229	94.05 239	80.78 228	91.71 328	95.38 219	81.55 318	88.63 194	93.91 230	75.04 214	95.47 377	82.47 252	91.61 236	96.57 192

IMVS_0407		89.85 170	89.51 161	90.88 230	93.72 259	77.75 319	93.07 278	95.34 224	85.53 205	88.34 200	94.49 201	77.69 178	97.60 227	84.75 214	92.65 221	97.28 134

viewmambaseed2359dif		90.04 160	89.78 154	90.83 231	92.85 300	77.92 308	92.23 313	95.01 241	81.90 303	90.20 160	95.45 148	79.64 152	97.34 263	87.52 174	93.17 207	97.23 143

sd_testset		88.59 213	87.85 215	90.83 231	96.00 116	80.42 238	92.35 307	94.71 266	88.73 101	86.85 233	95.20 165	67.31 322	96.43 330	79.64 305	89.85 267	95.63 236

PVSNet_BlendedMVS		89.98 162	89.70 155	90.82 233	96.12 106	81.25 206	93.92 232	96.83 79	83.49 264	89.10 183	92.26 285	81.04 128	98.85 98	86.72 187	87.86 301	92.35 378

cascas		86.43 298	84.98 308	90.80 234	92.10 321	80.92 223	90.24 364	95.91 170	73.10 419	83.57 330	88.39 395	65.15 348	97.46 244	84.90 212	91.43 238	94.03 305

ECVR-MVS		89.09 196	88.53 192	90.77 235	95.62 139	75.89 351	96.16 55	84.22 444	87.89 133	90.20 160	96.65 86	63.19 363	98.10 173	85.90 198	96.94 107	98.33 46

GA-MVS		86.61 288	85.27 302	90.66 236	91.33 351	78.71 285	90.40 359	93.81 306	85.34 213	85.12 285	89.57 376	61.25 379	97.11 283	80.99 285	89.59 273	96.15 208

thres600view7		87.65 239	86.67 247	90.59 237	96.08 112	78.72 283	94.88 154	91.58 367	87.06 159	88.08 205	92.30 283	68.91 312	98.10 173	70.05 394	91.10 241	94.96 259

thres400		87.62 244	86.64 248	90.57 238	95.99 119	78.64 286	94.58 175	91.98 356	86.94 165	88.09 203	91.77 305	69.18 308	98.10 173	70.13 391	91.10 241	94.96 259

baseline1		88.10 226	87.28 228	90.57 238	94.96 172	80.07 248	94.27 201	91.29 376	86.74 170	87.41 220	94.00 223	76.77 187	96.20 341	80.77 288	79.31 403	95.44 240

viewdifsd2359ckpt11		89.43 183	89.05 177	90.56 240	92.89 298	77.00 333	92.81 291	94.52 273	87.03 160	89.77 170	95.79 133	74.67 221	97.51 235	88.97 150	84.98 327	97.17 146

viewmsd2359difaftdt		89.43 183	89.05 177	90.56 240	92.89 298	77.00 333	92.81 291	94.52 273	87.03 160	89.77 170	95.79 133	74.67 221	97.51 235	88.97 150	84.98 327	97.17 146

FC-MVSNet-test		90.27 152	90.18 140	90.53 242	93.71 263	79.85 259	95.77 93	97.59 4	89.31 78	86.27 247	94.67 192	81.93 118	97.01 291	84.26 224	88.09 297	94.71 271

PAPM		86.68 287	85.39 297	90.53 242	93.05 289	79.33 275	89.79 376	94.77 264	78.82 357	81.95 353	93.24 252	76.81 185	97.30 265	66.94 411	93.16 208	94.95 263

WR-MVS		88.38 218	87.67 218	90.52 244	93.30 277	80.18 243	93.26 268	95.96 165	88.57 109	85.47 271	92.81 267	76.12 195	96.91 298	81.24 280	82.29 360	94.47 288

SSM_04072		88.57 215	87.92 212	90.51 245	94.76 185	82.66 165	79.84 455	94.64 270	85.18 215	88.96 187	95.00 173	76.00 198	92.03 425	83.74 233	93.15 209	96.85 177

MVSTER		88.84 204	88.29 202	90.51 245	92.95 295	80.44 237	93.73 243	95.01 241	84.66 239	87.15 224	93.12 257	72.79 254	97.21 276	87.86 166	87.36 309	93.87 312

testdata				90.49 247	96.40 96	77.89 311		95.37 221	72.51 424	93.63 73	96.69 82	82.08 114	97.65 222	83.08 240	97.39 97	95.94 220

test1111		89.10 194	88.64 189	90.48 248	95.53 144	74.97 361	96.08 64	84.89 442	88.13 124	90.16 164	96.65 86	63.29 361	98.10 173	86.14 193	96.90 109	98.39 41

tt0805		86.92 276	85.74 291	90.48 248	92.22 315	79.98 255	95.63 107	94.88 255	83.83 254	84.74 294	92.80 268	57.61 406	97.67 219	85.48 204	84.42 332	93.79 317

jajsoiax		88.24 223	87.50 221	90.48 248	90.89 372	80.14 245	95.31 120	95.65 197	84.97 227	84.24 313	94.02 221	65.31 347	97.42 250	88.56 157	88.52 288	93.89 308

PatchMatch-RL		86.77 284	85.54 293	90.47 251	95.88 124	82.71 163	90.54 357	92.31 344	79.82 342	84.32 310	91.57 317	68.77 314	96.39 332	73.16 370	93.48 199	92.32 379

tfpn200view9		87.58 247	86.64 248	90.41 252	95.99 119	78.64 286	94.58 175	91.98 356	86.94 165	88.09 203	91.77 305	69.18 308	98.10 173	70.13 391	91.10 241	94.48 286

VPNet		88.20 224	87.47 223	90.39 253	93.56 270	79.46 267	94.04 220	95.54 205	88.67 104	86.96 226	94.58 199	69.33 302	97.15 278	84.05 227	80.53 389	94.56 278

ACMH	80.38 17	85.36 317	83.68 334	90.39 253	94.45 215	80.63 231	94.73 167	94.85 257	82.09 295	77.24 404	92.65 272	60.01 390	97.58 229	72.25 375	84.87 329	92.96 357
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 242	86.71 244	90.38 255	96.12 106	78.55 289	95.03 146	91.58 367	87.15 156	88.06 206	92.29 284	68.91 312	98.10 173	70.13 391	91.10 241	94.48 286

mvs_tets		88.06 229	87.28 228	90.38 255	90.94 368	79.88 257	95.22 131	95.66 195	85.10 223	84.21 314	93.94 226	63.53 359	97.40 258	88.50 158	88.40 292	93.87 312

1314		87.51 250	86.57 253	90.34 257	92.42 312	79.74 262	92.63 297	95.35 223	78.35 366	80.14 376	91.62 313	74.05 233	97.15 278	81.05 281	93.53 195	94.12 298

LTVRE_ROB	82.13 13	86.26 301	84.90 311	90.34 257	94.44 216	81.50 196	92.31 311	94.89 253	83.03 276	79.63 386	92.67 271	69.69 296	97.79 211	71.20 380	86.26 318	91.72 389
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		89.03 200	88.64 189	90.21 259	90.74 379	79.28 276	95.96 78	95.90 171	84.66 239	85.33 283	92.94 262	74.02 234	97.30 265	89.64 140	88.53 287	94.05 304

v2v482		87.84 232	87.06 232	90.17 260	90.99 364	79.23 279	94.00 226	95.13 234	84.87 230	85.53 266	92.07 296	74.45 225	97.45 245	84.71 219	81.75 368	93.85 315

pmmvs4		85.43 315	83.86 332	90.16 261	90.02 397	82.97 154	90.27 360	92.67 335	75.93 391	80.73 367	91.74 307	71.05 272	95.73 366	78.85 315	83.46 346	91.78 388

V42		87.68 237	86.86 237	90.15 262	90.58 384	80.14 245	94.24 204	95.28 228	83.66 258	85.67 261	91.33 319	74.73 219	97.41 256	84.43 223	81.83 366	92.89 360

MSDG		84.86 330	83.09 343	90.14 263	93.80 255	80.05 250	89.18 389	93.09 322	78.89 354	78.19 396	91.91 302	65.86 345	97.27 269	68.47 400	88.45 290	93.11 352

sc_t1		81.53 369	78.67 390	90.12 264	90.78 376	78.64 286	93.91 234	90.20 399	68.42 439	80.82 366	89.88 369	46.48 444	96.76 303	76.03 346	71.47 428	94.96 259

anonymousdsp		87.84 232	87.09 231	90.12 264	89.13 408	80.54 235	94.67 171	95.55 203	82.05 296	83.82 321	92.12 290	71.47 269	97.15 278	87.15 180	87.80 304	92.67 366

thres200		87.21 266	86.24 267	90.12 264	95.36 148	78.53 290	93.26 268	92.10 350	86.42 179	88.00 208	91.11 330	69.24 307	98.00 191	69.58 395	91.04 247	93.83 316

CR-MVSNet		85.35 318	83.76 333	90.12 264	90.58 384	79.34 272	85.24 433	91.96 358	78.27 368	85.55 264	87.87 405	71.03 273	95.61 369	73.96 365	89.36 276	95.40 242

v1144		87.61 245	86.79 242	90.06 268	91.01 363	79.34 272	93.95 229	95.42 218	83.36 269	85.66 262	91.31 322	74.98 215	97.42 250	83.37 237	82.06 362	93.42 338

XXY-MVS		87.65 239	86.85 238	90.03 269	92.14 318	80.60 233	93.76 241	95.23 230	82.94 279	84.60 296	94.02 221	74.27 227	95.49 376	81.04 282	83.68 342	94.01 306

Vis-MVSNet (Re-imp)		89.59 176	89.44 163	90.03 269	95.74 129	75.85 352	95.61 108	90.80 390	87.66 144	87.83 212	95.40 152	76.79 186	96.46 328	78.37 317	96.73 115	97.80 105

test2506		87.21 266	86.28 265	90.02 271	95.62 139	73.64 377	96.25 50	71.38 467	87.89 133	90.45 154	96.65 86	55.29 418	98.09 181	86.03 197	96.94 107	98.33 46

BH-untuned		88.60 212	88.13 206	90.01 272	95.24 156	78.50 292	93.29 266	94.15 292	84.75 235	84.46 302	93.40 244	75.76 204	97.40 258	77.59 327	94.52 172	94.12 298

v1192		87.25 262	86.33 262	90.00 273	90.76 378	79.04 280	93.80 239	95.48 208	82.57 286	85.48 270	91.18 326	73.38 248	97.42 250	82.30 256	82.06 362	93.53 332

v7n		86.81 279	85.76 289	89.95 274	90.72 380	79.25 278	95.07 143	95.92 168	84.45 242	82.29 346	90.86 337	72.60 258	97.53 233	79.42 310	80.52 390	93.08 354

testing91		87.11 271	86.18 268	89.92 275	94.43 217	75.38 360	91.53 333	92.27 346	86.48 176	86.50 238	90.24 355	61.19 382	97.53 233	82.10 261	90.88 249	96.84 180

IMVS_0404		87.60 246	86.84 239	89.89 276	93.72 259	77.75 319	88.56 398	95.34 224	85.53 205	79.98 380	94.49 201	66.54 338	94.64 390	84.75 214	92.65 221	97.28 134

v8		87.50 252	86.71 244	89.89 276	91.37 348	79.40 269	94.50 180	95.38 219	84.81 233	83.60 329	91.33 319	76.05 196	97.42 250	82.84 246	80.51 391	92.84 362

v10		87.25 262	86.38 259	89.85 278	91.19 354	79.50 265	94.48 181	95.45 213	83.79 256	83.62 328	91.19 324	75.13 212	97.42 250	81.94 266	80.60 386	92.63 368

baseline2		86.50 294	85.39 297	89.84 279	91.12 359	76.70 340	91.88 323	88.58 422	82.35 291	79.95 381	90.95 335	73.42 246	97.63 225	80.27 298	89.95 264	95.19 249

pm-mvs1		86.61 288	85.54 293	89.82 280	91.44 343	80.18 243	95.28 126	94.85 257	83.84 253	81.66 355	92.62 273	72.45 261	96.48 325	79.67 304	78.06 406	92.82 363

TR-MVS		86.78 281	85.76 289	89.82 280	94.37 220	78.41 294	92.47 302	92.83 329	81.11 328	86.36 244	92.40 279	68.73 315	97.48 240	73.75 368	89.85 267	93.57 331

ACMH+	81.04 14	85.05 325	83.46 337	89.82 280	94.66 196	79.37 270	94.44 186	94.12 295	82.19 294	78.04 398	92.82 266	58.23 403	97.54 232	73.77 367	82.90 354	92.54 369

EI-MVSNet		89.10 194	88.86 186	89.80 283	91.84 330	78.30 299	93.70 246	95.01 241	85.73 196	87.15 224	95.28 158	79.87 142	97.21 276	83.81 231	87.36 309	93.88 311

v144192		87.19 268	86.35 261	89.74 284	90.64 382	78.24 301	93.92 232	95.43 216	81.93 301	85.51 268	91.05 333	74.21 230	97.45 245	82.86 245	81.56 370	93.53 332

COLMAP_ROB	80.39 16	83.96 344	82.04 353	89.74 284	95.28 152	79.75 261	94.25 202	92.28 345	75.17 398	78.02 399	93.77 236	58.60 402	97.84 209	65.06 422	85.92 319	91.63 391
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 300	85.18 304	89.73 286	92.15 317	76.60 341	91.12 344	91.69 363	83.53 263	85.50 269	88.81 388	66.79 331	96.48 325	76.65 336	90.35 256	96.12 211

IterMVS-LS		88.36 220	87.91 214	89.70 287	93.80 255	78.29 300	93.73 243	95.08 239	85.73 196	84.75 293	91.90 303	79.88 141	96.92 297	83.83 230	82.51 356	93.89 308
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 297	85.35 300	89.69 288	94.29 227	75.40 359	91.30 338	90.53 394	84.76 234	85.06 287	90.13 361	58.95 401	97.45 245	82.08 262	91.09 245	96.21 206

testing99		86.72 285	85.73 292	89.69 288	94.23 229	74.91 363	91.35 337	90.97 384	86.14 187	86.36 244	90.22 356	59.41 395	97.48 240	82.24 258	90.66 251	96.69 187

v1921920		86.97 275	86.06 275	89.69 288	90.53 387	78.11 304	93.80 239	95.43 216	81.90 303	85.33 283	91.05 333	72.66 255	97.41 256	82.05 264	81.80 367	93.53 332

icg_test_0407_2		89.15 192	88.97 179	89.68 291	93.72 259	77.75 319	88.26 403	95.34 224	85.53 205	88.34 200	94.49 201	77.69 178	93.99 401	84.75 214	92.65 221	97.28 134

VortexMVS		88.42 216	88.01 208	89.63 292	93.89 250	78.82 282	93.82 238	95.47 209	86.67 173	84.53 300	91.99 299	72.62 257	96.65 309	89.02 149	84.09 336	93.41 339

Fast-Effi-MVS+-dtu		87.44 253	86.72 243	89.63 292	92.04 322	77.68 324	94.03 221	93.94 298	85.81 193	82.42 345	91.32 321	70.33 287	97.06 287	80.33 297	90.23 258	94.14 297

v1240		86.78 281	85.85 284	89.56 294	90.45 389	77.79 316	93.61 249	95.37 221	81.65 313	85.43 275	91.15 328	71.50 268	97.43 249	81.47 277	82.05 364	93.47 336

Effi-MVS+-dtu		88.65 210	88.35 198	89.54 295	93.33 276	76.39 345	94.47 184	94.36 282	87.70 141	85.43 275	89.56 377	73.45 244	97.26 271	85.57 203	91.28 240	94.97 256

AllTest		83.42 351	81.39 357	89.52 296	95.01 166	77.79 316	93.12 272	90.89 388	77.41 375	76.12 413	93.34 245	54.08 424	97.51 235	68.31 402	84.27 334	93.26 342

TestCases				89.52 296	95.01 166	77.79 316		90.89 388	77.41 375	76.12 413	93.34 245	54.08 424	97.51 235	68.31 402	84.27 334	93.26 342

mvs_anonymous		89.37 189	89.32 168	89.51 298	93.47 272	74.22 370	91.65 331	94.83 259	82.91 280	85.45 272	93.79 234	81.23 127	96.36 335	86.47 189	94.09 182	97.94 89

XVG-ACMP-BASELINE		86.00 303	84.84 313	89.45 299	91.20 353	78.00 306	91.70 329	95.55 203	85.05 225	82.97 339	92.25 286	54.49 422	97.48 240	82.93 243	87.45 308	92.89 360

testing222		84.84 331	83.32 338	89.43 300	94.15 236	75.94 350	91.09 345	89.41 420	84.90 228	85.78 258	89.44 378	52.70 429	96.28 339	70.80 386	91.57 237	96.07 215

MVP-Stereo		85.97 304	84.86 312	89.32 301	90.92 370	82.19 180	92.11 318	94.19 289	78.76 359	78.77 395	91.63 312	68.38 319	96.56 319	75.01 355	93.95 184	89.20 429
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 307	84.70 315	89.29 302	91.76 334	75.54 356	88.49 399	91.30 375	81.63 315	85.05 288	88.70 392	71.71 265	96.24 340	74.61 360	89.05 282	96.08 214
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 273	86.32 263	89.21 303	90.94 368	77.26 329	93.71 245	94.43 277	84.84 232	84.36 308	90.80 341	76.04 197	97.05 289	82.12 260	79.60 400	93.31 341

tfpnnormal		84.72 333	83.23 341	89.20 304	92.79 302	80.05 250	94.48 181	95.81 179	82.38 289	81.08 363	91.21 323	69.01 311	96.95 295	61.69 433	80.59 387	90.58 416

cl22		86.78 281	85.98 278	89.18 305	92.34 313	77.62 325	90.84 350	94.13 294	81.33 322	83.97 319	90.15 360	73.96 235	96.60 316	84.19 225	82.94 351	93.33 340

BH-w/o		87.57 248	87.05 233	89.12 306	94.90 178	77.90 310	92.41 303	93.51 313	82.89 281	83.70 325	91.34 318	75.75 205	97.07 286	75.49 348	93.49 197	92.39 376

WR-MVS_H		87.80 234	87.37 225	89.10 307	93.23 278	78.12 303	95.61 108	97.30 33	87.90 131	83.72 324	92.01 298	79.65 151	96.01 350	76.36 340	80.54 388	93.16 350

miper_enhance_ethall		86.90 277	86.18 268	89.06 308	91.66 339	77.58 326	90.22 366	94.82 260	79.16 350	84.48 301	89.10 382	79.19 156	96.66 308	84.06 226	82.94 351	92.94 358

c3_l		87.14 270	86.50 257	89.04 309	92.20 316	77.26 329	91.22 343	94.70 267	82.01 299	84.34 309	90.43 352	78.81 159	96.61 314	83.70 235	81.09 377	93.25 344

miper_ehance_all_eth		87.22 265	86.62 251	89.02 310	92.13 319	77.40 328	90.91 349	94.81 261	81.28 323	84.32 310	90.08 363	79.26 154	96.62 311	83.81 231	82.94 351	93.04 355

gg-mvs-nofinetune		81.77 363	79.37 378	88.99 311	90.85 374	77.73 323	86.29 425	79.63 455	74.88 403	83.19 338	69.05 458	60.34 387	96.11 345	75.46 349	94.64 168	93.11 352

ETVMVS		84.43 338	82.92 347	88.97 312	94.37 220	74.67 364	91.23 342	88.35 424	83.37 268	86.06 253	89.04 383	55.38 416	95.67 368	67.12 409	91.34 239	96.58 191

pmmvs6		83.42 351	81.60 355	88.87 313	88.01 423	77.87 312	94.96 149	94.24 288	74.67 404	78.80 394	91.09 331	60.17 389	96.49 324	77.06 335	75.40 420	92.23 381

test_cas_vis1_n_1920		88.83 207	88.85 187	88.78 314	91.15 358	76.72 339	93.85 237	94.93 251	83.23 273	92.81 93	96.00 116	61.17 383	94.45 391	91.67 110	94.84 160	95.17 250

MIMVSNet		82.59 357	80.53 362	88.76 315	91.51 341	78.32 298	86.57 424	90.13 402	79.32 346	80.70 368	88.69 393	52.98 428	93.07 417	66.03 417	88.86 284	94.90 264

cl____		86.52 293	85.78 286	88.75 316	92.03 323	76.46 343	90.74 351	94.30 284	81.83 309	83.34 335	90.78 342	75.74 207	96.57 317	81.74 272	81.54 371	93.22 346

DIV-MVS_self_test		86.53 292	85.78 286	88.75 316	92.02 324	76.45 344	90.74 351	94.30 284	81.83 309	83.34 335	90.82 340	75.75 205	96.57 317	81.73 273	81.52 372	93.24 345

CP-MVSNet		87.63 242	87.26 230	88.74 318	93.12 283	76.59 342	95.29 124	96.58 105	88.43 112	83.49 332	92.98 261	75.28 211	95.83 359	78.97 313	81.15 376	93.79 317

eth_miper_zixun_eth		86.50 294	85.77 288	88.68 319	91.94 325	75.81 353	90.47 358	94.89 253	82.05 296	84.05 316	90.46 351	75.96 200	96.77 302	82.76 249	79.36 402	93.46 337

CHOSEN 280x420		85.15 323	83.99 330	88.65 320	92.47 309	78.40 295	79.68 457	92.76 332	74.90 402	81.41 359	89.59 375	69.85 295	95.51 373	79.92 302	95.29 150	92.03 384

PS-CasMVS		87.32 259	86.88 236	88.63 321	92.99 293	76.33 347	95.33 119	96.61 103	88.22 120	83.30 337	93.07 259	73.03 252	95.79 363	78.36 318	81.00 382	93.75 324

TransMVSNet (Re)		84.43 338	83.06 345	88.54 322	91.72 335	78.44 293	95.18 137	92.82 331	82.73 284	79.67 385	92.12 290	73.49 243	95.96 352	71.10 384	68.73 439	91.21 403

tt0320-xc		79.63 392	76.66 401	88.52 323	91.03 362	78.72 283	93.00 281	89.53 419	66.37 443	76.11 415	87.11 416	46.36 446	95.32 381	72.78 372	67.67 440	91.51 395

EG-PatchMatch MVS		82.37 359	80.34 365	88.46 324	90.27 391	79.35 271	92.80 294	94.33 283	77.14 379	73.26 431	90.18 359	47.47 441	96.72 304	70.25 388	87.32 311	89.30 426

PEN-MVS		86.80 280	86.27 266	88.40 325	92.32 314	75.71 355	95.18 137	96.38 120	87.97 128	82.82 341	93.15 255	73.39 247	95.92 354	76.15 344	79.03 405	93.59 330

Baseline_NR-MVSNet		87.07 272	86.63 250	88.40 325	91.44 343	77.87 312	94.23 205	92.57 337	84.12 247	85.74 260	92.08 294	77.25 182	96.04 346	82.29 257	79.94 395	91.30 401

UBG		85.51 313	84.57 320	88.35 327	94.21 231	71.78 402	90.07 371	89.66 415	82.28 292	85.91 256	89.01 384	61.30 377	97.06 287	76.58 339	92.06 234	96.22 204

D2MVS		85.90 305	85.09 306	88.35 327	90.79 375	77.42 327	91.83 325	95.70 191	80.77 331	80.08 378	90.02 365	66.74 333	96.37 333	81.88 268	87.97 299	91.26 402

pmmvs5		84.21 340	82.84 350	88.34 329	88.95 410	76.94 335	92.41 303	91.91 360	75.63 393	80.28 373	91.18 326	64.59 353	95.57 370	77.09 334	83.47 345	92.53 370

mamv4		90.92 131	91.78 104	88.33 330	95.67 135	70.75 415	92.92 286	96.02 160	81.90 303	88.11 202	95.34 156	85.88 52	96.97 293	95.22 39	95.01 155	97.26 138

tt0320		80.13 385	77.41 394	88.29 331	90.50 388	78.02 305	93.10 275	90.71 392	66.06 446	76.75 408	86.97 417	49.56 436	95.40 378	71.65 376	71.41 429	91.46 398

LCM-MVSNet-Re		88.30 222	88.32 201	88.27 332	94.71 193	72.41 397	93.15 271	90.98 383	87.77 138	79.25 389	91.96 300	78.35 168	95.75 364	83.04 241	95.62 139	96.65 188

CostFormer		85.77 310	84.94 310	88.26 333	91.16 357	72.58 395	89.47 384	91.04 382	76.26 388	86.45 242	89.97 367	70.74 278	96.86 301	82.35 255	87.07 314	95.34 246

ITE_SJBPF				88.24 334	91.88 329	77.05 332		92.92 326	85.54 203	80.13 377	93.30 249	57.29 407	96.20 341	72.46 374	84.71 330	91.49 396

PVSNet	78.82 18	85.55 312	84.65 316	88.23 335	94.72 191	71.93 398	87.12 420	92.75 333	78.80 358	84.95 290	90.53 349	64.43 354	96.71 306	74.74 358	93.86 186	96.06 217

IterMVS-SCA-FT		85.45 314	84.53 321	88.18 336	91.71 336	76.87 336	90.19 368	92.65 336	85.40 212	81.44 358	90.54 348	66.79 331	95.00 387	81.04 282	81.05 378	92.66 367

EPNet_dtu		86.49 296	85.94 281	88.14 337	90.24 392	72.82 387	94.11 211	92.20 348	86.66 174	79.42 388	92.36 281	73.52 242	95.81 361	71.26 379	93.66 190	95.80 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 355	80.93 361	88.06 338	90.05 396	76.37 346	84.74 438	91.96 358	72.28 427	81.32 361	87.87 405	71.03 273	95.50 375	68.97 397	80.15 393	92.32 379

test_vis1_n_1920		89.39 188	89.84 151	88.04 339	92.97 294	72.64 392	94.71 169	96.03 159	86.18 185	91.94 122	96.56 94	61.63 372	95.74 365	93.42 60	95.11 154	95.74 231

DTE-MVSNet		86.11 302	85.48 295	87.98 340	91.65 340	74.92 362	94.93 151	95.75 184	87.36 151	82.26 347	93.04 260	72.85 253	95.82 360	74.04 363	77.46 411	93.20 348

PMMVS		85.71 311	84.96 309	87.95 341	88.90 411	77.09 331	88.68 396	90.06 404	72.32 426	86.47 239	90.76 343	72.15 263	94.40 393	81.78 271	93.49 197	92.36 377

GG-mvs-BLEND				87.94 342	89.73 403	77.91 309	87.80 409	78.23 460		80.58 370	83.86 435	59.88 391	95.33 380	71.20 380	92.22 232	90.60 415

MonoMVSNet		86.89 278	86.55 254	87.92 343	89.46 406	73.75 374	94.12 209	93.10 321	87.82 137	85.10 286	90.76 343	69.59 298	94.94 388	86.47 189	82.50 357	95.07 253

reproduce_monomvs		86.37 299	85.87 283	87.87 344	93.66 267	73.71 375	93.44 256	95.02 240	88.61 107	82.64 344	91.94 301	57.88 405	96.68 307	89.96 135	79.71 399	93.22 346

pmmvs-eth3d		80.97 378	78.72 389	87.74 345	84.99 441	79.97 256	90.11 370	91.65 365	75.36 395	73.51 429	86.03 424	59.45 394	93.96 404	75.17 352	72.21 425	89.29 428

MS-PatchMatch		85.05 325	84.16 325	87.73 346	91.42 346	78.51 291	91.25 341	93.53 312	77.50 374	80.15 375	91.58 315	61.99 369	95.51 373	75.69 347	94.35 177	89.16 430

mmtdpeth		85.04 327	84.15 326	87.72 347	93.11 284	75.74 354	94.37 195	92.83 329	84.98 226	89.31 180	86.41 421	61.61 374	97.14 281	92.63 76	62.11 450	90.29 417

test_0402		81.30 374	79.17 383	87.67 348	93.19 279	78.17 302	92.98 283	91.71 361	75.25 397	76.02 416	90.31 354	59.23 396	96.37 333	50.22 453	83.63 343	88.47 438

IterMVS		84.88 329	83.98 331	87.60 349	91.44 343	76.03 349	90.18 369	92.41 339	83.24 272	81.06 364	90.42 353	66.60 334	94.28 397	79.46 306	80.98 383	92.48 371
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 372	79.30 379	87.58 350	90.92 370	74.16 372	80.99 450	87.68 429	70.52 434	76.63 410	88.81 388	71.21 270	92.76 420	60.01 439	86.93 315	95.83 227

EPMVS		83.90 347	82.70 351	87.51 351	90.23 393	72.67 390	88.62 397	81.96 450	81.37 321	85.01 289	88.34 396	66.31 339	94.45 391	75.30 351	87.12 312	95.43 241

ADS-MVSNet2		81.66 366	79.71 375	87.50 352	91.35 349	74.19 371	83.33 443	88.48 423	72.90 421	82.24 348	85.77 427	64.98 349	93.20 415	64.57 424	83.74 340	95.12 251

OurMVSNet-221017-0		85.35 318	84.64 318	87.49 353	90.77 377	72.59 394	94.01 224	94.40 280	84.72 236	79.62 387	93.17 254	61.91 370	96.72 304	81.99 265	81.16 374	93.16 350

tpm2		84.08 342	82.94 346	87.48 354	91.39 347	71.27 407	89.23 388	90.37 396	71.95 428	84.64 295	89.33 379	67.30 323	96.55 321	75.17 352	87.09 313	94.63 272

RPSCF		85.07 324	84.27 322	87.48 354	92.91 297	70.62 417	91.69 330	92.46 338	76.20 389	82.67 343	95.22 161	63.94 357	97.29 268	77.51 329	85.80 320	94.53 279

myMVS_eth3d28		85.80 309	85.26 303	87.42 356	94.73 189	69.92 422	90.60 355	90.95 385	87.21 155	86.06 253	90.04 364	59.47 393	96.02 348	74.89 357	93.35 204	96.33 198

WBMVS		84.97 328	84.18 324	87.34 357	94.14 237	71.62 406	90.20 367	92.35 341	81.61 316	84.06 315	90.76 343	61.82 371	96.52 322	78.93 314	83.81 338	93.89 308

miper_lstm_enhance		85.27 321	84.59 319	87.31 358	91.28 352	74.63 365	87.69 414	94.09 296	81.20 327	81.36 360	89.85 371	74.97 216	94.30 396	81.03 284	79.84 398	93.01 356

FMVSNet5		81.52 370	79.60 376	87.27 359	91.17 355	77.95 307	91.49 334	92.26 347	76.87 381	76.16 412	87.91 404	51.67 430	92.34 423	67.74 406	81.16 374	91.52 394

USDC		82.76 354	81.26 359	87.26 360	91.17 355	74.55 366	89.27 386	93.39 315	78.26 369	75.30 420	92.08 294	54.43 423	96.63 310	71.64 377	85.79 321	90.61 413

test-LLR		85.87 306	85.41 296	87.25 361	90.95 366	71.67 404	89.55 380	89.88 411	83.41 266	84.54 298	87.95 402	67.25 324	95.11 384	81.82 269	93.37 202	94.97 256

test-mter		84.54 337	83.64 335	87.25 361	90.95 366	71.67 404	89.55 380	89.88 411	79.17 349	84.54 298	87.95 402	55.56 413	95.11 384	81.82 269	93.37 202	94.97 256

JIA-IIPM		81.04 375	78.98 387	87.25 361	88.64 412	73.48 379	81.75 449	89.61 417	73.19 418	82.05 351	73.71 454	66.07 344	95.87 357	71.18 382	84.60 331	92.41 375

TDRefinement		79.81 389	77.34 395	87.22 364	79.24 457	75.48 357	93.12 272	92.03 353	76.45 384	75.01 421	91.58 315	49.19 437	96.44 329	70.22 390	69.18 436	89.75 422

tpmvs		83.35 353	82.07 352	87.20 365	91.07 361	71.00 413	88.31 402	91.70 362	78.91 352	80.49 372	87.18 414	69.30 305	97.08 284	68.12 405	83.56 344	93.51 335

ppachtmachnet_test		81.84 362	80.07 370	87.15 366	88.46 416	74.43 369	89.04 392	92.16 349	75.33 396	77.75 401	88.99 385	66.20 341	95.37 379	65.12 421	77.60 409	91.65 390

dmvs_re		84.20 341	83.22 342	87.14 367	91.83 332	77.81 314	90.04 372	90.19 400	84.70 238	81.49 356	89.17 381	64.37 355	91.13 436	71.58 378	85.65 322	92.46 373

tpm cat1		81.96 360	80.27 366	87.01 368	91.09 360	71.02 412	87.38 418	91.53 370	66.25 444	80.17 374	86.35 423	68.22 320	96.15 344	69.16 396	82.29 360	93.86 314

test_fmvs1_n		87.03 274	87.04 234	86.97 369	89.74 402	71.86 399	94.55 177	94.43 277	78.47 363	91.95 121	95.50 147	51.16 432	93.81 405	93.02 68	94.56 170	95.26 247

OpenMVS_ROB	74.94 19	79.51 393	77.03 400	86.93 370	87.00 429	76.23 348	92.33 309	90.74 391	68.93 438	74.52 425	88.23 399	49.58 435	96.62 311	57.64 445	84.29 333	87.94 441

SixPastTwentyTwo		83.91 346	82.90 348	86.92 371	90.99 364	70.67 416	93.48 253	91.99 355	85.54 203	77.62 403	92.11 292	60.59 386	96.87 300	76.05 345	77.75 408	93.20 348

ADS-MVSNet		81.56 368	79.78 372	86.90 372	91.35 349	71.82 400	83.33 443	89.16 421	72.90 421	82.24 348	85.77 427	64.98 349	93.76 406	64.57 424	83.74 340	95.12 251

PatchT		82.68 356	81.27 358	86.89 373	90.09 395	70.94 414	84.06 440	90.15 401	74.91 401	85.63 263	83.57 437	69.37 301	94.87 389	65.19 419	88.50 289	94.84 266

tpm		84.73 332	84.02 329	86.87 374	90.33 390	68.90 425	89.06 391	89.94 408	80.85 330	85.75 259	89.86 370	68.54 317	95.97 351	77.76 325	84.05 337	95.75 230

Patchmatch-RL test		81.67 365	79.96 371	86.81 375	85.42 439	71.23 408	82.17 448	87.50 430	78.47 363	77.19 405	82.50 444	70.81 277	93.48 410	82.66 250	72.89 424	95.71 234

test_vis1_n		86.56 291	86.49 258	86.78 376	88.51 413	72.69 389	94.68 170	93.78 308	79.55 345	90.70 149	95.31 157	48.75 438	93.28 413	93.15 64	93.99 183	94.38 290

testing3-2		86.72 285	86.71 244	86.74 377	96.11 109	65.92 437	93.39 258	89.65 416	89.46 71	87.84 211	92.79 269	59.17 398	97.60 227	81.31 278	90.72 250	96.70 186

test_fmvs1		87.34 257	87.56 220	86.68 378	90.59 383	71.80 401	94.01 224	94.04 297	78.30 367	91.97 119	95.22 161	56.28 411	93.71 407	92.89 69	94.71 163	94.52 280

MDA-MVSNet-bldmvs		78.85 398	76.31 403	86.46 379	89.76 401	73.88 373	88.79 394	90.42 395	79.16 350	59.18 454	88.33 397	60.20 388	94.04 399	62.00 432	68.96 437	91.48 397

mvs5depth		80.98 377	79.15 384	86.45 380	84.57 442	73.29 382	87.79 410	91.67 364	80.52 333	82.20 350	89.72 373	55.14 419	95.93 353	73.93 366	66.83 442	90.12 419

tpmrst		85.35 318	84.99 307	86.43 381	90.88 373	67.88 430	88.71 395	91.43 373	80.13 337	86.08 252	88.80 390	73.05 251	96.02 348	82.48 251	83.40 348	95.40 242

TESTMET0.1,1		83.74 349	82.85 349	86.42 382	89.96 398	71.21 409	89.55 380	87.88 426	77.41 375	83.37 334	87.31 410	56.71 409	93.65 409	80.62 292	92.85 218	94.40 289

our_test_3		81.93 361	80.46 364	86.33 383	88.46 416	73.48 379	88.46 400	91.11 378	76.46 383	76.69 409	88.25 398	66.89 329	94.36 394	68.75 398	79.08 404	91.14 405

lessismore_v0				86.04 384	88.46 416	68.78 426		80.59 453		73.01 432	90.11 362	55.39 415	96.43 330	75.06 354	65.06 445	92.90 359

TinyColmap		79.76 390	77.69 393	85.97 385	91.71 336	73.12 383	89.55 380	90.36 397	75.03 399	72.03 435	90.19 358	46.22 447	96.19 343	63.11 428	81.03 379	88.59 437

KD-MVS_2432*1600		78.50 399	76.02 407	85.93 386	86.22 432	74.47 367	84.80 436	92.33 342	79.29 347	76.98 406	85.92 425	53.81 426	93.97 402	67.39 407	57.42 455	89.36 424

miper_refine_blended		78.50 399	76.02 407	85.93 386	86.22 432	74.47 367	84.80 436	92.33 342	79.29 347	76.98 406	85.92 425	53.81 426	93.97 402	67.39 407	57.42 455	89.36 424

K. test v3		81.59 367	80.15 369	85.91 388	89.89 400	69.42 424	92.57 299	87.71 428	85.56 202	73.44 430	89.71 374	55.58 412	95.52 372	77.17 332	69.76 433	92.78 364

SSC-MVS3.2		84.60 336	84.19 323	85.85 389	92.74 304	68.07 427	88.15 405	93.81 306	87.42 149	83.76 323	91.07 332	62.91 364	95.73 366	74.56 361	83.24 349	93.75 324

mvsany_test1		85.42 316	85.30 301	85.77 390	87.95 425	75.41 358	87.61 417	80.97 452	76.82 382	88.68 193	95.83 130	77.44 181	90.82 438	85.90 198	86.51 316	91.08 409

MIMVSNet1		79.38 394	77.28 396	85.69 391	86.35 431	73.67 376	91.61 332	92.75 333	78.11 372	72.64 433	88.12 400	48.16 439	91.97 429	60.32 436	77.49 410	91.43 399

UWE-MVS		83.69 350	83.09 343	85.48 392	93.06 288	65.27 442	90.92 348	86.14 434	79.90 340	86.26 248	90.72 346	57.17 408	95.81 361	71.03 385	92.62 226	95.35 245

UnsupCasMVSNet_eth		80.07 386	78.27 392	85.46 393	85.24 440	72.63 393	88.45 401	94.87 256	82.99 278	71.64 438	88.07 401	56.34 410	91.75 431	73.48 369	63.36 448	92.01 385

CL-MVSNet_self_test		81.74 364	80.53 362	85.36 394	85.96 434	72.45 396	90.25 362	93.07 323	81.24 325	79.85 384	87.29 411	70.93 275	92.52 421	66.95 410	69.23 435	91.11 407

MDA-MVSNet_test_wron		79.21 396	77.19 398	85.29 395	88.22 420	72.77 388	85.87 427	90.06 404	74.34 406	62.62 451	87.56 408	66.14 342	91.99 428	66.90 414	73.01 422	91.10 408

YYNet1		79.22 395	77.20 397	85.28 396	88.20 421	72.66 391	85.87 427	90.05 406	74.33 407	62.70 449	87.61 407	66.09 343	92.03 425	66.94 411	72.97 423	91.15 404

WB-MVSnew		83.77 348	83.28 339	85.26 397	91.48 342	71.03 411	91.89 322	87.98 425	78.91 352	84.78 292	90.22 356	69.11 310	94.02 400	64.70 423	90.44 253	90.71 411

dp		81.47 371	80.23 367	85.17 398	89.92 399	65.49 440	86.74 422	90.10 403	76.30 387	81.10 362	87.12 415	62.81 365	95.92 354	68.13 404	79.88 396	94.09 301

UnsupCasMVSNet_bld		76.23 409	73.27 413	85.09 399	83.79 444	72.92 385	85.65 430	93.47 314	71.52 429	68.84 444	79.08 449	49.77 434	93.21 414	66.81 415	60.52 452	89.13 432

SD_0403		84.71 334	84.65 316	84.92 400	92.95 295	65.95 436	92.07 321	93.23 318	83.82 255	79.03 390	93.73 239	73.90 236	92.91 419	63.02 430	90.05 260	95.89 223

Anonymous20231206		81.03 376	79.77 374	84.82 401	87.85 426	70.26 419	91.42 335	92.08 351	73.67 413	77.75 401	89.25 380	62.43 367	93.08 416	61.50 434	82.00 365	91.12 406

FE-MVSNET		78.19 401	76.03 406	84.69 402	83.70 445	73.31 381	90.58 356	90.00 407	77.11 380	71.91 436	85.47 429	55.53 414	91.94 430	59.69 440	70.24 431	88.83 434

test0.0.03 1		82.41 358	81.69 354	84.59 403	88.23 419	72.89 386	90.24 364	87.83 427	83.41 266	79.86 383	89.78 372	67.25 324	88.99 448	65.18 420	83.42 347	91.90 387

CMPMVS	59.16 21	80.52 380	79.20 382	84.48 404	83.98 443	67.63 433	89.95 375	93.84 305	64.79 448	66.81 446	91.14 329	57.93 404	95.17 382	76.25 342	88.10 295	90.65 412
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 335	84.79 314	84.37 405	91.84 330	64.92 443	93.70 246	91.47 372	66.19 445	86.16 251	95.28 158	67.18 326	93.33 412	80.89 287	90.42 255	94.88 265

PVSNet_0	73.20 20	77.22 405	74.83 411	84.37 405	90.70 381	71.10 410	83.09 445	89.67 414	72.81 423	73.93 428	83.13 439	60.79 385	93.70 408	68.54 399	50.84 460	88.30 439

LF4IMVS		80.37 383	79.07 386	84.27 407	86.64 430	69.87 423	89.39 385	91.05 381	76.38 385	74.97 422	90.00 366	47.85 440	94.25 398	74.55 362	80.82 385	88.69 436

Anonymous20240521		80.44 382	79.21 381	84.11 408	85.75 437	67.89 429	92.86 290	93.23 318	75.61 394	75.59 419	87.47 409	50.03 433	94.33 395	71.14 383	81.21 373	90.12 419

PM-MVS		78.11 402	76.12 405	84.09 409	83.54 446	70.08 420	88.97 393	85.27 441	79.93 339	74.73 424	86.43 420	34.70 458	93.48 410	79.43 309	72.06 426	88.72 435

test_fmvs2		83.98 343	84.03 328	83.83 410	87.16 428	67.53 434	93.93 231	92.89 327	77.62 373	86.89 232	93.53 242	47.18 442	92.02 427	90.54 129	86.51 316	91.93 386

testgi		80.94 379	80.20 368	83.18 411	87.96 424	66.29 435	91.28 339	90.70 393	83.70 257	78.12 397	92.84 264	51.37 431	90.82 438	63.34 427	82.46 358	92.43 374

KD-MVS_self_test		80.20 384	79.24 380	83.07 412	85.64 438	65.29 441	91.01 347	93.93 299	78.71 361	76.32 411	86.40 422	59.20 397	92.93 418	72.59 373	69.35 434	91.00 410

testing3		80.46 381	79.59 377	83.06 413	93.44 274	64.64 444	93.33 260	85.47 439	84.34 244	79.93 382	90.84 339	44.35 450	92.39 422	57.06 447	87.56 305	92.16 383

ambc				83.06 413	79.99 455	63.51 448	77.47 458	92.86 328		74.34 427	84.45 434	28.74 459	95.06 386	73.06 371	68.89 438	90.61 413

test20.03		79.95 388	79.08 385	82.55 415	85.79 436	67.74 432	91.09 345	91.08 379	81.23 326	74.48 426	89.96 368	61.63 372	90.15 440	60.08 437	76.38 416	89.76 421

MVStest1		72.91 413	69.70 418	82.54 416	78.14 458	73.05 384	88.21 404	86.21 433	60.69 452	64.70 447	90.53 349	46.44 445	85.70 455	58.78 443	53.62 457	88.87 433

test_vis1_rt		77.96 403	76.46 402	82.48 417	85.89 435	71.74 403	90.25 362	78.89 456	71.03 433	71.30 439	81.35 446	42.49 452	91.05 437	84.55 221	82.37 359	84.65 444

EU-MVSNet		81.32 373	80.95 360	82.42 418	88.50 415	63.67 447	93.32 261	91.33 374	64.02 449	80.57 371	92.83 265	61.21 381	92.27 424	76.34 341	80.38 392	91.32 400

myMVS_eth3d		79.67 391	78.79 388	82.32 419	91.92 326	64.08 445	89.75 378	87.40 431	81.72 311	78.82 392	87.20 412	45.33 448	91.29 434	59.09 442	87.84 302	91.60 392

ttmdpeth		76.55 407	74.64 412	82.29 420	82.25 451	67.81 431	89.76 377	85.69 437	70.35 435	75.76 417	91.69 308	46.88 443	89.77 442	66.16 416	63.23 449	89.30 426

pmmvs3		71.81 416	68.71 419	81.11 421	75.86 460	70.42 418	86.74 422	83.66 445	58.95 455	68.64 445	80.89 447	36.93 456	89.52 444	63.10 429	63.59 447	83.39 445

Syy-MVS		80.07 386	79.78 372	80.94 422	91.92 326	59.93 454	89.75 378	87.40 431	81.72 311	78.82 392	87.20 412	66.29 340	91.29 434	47.06 455	87.84 302	91.60 392

UWE-MVS-28		78.98 397	78.38 391	80.80 423	88.18 422	60.66 453	90.65 353	78.51 457	78.84 356	77.93 400	90.93 336	59.08 399	89.02 447	50.96 452	90.33 257	92.72 365

new-patchmatchnet		76.41 408	75.17 410	80.13 424	82.65 450	59.61 455	87.66 415	91.08 379	78.23 370	69.85 442	83.22 438	54.76 420	91.63 433	64.14 426	64.89 446	89.16 430

mvsany_test3		74.95 410	73.26 414	80.02 425	74.61 461	63.16 449	85.53 431	78.42 458	74.16 408	74.89 423	86.46 419	36.02 457	89.09 446	82.39 254	66.91 441	87.82 442

test_fmvs3		77.67 404	77.16 399	79.22 426	79.52 456	61.14 451	92.34 308	91.64 366	73.98 410	78.86 391	86.59 418	27.38 462	87.03 450	88.12 163	75.97 418	89.50 423

DSMNet-mixed		76.94 406	76.29 404	78.89 427	83.10 448	56.11 463	87.78 411	79.77 454	60.65 453	75.64 418	88.71 391	61.56 375	88.34 449	60.07 438	89.29 278	92.21 382

EGC-MVSNET		61.97 424	56.37 429	78.77 428	89.63 404	73.50 378	89.12 390	82.79 447	0.21 474	1.24 475	84.80 432	39.48 453	90.04 441	44.13 457	75.94 419	72.79 456

new_pmnet		72.15 414	70.13 417	78.20 429	82.95 449	65.68 438	83.91 441	82.40 449	62.94 451	64.47 448	79.82 448	42.85 451	86.26 454	57.41 446	74.44 421	82.65 449

MVS-HIRNet		73.70 412	72.20 415	78.18 430	91.81 333	56.42 462	82.94 446	82.58 448	55.24 456	68.88 443	66.48 459	55.32 417	95.13 383	58.12 444	88.42 291	83.01 447

LCM-MVSNet		66.00 421	62.16 426	77.51 431	64.51 471	58.29 457	83.87 442	90.90 387	48.17 460	54.69 457	73.31 455	16.83 471	86.75 451	65.47 418	61.67 451	87.48 443

APD_test1		69.04 417	66.26 423	77.36 432	80.51 454	62.79 450	85.46 432	83.51 446	54.11 458	59.14 455	84.79 433	23.40 465	89.61 443	55.22 448	70.24 431	79.68 453

test_f		71.95 415	70.87 416	75.21 433	74.21 463	59.37 456	85.07 435	85.82 436	65.25 447	70.42 441	83.13 439	23.62 463	82.93 461	78.32 319	71.94 427	83.33 446

ANet_high		58.88 428	54.22 433	72.86 434	56.50 474	56.67 459	80.75 451	86.00 435	73.09 420	37.39 466	64.63 462	22.17 466	79.49 464	43.51 458	23.96 468	82.43 450

test_vis3_rt		65.12 422	62.60 424	72.69 435	71.44 464	60.71 452	87.17 419	65.55 468	63.80 450	53.22 458	65.65 461	14.54 472	89.44 445	76.65 336	65.38 444	67.91 459

FPMVS		64.63 423	62.55 425	70.88 436	70.80 465	56.71 458	84.42 439	84.42 443	51.78 459	49.57 459	81.61 445	23.49 464	81.48 462	40.61 462	76.25 417	74.46 455

dmvs_testset		74.57 411	75.81 409	70.86 437	87.72 427	40.47 472	87.05 421	77.90 462	82.75 283	71.15 440	85.47 429	67.98 321	84.12 459	45.26 456	76.98 415	88.00 440

N_pmnet		68.89 418	68.44 420	70.23 438	89.07 409	28.79 477	88.06 406	19.50 477	69.47 437	71.86 437	84.93 431	61.24 380	91.75 431	54.70 449	77.15 412	90.15 418

testf1		59.54 426	56.11 430	69.85 439	69.28 466	56.61 460	80.37 452	76.55 465	42.58 463	45.68 462	75.61 450	11.26 473	84.18 457	43.20 459	60.44 453	68.75 457

APD_test2		59.54 426	56.11 430	69.85 439	69.28 466	56.61 460	80.37 452	76.55 465	42.58 463	45.68 462	75.61 450	11.26 473	84.18 457	43.20 459	60.44 453	68.75 457

WB-MVS		67.92 419	67.49 421	69.21 441	81.09 452	41.17 471	88.03 407	78.00 461	73.50 415	62.63 450	83.11 441	63.94 357	86.52 452	25.66 467	51.45 459	79.94 452

PMMVS2		59.60 425	56.40 428	69.21 441	68.83 468	46.58 467	73.02 462	77.48 463	55.07 457	49.21 460	72.95 456	17.43 470	80.04 463	49.32 454	44.33 463	80.99 451

SSC-MVS		67.06 420	66.56 422	68.56 443	80.54 453	40.06 473	87.77 412	77.37 464	72.38 425	61.75 452	82.66 443	63.37 360	86.45 453	24.48 468	48.69 462	79.16 454

Gipuma		57.99 430	54.91 432	67.24 444	88.51 413	65.59 439	52.21 465	90.33 398	43.58 462	42.84 465	51.18 466	20.29 468	85.07 456	34.77 463	70.45 430	51.05 465
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 432	48.46 436	63.48 445	45.72 476	46.20 468	73.41 461	78.31 459	41.03 465	30.06 468	65.68 460	6.05 475	83.43 460	30.04 465	65.86 443	60.80 460
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 429	58.24 427	60.56 446	83.13 447	45.09 470	82.32 447	48.22 476	67.61 441	61.70 453	69.15 457	38.75 454	76.05 465	32.01 464	41.31 464	60.55 461

MVE	39.65 23	43.39 434	38.59 440	57.77 447	56.52 473	48.77 466	55.38 464	58.64 472	29.33 468	28.96 469	52.65 465	4.68 476	64.62 469	28.11 466	33.07 466	59.93 462
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 433	48.47 435	56.66 448	52.26 475	18.98 479	41.51 467	81.40 451	10.10 469	44.59 464	75.01 453	28.51 460	68.16 466	53.54 450	49.31 461	82.83 448

DeepMVS_CX				56.31 449	74.23 462	51.81 465		56.67 473	44.85 461	48.54 461	75.16 452	27.87 461	58.74 471	40.92 461	52.22 458	58.39 463

kuosan		53.51 431	53.30 434	54.13 450	76.06 459	45.36 469	80.11 454	48.36 475	59.63 454	54.84 456	63.43 463	37.41 455	62.07 470	20.73 470	39.10 465	54.96 464

E-PMN		43.23 435	42.29 437	46.03 451	65.58 470	37.41 474	73.51 460	64.62 469	33.99 466	28.47 470	47.87 467	19.90 469	67.91 467	22.23 469	24.45 467	32.77 466

EMVS		42.07 436	41.12 438	44.92 452	63.45 472	35.56 476	73.65 459	63.48 470	33.05 467	26.88 471	45.45 468	21.27 467	67.14 468	19.80 471	23.02 469	32.06 467

tmp_tt		35.64 437	39.24 439	24.84 453	14.87 477	23.90 478	62.71 463	51.51 474	6.58 471	36.66 467	62.08 464	44.37 449	30.34 473	52.40 451	22.00 470	20.27 468

wuyk23d		21.27 439	20.48 442	23.63 454	68.59 469	36.41 475	49.57 466	6.85 478	9.37 470	7.89 472	4.46 474	4.03 477	31.37 472	17.47 472	16.07 471	3.12 469

test123		8.76 441	11.22 444	1.39 455	0.85 479	0.97 480	85.76 429	0.35 480	0.54 473	2.45 474	8.14 473	0.60 478	0.48 474	2.16 474	0.17 473	2.71 470

testmvs		8.92 440	11.52 443	1.12 456	1.06 478	0.46 481	86.02 426	0.65 479	0.62 472	2.74 473	9.52 472	0.31 479	0.45 475	2.38 473	0.39 472	2.46 471

mmdepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

monomultidepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

test_blank		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet_test		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

DCPMVS		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

cdsmvs_eth3d_5k		22.14 438	29.52 441	0.00 457	0.00 480	0.00 482	0.00 468	95.76 183	0.00 475	0.00 476	94.29 210	75.66 208	0.00 476	0.00 475	0.00 474	0.00 472

pcd_1.5k_mvsjas		6.64 443	8.86 446	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	79.70 145	0.00 476	0.00 475	0.00 474	0.00 472

sosnet-low-res		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

sosnet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uncertanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

Regformer		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

ab-mvs-re		7.82 442	10.43 445	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	93.88 231	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

WAC-MVS						64.08 445								59.14 441

FOURS1					98.86 1	85.54 69	98.29 1	97.49 8	89.79 62	96.29 28

PC_three_1452									82.47 287	97.09 17	97.07 67	92.72 1	98.04 187	92.70 75	99.02 12	98.86 12

test_one_0601					98.58 11	85.83 63		97.44 17	91.05 22	96.78 24	98.06 21	91.45 11

eth-test2					0.00 480

eth-test					0.00 480

ZD-MVS					98.15 36	86.62 33		97.07 56	83.63 259	94.19 59	96.91 73	87.57 31	99.26 46	91.99 100	98.44 53

RE-MVS-def			93.68 68		97.92 45	84.57 89	96.28 46	96.76 88	87.46 146	93.75 70	97.43 46	82.94 96		92.73 71	97.80 87	97.88 96

IU-MVS					98.77 5	86.00 52		96.84 78	81.26 324	97.26 13				95.50 35	99.13 3	99.03 8

test_241102_TWO								97.44 17	90.31 40	97.62 8	98.07 19	91.46 10	99.58 10	95.66 29	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 54		97.44 17	90.26 46	97.71 2	97.96 30	92.31 4	99.38 31

9.14			94.47 31		97.79 54		96.08 64	97.44 17	86.13 189	95.10 49	97.40 48	88.34 22	99.22 48	93.25 63	98.70 34

save fliter					97.85 51	85.63 68	95.21 134	96.82 81	89.44 72

test_0728_THIRD									90.75 28	97.04 19	98.05 24	92.09 6	99.55 16	95.64 31	99.13 3	99.13 2

test0726					98.78 3	85.93 57	97.19 12	97.47 13	90.27 44	97.64 6	98.13 6	91.47 8

GSMVS																96.12 211

test_part2					98.55 12	87.22 19				96.40 27

sam_mvs1												71.70 266				96.12 211

sam_mvs												70.60 280

MTGPA								96.97 61

test_post1							88.00 408				9.81 471	69.31 304	95.53 371	76.65 336

test_post											10.29 470	70.57 284	95.91 356

patchmatchnet-post											83.76 436	71.53 267	96.48 325

MTMP							96.16 55	60.64 471

gm-plane-assit					89.60 405	68.00 428			77.28 378		88.99 385		97.57 230	79.44 308

test9_res														91.91 104	98.71 32	98.07 78

TEST9					97.53 63	86.49 37	94.07 217	96.78 85	81.61 316	92.77 95	96.20 103	87.71 28	99.12 58

test_8					97.49 65	86.30 45	94.02 223	96.76 88	81.86 307	92.70 99	96.20 103	87.63 29	99.02 68

agg_prior2														90.54 129	98.68 37	98.27 59

agg_prior					97.38 68	85.92 59		96.72 95		92.16 114			98.97 82

test_prior4						85.96 56	94.11 211

test_prior2							94.12 209		87.67 143	92.63 103	96.39 98	86.62 41		91.50 113	98.67 40

旧先验2							93.36 259		71.25 431	94.37 55			97.13 282	86.74 185

新几何2							93.11 274

旧先验1					96.79 81	81.81 190		95.67 193			96.81 79	86.69 39			97.66 93	96.97 167

无先验							93.28 267	96.26 134	73.95 411				99.05 62	80.56 293		96.59 190

原ACMM2							92.94 285

test222					96.55 90	81.70 192	92.22 314	95.01 241	68.36 440	90.20 160	96.14 108	80.26 136			97.80 87	96.05 218

testdata2													98.75 110	78.30 320

segment_acmp												87.16 36

testdata1							92.15 316		87.94 129

plane_prior7					94.70 194	82.74 160

plane_prior6					94.52 209	82.75 158						74.23 228

plane_prior5								96.22 139					98.12 171	88.15 160	89.99 261	94.63 272

plane_prior4											94.86 181

plane_prior3						82.75 158			90.26 46	86.91 229

plane_prior2							95.85 87		90.81 26

plane_prior1					94.59 202

plane_prior						82.73 161	95.21 134		89.66 67						89.88 266

n2								0.00 481

nn								0.00 481

door-mid								85.49 438

test11								96.57 106

door								85.33 440

HQP5-MVS						81.56 194

HQP-NCC					94.17 233		94.39 191		88.81 97	85.43 275

ACMP_Plane					94.17 233		94.39 191		88.81 97	85.43 275

BP-MVS														87.11 182

HQP4-MVS										85.43 275			97.96 199			94.51 282

HQP3-MVS								96.04 157							89.77 270

HQP2-MVS												73.83 239

NP-MVS					94.37 220	82.42 173					93.98 224

MDTV_nov1_ep13_2view						55.91 464	87.62 416		73.32 417	84.59 297		70.33 287		74.65 359		95.50 239

MDTV_nov1_ep13			83.56 336		91.69 338	69.93 421	87.75 413	91.54 369	78.60 362	84.86 291	88.90 387	69.54 299	96.03 347	70.25 388	88.93 283

ACMMP++_ref															87.47 306

ACMMP++															88.01 298

Test By Simon												80.02 138

High-resolution multi-view benchmark