High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

DVP-MVS++		95.98 1	96.36 1	94.82 28	97.78 51	86.00 47	98.29 1	97.49 5	90.75 17	97.62 5	98.06 6	92.59 2	99.61 3	95.64 6	99.02 12	98.86 10

SED-MVS		95.91 2	96.28 2	94.80 30	98.77 5	85.99 49	97.13 14	97.44 14	90.31 26	97.71 1	98.07 4	92.31 4	99.58 8	95.66 4	99.13 3	98.84 13

DVP-MVS		95.67 3	96.02 3	94.64 36	98.78 3	85.93 52	97.09 16	96.73 76	90.27 29	97.04 10	98.05 8	91.47 8	99.55 14	95.62 8	99.08 7	98.45 34
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DPE-MVS		95.57 4	95.67 4	95.25 9	98.36 25	87.28 15	95.56 82	97.51 4	89.13 58	97.14 8	97.91 11	91.64 7	99.62 1	94.61 14	99.17 2	98.86 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		95.46 5	95.64 5	94.91 19	98.26 28	86.29 43	97.46 6	97.40 19	89.03 61	96.20 16	98.10 2	89.39 16	99.34 32	95.88 3	99.03 11	99.10 4

MSP-MVS		95.42 6	95.56 6	94.98 17	98.49 17	86.52 33	96.91 25	97.47 10	91.73 8	96.10 17	96.69 53	89.90 12	99.30 38	94.70 12	98.04 63	99.13 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CNVR-MVS		95.40 7	95.37 7	95.50 7	98.11 36	88.51 7	95.29 92	96.96 50	92.09 4	95.32 22	97.08 36	89.49 15	99.33 35	95.10 11	98.85 19	98.66 19

SMA-MVS		95.20 8	95.07 10	95.59 5	98.14 35	88.48 8	96.26 45	97.28 29	85.90 137	97.67 3	98.10 2	88.41 20	99.56 10	94.66 13	99.19 1	98.71 18
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 23	96.99 72	86.33 39	97.33 7	97.30 27	91.38 10	95.39 21	97.46 17	88.98 19	99.40 28	94.12 18	98.89 18	98.82 15
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 10	94.91 12	95.83 4	98.25 29	89.65 4	95.92 63	96.96 50	91.75 7	94.02 35	96.83 48	88.12 24	99.55 14	93.41 28	98.94 16	98.28 48

SF-MVS		94.97 11	94.90 13	95.20 10	97.84 47	87.76 9	96.65 34	97.48 9	87.76 101	95.71 19	97.70 13	88.28 23	99.35 31	93.89 22	98.78 25	98.48 28

SD-MVS		94.96 12	95.33 8	93.88 54	97.25 69	86.69 25	96.19 48	97.11 41	90.42 25	96.95 12	97.27 25	89.53 14	96.91 234	94.38 16	98.85 19	98.03 68
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + MP.		94.85 13	94.94 11	94.58 39	98.25 29	86.33 39	96.11 53	96.62 85	88.14 89	96.10 17	96.96 42	89.09 18	98.94 73	94.48 15	98.68 35	98.48 28
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
NCCC		94.81 14	94.69 15	95.17 12	97.83 48	87.46 14	95.66 76	96.93 54	92.34 2	93.94 36	96.58 63	87.74 27	99.44 27	92.83 36	98.40 50	98.62 20

ACMMP_NAP		94.74 15	94.56 16	95.28 8	98.02 41	87.70 10	95.68 74	97.34 21	88.28 82	95.30 23	97.67 14	85.90 43	99.54 18	93.91 21	98.95 15	98.60 21

HFP-MVS		94.52 16	94.40 19	94.86 22	98.61 10	86.81 22	96.94 20	97.34 21	88.63 71	93.65 40	97.21 29	86.10 41	99.49 24	92.35 47	98.77 27	98.30 45

ZNCC-MVS		94.47 17	94.28 23	95.03 14	98.52 15	86.96 17	96.85 28	97.32 25	88.24 83	93.15 50	97.04 39	86.17 40	99.62 1	92.40 45	98.81 22	98.52 24

XVS		94.45 18	94.32 20	94.85 23	98.54 13	86.60 31	96.93 22	97.19 33	90.66 22	92.85 57	97.16 34	85.02 54	99.49 24	91.99 60	98.56 46	98.47 31

MCST-MVS		94.45 18	94.20 28	95.19 11	98.46 19	87.50 13	95.00 112	97.12 39	87.13 111	92.51 70	96.30 69	89.24 17	99.34 32	93.46 26	98.62 42	98.73 16

region2R		94.43 20	94.27 25	94.92 18	98.65 8	86.67 27	96.92 24	97.23 32	88.60 73	93.58 42	97.27 25	85.22 50	99.54 18	92.21 50	98.74 29	98.56 23

ACMMPR		94.43 20	94.28 23	94.91 19	98.63 9	86.69 25	96.94 20	97.32 25	88.63 71	93.53 45	97.26 27	85.04 53	99.54 18	92.35 47	98.78 25	98.50 25

MTAPA		94.42 22	94.22 26	95.00 16	98.42 21	86.95 18	94.36 156	96.97 48	91.07 11	93.14 51	97.56 15	84.30 62	99.56 10	93.43 27	98.75 28	98.47 31

CP-MVS		94.34 23	94.21 27	94.74 34	98.39 23	86.64 29	97.60 4	97.24 30	88.53 75	92.73 64	97.23 28	85.20 51	99.32 36	92.15 53	98.83 21	98.25 53

MP-MVS		94.25 24	94.07 32	94.77 32	98.47 18	86.31 41	96.71 31	96.98 47	89.04 60	91.98 79	97.19 31	85.43 48	99.56 10	92.06 59	98.79 23	98.44 35
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 25	94.07 32	94.75 33	98.06 39	86.90 20	95.88 64	96.94 53	85.68 143	95.05 25	97.18 32	87.31 33	99.07 51	91.90 66	98.61 44	98.28 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 26	94.17 30	94.43 44	98.21 32	85.78 59	96.40 39	96.90 57	88.20 87	94.33 29	97.40 20	84.75 59	99.03 56	93.35 29	97.99 64	98.48 28

GST-MVS		94.21 27	93.97 35	94.90 21	98.41 22	86.82 21	96.54 36	97.19 33	88.24 83	93.26 47	96.83 48	85.48 47	99.59 7	91.43 72	98.40 50	98.30 45

MP-MVS-pluss		94.21 27	94.00 34	94.85 23	98.17 33	86.65 28	94.82 123	97.17 37	86.26 130	92.83 59	97.87 12	85.57 46	99.56 10	94.37 17	98.92 17	98.34 40
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepPCF-MVS	89.96 1	94.20 29	94.77 14	92.49 101	96.52 87	80.00 204	94.00 180	97.08 42	90.05 33	95.65 20	97.29 24	89.66 13	98.97 70	93.95 20	98.71 30	98.50 25

CS-MVS		94.12 30	94.44 18	93.17 68	96.55 84	83.08 115	97.63 3	96.95 52	91.71 9	93.50 46	96.21 72	85.61 44	98.24 123	93.64 24	98.17 56	98.19 56

DeepC-MVS_fast	89.43 2	94.04 31	93.79 38	94.80 30	97.48 61	86.78 23	95.65 78	96.89 58	89.40 50	92.81 60	96.97 41	85.37 49	99.24 41	90.87 82	98.69 33	98.38 39
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS-test		94.02 32	94.29 22	93.24 65	96.69 78	83.24 108	97.49 5	96.92 55	92.14 3	92.90 55	95.77 93	85.02 54	98.33 118	93.03 33	98.62 42	98.13 60

HPM-MVS		94.02 32	93.88 36	94.43 44	98.39 23	85.78 59	97.25 10	97.07 43	86.90 119	92.62 67	96.80 52	84.85 58	99.17 45	92.43 43	98.65 40	98.33 41
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 34	93.78 39	94.63 37	98.50 16	85.90 56	96.87 26	96.91 56	88.70 69	91.83 87	97.17 33	83.96 66	99.55 14	91.44 71	98.64 41	98.43 36

PGM-MVS		93.96 35	93.72 41	94.68 35	98.43 20	86.22 44	95.30 90	97.78 1	87.45 107	93.26 47	97.33 23	84.62 60	99.51 22	90.75 84	98.57 45	98.32 44

PHI-MVS		93.89 36	93.65 44	94.62 38	96.84 75	86.43 36	96.69 32	97.49 5	85.15 157	93.56 44	96.28 70	85.60 45	99.31 37	92.45 42	98.79 23	98.12 62

SR-MVS-dyc-post		93.82 37	93.82 37	93.82 56	97.92 43	84.57 73	96.28 43	96.76 72	87.46 105	93.75 38	97.43 18	84.24 63	99.01 61	92.73 37	97.80 70	97.88 75

APD-MVS_3200maxsize		93.78 38	93.77 40	93.80 58	97.92 43	84.19 85	96.30 41	96.87 60	86.96 115	93.92 37	97.47 16	83.88 67	98.96 72	92.71 40	97.87 68	98.26 52

patch_mono-2		93.74 39	94.32 20	92.01 117	97.54 57	78.37 242	93.40 203	97.19 33	88.02 91	94.99 26	97.21 29	88.35 21	98.44 109	94.07 19	98.09 61	99.23 1

MSLP-MVS++		93.72 40	94.08 31	92.65 93	97.31 65	83.43 103	95.79 68	97.33 23	90.03 34	93.58 42	96.96 42	84.87 57	97.76 161	92.19 52	98.66 38	96.76 119

TSAR-MVS + GP.		93.66 41	93.41 45	94.41 46	96.59 82	86.78 23	94.40 149	93.93 234	89.77 42	94.21 30	95.59 100	87.35 32	98.61 95	92.72 39	96.15 98	97.83 79

CANet		93.54 42	93.20 49	94.55 40	95.65 117	85.73 61	94.94 115	96.69 81	91.89 6	90.69 103	95.88 87	81.99 90	99.54 18	93.14 32	97.95 66	98.39 37

dcpmvs_2		93.49 43	94.19 29	91.38 151	97.69 54	76.78 274	94.25 159	96.29 101	88.33 79	94.46 27	96.88 45	88.07 25	98.64 91	93.62 25	98.09 61	98.73 16

MVS_111021_HR		93.45 44	93.31 46	93.84 55	96.99 72	84.84 68	93.24 215	97.24 30	88.76 68	91.60 92	95.85 88	86.07 42	98.66 89	91.91 64	98.16 57	98.03 68

train_agg		93.44 45	93.08 50	94.52 41	97.53 58	86.49 34	94.07 172	96.78 69	81.86 227	92.77 61	96.20 73	87.63 29	99.12 49	92.14 54	98.69 33	97.94 71

DROMVSNet		93.44 45	93.71 42	92.63 94	95.21 131	82.43 136	97.27 9	96.71 79	90.57 24	92.88 56	95.80 91	83.16 71	98.16 129	93.68 23	98.14 58	97.31 96

DELS-MVS		93.43 47	93.25 47	93.97 51	95.42 124	85.04 67	93.06 222	97.13 38	90.74 19	91.84 85	95.09 116	86.32 39	99.21 43	91.22 73	98.45 48	97.65 84
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
HPM-MVS_fast		93.40 48	93.22 48	93.94 53	98.36 25	84.83 69	97.15 13	96.80 68	85.77 140	92.47 71	97.13 35	82.38 79	99.07 51	90.51 89	98.40 50	97.92 74

DeepC-MVS	88.79 3	93.31 49	92.99 52	94.26 49	96.07 102	85.83 57	94.89 118	96.99 46	89.02 63	89.56 117	97.37 22	82.51 78	99.38 29	92.20 51	98.30 53	97.57 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		93.27 50	92.75 56	94.85 23	95.70 116	87.66 11	96.33 40	96.41 95	90.00 35	94.09 33	94.60 137	82.33 81	98.62 94	92.40 45	92.86 158	98.27 50

ACMMP		93.24 51	92.88 54	94.30 48	98.09 38	85.33 65	96.86 27	97.45 13	88.33 79	90.15 112	97.03 40	81.44 93	99.51 22	90.85 83	95.74 101	98.04 67
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CSCG		93.23 52	93.05 51	93.76 59	98.04 40	84.07 87	96.22 47	97.37 20	84.15 173	90.05 113	95.66 97	87.77 26	99.15 48	89.91 92	98.27 54	98.07 64

alignmvs		93.08 53	92.50 60	94.81 29	95.62 119	87.61 12	95.99 59	96.07 119	89.77 42	94.12 32	94.87 122	80.56 99	98.66 89	92.42 44	93.10 154	98.15 59

EI-MVSNet-Vis-set		93.01 54	92.92 53	93.29 63	95.01 138	83.51 102	94.48 141	95.77 140	90.87 13	92.52 69	96.67 55	84.50 61	99.00 65	91.99 60	94.44 129	97.36 95

casdiffmvs_mvg		92.96 55	92.83 55	93.35 62	94.59 161	83.40 105	95.00 112	96.34 99	90.30 28	92.05 77	96.05 81	83.43 69	98.15 130	92.07 56	95.67 102	98.49 27
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		92.83 56	92.54 59	93.68 60	96.10 100	84.71 71	95.66 76	96.39 96	91.92 5	93.22 49	96.49 65	83.16 71	98.87 77	84.47 158	95.47 107	97.45 94

CDPH-MVS		92.83 56	92.30 62	94.44 42	97.79 49	86.11 46	94.06 174	96.66 82	80.09 254	92.77 61	96.63 60	86.62 36	99.04 55	87.40 120	98.66 38	98.17 58

ETV-MVS		92.74 58	92.66 57	92.97 78	95.20 132	84.04 89	95.07 108	96.51 91	90.73 20	92.96 54	91.19 256	84.06 64	98.34 116	91.72 68	96.54 92	96.54 128

EI-MVSNet-UG-set		92.74 58	92.62 58	93.12 70	94.86 149	83.20 110	94.40 149	95.74 143	90.71 21	92.05 77	96.60 62	84.00 65	98.99 67	91.55 69	93.63 139	97.17 103

DPM-MVS		92.58 60	91.74 68	95.08 13	96.19 95	89.31 5	92.66 232	96.56 90	83.44 191	91.68 91	95.04 117	86.60 38	98.99 67	85.60 144	97.92 67	96.93 115

casdiffmvs		92.51 61	92.43 61	92.74 88	94.41 173	81.98 146	94.54 139	96.23 108	89.57 46	91.96 81	96.17 77	82.58 77	98.01 148	90.95 80	95.45 109	98.23 54
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_111021_LR		92.47 62	92.29 63	92.98 77	95.99 106	84.43 82	93.08 220	96.09 117	88.20 87	91.12 99	95.72 96	81.33 95	97.76 161	91.74 67	97.37 77	96.75 120

3Dnovator+	87.14 4	92.42 63	91.37 71	95.55 6	95.63 118	88.73 6	97.07 18	96.77 71	90.84 14	84.02 241	96.62 61	75.95 150	99.34 32	87.77 114	97.68 73	98.59 22

baseline		92.39 64	92.29 63	92.69 92	94.46 170	81.77 151	94.14 165	96.27 103	89.22 54	91.88 83	96.00 82	82.35 80	97.99 150	91.05 75	95.27 114	98.30 45

VNet		92.24 65	91.91 66	93.24 65	96.59 82	83.43 103	94.84 122	96.44 93	89.19 56	94.08 34	95.90 86	77.85 134	98.17 128	88.90 102	93.38 148	98.13 60

CPTT-MVS		91.99 66	91.80 67	92.55 98	98.24 31	81.98 146	96.76 30	96.49 92	81.89 226	90.24 108	96.44 67	78.59 123	98.61 95	89.68 93	97.85 69	97.06 107

EIA-MVS		91.95 67	91.94 65	91.98 121	95.16 133	80.01 203	95.36 85	96.73 76	88.44 76	89.34 121	92.16 222	83.82 68	98.45 108	89.35 96	97.06 80	97.48 92

DP-MVS Recon		91.95 67	91.28 73	93.96 52	98.33 27	85.92 54	94.66 133	96.66 82	82.69 208	90.03 114	95.82 90	82.30 82	99.03 56	84.57 156	96.48 95	96.91 116

EPNet		91.79 69	91.02 79	94.10 50	90.10 310	85.25 66	96.03 58	92.05 281	92.83 1	87.39 156	95.78 92	79.39 114	99.01 61	88.13 110	97.48 75	98.05 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		91.77 70	91.70 69	92.00 120	97.08 71	80.03 202	93.60 197	95.18 181	87.85 99	90.89 101	96.47 66	82.06 88	98.36 113	85.07 148	97.04 81	97.62 85

Vis-MVSNet		91.75 71	91.23 74	93.29 63	95.32 126	83.78 94	96.14 51	95.98 124	89.89 36	90.45 105	96.58 63	75.09 162	98.31 121	84.75 154	96.90 84	97.78 82
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 72	90.82 83	94.44 42	94.59 161	86.37 38	97.18 12	97.02 45	89.20 55	84.31 237	96.66 56	73.74 186	99.17 45	86.74 130	97.96 65	97.79 81

EPP-MVSNet		91.70 73	91.56 70	92.13 116	95.88 109	80.50 187	97.33 7	95.25 177	86.15 133	89.76 116	95.60 99	83.42 70	98.32 120	87.37 122	93.25 151	97.56 90

MVSFormer		91.68 74	91.30 72	92.80 84	93.86 194	83.88 92	95.96 61	95.90 131	84.66 168	91.76 88	94.91 120	77.92 131	97.30 205	89.64 94	97.11 78	97.24 99

Effi-MVS+		91.59 75	91.11 76	93.01 76	94.35 177	83.39 106	94.60 135	95.10 185	87.10 112	90.57 104	93.10 194	81.43 94	98.07 144	89.29 98	94.48 127	97.59 88

IS-MVSNet		91.43 76	91.09 78	92.46 102	95.87 111	81.38 163	96.95 19	93.69 245	89.72 44	89.50 119	95.98 83	78.57 124	97.77 160	83.02 176	96.50 94	98.22 55

PVSNet_Blended_VisFu		91.38 77	90.91 81	92.80 84	96.39 90	83.17 111	94.87 120	96.66 82	83.29 195	89.27 122	94.46 141	80.29 101	99.17 45	87.57 118	95.37 110	96.05 146

diffmvs		91.37 78	91.23 74	91.77 136	93.09 216	80.27 190	92.36 241	95.52 160	87.03 114	91.40 96	94.93 119	80.08 103	97.44 189	92.13 55	94.56 124	97.61 86
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		91.31 79	91.11 76	91.93 125	94.37 174	80.14 195	93.46 202	95.80 138	86.46 126	91.35 97	93.77 173	82.21 84	98.09 141	87.57 118	94.95 116	97.55 91

OMC-MVS		91.23 80	90.62 85	93.08 72	96.27 93	84.07 87	93.52 199	95.93 127	86.95 116	89.51 118	96.13 79	78.50 125	98.35 115	85.84 142	92.90 157	96.83 118

PAPM_NR		91.22 81	90.78 84	92.52 100	97.60 56	81.46 160	94.37 155	96.24 107	86.39 128	87.41 153	94.80 128	82.06 88	98.48 102	82.80 182	95.37 110	97.61 86

PS-MVSNAJ		91.18 82	90.92 80	91.96 123	95.26 129	82.60 135	92.09 252	95.70 145	86.27 129	91.84 85	92.46 212	79.70 109	98.99 67	89.08 100	95.86 100	94.29 216

xiu_mvs_v2_base		91.13 83	90.89 82	91.86 130	94.97 141	82.42 137	92.24 246	95.64 152	86.11 136	91.74 90	93.14 192	79.67 112	98.89 76	89.06 101	95.46 108	94.28 217

nrg030		91.08 84	90.39 86	93.17 68	93.07 217	86.91 19	96.41 37	96.26 104	88.30 81	88.37 136	94.85 125	82.19 85	97.64 172	91.09 74	82.95 269	94.96 180

lupinMVS		90.92 85	90.21 89	93.03 75	93.86 194	83.88 92	92.81 229	93.86 238	79.84 257	91.76 88	94.29 147	77.92 131	98.04 146	90.48 90	97.11 78	97.17 103

h-mvs33		90.80 86	90.15 92	92.75 87	96.01 104	82.66 132	95.43 84	95.53 159	89.80 38	93.08 52	95.64 98	75.77 151	99.00 65	92.07 56	78.05 324	96.60 124

jason		90.80 86	90.10 93	92.90 81	93.04 219	83.53 101	93.08 220	94.15 227	80.22 251	91.41 95	94.91 120	76.87 138	97.93 155	90.28 91	96.90 84	97.24 99
jason: jason.
VDD-MVS		90.74 88	89.92 100	93.20 67	96.27 93	83.02 117	95.73 71	93.86 238	88.42 78	92.53 68	96.84 47	62.09 297	98.64 91	90.95 80	92.62 161	97.93 73

PVSNet_Blended		90.73 89	90.32 88	91.98 121	96.12 97	81.25 165	92.55 236	96.83 64	82.04 220	89.10 124	92.56 210	81.04 97	98.85 81	86.72 132	95.91 99	95.84 153

test_yl		90.69 90	90.02 98	92.71 89	95.72 114	82.41 139	94.11 167	95.12 183	85.63 145	91.49 93	94.70 131	74.75 166	98.42 111	86.13 137	92.53 162	97.31 96

DCV-MVSNet		90.69 90	90.02 98	92.71 89	95.72 114	82.41 139	94.11 167	95.12 183	85.63 145	91.49 93	94.70 131	74.75 166	98.42 111	86.13 137	92.53 162	97.31 96

API-MVS		90.66 92	90.07 94	92.45 103	96.36 91	84.57 73	96.06 57	95.22 180	82.39 211	89.13 123	94.27 150	80.32 100	98.46 105	80.16 229	96.71 89	94.33 213

xiu_mvs_v1_base_debu		90.64 93	90.05 95	92.40 104	93.97 191	84.46 79	93.32 205	95.46 162	85.17 154	92.25 72	94.03 155	70.59 221	98.57 98	90.97 77	94.67 119	94.18 218

xiu_mvs_v1_base		90.64 93	90.05 95	92.40 104	93.97 191	84.46 79	93.32 205	95.46 162	85.17 154	92.25 72	94.03 155	70.59 221	98.57 98	90.97 77	94.67 119	94.18 218

xiu_mvs_v1_base_debi		90.64 93	90.05 95	92.40 104	93.97 191	84.46 79	93.32 205	95.46 162	85.17 154	92.25 72	94.03 155	70.59 221	98.57 98	90.97 77	94.67 119	94.18 218

HQP_MVS		90.60 96	90.19 90	91.82 133	94.70 157	82.73 128	95.85 65	96.22 109	90.81 15	86.91 165	94.86 123	74.23 174	98.12 131	88.15 108	89.99 185	94.63 192

FIs		90.51 97	90.35 87	90.99 172	93.99 190	80.98 173	95.73 71	97.54 3	89.15 57	86.72 170	94.68 133	81.83 92	97.24 213	85.18 147	88.31 219	94.76 190

MAR-MVS		90.30 98	89.37 109	93.07 74	96.61 81	84.48 78	95.68 74	95.67 147	82.36 213	87.85 144	92.85 199	76.63 144	98.80 85	80.01 230	96.68 90	95.91 149
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
FC-MVSNet-test		90.27 99	90.18 91	90.53 183	93.71 200	79.85 209	95.77 69	97.59 2	89.31 52	86.27 179	94.67 134	81.93 91	97.01 228	84.26 160	88.09 223	94.71 191

CANet_DTU		90.26 100	89.41 108	92.81 83	93.46 208	83.01 118	93.48 200	94.47 214	89.43 49	87.76 148	94.23 151	70.54 225	99.03 56	84.97 149	96.39 96	96.38 131

OPM-MVS		90.12 101	89.56 103	91.82 133	93.14 214	83.90 91	94.16 164	95.74 143	88.96 64	87.86 143	95.43 104	72.48 202	97.91 156	88.10 112	90.18 184	93.65 252
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 102	89.13 115	92.95 79	96.71 77	82.32 141	96.08 54	89.91 330	86.79 120	92.15 76	96.81 50	62.60 295	98.34 116	87.18 124	93.90 135	98.19 56

GeoE		90.05 103	89.43 107	91.90 129	95.16 133	80.37 189	95.80 67	94.65 211	83.90 178	87.55 152	94.75 130	78.18 129	97.62 174	81.28 209	93.63 139	97.71 83

PAPR		90.02 104	89.27 114	92.29 112	95.78 112	80.95 175	92.68 231	96.22 109	81.91 224	86.66 171	93.75 175	82.23 83	98.44 109	79.40 240	94.79 117	97.48 92

PVSNet_BlendedMVS		89.98 105	89.70 101	90.82 176	96.12 97	81.25 165	93.92 185	96.83 64	83.49 190	89.10 124	92.26 220	81.04 97	98.85 81	86.72 132	87.86 227	92.35 297

PS-MVSNAJss		89.97 106	89.62 102	91.02 169	91.90 248	80.85 178	95.26 95	95.98 124	86.26 130	86.21 180	94.29 147	79.70 109	97.65 169	88.87 103	88.10 221	94.57 197

mvsmamba		89.96 107	89.50 104	91.33 154	92.90 225	81.82 149	96.68 33	92.37 269	89.03 61	87.00 161	94.85 125	73.05 194	97.65 169	91.03 76	88.63 210	94.51 202

XVG-OURS-SEG-HR		89.95 108	89.45 105	91.47 148	94.00 189	81.21 168	91.87 255	96.06 121	85.78 139	88.55 132	95.73 95	74.67 170	97.27 209	88.71 104	89.64 194	95.91 149

UGNet		89.95 108	88.95 119	92.95 79	94.51 167	83.31 107	95.70 73	95.23 178	89.37 51	87.58 150	93.94 163	64.00 286	98.78 86	83.92 165	96.31 97	96.74 121
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
UniMVSNet_NR-MVSNet		89.92 110	89.29 112	91.81 135	93.39 209	83.72 95	94.43 147	97.12 39	89.80 38	86.46 173	93.32 183	83.16 71	97.23 214	84.92 150	81.02 298	94.49 207

AdaColmap		89.89 111	89.07 116	92.37 107	97.41 62	83.03 116	94.42 148	95.92 128	82.81 206	86.34 178	94.65 135	73.89 182	99.02 59	80.69 220	95.51 105	95.05 175

hse-mvs2		89.88 112	89.34 110	91.51 145	94.83 151	81.12 170	93.94 183	93.91 237	89.80 38	93.08 52	93.60 177	75.77 151	97.66 168	92.07 56	77.07 331	95.74 158

UniMVSNet (Re)		89.80 113	89.07 116	92.01 117	93.60 204	84.52 76	94.78 126	97.47 10	89.26 53	86.44 176	92.32 217	82.10 86	97.39 201	84.81 153	80.84 302	94.12 222

HQP-MVS		89.80 113	89.28 113	91.34 153	94.17 180	81.56 154	94.39 151	96.04 122	88.81 65	85.43 204	93.97 162	73.83 184	97.96 152	87.11 127	89.77 192	94.50 205

FA-MVS(test-final)		89.66 115	88.91 121	91.93 125	94.57 164	80.27 190	91.36 266	94.74 208	84.87 162	89.82 115	92.61 209	74.72 169	98.47 104	83.97 164	93.53 142	97.04 109

VPA-MVSNet		89.62 116	88.96 118	91.60 141	93.86 194	82.89 123	95.46 83	97.33 23	87.91 94	88.43 135	93.31 184	74.17 177	97.40 198	87.32 123	82.86 274	94.52 200

WTY-MVS		89.60 117	88.92 120	91.67 139	95.47 123	81.15 169	92.38 240	94.78 206	83.11 198	89.06 126	94.32 145	78.67 122	96.61 248	81.57 206	90.89 178	97.24 99

Vis-MVSNet (Re-imp)		89.59 118	89.44 106	90.03 210	95.74 113	75.85 286	95.61 80	90.80 316	87.66 104	87.83 145	95.40 105	76.79 140	96.46 261	78.37 245	96.73 88	97.80 80

VDDNet		89.56 119	88.49 135	92.76 86	95.07 137	82.09 143	96.30 41	93.19 252	81.05 246	91.88 83	96.86 46	61.16 307	98.33 118	88.43 107	92.49 164	97.84 78

114514_t		89.51 120	88.50 133	92.54 99	98.11 36	81.99 145	95.16 103	96.36 98	70.19 346	85.81 185	95.25 109	76.70 142	98.63 93	82.07 194	96.86 87	97.00 112

QAPM		89.51 120	88.15 144	93.59 61	94.92 145	84.58 72	96.82 29	96.70 80	78.43 279	83.41 256	96.19 76	73.18 193	99.30 38	77.11 261	96.54 92	96.89 117

CLD-MVS		89.47 122	88.90 122	91.18 159	94.22 179	82.07 144	92.13 250	96.09 117	87.90 95	85.37 210	92.45 213	74.38 172	97.56 177	87.15 125	90.43 180	93.93 231
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		89.45 123	88.90 122	91.12 161	94.47 168	81.49 158	95.30 90	96.14 114	86.73 122	85.45 201	95.16 113	69.89 231	98.10 133	87.70 116	89.23 201	93.77 245

CDS-MVSNet		89.45 123	88.51 132	92.29 112	93.62 203	83.61 100	93.01 223	94.68 210	81.95 222	87.82 146	93.24 188	78.69 121	96.99 229	80.34 226	93.23 152	96.28 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
iter_conf_final		89.42 125	88.69 126	91.60 141	95.12 136	82.93 121	95.75 70	92.14 278	87.32 109	87.12 160	94.07 153	67.09 261	97.55 178	90.61 86	89.01 205	94.32 214

Fast-Effi-MVS+		89.41 126	88.64 127	91.71 138	94.74 153	80.81 179	93.54 198	95.10 185	83.11 198	86.82 169	90.67 273	79.74 108	97.75 164	80.51 224	93.55 141	96.57 126

ab-mvs		89.41 126	88.35 137	92.60 95	95.15 135	82.65 133	92.20 248	95.60 154	83.97 177	88.55 132	93.70 176	74.16 178	98.21 127	82.46 187	89.37 197	96.94 114

XVG-OURS		89.40 128	88.70 125	91.52 144	94.06 183	81.46 160	91.27 268	96.07 119	86.14 134	88.89 128	95.77 93	68.73 251	97.26 211	87.39 121	89.96 187	95.83 154

mvs_anonymous		89.37 129	89.32 111	89.51 233	93.47 207	74.22 299	91.65 262	94.83 202	82.91 204	85.45 201	93.79 171	81.23 96	96.36 267	86.47 134	94.09 132	97.94 71

DU-MVS		89.34 130	88.50 133	91.85 132	93.04 219	83.72 95	94.47 144	96.59 87	89.50 47	86.46 173	93.29 186	77.25 136	97.23 214	84.92 150	81.02 298	94.59 195

TAMVS		89.21 131	88.29 141	91.96 123	93.71 200	82.62 134	93.30 209	94.19 225	82.22 215	87.78 147	93.94 163	78.83 118	96.95 231	77.70 254	92.98 156	96.32 132

ACMM	84.12 9	89.14 132	88.48 136	91.12 161	94.65 160	81.22 167	95.31 88	96.12 116	85.31 153	85.92 184	94.34 143	70.19 229	98.06 145	85.65 143	88.86 208	94.08 226
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 133	88.64 127	90.48 189	95.53 122	74.97 292	96.08 54	84.89 352	88.13 90	90.16 111	96.65 57	63.29 291	98.10 133	86.14 135	96.90 84	98.39 37

EI-MVSNet		89.10 133	88.86 124	89.80 222	91.84 250	78.30 244	93.70 194	95.01 188	85.73 141	87.15 158	95.28 107	79.87 106	97.21 216	83.81 167	87.36 233	93.88 234

ECVR-MVS		89.09 135	88.53 131	90.77 178	95.62 119	75.89 285	96.16 49	84.22 354	87.89 97	90.20 109	96.65 57	63.19 293	98.10 133	85.90 140	96.94 82	98.33 41

RRT_MVS		89.09 135	88.62 130	90.49 187	92.85 226	79.65 213	96.41 37	94.41 217	88.22 85	85.50 197	94.77 129	69.36 239	97.31 204	89.33 97	86.73 240	94.51 202

CNLPA		89.07 137	87.98 148	92.34 108	96.87 74	84.78 70	94.08 171	93.24 250	81.41 237	84.46 227	95.13 115	75.57 158	96.62 245	77.21 259	93.84 137	95.61 162

PLC	84.53 7	89.06 138	88.03 147	92.15 115	97.27 68	82.69 131	94.29 157	95.44 167	79.71 259	84.01 242	94.18 152	76.68 143	98.75 87	77.28 258	93.41 147	95.02 176
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 139	88.64 127	90.21 200	90.74 295	79.28 225	95.96 61	95.90 131	84.66 168	85.33 212	92.94 198	74.02 180	97.30 205	89.64 94	88.53 212	94.05 228

HY-MVS	83.01 12	89.03 139	87.94 150	92.29 112	94.86 149	82.77 124	92.08 253	94.49 213	81.52 236	86.93 163	92.79 205	78.32 128	98.23 124	79.93 231	90.55 179	95.88 151

ACMP	84.23 8	89.01 141	88.35 137	90.99 172	94.73 154	81.27 164	95.07 108	95.89 133	86.48 125	83.67 249	94.30 146	69.33 240	97.99 150	87.10 129	88.55 211	93.72 249
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 142	88.26 143	90.94 175	94.05 184	80.78 180	91.71 259	95.38 171	81.55 235	88.63 131	93.91 167	75.04 163	95.47 303	82.47 186	91.61 169	96.57 126

iter_conf05		88.85 143	88.08 146	91.17 160	94.27 178	81.64 153	95.18 100	92.15 277	86.23 132	87.28 157	94.07 153	63.89 289	97.55 178	90.63 85	89.00 206	94.32 214

TranMVSNet+NR-MVSNet		88.84 144	87.95 149	91.49 146	92.68 230	83.01 118	94.92 117	96.31 100	89.88 37	85.53 194	93.85 170	76.63 144	96.96 230	81.91 198	79.87 315	94.50 205

CHOSEN 1792x2688		88.84 144	87.69 153	92.30 111	96.14 96	81.42 162	90.01 292	95.86 135	74.52 318	87.41 153	93.94 163	75.46 159	98.36 113	80.36 225	95.53 104	97.12 106

MVSTER		88.84 144	88.29 141	90.51 186	92.95 223	80.44 188	93.73 191	95.01 188	84.66 168	87.15 158	93.12 193	72.79 198	97.21 216	87.86 113	87.36 233	93.87 235

OpenMVS	83.78 11	88.74 147	87.29 163	93.08 72	92.70 229	85.39 64	96.57 35	96.43 94	78.74 274	80.85 285	96.07 80	69.64 235	99.01 61	78.01 252	96.65 91	94.83 187

thisisatest0530		88.67 148	87.61 155	91.86 130	94.87 148	80.07 198	94.63 134	89.90 331	84.00 176	88.46 134	93.78 172	66.88 265	98.46 105	83.30 172	92.65 160	97.06 107

Effi-MVS+-dtu		88.65 149	88.35 137	89.54 230	93.33 210	76.39 280	94.47 144	94.36 219	87.70 102	85.43 204	89.56 295	73.45 189	97.26 211	85.57 145	91.28 171	94.97 177

tttt0517		88.61 150	87.78 152	91.11 164	94.96 142	77.81 257	95.35 86	89.69 334	85.09 159	88.05 141	94.59 138	66.93 263	98.48 102	83.27 173	92.13 167	97.03 110

BH-untuned		88.60 151	88.13 145	90.01 213	95.24 130	78.50 238	93.29 210	94.15 227	84.75 166	84.46 227	93.40 180	75.76 153	97.40 198	77.59 255	94.52 126	94.12 222

NR-MVSNet		88.58 152	87.47 159	91.93 125	93.04 219	84.16 86	94.77 127	96.25 106	89.05 59	80.04 299	93.29 186	79.02 117	97.05 226	81.71 205	80.05 312	94.59 195

1112_ss		88.42 153	87.33 162	91.72 137	94.92 145	80.98 173	92.97 225	94.54 212	78.16 285	83.82 245	93.88 168	78.78 120	97.91 156	79.45 236	89.41 196	96.26 135

WR-MVS		88.38 154	87.67 154	90.52 185	93.30 211	80.18 193	93.26 212	95.96 126	88.57 74	85.47 200	92.81 203	76.12 146	96.91 234	81.24 210	82.29 278	94.47 210

BH-RMVSNet		88.37 155	87.48 158	91.02 169	95.28 127	79.45 217	92.89 227	93.07 254	85.45 150	86.91 165	94.84 127	70.35 226	97.76 161	73.97 289	94.59 123	95.85 152

IterMVS-LS		88.36 156	87.91 151	89.70 226	93.80 197	78.29 245	93.73 191	95.08 187	85.73 141	84.75 219	91.90 236	79.88 105	96.92 233	83.83 166	82.51 275	93.89 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 157	86.13 203	94.85 23	98.54 13	86.60 31	96.93 22	97.19 33	90.66 22	92.85 57	23.41 373	85.02 54	99.49 24	91.99 60	98.56 46	98.47 31

LCM-MVSNet-Re		88.30 158	88.32 140	88.27 260	94.71 156	72.41 320	93.15 216	90.98 311	87.77 100	79.25 307	91.96 234	78.35 127	95.75 292	83.04 175	95.62 103	96.65 123

jajsoiax		88.24 159	87.50 157	90.48 189	90.89 289	80.14 195	95.31 88	95.65 151	84.97 161	84.24 238	94.02 158	65.31 280	97.42 191	88.56 105	88.52 213	93.89 232

VPNet		88.20 160	87.47 159	90.39 194	93.56 205	79.46 216	94.04 175	95.54 158	88.67 70	86.96 162	94.58 139	69.33 240	97.15 218	84.05 163	80.53 307	94.56 198

TAPA-MVS	84.62 6	88.16 161	87.01 171	91.62 140	96.64 80	80.65 182	94.39 151	96.21 112	76.38 298	86.19 181	95.44 102	79.75 107	98.08 143	62.75 346	95.29 112	96.13 139
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 162	87.28 164	90.57 181	94.96 142	80.07 198	94.27 158	91.29 304	86.74 121	87.41 153	94.00 160	76.77 141	96.20 272	80.77 218	79.31 320	95.44 164

Anonymous20240529		88.09 163	86.59 187	92.58 97	96.53 86	81.92 148	95.99 59	95.84 136	74.11 322	89.06 126	95.21 112	61.44 302	98.81 84	83.67 170	87.47 230	97.01 111

HyFIR lowres test		88.09 163	86.81 175	91.93 125	96.00 105	80.63 183	90.01 292	95.79 139	73.42 328	87.68 149	92.10 228	73.86 183	97.96 152	80.75 219	91.70 168	97.19 102

mvs_tets		88.06 165	87.28 164	90.38 196	90.94 285	79.88 207	95.22 97	95.66 149	85.10 158	84.21 239	93.94 163	63.53 290	97.40 198	88.50 106	88.40 217	93.87 235

F-COLMAP		87.95 166	86.80 176	91.40 150	96.35 92	80.88 177	94.73 129	95.45 165	79.65 260	82.04 273	94.61 136	71.13 212	98.50 101	76.24 270	91.05 176	94.80 189

LS3D		87.89 167	86.32 197	92.59 96	96.07 102	82.92 122	95.23 96	94.92 195	75.66 305	82.89 263	95.98 83	72.48 202	99.21 43	68.43 320	95.23 115	95.64 161

anonymousdsp		87.84 168	87.09 167	90.12 206	89.13 322	80.54 186	94.67 132	95.55 156	82.05 218	83.82 245	92.12 225	71.47 210	97.15 218	87.15 125	87.80 229	92.67 286

v2v482		87.84 168	87.06 168	90.17 202	90.99 281	79.23 228	94.00 180	95.13 182	84.87 162	85.53 194	92.07 231	74.45 171	97.45 187	84.71 155	81.75 286	93.85 238

WR-MVS_H		87.80 170	87.37 161	89.10 241	93.23 212	78.12 248	95.61 80	97.30 27	87.90 95	83.72 247	92.01 233	79.65 113	96.01 280	76.36 267	80.54 306	93.16 271

AUN-MVS		87.78 171	86.54 189	91.48 147	94.82 152	81.05 171	93.91 187	93.93 234	83.00 201	86.93 163	93.53 178	69.50 237	97.67 166	86.14 135	77.12 330	95.73 159

PCF-MVS	84.11 10	87.74 172	86.08 207	92.70 91	94.02 185	84.43 82	89.27 302	95.87 134	73.62 327	84.43 229	94.33 144	78.48 126	98.86 79	70.27 306	94.45 128	94.81 188
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 173	86.13 203	92.31 110	96.66 79	80.74 181	94.87 120	91.49 299	80.47 250	89.46 120	95.44 102	54.72 334	98.23 124	82.19 192	89.89 189	97.97 70

V42		87.68 173	86.86 173	90.15 204	90.58 301	80.14 195	94.24 161	95.28 176	83.66 184	85.67 188	91.33 251	74.73 168	97.41 196	84.43 159	81.83 284	92.89 281

thres600view7		87.65 175	86.67 182	90.59 180	96.08 101	78.72 231	94.88 119	91.58 295	87.06 113	88.08 139	92.30 218	68.91 248	98.10 133	70.05 313	91.10 172	94.96 180

XXY-MVS		87.65 175	86.85 174	90.03 210	92.14 240	80.60 185	93.76 190	95.23 178	82.94 203	84.60 222	94.02 158	74.27 173	95.49 302	81.04 212	83.68 262	94.01 230

Test_1112_low_res		87.65 175	86.51 190	91.08 165	94.94 144	79.28 225	91.77 257	94.30 221	76.04 303	83.51 254	92.37 215	77.86 133	97.73 165	78.69 244	89.13 203	96.22 136

thres100view900		87.63 178	86.71 180	90.38 196	96.12 97	78.55 235	95.03 111	91.58 295	87.15 110	88.06 140	92.29 219	68.91 248	98.10 133	70.13 310	91.10 172	94.48 208

CP-MVSNet		87.63 178	87.26 166	88.74 250	93.12 215	76.59 277	95.29 92	96.58 88	88.43 77	83.49 255	92.98 197	75.28 160	95.83 288	78.97 242	81.15 294	93.79 240

thres400		87.62 180	86.64 183	90.57 181	95.99 106	78.64 233	94.58 136	91.98 285	86.94 117	88.09 137	91.77 238	69.18 245	98.10 133	70.13 310	91.10 172	94.96 180

v1144		87.61 181	86.79 177	90.06 209	91.01 280	79.34 221	93.95 182	95.42 170	83.36 194	85.66 189	91.31 254	74.98 164	97.42 191	83.37 171	82.06 280	93.42 261

bld_raw_dy_0_64		87.60 182	86.73 178	90.21 200	91.72 254	80.26 192	95.09 107	88.61 339	85.68 143	85.55 191	94.38 142	63.93 288	96.66 242	87.73 115	87.84 228	93.72 249

tfpn200view9		87.58 183	86.64 183	90.41 193	95.99 106	78.64 233	94.58 136	91.98 285	86.94 117	88.09 137	91.77 238	69.18 245	98.10 133	70.13 310	91.10 172	94.48 208

BH-w/o		87.57 184	87.05 169	89.12 240	94.90 147	77.90 253	92.41 238	93.51 247	82.89 205	83.70 248	91.34 250	75.75 154	97.07 224	75.49 275	93.49 144	92.39 295

UniMVSNet_ETH3D		87.53 185	86.37 194	91.00 171	92.44 233	78.96 230	94.74 128	95.61 153	84.07 175	85.36 211	94.52 140	59.78 315	97.34 203	82.93 177	87.88 226	96.71 122

ET-MVSNet_ETH3D		87.51 186	85.91 215	92.32 109	93.70 202	83.93 90	92.33 243	90.94 312	84.16 172	72.09 345	92.52 211	69.90 230	95.85 287	89.20 99	88.36 218	97.17 103

1314		87.51 186	86.57 188	90.34 198	92.42 234	79.74 211	92.63 233	95.35 175	78.35 280	80.14 296	91.62 245	74.05 179	97.15 218	81.05 211	93.53 142	94.12 222

v8		87.50 188	86.71 180	89.89 216	91.37 267	79.40 218	94.50 140	95.38 171	84.81 165	83.60 252	91.33 251	76.05 147	97.42 191	82.84 180	80.51 309	92.84 283

Fast-Effi-MVS+-dtu		87.44 189	86.72 179	89.63 228	92.04 244	77.68 262	94.03 176	93.94 233	85.81 138	82.42 267	91.32 253	70.33 227	97.06 225	80.33 227	90.23 183	94.14 221

MVS		87.44 189	86.10 206	91.44 149	92.61 231	83.62 99	92.63 233	95.66 149	67.26 350	81.47 277	92.15 223	77.95 130	98.22 126	79.71 233	95.48 106	92.47 292

FE-MVS		87.40 191	86.02 209	91.57 143	94.56 165	79.69 212	90.27 282	93.72 244	80.57 249	88.80 129	91.62 245	65.32 279	98.59 97	74.97 283	94.33 131	96.44 129

FMVSNet3		87.40 191	86.11 205	91.30 155	93.79 199	83.64 98	94.20 163	94.81 204	83.89 179	84.37 230	91.87 237	68.45 254	96.56 253	78.23 249	85.36 247	93.70 251

test_fmvs1		87.34 193	87.56 156	86.68 299	90.59 300	71.80 324	94.01 178	94.04 232	78.30 281	91.97 80	95.22 110	56.28 327	93.71 326	92.89 35	94.71 118	94.52 200

thisisatest0515		87.33 194	85.99 210	91.37 152	93.49 206	79.55 214	90.63 278	89.56 337	80.17 252	87.56 151	90.86 267	67.07 262	98.28 122	81.50 207	93.02 155	96.29 133

PS-CasMVS		87.32 195	86.88 172	88.63 253	92.99 222	76.33 282	95.33 87	96.61 86	88.22 85	83.30 260	93.07 195	73.03 196	95.79 291	78.36 246	81.00 300	93.75 247

GBi-Net		87.26 196	85.98 211	91.08 165	94.01 186	83.10 112	95.14 104	94.94 191	83.57 186	84.37 230	91.64 241	66.59 270	96.34 268	78.23 249	85.36 247	93.79 240

test1		87.26 196	85.98 211	91.08 165	94.01 186	83.10 112	95.14 104	94.94 191	83.57 186	84.37 230	91.64 241	66.59 270	96.34 268	78.23 249	85.36 247	93.79 240

v1192		87.25 198	86.33 196	90.00 214	90.76 294	79.04 229	93.80 188	95.48 161	82.57 209	85.48 199	91.18 258	73.38 192	97.42 191	82.30 190	82.06 280	93.53 255

v10		87.25 198	86.38 193	89.85 217	91.19 273	79.50 215	94.48 141	95.45 165	83.79 182	83.62 251	91.19 256	75.13 161	97.42 191	81.94 197	80.60 304	92.63 288

DP-MVS		87.25 198	85.36 230	92.90 81	97.65 55	83.24 108	94.81 124	92.00 283	74.99 313	81.92 275	95.00 118	72.66 199	99.05 53	66.92 331	92.33 165	96.40 130

miper_ehance_all_eth		87.22 201	86.62 186	89.02 244	92.13 241	77.40 267	90.91 274	94.81 204	81.28 240	84.32 235	90.08 284	79.26 115	96.62 245	83.81 167	82.94 270	93.04 276

test2506		87.21 202	86.28 199	90.02 212	95.62 119	73.64 304	96.25 46	71.38 373	87.89 97	90.45 105	96.65 57	55.29 332	98.09 141	86.03 139	96.94 82	98.33 41

thres200		87.21 202	86.24 201	90.12 206	95.36 125	78.53 236	93.26 212	92.10 279	86.42 127	88.00 142	91.11 262	69.24 244	98.00 149	69.58 314	91.04 177	93.83 239

v144192		87.19 204	86.35 195	89.74 223	90.64 298	78.24 246	93.92 185	95.43 168	81.93 223	85.51 196	91.05 264	74.21 176	97.45 187	82.86 179	81.56 288	93.53 255

FMVSNet2		87.19 204	85.82 217	91.30 155	94.01 186	83.67 97	94.79 125	94.94 191	83.57 186	83.88 244	92.05 232	66.59 270	96.51 256	77.56 256	85.01 250	93.73 248

c3_l		87.14 206	86.50 191	89.04 243	92.20 238	77.26 268	91.22 270	94.70 209	82.01 221	84.34 234	90.43 277	78.81 119	96.61 248	83.70 169	81.09 295	93.25 266

Baseline_NR-MVSNet		87.07 207	86.63 185	88.40 256	91.44 262	77.87 255	94.23 162	92.57 266	84.12 174	85.74 187	92.08 229	77.25 136	96.04 277	82.29 191	79.94 313	91.30 315

v148		87.04 208	86.32 197	89.21 237	90.94 285	77.26 268	93.71 193	94.43 215	84.84 164	84.36 233	90.80 270	76.04 148	97.05 226	82.12 193	79.60 317	93.31 263

test_fmvs1_n		87.03 209	87.04 170	86.97 291	89.74 318	71.86 322	94.55 138	94.43 215	78.47 277	91.95 82	95.50 101	51.16 344	93.81 324	93.02 34	94.56 124	95.26 170

v1921920		86.97 210	86.06 208	89.69 227	90.53 304	78.11 249	93.80 188	95.43 168	81.90 225	85.33 212	91.05 264	72.66 199	97.41 196	82.05 195	81.80 285	93.53 255

tt0805		86.92 211	85.74 223	90.48 189	92.22 237	79.98 205	95.63 79	94.88 198	83.83 181	84.74 220	92.80 204	57.61 323	97.67 166	85.48 146	84.42 254	93.79 240

miper_enhance_ethall		86.90 212	86.18 202	89.06 242	91.66 259	77.58 264	90.22 288	94.82 203	79.16 266	84.48 226	89.10 298	79.19 116	96.66 242	84.06 162	82.94 270	92.94 279

v7n		86.81 213	85.76 221	89.95 215	90.72 296	79.25 227	95.07 108	95.92 128	84.45 171	82.29 268	90.86 267	72.60 201	97.53 181	79.42 239	80.52 308	93.08 275

PEN-MVS		86.80 214	86.27 200	88.40 256	92.32 236	75.71 288	95.18 100	96.38 97	87.97 92	82.82 264	93.15 191	73.39 191	95.92 283	76.15 271	79.03 322	93.59 253

cl22		86.78 215	85.98 211	89.18 239	92.34 235	77.62 263	90.84 275	94.13 229	81.33 239	83.97 243	90.15 282	73.96 181	96.60 250	84.19 161	82.94 270	93.33 262

v1240		86.78 215	85.85 216	89.56 229	90.45 305	77.79 258	93.61 196	95.37 173	81.65 231	85.43 204	91.15 260	71.50 209	97.43 190	81.47 208	82.05 282	93.47 259

TR-MVS		86.78 215	85.76 221	89.82 219	94.37 174	78.41 240	92.47 237	92.83 259	81.11 245	86.36 177	92.40 214	68.73 251	97.48 184	73.75 292	89.85 191	93.57 254

PatchMatch-RL		86.77 218	85.54 224	90.47 192	95.88 109	82.71 130	90.54 279	92.31 272	79.82 258	84.32 235	91.57 249	68.77 250	96.39 264	73.16 294	93.48 146	92.32 298

PAPM		86.68 219	85.39 228	90.53 183	93.05 218	79.33 224	89.79 295	94.77 207	78.82 271	81.95 274	93.24 188	76.81 139	97.30 205	66.94 329	93.16 153	94.95 183

pm-mvs1		86.61 220	85.54 224	89.82 219	91.44 262	80.18 193	95.28 94	94.85 200	83.84 180	81.66 276	92.62 208	72.45 204	96.48 258	79.67 234	78.06 323	92.82 284

GA-MVS		86.61 220	85.27 232	90.66 179	91.33 270	78.71 232	90.40 281	93.81 241	85.34 152	85.12 214	89.57 294	61.25 304	97.11 222	80.99 215	89.59 195	96.15 137

Anonymous20231211		86.59 222	85.13 234	90.98 174	96.52 87	81.50 156	96.14 51	96.16 113	73.78 325	83.65 250	92.15 223	63.26 292	97.37 202	82.82 181	81.74 287	94.06 227

test_vis1_n		86.56 223	86.49 192	86.78 298	88.51 327	72.69 313	94.68 131	93.78 242	79.55 261	90.70 102	95.31 106	48.75 349	93.28 332	93.15 31	93.99 133	94.38 212

DIV-MVS_self_test		86.53 224	85.78 218	88.75 248	92.02 246	76.45 279	90.74 276	94.30 221	81.83 229	83.34 258	90.82 269	75.75 154	96.57 251	81.73 204	81.52 290	93.24 267

cl____		86.52 225	85.78 218	88.75 248	92.03 245	76.46 278	90.74 276	94.30 221	81.83 229	83.34 258	90.78 271	75.74 156	96.57 251	81.74 203	81.54 289	93.22 268

eth_miper_zixun_eth		86.50 226	85.77 220	88.68 251	91.94 247	75.81 287	90.47 280	94.89 196	82.05 218	84.05 240	90.46 276	75.96 149	96.77 238	82.76 183	79.36 319	93.46 260

baseline2		86.50 226	85.39 228	89.84 218	91.12 277	76.70 275	91.88 254	88.58 340	82.35 214	79.95 300	90.95 266	73.42 190	97.63 173	80.27 228	89.95 188	95.19 172

EPNet_dtu		86.49 228	85.94 214	88.14 265	90.24 308	72.82 311	94.11 167	92.20 275	86.66 124	79.42 306	92.36 216	73.52 187	95.81 290	71.26 300	93.66 138	95.80 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		86.43 229	84.98 237	90.80 177	92.10 243	80.92 176	90.24 286	95.91 130	73.10 331	83.57 253	88.39 309	65.15 281	97.46 186	84.90 152	91.43 170	94.03 229

SCA		86.32 230	85.18 233	89.73 225	92.15 239	76.60 276	91.12 271	91.69 292	83.53 189	85.50 197	88.81 302	66.79 266	96.48 258	76.65 264	90.35 182	96.12 140

LTVRE_ROB	82.13 13	86.26 231	84.90 240	90.34 198	94.44 172	81.50 156	92.31 245	94.89 196	83.03 200	79.63 304	92.67 206	69.69 234	97.79 159	71.20 301	86.26 243	91.72 307
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DTE-MVSNet		86.11 232	85.48 226	87.98 268	91.65 260	74.92 293	94.93 116	95.75 142	87.36 108	82.26 269	93.04 196	72.85 197	95.82 289	74.04 288	77.46 328	93.20 269

XVG-ACMP-BASELINE		86.00 233	84.84 242	89.45 234	91.20 272	78.00 250	91.70 260	95.55 156	85.05 160	82.97 262	92.25 221	54.49 335	97.48 184	82.93 177	87.45 232	92.89 281

MVP-Stereo		85.97 234	84.86 241	89.32 235	90.92 287	82.19 142	92.11 251	94.19 225	78.76 273	78.77 310	91.63 244	68.38 255	96.56 253	75.01 282	93.95 134	89.20 340
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 235	85.09 235	88.35 258	90.79 292	77.42 266	91.83 256	95.70 145	80.77 248	80.08 298	90.02 285	66.74 268	96.37 265	81.88 199	87.97 225	91.26 316

test-LLR		85.87 236	85.41 227	87.25 284	90.95 283	71.67 326	89.55 296	89.88 332	83.41 192	84.54 224	87.95 316	67.25 258	95.11 308	81.82 200	93.37 149	94.97 177

FMVSNet1		85.85 237	84.11 251	91.08 165	92.81 227	83.10 112	95.14 104	94.94 191	81.64 232	82.68 265	91.64 241	59.01 319	96.34 268	75.37 277	83.78 259	93.79 240

PatchmatchNet		85.85 237	84.70 244	89.29 236	91.76 253	75.54 289	88.49 314	91.30 303	81.63 233	85.05 215	88.70 306	71.71 206	96.24 271	74.61 286	89.05 204	96.08 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		85.77 239	84.94 239	88.26 261	91.16 276	72.58 318	89.47 300	91.04 310	76.26 301	86.45 175	89.97 287	70.74 219	96.86 237	82.35 189	87.07 238	95.34 169

PMMVS		85.71 240	84.96 238	87.95 269	88.90 325	77.09 270	88.68 312	90.06 326	72.32 337	86.47 172	90.76 272	72.15 205	94.40 314	81.78 202	93.49 144	92.36 296

PVSNet	78.82 18	85.55 241	84.65 245	88.23 263	94.72 155	71.93 321	87.12 329	92.75 262	78.80 272	84.95 217	90.53 275	64.43 285	96.71 241	74.74 284	93.86 136	96.06 145

IterMVS-SCA-FT		85.45 242	84.53 248	88.18 264	91.71 256	76.87 273	90.19 289	92.65 265	85.40 151	81.44 278	90.54 274	66.79 266	95.00 311	81.04 212	81.05 296	92.66 287

pmmvs4		85.43 243	83.86 256	90.16 203	90.02 313	82.97 120	90.27 282	92.67 264	75.93 304	80.73 286	91.74 240	71.05 213	95.73 293	78.85 243	83.46 266	91.78 306

mvsany_test1		85.42 244	85.30 231	85.77 309	87.95 338	75.41 291	87.61 326	80.97 362	76.82 295	88.68 130	95.83 89	77.44 135	90.82 350	85.90 140	86.51 241	91.08 324

ACMH	80.38 17	85.36 245	83.68 258	90.39 194	94.45 171	80.63 183	94.73 129	94.85 200	82.09 217	77.24 318	92.65 207	60.01 313	97.58 175	72.25 298	84.87 251	92.96 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 246	84.64 246	87.49 278	90.77 293	72.59 317	94.01 178	94.40 218	84.72 167	79.62 305	93.17 190	61.91 299	96.72 239	81.99 196	81.16 292	93.16 271

CR-MVSNet		85.35 246	83.76 257	90.12 206	90.58 301	79.34 221	85.24 341	91.96 287	78.27 282	85.55 191	87.87 319	71.03 214	95.61 294	73.96 290	89.36 198	95.40 166

tpmrst		85.35 246	84.99 236	86.43 301	90.88 290	67.88 347	88.71 311	91.43 301	80.13 253	86.08 183	88.80 304	73.05 194	96.02 279	82.48 185	83.40 268	95.40 166

miper_lstm_enhance		85.27 249	84.59 247	87.31 281	91.28 271	74.63 294	87.69 323	94.09 231	81.20 244	81.36 280	89.85 290	74.97 165	94.30 317	81.03 214	79.84 316	93.01 277

IB-MVS	80.51 15	85.24 250	83.26 262	91.19 158	92.13 241	79.86 208	91.75 258	91.29 304	83.28 196	80.66 288	88.49 308	61.28 303	98.46 105	80.99 215	79.46 318	95.25 171
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
CHOSEN 280x420		85.15 251	83.99 254	88.65 252	92.47 232	78.40 241	79.68 361	92.76 261	74.90 315	81.41 279	89.59 293	69.85 233	95.51 299	79.92 232	95.29 112	92.03 302

RPSCF		85.07 252	84.27 249	87.48 279	92.91 224	70.62 336	91.69 261	92.46 267	76.20 302	82.67 266	95.22 110	63.94 287	97.29 208	77.51 257	85.80 245	94.53 199

MS-PatchMatch		85.05 253	84.16 250	87.73 272	91.42 265	78.51 237	91.25 269	93.53 246	77.50 288	80.15 295	91.58 247	61.99 298	95.51 299	75.69 274	94.35 130	89.16 341

ACMH+	81.04 14	85.05 253	83.46 261	89.82 219	94.66 159	79.37 219	94.44 146	94.12 230	82.19 216	78.04 313	92.82 202	58.23 321	97.54 180	73.77 291	82.90 273	92.54 289

IterMVS		84.88 255	83.98 255	87.60 274	91.44 262	76.03 284	90.18 290	92.41 268	83.24 197	81.06 284	90.42 278	66.60 269	94.28 318	79.46 235	80.98 301	92.48 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 256	83.09 264	90.14 205	93.80 197	80.05 200	89.18 305	93.09 253	78.89 269	78.19 311	91.91 235	65.86 278	97.27 209	68.47 319	88.45 215	93.11 273

tpm		84.73 257	84.02 253	86.87 296	90.33 306	68.90 343	89.06 307	89.94 329	80.85 247	85.75 186	89.86 289	68.54 253	95.97 281	77.76 253	84.05 258	95.75 157

tfpnnormal		84.72 258	83.23 263	89.20 238	92.79 228	80.05 200	94.48 141	95.81 137	82.38 212	81.08 283	91.21 255	69.01 247	96.95 231	61.69 348	80.59 305	90.58 330

CVMVSNet		84.69 259	84.79 243	84.37 320	91.84 250	64.92 356	93.70 194	91.47 300	66.19 352	86.16 182	95.28 107	67.18 260	93.33 331	80.89 217	90.42 181	94.88 185

test-mter		84.54 260	83.64 259	87.25 284	90.95 283	71.67 326	89.55 296	89.88 332	79.17 265	84.54 224	87.95 316	55.56 329	95.11 308	81.82 200	93.37 149	94.97 177

TransMVSNet (Re)		84.43 261	83.06 265	88.54 254	91.72 254	78.44 239	95.18 100	92.82 260	82.73 207	79.67 303	92.12 225	73.49 188	95.96 282	71.10 305	68.73 352	91.21 318

pmmvs5		84.21 262	82.84 269	88.34 259	88.95 324	76.94 272	92.41 238	91.91 289	75.63 306	80.28 293	91.18 258	64.59 284	95.57 295	77.09 262	83.47 265	92.53 290

tpm2		84.08 263	82.94 266	87.48 279	91.39 266	71.27 328	89.23 304	90.37 320	71.95 339	84.64 221	89.33 296	67.30 257	96.55 255	75.17 279	87.09 237	94.63 192

test_fmvs2		83.98 264	84.03 252	83.83 325	87.16 340	67.53 350	93.93 184	92.89 257	77.62 287	86.89 168	93.53 178	47.18 354	92.02 343	90.54 87	86.51 241	91.93 304

COLMAP_ROB	80.39 16	83.96 265	82.04 272	89.74 223	95.28 127	79.75 210	94.25 159	92.28 273	75.17 311	78.02 314	93.77 173	58.60 320	97.84 158	65.06 339	85.92 244	91.63 309
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 266	81.53 276	91.21 157	90.58 301	79.34 221	85.24 341	96.76 72	71.44 341	85.55 191	82.97 349	70.87 217	98.91 75	61.01 350	89.36 198	95.40 166

SixPastTwentyTwo		83.91 267	82.90 267	86.92 293	90.99 281	70.67 335	93.48 200	91.99 284	85.54 148	77.62 317	92.11 227	60.59 309	96.87 236	76.05 272	77.75 325	93.20 269

EPMVS		83.90 268	82.70 270	87.51 276	90.23 309	72.67 314	88.62 313	81.96 360	81.37 238	85.01 216	88.34 310	66.31 273	94.45 313	75.30 278	87.12 236	95.43 165

TESTMET0.1,1		83.74 269	82.85 268	86.42 302	89.96 314	71.21 330	89.55 296	87.88 342	77.41 289	83.37 257	87.31 324	56.71 325	93.65 328	80.62 222	92.85 159	94.40 211

MVS_0304		83.46 270	81.92 273	88.10 266	90.63 299	77.49 265	93.26 212	93.75 243	80.04 255	80.44 292	87.24 326	47.94 351	95.55 296	75.79 273	88.16 220	91.26 316

pmmvs6		83.42 271	81.60 275	88.87 246	88.01 336	77.87 255	94.96 114	94.24 224	74.67 317	78.80 309	91.09 263	60.17 312	96.49 257	77.06 263	75.40 336	92.23 300

AllTest		83.42 271	81.39 277	89.52 231	95.01 138	77.79 258	93.12 217	90.89 314	77.41 289	76.12 326	93.34 181	54.08 337	97.51 182	68.31 321	84.27 256	93.26 264

tpmvs		83.35 273	82.07 271	87.20 288	91.07 279	71.00 333	88.31 317	91.70 291	78.91 268	80.49 291	87.18 327	69.30 243	97.08 223	68.12 324	83.56 264	93.51 258

USDC		82.76 274	81.26 279	87.26 283	91.17 274	74.55 295	89.27 302	93.39 249	78.26 283	75.30 331	92.08 229	54.43 336	96.63 244	71.64 299	85.79 246	90.61 327

Patchmtry		82.71 275	80.93 281	88.06 267	90.05 312	76.37 281	84.74 346	91.96 287	72.28 338	81.32 281	87.87 319	71.03 214	95.50 301	68.97 316	80.15 311	92.32 298

PatchT		82.68 276	81.27 278	86.89 295	90.09 311	70.94 334	84.06 348	90.15 323	74.91 314	85.63 190	83.57 345	69.37 238	94.87 312	65.19 336	88.50 214	94.84 186

MIMVSNet		82.59 277	80.53 282	88.76 247	91.51 261	78.32 243	86.57 332	90.13 324	79.32 262	80.70 287	88.69 307	52.98 341	93.07 336	66.03 334	88.86 208	94.90 184

test0.0.03 1		82.41 278	81.69 274	84.59 318	88.23 333	72.89 310	90.24 286	87.83 343	83.41 192	79.86 301	89.78 291	67.25 258	88.99 358	65.18 337	83.42 267	91.90 305

EG-PatchMatch MVS		82.37 279	80.34 285	88.46 255	90.27 307	79.35 220	92.80 230	94.33 220	77.14 293	73.26 342	90.18 281	47.47 353	96.72 239	70.25 307	87.32 235	89.30 338

tpm cat1		81.96 280	80.27 286	87.01 290	91.09 278	71.02 332	87.38 327	91.53 298	66.25 351	80.17 294	86.35 333	68.22 256	96.15 275	69.16 315	82.29 278	93.86 237

our_test_3		81.93 281	80.46 284	86.33 303	88.46 330	73.48 306	88.46 315	91.11 306	76.46 296	76.69 322	88.25 312	66.89 264	94.36 315	68.75 317	79.08 321	91.14 320

ppachtmachnet_test		81.84 282	80.07 290	87.15 289	88.46 330	74.43 298	89.04 308	92.16 276	75.33 309	77.75 315	88.99 299	66.20 274	95.37 304	65.12 338	77.60 326	91.65 308

gg-mvs-nofinetune		81.77 283	79.37 296	88.99 245	90.85 291	77.73 261	86.29 333	79.63 365	74.88 316	83.19 261	69.05 363	60.34 310	96.11 276	75.46 276	94.64 122	93.11 273

CL-MVSNet_self_test		81.74 284	80.53 282	85.36 312	85.96 346	72.45 319	90.25 284	93.07 254	81.24 242	79.85 302	87.29 325	70.93 216	92.52 339	66.95 328	69.23 348	91.11 322

Patchmatch-RL test		81.67 285	79.96 291	86.81 297	85.42 351	71.23 329	82.17 355	87.50 346	78.47 277	77.19 319	82.50 350	70.81 218	93.48 329	82.66 184	72.89 340	95.71 160

ADS-MVSNet2		81.66 286	79.71 294	87.50 277	91.35 268	74.19 300	83.33 351	88.48 341	72.90 333	82.24 270	85.77 337	64.98 282	93.20 334	64.57 340	83.74 260	95.12 173

K. test v3		81.59 287	80.15 289	85.91 308	89.89 316	69.42 342	92.57 235	87.71 344	85.56 147	73.44 341	89.71 292	55.58 328	95.52 298	77.17 260	69.76 346	92.78 285

ADS-MVSNet		81.56 288	79.78 292	86.90 294	91.35 268	71.82 323	83.33 351	89.16 338	72.90 333	82.24 270	85.77 337	64.98 282	93.76 325	64.57 340	83.74 260	95.12 173

FMVSNet5		81.52 289	79.60 295	87.27 282	91.17 274	77.95 251	91.49 264	92.26 274	76.87 294	76.16 325	87.91 318	51.67 342	92.34 340	67.74 325	81.16 292	91.52 310

dp		81.47 290	80.23 287	85.17 315	89.92 315	65.49 354	86.74 330	90.10 325	76.30 300	81.10 282	87.12 328	62.81 294	95.92 283	68.13 323	79.88 314	94.09 225

Patchmatch-test		81.37 291	79.30 297	87.58 275	90.92 287	74.16 301	80.99 357	87.68 345	70.52 345	76.63 323	88.81 302	71.21 211	92.76 338	60.01 354	86.93 239	95.83 154

EU-MVSNet		81.32 292	80.95 280	82.42 331	88.50 329	63.67 357	93.32 205	91.33 302	64.02 355	80.57 290	92.83 201	61.21 306	92.27 341	76.34 268	80.38 310	91.32 314

test_0402		81.30 293	79.17 301	87.67 273	93.19 213	78.17 247	92.98 224	91.71 290	75.25 310	76.02 328	90.31 279	59.23 317	96.37 265	50.22 362	83.63 263	88.47 347

JIA-IIPM		81.04 294	78.98 304	87.25 284	88.64 326	73.48 306	81.75 356	89.61 336	73.19 330	82.05 272	73.71 360	66.07 277	95.87 286	71.18 303	84.60 253	92.41 294

Anonymous20231206		81.03 295	79.77 293	84.82 317	87.85 339	70.26 338	91.42 265	92.08 280	73.67 326	77.75 315	89.25 297	62.43 296	93.08 335	61.50 349	82.00 283	91.12 321

pmmvs-eth3d		80.97 296	78.72 305	87.74 271	84.99 353	79.97 206	90.11 291	91.65 293	75.36 308	73.51 340	86.03 334	59.45 316	93.96 323	75.17 279	72.21 341	89.29 339

testgi		80.94 297	80.20 288	83.18 326	87.96 337	66.29 351	91.28 267	90.70 318	83.70 183	78.12 312	92.84 200	51.37 343	90.82 350	63.34 343	82.46 276	92.43 293

CMPMVS	59.16 21	80.52 298	79.20 300	84.48 319	83.98 354	67.63 349	89.95 294	93.84 240	64.79 354	66.81 355	91.14 261	57.93 322	95.17 306	76.25 269	88.10 221	90.65 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20240521		80.44 299	79.21 299	84.11 323	85.75 349	67.89 346	92.86 228	93.23 251	75.61 307	75.59 330	87.47 323	50.03 345	94.33 316	71.14 304	81.21 291	90.12 332

LF4IMVS		80.37 300	79.07 303	84.27 322	86.64 342	69.87 341	89.39 301	91.05 309	76.38 298	74.97 333	90.00 286	47.85 352	94.25 319	74.55 287	80.82 303	88.69 345

KD-MVS_self_test		80.20 301	79.24 298	83.07 327	85.64 350	65.29 355	91.01 273	93.93 234	78.71 275	76.32 324	86.40 332	59.20 318	92.93 337	72.59 296	69.35 347	91.00 325

UnsupCasMVSNet_eth		80.07 302	78.27 306	85.46 311	85.24 352	72.63 316	88.45 316	94.87 199	82.99 202	71.64 348	88.07 315	56.34 326	91.75 346	73.48 293	63.36 359	92.01 303

test20.03		79.95 303	79.08 302	82.55 329	85.79 348	67.74 348	91.09 272	91.08 307	81.23 243	74.48 337	89.96 288	61.63 300	90.15 352	60.08 352	76.38 332	89.76 333

TDRefinement		79.81 304	77.34 308	87.22 287	79.24 363	75.48 290	93.12 217	92.03 282	76.45 297	75.01 332	91.58 247	49.19 348	96.44 262	70.22 309	69.18 349	89.75 334

TinyColmap		79.76 305	77.69 307	85.97 305	91.71 256	73.12 308	89.55 296	90.36 321	75.03 312	72.03 346	90.19 280	46.22 355	96.19 274	63.11 344	81.03 297	88.59 346

OpenMVS_ROB	74.94 19	79.51 306	77.03 313	86.93 292	87.00 341	76.23 283	92.33 243	90.74 317	68.93 348	74.52 336	88.23 313	49.58 347	96.62 245	57.64 356	84.29 255	87.94 349

MIMVSNet1		79.38 307	77.28 309	85.69 310	86.35 343	73.67 303	91.61 263	92.75 262	78.11 286	72.64 344	88.12 314	48.16 350	91.97 345	60.32 351	77.49 327	91.43 313

YYNet1		79.22 308	77.20 310	85.28 314	88.20 335	72.66 315	85.87 335	90.05 328	74.33 320	62.70 357	87.61 321	66.09 276	92.03 342	66.94 329	72.97 339	91.15 319

MDA-MVSNet_test_wron		79.21 309	77.19 311	85.29 313	88.22 334	72.77 312	85.87 335	90.06 326	74.34 319	62.62 358	87.56 322	66.14 275	91.99 344	66.90 332	73.01 338	91.10 323

MDA-MVSNet-bldmvs		78.85 310	76.31 315	86.46 300	89.76 317	73.88 302	88.79 310	90.42 319	79.16 266	59.18 359	88.33 311	60.20 311	94.04 320	62.00 347	68.96 350	91.48 312

KD-MVS_2432*1600		78.50 311	76.02 318	85.93 306	86.22 344	74.47 296	84.80 344	92.33 270	79.29 263	76.98 320	85.92 335	53.81 339	93.97 321	67.39 326	57.42 364	89.36 336

miper_refine_blended		78.50 311	76.02 318	85.93 306	86.22 344	74.47 296	84.80 344	92.33 270	79.29 263	76.98 320	85.92 335	53.81 339	93.97 321	67.39 326	57.42 364	89.36 336

PM-MVS		78.11 313	76.12 317	84.09 324	83.54 356	70.08 339	88.97 309	85.27 351	79.93 256	74.73 335	86.43 331	34.70 362	93.48 329	79.43 238	72.06 342	88.72 344

test_vis1_rt		77.96 314	76.46 314	82.48 330	85.89 347	71.74 325	90.25 284	78.89 366	71.03 344	71.30 349	81.35 352	42.49 358	91.05 349	84.55 157	82.37 277	84.65 352

test_fmvs3		77.67 315	77.16 312	79.22 335	79.52 362	61.14 361	92.34 242	91.64 294	73.98 323	78.86 308	86.59 329	27.38 366	87.03 360	88.12 111	75.97 334	89.50 335

PVSNet_0	73.20 20	77.22 316	74.83 321	84.37 320	90.70 297	71.10 331	83.09 353	89.67 335	72.81 335	73.93 339	83.13 347	60.79 308	93.70 327	68.54 318	50.84 367	88.30 348

DSMNet-mixed		76.94 317	76.29 316	78.89 336	83.10 357	56.11 371	87.78 321	79.77 364	60.65 358	75.64 329	88.71 305	61.56 301	88.34 359	60.07 353	89.29 200	92.21 301

new-patchmatchnet		76.41 318	75.17 320	80.13 333	82.65 359	59.61 363	87.66 324	91.08 307	78.23 284	69.85 351	83.22 346	54.76 333	91.63 348	64.14 342	64.89 357	89.16 341

UnsupCasMVSNet_bld		76.23 319	73.27 322	85.09 316	83.79 355	72.92 309	85.65 338	93.47 248	71.52 340	68.84 353	79.08 355	49.77 346	93.21 333	66.81 333	60.52 361	89.13 343

mvsany_test3		74.95 320	73.26 323	80.02 334	74.61 365	63.16 359	85.53 339	78.42 367	74.16 321	74.89 334	86.46 330	36.02 361	89.09 357	82.39 188	66.91 353	87.82 350

MVS-HIRNet		73.70 321	72.20 324	78.18 339	91.81 252	56.42 370	82.94 354	82.58 358	55.24 360	68.88 352	66.48 364	55.32 331	95.13 307	58.12 355	88.42 216	83.01 355

new_pmnet		72.15 322	70.13 326	78.20 338	82.95 358	65.68 352	83.91 349	82.40 359	62.94 357	64.47 356	79.82 354	42.85 357	86.26 362	57.41 357	74.44 337	82.65 357

test_f		71.95 323	70.87 325	75.21 342	74.21 367	59.37 364	85.07 343	85.82 348	65.25 353	70.42 350	83.13 347	23.62 367	82.93 367	78.32 247	71.94 343	83.33 354

pmmvs3		71.81 324	68.71 327	81.11 332	75.86 364	70.42 337	86.74 330	83.66 355	58.95 359	68.64 354	80.89 353	36.93 360	89.52 355	63.10 345	63.59 358	83.39 353

APD_test1		69.04 325	66.26 329	77.36 341	80.51 360	62.79 360	85.46 340	83.51 356	54.11 362	59.14 360	84.79 341	23.40 369	89.61 354	55.22 358	70.24 345	79.68 360

N_pmnet		68.89 326	68.44 328	70.23 346	89.07 323	28.79 380	88.06 318	19.50 381	69.47 347	71.86 347	84.93 339	61.24 305	91.75 346	54.70 359	77.15 329	90.15 331

LCM-MVSNet		66.00 327	62.16 332	77.51 340	64.51 375	58.29 365	83.87 350	90.90 313	48.17 364	54.69 361	73.31 361	16.83 375	86.75 361	65.47 335	61.67 360	87.48 351

test_vis3_rt		65.12 328	62.60 330	72.69 344	71.44 368	60.71 362	87.17 328	65.55 374	63.80 356	53.22 362	65.65 366	14.54 376	89.44 356	76.65 264	65.38 355	67.91 365

FPMVS		64.63 329	62.55 331	70.88 345	70.80 369	56.71 366	84.42 347	84.42 353	51.78 363	49.57 363	81.61 351	23.49 368	81.48 368	40.61 369	76.25 333	74.46 361

EGC-MVSNET		61.97 330	56.37 334	78.77 337	89.63 320	73.50 305	89.12 306	82.79 357	0.21 378	1.24 379	84.80 340	39.48 359	90.04 353	44.13 364	75.94 335	72.79 362

PMMVS2		59.60 331	56.40 333	69.21 349	68.83 372	46.58 375	73.02 366	77.48 370	55.07 361	49.21 364	72.95 362	17.43 374	80.04 369	49.32 363	44.33 369	80.99 359

testf1		59.54 332	56.11 335	69.85 347	69.28 370	56.61 368	80.37 359	76.55 371	42.58 367	45.68 366	75.61 356	11.26 377	84.18 364	43.20 366	60.44 362	68.75 363

APD_test2		59.54 332	56.11 335	69.85 347	69.28 370	56.61 368	80.37 359	76.55 371	42.58 367	45.68 366	75.61 356	11.26 377	84.18 364	43.20 366	60.44 362	68.75 363

ANet_high		58.88 334	54.22 338	72.86 343	56.50 378	56.67 367	80.75 358	86.00 347	73.09 332	37.39 370	64.63 367	22.17 370	79.49 370	43.51 365	23.96 372	82.43 358

Gipuma		57.99 335	54.91 337	67.24 350	88.51 327	65.59 353	52.21 369	90.33 322	43.58 366	42.84 369	51.18 370	20.29 372	85.07 363	34.77 370	70.45 344	51.05 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 336	48.46 340	63.48 351	45.72 380	46.20 376	73.41 365	78.31 368	41.03 369	30.06 372	65.68 365	6.05 379	83.43 366	30.04 371	65.86 354	60.80 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 337	48.47 339	56.66 353	52.26 379	18.98 382	41.51 371	81.40 361	10.10 373	44.59 368	75.01 359	28.51 364	68.16 371	53.54 360	49.31 368	82.83 356

MVE	39.65 23	43.39 338	38.59 344	57.77 352	56.52 377	48.77 374	55.38 368	58.64 378	29.33 372	28.96 373	52.65 369	4.68 380	64.62 374	28.11 372	33.07 370	59.93 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 339	42.29 341	46.03 355	65.58 374	37.41 377	73.51 364	64.62 375	33.99 370	28.47 374	47.87 371	19.90 373	67.91 372	22.23 373	24.45 371	32.77 370

EMVS		42.07 340	41.12 342	44.92 356	63.45 376	35.56 379	73.65 363	63.48 376	33.05 371	26.88 375	45.45 372	21.27 371	67.14 373	19.80 374	23.02 373	32.06 371

tmp_tt		35.64 341	39.24 343	24.84 357	14.87 381	23.90 381	62.71 367	51.51 380	6.58 375	36.66 371	62.08 368	44.37 356	30.34 377	52.40 361	22.00 374	20.27 372

cdsmvs_eth3d_5k		22.14 342	29.52 345	0.00 361	0.00 384	0.00 385	0.00 372	95.76 141	0.00 379	0.00 380	94.29 147	75.66 157	0.00 380	0.00 378	0.00 378	0.00 376

wuyk23d		21.27 343	20.48 346	23.63 358	68.59 373	36.41 378	49.57 370	6.85 382	9.37 374	7.89 376	4.46 378	4.03 381	31.37 376	17.47 375	16.07 375	3.12 373

testmvs		8.92 344	11.52 347	1.12 360	1.06 382	0.46 384	86.02 334	0.65 383	0.62 376	2.74 377	9.52 376	0.31 383	0.45 379	2.38 376	0.39 376	2.46 375

test123		8.76 345	11.22 348	1.39 359	0.85 383	0.97 383	85.76 337	0.35 384	0.54 377	2.45 378	8.14 377	0.60 382	0.48 378	2.16 377	0.17 377	2.71 374

ab-mvs-re		7.82 346	10.43 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	93.88 168	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		6.64 347	8.86 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	79.70 109	0.00 380	0.00 378	0.00 378	0.00 376

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					98.86 1	85.54 63	98.29 1	97.49 5	89.79 41	96.29 15

MSC_two_6792asdad				96.52 1	97.78 51	90.86 1		96.85 61					99.61 3	96.03 1	99.06 9	99.07 5

PC_three_1452									82.47 210	97.09 9	97.07 38	92.72 1	98.04 146	92.70 41	99.02 12	98.86 10

No_MVS				96.52 1	97.78 51	90.86 1		96.85 61					99.61 3	96.03 1	99.06 9	99.07 5

test_one_0601					98.58 11	85.83 57		97.44 14	91.05 12	96.78 13	98.06 6	91.45 11

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					98.15 34	86.62 30		97.07 43	83.63 185	94.19 31	96.91 44	87.57 31	99.26 40	91.99 60	98.44 49

RE-MVS-def			93.68 43		97.92 43	84.57 73	96.28 43	96.76 72	87.46 105	93.75 38	97.43 18	82.94 74		92.73 37	97.80 70	97.88 75

IU-MVS					98.77 5	86.00 47		96.84 63	81.26 241	97.26 7				95.50 10	99.13 3	99.03 7

OPU-MVS				96.21 3	98.00 42	90.85 3	97.13 14				97.08 36	92.59 2	98.94 73	92.25 49	98.99 14	98.84 13

test_241102_TWO								97.44 14	90.31 26	97.62 5	98.07 4	91.46 10	99.58 8	95.66 4	99.12 6	98.98 9

test_241102_ONE					98.77 5	85.99 49		97.44 14	90.26 31	97.71 1	97.96 10	92.31 4	99.38 29

9.14			94.47 17		97.79 49		96.08 54	97.44 14	86.13 135	95.10 24	97.40 20	88.34 22	99.22 42	93.25 30	98.70 32

save fliter					97.85 46	85.63 62	95.21 98	96.82 66	89.44 48

test_0728_THIRD									90.75 17	97.04 10	98.05 8	92.09 6	99.55 14	95.64 6	99.13 3	99.13 2

test_0728_SECOND				95.01 15	98.79 2	86.43 36	97.09 16	97.49 5					99.61 3	95.62 8	99.08 7	98.99 8

test0726					98.78 3	85.93 52	97.19 11	97.47 10	90.27 29	97.64 4	98.13 1	91.47 8

GSMVS																96.12 140

test_part2					98.55 12	87.22 16				96.40 14

sam_mvs1												71.70 207				96.12 140

sam_mvs												70.60 220

ambc				83.06 328	79.99 361	63.51 358	77.47 362	92.86 258		74.34 338	84.45 342	28.74 363	95.06 310	73.06 295	68.89 351	90.61 327

MTGPA								96.97 48

test_post1							88.00 319				9.81 375	69.31 242	95.53 297	76.65 264

test_post											10.29 374	70.57 224	95.91 285

patchmatchnet-post											83.76 344	71.53 208	96.48 258

GG-mvs-BLEND				87.94 270	89.73 319	77.91 252	87.80 320	78.23 369		80.58 289	83.86 343	59.88 314	95.33 305	71.20 301	92.22 166	90.60 329

MTMP							96.16 49	60.64 377

gm-plane-assit					89.60 321	68.00 345			77.28 292		88.99 299		97.57 176	79.44 237

test9_res														91.91 64	98.71 30	98.07 64

TEST9					97.53 58	86.49 34	94.07 172	96.78 69	81.61 234	92.77 61	96.20 73	87.71 28	99.12 49

test_8					97.49 60	86.30 42	94.02 177	96.76 72	81.86 227	92.70 65	96.20 73	87.63 29	99.02 59

agg_prior2														90.54 87	98.68 35	98.27 50

agg_prior					97.38 63	85.92 54		96.72 78		92.16 75			98.97 70

TestCases				89.52 231	95.01 138	77.79 258		90.89 314	77.41 289	76.12 326	93.34 181	54.08 337	97.51 182	68.31 321	84.27 256	93.26 264

test_prior4						85.96 51	94.11 167

test_prior2							94.12 166		87.67 103	92.63 66	96.39 68	86.62 36		91.50 70	98.67 37

test_prior				93.82 56	97.29 67	84.49 77		96.88 59					98.87 77			98.11 63

旧先验2							93.36 204		71.25 342	94.37 28			97.13 221	86.74 130

新几何2							93.11 219

新几何1				93.10 71	97.30 66	84.35 84		95.56 155	71.09 343	91.26 98	96.24 71	82.87 75	98.86 79	79.19 241	98.10 60	96.07 144

旧先验1					96.79 76	81.81 150		95.67 147			96.81 50	86.69 35			97.66 74	96.97 113

无先验							93.28 211	96.26 104	73.95 324				99.05 53	80.56 223		96.59 125

原ACMM2							92.94 226

原ACMM1				92.01 117	97.34 64	81.05 171		96.81 67	78.89 269	90.45 105	95.92 85	82.65 76	98.84 83	80.68 221	98.26 55	96.14 138

test222					96.55 84	81.70 152	92.22 247	95.01 188	68.36 349	90.20 109	96.14 78	80.26 102			97.80 70	96.05 146

testdata2													98.75 87	78.30 248

segment_acmp												87.16 34

testdata				90.49 187	96.40 89	77.89 254		95.37 173	72.51 336	93.63 41	96.69 53	82.08 87	97.65 169	83.08 174	97.39 76	95.94 148

testdata1							92.15 249		87.94 93

test12				94.34 47	97.13 70	86.15 45		96.29 101		91.04 100		85.08 52	99.01 61		98.13 59	97.86 77

plane_prior7					94.70 157	82.74 127

plane_prior6					94.52 166	82.75 125						74.23 174

plane_prior5								96.22 109					98.12 131	88.15 108	89.99 185	94.63 192

plane_prior4											94.86 123

plane_prior3						82.75 125			90.26 31	86.91 165

plane_prior2							95.85 65		90.81 15

plane_prior1					94.59 161

plane_prior						82.73 128	95.21 98		89.66 45						89.88 190

n2								0.00 385

nn								0.00 385

door-mid								85.49 349

lessismore_v0				86.04 304	88.46 330	68.78 344		80.59 363		73.01 343	90.11 283	55.39 330	96.43 263	75.06 281	65.06 356	92.90 280

LGP-MVS_train				91.12 161	94.47 168	81.49 158		96.14 114	86.73 122	85.45 201	95.16 113	69.89 231	98.10 133	87.70 116	89.23 201	93.77 245

test11								96.57 89

door								85.33 350

HQP5-MVS						81.56 154

HQP-NCC					94.17 180		94.39 151		88.81 65	85.43 204

ACMP_Plane					94.17 180		94.39 151		88.81 65	85.43 204

BP-MVS														87.11 127

HQP4-MVS										85.43 204			97.96 152			94.51 202

HQP3-MVS								96.04 122							89.77 192

HQP2-MVS												73.83 184

NP-MVS					94.37 174	82.42 137					93.98 161

MDTV_nov1_ep13_2view						55.91 372	87.62 325		73.32 329	84.59 223		70.33 227		74.65 285		95.50 163

MDTV_nov1_ep13			83.56 260		91.69 258	69.93 340	87.75 322	91.54 297	78.60 276	84.86 218	88.90 301	69.54 236	96.03 278	70.25 307	88.93 207

ACMMP++_ref															87.47 230

ACMMP++															88.01 224

Test By Simon												80.02 104

ITE_SJBPF				88.24 262	91.88 249	77.05 271		92.92 256	85.54 148	80.13 297	93.30 185	57.29 324	96.20 272	72.46 297	84.71 252	91.49 311

DeepMVS_CX				56.31 354	74.23 366	51.81 373		56.67 379	44.85 365	48.54 365	75.16 358	27.87 365	58.74 375	40.92 368	52.22 366	58.39 368

High-resolution multi-view benchmark