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.

test0726					98.78 2	85.93 58	97.19 6	97.47 8	90.27 28	97.64 4	98.13 1	91.47 6

SMA-MVS		95.20 7	95.07 9	95.59 3	98.14 36	88.48 6	96.26 40	97.28 28	85.90 136	97.67 3	98.10 2	88.41 17	99.56 7	94.66 10	99.19 1	98.71 12
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
APDe-MVS		95.46 4	95.64 4	94.91 22	98.26 28	86.29 49	97.46 2	97.40 17	89.03 61	96.20 12	98.10 2	89.39 13	99.34 33	95.88 1	99.03 9	99.10 3

SED-MVS		95.91 1	96.28 1	94.80 33	98.77 4	85.99 55	97.13 9	97.44 12	90.31 26	97.71 1	98.07 4	92.31 2	99.58 5	95.66 2	99.13 3	98.84 8

test_241102_TWO								97.44 12	90.31 26	97.62 5	98.07 4	91.46 8	99.58 5	95.66 2	99.12 6	98.98 6

DVP-MVS		95.67 2	96.02 2	94.64 40	98.78 2	85.93 58	97.09 11	96.73 76	90.27 28	97.04 8	98.05 6	91.47 6	99.55 12	95.62 5	99.08 7	98.45 32
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
test_0728_THIRD									90.75 19	97.04 8	98.05 6	92.09 4	99.55 12	95.64 4	99.13 3	99.13 1

test_241102_ONE					98.77 4	85.99 55		97.44 12	90.26 30	97.71 1	97.96 8	92.31 2	99.38 29

DPE-MVS		95.57 3	95.67 3	95.25 7	98.36 25	87.28 15	95.56 76	97.51 4	89.13 58	97.14 7	97.91 9	91.64 5	99.62 1	94.61 11	99.17 2	98.86 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		94.21 33	94.00 37	94.85 27	98.17 34	86.65 33	94.82 120	97.17 38	86.26 130	92.83 63	97.87 10	85.57 52	99.56 7	94.37 14	98.92 13	98.34 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xxxxxxxxxxxxxcwj		94.65 15	94.70 14	94.48 47	97.85 48	85.63 68	95.21 94	95.47 169	89.44 47	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

SF-MVS		94.97 10	94.90 12	95.20 8	97.84 50	87.76 8	96.65 28	97.48 7	87.76 96	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

ACMMP_NAP		94.74 14	94.56 16	95.28 6	98.02 43	87.70 10	95.68 69	97.34 19	88.28 81	95.30 20	97.67 13	85.90 49	99.54 16	93.91 17	98.95 11	98.60 17

zzz-MVS		94.47 18	94.30 21	95.00 16	98.42 20	86.95 18	95.06 106	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

MTAPA		94.42 24	94.22 25	95.00 16	98.42 20	86.95 18	94.36 156	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

APD-MVS_3200maxsize		93.78 45	93.77 44	93.80 68	97.92 45	84.19 96	96.30 36	96.87 61	86.96 113	93.92 37	97.47 16	83.88 74	98.96 83	92.71 38	97.87 71	98.26 49

SteuartSystems-ACMMP		95.20 7	95.32 8	94.85 27	96.99 76	86.33 45	97.33 3	97.30 26	91.38 11	95.39 18	97.46 17	88.98 16	99.40 28	94.12 15	98.89 14	98.82 10
Skip Steuart: Steuart Systems R&D Blog.
test1172		93.97 39	94.07 34	93.66 71	98.11 37	83.45 115	96.26 40	96.84 62	88.33 78	94.19 30	97.43 18	84.24 69	99.01 70	93.26 27	97.98 67	98.52 20

SR-MVS-dyc-post		93.82 44	93.82 40	93.82 64	97.92 45	84.57 81	96.28 38	96.76 72	87.46 103	93.75 40	97.43 18	84.24 69	99.01 70	92.73 35	97.80 73	97.88 77

RE-MVS-def			93.68 46		97.92 45	84.57 81	96.28 38	96.76 72	87.46 103	93.75 40	97.43 18	82.94 80		92.73 35	97.80 73	97.88 77

9.14			94.47 17		97.79 52		96.08 50	97.44 12	86.13 134	95.10 22	97.40 21	88.34 18	99.22 46	93.25 28	98.70 32

SR-MVS		94.23 31	94.17 30	94.43 50	98.21 33	85.78 65	96.40 34	96.90 57	88.20 85	94.33 27	97.40 21	84.75 64	99.03 64	93.35 25	97.99 65	98.48 24

DeepC-MVS	88.79 3	93.31 56	92.99 61	94.26 55	96.07 105	85.83 64	94.89 115	96.99 47	89.02 62	89.56 117	97.37 23	82.51 85	99.38 29	92.20 48	98.30 57	97.57 91
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		93.96 40	93.72 45	94.68 38	98.43 19	86.22 50	95.30 86	97.78 1	87.45 105	93.26 51	97.33 24	84.62 65	99.51 21	90.75 85	98.57 46	98.32 40

ETH3D-3000-0.1		94.61 16	94.44 18	95.12 11	97.70 55	87.71 9	95.98 56	97.44 12	86.67 122	95.25 21	97.31 25	87.73 25	99.24 44	93.11 31	98.76 26	98.40 35

DeepPCF-MVS	89.96 1	94.20 35	94.77 13	92.49 110	96.52 90	80.00 210	94.00 180	97.08 43	90.05 32	95.65 17	97.29 26	89.66 10	98.97 80	93.95 16	98.71 30	98.50 22

region2R		94.43 22	94.27 24	94.92 20	98.65 7	86.67 32	96.92 19	97.23 32	88.60 72	93.58 47	97.27 27	85.22 56	99.54 16	92.21 47	98.74 29	98.56 19

SD-MVS		94.96 11	95.33 7	93.88 62	97.25 73	86.69 30	96.19 43	97.11 42	90.42 25	96.95 10	97.27 27	89.53 11	96.91 237	94.38 13	98.85 15	98.03 67
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
ACMMPR		94.43 22	94.28 22	94.91 22	98.63 8	86.69 30	96.94 15	97.32 24	88.63 70	93.53 50	97.26 29	85.04 59	99.54 16	92.35 44	98.78 21	98.50 22

CP-MVS		94.34 26	94.21 27	94.74 37	98.39 23	86.64 34	97.60 1	97.24 30	88.53 74	92.73 68	97.23 30	85.20 57	99.32 37	92.15 50	98.83 17	98.25 50

abl_6		93.18 61	93.05 59	93.57 73	97.52 60	84.27 95	95.53 77	96.67 83	87.85 93	93.20 54	97.22 31	80.35 107	99.18 49	91.91 61	97.21 83	97.26 100

HFP-MVS		94.52 17	94.40 19	94.86 25	98.61 9	86.81 24	96.94 15	97.34 19	88.63 70	93.65 43	97.21 32	86.10 45	99.49 23	92.35 44	98.77 24	98.30 41

#test#		94.32 28	94.14 31	94.86 25	98.61 9	86.81 24	96.43 32	97.34 19	87.51 102	93.65 43	97.21 32	86.10 45	99.49 23	91.68 68	98.77 24	98.30 41

MP-MVS		94.25 29	94.07 34	94.77 35	98.47 17	86.31 47	96.71 26	96.98 48	89.04 60	91.98 85	97.19 34	85.43 54	99.56 7	92.06 55	98.79 19	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 30	94.07 34	94.75 36	98.06 41	86.90 21	95.88 60	96.94 54	85.68 142	95.05 23	97.18 35	87.31 31	99.07 58	91.90 64	98.61 45	98.28 45
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		93.99 38	93.78 43	94.63 41	98.50 15	85.90 63	96.87 21	96.91 56	88.70 68	91.83 91	97.17 36	83.96 73	99.55 12	91.44 73	98.64 43	98.43 34

XVS		94.45 20	94.32 20	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 61	97.16 37	85.02 60	99.49 23	91.99 56	98.56 47	98.47 28

HPM-MVS_fast		93.40 55	93.22 55	93.94 61	98.36 25	84.83 76	97.15 8	96.80 68	85.77 139	92.47 76	97.13 38	82.38 86	99.07 58	90.51 87	98.40 53	97.92 75

ETH3D cwj APD-0.16		93.91 41	93.53 49	95.06 13	96.76 81	87.78 7	94.92 113	97.21 34	84.33 172	93.89 38	97.09 39	87.20 33	99.29 41	91.90 64	98.44 51	98.12 59

OPU-MVS				96.21 1	98.00 44	90.85 1	97.13 9				97.08 40	92.59 1	98.94 84	92.25 46	98.99 10	98.84 8

CNVR-MVS		95.40 6	95.37 6	95.50 5	98.11 37	88.51 5	95.29 88	96.96 52	92.09 3	95.32 19	97.08 40	89.49 12	99.33 36	95.10 8	98.85 15	98.66 14

ZNCC-MVS		94.47 18	94.28 22	95.03 14	98.52 14	86.96 17	96.85 23	97.32 24	88.24 82	93.15 55	97.04 42	86.17 44	99.62 1	92.40 42	98.81 18	98.52 20

testtj		94.39 25	94.18 29	95.00 16	98.24 31	86.77 28	96.16 44	97.23 32	87.28 107	94.85 24	97.04 42	86.99 37	99.52 20	91.54 70	98.33 56	98.71 12

ACMMP		93.24 59	92.88 64	94.30 54	98.09 40	85.33 72	96.86 22	97.45 11	88.33 78	90.15 113	97.03 44	81.44 100	99.51 21	90.85 84	95.74 106	98.04 66
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
DeepC-MVS_fast	89.43 2	94.04 36	93.79 42	94.80 33	97.48 62	86.78 26	95.65 73	96.89 58	89.40 50	92.81 64	96.97 45	85.37 55	99.24 44	90.87 83	98.69 33	98.38 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		94.85 12	94.94 10	94.58 43	98.25 29	86.33 45	96.11 49	96.62 88	88.14 87	96.10 13	96.96 46	89.09 15	98.94 84	94.48 12	98.68 35	98.48 24
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSLP-MVS++		93.72 46	94.08 33	92.65 103	97.31 67	83.43 116	95.79 64	97.33 22	90.03 33	93.58 47	96.96 46	84.87 62	97.76 164	92.19 49	98.66 40	96.76 121

ZD-MVS					98.15 35	86.62 35		97.07 44	83.63 185	94.19 30	96.91 48	87.57 29	99.26 43	91.99 56	98.44 51

VDDNet		89.56 127	88.49 140	92.76 96	95.07 140	82.09 151	96.30 36	93.19 255	81.05 247	91.88 87	96.86 49	61.16 310	98.33 127	88.43 107	92.49 164	97.84 80

VDD-MVS		90.74 97	89.92 109	93.20 77	96.27 96	83.02 127	95.73 66	93.86 243	88.42 77	92.53 73	96.84 50	62.09 300	98.64 104	90.95 81	92.62 161	97.93 74

GST-MVS		94.21 33	93.97 38	94.90 24	98.41 22	86.82 23	96.54 31	97.19 35	88.24 82	93.26 51	96.83 51	85.48 53	99.59 4	91.43 74	98.40 53	98.30 41

HPM-MVS++		95.14 9	94.91 11	95.83 2	98.25 29	89.65 2	95.92 59	96.96 52	91.75 7	94.02 35	96.83 51	88.12 21	99.55 12	93.41 24	98.94 12	98.28 45

旧先验1					96.79 80	81.81 157		95.67 153			96.81 53	86.69 38			97.66 77	96.97 114

LFMVS		90.08 112	89.13 125	92.95 89	96.71 82	82.32 149	96.08 50	89.91 330	86.79 118	92.15 83	96.81 53	62.60 297	98.34 125	87.18 123	93.90 135	98.19 53

HPM-MVS		94.02 37	93.88 39	94.43 50	98.39 23	85.78 65	97.25 5	97.07 44	86.90 117	92.62 72	96.80 55	84.85 63	99.17 50	92.43 40	98.65 42	98.33 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-3		93.68 47	93.64 48	93.81 67	95.36 128	84.61 79	94.68 128	95.83 142	91.27 12	93.60 46	96.71 56	85.75 50	98.86 91	92.87 33	96.65 94	97.96 71

Regformer-4		93.91 41	93.81 41	94.19 57	95.36 128	85.47 70	94.68 128	96.41 99	91.60 10	93.75 40	96.71 56	85.95 48	99.10 57	93.21 29	96.65 94	98.01 69

MSP-MVS		95.42 5	95.56 5	94.98 19	98.49 16	86.52 38	96.91 20	97.47 8	91.73 8	96.10 13	96.69 58	89.90 9	99.30 39	94.70 9	98.04 64	99.13 1
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
testdata				90.49 192	96.40 92	77.89 256		95.37 181	72.51 330	93.63 45	96.69 58	82.08 94	97.65 173	83.08 168	97.39 81	95.94 150

EI-MVSNet-Vis-set		93.01 63	92.92 63	93.29 74	95.01 141	83.51 114	94.48 140	95.77 146	90.87 15	92.52 74	96.67 60	84.50 66	99.00 75	91.99 56	94.44 131	97.36 97

3Dnovator	86.66 5	91.73 81	90.82 92	94.44 48	94.59 165	86.37 43	97.18 7	97.02 46	89.20 55	84.31 232	96.66 61	73.74 192	99.17 50	86.74 129	97.96 68	97.79 83

CDPH-MVS		92.83 65	92.30 71	94.44 48	97.79 52	86.11 52	94.06 175	96.66 84	80.09 255	92.77 65	96.63 62	86.62 39	99.04 63	87.40 119	98.66 40	98.17 54

3Dnovator+	87.14 4	92.42 72	91.37 80	95.55 4	95.63 122	88.73 4	97.07 13	96.77 71	90.84 16	84.02 237	96.62 63	75.95 157	99.34 33	87.77 114	97.68 76	98.59 18

EI-MVSNet-UG-set		92.74 67	92.62 67	93.12 80	94.86 152	83.20 121	94.40 148	95.74 149	90.71 22	92.05 84	96.60 64	84.00 72	98.99 77	91.55 69	93.63 139	97.17 105

NCCC		94.81 13	94.69 15	95.17 10	97.83 51	87.46 14	95.66 71	96.93 55	92.34 2	93.94 36	96.58 65	87.74 24	99.44 27	92.83 34	98.40 53	98.62 16

Vis-MVSNet		91.75 80	91.23 83	93.29 74	95.32 131	83.78 106	96.14 46	95.98 128	89.89 35	90.45 109	96.58 65	75.09 169	98.31 129	84.75 150	96.90 88	97.78 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-1		94.22 32	94.13 32	94.51 46	95.54 124	86.36 44	94.57 136	96.44 96	91.69 9	94.32 28	96.56 67	87.05 36	99.03 64	93.35 25	97.65 78	98.15 56

Regformer-2		94.33 27	94.22 25	94.68 38	95.54 124	86.75 29	94.57 136	96.70 80	91.84 6	94.41 25	96.56 67	87.19 34	99.13 54	93.50 20	97.65 78	98.16 55

UA-Net		92.83 65	92.54 68	93.68 70	96.10 103	84.71 78	95.66 71	96.39 101	91.92 4	93.22 53	96.49 69	83.16 78	98.87 88	84.47 153	95.47 111	97.45 96

MG-MVS		91.77 79	91.70 78	92.00 131	97.08 75	80.03 208	93.60 197	95.18 189	87.85 93	90.89 106	96.47 70	82.06 95	98.36 122	85.07 144	97.04 87	97.62 87

CPTT-MVS		91.99 75	91.80 76	92.55 107	98.24 31	81.98 154	96.76 25	96.49 95	81.89 227	90.24 111	96.44 71	78.59 131	98.61 107	89.68 92	97.85 72	97.06 109

test_prior3		93.60 50	93.53 49	93.82 64	97.29 69	84.49 85	94.12 166	96.88 59	87.67 99	92.63 70	96.39 72	86.62 39	98.87 88	91.50 71	98.67 37	98.11 61

test_prior2							94.12 166		87.67 99	92.63 70	96.39 72	86.62 39		91.50 71	98.67 37

MCST-MVS		94.45 20	94.20 28	95.19 9	98.46 18	87.50 13	95.00 108	97.12 40	87.13 109	92.51 75	96.30 74	89.24 14	99.34 33	93.46 21	98.62 44	98.73 11

ETH3 D test6400		93.64 49	93.22 55	94.92 20	97.79 52	86.84 22	95.31 83	97.26 29	82.67 209	93.81 39	96.29 75	87.29 32	99.27 42	89.87 91	98.67 37	98.65 15

PHI-MVS		93.89 43	93.65 47	94.62 42	96.84 79	86.43 41	96.69 27	97.49 5	85.15 158	93.56 49	96.28 76	85.60 51	99.31 38	92.45 39	98.79 19	98.12 59

新几何1				93.10 81	97.30 68	84.35 94		95.56 161	71.09 337	91.26 102	96.24 77	82.87 82	98.86 91	79.19 235	98.10 62	96.07 146

agg_prior1		93.29 57	92.97 62	94.26 55	97.38 64	85.92 60	93.92 184	96.72 78	81.96 222	92.16 81	96.23 78	87.85 22	98.97 80	91.95 60	98.55 49	97.90 76

1121		90.42 107	89.49 113	93.20 77	97.27 71	84.46 88	92.63 232	95.51 167	71.01 338	91.20 103	96.21 79	82.92 81	99.05 60	80.56 216	98.07 63	96.10 144

TEST9					97.53 58	86.49 39	94.07 173	96.78 69	81.61 235	92.77 65	96.20 80	87.71 26	99.12 55

train_agg		93.44 53	93.08 58	94.52 45	97.53 58	86.49 39	94.07 173	96.78 69	81.86 228	92.77 65	96.20 80	87.63 27	99.12 55	92.14 51	98.69 33	97.94 72

test_8					97.49 61	86.30 48	94.02 178	96.76 72	81.86 228	92.70 69	96.20 80	87.63 27	99.02 68

QAPM		89.51 128	88.15 149	93.59 72	94.92 148	84.58 80	96.82 24	96.70 80	78.43 278	83.41 253	96.19 83	73.18 200	99.30 39	77.11 255	96.54 97	96.89 118

casdiffmvs		92.51 70	92.43 70	92.74 98	94.41 174	81.98 154	94.54 138	96.23 111	89.57 45	91.96 86	96.17 84	82.58 84	98.01 151	90.95 81	95.45 113	98.23 51

test222					96.55 88	81.70 159	92.22 246	95.01 196	68.36 343	90.20 112	96.14 85	80.26 110			97.80 73	96.05 148

OMC-MVS		91.23 89	90.62 94	93.08 82	96.27 96	84.07 98	93.52 199	95.93 132	86.95 114	89.51 118	96.13 86	78.50 133	98.35 124	85.84 137	92.90 157	96.83 119

OpenMVS	83.78 11	88.74 153	87.29 168	93.08 82	92.70 229	85.39 71	96.57 30	96.43 98	78.74 274	80.85 282	96.07 87	69.64 242	99.01 70	78.01 246	96.65 94	94.83 190

baseline		92.39 73	92.29 72	92.69 102	94.46 171	81.77 158	94.14 165	96.27 106	89.22 54	91.88 87	96.00 88	82.35 87	97.99 153	91.05 77	95.27 118	98.30 41

IS-MVSNet		91.43 85	91.09 87	92.46 111	95.87 115	81.38 171	96.95 14	93.69 248	89.72 42	89.50 119	95.98 89	78.57 132	97.77 163	83.02 170	96.50 99	98.22 52

LS3D		87.89 173	86.32 200	92.59 105	96.07 105	82.92 131	95.23 92	94.92 204	75.66 301	82.89 260	95.98 89	72.48 208	99.21 47	68.43 312	95.23 119	95.64 163

原ACMM1				92.01 129	97.34 66	81.05 179		96.81 67	78.89 269	90.45 109	95.92 91	82.65 83	98.84 96	80.68 214	98.26 59	96.14 139

VNet		92.24 74	91.91 75	93.24 76	96.59 86	83.43 116	94.84 119	96.44 96	89.19 56	94.08 34	95.90 92	77.85 142	98.17 135	88.90 101	93.38 147	98.13 58

CANet		93.54 51	93.20 57	94.55 44	95.65 121	85.73 67	94.94 111	96.69 82	91.89 5	90.69 107	95.88 93	81.99 97	99.54 16	93.14 30	97.95 69	98.39 36

MVS_111021_HR		93.45 52	93.31 52	93.84 63	96.99 76	84.84 75	93.24 214	97.24 30	88.76 67	91.60 96	95.85 94	86.07 47	98.66 102	91.91 61	98.16 60	98.03 67

DP-MVS Recon		91.95 76	91.28 82	93.96 60	98.33 27	85.92 60	94.66 131	96.66 84	82.69 208	90.03 115	95.82 95	82.30 89	99.03 64	84.57 152	96.48 100	96.91 117

EPNet		91.79 78	91.02 88	94.10 58	90.10 309	85.25 73	96.03 53	92.05 280	92.83 1	87.39 154	95.78 96	79.39 122	99.01 70	88.13 111	97.48 80	98.05 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-OURS		89.40 136	88.70 134	91.52 152	94.06 183	81.46 168	91.27 267	96.07 122	86.14 133	88.89 128	95.77 97	68.73 257	97.26 211	87.39 120	89.96 187	95.83 156

XVG-OURS-SEG-HR		89.95 117	89.45 114	91.47 156	94.00 189	81.21 176	91.87 254	96.06 124	85.78 138	88.55 130	95.73 98	74.67 176	97.27 209	88.71 104	89.64 194	95.91 151

MVS_111021_LR		92.47 71	92.29 72	92.98 87	95.99 109	84.43 92	93.08 219	96.09 120	88.20 85	91.12 104	95.72 99	81.33 102	97.76 164	91.74 66	97.37 82	96.75 122

CSCG		93.23 60	93.05 59	93.76 69	98.04 42	84.07 98	96.22 42	97.37 18	84.15 174	90.05 114	95.66 100	87.77 23	99.15 53	89.91 90	98.27 58	98.07 63

hse-mvs3		90.80 95	90.15 101	92.75 97	96.01 107	82.66 141	95.43 79	95.53 165	89.80 37	93.08 58	95.64 101	75.77 158	99.00 75	92.07 53	78.05 320	96.60 126

EPP-MVSNet		91.70 82	91.56 79	92.13 127	95.88 113	80.50 195	97.33 3	95.25 185	86.15 132	89.76 116	95.60 102	83.42 76	98.32 128	87.37 121	93.25 150	97.56 92

TSAR-MVS + GP.		93.66 48	93.41 51	94.41 52	96.59 86	86.78 26	94.40 148	93.93 239	89.77 40	94.21 29	95.59 103	87.35 30	98.61 107	92.72 37	96.15 103	97.83 81

Anonymous202405211		87.68 179	86.13 205	92.31 120	96.66 83	80.74 189	94.87 117	91.49 297	80.47 251	89.46 120	95.44 104	54.72 335	98.23 131	82.19 185	89.89 189	97.97 70

TAPA-MVS	84.62 6	88.16 167	87.01 175	91.62 150	96.64 84	80.65 190	94.39 150	96.21 115	76.38 294	86.19 177	95.44 104	79.75 115	98.08 146	62.75 338	95.29 116	96.13 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		90.12 111	89.56 112	91.82 143	93.14 215	83.90 102	94.16 164	95.74 149	88.96 63	87.86 141	95.43 106	72.48 208	97.91 159	88.10 112	90.18 184	93.65 250

Vis-MVSNet (Re-imp)		89.59 126	89.44 115	90.03 212	95.74 117	75.85 287	95.61 74	90.80 315	87.66 101	87.83 143	95.40 107	76.79 147	96.46 263	78.37 240	96.73 91	97.80 82

EI-MVSNet		89.10 141	88.86 133	89.80 223	91.84 250	78.30 246	93.70 194	95.01 196	85.73 140	87.15 155	95.28 108	79.87 114	97.21 216	83.81 161	87.36 229	93.88 234

CVMVSNet		84.69 260	84.79 243	84.37 319	91.84 250	64.92 350	93.70 194	91.47 298	66.19 346	86.16 178	95.28 108	67.18 266	93.33 331	80.89 210	90.42 181	94.88 188

114514_t		89.51 128	88.50 138	92.54 108	98.11 37	81.99 153	95.16 99	96.36 103	70.19 340	85.81 181	95.25 110	76.70 149	98.63 105	82.07 187	96.86 90	97.00 113

RPSCF		85.07 252	84.27 249	87.48 282	92.91 226	70.62 331	91.69 261	92.46 269	76.20 298	82.67 263	95.22 111	63.94 293	97.29 208	77.51 251	85.80 240	94.53 204

Anonymous20240529		88.09 169	86.59 190	92.58 106	96.53 89	81.92 156	95.99 54	95.84 141	74.11 317	89.06 126	95.21 112	61.44 305	98.81 97	83.67 164	87.47 226	97.01 112

LPG-MVS_test		89.45 131	88.90 131	91.12 166	94.47 169	81.49 166	95.30 86	96.14 117	86.73 120	85.45 197	95.16 113	69.89 238	98.10 139	87.70 115	89.23 201	93.77 244

LGP-MVS_train				91.12 166	94.47 169	81.49 166		96.14 117	86.73 120	85.45 197	95.16 113	69.89 238	98.10 139	87.70 115	89.23 201	93.77 244

CNLPA		89.07 142	87.98 153	92.34 118	96.87 78	84.78 77	94.08 172	93.24 253	81.41 238	84.46 222	95.13 115	75.57 165	96.62 247	77.21 253	93.84 137	95.61 164

CS-MVS		93.01 63	93.28 53	92.21 125	94.70 160	81.67 161	96.60 29	96.65 87	89.58 44	92.34 77	95.10 116	83.39 77	98.15 136	93.11 31	97.99 65	96.82 120

DELS-MVS		93.43 54	93.25 54	93.97 59	95.42 127	85.04 74	93.06 221	97.13 39	90.74 20	91.84 89	95.09 117	86.32 43	99.21 47	91.22 75	98.45 50	97.65 86
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
DPM-MVS		92.58 69	91.74 77	95.08 12	96.19 98	89.31 3	92.66 231	96.56 93	83.44 191	91.68 95	95.04 118	86.60 42	98.99 77	85.60 140	97.92 70	96.93 116

DP-MVS		87.25 201	85.36 231	92.90 91	97.65 56	83.24 120	94.81 121	92.00 282	74.99 309	81.92 272	95.00 119	72.66 205	99.05 60	66.92 323	92.33 165	96.40 131

diffmvs		91.37 87	91.23 83	91.77 146	93.09 217	80.27 198	92.36 241	95.52 166	87.03 112	91.40 100	94.93 120	80.08 111	97.44 190	92.13 52	94.56 127	97.61 88

MVSFormer		91.68 83	91.30 81	92.80 94	93.86 194	83.88 103	95.96 57	95.90 136	84.66 168	91.76 92	94.91 121	77.92 139	97.30 205	89.64 93	97.11 84	97.24 101

jason		90.80 95	90.10 102	92.90 91	93.04 220	83.53 113	93.08 219	94.15 233	80.22 252	91.41 99	94.91 121	76.87 145	97.93 158	90.28 89	96.90 88	97.24 101
jason: jason.
alignmvs		93.08 62	92.50 69	94.81 32	95.62 123	87.61 12	95.99 54	96.07 122	89.77 40	94.12 32	94.87 123	80.56 106	98.66 102	92.42 41	93.10 153	98.15 56

HQP_MVS		90.60 105	90.19 99	91.82 143	94.70 160	82.73 137	95.85 61	96.22 112	90.81 17	86.91 162	94.86 124	74.23 180	98.12 137	88.15 109	89.99 185	94.63 196

plane_prior4											94.86 124

nrg030		91.08 93	90.39 95	93.17 79	93.07 218	86.91 20	96.41 33	96.26 107	88.30 80	88.37 134	94.85 126	82.19 92	97.64 175	91.09 76	82.95 264	94.96 183

BH-RMVSNet		88.37 161	87.48 163	91.02 174	95.28 132	79.45 220	92.89 226	93.07 257	85.45 149	86.91 162	94.84 127	70.35 233	97.76 164	73.97 281	94.59 126	95.85 154

PAPM_NR		91.22 90	90.78 93	92.52 109	97.60 57	81.46 168	94.37 155	96.24 110	86.39 128	87.41 151	94.80 128	82.06 95	98.48 113	82.80 176	95.37 114	97.61 88

GeoE		90.05 113	89.43 116	91.90 139	95.16 137	80.37 197	95.80 63	94.65 218	83.90 179	87.55 150	94.75 129	78.18 137	97.62 177	81.28 202	93.63 139	97.71 85

test_yl		90.69 99	90.02 107	92.71 99	95.72 118	82.41 147	94.11 168	95.12 191	85.63 143	91.49 97	94.70 130	74.75 173	98.42 120	86.13 135	92.53 162	97.31 98

DCV-MVSNet		90.69 99	90.02 107	92.71 99	95.72 118	82.41 147	94.11 168	95.12 191	85.63 143	91.49 97	94.70 130	74.75 173	98.42 120	86.13 135	92.53 162	97.31 98

FIs		90.51 106	90.35 96	90.99 177	93.99 190	80.98 181	95.73 66	97.54 3	89.15 57	86.72 166	94.68 132	81.83 99	97.24 213	85.18 143	88.31 216	94.76 193

FC-MVSNet-test		90.27 109	90.18 100	90.53 188	93.71 200	79.85 214	95.77 65	97.59 2	89.31 52	86.27 175	94.67 133	81.93 98	97.01 231	84.26 155	88.09 220	94.71 194

AdaColmap		89.89 120	89.07 126	92.37 117	97.41 63	83.03 126	94.42 147	95.92 133	82.81 206	86.34 174	94.65 134	73.89 188	99.02 68	80.69 213	95.51 109	95.05 178

F-COLMAP		87.95 172	86.80 180	91.40 158	96.35 95	80.88 185	94.73 126	95.45 173	79.65 261	82.04 270	94.61 135	71.13 219	98.50 112	76.24 263	91.05 176	94.80 192

canonicalmvs		93.27 58	92.75 65	94.85 27	95.70 120	87.66 11	96.33 35	96.41 99	90.00 34	94.09 33	94.60 136	82.33 88	98.62 106	92.40 42	92.86 158	98.27 47

tttt0517		88.61 156	87.78 157	91.11 169	94.96 145	77.81 259	95.35 81	89.69 334	85.09 160	88.05 139	94.59 137	66.93 269	98.48 113	83.27 167	92.13 167	97.03 111

VPNet		88.20 166	87.47 164	90.39 196	93.56 205	79.46 219	94.04 176	95.54 164	88.67 69	86.96 159	94.58 138	69.33 246	97.15 218	84.05 158	80.53 303	94.56 203

UniMVSNet_ETH3D		87.53 190	86.37 196	91.00 176	92.44 233	78.96 233	94.74 125	95.61 159	84.07 176	85.36 207	94.52 139	59.78 319	97.34 204	82.93 171	87.88 223	96.71 124

PVSNet_Blended_VisFu		91.38 86	90.91 90	92.80 94	96.39 93	83.17 122	94.87 117	96.66 84	83.29 195	89.27 122	94.46 140	80.29 109	99.17 50	87.57 117	95.37 114	96.05 148

ACMM	84.12 9	89.14 140	88.48 141	91.12 166	94.65 164	81.22 175	95.31 83	96.12 119	85.31 153	85.92 180	94.34 141	70.19 236	98.06 148	85.65 139	88.86 206	94.08 225
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PCF-MVS	84.11 10	87.74 178	86.08 209	92.70 101	94.02 185	84.43 92	89.27 299	95.87 139	73.62 321	84.43 224	94.33 142	78.48 134	98.86 91	70.27 298	94.45 130	94.81 191
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WTY-MVS		89.60 125	88.92 130	91.67 149	95.47 126	81.15 177	92.38 240	94.78 214	83.11 198	89.06 126	94.32 143	78.67 130	96.61 250	81.57 199	90.89 178	97.24 101

ACMP	84.23 8	89.01 146	88.35 142	90.99 177	94.73 157	81.27 172	95.07 103	95.89 138	86.48 124	83.67 246	94.30 144	69.33 246	97.99 153	87.10 128	88.55 208	93.72 248
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cdsmvs_eth3d_5k		22.14 333	29.52 336	0.00 350	0.00 371	0.00 372	0.00 362	95.76 147	0.00 367	0.00 368	94.29 145	75.66 164	0.00 368	0.00 366	0.00 366	0.00 364

PS-MVSNAJss		89.97 116	89.62 111	91.02 174	91.90 248	80.85 186	95.26 91	95.98 128	86.26 130	86.21 176	94.29 145	79.70 117	97.65 173	88.87 102	88.10 218	94.57 202

lupinMVS		90.92 94	90.21 98	93.03 85	93.86 194	83.88 103	92.81 228	93.86 243	79.84 258	91.76 92	94.29 145	77.92 139	98.04 149	90.48 88	97.11 84	97.17 105

API-MVS		90.66 101	90.07 103	92.45 112	96.36 94	84.57 81	96.06 52	95.22 188	82.39 211	89.13 123	94.27 148	80.32 108	98.46 115	80.16 223	96.71 92	94.33 214

CANet_DTU		90.26 110	89.41 117	92.81 93	93.46 208	83.01 128	93.48 200	94.47 221	89.43 49	87.76 146	94.23 149	70.54 232	99.03 64	84.97 145	96.39 101	96.38 132

PLC	84.53 7	89.06 143	88.03 151	92.15 126	97.27 71	82.69 140	94.29 158	95.44 175	79.71 260	84.01 238	94.18 150	76.68 150	98.75 100	77.28 252	93.41 146	95.02 179
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
xiu_mvs_v1_base_debu		90.64 102	90.05 104	92.40 113	93.97 191	84.46 88	93.32 204	95.46 170	85.17 155	92.25 78	94.03 151	70.59 228	98.57 109	90.97 78	94.67 122	94.18 217

xiu_mvs_v1_base		90.64 102	90.05 104	92.40 113	93.97 191	84.46 88	93.32 204	95.46 170	85.17 155	92.25 78	94.03 151	70.59 228	98.57 109	90.97 78	94.67 122	94.18 217

xiu_mvs_v1_base_debi		90.64 102	90.05 104	92.40 113	93.97 191	84.46 88	93.32 204	95.46 170	85.17 155	92.25 78	94.03 151	70.59 228	98.57 109	90.97 78	94.67 122	94.18 217

jajsoiax		88.24 165	87.50 162	90.48 193	90.89 289	80.14 201	95.31 83	95.65 157	84.97 162	84.24 234	94.02 154	65.31 286	97.42 192	88.56 105	88.52 210	93.89 232

XXY-MVS		87.65 181	86.85 178	90.03 212	92.14 239	80.60 193	93.76 190	95.23 186	82.94 203	84.60 217	94.02 154	74.27 179	95.49 304	81.04 205	83.68 257	94.01 229

baseline1		88.10 168	87.28 169	90.57 186	94.96 145	80.07 204	94.27 159	91.29 302	86.74 119	87.41 151	94.00 156	76.77 148	96.20 274	80.77 211	79.31 316	95.44 168

NP-MVS					94.37 175	82.42 145					93.98 157

HQP-MVS		89.80 122	89.28 122	91.34 160	94.17 180	81.56 162	94.39 150	96.04 126	88.81 64	85.43 200	93.97 158	73.83 190	97.96 155	87.11 126	89.77 192	94.50 207

mvs_tets		88.06 171	87.28 169	90.38 198	90.94 285	79.88 212	95.22 93	95.66 155	85.10 159	84.21 235	93.94 159	63.53 294	97.40 199	88.50 106	88.40 214	93.87 235

CHOSEN 1792x2688		88.84 150	87.69 158	92.30 121	96.14 99	81.42 170	90.01 289	95.86 140	74.52 314	87.41 151	93.94 159	75.46 166	98.36 122	80.36 219	95.53 108	97.12 108

UGNet		89.95 117	88.95 129	92.95 89	94.51 168	83.31 119	95.70 68	95.23 186	89.37 51	87.58 148	93.94 159	64.00 292	98.78 99	83.92 159	96.31 102	96.74 123
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
TAMVS		89.21 139	88.29 146	91.96 134	93.71 200	82.62 143	93.30 208	94.19 231	82.22 215	87.78 145	93.94 159	78.83 126	96.95 234	77.70 248	92.98 156	96.32 133

sss		88.93 148	88.26 148	90.94 180	94.05 184	80.78 188	91.71 259	95.38 179	81.55 236	88.63 129	93.91 163	75.04 170	95.47 305	82.47 180	91.61 169	96.57 128

1112_ss		88.42 159	87.33 167	91.72 147	94.92 148	80.98 181	92.97 224	94.54 219	78.16 283	83.82 242	93.88 164	78.78 128	97.91 159	79.45 230	89.41 196	96.26 136

ab-mvs-re		7.82 337	10.43 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	93.88 164	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

TranMVSNet+NR-MVSNet		88.84 150	87.95 154	91.49 154	92.68 230	83.01 128	94.92 113	96.31 104	89.88 36	85.53 190	93.85 166	76.63 151	96.96 233	81.91 191	79.87 311	94.50 207

RRT_MVS		88.86 149	87.68 159	92.39 116	92.02 245	86.09 53	94.38 154	94.94 199	85.45 149	87.14 157	93.84 167	65.88 284	97.11 222	88.73 103	86.77 236	93.98 230

mvs_anonymous		89.37 137	89.32 120	89.51 234	93.47 207	74.22 298	91.65 262	94.83 210	82.91 204	85.45 197	93.79 168	81.23 103	96.36 269	86.47 133	94.09 133	97.94 72

thisisatest0530		88.67 154	87.61 161	91.86 140	94.87 151	80.07 204	94.63 132	89.90 331	84.00 177	88.46 132	93.78 169	66.88 271	98.46 115	83.30 166	92.65 160	97.06 109

MVS_Test		91.31 88	91.11 85	91.93 136	94.37 175	80.14 201	93.46 202	95.80 144	86.46 125	91.35 101	93.77 170	82.21 91	98.09 145	87.57 117	94.95 120	97.55 93

COLMAP_ROB	80.39 16	83.96 265	82.04 272	89.74 224	95.28 132	79.75 215	94.25 160	92.28 274	75.17 307	78.02 310	93.77 170	58.60 324	97.84 161	65.06 331	85.92 238	91.63 307
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PAPR		90.02 114	89.27 123	92.29 122	95.78 116	80.95 183	92.68 230	96.22 112	81.91 225	86.66 167	93.75 172	82.23 90	98.44 119	79.40 234	94.79 121	97.48 94

test_part1		89.00 147	87.99 152	92.04 128	95.94 112	83.81 105	96.14 46	96.05 125	86.44 126	85.69 184	93.73 173	71.57 214	97.66 171	85.80 138	80.54 301	94.66 195

ab-mvs		89.41 134	88.35 142	92.60 104	95.15 139	82.65 142	92.20 247	95.60 160	83.97 178	88.55 130	93.70 174	74.16 184	98.21 134	82.46 181	89.37 197	96.94 115

hse-mvs2		89.88 121	89.34 119	91.51 153	94.83 154	81.12 178	93.94 183	93.91 242	89.80 37	93.08 58	93.60 175	75.77 158	97.66 171	92.07 53	77.07 327	95.74 160

AUN-MVS		87.78 177	86.54 192	91.48 155	94.82 155	81.05 179	93.91 187	93.93 239	83.00 201	86.93 160	93.53 176	69.50 244	97.67 170	86.14 134	77.12 326	95.73 161

BH-untuned		88.60 157	88.13 150	90.01 214	95.24 135	78.50 241	93.29 209	94.15 233	84.75 166	84.46 222	93.40 177	75.76 160	97.40 199	77.59 249	94.52 128	94.12 221

AllTest		83.42 271	81.39 277	89.52 232	95.01 141	77.79 260	93.12 216	90.89 313	77.41 286	76.12 322	93.34 178	54.08 338	97.51 183	68.31 313	84.27 251	93.26 262

TestCases				89.52 232	95.01 141	77.79 260		90.89 313	77.41 286	76.12 322	93.34 178	54.08 338	97.51 183	68.31 313	84.27 251	93.26 262

UniMVSNet_NR-MVSNet		89.92 119	89.29 121	91.81 145	93.39 209	83.72 107	94.43 146	97.12 40	89.80 37	86.46 169	93.32 180	83.16 78	97.23 214	84.92 146	81.02 293	94.49 209

VPA-MVSNet		89.62 124	88.96 128	91.60 151	93.86 194	82.89 132	95.46 78	97.33 22	87.91 90	88.43 133	93.31 181	74.17 183	97.40 199	87.32 122	82.86 269	94.52 205

ITE_SJBPF				88.24 265	91.88 249	77.05 274		92.92 259	85.54 146	80.13 294	93.30 182	57.29 327	96.20 274	72.46 289	84.71 247	91.49 309

DU-MVS		89.34 138	88.50 138	91.85 142	93.04 220	83.72 107	94.47 143	96.59 90	89.50 46	86.46 169	93.29 183	77.25 143	97.23 214	84.92 146	81.02 293	94.59 200

NR-MVSNet		88.58 158	87.47 164	91.93 136	93.04 220	84.16 97	94.77 124	96.25 109	89.05 59	80.04 296	93.29 183	79.02 125	97.05 228	81.71 198	80.05 308	94.59 200

CDS-MVSNet		89.45 131	88.51 137	92.29 122	93.62 203	83.61 112	93.01 222	94.68 217	81.95 223	87.82 144	93.24 185	78.69 129	96.99 232	80.34 220	93.23 151	96.28 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		86.68 221	85.39 229	90.53 188	93.05 219	79.33 227	89.79 292	94.77 215	78.82 271	81.95 271	93.24 185	76.81 146	97.30 205	66.94 321	93.16 152	94.95 186

OurMVSNet-221017-0		85.35 246	84.64 246	87.49 281	90.77 293	72.59 315	94.01 179	94.40 223	84.72 167	79.62 302	93.17 187	61.91 302	96.72 242	81.99 189	81.16 287	93.16 270

PEN-MVS		86.80 216	86.27 202	88.40 259	92.32 236	75.71 289	95.18 97	96.38 102	87.97 88	82.82 261	93.15 188	73.39 198	95.92 285	76.15 264	79.03 318	93.59 251

xiu_mvs_v2_base		91.13 92	90.89 91	91.86 140	94.97 144	82.42 145	92.24 245	95.64 158	86.11 135	91.74 94	93.14 189	79.67 120	98.89 87	89.06 100	95.46 112	94.28 216

MVSTER		88.84 150	88.29 146	90.51 191	92.95 225	80.44 196	93.73 191	95.01 196	84.66 168	87.15 155	93.12 190	72.79 204	97.21 216	87.86 113	87.36 229	93.87 235

Effi-MVS+		91.59 84	91.11 85	93.01 86	94.35 178	83.39 118	94.60 133	95.10 193	87.10 110	90.57 108	93.10 191	81.43 101	98.07 147	89.29 97	94.48 129	97.59 90

PS-CasMVS		87.32 198	86.88 176	88.63 255	92.99 224	76.33 284	95.33 82	96.61 89	88.22 84	83.30 257	93.07 192	73.03 202	95.79 293	78.36 241	81.00 295	93.75 246

DTE-MVSNet		86.11 233	85.48 227	87.98 271	91.65 259	74.92 292	94.93 112	95.75 148	87.36 106	82.26 266	93.04 193	72.85 203	95.82 291	74.04 280	77.46 324	93.20 268

CP-MVSNet		87.63 184	87.26 171	88.74 252	93.12 216	76.59 279	95.29 88	96.58 91	88.43 76	83.49 252	92.98 194	75.28 167	95.83 290	78.97 236	81.15 289	93.79 240

test_djsdf		89.03 144	88.64 135	90.21 203	90.74 295	79.28 228	95.96 57	95.90 136	84.66 168	85.33 208	92.94 195	74.02 186	97.30 205	89.64 93	88.53 209	94.05 227

RRT_test8_iter05		86.90 212	86.36 197	88.52 257	93.00 223	73.27 306	94.32 157	95.96 130	85.50 148	84.26 233	92.86 196	60.76 312	97.70 169	88.32 108	82.29 272	94.60 199

MAR-MVS		90.30 108	89.37 118	93.07 84	96.61 85	84.48 87	95.68 69	95.67 153	82.36 213	87.85 142	92.85 197	76.63 151	98.80 98	80.01 224	96.68 93	95.91 151
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
testgi		80.94 297	80.20 288	83.18 324	87.96 334	66.29 345	91.28 266	90.70 317	83.70 183	78.12 308	92.84 198	51.37 344	90.82 347	63.34 335	82.46 271	92.43 292

EU-MVSNet		81.32 292	80.95 280	82.42 328	88.50 325	63.67 351	93.32 204	91.33 300	64.02 348	80.57 287	92.83 199	61.21 309	92.27 340	76.34 261	80.38 306	91.32 312

ACMH+	81.04 14	85.05 253	83.46 261	89.82 220	94.66 163	79.37 222	94.44 145	94.12 236	82.19 216	78.04 309	92.82 200	58.23 325	97.54 181	73.77 283	82.90 268	92.54 288

mvs-test1		89.45 131	89.14 124	90.38 198	93.33 210	77.63 265	94.95 110	94.36 224	87.70 97	87.10 158	92.81 201	73.45 195	98.03 150	85.57 141	93.04 154	95.48 166

WR-MVS		88.38 160	87.67 160	90.52 190	93.30 212	80.18 199	93.26 211	95.96 130	88.57 73	85.47 196	92.81 201	76.12 153	96.91 237	81.24 203	82.29 272	94.47 212

HY-MVS	83.01 12	89.03 144	87.94 155	92.29 122	94.86 152	82.77 133	92.08 252	94.49 220	81.52 237	86.93 160	92.79 203	78.32 136	98.23 131	79.93 225	90.55 179	95.88 153

LTVRE_ROB	82.13 13	86.26 232	84.90 240	90.34 201	94.44 173	81.50 164	92.31 244	94.89 205	83.03 200	79.63 301	92.67 204	69.69 241	97.79 162	71.20 293	86.26 237	91.72 305
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
ACMH	80.38 17	85.36 245	83.68 257	90.39 196	94.45 172	80.63 191	94.73 126	94.85 208	82.09 217	77.24 314	92.65 205	60.01 317	97.58 178	72.25 290	84.87 246	92.96 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		86.61 222	85.54 225	89.82 220	91.44 261	80.18 199	95.28 90	94.85 208	83.84 181	81.66 273	92.62 206	72.45 210	96.48 260	79.67 228	78.06 319	92.82 283

PVSNet_Blended		90.73 98	90.32 97	91.98 132	96.12 100	81.25 173	92.55 236	96.83 64	82.04 220	89.10 124	92.56 207	81.04 104	98.85 94	86.72 131	95.91 104	95.84 155

ET-MVSNet_ETH3D		87.51 191	85.91 216	92.32 119	93.70 202	83.93 101	92.33 242	90.94 311	84.16 173	72.09 340	92.52 208	69.90 237	95.85 289	89.20 98	88.36 215	97.17 105

PS-MVSNAJ		91.18 91	90.92 89	91.96 134	95.26 134	82.60 144	92.09 251	95.70 151	86.27 129	91.84 89	92.46 209	79.70 117	98.99 77	89.08 99	95.86 105	94.29 215

CLD-MVS		89.47 130	88.90 131	91.18 165	94.22 179	82.07 152	92.13 249	96.09 120	87.90 91	85.37 206	92.45 210	74.38 178	97.56 180	87.15 124	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
TR-MVS		86.78 217	85.76 222	89.82 220	94.37 175	78.41 243	92.47 237	92.83 261	81.11 246	86.36 173	92.40 211	68.73 257	97.48 185	73.75 284	89.85 191	93.57 252

Test_1112_low_res		87.65 181	86.51 193	91.08 170	94.94 147	79.28 228	91.77 256	94.30 227	76.04 299	83.51 251	92.37 212	77.86 141	97.73 168	78.69 239	89.13 203	96.22 137

EPNet_dtu		86.49 229	85.94 215	88.14 268	90.24 307	72.82 310	94.11 168	92.20 276	86.66 123	79.42 303	92.36 213	73.52 193	95.81 292	71.26 292	93.66 138	95.80 158
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet (Re)		89.80 122	89.07 126	92.01 129	93.60 204	84.52 84	94.78 123	97.47 8	89.26 53	86.44 172	92.32 214	82.10 93	97.39 202	84.81 149	80.84 297	94.12 221

thres600view7		87.65 181	86.67 185	90.59 185	96.08 104	78.72 234	94.88 116	91.58 293	87.06 111	88.08 137	92.30 215	68.91 254	98.10 139	70.05 305	91.10 172	94.96 183

thres100view900		87.63 184	86.71 183	90.38 198	96.12 100	78.55 238	95.03 107	91.58 293	87.15 108	88.06 138	92.29 216	68.91 254	98.10 139	70.13 302	91.10 172	94.48 210

PVSNet_BlendedMVS		89.98 115	89.70 110	90.82 181	96.12 100	81.25 173	93.92 184	96.83 64	83.49 190	89.10 124	92.26 217	81.04 104	98.85 94	86.72 131	87.86 224	92.35 296

XVG-ACMP-BASELINE		86.00 234	84.84 242	89.45 235	91.20 272	78.00 252	91.70 260	95.55 162	85.05 161	82.97 259	92.25 218	54.49 336	97.48 185	82.93 171	87.45 228	92.89 280

EIA-MVS		91.95 76	91.94 74	91.98 132	95.16 137	80.01 209	95.36 80	96.73 76	88.44 75	89.34 121	92.16 219	83.82 75	98.45 118	89.35 96	97.06 86	97.48 94

Anonymous20231211		86.59 224	85.13 234	90.98 179	96.52 90	81.50 164	96.14 46	96.16 116	73.78 319	83.65 247	92.15 220	63.26 295	97.37 203	82.82 175	81.74 282	94.06 226

MVS		87.44 194	86.10 208	91.44 157	92.61 231	83.62 111	92.63 232	95.66 155	67.26 344	81.47 274	92.15 220	77.95 138	98.22 133	79.71 227	95.48 110	92.47 291

anonymousdsp		87.84 174	87.09 172	90.12 208	89.13 319	80.54 194	94.67 130	95.55 162	82.05 218	83.82 242	92.12 222	71.47 217	97.15 218	87.15 124	87.80 225	92.67 285

TransMVSNet (Re)		84.43 262	83.06 265	88.54 256	91.72 254	78.44 242	95.18 97	92.82 262	82.73 207	79.67 300	92.12 222	73.49 194	95.96 284	71.10 297	68.73 344	91.21 316

SixPastTwentyTwo		83.91 267	82.90 267	86.92 295	90.99 281	70.67 330	93.48 200	91.99 283	85.54 146	77.62 313	92.11 224	60.59 313	96.87 239	76.05 265	77.75 321	93.20 268

HyFIR lowres test		88.09 169	86.81 179	91.93 136	96.00 108	80.63 191	90.01 289	95.79 145	73.42 322	87.68 147	92.10 225	73.86 189	97.96 155	80.75 212	91.70 168	97.19 104

Baseline_NR-MVSNet		87.07 209	86.63 188	88.40 259	91.44 261	77.87 257	94.23 162	92.57 268	84.12 175	85.74 183	92.08 226	77.25 143	96.04 279	82.29 184	79.94 309	91.30 313

USDC		82.76 274	81.26 279	87.26 286	91.17 274	74.55 294	89.27 299	93.39 252	78.26 281	75.30 327	92.08 226	54.43 337	96.63 246	71.64 291	85.79 241	90.61 324

v2v482		87.84 174	87.06 173	90.17 204	90.99 281	79.23 231	94.00 180	95.13 190	84.87 163	85.53 190	92.07 228	74.45 177	97.45 188	84.71 151	81.75 281	93.85 238

FMVSNet2		87.19 206	85.82 218	91.30 161	94.01 186	83.67 109	94.79 122	94.94 199	83.57 186	83.88 240	92.05 229	66.59 276	96.51 258	77.56 250	85.01 245	93.73 247

WR-MVS_H		87.80 176	87.37 166	89.10 242	93.23 213	78.12 250	95.61 74	97.30 26	87.90 91	83.72 244	92.01 230	79.65 121	96.01 282	76.36 260	80.54 301	93.16 270

LCM-MVSNet-Re		88.30 164	88.32 145	88.27 263	94.71 159	72.41 318	93.15 215	90.98 309	87.77 95	79.25 304	91.96 231	78.35 135	95.75 294	83.04 169	95.62 107	96.65 125

MSDG		84.86 257	83.09 264	90.14 207	93.80 197	80.05 206	89.18 302	93.09 256	78.89 269	78.19 307	91.91 232	65.86 285	97.27 209	68.47 311	88.45 212	93.11 272

IterMVS-LS		88.36 162	87.91 156	89.70 227	93.80 197	78.29 247	93.73 191	95.08 195	85.73 140	84.75 215	91.90 233	79.88 113	96.92 236	83.83 160	82.51 270	93.89 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet3		87.40 196	86.11 207	91.30 161	93.79 199	83.64 110	94.20 163	94.81 212	83.89 180	84.37 225	91.87 234	68.45 260	96.56 255	78.23 243	85.36 242	93.70 249

tfpn200view9		87.58 188	86.64 186	90.41 195	95.99 109	78.64 236	94.58 134	91.98 284	86.94 115	88.09 135	91.77 235	69.18 251	98.10 139	70.13 302	91.10 172	94.48 210

thres400		87.62 186	86.64 186	90.57 186	95.99 109	78.64 236	94.58 134	91.98 284	86.94 115	88.09 135	91.77 235	69.18 251	98.10 139	70.13 302	91.10 172	94.96 183

pmmvs4		85.43 244	83.86 255	90.16 205	90.02 312	82.97 130	90.27 281	92.67 266	75.93 300	80.73 283	91.74 237	71.05 220	95.73 295	78.85 237	83.46 261	91.78 304

GBi-Net		87.26 199	85.98 212	91.08 170	94.01 186	83.10 123	95.14 100	94.94 199	83.57 186	84.37 225	91.64 238	66.59 276	96.34 270	78.23 243	85.36 242	93.79 240

test1		87.26 199	85.98 212	91.08 170	94.01 186	83.10 123	95.14 100	94.94 199	83.57 186	84.37 225	91.64 238	66.59 276	96.34 270	78.23 243	85.36 242	93.79 240

FMVSNet1		85.85 238	84.11 251	91.08 170	92.81 227	83.10 123	95.14 100	94.94 199	81.64 233	82.68 262	91.64 238	59.01 323	96.34 270	75.37 270	83.78 254	93.79 240

MVP-Stereo		85.97 235	84.86 241	89.32 236	90.92 287	82.19 150	92.11 250	94.19 231	78.76 273	78.77 306	91.63 241	68.38 261	96.56 255	75.01 275	93.95 134	89.20 336
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		87.51 191	86.57 191	90.34 201	92.42 234	79.74 216	92.63 232	95.35 183	78.35 279	80.14 293	91.62 242	74.05 185	97.15 218	81.05 204	93.53 142	94.12 221

MS-PatchMatch		85.05 253	84.16 250	87.73 275	91.42 265	78.51 240	91.25 268	93.53 249	77.50 285	80.15 292	91.58 243	61.99 301	95.51 301	75.69 267	94.35 132	89.16 337

TDRefinement		79.81 304	77.34 308	87.22 290	79.24 355	75.48 291	93.12 216	92.03 281	76.45 293	75.01 328	91.58 243	49.19 348	96.44 264	70.22 301	69.18 341	89.75 331

PatchMatch-RL		86.77 220	85.54 225	90.47 194	95.88 113	82.71 139	90.54 278	92.31 273	79.82 259	84.32 230	91.57 245	68.77 256	96.39 266	73.16 286	93.48 145	92.32 297

BH-w/o		87.57 189	87.05 174	89.12 241	94.90 150	77.90 255	92.41 238	93.51 250	82.89 205	83.70 245	91.34 246	75.75 161	97.07 226	75.49 268	93.49 143	92.39 294

v8		87.50 193	86.71 183	89.89 217	91.37 267	79.40 221	94.50 139	95.38 179	84.81 165	83.60 249	91.33 247	76.05 154	97.42 192	82.84 174	80.51 305	92.84 282

V42		87.68 179	86.86 177	90.15 206	90.58 300	80.14 201	94.24 161	95.28 184	83.66 184	85.67 185	91.33 247	74.73 175	97.41 197	84.43 154	81.83 279	92.89 280

Fast-Effi-MVS+-dtu		87.44 194	86.72 182	89.63 229	92.04 243	77.68 264	94.03 177	93.94 238	85.81 137	82.42 264	91.32 249	70.33 234	97.06 227	80.33 221	90.23 183	94.14 220

v1144		87.61 187	86.79 181	90.06 211	91.01 280	79.34 224	93.95 182	95.42 178	83.36 194	85.66 186	91.31 250	74.98 171	97.42 192	83.37 165	82.06 275	93.42 259

tfpnnormal		84.72 259	83.23 263	89.20 239	92.79 228	80.05 206	94.48 140	95.81 143	82.38 212	81.08 280	91.21 251	69.01 253	96.95 234	61.69 340	80.59 300	90.58 327

ETV-MVS		92.74 67	92.66 66	92.97 88	95.20 136	84.04 100	95.07 103	96.51 94	90.73 21	92.96 60	91.19 252	84.06 71	98.34 125	91.72 67	96.54 97	96.54 130

v10		87.25 201	86.38 195	89.85 218	91.19 273	79.50 218	94.48 140	95.45 173	83.79 182	83.62 248	91.19 252	75.13 168	97.42 192	81.94 190	80.60 299	92.63 287

pmmvs5		84.21 263	82.84 269	88.34 262	88.95 321	76.94 275	92.41 238	91.91 288	75.63 302	80.28 290	91.18 254	64.59 290	95.57 297	77.09 256	83.47 260	92.53 289

v1192		87.25 201	86.33 199	90.00 215	90.76 294	79.04 232	93.80 188	95.48 168	82.57 210	85.48 195	91.18 254	73.38 199	97.42 192	82.30 183	82.06 275	93.53 253

v1240		86.78 217	85.85 217	89.56 230	90.45 304	77.79 260	93.61 196	95.37 181	81.65 232	85.43 200	91.15 256	71.50 216	97.43 191	81.47 201	82.05 277	93.47 257

CMPMVS	59.16 21	80.52 298	79.20 300	84.48 318	83.98 348	67.63 344	89.95 291	93.84 245	64.79 347	66.81 348	91.14 257	57.93 326	95.17 308	76.25 262	88.10 218	90.65 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thres200		87.21 205	86.24 203	90.12 208	95.36 128	78.53 239	93.26 211	92.10 278	86.42 127	88.00 140	91.11 258	69.24 250	98.00 152	69.58 306	91.04 177	93.83 239

pmmvs6		83.42 271	81.60 275	88.87 247	88.01 333	77.87 257	94.96 109	94.24 230	74.67 313	78.80 305	91.09 259	60.17 316	96.49 259	77.06 257	75.40 330	92.23 299

v144192		87.19 206	86.35 198	89.74 224	90.64 298	78.24 248	93.92 184	95.43 176	81.93 224	85.51 192	91.05 260	74.21 182	97.45 188	82.86 173	81.56 283	93.53 253

v1921920		86.97 211	86.06 210	89.69 228	90.53 303	78.11 251	93.80 188	95.43 176	81.90 226	85.33 208	91.05 260	72.66 205	97.41 197	82.05 188	81.80 280	93.53 253

baseline2		86.50 227	85.39 229	89.84 219	91.12 277	76.70 277	91.88 253	88.58 339	82.35 214	79.95 297	90.95 262	73.42 197	97.63 176	80.27 222	89.95 188	95.19 175

thisisatest0515		87.33 197	85.99 211	91.37 159	93.49 206	79.55 217	90.63 277	89.56 337	80.17 253	87.56 149	90.86 263	67.07 268	98.28 130	81.50 200	93.02 155	96.29 134

v7n		86.81 215	85.76 222	89.95 216	90.72 296	79.25 230	95.07 103	95.92 133	84.45 171	82.29 265	90.86 263	72.60 207	97.53 182	79.42 233	80.52 304	93.08 274

cl-mvsnet1		86.53 225	85.78 219	88.75 250	92.02 245	76.45 281	90.74 275	94.30 227	81.83 230	83.34 255	90.82 265	75.75 161	96.57 253	81.73 197	81.52 285	93.24 265

v148		87.04 210	86.32 200	89.21 238	90.94 285	77.26 271	93.71 193	94.43 222	84.84 164	84.36 228	90.80 266	76.04 155	97.05 228	82.12 186	79.60 313	93.31 261

cl-mvsnet____		86.52 226	85.78 219	88.75 250	92.03 244	76.46 280	90.74 275	94.30 227	81.83 230	83.34 255	90.78 267	75.74 163	96.57 253	81.74 196	81.54 284	93.22 267

PMMVS		85.71 241	84.96 238	87.95 272	88.90 322	77.09 273	88.68 309	90.06 326	72.32 331	86.47 168	90.76 268	72.15 211	94.40 316	81.78 195	93.49 143	92.36 295

bset_n11_16_dypcd		86.83 214	85.55 224	90.65 184	88.22 330	81.70 159	88.88 306	90.42 318	85.26 154	85.49 194	90.69 269	67.11 267	97.02 230	89.51 95	84.39 249	93.23 266

Fast-Effi-MVS+		89.41 134	88.64 135	91.71 148	94.74 156	80.81 187	93.54 198	95.10 193	83.11 198	86.82 165	90.67 270	79.74 116	97.75 167	80.51 218	93.55 141	96.57 128

IterMVS-SCA-FT		85.45 243	84.53 248	88.18 267	91.71 255	76.87 276	90.19 286	92.65 267	85.40 151	81.44 275	90.54 271	66.79 272	95.00 313	81.04 205	81.05 291	92.66 286

PVSNet	78.82 18	85.55 242	84.65 245	88.23 266	94.72 158	71.93 319	87.12 324	92.75 264	78.80 272	84.95 213	90.53 272	64.43 291	96.71 244	74.74 276	93.86 136	96.06 147

eth_miper_zixun_eth		86.50 227	85.77 221	88.68 253	91.94 247	75.81 288	90.47 279	94.89 205	82.05 218	84.05 236	90.46 273	75.96 156	96.77 241	82.76 177	79.36 315	93.46 258

cl_fuxian		87.14 208	86.50 194	89.04 244	92.20 237	77.26 271	91.22 269	94.70 216	82.01 221	84.34 229	90.43 274	78.81 127	96.61 250	83.70 163	81.09 290	93.25 264

IterMVS		84.88 256	83.98 254	87.60 277	91.44 261	76.03 286	90.18 287	92.41 270	83.24 197	81.06 281	90.42 275	66.60 275	94.28 320	79.46 229	80.98 296	92.48 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_0402		81.30 293	79.17 301	87.67 276	93.19 214	78.17 249	92.98 223	91.71 289	75.25 306	76.02 324	90.31 276	59.23 321	96.37 267	50.22 353	83.63 258	88.47 343

TinyColmap		79.76 305	77.69 307	85.97 305	91.71 255	73.12 307	89.55 293	90.36 321	75.03 308	72.03 341	90.19 277	46.22 353	96.19 276	63.11 336	81.03 292	88.59 342

EG-PatchMatch MVS		82.37 279	80.34 285	88.46 258	90.27 306	79.35 223	92.80 229	94.33 226	77.14 290	73.26 337	90.18 278	47.47 352	96.72 242	70.25 299	87.32 231	89.30 334

cl-mvsnet2		86.78 217	85.98 212	89.18 240	92.34 235	77.62 266	90.84 274	94.13 235	81.33 240	83.97 239	90.15 279	73.96 187	96.60 252	84.19 156	82.94 265	93.33 260

lessismore_v0				86.04 304	88.46 326	68.78 339		80.59 356		73.01 338	90.11 280	55.39 332	96.43 265	75.06 274	65.06 346	92.90 279

miper_ehance_all_eth		87.22 204	86.62 189	89.02 245	92.13 240	77.40 270	90.91 273	94.81 212	81.28 241	84.32 230	90.08 281	79.26 123	96.62 247	83.81 161	82.94 265	93.04 275

D2MVS		85.90 236	85.09 235	88.35 261	90.79 292	77.42 269	91.83 255	95.70 151	80.77 249	80.08 295	90.02 282	66.74 274	96.37 267	81.88 192	87.97 222	91.26 314

LF4IMVS		80.37 300	79.07 303	84.27 321	86.64 337	69.87 336	89.39 298	91.05 307	76.38 294	74.97 329	90.00 283	47.85 351	94.25 321	74.55 279	80.82 298	88.69 341

CostFormer		85.77 240	84.94 239	88.26 264	91.16 276	72.58 316	89.47 297	91.04 308	76.26 297	86.45 171	89.97 284	70.74 226	96.86 240	82.35 182	87.07 234	95.34 173

test20.03		79.95 303	79.08 302	82.55 327	85.79 342	67.74 343	91.09 271	91.08 305	81.23 244	74.48 332	89.96 285	61.63 303	90.15 348	60.08 344	76.38 328	89.76 330

tpm		84.73 258	84.02 252	86.87 298	90.33 305	68.90 338	89.06 303	89.94 329	80.85 248	85.75 182	89.86 286	68.54 259	95.97 283	77.76 247	84.05 253	95.75 159

miper_lstm_enhance		85.27 249	84.59 247	87.31 284	91.28 271	74.63 293	87.69 320	94.09 237	81.20 245	81.36 277	89.85 287	74.97 172	94.30 319	81.03 207	79.84 312	93.01 276

test0.0.03 1		82.41 278	81.69 274	84.59 317	88.23 329	72.89 309	90.24 283	87.83 342	83.41 192	79.86 298	89.78 288	67.25 264	88.99 350	65.18 329	83.42 262	91.90 303

K. test v3		81.59 287	80.15 289	85.91 308	89.89 315	69.42 337	92.57 235	87.71 343	85.56 145	73.44 336	89.71 289	55.58 330	95.52 300	77.17 254	69.76 338	92.78 284

CHOSEN 280x420		85.15 251	83.99 253	88.65 254	92.47 232	78.40 244	79.68 351	92.76 263	74.90 311	81.41 276	89.59 290	69.85 240	95.51 301	79.92 226	95.29 116	92.03 301

GA-MVS		86.61 222	85.27 232	90.66 183	91.33 270	78.71 235	90.40 280	93.81 246	85.34 152	85.12 210	89.57 291	61.25 307	97.11 222	80.99 208	89.59 195	96.15 138

Effi-MVS+-dtu		88.65 155	88.35 142	89.54 231	93.33 210	76.39 282	94.47 143	94.36 224	87.70 97	85.43 200	89.56 292	73.45 195	97.26 211	85.57 141	91.28 171	94.97 180

tpm2		84.08 264	82.94 266	87.48 282	91.39 266	71.27 323	89.23 301	90.37 320	71.95 333	84.64 216	89.33 293	67.30 263	96.55 257	75.17 272	87.09 233	94.63 196

Anonymous20231206		81.03 295	79.77 293	84.82 316	87.85 335	70.26 333	91.42 265	92.08 279	73.67 320	77.75 311	89.25 294	62.43 299	93.08 334	61.50 341	82.00 278	91.12 319

miper_enhance_ethall		86.90 212	86.18 204	89.06 243	91.66 258	77.58 267	90.22 285	94.82 211	79.16 266	84.48 221	89.10 295	79.19 124	96.66 245	84.06 157	82.94 265	92.94 278

ppachtmachnet_test		81.84 282	80.07 290	87.15 292	88.46 326	74.43 297	89.04 304	92.16 277	75.33 305	77.75 311	88.99 296	66.20 280	95.37 306	65.12 330	77.60 322	91.65 306

gm-plane-assit					89.60 318	68.00 340			77.28 289		88.99 296		97.57 179	79.44 231

MDTV_nov1_ep13			83.56 260		91.69 257	69.93 335	87.75 319	91.54 295	78.60 276	84.86 214	88.90 298	69.54 243	96.03 280	70.25 299	88.93 205

SCA		86.32 231	85.18 233	89.73 226	92.15 238	76.60 278	91.12 270	91.69 291	83.53 189	85.50 193	88.81 299	66.79 272	96.48 260	76.65 258	90.35 182	96.12 141

Patchmatch-test		81.37 291	79.30 297	87.58 278	90.92 287	74.16 300	80.99 349	87.68 344	70.52 339	76.63 319	88.81 299	71.21 218	92.76 337	60.01 346	86.93 235	95.83 156

tpmrst		85.35 246	84.99 236	86.43 301	90.88 290	67.88 342	88.71 308	91.43 299	80.13 254	86.08 179	88.80 301	73.05 201	96.02 281	82.48 179	83.40 263	95.40 170

DSMNet-mixed		76.94 315	76.29 314	78.89 331	83.10 351	56.11 358	87.78 318	79.77 357	60.65 350	75.64 325	88.71 302	61.56 304	88.34 351	60.07 345	89.29 200	92.21 300

PatchmatchNet		85.85 238	84.70 244	89.29 237	91.76 253	75.54 290	88.49 311	91.30 301	81.63 234	85.05 211	88.70 303	71.71 212	96.24 273	74.61 278	89.05 204	96.08 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		82.59 277	80.53 282	88.76 249	91.51 260	78.32 245	86.57 327	90.13 324	79.32 262	80.70 284	88.69 304	52.98 342	93.07 335	66.03 326	88.86 206	94.90 187

IB-MVS	80.51 15	85.24 250	83.26 262	91.19 164	92.13 240	79.86 213	91.75 257	91.29 302	83.28 196	80.66 285	88.49 305	61.28 306	98.46 115	80.99 208	79.46 314	95.25 174
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
cascas		86.43 230	84.98 237	90.80 182	92.10 242	80.92 184	90.24 283	95.91 135	73.10 325	83.57 250	88.39 306	65.15 287	97.46 187	84.90 148	91.43 170	94.03 228

EPMVS		83.90 268	82.70 270	87.51 279	90.23 308	72.67 312	88.62 310	81.96 354	81.37 239	85.01 212	88.34 307	66.31 279	94.45 315	75.30 271	87.12 232	95.43 169

MDA-MVSNet-bldmvs		78.85 310	76.31 313	86.46 300	89.76 316	73.88 301	88.79 307	90.42 318	79.16 266	59.18 352	88.33 308	60.20 315	94.04 322	62.00 339	68.96 342	91.48 310

our_test_3		81.93 281	80.46 284	86.33 303	88.46 326	73.48 304	88.46 312	91.11 304	76.46 292	76.69 318	88.25 309	66.89 270	94.36 317	68.75 309	79.08 317	91.14 318

OpenMVS_ROB	74.94 19	79.51 306	77.03 312	86.93 294	87.00 336	76.23 285	92.33 242	90.74 316	68.93 342	74.52 331	88.23 310	49.58 347	96.62 247	57.64 348	84.29 250	87.94 345

MIMVSNet1		79.38 307	77.28 309	85.69 309	86.35 338	73.67 303	91.61 263	92.75 264	78.11 284	72.64 339	88.12 311	48.16 349	91.97 343	60.32 343	77.49 323	91.43 311

UnsupCasMVSNet_eth		80.07 302	78.27 306	85.46 310	85.24 346	72.63 314	88.45 313	94.87 207	82.99 202	71.64 343	88.07 312	56.34 329	91.75 344	73.48 285	63.36 349	92.01 302

test-LLR		85.87 237	85.41 228	87.25 287	90.95 283	71.67 321	89.55 293	89.88 332	83.41 192	84.54 219	87.95 313	67.25 264	95.11 310	81.82 193	93.37 148	94.97 180

test-mter		84.54 261	83.64 259	87.25 287	90.95 283	71.67 321	89.55 293	89.88 332	79.17 265	84.54 219	87.95 313	55.56 331	95.11 310	81.82 193	93.37 148	94.97 180

FMVSNet5		81.52 289	79.60 295	87.27 285	91.17 274	77.95 253	91.49 264	92.26 275	76.87 291	76.16 321	87.91 315	51.67 343	92.34 339	67.74 317	81.16 287	91.52 308

CR-MVSNet		85.35 246	83.76 256	90.12 208	90.58 300	79.34 224	85.24 334	91.96 286	78.27 280	85.55 188	87.87 316	71.03 221	95.61 296	73.96 282	89.36 198	95.40 170

Patchmtry		82.71 275	80.93 281	88.06 270	90.05 311	76.37 283	84.74 338	91.96 286	72.28 332	81.32 278	87.87 316	71.03 221	95.50 303	68.97 308	80.15 307	92.32 297

YYNet1		79.22 308	77.20 310	85.28 313	88.20 332	72.66 313	85.87 330	90.05 328	74.33 316	62.70 350	87.61 318	66.09 282	92.03 341	66.94 321	72.97 333	91.15 317

MDA-MVSNet_test_wron		79.21 309	77.19 311	85.29 312	88.22 330	72.77 311	85.87 330	90.06 326	74.34 315	62.62 351	87.56 319	66.14 281	91.99 342	66.90 324	73.01 332	91.10 321

Anonymous20240521		80.44 299	79.21 299	84.11 322	85.75 343	67.89 341	92.86 227	93.23 254	75.61 303	75.59 326	87.47 320	50.03 345	94.33 318	71.14 296	81.21 286	90.12 329

DWT-MVSNet_test		84.95 255	83.68 257	88.77 248	91.43 264	73.75 302	91.74 258	90.98 309	80.66 250	83.84 241	87.36 321	62.44 298	97.11 222	78.84 238	85.81 239	95.46 167

TESTMET0.1,1		83.74 269	82.85 268	86.42 302	89.96 313	71.21 325	89.55 293	87.88 341	77.41 286	83.37 254	87.31 322	56.71 328	93.65 328	80.62 215	92.85 159	94.40 213

CL-MVSNet_2432*1600		81.74 284	80.53 282	85.36 311	85.96 341	72.45 317	90.25 282	93.07 257	81.24 243	79.85 299	87.29 323	70.93 223	92.52 338	66.95 320	69.23 340	91.11 320

MVS_0304		83.46 270	81.92 273	88.10 269	90.63 299	77.49 268	93.26 211	93.75 247	80.04 256	80.44 289	87.24 324	47.94 350	95.55 298	75.79 266	88.16 217	91.26 314

tpmvs		83.35 273	82.07 271	87.20 291	91.07 279	71.00 328	88.31 314	91.70 290	78.91 268	80.49 288	87.18 325	69.30 249	97.08 225	68.12 316	83.56 259	93.51 256

dp		81.47 290	80.23 287	85.17 314	89.92 314	65.49 348	86.74 325	90.10 325	76.30 296	81.10 279	87.12 326	62.81 296	95.92 285	68.13 315	79.88 310	94.09 224

PM-MVS		78.11 313	76.12 315	84.09 323	83.54 350	70.08 334	88.97 305	85.27 349	79.93 257	74.73 330	86.43 327	34.70 357	93.48 329	79.43 232	72.06 336	88.72 340

DIV-MVS_2432*1600		80.20 301	79.24 298	83.07 325	85.64 344	65.29 349	91.01 272	93.93 239	78.71 275	76.32 320	86.40 328	59.20 322	92.93 336	72.59 288	69.35 339	91.00 322

tpm cat1		81.96 280	80.27 286	87.01 293	91.09 278	71.02 327	87.38 323	91.53 296	66.25 345	80.17 291	86.35 329	68.22 262	96.15 277	69.16 307	82.29 272	93.86 237

pmmvs-eth3d		80.97 296	78.72 305	87.74 274	84.99 347	79.97 211	90.11 288	91.65 292	75.36 304	73.51 335	86.03 330	59.45 320	93.96 325	75.17 272	72.21 335	89.29 335

KD-MVS_2432*1600		78.50 311	76.02 316	85.93 306	86.22 339	74.47 295	84.80 336	92.33 271	79.29 263	76.98 316	85.92 331	53.81 340	93.97 323	67.39 318	57.42 352	89.36 332

miper_refine_blended		78.50 311	76.02 316	85.93 306	86.22 339	74.47 295	84.80 336	92.33 271	79.29 263	76.98 316	85.92 331	53.81 340	93.97 323	67.39 318	57.42 352	89.36 332

ADS-MVSNet2		81.66 286	79.71 294	87.50 280	91.35 268	74.19 299	83.33 343	88.48 340	72.90 327	82.24 267	85.77 333	64.98 288	93.20 333	64.57 332	83.74 255	95.12 176

ADS-MVSNet		81.56 288	79.78 292	86.90 296	91.35 268	71.82 320	83.33 343	89.16 338	72.90 327	82.24 267	85.77 333	64.98 288	93.76 326	64.57 332	83.74 255	95.12 176

N_pmnet		68.89 321	68.44 324	70.23 337	89.07 320	28.79 367	88.06 315	19.50 368	69.47 341	71.86 342	84.93 335	61.24 308	91.75 344	54.70 350	77.15 325	90.15 328

ambc				83.06 326	79.99 354	63.51 352	77.47 352	92.86 260		74.34 333	84.45 336	28.74 358	95.06 312	73.06 287	68.89 343	90.61 324

GG-mvs-BLEND				87.94 273	89.73 317	77.91 254	87.80 317	78.23 360		80.58 286	83.86 337	59.88 318	95.33 307	71.20 293	92.22 166	90.60 326

patchmatchnet-post											83.76 338	71.53 215	96.48 260

PatchT		82.68 276	81.27 278	86.89 297	90.09 310	70.94 329	84.06 340	90.15 323	74.91 310	85.63 187	83.57 339	69.37 245	94.87 314	65.19 328	88.50 211	94.84 189

new-patchmatchnet		76.41 316	75.17 318	80.13 330	82.65 353	59.61 353	87.66 321	91.08 305	78.23 282	69.85 344	83.22 340	54.76 334	91.63 346	64.14 334	64.89 347	89.16 337

PVSNet_0	73.20 20	77.22 314	74.83 319	84.37 319	90.70 297	71.10 326	83.09 345	89.67 335	72.81 329	73.93 334	83.13 341	60.79 311	93.70 327	68.54 310	50.84 355	88.30 344

RPMNet		83.95 266	81.53 276	91.21 163	90.58 300	79.34 224	85.24 334	96.76 72	71.44 335	85.55 188	82.97 342	70.87 224	98.91 86	61.01 342	89.36 198	95.40 170

Patchmatch-RL test		81.67 285	79.96 291	86.81 299	85.42 345	71.23 324	82.17 347	87.50 345	78.47 277	77.19 315	82.50 343	70.81 225	93.48 329	82.66 178	72.89 334	95.71 162

FPMVS		64.63 323	62.55 325	70.88 336	70.80 358	56.71 355	84.42 339	84.42 350	51.78 354	49.57 354	81.61 344	23.49 361	81.48 356	40.61 357	76.25 329	74.46 353

pmmvs3		71.81 320	68.71 323	81.11 329	75.86 356	70.42 332	86.74 325	83.66 351	58.95 351	68.64 347	80.89 345	36.93 356	89.52 349	63.10 337	63.59 348	83.39 347

new_pmnet		72.15 319	70.13 322	78.20 332	82.95 352	65.68 346	83.91 341	82.40 353	62.94 349	64.47 349	79.82 346	42.85 355	86.26 353	57.41 349	74.44 331	82.65 350

UnsupCasMVSNet_bld		76.23 317	73.27 320	85.09 315	83.79 349	72.92 308	85.65 333	93.47 251	71.52 334	68.84 346	79.08 347	49.77 346	93.21 332	66.81 325	60.52 351	89.13 339

DeepMVS_CX				56.31 343	74.23 357	51.81 360		56.67 366	44.85 356	48.54 356	75.16 348	27.87 360	58.74 363	40.92 356	52.22 354	58.39 356

test_method		50.52 328	48.47 330	56.66 342	52.26 366	18.98 369	41.51 361	81.40 355	10.10 362	44.59 357	75.01 349	28.51 359	68.16 359	53.54 351	49.31 356	82.83 349

JIA-IIPM		81.04 294	78.98 304	87.25 287	88.64 323	73.48 304	81.75 348	89.61 336	73.19 324	82.05 269	73.71 350	66.07 283	95.87 288	71.18 295	84.60 248	92.41 293

LCM-MVSNet		66.00 322	62.16 326	77.51 334	64.51 362	58.29 354	83.87 342	90.90 312	48.17 355	54.69 353	73.31 351	16.83 367	86.75 352	65.47 327	61.67 350	87.48 346

PMMVS2		59.60 324	56.40 327	69.21 338	68.83 359	46.58 362	73.02 356	77.48 361	55.07 353	49.21 355	72.95 352	17.43 366	80.04 357	49.32 354	44.33 357	80.99 352

gg-mvs-nofinetune		81.77 283	79.37 296	88.99 246	90.85 291	77.73 263	86.29 328	79.63 358	74.88 312	83.19 258	69.05 353	60.34 314	96.11 278	75.46 269	94.64 125	93.11 272

MVS-HIRNet		73.70 318	72.20 321	78.18 333	91.81 252	56.42 357	82.94 346	82.58 352	55.24 352	68.88 345	66.48 354	55.32 333	95.13 309	58.12 347	88.42 213	83.01 348

PMVS	47.18 22	52.22 327	48.46 331	63.48 340	45.72 367	46.20 363	73.41 355	78.31 359	41.03 358	30.06 361	65.68 355	6.05 368	83.43 355	30.04 359	65.86 345	60.80 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ANet_high		58.88 325	54.22 329	72.86 335	56.50 365	56.67 356	80.75 350	86.00 346	73.09 326	37.39 359	64.63 356	22.17 362	79.49 358	43.51 355	23.96 360	82.43 351

tmp_tt		35.64 332	39.24 334	24.84 346	14.87 368	23.90 368	62.71 357	51.51 367	6.58 364	36.66 360	62.08 357	44.37 354	30.34 365	52.40 352	22.00 362	20.27 360

MVE	39.65 23	43.39 329	38.59 335	57.77 341	56.52 364	48.77 361	55.38 358	58.64 365	29.33 361	28.96 362	52.65 358	4.68 369	64.62 362	28.11 360	33.07 358	59.93 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		57.99 326	54.91 328	67.24 339	88.51 324	65.59 347	52.21 359	90.33 322	43.58 357	42.84 358	51.18 359	20.29 364	85.07 354	34.77 358	70.45 337	51.05 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		43.23 330	42.29 332	46.03 344	65.58 361	37.41 364	73.51 354	64.62 362	33.99 359	28.47 363	47.87 360	19.90 365	67.91 360	22.23 361	24.45 359	32.77 358

EMVS		42.07 331	41.12 333	44.92 345	63.45 363	35.56 366	73.65 353	63.48 363	33.05 360	26.88 364	45.45 361	21.27 363	67.14 361	19.80 362	23.02 361	32.06 359

X-MVStestdata		88.31 163	86.13 205	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 61	23.41 362	85.02 60	99.49 23	91.99 56	98.56 47	98.47 28

test_post											10.29 363	70.57 231	95.91 287

test_post1							88.00 316				9.81 364	69.31 248	95.53 299	76.65 258

testmvs		8.92 335	11.52 338	1.12 349	1.06 369	0.46 371	86.02 329	0.65 370	0.62 365	2.74 366	9.52 365	0.31 372	0.45 367	2.38 364	0.39 364	2.46 363

test123		8.76 336	11.22 339	1.39 348	0.85 370	0.97 370	85.76 332	0.35 371	0.54 366	2.45 367	8.14 366	0.60 371	0.48 366	2.16 365	0.17 365	2.71 362

wuyk23d		21.27 334	20.48 337	23.63 347	68.59 360	36.41 365	49.57 360	6.85 369	9.37 363	7.89 365	4.46 367	4.03 370	31.37 364	17.47 363	16.07 363	3.12 361

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		6.64 338	8.86 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	79.70 117	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

IU-MVS					98.77 4	86.00 54		96.84 62	81.26 242	97.26 6				95.50 7	99.13 3	99.03 4

save fliter					97.85 48	85.63 68	95.21 94	96.82 66	89.44 47

test_0728_SECOND				95.01 15	98.79 1	86.43 41	97.09 11	97.49 5					99.61 3	95.62 5	99.08 7	98.99 5

GSMVS																96.12 141

test_part2					98.55 11	87.22 16				96.40 11

sam_mvs1												71.70 213				96.12 141

sam_mvs												70.60 227

MTGPA								96.97 49

MTMP							96.16 44	60.64 364

test9_res														91.91 61	98.71 30	98.07 63

agg_prior2														90.54 86	98.68 35	98.27 47

agg_prior					97.38 64	85.92 60		96.72 78		92.16 81			98.97 80

test_prior4						85.96 57	94.11 168

test_prior				93.82 64	97.29 69	84.49 85		96.88 59					98.87 88			98.11 61

旧先验2							93.36 203		71.25 336	94.37 26			97.13 221	86.74 129

新几何2							93.11 218

无先验							93.28 210	96.26 107	73.95 318				99.05 60	80.56 216		96.59 127

原ACMM2							92.94 225

testdata2													98.75 100	78.30 242

segment_acmp												87.16 35

testdata1							92.15 248		87.94 89

test12				94.34 53	97.13 74	86.15 51		96.29 105		91.04 105		85.08 58	99.01 70		98.13 61	97.86 79

plane_prior7					94.70 160	82.74 136

plane_prior6					94.52 167	82.75 134						74.23 180

plane_prior5								96.22 112					98.12 137	88.15 109	89.99 185	94.63 196

plane_prior3						82.75 134			90.26 30	86.91 162

plane_prior2							95.85 61		90.81 17

plane_prior1					94.59 165

plane_prior						82.73 137	95.21 94		89.66 43						89.88 190

n2								0.00 372

nn								0.00 372

door-mid								85.49 347

test11								96.57 92

door								85.33 348

HQP5-MVS						81.56 162

HQP-NCC					94.17 180		94.39 150		88.81 64	85.43 200

ACMP_Plane					94.17 180		94.39 150		88.81 64	85.43 200

BP-MVS														87.11 126

HQP4-MVS										85.43 200			97.96 155			94.51 206

HQP3-MVS								96.04 126							89.77 192

HQP2-MVS												73.83 190

MDTV_nov1_ep13_2view						55.91 359	87.62 322		73.32 323	84.59 218		70.33 234		74.65 277		95.50 165

ACMMP++_ref															87.47 226

ACMMP++															88.01 221

Test By Simon												80.02 112

High-resolution multi-view benchmark