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 59	97.19 7	97.47 8	90.27 29	97.64 4	98.13 1	91.47 7

SMA-MVS		95.20 7	95.07 9	95.59 4	98.14 36	88.48 7	96.26 41	97.28 28	85.90 138	97.67 3	98.10 2	88.41 18	99.56 8	94.66 11	99.19 1	98.71 14
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 23	98.26 28	86.29 50	97.46 2	97.40 17	89.03 62	96.20 13	98.10 2	89.39 14	99.34 34	95.88 2	99.03 10	99.10 3

SED-MVS		95.91 1	96.28 1	94.80 34	98.77 4	85.99 56	97.13 10	97.44 12	90.31 27	97.71 1	98.07 4	92.31 3	99.58 6	95.66 3	99.13 3	98.84 10

test_241102_TWO								97.44 12	90.31 27	97.62 5	98.07 4	91.46 9	99.58 6	95.66 3	99.12 6	98.98 7

DVP-MVS		95.67 2	96.02 2	94.64 41	98.78 2	85.93 59	97.09 12	96.73 77	90.27 29	97.04 9	98.05 6	91.47 7	99.55 13	95.62 6	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
test_0728_THIRD									90.75 19	97.04 9	98.05 6	92.09 5	99.55 13	95.64 5	99.13 3	99.13 1

test_241102_ONE					98.77 4	85.99 56		97.44 12	90.26 31	97.71 1	97.96 8	92.31 3	99.38 30

DPE-MVS		95.57 3	95.67 3	95.25 8	98.36 25	87.28 16	95.56 77	97.51 4	89.13 58	97.14 7	97.91 9	91.64 6	99.62 1	94.61 12	99.17 2	98.86 8
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 28	98.17 34	86.65 34	94.82 122	97.17 38	86.26 132	92.83 65	97.87 10	85.57 53	99.56 8	94.37 15	98.92 15	98.34 40
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xxxxxxxxxxxxxcwj		94.65 15	94.70 14	94.48 48	97.85 48	85.63 69	95.21 96	95.47 172	89.44 47	95.71 16	97.70 11	88.28 20	99.35 32	93.89 19	98.78 23	98.48 26

SF-MVS		94.97 10	94.90 12	95.20 9	97.84 50	87.76 9	96.65 29	97.48 7	87.76 98	95.71 16	97.70 11	88.28 20	99.35 32	93.89 19	98.78 23	98.48 26

ACMMP_NAP		94.74 14	94.56 16	95.28 7	98.02 43	87.70 11	95.68 70	97.34 19	88.28 82	95.30 21	97.67 13	85.90 50	99.54 17	93.91 18	98.95 13	98.60 19

zzz-MVS		94.47 18	94.30 21	95.00 17	98.42 20	86.95 19	95.06 108	96.97 49	91.07 13	93.14 57	97.56 14	84.30 68	99.56 8	93.43 24	98.75 29	98.47 30

MTAPA		94.42 24	94.22 25	95.00 17	98.42 20	86.95 19	94.36 158	96.97 49	91.07 13	93.14 57	97.56 14	84.30 68	99.56 8	93.43 24	98.75 29	98.47 30

APD-MVS_3200maxsize		93.78 45	93.77 44	93.80 69	97.92 45	84.19 97	96.30 37	96.87 61	86.96 115	93.92 38	97.47 16	83.88 76	98.96 84	92.71 39	97.87 74	98.26 51

SteuartSystems-ACMMP		95.20 7	95.32 8	94.85 28	96.99 77	86.33 46	97.33 3	97.30 26	91.38 11	95.39 19	97.46 17	88.98 17	99.40 29	94.12 16	98.89 16	98.82 12
Skip Steuart: Steuart Systems R&D Blog.
test1172		93.97 39	94.07 34	93.66 72	98.11 37	83.45 116	96.26 41	96.84 63	88.33 79	94.19 31	97.43 18	84.24 70	99.01 71	93.26 29	97.98 70	98.52 22

SR-MVS-dyc-post		93.82 44	93.82 40	93.82 65	97.92 45	84.57 82	96.28 39	96.76 73	87.46 105	93.75 41	97.43 18	84.24 70	99.01 71	92.73 36	97.80 76	97.88 79

RE-MVS-def			93.68 47		97.92 45	84.57 82	96.28 39	96.76 73	87.46 105	93.75 41	97.43 18	82.94 83		92.73 36	97.80 76	97.88 79

9.14			94.47 17		97.79 52		96.08 51	97.44 12	86.13 136	95.10 23	97.40 21	88.34 19	99.22 47	93.25 30	98.70 34

SR-MVS		94.23 31	94.17 30	94.43 51	98.21 33	85.78 66	96.40 35	96.90 57	88.20 86	94.33 28	97.40 21	84.75 65	99.03 65	93.35 27	97.99 69	98.48 26

DeepC-MVS	88.79 3	93.31 57	92.99 61	94.26 56	96.07 106	85.83 65	94.89 117	96.99 47	89.02 63	89.56 120	97.37 23	82.51 88	99.38 30	92.20 51	98.30 59	97.57 93
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 39	98.43 19	86.22 51	95.30 87	97.78 1	87.45 107	93.26 52	97.33 24	84.62 66	99.51 22	90.75 89	98.57 48	98.32 42

ETH3D-3000-0.1		94.61 16	94.44 18	95.12 12	97.70 56	87.71 10	95.98 57	97.44 12	86.67 124	95.25 22	97.31 25	87.73 26	99.24 45	93.11 33	98.76 28	98.40 37

DeepPCF-MVS	89.96 1	94.20 35	94.77 13	92.49 112	96.52 91	80.00 213	94.00 182	97.08 43	90.05 33	95.65 18	97.29 26	89.66 11	98.97 81	93.95 17	98.71 32	98.50 24

region2R		94.43 22	94.27 24	94.92 21	98.65 7	86.67 33	96.92 20	97.23 32	88.60 73	93.58 48	97.27 27	85.22 57	99.54 17	92.21 50	98.74 31	98.56 21

SD-MVS		94.96 11	95.33 7	93.88 63	97.25 74	86.69 31	96.19 44	97.11 42	90.42 26	96.95 11	97.27 27	89.53 12	96.91 241	94.38 14	98.85 17	98.03 69
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 23	98.63 8	86.69 31	96.94 16	97.32 24	88.63 71	93.53 51	97.26 29	85.04 60	99.54 17	92.35 47	98.78 23	98.50 24

CP-MVS		94.34 26	94.21 27	94.74 38	98.39 23	86.64 35	97.60 1	97.24 30	88.53 75	92.73 70	97.23 30	85.20 58	99.32 38	92.15 53	98.83 19	98.25 52

abl_6		93.18 62	93.05 59	93.57 74	97.52 61	84.27 96	95.53 78	96.67 86	87.85 95	93.20 55	97.22 31	80.35 110	99.18 50	91.91 64	97.21 87	97.26 103

HFP-MVS		94.52 17	94.40 19	94.86 26	98.61 9	86.81 25	96.94 16	97.34 19	88.63 71	93.65 44	97.21 32	86.10 46	99.49 24	92.35 47	98.77 26	98.30 43

#test#		94.32 28	94.14 31	94.86 26	98.61 9	86.81 25	96.43 32	97.34 19	87.51 104	93.65 44	97.21 32	86.10 46	99.49 24	91.68 71	98.77 26	98.30 43

MP-MVS		94.25 29	94.07 34	94.77 36	98.47 17	86.31 48	96.71 27	96.98 48	89.04 60	91.98 87	97.19 34	85.43 55	99.56 8	92.06 58	98.79 21	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 30	94.07 34	94.75 37	98.06 41	86.90 22	95.88 61	96.94 54	85.68 144	95.05 24	97.18 35	87.31 32	99.07 59	91.90 67	98.61 47	98.28 47
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 42	98.50 15	85.90 64	96.87 22	96.91 56	88.70 69	91.83 93	97.17 36	83.96 75	99.55 13	91.44 76	98.64 45	98.43 36

XVS		94.45 20	94.32 20	94.85 28	98.54 12	86.60 37	96.93 18	97.19 35	90.66 23	92.85 63	97.16 37	85.02 61	99.49 24	91.99 59	98.56 49	98.47 30

HPM-MVS_fast		93.40 56	93.22 55	93.94 62	98.36 25	84.83 77	97.15 9	96.80 69	85.77 141	92.47 78	97.13 38	82.38 89	99.07 59	90.51 91	98.40 55	97.92 77

ETH3D cwj APD-0.16		93.91 41	93.53 50	95.06 14	96.76 82	87.78 8	94.92 115	97.21 34	84.33 174	93.89 39	97.09 39	87.20 34	99.29 42	91.90 67	98.44 53	98.12 61

OPU-MVS				96.21 2	98.00 44	90.85 2	97.13 10				97.08 40	92.59 2	98.94 85	92.25 49	98.99 12	98.84 10

CNVR-MVS		95.40 6	95.37 6	95.50 6	98.11 37	88.51 6	95.29 90	96.96 52	92.09 3	95.32 20	97.08 40	89.49 13	99.33 37	95.10 9	98.85 17	98.66 16

PC_three_1452									82.47 213	97.09 8	97.07 42	92.72 1	98.04 152	92.70 40	99.02 11	98.86 8

ZNCC-MVS		94.47 18	94.28 22	95.03 15	98.52 14	86.96 18	96.85 24	97.32 24	88.24 83	93.15 56	97.04 43	86.17 45	99.62 1	92.40 45	98.81 20	98.52 22

testtj		94.39 25	94.18 29	95.00 17	98.24 31	86.77 29	96.16 45	97.23 32	87.28 109	94.85 25	97.04 43	86.99 38	99.52 21	91.54 73	98.33 58	98.71 14

ACMMP		93.24 60	92.88 64	94.30 55	98.09 40	85.33 73	96.86 23	97.45 11	88.33 79	90.15 116	97.03 45	81.44 103	99.51 22	90.85 87	95.74 110	98.04 68
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 34	97.48 63	86.78 27	95.65 74	96.89 58	89.40 50	92.81 66	96.97 46	85.37 56	99.24 45	90.87 86	98.69 35	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
TSAR-MVS + MP.		94.85 12	94.94 10	94.58 44	98.25 29	86.33 46	96.11 50	96.62 90	88.14 89	96.10 14	96.96 47	89.09 16	98.94 85	94.48 13	98.68 37	98.48 26
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	92.65 104	97.31 68	83.43 117	95.79 65	97.33 22	90.03 34	93.58 48	96.96 47	84.87 63	97.76 168	92.19 52	98.66 42	96.76 123

ZD-MVS					98.15 35	86.62 36		97.07 44	83.63 187	94.19 31	96.91 49	87.57 30	99.26 44	91.99 59	98.44 53

VDDNet		89.56 129	88.49 142	92.76 97	95.07 142	82.09 153	96.30 37	93.19 258	81.05 250	91.88 89	96.86 50	61.16 313	98.33 128	88.43 111	92.49 168	97.84 82

VDD-MVS		90.74 99	89.92 111	93.20 78	96.27 97	83.02 128	95.73 67	93.86 246	88.42 78	92.53 75	96.84 51	62.09 303	98.64 105	90.95 84	92.62 165	97.93 76

GST-MVS		94.21 33	93.97 38	94.90 25	98.41 22	86.82 24	96.54 31	97.19 35	88.24 83	93.26 52	96.83 52	85.48 54	99.59 5	91.43 77	98.40 55	98.30 43

HPM-MVS++		95.14 9	94.91 11	95.83 3	98.25 29	89.65 3	95.92 60	96.96 52	91.75 7	94.02 36	96.83 52	88.12 22	99.55 13	93.41 26	98.94 14	98.28 47

旧先验1					96.79 81	81.81 159		95.67 156			96.81 54	86.69 39			97.66 80	96.97 117

LFMVS		90.08 114	89.13 127	92.95 90	96.71 83	82.32 151	96.08 51	89.91 333	86.79 120	92.15 84	96.81 54	62.60 300	98.34 126	87.18 127	93.90 139	98.19 55

HPM-MVS		94.02 37	93.88 39	94.43 51	98.39 23	85.78 66	97.25 6	97.07 44	86.90 119	92.62 74	96.80 56	84.85 64	99.17 51	92.43 43	98.65 44	98.33 41
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 49	93.81 68	95.36 129	84.61 80	94.68 130	95.83 145	91.27 12	93.60 47	96.71 57	85.75 51	98.86 92	92.87 34	96.65 98	97.96 73

Regformer-4		93.91 41	93.81 41	94.19 58	95.36 129	85.47 71	94.68 130	96.41 102	91.60 10	93.75 41	96.71 57	85.95 49	99.10 58	93.21 31	96.65 98	98.01 71

MSP-MVS		95.42 5	95.56 5	94.98 20	98.49 16	86.52 39	96.91 21	97.47 8	91.73 8	96.10 14	96.69 59	89.90 10	99.30 40	94.70 10	98.04 68	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				90.49 195	96.40 93	77.89 259		95.37 184	72.51 333	93.63 46	96.69 59	82.08 97	97.65 177	83.08 172	97.39 85	95.94 154

EI-MVSNet-Vis-set		93.01 64	92.92 63	93.29 75	95.01 143	83.51 115	94.48 142	95.77 149	90.87 15	92.52 76	96.67 61	84.50 67	99.00 76	91.99 59	94.44 135	97.36 99

3Dnovator	86.66 5	91.73 83	90.82 94	94.44 49	94.59 167	86.37 44	97.18 8	97.02 46	89.20 55	84.31 235	96.66 62	73.74 195	99.17 51	86.74 133	97.96 71	97.79 85

CDPH-MVS		92.83 65	92.30 73	94.44 49	97.79 52	86.11 53	94.06 177	96.66 87	80.09 258	92.77 67	96.63 63	86.62 40	99.04 64	87.40 123	98.66 42	98.17 56

3Dnovator+	87.14 4	92.42 74	91.37 82	95.55 5	95.63 123	88.73 5	97.07 14	96.77 72	90.84 16	84.02 240	96.62 64	75.95 160	99.34 34	87.77 118	97.68 79	98.59 20

EI-MVSNet-UG-set		92.74 67	92.62 69	93.12 81	94.86 155	83.20 122	94.40 150	95.74 152	90.71 22	92.05 86	96.60 65	84.00 74	98.99 78	91.55 72	93.63 143	97.17 108

NCCC		94.81 13	94.69 15	95.17 11	97.83 51	87.46 15	95.66 72	96.93 55	92.34 2	93.94 37	96.58 66	87.74 25	99.44 28	92.83 35	98.40 55	98.62 18

Vis-MVSNet		91.75 82	91.23 85	93.29 75	95.32 132	83.78 107	96.14 47	95.98 131	89.89 36	90.45 112	96.58 66	75.09 172	98.31 130	84.75 154	96.90 92	97.78 86
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 47	95.54 125	86.36 45	94.57 138	96.44 99	91.69 9	94.32 29	96.56 68	87.05 37	99.03 65	93.35 27	97.65 81	98.15 58

Regformer-2		94.33 27	94.22 25	94.68 39	95.54 125	86.75 30	94.57 138	96.70 82	91.84 6	94.41 26	96.56 68	87.19 35	99.13 55	93.50 22	97.65 81	98.16 57

UA-Net		92.83 65	92.54 70	93.68 71	96.10 104	84.71 79	95.66 72	96.39 104	91.92 4	93.22 54	96.49 70	83.16 80	98.87 89	84.47 157	95.47 115	97.45 98

MG-MVS		91.77 81	91.70 80	92.00 133	97.08 76	80.03 211	93.60 199	95.18 192	87.85 95	90.89 109	96.47 71	82.06 98	98.36 123	85.07 148	97.04 91	97.62 89

CPTT-MVS		91.99 77	91.80 78	92.55 109	98.24 31	81.98 156	96.76 26	96.49 98	81.89 230	90.24 114	96.44 72	78.59 134	98.61 108	89.68 96	97.85 75	97.06 112

test_prior3		93.60 50	93.53 50	93.82 65	97.29 70	84.49 86	94.12 168	96.88 59	87.67 101	92.63 72	96.39 73	86.62 40	98.87 89	91.50 74	98.67 39	98.11 63

test_prior2							94.12 168		87.67 101	92.63 72	96.39 73	86.62 40		91.50 74	98.67 39

MCST-MVS		94.45 20	94.20 28	95.19 10	98.46 18	87.50 14	95.00 110	97.12 40	87.13 111	92.51 77	96.30 75	89.24 15	99.34 34	93.46 23	98.62 46	98.73 13

ETH3 D test6400		93.64 49	93.22 55	94.92 21	97.79 52	86.84 23	95.31 84	97.26 29	82.67 211	93.81 40	96.29 76	87.29 33	99.27 43	89.87 95	98.67 39	98.65 17

PHI-MVS		93.89 43	93.65 48	94.62 43	96.84 80	86.43 42	96.69 28	97.49 5	85.15 160	93.56 50	96.28 77	85.60 52	99.31 39	92.45 42	98.79 21	98.12 61

新几何1				93.10 82	97.30 69	84.35 95		95.56 164	71.09 340	91.26 104	96.24 78	82.87 85	98.86 92	79.19 239	98.10 66	96.07 150

agg_prior1		93.29 58	92.97 62	94.26 56	97.38 65	85.92 61	93.92 186	96.72 79	81.96 225	92.16 82	96.23 79	87.85 23	98.97 81	91.95 63	98.55 51	97.90 78

1121		90.42 109	89.49 115	93.20 78	97.27 72	84.46 89	92.63 234	95.51 170	71.01 341	91.20 105	96.21 80	82.92 84	99.05 61	80.56 220	98.07 67	96.10 148

TEST9					97.53 59	86.49 40	94.07 175	96.78 70	81.61 238	92.77 67	96.20 81	87.71 27	99.12 56

train_agg		93.44 53	93.08 58	94.52 46	97.53 59	86.49 40	94.07 175	96.78 70	81.86 231	92.77 67	96.20 81	87.63 28	99.12 56	92.14 54	98.69 35	97.94 74

test_8					97.49 62	86.30 49	94.02 180	96.76 73	81.86 231	92.70 71	96.20 81	87.63 28	99.02 69

QAPM		89.51 130	88.15 151	93.59 73	94.92 150	84.58 81	96.82 25	96.70 82	78.43 281	83.41 256	96.19 84	73.18 203	99.30 40	77.11 259	96.54 101	96.89 121

casdiffmvs		92.51 72	92.43 72	92.74 99	94.41 176	81.98 156	94.54 140	96.23 114	89.57 45	91.96 88	96.17 85	82.58 87	98.01 155	90.95 84	95.45 117	98.23 53

test222					96.55 89	81.70 161	92.22 248	95.01 199	68.36 346	90.20 115	96.14 86	80.26 113			97.80 76	96.05 152

OMC-MVS		91.23 91	90.62 96	93.08 83	96.27 97	84.07 99	93.52 201	95.93 135	86.95 116	89.51 121	96.13 87	78.50 136	98.35 125	85.84 141	92.90 161	96.83 122

OpenMVS	83.78 11	88.74 155	87.29 170	93.08 83	92.70 232	85.39 72	96.57 30	96.43 101	78.74 277	80.85 285	96.07 88	69.64 245	99.01 71	78.01 250	96.65 98	94.83 194

baseline		92.39 75	92.29 74	92.69 103	94.46 173	81.77 160	94.14 167	96.27 109	89.22 54	91.88 89	96.00 89	82.35 90	97.99 157	91.05 80	95.27 122	98.30 43

IS-MVSNet		91.43 87	91.09 89	92.46 113	95.87 116	81.38 172	96.95 15	93.69 251	89.72 43	89.50 122	95.98 90	78.57 135	97.77 167	83.02 174	96.50 103	98.22 54

LS3D		87.89 175	86.32 202	92.59 107	96.07 106	82.92 132	95.23 94	94.92 207	75.66 304	82.89 263	95.98 90	72.48 211	99.21 48	68.43 316	95.23 123	95.64 167

原ACMM1				92.01 131	97.34 67	81.05 181		96.81 68	78.89 272	90.45 112	95.92 92	82.65 86	98.84 97	80.68 218	98.26 61	96.14 143

VNet		92.24 76	91.91 77	93.24 77	96.59 87	83.43 117	94.84 121	96.44 99	89.19 56	94.08 35	95.90 93	77.85 145	98.17 136	88.90 105	93.38 151	98.13 60

CANet		93.54 51	93.20 57	94.55 45	95.65 122	85.73 68	94.94 113	96.69 84	91.89 5	90.69 110	95.88 94	81.99 100	99.54 17	93.14 32	97.95 72	98.39 38

MVS_111021_HR		93.45 52	93.31 53	93.84 64	96.99 77	84.84 76	93.24 216	97.24 30	88.76 68	91.60 98	95.85 95	86.07 48	98.66 103	91.91 64	98.16 63	98.03 69

DP-MVS Recon		91.95 78	91.28 84	93.96 61	98.33 27	85.92 61	94.66 133	96.66 87	82.69 210	90.03 118	95.82 96	82.30 92	99.03 65	84.57 156	96.48 104	96.91 120

DROMVSNet		93.44 53	93.71 46	92.63 105	95.21 137	82.43 146	97.27 5	96.71 81	90.57 25	92.88 62	95.80 97	83.16 80	98.16 137	93.68 21	98.14 64	97.31 100

EPNet		91.79 80	91.02 90	94.10 59	90.10 312	85.25 74	96.03 54	92.05 283	92.83 1	87.39 157	95.78 98	79.39 125	99.01 71	88.13 115	97.48 83	98.05 67
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-OURS		89.40 138	88.70 136	91.52 155	94.06 186	81.46 169	91.27 269	96.07 125	86.14 135	88.89 131	95.77 99	68.73 260	97.26 215	87.39 124	89.96 191	95.83 160

XVG-OURS-SEG-HR		89.95 119	89.45 116	91.47 159	94.00 192	81.21 178	91.87 256	96.06 127	85.78 140	88.55 133	95.73 100	74.67 179	97.27 213	88.71 108	89.64 198	95.91 155

MVS_111021_LR		92.47 73	92.29 74	92.98 88	95.99 110	84.43 93	93.08 221	96.09 123	88.20 86	91.12 106	95.72 101	81.33 105	97.76 168	91.74 69	97.37 86	96.75 124

CSCG		93.23 61	93.05 59	93.76 70	98.04 42	84.07 99	96.22 43	97.37 18	84.15 176	90.05 117	95.66 102	87.77 24	99.15 54	89.91 94	98.27 60	98.07 65

hse-mvs3		90.80 97	90.15 103	92.75 98	96.01 108	82.66 142	95.43 80	95.53 168	89.80 38	93.08 59	95.64 103	75.77 161	99.00 76	92.07 56	78.05 324	96.60 129

EPP-MVSNet		91.70 84	91.56 81	92.13 128	95.88 114	80.50 198	97.33 3	95.25 188	86.15 134	89.76 119	95.60 104	83.42 79	98.32 129	87.37 125	93.25 154	97.56 94

TSAR-MVS + GP.		93.66 48	93.41 52	94.41 53	96.59 87	86.78 27	94.40 150	93.93 242	89.77 41	94.21 30	95.59 105	87.35 31	98.61 108	92.72 38	96.15 107	97.83 83

Anonymous202405211		87.68 181	86.13 207	92.31 122	96.66 84	80.74 191	94.87 119	91.49 300	80.47 254	89.46 123	95.44 106	54.72 338	98.23 132	82.19 189	89.89 193	97.97 72

TAPA-MVS	84.62 6	88.16 169	87.01 177	91.62 152	96.64 85	80.65 192	94.39 152	96.21 118	76.38 297	86.19 180	95.44 106	79.75 118	98.08 148	62.75 342	95.29 120	96.13 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		90.12 113	89.56 114	91.82 145	93.14 218	83.90 103	94.16 166	95.74 152	88.96 64	87.86 144	95.43 108	72.48 211	97.91 163	88.10 116	90.18 188	93.65 254
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet (Re-imp)		89.59 128	89.44 117	90.03 215	95.74 118	75.85 290	95.61 75	90.80 318	87.66 103	87.83 146	95.40 109	76.79 150	96.46 267	78.37 244	96.73 95	97.80 84

EI-MVSNet		89.10 143	88.86 135	89.80 226	91.84 253	78.30 249	93.70 196	95.01 199	85.73 142	87.15 158	95.28 110	79.87 117	97.21 220	83.81 165	87.36 233	93.88 238

CVMVSNet		84.69 262	84.79 245	84.37 322	91.84 253	64.92 353	93.70 196	91.47 301	66.19 349	86.16 181	95.28 110	67.18 269	93.33 335	80.89 214	90.42 185	94.88 192

114514_t		89.51 130	88.50 140	92.54 110	98.11 37	81.99 155	95.16 101	96.36 106	70.19 343	85.81 184	95.25 112	76.70 152	98.63 106	82.07 191	96.86 94	97.00 116

RPSCF		85.07 254	84.27 251	87.48 285	92.91 229	70.62 334	91.69 263	92.46 272	76.20 301	82.67 266	95.22 113	63.94 296	97.29 212	77.51 255	85.80 244	94.53 208

Anonymous20240529		88.09 171	86.59 192	92.58 108	96.53 90	81.92 158	95.99 55	95.84 144	74.11 320	89.06 129	95.21 114	61.44 308	98.81 98	83.67 168	87.47 230	97.01 115

LPG-MVS_test		89.45 133	88.90 133	91.12 169	94.47 171	81.49 167	95.30 87	96.14 120	86.73 122	85.45 200	95.16 115	69.89 241	98.10 140	87.70 119	89.23 205	93.77 248

LGP-MVS_train				91.12 169	94.47 171	81.49 167		96.14 120	86.73 122	85.45 200	95.16 115	69.89 241	98.10 140	87.70 119	89.23 205	93.77 248

CNLPA		89.07 144	87.98 155	92.34 120	96.87 79	84.78 78	94.08 174	93.24 256	81.41 241	84.46 225	95.13 117	75.57 168	96.62 251	77.21 257	93.84 141	95.61 168

DELS-MVS		93.43 55	93.25 54	93.97 60	95.42 128	85.04 75	93.06 223	97.13 39	90.74 20	91.84 91	95.09 118	86.32 44	99.21 48	91.22 78	98.45 52	97.65 88
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 79	95.08 13	96.19 99	89.31 4	92.66 233	96.56 96	83.44 193	91.68 97	95.04 119	86.60 43	98.99 78	85.60 144	97.92 73	96.93 119

DP-MVS		87.25 203	85.36 233	92.90 92	97.65 57	83.24 121	94.81 123	92.00 285	74.99 312	81.92 275	95.00 120	72.66 208	99.05 61	66.92 327	92.33 169	96.40 134

diffmvs		91.37 89	91.23 85	91.77 148	93.09 220	80.27 201	92.36 243	95.52 169	87.03 114	91.40 102	94.93 121	80.08 114	97.44 194	92.13 55	94.56 131	97.61 90

MVSFormer		91.68 85	91.30 83	92.80 95	93.86 197	83.88 104	95.96 58	95.90 139	84.66 170	91.76 94	94.91 122	77.92 142	97.30 209	89.64 97	97.11 88	97.24 104

jason		90.80 97	90.10 104	92.90 92	93.04 223	83.53 114	93.08 221	94.15 236	80.22 255	91.41 101	94.91 122	76.87 148	97.93 162	90.28 93	96.90 92	97.24 104
jason: jason.
alignmvs		93.08 63	92.50 71	94.81 33	95.62 124	87.61 13	95.99 55	96.07 125	89.77 41	94.12 33	94.87 124	80.56 109	98.66 103	92.42 44	93.10 157	98.15 58

HQP_MVS		90.60 107	90.19 101	91.82 145	94.70 163	82.73 138	95.85 62	96.22 115	90.81 17	86.91 165	94.86 125	74.23 183	98.12 138	88.15 113	89.99 189	94.63 200

plane_prior4											94.86 125

nrg030		91.08 95	90.39 97	93.17 80	93.07 221	86.91 21	96.41 34	96.26 110	88.30 81	88.37 137	94.85 127	82.19 95	97.64 179	91.09 79	82.95 268	94.96 187

BH-RMVSNet		88.37 163	87.48 165	91.02 177	95.28 133	79.45 223	92.89 228	93.07 260	85.45 151	86.91 165	94.84 128	70.35 236	97.76 168	73.97 285	94.59 130	95.85 158

PAPM_NR		91.22 92	90.78 95	92.52 111	97.60 58	81.46 169	94.37 157	96.24 113	86.39 130	87.41 154	94.80 129	82.06 98	98.48 114	82.80 180	95.37 118	97.61 90

GeoE		90.05 115	89.43 118	91.90 141	95.16 139	80.37 200	95.80 64	94.65 221	83.90 181	87.55 153	94.75 130	78.18 140	97.62 181	81.28 206	93.63 143	97.71 87

test_yl		90.69 101	90.02 109	92.71 100	95.72 119	82.41 149	94.11 170	95.12 194	85.63 145	91.49 99	94.70 131	74.75 176	98.42 121	86.13 139	92.53 166	97.31 100

DCV-MVSNet		90.69 101	90.02 109	92.71 100	95.72 119	82.41 149	94.11 170	95.12 194	85.63 145	91.49 99	94.70 131	74.75 176	98.42 121	86.13 139	92.53 166	97.31 100

FIs		90.51 108	90.35 98	90.99 180	93.99 193	80.98 183	95.73 67	97.54 3	89.15 57	86.72 169	94.68 133	81.83 102	97.24 217	85.18 147	88.31 220	94.76 197

FC-MVSNet-test		90.27 111	90.18 102	90.53 191	93.71 203	79.85 217	95.77 66	97.59 2	89.31 52	86.27 178	94.67 134	81.93 101	97.01 235	84.26 159	88.09 224	94.71 198

AdaColmap		89.89 122	89.07 128	92.37 119	97.41 64	83.03 127	94.42 149	95.92 136	82.81 208	86.34 177	94.65 135	73.89 191	99.02 69	80.69 217	95.51 113	95.05 182

F-COLMAP		87.95 174	86.80 182	91.40 161	96.35 96	80.88 187	94.73 128	95.45 176	79.65 264	82.04 273	94.61 136	71.13 222	98.50 113	76.24 267	91.05 180	94.80 196

canonicalmvs		93.27 59	92.75 66	94.85 28	95.70 121	87.66 12	96.33 36	96.41 102	90.00 35	94.09 34	94.60 137	82.33 91	98.62 107	92.40 45	92.86 162	98.27 49

tttt0517		88.61 158	87.78 159	91.11 172	94.96 147	77.81 262	95.35 82	89.69 337	85.09 162	88.05 142	94.59 138	66.93 272	98.48 114	83.27 171	92.13 171	97.03 114

VPNet		88.20 168	87.47 166	90.39 199	93.56 208	79.46 222	94.04 178	95.54 167	88.67 70	86.96 162	94.58 139	69.33 249	97.15 222	84.05 162	80.53 307	94.56 207

CS-MVS		92.55 70	92.87 65	91.58 154	94.21 182	80.54 196	95.30 87	96.68 85	88.18 88	92.09 85	94.57 140	84.06 72	98.05 151	92.56 41	98.19 62	96.15 141

UniMVSNet_ETH3D		87.53 192	86.37 198	91.00 179	92.44 236	78.96 236	94.74 127	95.61 162	84.07 178	85.36 210	94.52 141	59.78 322	97.34 208	82.93 175	87.88 227	96.71 126

PVSNet_Blended_VisFu		91.38 88	90.91 92	92.80 95	96.39 94	83.17 123	94.87 119	96.66 87	83.29 197	89.27 125	94.46 142	80.29 112	99.17 51	87.57 121	95.37 118	96.05 152

CS-MVS-test		92.55 70	92.72 67	92.02 130	94.87 153	81.34 173	96.43 32	96.57 94	89.04 60	91.05 107	94.41 143	83.85 77	98.09 146	90.83 88	97.47 84	96.64 128

ACMM	84.12 9	89.14 142	88.48 143	91.12 169	94.65 166	81.22 177	95.31 84	96.12 122	85.31 155	85.92 183	94.34 144	70.19 239	98.06 150	85.65 143	88.86 210	94.08 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PCF-MVS	84.11 10	87.74 180	86.08 211	92.70 102	94.02 188	84.43 93	89.27 301	95.87 142	73.62 324	84.43 227	94.33 145	78.48 137	98.86 92	70.27 302	94.45 134	94.81 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WTY-MVS		89.60 127	88.92 132	91.67 151	95.47 127	81.15 179	92.38 242	94.78 217	83.11 200	89.06 129	94.32 146	78.67 133	96.61 254	81.57 203	90.89 182	97.24 104

ACMP	84.23 8	89.01 148	88.35 144	90.99 180	94.73 160	81.27 174	95.07 105	95.89 141	86.48 126	83.67 249	94.30 147	69.33 249	97.99 157	87.10 132	88.55 212	93.72 252
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cdsmvs_eth3d_5k		22.14 335	29.52 338	0.00 353	0.00 374	0.00 375	0.00 364	95.76 150	0.00 370	0.00 371	94.29 148	75.66 167	0.00 372	0.00 370	0.00 370	0.00 368

PS-MVSNAJss		89.97 118	89.62 113	91.02 177	91.90 251	80.85 188	95.26 93	95.98 131	86.26 132	86.21 179	94.29 148	79.70 120	97.65 177	88.87 106	88.10 222	94.57 206

lupinMVS		90.92 96	90.21 100	93.03 86	93.86 197	83.88 104	92.81 230	93.86 246	79.84 261	91.76 94	94.29 148	77.92 142	98.04 152	90.48 92	97.11 88	97.17 108

API-MVS		90.66 103	90.07 105	92.45 114	96.36 95	84.57 82	96.06 53	95.22 191	82.39 214	89.13 126	94.27 151	80.32 111	98.46 116	80.16 227	96.71 96	94.33 218

CANet_DTU		90.26 112	89.41 119	92.81 94	93.46 211	83.01 129	93.48 202	94.47 224	89.43 49	87.76 149	94.23 152	70.54 235	99.03 65	84.97 149	96.39 105	96.38 135

PLC	84.53 7	89.06 145	88.03 153	92.15 127	97.27 72	82.69 141	94.29 160	95.44 178	79.71 263	84.01 241	94.18 153	76.68 153	98.75 101	77.28 256	93.41 150	95.02 183
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
xiu_mvs_v1_base_debu		90.64 104	90.05 106	92.40 115	93.97 194	84.46 89	93.32 206	95.46 173	85.17 157	92.25 79	94.03 154	70.59 231	98.57 110	90.97 81	94.67 126	94.18 221

xiu_mvs_v1_base		90.64 104	90.05 106	92.40 115	93.97 194	84.46 89	93.32 206	95.46 173	85.17 157	92.25 79	94.03 154	70.59 231	98.57 110	90.97 81	94.67 126	94.18 221

xiu_mvs_v1_base_debi		90.64 104	90.05 106	92.40 115	93.97 194	84.46 89	93.32 206	95.46 173	85.17 157	92.25 79	94.03 154	70.59 231	98.57 110	90.97 81	94.67 126	94.18 221

jajsoiax		88.24 167	87.50 164	90.48 196	90.89 292	80.14 204	95.31 84	95.65 160	84.97 164	84.24 237	94.02 157	65.31 289	97.42 196	88.56 109	88.52 214	93.89 236

XXY-MVS		87.65 183	86.85 180	90.03 215	92.14 242	80.60 195	93.76 192	95.23 189	82.94 205	84.60 220	94.02 157	74.27 182	95.49 308	81.04 209	83.68 261	94.01 233

baseline1		88.10 170	87.28 171	90.57 189	94.96 147	80.07 207	94.27 161	91.29 305	86.74 121	87.41 154	94.00 159	76.77 151	96.20 278	80.77 215	79.31 320	95.44 172

NP-MVS					94.37 177	82.42 147					93.98 160

HQP-MVS		89.80 124	89.28 124	91.34 163	94.17 183	81.56 163	94.39 152	96.04 129	88.81 65	85.43 203	93.97 161	73.83 193	97.96 159	87.11 130	89.77 196	94.50 211

mvs_tets		88.06 173	87.28 171	90.38 201	90.94 288	79.88 215	95.22 95	95.66 158	85.10 161	84.21 238	93.94 162	63.53 297	97.40 203	88.50 110	88.40 218	93.87 239

CHOSEN 1792x2688		88.84 152	87.69 160	92.30 123	96.14 100	81.42 171	90.01 291	95.86 143	74.52 317	87.41 154	93.94 162	75.46 169	98.36 123	80.36 223	95.53 112	97.12 111

UGNet		89.95 119	88.95 131	92.95 90	94.51 170	83.31 120	95.70 69	95.23 189	89.37 51	87.58 151	93.94 162	64.00 295	98.78 100	83.92 163	96.31 106	96.74 125
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		89.21 141	88.29 148	91.96 136	93.71 203	82.62 144	93.30 210	94.19 234	82.22 218	87.78 148	93.94 162	78.83 129	96.95 238	77.70 252	92.98 160	96.32 136

sss		88.93 150	88.26 150	90.94 183	94.05 187	80.78 190	91.71 261	95.38 182	81.55 239	88.63 132	93.91 166	75.04 173	95.47 309	82.47 184	91.61 173	96.57 131

1112_ss		88.42 161	87.33 169	91.72 149	94.92 150	80.98 183	92.97 226	94.54 222	78.16 286	83.82 245	93.88 167	78.78 131	97.91 163	79.45 234	89.41 200	96.26 139

ab-mvs-re		7.82 339	10.43 342	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	93.88 167	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

TranMVSNet+NR-MVSNet		88.84 152	87.95 156	91.49 157	92.68 233	83.01 129	94.92 115	96.31 107	89.88 37	85.53 193	93.85 169	76.63 154	96.96 237	81.91 195	79.87 315	94.50 211

RRT_MVS		88.86 151	87.68 161	92.39 118	92.02 248	86.09 54	94.38 156	94.94 202	85.45 151	87.14 160	93.84 170	65.88 287	97.11 226	88.73 107	86.77 240	93.98 234

mvs_anonymous		89.37 139	89.32 122	89.51 237	93.47 210	74.22 301	91.65 264	94.83 213	82.91 206	85.45 200	93.79 171	81.23 106	96.36 273	86.47 137	94.09 137	97.94 74

thisisatest0530		88.67 156	87.61 163	91.86 142	94.87 153	80.07 207	94.63 134	89.90 334	84.00 179	88.46 135	93.78 172	66.88 274	98.46 116	83.30 170	92.65 164	97.06 112

MVS_Test		91.31 90	91.11 87	91.93 138	94.37 177	80.14 204	93.46 204	95.80 147	86.46 127	91.35 103	93.77 173	82.21 94	98.09 146	87.57 121	94.95 124	97.55 95

COLMAP_ROB	80.39 16	83.96 267	82.04 274	89.74 227	95.28 133	79.75 218	94.25 162	92.28 277	75.17 310	78.02 313	93.77 173	58.60 327	97.84 165	65.06 335	85.92 242	91.63 311
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PAPR		90.02 116	89.27 125	92.29 124	95.78 117	80.95 185	92.68 232	96.22 115	81.91 228	86.66 170	93.75 175	82.23 93	98.44 120	79.40 238	94.79 125	97.48 96

test_part1		89.00 149	87.99 154	92.04 129	95.94 113	83.81 106	96.14 47	96.05 128	86.44 128	85.69 187	93.73 176	71.57 217	97.66 175	85.80 142	80.54 305	94.66 199

ab-mvs		89.41 136	88.35 144	92.60 106	95.15 141	82.65 143	92.20 249	95.60 163	83.97 180	88.55 133	93.70 177	74.16 187	98.21 135	82.46 185	89.37 201	96.94 118

hse-mvs2		89.88 123	89.34 121	91.51 156	94.83 157	81.12 180	93.94 185	93.91 245	89.80 38	93.08 59	93.60 178	75.77 161	97.66 175	92.07 56	77.07 331	95.74 164

AUN-MVS		87.78 179	86.54 194	91.48 158	94.82 158	81.05 181	93.91 189	93.93 242	83.00 203	86.93 163	93.53 179	69.50 247	97.67 174	86.14 138	77.12 330	95.73 165

BH-untuned		88.60 159	88.13 152	90.01 217	95.24 136	78.50 244	93.29 211	94.15 236	84.75 168	84.46 225	93.40 180	75.76 163	97.40 203	77.59 253	94.52 132	94.12 225

AllTest		83.42 273	81.39 279	89.52 235	95.01 143	77.79 263	93.12 218	90.89 316	77.41 289	76.12 325	93.34 181	54.08 341	97.51 187	68.31 317	84.27 255	93.26 266

TestCases				89.52 235	95.01 143	77.79 263		90.89 316	77.41 289	76.12 325	93.34 181	54.08 341	97.51 187	68.31 317	84.27 255	93.26 266

UniMVSNet_NR-MVSNet		89.92 121	89.29 123	91.81 147	93.39 212	83.72 108	94.43 148	97.12 40	89.80 38	86.46 172	93.32 183	83.16 80	97.23 218	84.92 150	81.02 297	94.49 213

VPA-MVSNet		89.62 126	88.96 130	91.60 153	93.86 197	82.89 133	95.46 79	97.33 22	87.91 92	88.43 136	93.31 184	74.17 186	97.40 203	87.32 126	82.86 273	94.52 209

ITE_SJBPF				88.24 268	91.88 252	77.05 277		92.92 262	85.54 148	80.13 297	93.30 185	57.29 330	96.20 278	72.46 293	84.71 251	91.49 313

DU-MVS		89.34 140	88.50 140	91.85 144	93.04 223	83.72 108	94.47 145	96.59 92	89.50 46	86.46 172	93.29 186	77.25 146	97.23 218	84.92 150	81.02 297	94.59 204

NR-MVSNet		88.58 160	87.47 166	91.93 138	93.04 223	84.16 98	94.77 126	96.25 112	89.05 59	80.04 299	93.29 186	79.02 128	97.05 232	81.71 202	80.05 312	94.59 204

CDS-MVSNet		89.45 133	88.51 139	92.29 124	93.62 206	83.61 113	93.01 224	94.68 220	81.95 226	87.82 147	93.24 188	78.69 132	96.99 236	80.34 224	93.23 155	96.28 138
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		86.68 223	85.39 231	90.53 191	93.05 222	79.33 230	89.79 294	94.77 218	78.82 274	81.95 274	93.24 188	76.81 149	97.30 209	66.94 325	93.16 156	94.95 190

OurMVSNet-221017-0		85.35 248	84.64 248	87.49 284	90.77 296	72.59 318	94.01 181	94.40 226	84.72 169	79.62 305	93.17 190	61.91 305	96.72 246	81.99 193	81.16 291	93.16 274

PEN-MVS		86.80 218	86.27 204	88.40 262	92.32 239	75.71 292	95.18 99	96.38 105	87.97 90	82.82 264	93.15 191	73.39 201	95.92 289	76.15 268	79.03 322	93.59 255

xiu_mvs_v2_base		91.13 94	90.89 93	91.86 142	94.97 146	82.42 147	92.24 247	95.64 161	86.11 137	91.74 96	93.14 192	79.67 123	98.89 88	89.06 104	95.46 116	94.28 220

MVSTER		88.84 152	88.29 148	90.51 194	92.95 228	80.44 199	93.73 193	95.01 199	84.66 170	87.15 158	93.12 193	72.79 207	97.21 220	87.86 117	87.36 233	93.87 239

Effi-MVS+		91.59 86	91.11 87	93.01 87	94.35 180	83.39 119	94.60 135	95.10 196	87.10 112	90.57 111	93.10 194	81.43 104	98.07 149	89.29 101	94.48 133	97.59 92

PS-CasMVS		87.32 200	86.88 178	88.63 258	92.99 227	76.33 287	95.33 83	96.61 91	88.22 85	83.30 260	93.07 195	73.03 205	95.79 297	78.36 245	81.00 299	93.75 250

DTE-MVSNet		86.11 235	85.48 229	87.98 274	91.65 262	74.92 295	94.93 114	95.75 151	87.36 108	82.26 269	93.04 196	72.85 206	95.82 295	74.04 284	77.46 328	93.20 272

CP-MVSNet		87.63 186	87.26 173	88.74 255	93.12 219	76.59 282	95.29 90	96.58 93	88.43 77	83.49 255	92.98 197	75.28 170	95.83 294	78.97 240	81.15 293	93.79 244

test_djsdf		89.03 146	88.64 137	90.21 206	90.74 298	79.28 231	95.96 58	95.90 139	84.66 170	85.33 211	92.94 198	74.02 189	97.30 209	89.64 97	88.53 213	94.05 231

RRT_test8_iter05		86.90 214	86.36 199	88.52 260	93.00 226	73.27 309	94.32 159	95.96 133	85.50 150	84.26 236	92.86 199	60.76 315	97.70 173	88.32 112	82.29 276	94.60 203

MAR-MVS		90.30 110	89.37 120	93.07 85	96.61 86	84.48 88	95.68 70	95.67 156	82.36 216	87.85 145	92.85 200	76.63 154	98.80 99	80.01 228	96.68 97	95.91 155
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		80.94 299	80.20 290	83.18 327	87.96 337	66.29 348	91.28 268	90.70 320	83.70 185	78.12 311	92.84 201	51.37 347	90.82 351	63.34 339	82.46 275	92.43 296

EU-MVSNet		81.32 294	80.95 282	82.42 331	88.50 328	63.67 354	93.32 206	91.33 303	64.02 351	80.57 290	92.83 202	61.21 312	92.27 344	76.34 265	80.38 310	91.32 316

ACMH+	81.04 14	85.05 255	83.46 263	89.82 223	94.66 165	79.37 225	94.44 147	94.12 239	82.19 219	78.04 312	92.82 203	58.23 328	97.54 185	73.77 287	82.90 272	92.54 292

mvs-test1		89.45 133	89.14 126	90.38 201	93.33 213	77.63 268	94.95 112	94.36 227	87.70 99	87.10 161	92.81 204	73.45 198	98.03 154	85.57 145	93.04 158	95.48 170

WR-MVS		88.38 162	87.67 162	90.52 193	93.30 215	80.18 202	93.26 213	95.96 133	88.57 74	85.47 199	92.81 204	76.12 156	96.91 241	81.24 207	82.29 276	94.47 216

HY-MVS	83.01 12	89.03 146	87.94 157	92.29 124	94.86 155	82.77 134	92.08 254	94.49 223	81.52 240	86.93 163	92.79 206	78.32 139	98.23 132	79.93 229	90.55 183	95.88 157

LTVRE_ROB	82.13 13	86.26 234	84.90 242	90.34 204	94.44 175	81.50 165	92.31 246	94.89 208	83.03 202	79.63 304	92.67 207	69.69 244	97.79 166	71.20 297	86.26 241	91.72 309
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	85.36 247	83.68 259	90.39 199	94.45 174	80.63 193	94.73 128	94.85 211	82.09 220	77.24 317	92.65 208	60.01 320	97.58 182	72.25 294	84.87 250	92.96 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		86.61 224	85.54 227	89.82 223	91.44 264	80.18 202	95.28 92	94.85 211	83.84 183	81.66 276	92.62 209	72.45 213	96.48 264	79.67 232	78.06 323	92.82 287

PVSNet_Blended		90.73 100	90.32 99	91.98 134	96.12 101	81.25 175	92.55 238	96.83 65	82.04 223	89.10 127	92.56 210	81.04 107	98.85 95	86.72 135	95.91 108	95.84 159

ET-MVSNet_ETH3D		87.51 193	85.91 218	92.32 121	93.70 205	83.93 102	92.33 244	90.94 314	84.16 175	72.09 343	92.52 211	69.90 240	95.85 293	89.20 102	88.36 219	97.17 108

PS-MVSNAJ		91.18 93	90.92 91	91.96 136	95.26 135	82.60 145	92.09 253	95.70 154	86.27 131	91.84 91	92.46 212	79.70 120	98.99 78	89.08 103	95.86 109	94.29 219

CLD-MVS		89.47 132	88.90 133	91.18 168	94.22 181	82.07 154	92.13 251	96.09 123	87.90 93	85.37 209	92.45 213	74.38 181	97.56 184	87.15 128	90.43 184	93.93 235
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TR-MVS		86.78 219	85.76 224	89.82 223	94.37 177	78.41 246	92.47 239	92.83 264	81.11 249	86.36 176	92.40 214	68.73 260	97.48 189	73.75 288	89.85 195	93.57 256

Test_1112_low_res		87.65 183	86.51 195	91.08 173	94.94 149	79.28 231	91.77 258	94.30 230	76.04 302	83.51 254	92.37 215	77.86 144	97.73 172	78.69 243	89.13 207	96.22 140

EPNet_dtu		86.49 231	85.94 217	88.14 271	90.24 310	72.82 313	94.11 170	92.20 279	86.66 125	79.42 306	92.36 216	73.52 196	95.81 296	71.26 296	93.66 142	95.80 162
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet (Re)		89.80 124	89.07 128	92.01 131	93.60 207	84.52 85	94.78 125	97.47 8	89.26 53	86.44 175	92.32 217	82.10 96	97.39 206	84.81 153	80.84 301	94.12 225

thres600view7		87.65 183	86.67 187	90.59 188	96.08 105	78.72 237	94.88 118	91.58 296	87.06 113	88.08 140	92.30 218	68.91 257	98.10 140	70.05 309	91.10 176	94.96 187

thres100view900		87.63 186	86.71 185	90.38 201	96.12 101	78.55 241	95.03 109	91.58 296	87.15 110	88.06 141	92.29 219	68.91 257	98.10 140	70.13 306	91.10 176	94.48 214

PVSNet_BlendedMVS		89.98 117	89.70 112	90.82 184	96.12 101	81.25 175	93.92 186	96.83 65	83.49 192	89.10 127	92.26 220	81.04 107	98.85 95	86.72 135	87.86 228	92.35 300

XVG-ACMP-BASELINE		86.00 236	84.84 244	89.45 238	91.20 275	78.00 255	91.70 262	95.55 165	85.05 163	82.97 262	92.25 221	54.49 339	97.48 189	82.93 175	87.45 232	92.89 284

EIA-MVS		91.95 78	91.94 76	91.98 134	95.16 139	80.01 212	95.36 81	96.73 77	88.44 76	89.34 124	92.16 222	83.82 78	98.45 119	89.35 100	97.06 90	97.48 96

Anonymous20231211		86.59 226	85.13 236	90.98 182	96.52 91	81.50 165	96.14 47	96.16 119	73.78 322	83.65 250	92.15 223	63.26 298	97.37 207	82.82 179	81.74 286	94.06 230

MVS		87.44 196	86.10 210	91.44 160	92.61 234	83.62 112	92.63 234	95.66 158	67.26 347	81.47 277	92.15 223	77.95 141	98.22 134	79.71 231	95.48 114	92.47 295

anonymousdsp		87.84 176	87.09 174	90.12 211	89.13 322	80.54 196	94.67 132	95.55 165	82.05 221	83.82 245	92.12 225	71.47 220	97.15 222	87.15 128	87.80 229	92.67 289

TransMVSNet (Re)		84.43 264	83.06 267	88.54 259	91.72 257	78.44 245	95.18 99	92.82 265	82.73 209	79.67 303	92.12 225	73.49 197	95.96 288	71.10 301	68.73 348	91.21 320

SixPastTwentyTwo		83.91 269	82.90 269	86.92 298	90.99 284	70.67 333	93.48 202	91.99 286	85.54 148	77.62 316	92.11 227	60.59 316	96.87 243	76.05 269	77.75 325	93.20 272

HyFIR lowres test		88.09 171	86.81 181	91.93 138	96.00 109	80.63 193	90.01 291	95.79 148	73.42 325	87.68 150	92.10 228	73.86 192	97.96 159	80.75 216	91.70 172	97.19 107

Baseline_NR-MVSNet		87.07 211	86.63 190	88.40 262	91.44 264	77.87 260	94.23 164	92.57 271	84.12 177	85.74 186	92.08 229	77.25 146	96.04 283	82.29 188	79.94 313	91.30 317

USDC		82.76 276	81.26 281	87.26 289	91.17 277	74.55 297	89.27 301	93.39 255	78.26 284	75.30 330	92.08 229	54.43 340	96.63 250	71.64 295	85.79 245	90.61 328

v2v482		87.84 176	87.06 175	90.17 207	90.99 284	79.23 234	94.00 182	95.13 193	84.87 165	85.53 193	92.07 231	74.45 180	97.45 192	84.71 155	81.75 285	93.85 242

FMVSNet2		87.19 208	85.82 220	91.30 164	94.01 189	83.67 110	94.79 124	94.94 202	83.57 188	83.88 243	92.05 232	66.59 279	96.51 262	77.56 254	85.01 249	93.73 251

WR-MVS_H		87.80 178	87.37 168	89.10 245	93.23 216	78.12 253	95.61 75	97.30 26	87.90 93	83.72 247	92.01 233	79.65 124	96.01 286	76.36 264	80.54 305	93.16 274

LCM-MVSNet-Re		88.30 166	88.32 147	88.27 266	94.71 162	72.41 321	93.15 217	90.98 312	87.77 97	79.25 307	91.96 234	78.35 138	95.75 298	83.04 173	95.62 111	96.65 127

MSDG		84.86 259	83.09 266	90.14 210	93.80 200	80.05 209	89.18 304	93.09 259	78.89 272	78.19 310	91.91 235	65.86 288	97.27 213	68.47 315	88.45 216	93.11 276

IterMVS-LS		88.36 164	87.91 158	89.70 230	93.80 200	78.29 250	93.73 193	95.08 198	85.73 142	84.75 218	91.90 236	79.88 116	96.92 240	83.83 164	82.51 274	93.89 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet3		87.40 198	86.11 209	91.30 164	93.79 202	83.64 111	94.20 165	94.81 215	83.89 182	84.37 228	91.87 237	68.45 263	96.56 259	78.23 247	85.36 246	93.70 253

tfpn200view9		87.58 190	86.64 188	90.41 198	95.99 110	78.64 239	94.58 136	91.98 287	86.94 117	88.09 138	91.77 238	69.18 254	98.10 140	70.13 306	91.10 176	94.48 214

thres400		87.62 188	86.64 188	90.57 189	95.99 110	78.64 239	94.58 136	91.98 287	86.94 117	88.09 138	91.77 238	69.18 254	98.10 140	70.13 306	91.10 176	94.96 187

pmmvs4		85.43 246	83.86 257	90.16 208	90.02 315	82.97 131	90.27 283	92.67 269	75.93 303	80.73 286	91.74 240	71.05 223	95.73 299	78.85 241	83.46 265	91.78 308

GBi-Net		87.26 201	85.98 214	91.08 173	94.01 189	83.10 124	95.14 102	94.94 202	83.57 188	84.37 228	91.64 241	66.59 279	96.34 274	78.23 247	85.36 246	93.79 244

test1		87.26 201	85.98 214	91.08 173	94.01 189	83.10 124	95.14 102	94.94 202	83.57 188	84.37 228	91.64 241	66.59 279	96.34 274	78.23 247	85.36 246	93.79 244

FMVSNet1		85.85 240	84.11 253	91.08 173	92.81 230	83.10 124	95.14 102	94.94 202	81.64 236	82.68 265	91.64 241	59.01 326	96.34 274	75.37 274	83.78 258	93.79 244

MVP-Stereo		85.97 237	84.86 243	89.32 239	90.92 290	82.19 152	92.11 252	94.19 234	78.76 276	78.77 309	91.63 244	68.38 264	96.56 259	75.01 279	93.95 138	89.20 340
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		87.51 193	86.57 193	90.34 204	92.42 237	79.74 219	92.63 234	95.35 186	78.35 282	80.14 296	91.62 245	74.05 188	97.15 222	81.05 208	93.53 146	94.12 225

MS-PatchMatch		85.05 255	84.16 252	87.73 278	91.42 268	78.51 243	91.25 270	93.53 252	77.50 288	80.15 295	91.58 246	61.99 304	95.51 305	75.69 271	94.35 136	89.16 341

TDRefinement		79.81 306	77.34 310	87.22 293	79.24 358	75.48 294	93.12 218	92.03 284	76.45 296	75.01 331	91.58 246	49.19 351	96.44 268	70.22 305	69.18 345	89.75 335

PatchMatch-RL		86.77 222	85.54 227	90.47 197	95.88 114	82.71 140	90.54 280	92.31 276	79.82 262	84.32 233	91.57 248	68.77 259	96.39 270	73.16 290	93.48 149	92.32 301

BH-w/o		87.57 191	87.05 176	89.12 244	94.90 152	77.90 258	92.41 240	93.51 253	82.89 207	83.70 248	91.34 249	75.75 164	97.07 230	75.49 272	93.49 147	92.39 298

v8		87.50 195	86.71 185	89.89 220	91.37 270	79.40 224	94.50 141	95.38 182	84.81 167	83.60 252	91.33 250	76.05 157	97.42 196	82.84 178	80.51 309	92.84 286

V42		87.68 181	86.86 179	90.15 209	90.58 303	80.14 204	94.24 163	95.28 187	83.66 186	85.67 188	91.33 250	74.73 178	97.41 201	84.43 158	81.83 283	92.89 284

Fast-Effi-MVS+-dtu		87.44 196	86.72 184	89.63 232	92.04 246	77.68 267	94.03 179	93.94 241	85.81 139	82.42 267	91.32 252	70.33 237	97.06 231	80.33 225	90.23 187	94.14 224

v1144		87.61 189	86.79 183	90.06 214	91.01 283	79.34 227	93.95 184	95.42 181	83.36 196	85.66 189	91.31 253	74.98 174	97.42 196	83.37 169	82.06 279	93.42 263

tfpnnormal		84.72 261	83.23 265	89.20 242	92.79 231	80.05 209	94.48 142	95.81 146	82.38 215	81.08 283	91.21 254	69.01 256	96.95 238	61.69 344	80.59 304	90.58 331

ETV-MVS		92.74 67	92.66 68	92.97 89	95.20 138	84.04 101	95.07 105	96.51 97	90.73 21	92.96 61	91.19 255	84.06 72	98.34 126	91.72 70	96.54 101	96.54 133

v10		87.25 203	86.38 197	89.85 221	91.19 276	79.50 221	94.48 142	95.45 176	83.79 184	83.62 251	91.19 255	75.13 171	97.42 196	81.94 194	80.60 303	92.63 291

pmmvs5		84.21 265	82.84 271	88.34 265	88.95 324	76.94 278	92.41 240	91.91 291	75.63 305	80.28 293	91.18 257	64.59 293	95.57 301	77.09 260	83.47 264	92.53 293

v1192		87.25 203	86.33 201	90.00 218	90.76 297	79.04 235	93.80 190	95.48 171	82.57 212	85.48 198	91.18 257	73.38 202	97.42 196	82.30 187	82.06 279	93.53 257

v1240		86.78 219	85.85 219	89.56 233	90.45 307	77.79 263	93.61 198	95.37 184	81.65 235	85.43 203	91.15 259	71.50 219	97.43 195	81.47 205	82.05 281	93.47 261

CMPMVS	59.16 21	80.52 300	79.20 302	84.48 321	83.98 351	67.63 347	89.95 293	93.84 248	64.79 350	66.81 351	91.14 260	57.93 329	95.17 312	76.25 266	88.10 222	90.65 327
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thres200		87.21 207	86.24 205	90.12 211	95.36 129	78.53 242	93.26 213	92.10 281	86.42 129	88.00 143	91.11 261	69.24 253	98.00 156	69.58 310	91.04 181	93.83 243

pmmvs6		83.42 273	81.60 277	88.87 250	88.01 336	77.87 260	94.96 111	94.24 233	74.67 316	78.80 308	91.09 262	60.17 319	96.49 263	77.06 261	75.40 334	92.23 303

v144192		87.19 208	86.35 200	89.74 227	90.64 301	78.24 251	93.92 186	95.43 179	81.93 227	85.51 195	91.05 263	74.21 185	97.45 192	82.86 177	81.56 287	93.53 257

v1921920		86.97 213	86.06 212	89.69 231	90.53 306	78.11 254	93.80 190	95.43 179	81.90 229	85.33 211	91.05 263	72.66 208	97.41 201	82.05 192	81.80 284	93.53 257

baseline2		86.50 229	85.39 231	89.84 222	91.12 280	76.70 280	91.88 255	88.58 342	82.35 217	79.95 300	90.95 265	73.42 200	97.63 180	80.27 226	89.95 192	95.19 179

thisisatest0515		87.33 199	85.99 213	91.37 162	93.49 209	79.55 220	90.63 279	89.56 340	80.17 256	87.56 152	90.86 266	67.07 271	98.28 131	81.50 204	93.02 159	96.29 137

v7n		86.81 217	85.76 224	89.95 219	90.72 299	79.25 233	95.07 105	95.92 136	84.45 173	82.29 268	90.86 266	72.60 210	97.53 186	79.42 237	80.52 308	93.08 278

cl-mvsnet1		86.53 227	85.78 221	88.75 253	92.02 248	76.45 284	90.74 277	94.30 230	81.83 233	83.34 258	90.82 268	75.75 164	96.57 257	81.73 201	81.52 289	93.24 269

v148		87.04 212	86.32 202	89.21 241	90.94 288	77.26 274	93.71 195	94.43 225	84.84 166	84.36 231	90.80 269	76.04 158	97.05 232	82.12 190	79.60 317	93.31 265

cl-mvsnet____		86.52 228	85.78 221	88.75 253	92.03 247	76.46 283	90.74 277	94.30 230	81.83 233	83.34 258	90.78 270	75.74 166	96.57 257	81.74 200	81.54 288	93.22 271

PMMVS		85.71 243	84.96 240	87.95 275	88.90 325	77.09 276	88.68 311	90.06 329	72.32 334	86.47 171	90.76 271	72.15 214	94.40 320	81.78 199	93.49 147	92.36 299

bset_n11_16_dypcd		86.83 216	85.55 226	90.65 187	88.22 333	81.70 161	88.88 308	90.42 321	85.26 156	85.49 197	90.69 272	67.11 270	97.02 234	89.51 99	84.39 253	93.23 270

Fast-Effi-MVS+		89.41 136	88.64 137	91.71 150	94.74 159	80.81 189	93.54 200	95.10 196	83.11 200	86.82 168	90.67 273	79.74 119	97.75 171	80.51 222	93.55 145	96.57 131

IterMVS-SCA-FT		85.45 245	84.53 250	88.18 270	91.71 258	76.87 279	90.19 288	92.65 270	85.40 153	81.44 278	90.54 274	66.79 275	95.00 317	81.04 209	81.05 295	92.66 290

PVSNet	78.82 18	85.55 244	84.65 247	88.23 269	94.72 161	71.93 322	87.12 326	92.75 267	78.80 275	84.95 216	90.53 275	64.43 294	96.71 248	74.74 280	93.86 140	96.06 151

eth_miper_zixun_eth		86.50 229	85.77 223	88.68 256	91.94 250	75.81 291	90.47 281	94.89 208	82.05 221	84.05 239	90.46 276	75.96 159	96.77 245	82.76 181	79.36 319	93.46 262

cl_fuxian		87.14 210	86.50 196	89.04 247	92.20 240	77.26 274	91.22 271	94.70 219	82.01 224	84.34 232	90.43 277	78.81 130	96.61 254	83.70 167	81.09 294	93.25 268

IterMVS		84.88 258	83.98 256	87.60 280	91.44 264	76.03 289	90.18 289	92.41 273	83.24 199	81.06 284	90.42 278	66.60 278	94.28 324	79.46 233	80.98 300	92.48 294
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_0402		81.30 295	79.17 303	87.67 279	93.19 217	78.17 252	92.98 225	91.71 292	75.25 309	76.02 327	90.31 279	59.23 324	96.37 271	50.22 357	83.63 262	88.47 347

TinyColmap		79.76 307	77.69 309	85.97 308	91.71 258	73.12 310	89.55 295	90.36 324	75.03 311	72.03 344	90.19 280	46.22 356	96.19 280	63.11 340	81.03 296	88.59 346

EG-PatchMatch MVS		82.37 281	80.34 287	88.46 261	90.27 309	79.35 226	92.80 231	94.33 229	77.14 293	73.26 340	90.18 281	47.47 355	96.72 246	70.25 303	87.32 235	89.30 338

cl-mvsnet2		86.78 219	85.98 214	89.18 243	92.34 238	77.62 269	90.84 276	94.13 238	81.33 243	83.97 242	90.15 282	73.96 190	96.60 256	84.19 160	82.94 269	93.33 264

lessismore_v0				86.04 307	88.46 329	68.78 342		80.59 359		73.01 341	90.11 283	55.39 335	96.43 269	75.06 278	65.06 350	92.90 283

miper_ehance_all_eth		87.22 206	86.62 191	89.02 248	92.13 243	77.40 273	90.91 275	94.81 215	81.28 244	84.32 233	90.08 284	79.26 126	96.62 251	83.81 165	82.94 269	93.04 279

D2MVS		85.90 238	85.09 237	88.35 264	90.79 295	77.42 272	91.83 257	95.70 154	80.77 252	80.08 298	90.02 285	66.74 277	96.37 271	81.88 196	87.97 226	91.26 318

LF4IMVS		80.37 302	79.07 305	84.27 324	86.64 340	69.87 339	89.39 300	91.05 310	76.38 297	74.97 332	90.00 286	47.85 354	94.25 325	74.55 283	80.82 302	88.69 345

CostFormer		85.77 242	84.94 241	88.26 267	91.16 279	72.58 319	89.47 299	91.04 311	76.26 300	86.45 174	89.97 287	70.74 229	96.86 244	82.35 186	87.07 238	95.34 177

test20.03		79.95 305	79.08 304	82.55 330	85.79 345	67.74 346	91.09 273	91.08 308	81.23 247	74.48 335	89.96 288	61.63 306	90.15 352	60.08 348	76.38 332	89.76 334

tpm		84.73 260	84.02 254	86.87 301	90.33 308	68.90 341	89.06 305	89.94 332	80.85 251	85.75 185	89.86 289	68.54 262	95.97 287	77.76 251	84.05 257	95.75 163

miper_lstm_enhance		85.27 251	84.59 249	87.31 287	91.28 274	74.63 296	87.69 322	94.09 240	81.20 248	81.36 280	89.85 290	74.97 175	94.30 323	81.03 211	79.84 316	93.01 280

test0.0.03 1		82.41 280	81.69 276	84.59 320	88.23 332	72.89 312	90.24 285	87.83 345	83.41 194	79.86 301	89.78 291	67.25 267	88.99 354	65.18 333	83.42 266	91.90 307

K. test v3		81.59 289	80.15 291	85.91 311	89.89 318	69.42 340	92.57 237	87.71 346	85.56 147	73.44 339	89.71 292	55.58 333	95.52 304	77.17 258	69.76 342	92.78 288

CHOSEN 280x420		85.15 253	83.99 255	88.65 257	92.47 235	78.40 247	79.68 353	92.76 266	74.90 314	81.41 279	89.59 293	69.85 243	95.51 305	79.92 230	95.29 120	92.03 305

GA-MVS		86.61 224	85.27 234	90.66 186	91.33 273	78.71 238	90.40 282	93.81 249	85.34 154	85.12 213	89.57 294	61.25 310	97.11 226	80.99 212	89.59 199	96.15 141

Effi-MVS+-dtu		88.65 157	88.35 144	89.54 234	93.33 213	76.39 285	94.47 145	94.36 227	87.70 99	85.43 203	89.56 295	73.45 198	97.26 215	85.57 145	91.28 175	94.97 184

tpm2		84.08 266	82.94 268	87.48 285	91.39 269	71.27 326	89.23 303	90.37 323	71.95 336	84.64 219	89.33 296	67.30 266	96.55 261	75.17 276	87.09 237	94.63 200

Anonymous20231206		81.03 297	79.77 295	84.82 319	87.85 338	70.26 336	91.42 267	92.08 282	73.67 323	77.75 314	89.25 297	62.43 302	93.08 338	61.50 345	82.00 282	91.12 323

miper_enhance_ethall		86.90 214	86.18 206	89.06 246	91.66 261	77.58 270	90.22 287	94.82 214	79.16 269	84.48 224	89.10 298	79.19 127	96.66 249	84.06 161	82.94 269	92.94 282

ppachtmachnet_test		81.84 284	80.07 292	87.15 295	88.46 329	74.43 300	89.04 306	92.16 280	75.33 308	77.75 314	88.99 299	66.20 283	95.37 310	65.12 334	77.60 326	91.65 310

gm-plane-assit					89.60 321	68.00 343			77.28 292		88.99 299		97.57 183	79.44 235

MDTV_nov1_ep13			83.56 262		91.69 260	69.93 338	87.75 321	91.54 298	78.60 279	84.86 217	88.90 301	69.54 246	96.03 284	70.25 303	88.93 209

SCA		86.32 233	85.18 235	89.73 229	92.15 241	76.60 281	91.12 272	91.69 294	83.53 191	85.50 196	88.81 302	66.79 275	96.48 264	76.65 262	90.35 186	96.12 145

Patchmatch-test		81.37 293	79.30 299	87.58 281	90.92 290	74.16 303	80.99 351	87.68 347	70.52 342	76.63 322	88.81 302	71.21 221	92.76 341	60.01 350	86.93 239	95.83 160

tpmrst		85.35 248	84.99 238	86.43 304	90.88 293	67.88 345	88.71 310	91.43 302	80.13 257	86.08 182	88.80 304	73.05 204	96.02 285	82.48 183	83.40 267	95.40 174

DSMNet-mixed		76.94 317	76.29 316	78.89 334	83.10 354	56.11 361	87.78 320	79.77 360	60.65 353	75.64 328	88.71 305	61.56 307	88.34 355	60.07 349	89.29 204	92.21 304

PatchmatchNet		85.85 240	84.70 246	89.29 240	91.76 256	75.54 293	88.49 313	91.30 304	81.63 237	85.05 214	88.70 306	71.71 215	96.24 277	74.61 282	89.05 208	96.08 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		82.59 279	80.53 284	88.76 252	91.51 263	78.32 248	86.57 329	90.13 327	79.32 265	80.70 287	88.69 307	52.98 345	93.07 339	66.03 330	88.86 210	94.90 191

IB-MVS	80.51 15	85.24 252	83.26 264	91.19 167	92.13 243	79.86 216	91.75 259	91.29 305	83.28 198	80.66 288	88.49 308	61.28 309	98.46 116	80.99 212	79.46 318	95.25 178
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		86.43 232	84.98 239	90.80 185	92.10 245	80.92 186	90.24 285	95.91 138	73.10 328	83.57 253	88.39 309	65.15 290	97.46 191	84.90 152	91.43 174	94.03 232

EPMVS		83.90 270	82.70 272	87.51 282	90.23 311	72.67 315	88.62 312	81.96 357	81.37 242	85.01 215	88.34 310	66.31 282	94.45 319	75.30 275	87.12 236	95.43 173

MDA-MVSNet-bldmvs		78.85 312	76.31 315	86.46 303	89.76 319	73.88 304	88.79 309	90.42 321	79.16 269	59.18 355	88.33 311	60.20 318	94.04 326	62.00 343	68.96 346	91.48 314

our_test_3		81.93 283	80.46 286	86.33 306	88.46 329	73.48 307	88.46 314	91.11 307	76.46 295	76.69 321	88.25 312	66.89 273	94.36 321	68.75 313	79.08 321	91.14 322

OpenMVS_ROB	74.94 19	79.51 308	77.03 314	86.93 297	87.00 339	76.23 288	92.33 244	90.74 319	68.93 345	74.52 334	88.23 313	49.58 350	96.62 251	57.64 352	84.29 254	87.94 349

MIMVSNet1		79.38 309	77.28 311	85.69 312	86.35 341	73.67 306	91.61 265	92.75 267	78.11 287	72.64 342	88.12 314	48.16 352	91.97 347	60.32 347	77.49 327	91.43 315

UnsupCasMVSNet_eth		80.07 304	78.27 308	85.46 313	85.24 349	72.63 317	88.45 315	94.87 210	82.99 204	71.64 346	88.07 315	56.34 332	91.75 348	73.48 289	63.36 353	92.01 306

test-LLR		85.87 239	85.41 230	87.25 290	90.95 286	71.67 324	89.55 295	89.88 335	83.41 194	84.54 222	87.95 316	67.25 267	95.11 314	81.82 197	93.37 152	94.97 184

test-mter		84.54 263	83.64 261	87.25 290	90.95 286	71.67 324	89.55 295	89.88 335	79.17 268	84.54 222	87.95 316	55.56 334	95.11 314	81.82 197	93.37 152	94.97 184

FMVSNet5		81.52 291	79.60 297	87.27 288	91.17 277	77.95 256	91.49 266	92.26 278	76.87 294	76.16 324	87.91 318	51.67 346	92.34 343	67.74 321	81.16 291	91.52 312

CR-MVSNet		85.35 248	83.76 258	90.12 211	90.58 303	79.34 227	85.24 336	91.96 289	78.27 283	85.55 191	87.87 319	71.03 224	95.61 300	73.96 286	89.36 202	95.40 174

Patchmtry		82.71 277	80.93 283	88.06 273	90.05 314	76.37 286	84.74 340	91.96 289	72.28 335	81.32 281	87.87 319	71.03 224	95.50 307	68.97 312	80.15 311	92.32 301

YYNet1		79.22 310	77.20 312	85.28 316	88.20 335	72.66 316	85.87 332	90.05 331	74.33 319	62.70 353	87.61 321	66.09 285	92.03 345	66.94 325	72.97 337	91.15 321

MDA-MVSNet_test_wron		79.21 311	77.19 313	85.29 315	88.22 333	72.77 314	85.87 332	90.06 329	74.34 318	62.62 354	87.56 322	66.14 284	91.99 346	66.90 328	73.01 336	91.10 325

Anonymous20240521		80.44 301	79.21 301	84.11 325	85.75 346	67.89 344	92.86 229	93.23 257	75.61 306	75.59 329	87.47 323	50.03 348	94.33 322	71.14 300	81.21 290	90.12 333

DWT-MVSNet_test		84.95 257	83.68 259	88.77 251	91.43 267	73.75 305	91.74 260	90.98 312	80.66 253	83.84 244	87.36 324	62.44 301	97.11 226	78.84 242	85.81 243	95.46 171

TESTMET0.1,1		83.74 271	82.85 270	86.42 305	89.96 316	71.21 328	89.55 295	87.88 344	77.41 289	83.37 257	87.31 325	56.71 331	93.65 332	80.62 219	92.85 163	94.40 217

CL-MVSNet_2432*1600		81.74 286	80.53 284	85.36 314	85.96 344	72.45 320	90.25 284	93.07 260	81.24 246	79.85 302	87.29 326	70.93 226	92.52 342	66.95 324	69.23 344	91.11 324

MVS_0304		83.46 272	81.92 275	88.10 272	90.63 302	77.49 271	93.26 213	93.75 250	80.04 259	80.44 292	87.24 327	47.94 353	95.55 302	75.79 270	88.16 221	91.26 318

tpmvs		83.35 275	82.07 273	87.20 294	91.07 282	71.00 331	88.31 316	91.70 293	78.91 271	80.49 291	87.18 328	69.30 252	97.08 229	68.12 320	83.56 263	93.51 260

dp		81.47 292	80.23 289	85.17 317	89.92 317	65.49 351	86.74 327	90.10 328	76.30 299	81.10 282	87.12 329	62.81 299	95.92 289	68.13 319	79.88 314	94.09 228

PM-MVS		78.11 315	76.12 317	84.09 326	83.54 353	70.08 337	88.97 307	85.27 352	79.93 260	74.73 333	86.43 330	34.70 360	93.48 333	79.43 236	72.06 340	88.72 344

DIV-MVS_2432*1600		80.20 303	79.24 300	83.07 328	85.64 347	65.29 352	91.01 274	93.93 242	78.71 278	76.32 323	86.40 331	59.20 325	92.93 340	72.59 292	69.35 343	91.00 326

tpm cat1		81.96 282	80.27 288	87.01 296	91.09 281	71.02 330	87.38 325	91.53 299	66.25 348	80.17 294	86.35 332	68.22 265	96.15 281	69.16 311	82.29 276	93.86 241

pmmvs-eth3d		80.97 298	78.72 307	87.74 277	84.99 350	79.97 214	90.11 290	91.65 295	75.36 307	73.51 338	86.03 333	59.45 323	93.96 329	75.17 276	72.21 339	89.29 339

KD-MVS_2432*1600		78.50 313	76.02 318	85.93 309	86.22 342	74.47 298	84.80 338	92.33 274	79.29 266	76.98 319	85.92 334	53.81 343	93.97 327	67.39 322	57.42 356	89.36 336

miper_refine_blended		78.50 313	76.02 318	85.93 309	86.22 342	74.47 298	84.80 338	92.33 274	79.29 266	76.98 319	85.92 334	53.81 343	93.97 327	67.39 322	57.42 356	89.36 336

ADS-MVSNet2		81.66 288	79.71 296	87.50 283	91.35 271	74.19 302	83.33 345	88.48 343	72.90 330	82.24 270	85.77 336	64.98 291	93.20 337	64.57 336	83.74 259	95.12 180

ADS-MVSNet		81.56 290	79.78 294	86.90 299	91.35 271	71.82 323	83.33 345	89.16 341	72.90 330	82.24 270	85.77 336	64.98 291	93.76 330	64.57 336	83.74 259	95.12 180

N_pmnet		68.89 323	68.44 326	70.23 340	89.07 323	28.79 370	88.06 317	19.50 371	69.47 344	71.86 345	84.93 338	61.24 311	91.75 348	54.70 354	77.15 329	90.15 332

ambc				83.06 329	79.99 357	63.51 355	77.47 354	92.86 263		74.34 336	84.45 339	28.74 361	95.06 316	73.06 291	68.89 347	90.61 328

GG-mvs-BLEND				87.94 276	89.73 320	77.91 257	87.80 319	78.23 363		80.58 289	83.86 340	59.88 321	95.33 311	71.20 297	92.22 170	90.60 330

patchmatchnet-post											83.76 341	71.53 218	96.48 264

PatchT		82.68 278	81.27 280	86.89 300	90.09 313	70.94 332	84.06 342	90.15 326	74.91 313	85.63 190	83.57 342	69.37 248	94.87 318	65.19 332	88.50 215	94.84 193

new-patchmatchnet		76.41 318	75.17 320	80.13 333	82.65 356	59.61 356	87.66 323	91.08 308	78.23 285	69.85 347	83.22 343	54.76 337	91.63 350	64.14 338	64.89 351	89.16 341

PVSNet_0	73.20 20	77.22 316	74.83 321	84.37 322	90.70 300	71.10 329	83.09 347	89.67 338	72.81 332	73.93 337	83.13 344	60.79 314	93.70 331	68.54 314	50.84 359	88.30 348

RPMNet		83.95 268	81.53 278	91.21 166	90.58 303	79.34 227	85.24 336	96.76 73	71.44 338	85.55 191	82.97 345	70.87 227	98.91 87	61.01 346	89.36 202	95.40 174

Patchmatch-RL test		81.67 287	79.96 293	86.81 302	85.42 348	71.23 327	82.17 349	87.50 348	78.47 280	77.19 318	82.50 346	70.81 228	93.48 333	82.66 182	72.89 338	95.71 166

FPMVS		64.63 325	62.55 327	70.88 339	70.80 361	56.71 358	84.42 341	84.42 353	51.78 357	49.57 357	81.61 347	23.49 364	81.48 360	40.61 361	76.25 333	74.46 357

pmmvs3		71.81 322	68.71 325	81.11 332	75.86 359	70.42 335	86.74 327	83.66 354	58.95 354	68.64 350	80.89 348	36.93 359	89.52 353	63.10 341	63.59 352	83.39 351

new_pmnet		72.15 321	70.13 324	78.20 335	82.95 355	65.68 349	83.91 343	82.40 356	62.94 352	64.47 352	79.82 349	42.85 358	86.26 357	57.41 353	74.44 335	82.65 354

UnsupCasMVSNet_bld		76.23 319	73.27 322	85.09 318	83.79 352	72.92 311	85.65 335	93.47 254	71.52 337	68.84 349	79.08 350	49.77 349	93.21 336	66.81 329	60.52 355	89.13 343

DeepMVS_CX				56.31 346	74.23 360	51.81 363		56.67 369	44.85 359	48.54 359	75.16 351	27.87 363	58.74 367	40.92 360	52.22 358	58.39 360

test_method		50.52 330	48.47 332	56.66 345	52.26 369	18.98 372	41.51 363	81.40 358	10.10 365	44.59 360	75.01 352	28.51 362	68.16 363	53.54 355	49.31 360	82.83 353

JIA-IIPM		81.04 296	78.98 306	87.25 290	88.64 326	73.48 307	81.75 350	89.61 339	73.19 327	82.05 272	73.71 353	66.07 286	95.87 292	71.18 299	84.60 252	92.41 297

LCM-MVSNet		66.00 324	62.16 328	77.51 337	64.51 365	58.29 357	83.87 344	90.90 315	48.17 358	54.69 356	73.31 354	16.83 370	86.75 356	65.47 331	61.67 354	87.48 350

PMMVS2		59.60 326	56.40 329	69.21 341	68.83 362	46.58 365	73.02 358	77.48 364	55.07 356	49.21 358	72.95 355	17.43 369	80.04 361	49.32 358	44.33 361	80.99 356

gg-mvs-nofinetune		81.77 285	79.37 298	88.99 249	90.85 294	77.73 266	86.29 330	79.63 361	74.88 315	83.19 261	69.05 356	60.34 317	96.11 282	75.46 273	94.64 129	93.11 276

MVS-HIRNet		73.70 320	72.20 323	78.18 336	91.81 255	56.42 360	82.94 348	82.58 355	55.24 355	68.88 348	66.48 357	55.32 336	95.13 313	58.12 351	88.42 217	83.01 352

PMVS	47.18 22	52.22 329	48.46 333	63.48 343	45.72 370	46.20 366	73.41 357	78.31 362	41.03 361	30.06 364	65.68 358	6.05 371	83.43 359	30.04 363	65.86 349	60.80 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ANet_high		58.88 327	54.22 331	72.86 338	56.50 368	56.67 359	80.75 352	86.00 349	73.09 329	37.39 362	64.63 359	22.17 365	79.49 362	43.51 359	23.96 364	82.43 355

tmp_tt		35.64 334	39.24 336	24.84 349	14.87 371	23.90 371	62.71 359	51.51 370	6.58 367	36.66 363	62.08 360	44.37 357	30.34 369	52.40 356	22.00 366	20.27 364

MVE	39.65 23	43.39 331	38.59 337	57.77 344	56.52 367	48.77 364	55.38 360	58.64 368	29.33 364	28.96 365	52.65 361	4.68 372	64.62 366	28.11 364	33.07 362	59.93 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		57.99 328	54.91 330	67.24 342	88.51 327	65.59 350	52.21 361	90.33 325	43.58 360	42.84 361	51.18 362	20.29 367	85.07 358	34.77 362	70.45 341	51.05 361
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		43.23 332	42.29 334	46.03 347	65.58 364	37.41 367	73.51 356	64.62 365	33.99 362	28.47 366	47.87 363	19.90 368	67.91 364	22.23 365	24.45 363	32.77 362

EMVS		42.07 333	41.12 335	44.92 348	63.45 366	35.56 369	73.65 355	63.48 366	33.05 363	26.88 367	45.45 364	21.27 366	67.14 365	19.80 366	23.02 365	32.06 363

X-MVStestdata		88.31 165	86.13 207	94.85 28	98.54 12	86.60 37	96.93 18	97.19 35	90.66 23	92.85 63	23.41 365	85.02 61	99.49 24	91.99 59	98.56 49	98.47 30

test_post											10.29 366	70.57 234	95.91 291

test_post1							88.00 318				9.81 367	69.31 251	95.53 303	76.65 262

testmvs		8.92 337	11.52 340	1.12 352	1.06 372	0.46 374	86.02 331	0.65 373	0.62 368	2.74 369	9.52 368	0.31 375	0.45 371	2.38 368	0.39 368	2.46 367

test123		8.76 338	11.22 341	1.39 351	0.85 373	0.97 373	85.76 334	0.35 374	0.54 369	2.45 370	8.14 369	0.60 374	0.48 370	2.16 369	0.17 369	2.71 366

wuyk23d		21.27 336	20.48 339	23.63 350	68.59 363	36.41 368	49.57 362	6.85 372	9.37 366	7.89 368	4.46 370	4.03 373	31.37 368	17.47 367	16.07 367	3.12 365

uanet_test		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

pcd_1.5k_mvsjas		6.64 340	8.86 343	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	79.70 120	0.00 372	0.00 370	0.00 370	0.00 368

sosnet-low-res		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

sosnet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

uncertanet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

Regformer		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

uanet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

No_MVS				96.52 1	97.78 55	90.86 1		96.85 62					99.61 3	96.03 1	99.06 9	99.07 4

eth-test2					0.00 374

eth-test					0.00 374

IU-MVS					98.77 4	86.00 55		96.84 63	81.26 245	97.26 6				95.50 8	99.13 3	99.03 5

save fliter					97.85 48	85.63 69	95.21 96	96.82 67	89.44 47

test_0728_SECOND				95.01 16	98.79 1	86.43 42	97.09 12	97.49 5					99.61 3	95.62 6	99.08 7	98.99 6

GSMVS																96.12 145

test_part2					98.55 11	87.22 17				96.40 12

sam_mvs1												71.70 216				96.12 145

sam_mvs												70.60 230

MTGPA								96.97 49

MTMP							96.16 45	60.64 367

test9_res														91.91 64	98.71 32	98.07 65

agg_prior2														90.54 90	98.68 37	98.27 49

agg_prior					97.38 65	85.92 61		96.72 79		92.16 82			98.97 81

test_prior4						85.96 58	94.11 170

test_prior				93.82 65	97.29 70	84.49 86		96.88 59					98.87 89			98.11 63

旧先验2							93.36 205		71.25 339	94.37 27			97.13 225	86.74 133

新几何2							93.11 220

无先验							93.28 212	96.26 110	73.95 321				99.05 61	80.56 220		96.59 130

原ACMM2							92.94 227

testdata2													98.75 101	78.30 246

segment_acmp												87.16 36

testdata1							92.15 250		87.94 91

test12				94.34 54	97.13 75	86.15 52		96.29 108		91.04 108		85.08 59	99.01 71		98.13 65	97.86 81

plane_prior7					94.70 163	82.74 137

plane_prior6					94.52 169	82.75 135						74.23 183

plane_prior5								96.22 115					98.12 138	88.15 113	89.99 189	94.63 200

plane_prior3						82.75 135			90.26 31	86.91 165

plane_prior2							95.85 62		90.81 17

plane_prior1					94.59 167

plane_prior						82.73 138	95.21 96		89.66 44						89.88 194

n2								0.00 375

nn								0.00 375

door-mid								85.49 350

test11								96.57 94

door								85.33 351

HQP5-MVS						81.56 163

HQP-NCC					94.17 183		94.39 152		88.81 65	85.43 203

ACMP_Plane					94.17 183		94.39 152		88.81 65	85.43 203

BP-MVS														87.11 130

HQP4-MVS										85.43 203			97.96 159			94.51 210

HQP3-MVS								96.04 129							89.77 196

HQP2-MVS												73.83 193

MDTV_nov1_ep13_2view						55.91 362	87.62 324		73.32 326	84.59 221		70.33 237		74.65 281		95.50 169

ACMMP++_ref															87.47 230

ACMMP++															88.01 225

Test By Simon												80.02 115

High-resolution multi-view benchmark