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.

APDe-MVS		95.46 1	95.64 1	94.91 6	98.26 13	86.29 21	97.46 2	97.40 5	89.03 36	96.20 2	98.10 1	89.39 4	99.34 13	95.88 1	99.03 1	99.10 1

CNVR-MVS		95.40 2	95.37 2	95.50 3	98.11 16	88.51 3	95.29 49	96.96 26	92.09 3	95.32 5	97.08 16	89.49 3	99.33 16	95.10 2	98.85 4	98.66 4

SteuartSystems-ACMMP		95.20 3	95.32 4	94.85 9	96.99 38	86.33 19	97.33 3	97.30 11	91.38 8	95.39 4	97.46 3	88.98 6	99.40 11	94.12 4	98.89 3	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 4	94.91 5	95.83 1	98.25 14	89.65 1	95.92 31	96.96 26	91.75 5	94.02 12	96.83 22	88.12 7	99.55 2	93.41 8	98.94 2	98.28 17

SD-MVS		94.96 5	95.33 3	93.88 33	97.25 35	86.69 11	96.19 24	97.11 20	90.42 18	96.95 1	97.27 7	89.53 2	96.91 149	94.38 3	98.85 4	98.03 33

NCCC		94.81 6	94.69 7	95.17 5	97.83 22	87.46 5	95.66 39	96.93 29	92.34 2	93.94 13	96.58 34	87.74 9	99.44 10	92.83 12	98.40 25	98.62 5

XVS		94.45 7	94.32 8	94.85 9	98.54 3	86.60 14	96.93 11	97.19 15	90.66 16	92.85 23	97.16 14	85.02 30	99.49 7	91.99 22	98.56 22	98.47 9

MCST-MVS		94.45 7	94.20 12	95.19 4	98.46 7	87.50 4	95.00 62	97.12 18	87.13 65	92.51 33	96.30 42	89.24 5	99.34 13	93.46 7	98.62 19	98.73 3

ACMMPR		94.43 9	94.28 9	94.91 6	98.63 1	86.69 11	96.94 10	97.32 10	88.63 44	93.53 21	97.26 8	85.04 29	99.54 4	92.35 17	98.78 9	98.50 7

CP-MVS		94.34 10	94.21 11	94.74 15	98.39 9	86.64 13	97.60 1	97.24 13	88.53 46	92.73 27	97.23 9	85.20 27	99.32 17	92.15 20	98.83 6	98.25 20

Regformer-2		94.33 11	94.22 10	94.68 16	95.54 67	86.75 10	94.57 79	96.70 40	91.84 4	94.41 7	96.56 36	87.19 12	99.13 27	93.50 6	97.65 40	98.16 24

#test#		94.32 12	94.14 13	94.86 8	98.61 2	86.81 7	96.43 20	97.34 7	87.51 62	93.65 16	97.21 10	86.10 20	99.49 7	91.68 28	98.77 10	98.30 16

MP-MVS		94.25 13	94.07 16	94.77 13	98.47 6	86.31 20	96.71 17	96.98 25	89.04 35	91.98 37	97.19 11	85.43 25	99.56 1	92.06 21	98.79 7	98.44 11

APD-MVS		94.24 14	94.07 16	94.75 14	98.06 19	86.90 6	95.88 32	96.94 28	85.68 83	95.05 6	97.18 12	87.31 11	99.07 29	91.90 26	98.61 20	98.28 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-1		94.22 15	94.13 14	94.51 20	95.54 67	86.36 18	94.57 79	96.44 50	91.69 6	94.32 9	96.56 36	87.05 14	99.03 35	93.35 9	97.65 40	98.15 25

DeepPCF-MVS	89.96 1	94.20 16	94.77 6	92.49 67	96.52 49	80.00 128	94.00 114	97.08 21	90.05 20	95.65 3	97.29 6	89.66 1	98.97 44	93.95 5	98.71 11	98.50 7

DeepC-MVS_fast	89.43 2	94.04 17	93.79 20	94.80 12	97.48 26	86.78 8	95.65 40	96.89 31	89.40 29	92.81 25	96.97 18	85.37 26	99.24 20	90.87 35	98.69 12	98.38 13
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		94.02 18	93.88 18	94.43 23	98.39 9	85.78 29	97.25 5	97.07 22	86.90 68	92.62 30	96.80 25	84.85 33	99.17 23	92.43 16	98.65 17	98.33 14

mPP-MVS		93.99 19	93.78 21	94.63 18	98.50 5	85.90 27	96.87 13	96.91 30	88.70 42	91.83 41	97.17 13	83.96 37	99.55 2	91.44 32	98.64 18	98.43 12

PGM-MVS		93.96 20	93.72 23	94.68 16	98.43 8	86.22 22	95.30 47	97.78 1	87.45 63	93.26 22	97.33 5	84.62 34	99.51 5	90.75 37	98.57 21	98.32 15

Regformer-4		93.91 21	93.81 19	94.19 28	95.36 71	85.47 30	94.68 76	96.41 53	91.60 7	93.75 15	96.71 26	85.95 22	99.10 28	93.21 10	96.65 55	98.01 35

PHI-MVS		93.89 22	93.65 24	94.62 19	96.84 41	86.43 16	96.69 18	97.49 2	85.15 90	93.56 20	96.28 43	85.60 24	99.31 18	92.45 15	98.79 7	98.12 27

patchmatch test		93.78 23	93.77 22	93.80 38	97.92 21	84.19 49	96.30 21	96.87 34	86.96 66	93.92 14	97.47 2	83.88 38	98.96 45	92.71 14	97.87 35	98.26 19

MSLP-MVS		93.72 24	94.08 15	92.65 61	97.31 29	83.43 64	95.79 35	97.33 8	90.03 21	93.58 19	96.96 19	84.87 32	97.76 94	92.19 19	98.66 15	96.76 68

Regformer-3		93.68 25	93.64 25	93.81 37	95.36 71	84.61 38	94.68 76	95.83 84	91.27 9	93.60 18	96.71 26	85.75 23	98.86 48	92.87 11	96.65 55	97.96 36

TSAR-MVS		93.66 26	93.41 27	94.41 24	96.59 46	86.78 8	94.40 88	93.93 160	89.77 25	94.21 10	95.59 62	87.35 10	98.61 61	92.72 13	96.15 62	97.83 43

test_prior3		93.60 27	93.53 26	93.82 35	97.29 31	84.49 42	94.12 104	96.88 32	87.67 59	92.63 28	96.39 40	86.62 16	98.87 46	91.50 30	98.67 13	98.11 28

MVS_111021_HR		93.45 28	93.31 28	93.84 34	96.99 38	84.84 35	93.24 140	97.24 13	88.76 41	91.60 44	95.85 54	86.07 21	98.66 58	91.91 25	98.16 30	98.03 33

DELS-MVS		93.43 29	93.25 29	93.97 30	95.42 70	85.04 34	93.06 146	97.13 17	90.74 14	91.84 39	95.09 72	86.32 19	99.21 21	91.22 33	98.45 24	97.65 47
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
HPM-MVS_fast		93.40 30	93.22 30	93.94 32	98.36 11	84.83 36	97.15 7	96.80 38	85.77 80	92.47 34	97.13 15	82.38 45	99.07 29	90.51 38	98.40 25	97.92 39

DeepC-MVS	88.79 3	93.31 31	92.99 32	94.26 27	96.07 58	85.83 28	94.89 65	96.99 24	89.02 37	89.56 59	97.37 4	82.51 44	99.38 12	92.20 18	98.30 27	97.57 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP		93.24 32	92.88 34	94.30 26	98.09 18	85.33 32	96.86 14	97.45 4	88.33 49	90.15 55	97.03 17	81.44 52	99.51 5	90.85 36	95.74 65	98.04 32
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CSCG		93.23 33	93.05 31	93.76 39	98.04 20	84.07 51	96.22 23	97.37 6	84.15 106	90.05 56	95.66 60	87.77 8	99.15 26	89.91 41	98.27 28	98.07 30

EI-MVSNet-Vis		93.01 34	92.92 33	93.29 41	95.01 81	83.51 63	94.48 82	95.77 85	90.87 10	92.52 32	96.67 29	84.50 35	99.00 41	91.99 22	94.44 77	97.36 55

CDPH-MVS		92.83 35	92.30 36	94.44 21	97.79 23	86.11 24	94.06 111	96.66 42	80.09 170	92.77 26	96.63 31	86.62 16	99.04 34	87.40 60	98.66 15	98.17 23

EI-MVSNet-UG		92.74 36	92.62 35	93.12 46	94.86 86	83.20 68	94.40 88	95.74 87	90.71 15	92.05 36	96.60 33	84.00 36	98.99 42	91.55 29	93.63 84	97.17 59

MVS_111021_LR		92.47 37	92.29 37	92.98 52	95.99 60	84.43 46	93.08 144	96.09 68	88.20 51	91.12 48	95.72 59	81.33 53	97.76 94	91.74 27	97.37 45	96.75 69

3Dnovator+	87.14 4	92.42 38	91.37 42	95.55 2	95.63 66	88.73 2	97.07 8	96.77 39	90.84 11	84.02 149	96.62 32	75.95 108	99.34 13	87.77 56	97.68 38	98.59 6

VNet		92.24 39	91.91 38	93.24 43	96.59 46	83.43 64	94.84 67	96.44 50	89.19 33	94.08 11	95.90 53	77.85 86	98.17 77	88.90 46	93.38 90	98.13 26

CPTT-MVS		91.99 40	91.80 39	92.55 64	98.24 15	81.98 94	96.76 16	96.49 49	81.89 154	90.24 53	96.44 39	78.59 75	98.61 61	89.68 42	97.85 36	97.06 61

DP-MVS Recon		91.95 41	91.28 44	93.96 31	98.33 12	85.92 26	94.66 78	96.66 42	82.69 145	90.03 57	95.82 55	82.30 46	99.03 35	84.57 84	96.48 60	96.91 65

EPNet		91.79 42	91.02 47	94.10 29	90.10 212	85.25 33	96.03 28	92.05 186	92.83 1	87.39 79	95.78 56	79.39 69	99.01 38	88.13 54	97.48 42	98.05 31
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		91.77 43	91.70 40	92.00 79	97.08 37	80.03 127	93.60 131	95.18 119	87.85 57	90.89 50	96.47 38	82.06 50	98.36 68	85.07 77	97.04 48	97.62 48

Vis-MVSNet		91.75 44	91.23 45	93.29 41	95.32 73	83.78 55	96.14 25	95.98 74	89.89 22	90.45 51	96.58 34	75.09 119	98.31 73	84.75 82	96.90 49	97.78 46
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 45	90.82 50	94.44 21	94.59 95	86.37 17	97.18 6	97.02 23	89.20 32	84.31 145	96.66 30	73.74 135	99.17 23	86.74 69	97.96 34	97.79 45

EPP-MVSNet		91.70 46	91.56 41	92.13 76	95.88 61	80.50 121	97.33 3	95.25 113	86.15 77	89.76 58	95.60 61	83.42 39	98.32 72	87.37 62	93.25 94	97.56 52

MVSFormer		91.68 47	91.30 43	92.80 57	93.86 116	83.88 53	95.96 29	95.90 80	84.66 98	91.76 42	94.91 74	77.92 83	97.30 123	89.64 43	97.11 46	97.24 56

IS-MVSNet		91.43 48	91.09 46	92.46 68	95.87 63	81.38 104	96.95 9	93.69 165	89.72 26	89.50 61	95.98 50	78.57 76	97.77 93	83.02 100	96.50 59	98.22 21

PVSNet_Blended_VisFu		91.38 49	90.91 49	92.80 57	96.39 51	83.17 69	94.87 66	96.66 42	83.29 123	89.27 62	94.46 84	80.29 60	99.17 23	87.57 59	95.37 69	96.05 86

OMC-MVS		91.23 50	90.62 52	93.08 48	96.27 54	84.07 51	93.52 132	95.93 77	86.95 67	89.51 60	96.13 48	78.50 77	98.35 69	85.84 75	92.90 98	96.83 67

PAPM_NR		91.22 51	90.78 51	92.52 66	97.60 25	81.46 102	94.37 94	96.24 60	86.39 74	87.41 78	94.80 80	82.06 50	98.48 64	82.80 103	95.37 69	97.61 49

PS-MVSNAJ		91.18 52	90.92 48	91.96 81	95.26 76	82.60 88	92.09 169	95.70 88	86.27 75	91.84 39	92.46 134	79.70 66	98.99 42	89.08 45	95.86 64	94.29 139

lupinMVS		90.92 53	90.21 54	93.03 51	93.86 116	83.88 53	92.81 152	93.86 161	79.84 172	91.76 42	94.29 89	77.92 83	98.04 84	90.48 39	97.11 46	97.17 59

jason		90.80 54	90.10 56	92.90 55	93.04 134	83.53 62	93.08 144	94.15 148	80.22 168	91.41 45	94.91 74	76.87 89	97.93 88	90.28 40	96.90 49	97.24 56
jason: jason.
VDD-MVS		90.74 55	89.92 58	93.20 44	96.27 54	83.02 74	95.73 36	93.86 161	88.42 48	92.53 31	96.84 21	62.09 202	98.64 59	90.95 34	92.62 100	97.93 38

PVSNet_Blended		90.73 56	90.32 53	91.98 80	96.12 56	81.25 106	92.55 159	96.83 35	82.04 151	89.10 64	92.56 133	81.04 56	98.85 51	86.72 71	95.91 63	95.84 93

API-MVS		90.66 57	90.07 57	92.45 69	96.36 52	84.57 40	96.06 27	95.22 118	82.39 146	89.13 63	94.27 91	80.32 59	98.46 65	80.16 136	96.71 53	94.33 138

HQP_MVS		90.60 58	90.19 55	91.82 85	94.70 91	82.73 82	95.85 33	96.22 61	90.81 12	86.91 84	94.86 76	74.23 126	98.12 78	88.15 52	89.99 110	94.63 123

liao		90.42 59	89.49 61	93.20 44	97.27 33	84.46 45	92.63 155	95.51 100	71.01 233	91.20 47	96.21 45	82.92 41	99.05 31	80.56 130	98.07 33	96.10 82

MAR-MVS		90.30 60	89.37 64	93.07 50	96.61 45	84.48 44	95.68 38	95.67 89	82.36 147	87.85 72	92.85 125	76.63 94	98.80 54	80.01 137	96.68 54	95.91 89
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
LFMVS		90.08 61	89.13 69	92.95 53	96.71 43	82.32 90	96.08 26	89.91 226	86.79 69	92.15 35	96.81 23	62.60 199	98.34 70	87.18 64	93.90 80	98.19 22

PAPR		90.02 62	89.27 68	92.29 72	95.78 64	80.95 113	92.68 154	96.22 61	81.91 153	86.66 87	93.75 107	82.23 47	98.44 67	79.40 149	94.79 73	97.48 53

PVSNet_BlendedMVS		89.98 63	89.70 59	90.82 112	96.12 56	81.25 106	93.92 116	96.83 35	83.49 118	89.10 64	92.26 140	81.04 56	98.85 51	86.72 71	87.86 141	92.35 195

PS-MVSNAJss		89.97 64	89.62 60	91.02 108	91.90 150	80.85 116	95.26 51	95.98 74	86.26 76	86.21 95	94.29 89	79.70 66	97.65 98	88.87 47	88.10 138	94.57 128

XVG-OURS-SEG-HR		89.95 65	89.45 62	91.47 93	94.00 115	81.21 109	91.87 171	96.06 71	85.78 79	88.55 69	95.73 58	74.67 122	97.27 127	88.71 48	89.64 117	95.91 89

UGNet		89.95 65	88.95 74	92.95 53	94.51 98	83.31 66	95.70 37	95.23 116	89.37 30	87.58 76	93.94 97	64.00 195	98.78 55	83.92 92	96.31 61	96.74 70
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
UniMVSNet_NR-MVSNet		89.92 67	89.29 66	91.81 86	93.39 128	83.72 56	94.43 86	97.12 18	89.80 24	86.46 89	93.32 111	83.16 40	97.23 130	84.92 78	81.02 197	94.49 135

AdaColmap		89.89 68	89.07 70	92.37 70	97.41 27	83.03 73	94.42 87	95.92 78	82.81 140	86.34 94	94.65 81	73.89 132	99.02 37	80.69 127	95.51 67	95.05 111

UniMVSNet (Re)		89.80 69	89.07 70	92.01 77	93.60 124	84.52 41	94.78 70	97.47 3	89.26 31	86.44 92	92.32 139	82.10 48	97.39 122	84.81 81	80.84 201	94.12 140

HQP-MVS		89.80 69	89.28 67	91.34 96	94.17 106	81.56 97	94.39 90	96.04 72	88.81 38	85.43 120	93.97 96	73.83 133	97.96 86	87.11 66	89.77 115	94.50 133

VPA-MVSNet		89.62 71	88.96 73	91.60 90	93.86 116	82.89 77	95.46 43	97.33 8	87.91 54	88.43 71	93.31 112	74.17 128	97.40 119	87.32 63	82.86 178	94.52 131

WTY-MVS		89.60 72	88.92 75	91.67 88	95.47 69	81.15 110	92.38 162	94.78 138	83.11 126	89.06 66	94.32 87	78.67 74	96.61 159	81.57 119	90.89 106	97.24 56

Vis-MVSNet (Re-imp)		89.59 73	89.44 63	90.03 138	95.74 65	75.85 184	95.61 41	90.80 210	87.66 61	87.83 73	95.40 64	76.79 91	96.46 164	78.37 155	96.73 52	97.80 44

VDDNet		89.56 74	88.49 86	92.76 59	95.07 80	82.09 91	96.30 21	93.19 170	81.05 163	91.88 38	96.86 20	61.16 210	98.33 71	88.43 51	92.49 101	97.84 42

114514_t		89.51 75	88.50 84	92.54 65	98.11 16	81.99 93	95.16 57	96.36 55	70.19 234	85.81 100	95.25 67	76.70 92	98.63 60	82.07 111	96.86 51	97.00 62

QAPM		89.51 75	88.15 94	93.59 40	94.92 83	84.58 39	96.82 15	96.70 40	78.43 186	83.41 163	96.19 46	73.18 140	99.30 19	77.11 170	96.54 58	96.89 66

CLD-MVS		89.47 77	88.90 76	91.18 100	94.22 105	82.07 92	92.13 168	96.09 68	87.90 55	85.37 124	92.45 135	74.38 124	97.56 101	87.15 65	90.43 108	93.93 148
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 78	88.90 76	91.12 101	94.47 99	81.49 100	95.30 47	96.14 65	86.73 70	85.45 117	95.16 69	69.89 160	98.10 80	87.70 57	89.23 124	93.77 160

CDS-MVSNet		89.45 78	88.51 83	92.29 72	93.62 123	83.61 61	93.01 147	94.68 140	81.95 152	87.82 74	93.24 116	78.69 73	96.99 143	80.34 133	93.23 95	96.28 76
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ab-mvs		89.41 80	88.35 88	92.60 62	95.15 79	82.65 86	92.20 166	95.60 94	83.97 108	88.55 69	93.70 109	74.16 129	98.21 76	82.46 108	89.37 120	96.94 64

XVG-OURS		89.40 81	88.70 79	91.52 91	94.06 109	81.46 102	91.27 185	96.07 70	86.14 78	88.89 67	95.77 57	68.73 170	97.26 129	87.39 61	89.96 112	95.83 94

mvs_anonymous		89.37 82	89.32 65	89.51 149	93.47 127	74.22 190	91.65 179	94.83 136	82.91 138	85.45 117	93.79 105	81.23 55	96.36 169	86.47 73	94.09 78	97.94 37

DU-MVS		89.34 83	88.50 84	91.85 84	93.04 134	83.72 56	94.47 84	96.59 46	89.50 28	86.46 89	93.29 114	77.25 87	97.23 130	84.92 78	81.02 197	94.59 126

TAMVS		89.21 84	88.29 91	91.96 81	93.71 122	82.62 87	93.30 136	94.19 147	82.22 148	87.78 75	93.94 97	78.83 71	96.95 147	77.70 163	92.98 97	96.32 75

ACMM	84.12 9	89.14 85	88.48 87	91.12 101	94.65 94	81.22 108	95.31 45	96.12 67	85.31 89	85.92 99	94.34 85	70.19 159	98.06 83	85.65 76	88.86 129	94.08 144
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EI-MVSNet		89.10 86	88.86 78	89.80 145	91.84 152	78.30 167	93.70 127	95.01 123	85.73 81	87.15 80	95.28 65	79.87 64	97.21 132	83.81 94	87.36 144	93.88 151

CNLPA		89.07 87	87.98 97	92.34 71	96.87 40	84.78 37	94.08 110	93.24 169	81.41 159	84.46 138	95.13 71	75.57 114	96.62 157	77.21 168	93.84 82	95.61 99

PLC	84.53 7	89.06 88	88.03 96	92.15 75	97.27 33	82.69 85	94.29 98	95.44 104	79.71 174	84.01 150	94.18 92	76.68 93	98.75 56	77.28 167	93.41 89	95.02 112
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 89	88.64 82	90.21 126	90.74 203	79.28 154	95.96 29	95.90 80	84.66 98	85.33 126	92.94 124	74.02 131	97.30 123	89.64 43	88.53 132	94.05 145

HY-MVS	83.01 12	89.03 89	87.94 99	92.29 72	94.86 86	82.77 78	92.08 170	94.49 142	81.52 158	86.93 83	92.79 130	78.32 80	98.23 74	79.93 140	90.55 107	95.88 91

ACMP	84.23 8	89.01 91	88.35 88	90.99 110	94.73 88	81.27 105	95.07 61	95.89 82	86.48 73	83.67 157	94.30 88	69.33 166	97.99 85	87.10 68	88.55 131	93.72 164
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVS_Test		88.97 92	88.97 72	88.96 156	93.01 137	73.25 201	91.49 181	94.42 143	82.72 143	85.36 125	93.75 107	80.41 58	96.26 173	86.28 74	93.30 93	97.38 54

HyFIR		88.94 93	88.67 81	89.73 147	95.18 78	73.70 198	88.40 216	95.10 121	77.72 192	87.50 77	94.85 78	78.16 81	97.92 89	83.75 95	97.38 44	97.59 50

sss		88.93 94	88.26 93	90.94 111	94.05 110	80.78 117	91.71 176	95.38 105	81.55 157	88.63 68	93.91 101	75.04 120	95.47 197	82.47 107	91.61 103	96.57 73

TranMVSNet+NR-MVSNet		88.84 95	87.95 98	91.49 92	92.68 143	83.01 75	94.92 64	96.31 56	89.88 23	85.53 114	93.85 104	76.63 94	96.96 146	81.91 114	79.87 214	94.50 133

MVSTER		88.84 95	88.29 91	90.51 117	92.95 139	80.44 122	93.73 124	95.01 123	84.66 98	87.15 80	93.12 120	72.79 144	97.21 132	87.86 55	87.36 144	93.87 152

OpenMVS	83.78 11	88.74 97	87.29 108	93.08 48	92.70 142	85.39 31	96.57 19	96.43 52	78.74 184	80.85 186	96.07 49	69.64 163	99.01 38	78.01 161	96.65 55	94.83 120

BH-untuned		88.60 98	88.13 95	90.01 140	95.24 77	78.50 163	93.29 137	94.15 148	84.75 96	84.46 138	93.40 110	75.76 112	97.40 119	77.59 164	94.52 75	94.12 140

NR-MVSNet		88.58 99	87.47 104	91.93 83	93.04 134	84.16 50	94.77 71	96.25 59	89.05 34	80.04 194	93.29 114	79.02 70	97.05 142	81.71 118	80.05 209	94.59 126

diffmvs		88.54 100	88.69 80	88.08 182	93.51 126	71.68 216	90.93 188	94.84 135	80.77 165	85.65 110	93.92 100	81.28 54	94.65 213	83.26 97	94.03 79	97.91 40

1112_ss		88.42 101	87.33 107	91.72 87	94.92 83	80.98 112	92.97 149	94.54 141	78.16 190	83.82 154	93.88 102	78.78 72	97.91 90	79.45 147	89.41 119	96.26 77

WR-MVS		88.38 102	87.67 101	90.52 116	93.30 129	80.18 123	93.26 139	95.96 76	88.57 45	85.47 116	92.81 129	76.12 98	96.91 149	81.24 120	82.29 183	94.47 136

BH-RMVSNet		88.37 103	87.48 103	91.02 108	95.28 74	79.45 141	92.89 151	93.07 172	85.45 87	86.91 84	94.84 79	70.35 157	97.76 94	73.97 189	94.59 74	95.85 92

IterMVS-LS		88.36 104	87.91 100	89.70 148	93.80 119	78.29 168	93.73 124	95.08 122	85.73 81	84.75 132	91.90 152	79.88 63	96.92 148	83.83 93	82.51 181	93.89 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 105	86.13 131	94.85 9	98.54 3	86.60 14	96.93 11	97.19 15	90.66 16	92.85 23	23.41 261	85.02 30	99.49 7	91.99 22	98.56 22	98.47 9

LCM-MVSNet-Re		88.30 106	88.32 90	88.27 177	94.71 90	72.41 213	93.15 141	90.98 206	87.77 58	79.25 201	91.96 150	78.35 79	95.75 191	83.04 99	95.62 66	96.65 71

jajsoiax		88.24 107	87.50 102	90.48 119	90.89 199	80.14 124	95.31 45	95.65 93	84.97 93	84.24 147	94.02 93	65.31 190	97.42 115	88.56 49	88.52 133	93.89 149

VPNet		88.20 108	87.47 104	90.39 121	93.56 125	79.46 139	94.04 112	95.54 97	88.67 43	86.96 82	94.58 83	69.33 166	97.15 134	84.05 91	80.53 205	94.56 129

TAPA-MVS	84.62 6	88.16 109	87.01 118	91.62 89	96.64 44	80.65 118	94.39 90	96.21 64	76.38 197	86.19 96	95.44 63	79.75 65	98.08 82	62.75 231	95.29 71	96.13 81
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		88.06 110	87.28 109	90.38 123	90.94 198	79.88 129	95.22 53	95.66 91	85.10 91	84.21 148	93.94 97	63.53 197	97.40 119	88.50 50	88.40 137	93.87 152

v1neww		87.98 111	87.25 111	90.16 128	91.38 169	79.41 143	94.37 94	95.28 109	84.48 101	85.77 102	91.53 161	76.12 98	97.45 107	84.45 86	81.89 186	93.61 169

v7new		87.98 111	87.25 111	90.16 128	91.38 169	79.41 143	94.37 94	95.28 109	84.48 101	85.77 102	91.53 161	76.12 98	97.45 107	84.45 86	81.89 186	93.61 169

v6		87.98 111	87.25 111	90.16 128	91.36 172	79.39 148	94.37 94	95.27 112	84.48 101	85.78 101	91.51 163	76.15 97	97.46 105	84.46 85	81.88 188	93.62 168

F-COLMAP		87.95 114	86.80 123	91.40 95	96.35 53	80.88 115	94.73 72	95.45 102	79.65 175	82.04 178	94.61 82	71.13 151	98.50 63	76.24 174	91.05 105	94.80 122

LS3D		87.89 115	86.32 129	92.59 63	96.07 58	82.92 76	95.23 52	94.92 131	75.66 203	82.89 168	95.98 50	72.48 146	99.21 21	68.43 212	95.23 72	95.64 98

v1		87.85 116	87.10 114	90.11 136	91.21 185	79.24 156	94.09 108	95.24 114	84.44 104	85.70 107	91.31 167	75.96 107	97.45 107	84.18 89	81.73 191	93.64 166

divwei89l23v2f112		87.84 117	87.09 115	90.10 137	91.23 184	79.24 156	94.09 108	95.24 114	84.44 104	85.70 107	91.31 167	75.91 109	97.44 113	84.17 90	81.73 191	93.64 166

v2v482		87.84 117	87.06 116	90.17 127	90.99 194	79.23 158	94.00 114	95.13 120	84.87 94	85.53 114	92.07 147	74.45 123	97.45 107	84.71 83	81.75 190	93.85 155

WR-MVS_H		87.80 119	87.37 106	89.10 155	93.23 130	78.12 170	95.61 41	97.30 11	87.90 55	83.72 155	92.01 149	79.65 68	96.01 181	76.36 171	80.54 204	93.16 177

v7		87.75 120	86.96 119	90.12 133	91.20 186	79.50 134	94.28 99	95.46 101	83.45 120	85.75 104	91.56 160	75.13 117	97.43 114	83.60 96	82.18 185	93.42 174

PCF-MVS	84.11 10	87.74 121	86.08 134	92.70 60	94.02 111	84.43 46	89.27 204	95.87 83	73.62 213	84.43 140	94.33 86	78.48 78	98.86 48	70.27 204	94.45 76	94.81 121
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
V42		87.68 122	86.86 121	90.15 131	90.58 205	80.14 124	94.24 101	95.28 109	83.66 112	85.67 109	91.33 165	74.73 121	97.41 118	84.43 88	81.83 189	92.89 183

XXY-MVS		87.65 123	86.85 122	90.03 138	92.14 147	80.60 120	93.76 121	95.23 116	82.94 137	84.60 134	94.02 93	74.27 125	95.49 196	81.04 122	83.68 171	94.01 147

Test_1112_low_res		87.65 123	86.51 127	91.08 104	94.94 82	79.28 154	91.77 172	94.30 146	76.04 202	83.51 161	92.37 137	77.86 85	97.73 97	78.69 154	89.13 126	96.22 78

CP-MVSNet		87.63 125	87.26 110	88.74 160	93.12 132	76.59 179	95.29 49	96.58 47	88.43 47	83.49 162	92.98 123	75.28 116	95.83 187	78.97 151	81.15 195	93.79 156

BH-w/o		87.57 126	87.05 117	89.12 154	94.90 85	77.90 173	92.41 161	93.51 166	82.89 139	83.70 156	91.34 164	75.75 113	97.07 141	75.49 177	93.49 86	92.39 193

liao1		87.51 127	86.57 126	90.34 124	92.42 145	79.74 132	92.63 155	95.35 108	78.35 187	80.14 192	91.62 157	74.05 130	97.15 134	81.05 121	93.53 85	94.12 140

v8		87.50 128	86.71 124	89.89 141	91.37 171	79.40 147	94.50 81	95.38 105	84.81 95	83.60 159	91.33 165	76.05 102	97.42 115	82.84 102	80.51 206	92.84 185

HC-MVS		87.44 129	86.10 133	91.44 94	92.61 144	83.62 60	92.63 155	95.66 91	67.26 242	81.47 182	92.15 142	77.95 82	98.22 75	79.71 144	95.48 68	92.47 190

FMVSNet3		87.40 130	86.11 132	91.30 97	93.79 121	83.64 59	94.20 103	94.81 137	83.89 109	84.37 141	91.87 153	68.45 173	96.56 161	78.23 158	85.36 157	93.70 165

PS-CasMVS		87.32 131	86.88 120	88.63 162	92.99 138	76.33 181	95.33 44	96.61 45	88.22 50	83.30 165	93.07 121	73.03 142	95.79 190	78.36 156	81.00 199	93.75 162

GBi-Net		87.26 132	85.98 135	91.08 104	94.01 112	83.10 70	95.14 58	94.94 127	83.57 115	84.37 141	91.64 154	66.59 181	96.34 170	78.23 158	85.36 157	93.79 156

test1		87.26 132	85.98 135	91.08 104	94.01 112	83.10 70	95.14 58	94.94 127	83.57 115	84.37 141	91.64 154	66.59 181	96.34 170	78.23 158	85.36 157	93.79 156

v10		87.25 134	86.38 128	89.85 142	91.19 188	79.50 134	94.48 82	95.45 102	83.79 110	83.62 158	91.19 169	75.13 117	97.42 115	81.94 113	80.60 203	92.63 187

DP-MVS		87.25 134	85.36 146	92.90 55	97.65 24	83.24 67	94.81 68	92.00 188	74.99 207	81.92 180	95.00 73	72.66 145	99.05 31	66.92 218	92.33 102	96.40 74

FMVSNet2		87.19 136	85.82 138	91.30 97	94.01 112	83.67 58	94.79 69	94.94 127	83.57 115	83.88 151	92.05 148	66.59 181	96.51 163	77.56 165	85.01 161	93.73 163

Baseline_NR-MVSNet		87.07 137	86.63 125	88.40 175	91.44 163	77.87 175	94.23 102	92.57 178	84.12 107	85.74 106	92.08 145	77.25 87	96.04 178	82.29 110	79.94 212	91.30 211

PEN-MVS		86.80 138	86.27 130	88.40 175	92.32 146	75.71 185	95.18 55	96.38 54	87.97 52	82.82 169	93.15 119	73.39 138	95.92 184	76.15 175	79.03 217	93.59 171

TR-MVS		86.78 139	85.76 139	89.82 143	94.37 103	78.41 165	92.47 160	92.83 174	81.11 162	86.36 93	92.40 136	68.73 170	97.48 103	73.75 192	89.85 114	93.57 172

PatchMatch-RL		86.77 140	85.54 140	90.47 120	95.88 61	82.71 84	90.54 190	92.31 181	79.82 173	84.32 144	91.57 159	68.77 169	96.39 167	73.16 194	93.48 88	92.32 197

PAPM		86.68 141	85.39 145	90.53 115	93.05 133	79.33 153	89.79 197	94.77 139	78.82 182	81.95 179	93.24 116	76.81 90	97.30 123	66.94 216	93.16 96	94.95 116

GA-MVS		86.61 142	85.27 147	90.66 114	91.33 177	78.71 162	90.40 191	93.81 164	85.34 88	85.12 127	89.57 188	61.25 207	97.11 137	80.99 124	89.59 118	96.15 79

v52		86.50 143	85.53 142	89.39 151	89.17 220	78.99 160	94.72 75	95.54 97	83.59 113	82.10 175	90.60 174	71.59 150	97.45 107	82.52 104	79.99 211	91.73 203

V4		86.50 143	85.54 140	89.39 151	89.13 221	78.99 160	94.73 72	95.54 97	83.59 113	82.10 175	90.61 173	71.60 149	97.45 107	82.52 104	80.01 210	91.74 202

EPNet_dtu		86.49 145	85.94 137	88.14 181	90.24 210	72.82 205	94.11 106	92.20 184	86.66 72	79.42 200	92.36 138	73.52 136	95.81 189	71.26 200	93.66 83	95.80 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
cascas		86.43 146	84.98 150	90.80 113	92.10 149	80.92 114	90.24 192	95.91 79	73.10 216	83.57 160	88.39 197	65.15 191	97.46 105	84.90 80	91.43 104	94.03 146

LTVRE_ROB	82.13 13	86.26 147	84.90 153	90.34 124	94.44 102	81.50 99	92.31 164	94.89 132	83.03 132	79.63 198	92.67 131	69.69 162	97.79 92	71.20 201	86.26 151	91.72 204
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DTE-MVSNet		86.11 148	85.48 143	87.98 185	91.65 159	74.92 188	94.93 63	95.75 86	87.36 64	82.26 172	93.04 122	72.85 143	95.82 188	74.04 188	77.46 220	93.20 175

PatchFormer-LS_test		86.02 149	85.13 148	88.70 161	91.52 160	74.12 192	91.19 186	92.09 185	82.71 144	84.30 146	87.24 213	70.87 154	96.98 144	81.04 122	85.17 160	95.00 113

XVG-ACMP-BASELINE		86.00 150	84.84 154	89.45 150	91.20 186	78.00 171	91.70 177	95.55 96	85.05 92	82.97 166	92.25 141	54.49 228	97.48 103	82.93 101	87.45 143	92.89 183

test-LLR		85.87 151	85.41 144	87.25 197	90.95 196	71.67 217	89.55 198	89.88 227	83.41 121	84.54 136	87.95 203	67.25 176	95.11 210	81.82 115	93.37 91	94.97 114

FMVSNet1		85.85 152	84.11 161	91.08 104	92.81 141	83.10 70	95.14 58	94.94 127	81.64 155	82.68 170	91.64 154	59.01 216	96.34 170	75.37 178	83.78 168	93.79 156

PatchmatchNet		85.85 152	84.70 156	89.29 153	91.76 155	75.54 186	88.49 214	91.30 201	81.63 156	85.05 128	88.70 194	71.71 148	96.24 174	74.61 185	89.05 127	96.08 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		85.77 154	84.94 152	88.26 178	91.16 191	72.58 212	89.47 202	91.04 205	76.26 200	86.45 91	89.97 182	70.74 156	96.86 152	82.35 109	87.07 149	95.34 107

PMMVS		85.71 155	84.96 151	87.95 186	88.90 223	77.09 176	88.68 212	90.06 222	72.32 223	86.47 88	90.76 171	72.15 147	94.40 215	81.78 117	93.49 86	92.36 194

PVSNet	78.82 18	85.55 156	84.65 157	88.23 180	94.72 89	71.93 214	87.12 223	92.75 176	78.80 183	84.95 130	90.53 175	64.43 194	96.71 155	74.74 183	93.86 81	96.06 85

ACMH	80.38 17	85.36 157	83.68 169	90.39 121	94.45 101	80.63 119	94.73 72	94.85 134	82.09 150	77.24 207	92.65 132	60.01 214	97.58 100	72.25 196	84.87 162	92.96 181
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 158	84.64 158	87.49 192	90.77 202	72.59 211	94.01 113	94.40 144	84.72 97	79.62 199	93.17 118	61.91 203	96.72 153	81.99 112	81.16 193	93.16 177

CR-MVSNet		85.35 158	83.76 165	90.12 133	90.58 205	79.34 151	85.24 234	91.96 190	78.27 188	85.55 112	87.87 206	71.03 152	95.61 192	73.96 190	89.36 121	95.40 104

tpmrst		85.35 158	84.99 149	86.43 209	90.88 200	67.88 234	88.71 211	91.43 199	80.13 169	86.08 98	88.80 192	73.05 141	96.02 180	82.48 106	83.40 175	95.40 104

PT_06_test		85.24 161	84.34 159	87.92 187	90.78 201	74.02 193	91.74 174	94.96 126	83.48 119	82.90 167	87.36 210	73.23 139	96.61 159	75.30 179	79.34 216	93.14 179

IB-MVS	80.51 15	85.24 161	83.26 180	91.19 99	92.13 148	79.86 130	91.75 173	91.29 202	83.28 124	80.66 188	88.49 196	61.28 206	98.46 65	80.99 124	79.46 215	95.25 108
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
RPSCF		85.07 163	84.27 160	87.48 193	92.91 140	70.62 226	91.69 178	92.46 179	76.20 201	82.67 171	95.22 68	63.94 196	97.29 126	77.51 166	85.80 155	94.53 130

Test4		85.05 164	83.81 164	88.77 157	91.43 165	73.75 196	90.68 189	90.98 206	80.66 166	83.84 152	89.59 187	62.44 200	97.11 137	78.84 152	85.81 153	95.46 101

ACMH+	81.04 14	85.05 164	83.46 176	89.82 143	94.66 93	79.37 149	94.44 85	94.12 150	82.19 149	78.04 204	92.82 128	58.23 218	97.54 102	73.77 191	82.90 177	92.54 188

v18		84.97 166	83.76 165	88.60 165	91.36 172	79.41 143	93.82 119	94.04 151	83.00 135	76.61 208	86.60 216	76.19 96	95.43 198	80.39 132	71.79 231	90.96 215

v16		84.96 167	83.74 167	88.62 163	91.40 167	79.48 137	93.83 117	94.04 151	83.03 132	76.54 209	86.59 217	76.11 101	95.42 199	80.33 134	71.80 230	90.95 217

DWT-MVSNet_test		84.95 168	83.68 169	88.77 157	91.43 165	73.75 196	91.74 174	90.98 206	80.66 166	83.84 152	87.36 210	62.44 200	97.11 137	78.84 152	85.81 153	95.46 101

v17		84.93 169	83.70 168	88.62 163	91.36 172	79.48 137	93.83 117	94.03 153	83.04 131	76.51 210	86.57 218	76.05 102	95.42 199	80.31 135	71.65 232	90.96 215

IterMVS		84.88 170	83.98 163	87.60 189	91.44 163	76.03 183	90.18 194	92.41 180	83.24 125	81.06 185	90.42 176	66.60 180	94.28 216	79.46 146	80.98 200	92.48 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 171	83.09 181	90.14 132	93.80 119	80.05 126	89.18 207	93.09 171	78.89 180	78.19 202	91.91 151	65.86 189	97.27 127	68.47 211	88.45 135	93.11 180

v15		84.79 172	83.53 173	88.57 169	91.30 183	79.41 143	93.70 127	94.01 154	83.06 128	76.27 211	86.42 222	76.03 104	95.38 201	80.01 137	71.00 235	90.92 218

V14		84.79 172	83.52 174	88.57 169	91.32 179	79.43 142	93.72 126	94.01 154	83.06 128	76.22 212	86.43 219	76.01 105	95.37 202	79.96 139	70.99 236	90.91 219

V9		84.77 174	83.50 175	88.58 166	91.33 177	79.46 139	93.75 122	94.00 157	83.07 127	76.07 215	86.43 219	75.97 106	95.37 202	79.91 141	70.93 238	90.91 219

v12		84.74 175	83.46 176	88.58 166	91.32 179	79.50 134	93.75 122	94.01 154	83.06 128	75.98 217	86.41 223	75.82 111	95.36 204	79.87 142	70.89 239	90.89 221

tpm		84.73 176	84.02 162	86.87 207	90.33 208	68.90 232	89.06 208	89.94 225	80.85 164	85.75 104	89.86 184	68.54 172	95.97 182	77.76 162	84.05 167	95.75 96

v13		84.72 177	83.44 178	88.58 166	91.31 182	79.52 133	93.77 120	94.00 157	83.03 132	75.85 218	86.38 224	75.84 110	95.35 205	79.83 143	70.95 237	90.87 222

CVMVSNet		84.69 178	84.79 155	84.37 223	91.84 152	64.92 241	93.70 127	91.47 197	66.19 244	86.16 97	95.28 65	67.18 179	93.33 223	80.89 126	90.42 109	94.88 118

v11		84.67 179	83.41 179	88.44 174	91.32 179	79.13 159	93.69 130	93.99 159	82.81 140	76.20 213	86.24 226	75.48 115	95.35 205	79.53 145	71.48 234	90.85 223

test-mter		84.54 180	83.64 171	87.25 197	90.95 196	71.67 217	89.55 198	89.88 227	79.17 177	84.54 136	87.95 203	55.56 225	95.11 210	81.82 115	93.37 91	94.97 114

TransMVSNet (Re)		84.43 181	83.06 182	88.54 171	91.72 156	78.44 164	95.18 55	92.82 175	82.73 142	79.67 197	92.12 143	73.49 137	95.96 183	71.10 203	68.73 245	91.21 212

tpm2		84.08 182	82.94 183	87.48 193	91.39 168	71.27 219	89.23 206	90.37 215	71.95 226	84.64 133	89.33 189	67.30 175	96.55 162	75.17 181	87.09 148	94.63 123

COLMAP_ROB	80.39 16	83.96 183	82.04 190	89.74 146	95.28 74	79.75 131	94.25 100	92.28 182	75.17 205	78.02 205	93.77 106	58.60 217	97.84 91	65.06 225	85.92 152	91.63 205
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SixPastTwentyTwo		83.91 184	82.90 184	86.92 204	90.99 194	70.67 225	93.48 133	91.99 189	85.54 85	77.62 206	92.11 144	60.59 212	96.87 151	76.05 176	77.75 218	93.20 175

EPMVS		83.90 185	82.70 186	87.51 190	90.23 211	72.67 208	88.62 213	81.96 254	81.37 160	85.01 129	88.34 198	66.31 184	94.45 214	75.30 179	87.12 147	95.43 103

tpmp4_e23		83.87 186	82.33 187	88.48 172	91.46 162	72.82 205	89.82 196	91.57 194	73.02 218	81.86 181	89.05 190	66.20 185	96.97 145	71.57 199	86.39 150	95.66 97

TESTMET0.1,1		83.74 187	82.85 185	86.42 210	89.96 215	71.21 220	89.55 198	87.88 239	77.41 193	83.37 164	87.31 212	56.71 222	93.65 221	80.62 129	92.85 99	94.40 137

pm-mvs1		83.61 188	82.12 188	88.08 182	89.60 219	76.64 178	91.36 183	91.45 198	74.39 209	79.77 196	90.72 172	67.20 178	95.25 207	71.99 197	82.61 180	92.34 196

tpmvs		83.35 189	82.07 189	87.20 201	91.07 193	71.00 223	88.31 217	91.70 193	78.91 179	80.49 190	87.18 214	69.30 168	97.08 140	68.12 214	83.56 173	93.51 173

RPMNet		83.18 190	80.87 196	90.12 133	90.58 205	79.34 151	85.24 234	90.78 211	71.44 228	85.55 112	82.97 233	70.87 154	95.61 192	61.01 233	89.36 121	95.40 104

USDC		82.76 191	81.26 193	87.26 196	91.17 189	74.55 189	89.27 204	93.39 168	78.26 189	75.30 220	92.08 145	54.43 229	96.63 156	71.64 198	85.79 156	90.61 225

Patchmtry		82.71 192	80.93 195	88.06 184	90.05 214	76.37 180	84.74 236	91.96 190	72.28 224	81.32 183	87.87 206	71.03 152	95.50 195	68.97 209	80.15 208	92.32 197

PatchT		82.68 193	81.27 192	86.89 206	90.09 213	70.94 224	84.06 241	90.15 219	74.91 208	85.63 111	83.57 231	69.37 165	94.87 212	65.19 223	88.50 134	94.84 119

MIMVSNet		82.59 194	80.53 197	88.76 159	91.51 161	78.32 166	86.57 226	90.13 220	79.32 176	80.70 187	88.69 195	52.98 230	93.07 227	66.03 221	88.86 129	94.90 117

test0.0.03 1		82.41 195	81.69 191	84.59 221	88.23 228	72.89 204	90.24 192	87.83 240	83.41 121	79.86 195	89.78 185	67.25 176	88.99 239	65.18 224	83.42 174	91.90 201

EG-PatchMatch MVS		82.37 196	80.34 198	88.46 173	90.27 209	79.35 150	92.80 153	94.33 145	77.14 194	73.26 228	90.18 179	47.47 238	96.72 153	70.25 205	87.32 146	89.30 230

tpm cat1		81.96 197	80.27 199	87.01 202	91.09 192	71.02 222	87.38 222	91.53 196	66.25 243	80.17 191	86.35 225	68.22 174	96.15 177	69.16 208	82.29 183	93.86 154

ADS-MVSNet2		81.66 198	79.71 204	87.50 191	91.35 175	74.19 191	83.33 245	88.48 236	72.90 219	82.24 173	85.77 227	64.98 192	93.20 225	64.57 226	83.74 169	95.12 109

K. test v3		81.59 199	80.15 202	85.91 213	89.89 217	69.42 231	92.57 158	87.71 241	85.56 84	73.44 227	89.71 186	55.58 224	95.52 194	77.17 169	69.76 242	92.78 186

ADS-MVSNet		81.56 200	79.78 203	86.90 205	91.35 175	71.82 215	83.33 245	89.16 232	72.90 219	82.24 173	85.77 227	64.98 192	93.76 219	64.57 226	83.74 169	95.12 109

FMVSNet5		81.52 201	79.60 205	87.27 195	91.17 189	77.95 172	91.49 181	92.26 183	76.87 195	76.16 214	87.91 205	51.67 231	92.34 228	67.74 215	81.16 193	91.52 206

dp		81.47 202	80.23 200	85.17 219	89.92 216	65.49 240	86.74 224	90.10 221	76.30 199	81.10 184	87.12 215	62.81 198	95.92 184	68.13 213	79.88 213	94.09 143

EU-MVSNet		81.32 203	80.95 194	82.42 229	88.50 226	63.67 243	93.32 135	91.33 200	64.02 248	80.57 189	92.83 127	61.21 209	92.27 229	76.34 172	80.38 207	91.32 210

test_0402		81.30 204	79.17 207	87.67 188	93.19 131	78.17 169	92.98 148	91.71 192	75.25 204	76.02 216	90.31 177	59.23 215	96.37 168	50.22 245	83.63 172	88.47 238

JIA-IIPM		81.04 205	78.98 210	87.25 197	88.64 224	73.48 200	81.75 249	89.61 231	73.19 215	82.05 177	73.71 248	66.07 188	95.87 186	71.18 202	84.60 164	92.41 192

testgi		80.94 206	80.20 201	83.18 227	87.96 231	66.29 237	91.28 184	90.70 213	83.70 111	78.12 203	92.84 126	51.37 232	90.82 236	63.34 228	82.46 182	92.43 191

CMPMVS	59.16 21	80.52 207	79.20 206	84.48 222	83.98 240	67.63 236	89.95 195	93.84 163	64.79 247	66.81 241	91.14 170	57.93 220	95.17 208	76.25 173	88.10 138	90.65 224
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS		80.37 208	79.07 209	84.27 225	86.64 234	69.87 230	89.39 203	91.05 204	76.38 197	74.97 222	90.00 181	47.85 237	94.25 217	74.55 186	80.82 202	88.69 234

UnsupCasMVSNet_eth		80.07 209	78.27 211	85.46 216	85.24 238	72.63 210	88.45 215	94.87 133	82.99 136	71.64 235	88.07 202	56.34 223	91.75 233	73.48 193	63.36 250	92.01 200

test20.03		79.95 210	79.08 208	82.55 228	85.79 237	67.74 235	91.09 187	91.08 203	81.23 161	74.48 225	89.96 183	61.63 204	90.15 237	60.08 236	76.38 222	89.76 227

TDRefinement		79.81 211	77.34 213	87.22 200	79.24 249	75.48 187	93.12 142	92.03 187	76.45 196	75.01 221	91.58 158	49.19 235	96.44 165	70.22 207	69.18 243	89.75 228

TinyColmap		79.76 212	77.69 212	85.97 212	91.71 157	73.12 202	89.55 198	90.36 216	75.03 206	72.03 233	90.19 178	46.22 240	96.19 176	63.11 229	81.03 196	88.59 235

OpenMVS_ROB	74.94 19	79.51 213	77.03 217	86.93 203	87.00 233	76.23 182	92.33 163	90.74 212	68.93 237	74.52 224	88.23 200	49.58 234	96.62 157	57.64 239	84.29 166	87.94 240

MIMVSNet1		79.38 214	77.28 214	85.69 214	86.35 236	73.67 199	91.61 180	92.75 176	78.11 191	72.64 231	88.12 201	48.16 236	91.97 232	60.32 234	77.49 219	91.43 209

YYNet1		79.22 215	77.20 215	85.28 218	88.20 230	72.66 209	85.87 229	90.05 224	74.33 211	62.70 247	87.61 208	66.09 187	92.03 230	66.94 216	72.97 228	91.15 213

MDA-MVSNet_test_wron		79.21 216	77.19 216	85.29 217	88.22 229	72.77 207	85.87 229	90.06 222	74.34 210	62.62 248	87.56 209	66.14 186	91.99 231	66.90 219	73.01 227	91.10 214

MDA-MVSNet-bldmvs		78.85 217	76.31 218	86.46 208	89.76 218	73.88 195	88.79 210	90.42 214	79.16 178	59.18 249	88.33 199	60.20 213	94.04 218	62.00 232	68.96 244	91.48 208

PM-MVS		78.11 218	76.12 220	84.09 226	83.54 242	70.08 228	88.97 209	85.27 247	79.93 171	74.73 223	86.43 219	34.70 248	93.48 222	79.43 148	72.06 229	88.72 233

PVSNet_0	73.20 20	77.22 219	74.83 221	84.37 223	90.70 204	71.10 221	83.09 247	89.67 230	72.81 221	73.93 226	83.13 232	60.79 211	93.70 220	68.54 210	50.84 255	88.30 239

DSMNet-mixed		76.94 220	76.29 219	78.89 231	83.10 243	56.11 253	87.78 219	79.77 257	60.65 251	75.64 219	88.71 193	61.56 205	88.34 241	60.07 237	89.29 123	92.21 199

UnsupCasMVSNet_bld		76.23 221	73.27 223	85.09 220	83.79 241	72.92 203	85.65 233	93.47 167	71.52 227	68.84 238	79.08 243	49.77 233	93.21 224	66.81 220	60.52 252	89.13 231

LP		75.51 222	72.15 226	85.61 215	87.86 232	73.93 194	80.20 251	88.43 237	67.39 239	70.05 236	80.56 240	58.18 219	93.18 226	46.28 251	70.36 241	89.71 229

test2356		74.50 223	73.27 223	78.20 232	80.81 246	59.84 245	83.76 244	88.33 238	71.43 229	72.37 232	81.84 236	45.60 241	86.26 247	50.97 243	84.32 165	88.50 236

testus		74.41 224	73.35 222	77.59 236	82.49 245	57.08 249	86.02 227	90.21 218	72.28 224	72.89 230	84.32 230	37.08 246	86.96 245	52.24 242	82.65 179	88.73 232

MVS-HIRNet		73.70 225	72.20 225	78.18 234	91.81 154	56.42 252	82.94 248	82.58 252	55.24 254	68.88 237	66.48 251	55.32 227	95.13 209	58.12 238	88.42 136	83.01 245

test1235678		72.22 226	70.31 228	77.93 235	78.04 250	58.04 248	85.76 231	89.80 229	70.15 235	63.43 245	80.20 241	42.24 243	87.24 244	48.68 247	74.50 225	88.50 236

new_pmnet		72.15 227	70.13 229	78.20 232	82.95 244	65.68 238	83.91 242	82.40 253	62.94 250	64.47 244	79.82 242	42.85 242	86.26 247	57.41 240	74.44 226	82.65 246

pmmvs3		71.81 228	68.71 232	81.11 230	75.86 251	70.42 227	86.74 224	83.66 249	58.95 252	68.64 240	80.89 239	36.93 247	89.52 238	63.10 230	63.59 249	83.39 244

testpf		71.41 229	72.11 227	69.30 246	84.53 239	59.79 246	62.74 261	83.14 250	71.11 231	68.83 239	81.57 238	46.70 239	84.83 252	74.51 187	75.86 224	63.30 254

HyFIR lowres test		70.96 230	69.05 231	76.67 238	86.42 235	64.06 242	77.75 254	82.95 251	55.25 253	62.90 246	78.84 244	33.89 249	90.95 235	60.14 235	83.30 176	75.01 252

1111		70.54 231	69.71 230	73.04 241	79.30 247	44.83 261	84.23 239	88.96 233	67.33 240	65.42 242	82.28 234	41.11 244	88.11 242	47.12 249	71.60 233	86.19 242

N_pmnet		68.89 232	68.44 233	70.23 244	89.07 222	28.79 268	88.06 218	19.50 265	69.47 236	71.86 234	84.93 229	61.24 208	91.75 233	54.70 241	77.15 221	90.15 226

LCM-MVSNet		66.00 233	62.16 237	77.51 237	64.51 261	58.29 247	83.87 243	90.90 209	48.17 257	54.69 251	73.31 249	16.83 260	86.75 246	65.47 222	61.67 251	87.48 241

testmv		65.49 234	62.66 235	73.96 240	68.78 256	53.14 256	84.70 237	88.56 235	65.94 245	52.35 252	74.65 247	25.02 253	85.14 250	43.54 253	60.40 253	83.60 243

test12356		64.99 235	63.78 234	68.61 248	72.69 253	39.14 264	78.46 252	87.61 242	64.91 246	55.77 250	77.48 245	28.10 250	85.59 249	44.69 252	64.35 248	81.12 248

FPMVS		64.63 236	62.55 236	70.88 243	70.80 254	56.71 250	84.42 238	84.42 248	51.78 256	49.57 253	81.61 237	23.49 254	81.48 254	40.61 256	76.25 223	74.46 253

no-one		61.56 237	56.58 239	76.49 239	67.80 259	62.76 244	78.13 253	86.11 243	63.16 249	43.24 256	64.70 253	26.12 252	88.95 240	50.84 244	29.15 258	77.77 250

PMMVS2		59.60 238	56.40 240	69.21 247	68.83 255	46.58 259	73.02 259	77.48 259	55.07 255	49.21 254	72.95 250	17.43 259	80.04 255	49.32 246	44.33 256	80.99 249

ANet_high		58.88 239	54.22 242	72.86 242	56.50 265	56.67 251	80.75 250	86.00 244	73.09 217	37.39 258	64.63 254	22.17 255	79.49 257	43.51 254	23.96 262	82.43 247

Gipuma		57.99 240	54.91 241	67.24 249	88.51 225	65.59 239	52.21 263	90.33 217	43.58 260	42.84 257	51.18 257	20.29 257	85.07 251	34.77 258	70.45 240	51.05 259
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		57.63 241	61.79 238	45.14 255	79.30 247	44.83 261	84.23 239	88.96 233	67.33 240	65.42 242	82.28 234	41.11 244	88.11 242	47.12 249	0.39 265	2.46 264

PMVS	47.18 22	52.22 242	48.46 243	63.48 250	45.72 266	46.20 260	73.41 257	78.31 258	41.03 261	30.06 260	65.68 252	6.05 262	83.43 253	30.04 259	65.86 246	60.80 256
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		50.55 243	44.13 245	69.81 245	56.77 263	54.58 255	73.22 258	80.78 255	39.79 262	22.08 264	46.69 259	4.03 264	79.71 256	47.65 248	26.13 260	75.14 251

PNet_i23d		50.48 244	47.18 244	60.36 251	68.59 257	44.56 263	72.75 260	72.61 260	43.92 259	33.91 259	60.19 255	6.16 261	73.52 258	38.50 257	28.04 259	63.01 255

MVE	39.65 23	43.39 245	38.59 248	57.77 252	56.52 264	48.77 258	55.38 262	58.64 263	29.33 265	28.96 261	52.65 256	4.68 263	64.62 261	28.11 260	33.07 257	59.93 257
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 246	42.29 246	46.03 254	65.58 260	37.41 265	73.51 256	64.62 261	33.99 263	28.47 262	47.87 258	19.90 258	67.91 259	22.23 261	24.45 261	32.77 260

EMVS		42.07 247	41.12 247	44.92 256	63.45 262	35.56 267	73.65 255	63.48 262	33.05 264	26.88 263	45.45 260	21.27 256	67.14 260	19.80 262	23.02 263	32.06 261

wuyk23d		21.27 248	20.48 249	23.63 257	68.59 257	36.41 266	49.57 264	6.85 266	9.37 266	7.89 265	4.46 264	4.03 264	31.37 263	17.47 263	16.07 264	3.12 262

testmvs		8.92 249	11.52 250	1.12 259	1.06 267	0.46 270	86.02 227	0.65 267	0.62 267	2.74 266	9.52 262	0.31 267	0.45 265	2.38 264	0.39 265	2.46 264

test123		8.76 250	11.22 251	1.39 258	0.85 268	0.97 269	85.76 231	0.35 268	0.54 268	2.45 267	8.14 263	0.60 266	0.48 264	2.16 265	0.17 267	2.71 263

ab-mvs-re		7.82 251	10.43 252	0.00 260	0.00 269	0.00 271	0.00 265	0.00 269	0.00 269	0.00 268	93.88 102	0.00 268	0.00 266	0.00 266	0.00 268	0.00 266

Regformer		0.00 252	0.00 253	0.00 260	0.00 269	0.00 271	0.00 265	0.00 269	0.00 269	0.00 268	0.00 265	0.00 268	0.00 266	0.00 266	0.00 268	0.00 266

test_prior4						85.96 25	94.11 106

test_prior2							94.12 104		87.67 59	92.63 28	96.39 40	86.62 16		91.50 30	98.67 13

test_prior				93.82 35	97.29 31	84.49 42		96.88 32					98.87 46			98.11 28

旧先验2							93.36 134		71.25 230	94.37 8			97.13 136	86.74 69

新几何2							93.11 143

新几何1				93.10 47	97.30 30	84.35 48		95.56 95	71.09 232	91.26 46	96.24 44	82.87 42	98.86 48	79.19 150	98.10 32	96.07 84

旧先验1					96.79 42	81.81 95		95.67 89			96.81 23	86.69 15			97.66 39	96.97 63

无先验							93.28 138	96.26 58	73.95 212				99.05 31	80.56 130		96.59 72

原ACMM2							92.94 150

原ACMM1				92.01 77	97.34 28	81.05 111		96.81 37	78.89 180	90.45 51	95.92 52	82.65 43	98.84 53	80.68 128	98.26 29	96.14 80

test222					96.55 48	81.70 96	92.22 165	95.01 123	68.36 238	90.20 54	96.14 47	80.26 61			97.80 37	96.05 86

testdata2													98.75 56	78.30 157

segment_acmp												87.16 13

testdata				90.49 118	96.40 50	77.89 174		95.37 107	72.51 222	93.63 17	96.69 28	82.08 49	97.65 98	83.08 98	97.39 43	95.94 88

testdata1							92.15 167		87.94 53

test12				94.34 25	97.13 36	86.15 23		96.29 57		91.04 49		85.08 28	99.01 38		98.13 31	97.86 41

plane_prior7					94.70 91	82.74 81

plane_prior6					94.52 97	82.75 79						74.23 126

plane_prior5								96.22 61					98.12 78	88.15 52	89.99 110	94.63 123

plane_prior4											94.86 76

plane_prior3						82.75 79			90.26 19	86.91 84

plane_prior2							95.85 33		90.81 12

plane_prior1					94.59 95

plane_prior						82.73 82	95.21 54		89.66 27						89.88 113

n2								0.00 269

nn								0.00 269

door-mid								85.49 245

lessismore_v0				86.04 211	88.46 227	68.78 233		80.59 256		73.01 229	90.11 180	55.39 226	96.43 166	75.06 182	65.06 247	92.90 182

LGP-MVS_train				91.12 101	94.47 99	81.49 100		96.14 65	86.73 70	85.45 117	95.16 69	69.89 160	98.10 80	87.70 57	89.23 124	93.77 160

test11								96.57 48

door								85.33 246

HQP5-MVS						81.56 97

HQP-NCC					94.17 106		94.39 90		88.81 38	85.43 120

ACMP_Plane					94.17 106		94.39 90		88.81 38	85.43 120

BP-MVS														87.11 66

HQP4-MVS										85.43 120			97.96 86			94.51 132

HQP3-MVS								96.04 72							89.77 115

HQP2-MVS												73.83 133

NP-MVS					94.37 103	82.42 89					93.98 95

MDTV_nov1_ep13_2view						55.91 254	87.62 221		73.32 214	84.59 135		70.33 158		74.65 184		95.50 100

MDTV_nov1_ep13			83.56 172		91.69 158	69.93 229	87.75 220	91.54 195	78.60 185	84.86 131	88.90 191	69.54 164	96.03 179	70.25 205	88.93 128

ACMMP++_ref															87.47 142

ACMMP++															88.01 140

Test By Simon												80.02 62

ITE_SJBPF				88.24 179	91.88 151	77.05 177		92.92 173	85.54 85	80.13 193	93.30 113	57.29 221	96.20 175	72.46 195	84.71 163	91.49 207

DeepMVS_CX				56.31 253	74.23 252	51.81 257		56.67 264	44.85 258	48.54 255	75.16 246	27.87 251	58.74 262	40.92 255	52.22 254	58.39 258

High-resolution multi-view benchmark