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		89.15 1	89.63 1	87.73 13	94.49 6	71.69 26	93.83 2	93.96 2	75.70 57	91.06 1	96.03 1	76.84 2	97.03 3	89.09 1	95.65 8	94.47 7

HPM-MVS++		89.02 2	89.15 2	88.63 1	95.01 1	76.03 1	92.38 11	92.85 25	80.26 10	87.78 7	94.27 10	75.89 6	96.81 6	87.45 5	96.44 1	93.05 48

CNVR-MVS		88.93 3	89.13 3	88.33 3	94.77 2	73.82 4	90.51 33	93.00 18	80.90 7	88.06 6	94.06 16	76.43 3	96.84 5	88.48 2	95.99 2	94.34 10

SteuartSystems-ACMMP		88.72 4	88.86 4	88.32 4	92.14 37	72.96 13	93.73 3	93.67 6	80.19 11	88.10 5	94.80 2	73.76 14	97.11 1	87.51 4	95.82 6	94.90 3
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 5	88.56 5	86.73 27	92.24 36	69.03 53	89.57 48	93.39 9	77.53 32	89.79 3	94.12 14	78.98 1	96.58 14	85.66 6	95.72 7	94.58 4

SD-MVS		88.06 6	88.50 6	86.71 28	92.60 34	72.71 15	91.81 20	93.19 13	77.87 26	90.32 2	94.00 17	74.83 7	93.78 85	87.63 3	94.27 28	93.65 30

NCCC		88.06 6	88.01 7	88.24 5	94.41 10	73.62 5	91.22 26	92.83 26	81.50 4	85.79 13	93.47 24	73.02 17	97.00 4	84.90 9	94.94 15	94.10 15

MP-MVS		87.71 8	87.64 8	87.93 10	94.36 12	73.88 2	92.71 10	92.65 30	77.57 29	83.84 29	94.40 9	72.24 21	96.28 19	85.65 7	95.30 12	93.62 32

ACMMPR		87.44 9	87.23 11	88.08 6	94.64 3	73.59 6	93.04 5	93.20 12	76.78 43	84.66 22	94.52 3	68.81 35	96.65 9	84.53 12	94.90 16	94.00 20

APD-MVS		87.44 9	87.52 9	87.19 21	94.24 13	72.39 22	91.86 19	92.83 26	73.01 89	88.58 4	94.52 3	73.36 15	96.49 15	84.26 14	95.01 14	92.70 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		87.37 11	87.25 10	87.73 13	94.53 5	72.46 21	89.82 44	93.82 4	73.07 88	84.86 21	92.89 31	76.22 4	96.33 17	84.89 10	95.13 13	94.40 8

#test#		87.33 12	87.13 12	87.94 9	94.58 4	73.54 8	92.34 12	93.24 11	75.23 64	84.91 18	94.44 7	70.78 27	96.61 11	83.75 17	94.89 17	93.66 26

XVS		87.18 13	86.91 15	88.00 7	94.42 8	73.33 11	92.78 6	92.99 19	79.14 15	83.67 32	94.17 13	67.45 42	96.60 12	83.06 22	94.50 21	94.07 16

HPM-MVS		87.11 14	86.98 13	87.50 18	93.88 17	72.16 25	92.19 15	93.33 10	76.07 54	83.81 30	93.95 18	69.77 31	96.01 24	85.15 8	94.66 19	94.32 11

CP-MVS		87.11 14	86.92 14	87.68 17	94.20 15	73.86 3	93.98 1	92.82 28	76.62 45	83.68 31	94.46 6	67.93 38	95.95 25	84.20 15	94.39 24	93.23 43

DeepC-MVS	79.81 2	87.08 16	86.88 16	87.69 16	91.16 46	72.32 24	90.31 37	93.94 3	77.12 37	82.82 39	94.23 12	72.13 22	97.09 2	84.83 11	95.37 10	93.65 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 17	86.62 18	87.76 12	93.52 20	72.37 23	91.26 23	93.04 15	76.62 45	84.22 25	93.36 26	71.44 25	96.76 7	80.82 33	95.33 11	94.16 13
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior3		86.73 18	86.86 17	86.33 33	92.61 32	69.59 48	88.85 62	92.97 22	75.41 60	84.91 18	93.54 20	74.28 11	95.48 32	83.31 18	95.86 4	93.91 22

PGM-MVS		86.68 19	86.27 22	87.90 11	94.22 14	73.38 10	90.22 39	93.04 15	75.53 59	83.86 28	94.42 8	67.87 40	96.64 10	82.70 26	94.57 20	93.66 26

mPP-MVS		86.67 20	86.32 21	87.72 15	94.41 10	73.55 7	92.74 8	92.22 38	76.87 41	82.81 40	94.25 11	66.44 49	96.24 20	82.88 25	94.28 27	93.38 39

Regformer-2		86.63 21	86.53 19	86.95 24	89.33 70	71.24 29	88.43 69	92.05 43	82.50 1	86.88 9	90.09 71	74.45 9	95.61 28	84.38 13	90.63 51	94.01 19

PHI-MVS		86.43 22	86.17 24	87.24 20	90.88 51	70.96 32	92.27 14	94.07 1	72.45 100	85.22 16	91.90 42	69.47 32	96.42 16	83.28 20	95.94 3	94.35 9

Regformer-1		86.41 23	86.33 20	86.64 29	89.33 70	70.93 33	88.43 69	91.39 61	82.14 2	86.65 10	90.09 71	74.39 10	95.01 48	83.97 16	90.63 51	93.97 21

CSCG		86.41 23	86.19 23	87.07 23	92.91 28	72.48 20	90.81 29	93.56 7	73.95 75	83.16 37	91.07 55	75.94 5	95.19 40	79.94 39	94.38 25	93.55 35

patchmatch test		85.97 25	85.88 25	86.22 36	92.69 30	69.53 50	91.93 18	92.99 19	73.54 81	85.94 11	94.51 5	65.80 57	95.61 28	83.04 24	92.51 38	93.53 37

ACMMP		85.89 26	85.39 28	87.38 19	93.59 19	72.63 17	92.74 8	93.18 14	76.78 43	80.73 54	93.82 19	64.33 65	96.29 18	82.67 27	90.69 50	93.23 43
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
CDPH-MVS		85.76 27	85.29 31	87.17 22	93.49 21	71.08 30	88.58 67	92.42 34	68.32 143	84.61 23	93.48 22	72.32 20	96.15 22	79.00 40	95.43 9	94.28 12

TSAR-MVS		85.71 28	85.33 29	86.84 25	91.34 44	72.50 19	89.07 58	87.28 143	76.41 47	85.80 12	90.22 69	74.15 13	95.37 38	81.82 29	91.88 40	92.65 52

Regformer-4		85.68 29	85.45 27	86.35 32	88.95 82	69.67 47	88.29 77	91.29 63	81.73 3	85.36 15	90.01 73	72.62 18	95.35 39	83.28 20	87.57 75	94.03 18

3Dnovator+	77.84 4	85.48 30	84.47 35	88.51 2	91.08 47	73.49 9	93.18 4	93.78 5	80.79 8	76.66 105	93.37 25	60.40 128	96.75 8	77.20 53	93.73 31	95.29 1

MSLP-MVS		85.43 31	85.76 26	84.45 60	91.93 39	70.24 40	90.71 30	92.86 24	77.46 34	84.22 25	92.81 35	67.16 45	92.94 112	80.36 35	94.35 26	90.16 107

DELS-MVS		85.41 32	85.30 30	85.77 39	88.49 95	67.93 76	85.52 142	93.44 8	78.70 22	83.63 34	89.03 94	74.57 8	95.71 27	80.26 37	94.04 29	93.66 26
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		85.35 33	84.95 33	86.57 31	93.69 18	70.58 39	92.15 16	91.62 54	73.89 76	82.67 42	94.09 15	62.60 93	95.54 31	80.93 31	92.93 33	93.57 34

Regformer-3		85.23 34	85.07 32	85.70 40	88.95 82	69.01 55	88.29 77	89.91 96	80.95 6	85.01 17	90.01 73	72.45 19	94.19 69	82.50 28	87.57 75	93.90 24

MVS_111021_HR		85.14 35	84.75 34	86.32 35	91.65 42	72.70 16	85.98 133	90.33 82	76.11 53	82.08 44	91.61 48	71.36 26	94.17 70	81.02 30	92.58 37	92.08 61

EI-MVSNet-Vis		84.19 36	83.81 36	85.31 42	88.18 102	67.85 77	87.66 90	89.73 100	80.05 13	82.95 38	89.59 80	70.74 28	94.82 54	80.66 34	84.72 99	93.28 42

EI-MVSNet-UG		83.81 37	83.38 38	85.09 46	87.87 110	67.53 81	87.44 100	89.66 101	79.74 14	82.23 43	89.41 89	70.24 29	94.74 56	79.95 38	83.92 103	92.99 49

CPTT-MVS		83.73 38	83.33 39	84.92 51	93.28 23	70.86 35	92.09 17	90.38 79	68.75 140	79.57 59	92.83 33	60.60 124	93.04 111	80.92 32	91.56 44	90.86 85

EPNet		83.72 39	82.92 42	86.14 38	84.22 156	69.48 51	91.05 28	85.27 165	81.30 5	76.83 102	91.65 45	66.09 53	95.56 30	76.00 60	93.85 30	93.38 39
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP_MVS		83.64 40	83.14 40	85.14 45	90.08 60	68.71 64	91.25 24	92.44 31	79.12 17	78.92 66	91.00 58	60.42 126	95.38 36	78.71 43	86.32 88	91.33 74

Vis-MVSNet		83.46 41	82.80 44	85.43 41	90.25 58	68.74 63	90.30 38	90.13 89	76.33 52	80.87 53	92.89 31	61.00 119	94.20 68	72.45 88	90.97 48	93.35 41
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 42	83.45 37	83.28 84	92.74 29	62.28 159	88.17 80	89.50 103	75.22 65	81.49 47	92.74 36	66.75 46	95.11 42	72.85 81	91.58 43	92.45 53

EPP-MVSNet		83.40 43	83.02 41	84.57 56	90.13 59	64.47 131	92.32 13	90.73 72	74.45 70	79.35 62	91.10 53	69.05 34	95.12 41	72.78 82	87.22 79	94.13 14

3Dnovator	76.31 5	83.38 44	82.31 48	86.59 30	87.94 109	72.94 14	90.64 31	92.14 42	77.21 35	75.47 117	92.83 33	58.56 133	94.72 57	73.24 80	92.71 36	92.13 60

IS-MVSNet		83.15 45	82.81 43	84.18 68	89.94 62	63.30 146	91.59 21	88.46 133	79.04 19	79.49 60	92.16 37	65.10 61	94.28 64	67.71 114	91.86 41	94.95 2

DP-MVS Recon		83.11 46	82.09 50	86.15 37	94.44 7	70.92 34	88.79 64	92.20 39	70.53 121	79.17 63	91.03 57	64.12 67	96.03 23	68.39 113	90.14 56	91.50 72

PAPM_NR		83.02 47	82.41 45	84.82 53	92.47 35	66.37 95	87.93 86	91.80 50	73.82 77	77.32 94	90.66 62	67.90 39	94.90 51	70.37 101	89.48 62	93.19 45

VDD-MVS		83.01 48	82.36 47	84.96 49	91.02 48	66.40 94	88.91 60	88.11 135	77.57 29	84.39 24	93.29 27	52.19 167	93.91 79	77.05 54	88.70 69	94.57 5

MVSFormer		82.85 49	82.05 51	85.24 44	87.35 123	70.21 41	90.50 34	90.38 79	68.55 141	81.32 48	89.47 84	61.68 107	93.46 94	78.98 41	90.26 54	92.05 62

OMC-MVS		82.69 50	81.97 53	84.85 52	88.75 89	67.42 82	87.98 83	90.87 71	74.92 67	79.72 58	91.65 45	62.19 104	93.96 76	75.26 69	86.42 87	93.16 46

PVSNet_Blended_VisFu		82.62 51	81.83 54	84.96 49	90.80 53	69.76 46	88.74 65	91.70 53	69.39 132	78.96 65	88.46 107	65.47 58	94.87 53	74.42 72	88.57 70	90.24 105

MVS_111021_LR		82.61 52	82.11 49	84.11 69	88.82 86	71.58 27	85.15 143	86.16 158	74.69 68	80.47 55	91.04 56	62.29 101	90.55 158	80.33 36	90.08 57	90.20 106

HQP-MVS		82.61 52	82.02 52	84.37 61	89.33 70	66.98 89	89.17 53	92.19 40	76.41 47	77.23 97	90.23 68	60.17 129	95.11 42	77.47 51	85.99 92	91.03 79

CLD-MVS		82.31 54	81.65 55	84.29 65	88.47 96	67.73 80	85.81 138	92.35 36	75.78 55	78.33 78	86.58 147	64.01 68	94.35 62	76.05 59	87.48 77	90.79 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 55	82.41 45	81.62 131	90.82 52	60.93 164	84.47 154	89.78 98	76.36 51	84.07 27	91.88 43	64.71 64	90.26 160	70.68 98	88.89 66	93.66 26

LPG-MVS_test		82.08 56	81.27 58	84.50 58	89.23 78	68.76 61	90.22 39	91.94 47	75.37 62	76.64 107	91.51 50	54.29 154	94.91 49	78.44 45	83.78 104	89.83 120

PS-MVSNAJss		82.07 57	81.31 57	84.34 64	86.51 132	67.27 86	89.27 51	91.51 57	71.75 107	79.37 61	90.22 69	63.15 77	94.27 65	77.69 49	82.36 116	91.49 73

API-MVS		81.99 58	81.23 59	84.26 66	90.94 49	70.18 43	91.10 27	89.32 106	71.51 113	78.66 70	88.28 112	65.26 59	95.10 45	64.74 139	91.23 47	87.51 172

UniMVSNet_NR-MVSNet		81.88 59	81.54 56	82.92 100	88.46 97	63.46 142	87.13 110	92.37 35	80.19 11	78.38 76	89.14 91	71.66 24	93.05 109	70.05 103	76.46 168	92.25 56

MAR-MVS		81.84 60	80.70 64	85.27 43	91.32 45	71.53 28	89.82 44	90.92 70	69.77 128	78.50 72	86.21 154	62.36 100	94.52 61	65.36 133	92.05 39	89.77 123
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		81.82 61	81.23 59	83.57 79	91.89 40	63.43 144	89.84 43	81.85 196	77.04 39	83.21 35	93.10 28	52.26 166	93.43 98	71.98 94	89.95 59	93.85 25

PS-MVSNAJ		81.69 62	81.02 62	83.70 77	89.51 67	68.21 74	84.28 160	90.09 90	70.79 118	81.26 50	85.62 160	63.15 77	94.29 63	75.62 66	88.87 67	88.59 150

PAPR		81.66 63	80.89 63	83.99 74	90.27 57	64.00 140	86.76 121	91.77 52	68.84 139	77.13 101	89.50 81	67.63 41	94.88 52	67.55 115	88.52 71	93.09 47

UniMVSNet (Re)		81.60 64	81.11 61	83.09 91	88.38 99	64.41 133	87.60 91	93.02 17	78.42 25	78.56 71	88.16 113	69.78 30	93.26 100	69.58 107	76.49 167	91.60 68

VDDNet		81.52 65	80.67 65	84.05 72	90.44 55	64.13 137	89.73 46	85.91 161	71.11 115	83.18 36	93.48 22	50.54 176	93.49 93	73.40 79	88.25 73	94.54 6

ACMP	74.13 6	81.51 66	80.57 66	84.36 62	89.42 68	68.69 67	89.97 42	91.50 59	74.46 69	75.04 132	90.41 65	53.82 159	94.54 59	77.56 50	82.91 109	89.86 119
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 67	80.29 71	84.70 55	86.63 131	69.90 45	85.95 134	86.77 148	63.24 183	81.07 52	89.47 84	61.08 118	92.15 125	78.33 48	90.07 58	92.05 62
jason: jason.
lupinMVS		81.39 67	80.27 72	84.76 54	87.35 123	70.21 41	85.55 140	86.41 153	62.85 187	81.32 48	88.61 103	61.68 107	92.24 124	78.41 47	90.26 54	91.83 65

DU-MVS		81.12 69	80.52 68	82.90 101	87.80 113	63.46 142	87.02 114	91.87 49	79.01 20	78.38 76	89.07 92	65.02 62	93.05 109	70.05 103	76.46 168	92.20 57

PVSNet_Blended		80.98 70	80.34 69	82.90 101	88.85 84	65.40 108	84.43 157	92.00 44	67.62 146	78.11 86	85.05 165	66.02 55	94.27 65	71.52 96	89.50 61	89.01 133

QAPM		80.88 71	79.50 83	85.03 47	88.01 108	68.97 57	91.59 21	92.00 44	66.63 156	75.15 129	92.16 37	57.70 135	95.45 34	63.52 142	88.76 68	90.66 91

liao		80.84 72	79.77 76	84.05 72	93.11 25	70.78 36	84.66 150	85.42 164	57.37 222	81.76 46	92.02 39	63.41 71	94.12 71	67.28 118	92.93 33	87.26 178

TranMVSNet+NR-MVSNet		80.84 72	80.31 70	82.42 112	87.85 111	62.33 157	87.74 89	91.33 62	80.55 9	77.99 88	89.86 75	65.23 60	92.62 115	67.05 122	75.24 183	92.30 54

UGNet		80.83 74	79.59 78	84.54 57	88.04 106	68.09 75	89.42 49	88.16 134	76.95 40	76.22 111	89.46 86	49.30 181	93.94 78	68.48 112	90.31 53	91.60 68
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
XVG-OURS-SEG-HR		80.81 75	79.76 77	83.96 76	85.60 141	68.78 60	83.54 169	90.50 76	70.66 120	76.71 104	91.66 44	60.69 122	91.26 142	76.94 55	81.58 122	91.83 65

ACMM	73.20 8	80.78 76	79.84 75	83.58 78	89.31 75	68.37 70	89.99 41	91.60 55	70.28 124	77.25 95	89.66 78	53.37 161	93.53 92	74.24 74	82.85 110	88.85 137
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		80.68 77	79.51 82	84.20 67	94.09 16	67.27 86	89.64 47	91.11 67	58.75 213	74.08 138	90.72 61	58.10 134	95.04 47	69.70 106	89.42 63	90.30 104

VPA-MVSNet		80.60 78	80.55 67	80.76 145	88.07 105	60.80 167	86.86 116	91.58 56	75.67 58	80.24 56	89.45 88	63.34 72	90.25 161	70.51 100	79.22 142	91.23 77

PVSNet_BlendedMVS		80.60 78	80.02 73	82.36 114	88.85 84	65.40 108	86.16 131	92.00 44	69.34 134	78.11 86	86.09 156	66.02 55	94.27 65	71.52 96	82.06 117	87.39 174

AdaColmap		80.58 80	79.42 84	84.06 71	93.09 26	68.91 58	89.36 50	88.97 121	69.27 135	75.70 116	89.69 77	57.20 138	95.77 26	63.06 146	88.41 72	87.50 173

EI-MVSNet		80.52 81	79.98 74	82.12 115	84.28 153	63.19 149	86.41 127	88.95 123	74.18 72	78.69 68	87.54 122	66.62 47	92.43 120	72.57 87	80.57 132	90.74 88

XVG-OURS		80.41 82	79.23 92	83.97 75	85.64 140	69.02 54	83.03 172	90.39 78	71.09 116	77.63 93	91.49 52	54.62 153	91.35 140	75.71 64	83.47 106	91.54 70

v1neww		80.40 83	79.54 79	82.98 96	84.10 163	64.51 127	87.57 93	90.22 84	73.25 82	78.47 73	86.65 143	62.83 84	93.86 81	75.72 62	77.02 158	90.58 93

v7new		80.40 83	79.54 79	82.98 96	84.10 163	64.51 127	87.57 93	90.22 84	73.25 82	78.47 73	86.65 143	62.83 84	93.86 81	75.72 62	77.02 158	90.58 93

v6		80.40 83	79.54 79	82.98 96	84.09 165	64.50 129	87.57 93	90.22 84	73.25 82	78.47 73	86.63 145	62.84 83	93.86 81	75.73 61	77.02 158	90.58 93

PCF-MVS	73.52 7	80.38 86	78.84 97	85.01 48	87.71 116	68.99 56	83.65 166	91.46 60	63.00 185	77.77 91	90.28 66	66.10 52	95.09 46	61.40 161	88.22 74	90.94 83
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 87	77.83 114	88.00 7	94.42 8	73.33 11	92.78 6	92.99 19	79.14 15	83.67 32	12.47 261	67.45 42	96.60 12	83.06 22	94.50 21	94.07 16

test_djsdf		80.30 88	79.32 87	83.27 85	83.98 171	65.37 110	90.50 34	90.38 79	68.55 141	76.19 112	88.70 99	56.44 140	93.46 94	78.98 41	80.14 137	90.97 82

v7		80.24 89	79.26 91	83.15 89	84.07 169	64.94 118	87.56 96	90.67 73	72.26 103	78.28 79	86.51 150	61.45 111	94.03 75	75.14 70	77.41 153	90.49 98

v2v482		80.23 90	79.29 90	83.05 93	83.62 176	64.14 136	87.04 113	89.97 93	73.61 78	78.18 85	87.22 130	61.10 117	93.82 84	76.11 58	76.78 166	91.18 78

NR-MVSNet		80.23 90	79.38 85	82.78 108	87.80 113	63.34 145	86.31 129	91.09 68	79.01 20	72.17 154	89.07 92	67.20 44	92.81 114	66.08 128	75.65 176	92.20 57

divwei89l23v2f112		80.19 92	79.31 88	82.85 103	83.84 173	64.11 139	87.13 110	90.08 91	73.13 85	78.27 80	86.39 152	62.69 90	93.75 86	75.40 68	76.82 164	90.68 89

v1		80.19 92	79.31 88	82.85 103	83.83 174	64.12 138	87.14 108	90.07 92	73.13 85	78.27 80	86.38 153	62.72 89	93.75 86	75.41 67	76.82 164	90.68 89

IterMVS-LS		80.06 94	79.38 85	82.11 116	85.89 136	63.20 148	86.79 118	89.34 105	74.19 71	75.45 119	86.72 136	66.62 47	92.39 122	72.58 86	76.86 162	90.75 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v8		79.97 95	79.02 95	82.80 106	84.09 165	64.50 129	87.96 84	90.29 83	74.13 74	75.24 128	86.81 135	62.88 81	93.89 80	74.39 73	75.40 180	90.00 116

OpenMVS	72.83 10	79.77 96	78.33 106	84.09 70	85.17 143	69.91 44	90.57 32	90.97 69	66.70 152	72.17 154	91.91 41	54.70 151	93.96 76	61.81 159	90.95 49	88.41 158

v10		79.74 97	78.67 98	82.97 99	84.06 170	64.95 117	87.88 88	90.62 75	73.11 87	75.11 130	86.56 148	61.46 110	94.05 74	73.68 76	75.55 177	89.90 117

BH-RMVSNet		79.61 98	78.44 104	83.14 90	89.38 69	65.93 99	84.95 146	87.15 144	73.56 80	78.19 84	89.79 76	56.67 139	93.36 99	59.53 174	86.74 84	90.13 109

ab-mvs		79.51 99	78.97 96	81.14 141	88.46 97	60.91 165	83.84 164	89.24 110	70.36 123	79.03 64	88.87 96	63.23 76	90.21 162	65.12 134	82.57 114	92.28 55

WR-MVS		79.49 100	79.22 93	80.27 152	88.79 88	58.35 183	85.06 144	88.61 131	78.56 23	77.65 92	88.34 110	63.81 70	90.66 157	64.98 137	77.22 156	91.80 67

BH-untuned		79.47 101	78.60 101	82.05 117	89.19 80	65.91 100	86.07 132	88.52 132	72.18 104	75.42 120	87.69 117	61.15 116	93.54 91	60.38 167	86.83 83	86.70 190

mvs_anonymous		79.42 102	79.11 94	80.34 149	84.45 152	57.97 189	82.59 173	87.62 140	67.40 150	76.17 114	88.56 105	68.47 36	89.59 170	70.65 99	86.05 91	93.47 38

V42		79.38 103	78.24 109	82.83 105	81.10 207	65.50 107	85.55 140	89.82 97	71.57 112	78.21 83	86.12 155	60.66 123	93.18 104	75.64 65	75.46 179	89.81 122

jajsoiax		79.29 104	77.96 111	83.27 85	84.68 148	66.57 93	89.25 52	90.16 88	69.20 137	75.46 118	89.49 82	45.75 197	93.13 107	76.84 56	80.80 128	90.11 110

TAPA-MVS	73.13 9	79.15 105	77.94 112	82.79 107	89.59 66	62.99 153	88.16 81	91.51 57	65.77 163	77.14 100	91.09 54	60.91 120	93.21 101	50.26 212	87.05 81	92.17 59
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 106	77.77 115	83.22 87	84.70 147	66.37 95	89.17 53	90.19 87	69.38 133	75.40 121	89.46 86	44.17 204	93.15 105	76.78 57	80.70 130	90.14 108

CDS-MVSNet		79.07 107	77.70 116	83.17 88	87.60 118	68.23 73	84.40 158	86.20 157	67.49 148	76.36 109	86.54 149	61.54 109	90.79 155	61.86 158	87.33 78	90.49 98
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 108	77.88 113	82.38 113	83.07 184	64.80 120	84.08 163	88.95 123	69.01 138	78.69 68	87.17 132	54.70 151	92.43 120	74.69 71	80.57 132	89.89 118

TAMVS		78.89 109	77.51 118	83.03 94	87.80 113	67.79 79	84.72 149	85.05 168	67.63 145	76.75 103	87.70 116	62.25 102	90.82 154	58.53 180	87.13 80	90.49 98

diffmvs		78.78 110	78.64 100	79.22 163	84.48 151	56.11 212	82.12 179	87.53 142	65.53 166	76.66 105	88.73 98	68.25 37	87.97 186	68.73 110	85.70 95	93.59 33

VPNet		78.69 111	78.66 99	78.76 170	88.31 100	55.72 214	84.45 156	86.63 150	76.79 42	78.26 82	90.55 64	59.30 131	89.70 169	66.63 123	77.05 157	90.88 84

WR-MVS_H		78.51 112	78.49 102	78.56 172	88.02 107	56.38 208	88.43 69	92.67 29	77.14 36	73.89 139	87.55 121	66.25 51	89.24 175	58.92 176	73.55 195	90.06 114

MVS_Test		78.49 113	78.26 108	79.18 167	83.29 180	56.48 205	82.45 174	86.59 151	66.73 151	76.13 115	88.46 107	66.33 50	89.04 178	70.30 102	84.06 102	92.01 64

GBi-Net		78.40 114	77.40 119	81.40 136	87.60 118	63.01 150	88.39 73	89.28 107	71.63 109	75.34 123	87.28 126	54.80 148	91.11 145	62.72 147	79.57 138	90.09 111

test1		78.40 114	77.40 119	81.40 136	87.60 118	63.01 150	88.39 73	89.28 107	71.63 109	75.34 123	87.28 126	54.80 148	91.11 145	62.72 147	79.57 138	90.09 111

Vis-MVSNet (Re-imp)		78.36 116	78.45 103	78.07 178	88.64 91	51.78 227	86.70 122	79.63 210	74.14 73	75.11 130	90.83 60	61.29 114	89.75 168	58.10 184	91.60 42	92.69 51

CP-MVSNet		78.22 117	78.34 105	77.84 180	87.83 112	54.54 218	87.94 85	91.17 66	77.65 27	73.48 141	88.49 106	62.24 103	88.43 183	62.19 153	74.07 190	90.55 96

BH-w/o		78.21 118	77.33 121	80.84 143	88.81 87	65.13 114	84.87 147	87.85 137	69.75 129	74.52 136	84.74 170	61.34 112	93.11 108	58.24 183	85.84 94	84.27 210

FMVSNet2		78.20 119	77.21 122	81.20 139	87.60 118	62.89 154	87.47 99	89.02 114	71.63 109	75.29 127	87.28 126	54.80 148	91.10 148	62.38 151	79.38 141	89.61 125

HC-MVS		78.19 120	76.99 125	81.78 121	85.66 139	66.99 88	84.66 150	90.47 77	55.08 230	72.02 158	85.27 163	63.83 69	94.11 73	66.10 127	89.80 60	84.24 211

Baseline_NR-MVSNet		78.15 121	78.33 106	77.61 184	85.79 137	56.21 211	86.78 119	85.76 162	73.60 79	77.93 89	87.57 120	65.02 62	88.99 179	67.14 121	75.33 181	87.63 169

CNLPA		78.08 122	76.79 129	81.97 119	90.40 56	71.07 31	87.59 92	84.55 170	66.03 162	72.38 153	89.64 79	57.56 136	86.04 198	59.61 172	83.35 107	88.79 139

PLC	70.83 11	78.05 123	76.37 132	83.08 92	91.88 41	67.80 78	88.19 79	89.46 104	64.33 176	69.87 178	88.38 109	53.66 160	93.58 90	58.86 177	82.73 112	87.86 166
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PS-CasMVS		78.01 124	78.09 110	77.77 182	87.71 116	54.39 219	88.02 82	91.22 64	77.50 33	73.26 142	88.64 102	60.73 121	88.41 184	61.88 157	73.88 194	90.53 97

V4		77.95 125	76.37 132	82.67 109	79.40 221	65.52 105	86.43 125	89.94 94	72.28 101	72.14 157	84.95 166	55.72 143	93.44 96	73.64 77	72.86 198	89.05 131

HY-MVS	69.67 12	77.95 125	77.15 124	80.36 148	87.57 122	60.21 171	83.37 171	87.78 138	66.11 159	75.37 122	87.06 134	63.27 74	90.48 159	61.38 162	82.43 115	90.40 103

v52		77.94 127	76.37 132	82.67 109	79.39 222	65.52 105	86.43 125	89.94 94	72.28 101	72.15 156	84.94 167	55.70 144	93.44 96	73.64 77	72.84 199	89.06 130

FMVSNet3		77.88 128	76.85 127	80.97 142	86.84 129	62.36 156	86.52 124	88.77 130	71.13 114	75.34 123	86.66 142	54.07 157	91.10 148	62.72 147	79.57 138	89.45 127

PEN-MVS		77.73 129	77.69 117	77.84 180	87.07 128	53.91 221	87.91 87	91.18 65	77.56 31	73.14 144	88.82 97	61.23 115	89.17 176	59.95 169	72.37 200	90.43 101

v16		77.69 130	76.36 135	81.68 128	84.15 160	64.63 125	87.33 102	88.99 118	72.69 98	69.31 185	82.08 189	62.80 87	91.79 129	72.70 84	67.23 218	88.63 144

v17		77.68 131	76.35 136	81.69 127	84.15 160	64.65 123	87.33 102	88.99 118	72.70 97	69.25 186	82.07 190	62.82 86	91.79 129	72.69 85	67.15 220	88.63 144

PAPM		77.68 131	76.40 131	81.51 134	87.29 125	61.85 161	83.78 165	89.59 102	64.74 171	71.23 164	88.70 99	62.59 94	93.66 89	52.66 206	87.03 82	89.01 133

v18		77.67 133	76.35 136	81.64 130	84.09 165	64.47 131	87.27 105	89.01 116	72.59 99	69.39 182	82.04 191	62.85 82	91.80 128	72.72 83	67.20 219	88.63 144

V14		77.52 134	76.12 139	81.70 126	84.15 160	64.77 121	87.21 107	88.95 123	72.80 94	68.79 188	81.94 197	62.69 90	91.72 132	72.31 90	66.27 225	88.60 148

V9		77.52 134	76.11 142	81.73 125	84.19 159	64.89 119	87.26 106	88.94 126	72.87 93	68.65 191	81.96 196	62.65 92	91.72 132	72.27 91	66.24 226	88.60 148

v15		77.51 136	76.12 139	81.66 129	84.09 165	64.65 123	87.14 108	88.96 122	72.76 95	68.90 187	81.91 198	62.74 88	91.73 131	72.32 89	66.29 224	88.61 147

v12		77.51 136	76.09 143	81.76 124	84.22 156	64.99 116	87.30 104	88.93 127	72.92 90	68.48 193	81.97 194	62.54 96	91.70 135	72.24 92	66.21 228	88.58 151

v13		77.50 138	76.07 144	81.77 122	84.23 155	65.07 115	87.34 101	88.91 128	72.92 90	68.35 194	81.97 194	62.53 97	91.69 136	72.20 93	66.22 227	88.56 152

HyFIR		77.49 139	77.16 123	78.47 175	88.18 102	58.04 186	75.75 218	84.17 175	60.53 199	76.25 110	89.49 82	62.42 99	87.81 189	68.64 111	89.00 65	89.56 126

v11		77.45 140	76.06 145	81.59 133	84.22 156	64.52 126	87.11 112	89.02 114	72.76 95	68.76 189	81.90 199	62.09 105	91.71 134	71.98 94	66.73 221	88.56 152

FMVSNet1		77.44 141	76.12 139	81.40 136	86.81 130	63.01 150	88.39 73	89.28 107	70.49 122	74.39 137	87.28 126	49.06 183	91.11 145	60.91 164	78.52 144	90.09 111

TR-MVS		77.44 141	76.18 138	81.20 139	88.24 101	63.24 147	84.61 153	86.40 154	67.55 147	77.81 90	86.48 151	54.10 156	93.15 105	57.75 185	82.72 113	87.20 179

1112_ss		77.40 143	76.43 130	80.32 150	89.11 81	60.41 170	83.65 166	87.72 139	62.13 192	73.05 145	86.72 136	62.58 95	89.97 166	62.11 155	80.80 128	90.59 92

LCM-MVSNet-Re		77.05 144	76.94 126	77.36 186	87.20 126	51.60 228	80.06 193	80.46 204	75.20 66	67.69 198	86.72 136	62.48 98	88.98 180	63.44 143	89.25 64	91.51 71

DTE-MVSNet		76.99 145	76.80 128	77.54 185	86.24 134	53.06 224	87.52 97	90.66 74	77.08 38	72.50 151	88.67 101	60.48 125	89.52 171	57.33 189	70.74 208	90.05 115

LS3D		76.95 146	74.82 152	83.37 82	90.45 54	67.36 85	89.15 57	86.94 147	61.87 194	69.52 180	90.61 63	51.71 171	94.53 60	46.38 226	86.71 85	88.21 161

GA-MVS		76.87 147	75.17 150	81.97 119	82.75 191	62.58 155	81.44 184	86.35 156	72.16 106	74.74 134	82.89 180	46.20 192	92.02 127	68.85 109	81.09 125	91.30 76

DP-MVS		76.78 148	74.57 154	83.42 80	93.29 22	69.46 52	88.55 68	83.70 178	63.98 180	70.20 170	88.89 95	54.01 158	94.80 55	46.66 223	81.88 120	86.01 198

cascas		76.72 149	74.64 153	82.99 95	85.78 138	65.88 101	82.33 177	89.21 111	60.85 198	72.74 147	81.02 205	47.28 188	93.75 86	67.48 116	85.02 96	89.34 128

liao1		76.53 150	75.30 149	80.21 153	83.93 172	62.32 158	84.66 150	88.81 129	60.23 203	70.16 172	84.07 171	55.30 147	90.73 156	67.37 117	83.21 108	87.59 171

Test_1112_low_res		76.40 151	75.44 148	79.27 161	89.28 76	58.09 185	81.69 181	87.07 145	59.53 209	72.48 152	86.67 141	61.30 113	89.33 173	60.81 166	80.15 136	90.41 102

F-COLMAP		76.38 152	74.33 157	82.50 111	89.28 76	66.95 91	88.41 72	89.03 113	64.05 178	66.83 204	88.61 103	46.78 191	92.89 113	57.48 186	78.55 143	87.67 168

LTVRE_ROB	69.57 13	76.25 153	74.54 156	81.41 135	88.60 92	64.38 134	79.24 199	89.12 112	70.76 119	69.79 179	87.86 114	49.09 182	93.20 102	56.21 194	80.16 135	86.65 191
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
XVG-ACMP-BASELINE		76.11 154	74.27 158	81.62 131	83.20 181	64.67 122	83.60 168	89.75 99	69.75 129	71.85 159	87.09 133	32.78 235	92.11 126	69.99 105	80.43 134	88.09 163

ACMH+	68.96 14	76.01 155	74.01 159	82.03 118	88.60 92	65.31 111	88.86 61	87.55 141	70.25 125	67.75 197	87.47 124	41.27 215	93.19 103	58.37 181	75.94 173	87.60 170

ACMH	67.68 16	75.89 156	73.93 160	81.77 122	88.71 90	66.61 92	88.62 66	89.01 116	69.81 127	66.78 205	86.70 140	41.95 214	91.51 139	55.64 195	78.14 149	87.17 180
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 157	73.36 164	83.31 83	84.76 146	66.03 97	83.38 170	85.06 167	70.21 126	69.40 181	81.05 204	45.76 196	94.66 58	65.10 135	75.49 178	89.25 129
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
WTY-MVS		75.65 158	75.68 146	75.57 198	86.40 133	56.82 199	77.92 208	82.40 189	65.10 170	76.18 113	87.72 115	63.13 79	80.90 216	60.31 168	81.96 118	89.00 135

EPNet_dtu		75.46 159	74.86 151	77.23 188	82.57 194	54.60 217	86.89 115	83.09 183	71.64 108	66.25 209	85.86 158	55.99 142	88.04 185	54.92 196	86.55 86	89.05 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS		75.41 160	75.56 147	74.96 200	83.59 177	57.82 192	80.59 191	83.87 177	66.54 157	74.93 133	88.31 111	63.24 75	80.09 220	62.16 154	76.85 163	86.97 185

TransMVSNet (Re)		75.39 161	74.56 155	77.86 179	85.50 142	57.10 197	86.78 119	86.09 160	72.17 105	71.53 163	87.34 125	63.01 80	89.31 174	56.84 191	61.83 236	87.17 180

CostFormer		75.24 162	73.90 161	79.27 161	82.65 193	58.27 184	80.80 186	82.73 187	61.57 195	75.33 126	83.13 179	55.52 145	91.07 151	64.98 137	78.34 148	88.45 156

PT_06_test		75.20 163	73.77 162	79.49 160	82.69 192	60.19 172	82.34 175	86.99 146	69.71 131	72.52 150	78.31 218	56.27 141	90.07 165	62.03 156	73.11 197	88.23 159

PatchFormer-LS_test		74.50 164	73.05 165	78.86 169	82.95 187	59.55 176	81.65 182	82.30 191	67.44 149	71.62 162	78.15 221	52.34 164	88.92 182	65.05 136	75.90 174	88.12 162

IterMVS		74.29 165	72.94 166	78.35 176	81.53 202	63.49 141	81.58 183	82.49 188	68.06 144	69.99 175	83.69 175	51.66 172	85.54 201	65.85 130	71.64 205	86.01 198
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 166	72.42 169	79.80 156	83.76 175	59.59 174	85.92 136	86.64 149	66.39 158	66.96 203	87.58 119	39.46 220	91.60 138	65.76 131	69.27 211	88.22 160

EG-PatchMatch MVS		74.04 167	71.82 173	80.71 146	84.92 145	67.42 82	85.86 137	88.08 136	66.04 161	64.22 218	83.85 172	35.10 234	92.56 118	57.44 187	80.83 127	82.16 225

Test4		73.95 168	72.20 170	79.21 164	82.91 188	58.94 178	81.25 185	82.17 192	65.21 167	71.05 166	82.44 183	44.21 202	90.17 163	63.29 144	77.28 154	88.53 154

DWT-MVSNet_test		73.70 169	71.86 172	79.21 164	82.91 188	58.94 178	82.34 175	82.17 192	65.21 167	71.05 166	78.31 218	44.21 202	90.17 163	63.29 144	77.28 154	88.53 154

sss		73.60 170	73.64 163	73.51 210	82.80 190	55.01 216	76.12 213	81.69 197	62.47 191	74.68 135	85.85 159	57.32 137	78.11 227	60.86 165	80.93 126	87.39 174

tpmp4_e23		73.45 171	71.17 180	80.31 151	83.55 178	59.56 175	81.88 180	82.33 190	57.94 218	70.51 169	81.62 200	51.19 175	91.63 137	53.96 200	77.51 152	89.75 124

SixPastTwentyTwo		73.37 172	71.26 179	79.70 157	85.08 144	57.89 191	85.57 139	83.56 179	71.03 117	65.66 210	85.88 157	42.10 212	92.57 117	59.11 175	63.34 233	88.65 143

CR-MVSNet		73.37 172	71.27 178	79.67 158	81.32 205	65.19 112	75.92 215	80.30 206	59.92 206	72.73 148	81.19 202	52.50 162	86.69 193	59.84 170	77.71 150	87.11 183

MSDG		73.36 174	70.99 181	80.49 147	84.51 150	65.80 102	80.71 188	86.13 159	65.70 164	65.46 211	83.74 174	44.60 200	90.91 153	51.13 210	76.89 161	84.74 209

tpm2		73.26 175	71.46 175	78.63 171	83.34 179	56.71 202	80.65 189	80.40 205	56.63 225	73.55 140	82.02 192	51.80 170	91.24 143	56.35 193	78.42 147	87.95 164

RPSCF		73.23 176	71.46 175	78.54 173	82.50 195	59.85 173	82.18 178	82.84 186	58.96 211	71.15 165	89.41 89	45.48 199	84.77 204	58.82 178	71.83 204	91.02 81

PatchmatchNet		73.12 177	71.33 177	78.49 174	83.18 182	60.85 166	79.63 196	78.57 214	64.13 177	71.73 160	79.81 213	51.20 174	85.97 199	57.40 188	76.36 170	88.66 142
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	66.92 17	73.01 178	70.41 185	80.81 144	87.13 127	65.63 104	88.30 76	84.19 174	62.96 186	63.80 220	87.69 117	38.04 225	92.56 118	46.66 223	74.91 184	84.24 211
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 179	72.58 167	74.25 207	84.28 153	50.85 233	86.41 127	83.45 180	44.56 248	73.23 143	87.54 122	49.38 179	85.70 200	65.90 129	78.44 146	86.19 195

test-LLR		72.94 180	72.43 168	74.48 204	81.35 203	58.04 186	78.38 204	77.46 218	66.66 153	69.95 176	79.00 216	48.06 186	79.24 222	66.13 125	84.83 97	86.15 196

test_0402		72.79 181	70.44 184	79.84 155	88.13 104	65.99 98	85.93 135	84.29 172	65.57 165	67.40 200	85.49 161	46.92 190	92.61 116	35.88 242	74.38 189	80.94 227

tpmrst		72.39 182	72.13 171	73.18 212	80.54 212	49.91 235	79.91 195	79.08 212	63.11 184	71.69 161	79.95 210	55.32 146	82.77 211	65.66 132	73.89 193	86.87 186

PatchMatch-RL		72.38 183	70.90 182	76.80 191	88.60 92	67.38 84	79.53 197	76.17 224	62.75 189	69.36 183	82.00 193	45.51 198	84.89 203	53.62 201	80.58 131	78.12 235

tpm		72.37 184	71.71 174	74.35 206	82.19 197	52.00 225	79.22 200	77.29 220	64.56 173	72.95 146	83.68 176	51.35 173	83.26 210	58.33 182	75.80 175	87.81 167

PVSNet	64.34 18	72.08 185	70.87 183	75.69 197	86.21 135	56.44 207	74.37 226	80.73 202	62.06 193	70.17 171	82.23 187	42.86 208	83.31 209	54.77 197	84.45 100	87.32 176

RPMNet		71.62 186	68.94 193	79.67 158	81.32 205	65.19 112	75.92 215	78.30 216	57.60 220	72.73 148	76.45 228	52.30 165	86.69 193	48.14 220	77.71 150	87.11 183

test-mter		71.41 187	70.39 186	74.48 204	81.35 203	58.04 186	78.38 204	77.46 218	60.32 202	69.95 176	79.00 216	36.08 232	79.24 222	66.13 125	84.83 97	86.15 196

K. test v3		71.19 188	68.51 195	79.21 164	83.04 185	57.78 193	84.35 159	76.91 222	72.90 92	62.99 223	82.86 181	39.27 221	91.09 150	61.65 160	52.66 248	88.75 140

tpmvs		71.09 189	69.29 189	76.49 192	82.04 198	56.04 213	78.92 203	81.37 199	64.05 178	67.18 201	78.28 220	49.74 178	89.77 167	49.67 215	72.37 200	83.67 214

pm-mvs1		70.87 190	69.03 191	76.39 193	79.41 220	58.92 180	80.64 190	78.89 213	55.71 227	67.14 202	83.58 177	48.48 185	85.25 202	52.94 205	74.48 188	84.99 206

Patchmtry		70.74 191	69.16 190	75.49 199	80.72 209	54.07 220	74.94 225	80.30 206	58.34 214	70.01 173	81.19 202	52.50 162	86.54 195	53.37 202	71.09 206	85.87 200

MIMVSNet		70.69 192	69.30 188	74.88 201	84.52 149	56.35 209	75.87 217	79.42 211	64.59 172	67.76 196	82.41 184	41.10 216	81.54 215	46.64 225	81.34 123	86.75 189

tpm cat1		70.57 193	68.31 197	77.35 187	82.41 196	57.95 190	78.08 207	80.22 208	52.04 240	68.54 192	77.66 224	52.00 169	87.84 188	51.77 207	72.07 203	86.25 194

OpenMVS_ROB	64.09 19	70.56 194	68.19 198	77.65 183	80.26 213	59.41 177	85.01 145	82.96 185	58.76 212	65.43 212	82.33 185	37.63 228	91.23 144	45.34 230	76.03 172	82.32 223

USDC		70.33 195	68.37 196	76.21 195	80.60 211	56.23 210	79.19 201	86.49 152	60.89 197	61.29 224	85.47 162	31.78 236	89.47 172	53.37 202	76.21 171	82.94 222

CMPMVS	51.72 21	70.19 196	68.16 199	76.28 194	73.15 240	57.55 194	79.47 198	83.92 176	48.02 246	56.48 236	84.81 168	43.13 206	86.42 197	62.67 150	81.81 121	84.89 207
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TESTMET0.1,1		69.89 197	69.00 192	72.55 213	79.27 224	56.85 198	78.38 204	74.71 234	57.64 219	68.09 195	77.19 226	37.75 226	76.70 232	63.92 141	84.09 101	84.10 213

FMVSNet5		69.50 198	67.96 201	74.15 208	82.97 186	55.35 215	80.01 194	82.12 194	62.56 190	63.02 221	81.53 201	36.92 229	81.92 213	48.42 218	74.06 191	85.17 205

PMMVS		69.34 199	68.67 194	71.35 219	75.67 232	62.03 160	75.17 220	73.46 239	50.00 244	68.68 190	79.05 214	52.07 168	78.13 226	61.16 163	82.77 111	73.90 243

EPMVS		69.02 200	68.16 199	71.59 215	79.61 218	49.80 236	77.40 210	66.93 250	62.82 188	70.01 173	79.05 214	45.79 195	77.86 229	56.58 192	75.26 182	87.13 182

MIMVSNet1		68.58 201	66.78 206	73.98 209	80.07 214	51.82 226	80.77 187	84.37 171	64.40 175	59.75 227	82.16 188	36.47 230	83.63 207	42.73 233	70.33 209	86.48 193

EU-MVSNet		68.53 202	67.61 204	71.31 220	78.51 226	47.01 241	84.47 154	84.27 173	42.27 249	66.44 208	84.79 169	40.44 218	83.76 205	58.76 179	68.54 217	83.17 217

PatchT		68.46 203	67.85 202	70.29 222	80.70 210	43.93 245	72.47 229	74.88 230	60.15 204	70.55 168	76.57 227	49.94 177	81.59 214	50.58 211	74.83 185	85.34 202

test0.0.03 1		68.00 204	67.69 203	68.90 226	77.55 227	47.43 239	75.70 219	72.95 241	66.66 153	66.56 206	82.29 186	48.06 186	75.87 235	44.97 231	74.51 187	83.41 216

TDRefinement		67.49 205	64.34 211	76.92 189	73.47 239	61.07 163	84.86 148	82.98 184	59.77 207	58.30 230	85.13 164	26.06 239	87.89 187	47.92 221	60.59 240	81.81 226

test20.03		67.45 206	66.95 205	68.94 225	75.48 235	44.84 243	77.50 209	77.67 217	66.66 153	63.01 222	83.80 173	47.02 189	78.40 225	42.53 234	68.86 215	83.58 215

UnsupCasMVSNet_eth		67.33 207	65.99 208	71.37 217	73.48 238	51.47 230	75.16 221	85.19 166	65.20 169	60.78 226	80.93 207	42.35 209	77.20 231	57.12 190	53.69 247	85.44 201

TinyColmap		67.30 208	64.81 210	74.76 203	81.92 199	56.68 203	80.29 192	81.49 198	60.33 201	56.27 237	83.22 178	24.77 241	87.66 190	45.52 228	69.47 210	79.95 231

dp		66.80 209	65.43 209	70.90 221	79.74 217	48.82 238	75.12 223	74.77 232	59.61 208	64.08 219	77.23 225	42.89 207	80.72 217	48.86 217	66.58 223	83.16 218

MDA-MVSNet-bldmvs		66.68 210	63.66 213	75.75 196	79.28 223	60.56 169	73.92 227	78.35 215	64.43 174	50.13 247	79.87 212	44.02 205	83.67 206	46.10 227	56.86 242	83.03 220

testgi		66.67 211	66.53 207	67.08 231	75.62 233	41.69 250	75.93 214	76.50 223	66.11 159	65.20 215	86.59 146	35.72 233	74.71 238	43.71 232	73.38 196	84.84 208

PM-MVS		66.41 212	64.14 212	73.20 211	73.92 236	56.45 206	78.97 202	64.96 254	63.88 182	64.72 216	80.24 209	19.84 248	83.44 208	66.24 124	64.52 231	79.71 232

JIA-IIPM		66.32 213	62.82 218	76.82 190	77.09 228	61.72 162	65.34 250	75.38 226	58.04 217	64.51 217	62.32 247	42.05 213	86.51 196	51.45 209	69.22 212	82.21 224

ADS-MVSNet2		66.20 214	63.33 214	74.82 202	79.92 215	58.75 181	67.55 246	75.19 228	53.37 236	65.25 213	75.86 229	42.32 210	80.53 218	41.57 235	68.91 213	85.18 203

YYNet1		65.03 215	62.91 216	71.38 216	75.85 231	56.60 204	69.12 240	74.66 236	57.28 223	54.12 239	77.87 223	45.85 194	74.48 239	49.95 213	61.52 238	83.05 219

MDA-MVSNet_test_wron		65.03 215	62.92 215	71.37 217	75.93 230	56.73 200	69.09 241	74.73 233	57.28 223	54.03 240	77.89 222	45.88 193	74.39 240	49.89 214	61.55 237	82.99 221

ADS-MVSNet		64.36 217	62.88 217	68.78 228	79.92 215	47.17 240	67.55 246	71.18 244	53.37 236	65.25 213	75.86 229	42.32 210	73.99 242	41.57 235	68.91 213	85.18 203

LF4IMVS		64.02 218	62.19 219	69.50 224	70.90 244	53.29 223	76.13 212	77.18 221	52.65 239	58.59 228	80.98 206	23.55 242	76.52 233	53.06 204	66.66 222	78.68 234

UnsupCasMVSNet_bld		63.70 219	61.53 220	70.21 223	73.69 237	51.39 231	72.82 228	81.89 195	55.63 228	57.81 231	71.80 236	38.67 223	78.61 224	49.26 216	52.21 249	80.63 228

PVSNet_0	57.27 20	61.67 220	59.27 221	68.85 227	79.61 218	57.44 195	68.01 244	73.44 240	55.93 226	58.54 229	70.41 239	44.58 201	77.55 230	47.01 222	35.91 254	71.55 245

LP		61.36 221	57.78 224	72.09 214	75.54 234	58.53 182	67.16 248	75.22 227	51.90 241	54.13 238	69.97 240	37.73 227	80.45 219	32.74 246	55.63 244	77.29 238

test2356		59.50 222	58.08 222	63.74 235	71.23 243	41.88 248	67.59 245	72.42 243	53.72 235	57.65 232	70.74 238	26.31 238	72.40 245	32.03 249	71.06 207	76.93 240

MVS-HIRNet		59.14 223	57.67 225	63.57 236	81.65 201	43.50 246	71.73 230	65.06 253	39.59 253	51.43 245	57.73 250	38.34 224	82.58 212	39.53 238	73.95 192	64.62 250

testus		59.00 224	57.91 223	62.25 238	72.25 241	39.09 253	69.74 235	75.02 229	53.04 238	57.21 234	73.72 234	18.76 250	70.33 249	32.86 245	68.57 216	77.35 237

test1235678		58.74 225	56.89 228	64.30 233	69.70 245	41.87 249	71.05 231	74.87 231	54.06 232	50.63 246	71.53 237	25.30 240	74.10 241	31.80 250	63.10 234	76.93 240

pmmvs3		57.79 226	54.26 230	68.37 229	64.02 252	56.72 201	75.12 223	65.17 252	40.20 251	52.93 243	69.86 241	20.36 246	75.48 237	45.45 229	55.25 246	72.90 244

DSMNet-mixed		57.77 227	56.90 227	60.38 239	67.70 250	35.61 256	69.18 239	53.97 257	32.30 260	57.49 233	79.88 211	40.39 219	68.57 252	38.78 239	72.37 200	76.97 239

1111		57.11 228	56.82 229	57.97 242	69.10 246	28.28 261	68.90 242	74.54 237	54.01 233	53.71 241	74.51 232	23.09 243	67.90 253	32.28 247	61.26 239	77.73 236

testpf		56.51 229	57.58 226	53.30 246	71.99 242	41.19 251	46.89 261	69.32 248	58.06 216	52.87 244	69.45 242	27.99 237	72.73 244	59.59 173	62.07 235	45.98 256

LCM-MVSNet		54.25 230	49.68 237	67.97 230	53.73 259	45.28 242	66.85 249	80.78 201	35.96 255	39.45 253	62.23 248	8.70 259	78.06 228	48.24 219	51.20 250	80.57 229

testmv		53.85 231	51.03 233	62.31 237	61.46 254	38.88 254	70.95 234	74.69 235	51.11 243	41.26 250	66.85 243	14.28 253	72.13 246	29.19 252	49.51 251	75.93 242

FPMVS		53.68 232	51.64 232	59.81 240	65.08 251	51.03 232	69.48 238	69.58 246	41.46 250	40.67 251	72.32 235	16.46 252	70.00 250	24.24 256	65.42 229	58.40 252

N_pmnet		52.79 233	53.26 231	51.40 249	78.99 225	7.68 268	69.52 237	3.89 265	51.63 242	57.01 235	74.98 231	40.83 217	65.96 255	37.78 240	64.67 230	80.56 230

HyFIR lowres test		51.79 234	50.01 236	57.11 243	68.82 248	49.21 237	60.50 253	53.26 258	34.52 256	43.77 249	64.94 246	20.34 247	71.75 247	39.87 237	64.06 232	50.39 253

no-one		51.08 235	45.79 240	66.95 232	57.92 257	50.49 234	59.63 256	76.04 225	48.04 245	31.85 254	56.10 253	19.12 249	80.08 221	36.89 241	26.52 256	70.29 246

new_pmnet		50.91 236	50.29 234	52.78 247	68.58 249	34.94 259	63.71 252	56.63 256	39.73 252	44.95 248	65.47 245	21.93 245	58.48 257	34.98 243	56.62 243	64.92 249

ANet_high		50.57 237	46.10 239	63.99 234	48.67 262	39.13 252	70.99 233	80.85 200	61.39 196	31.18 256	57.70 251	17.02 251	73.65 243	31.22 251	15.89 263	79.18 233

test12356		49.28 238	48.51 238	51.59 248	62.06 253	19.11 266	60.40 254	72.45 242	47.60 247	40.64 252	65.68 244	13.84 254	68.72 251	27.29 254	46.67 253	66.94 248

.test1245		45.55 239	50.02 235	32.14 255	69.10 246	28.28 261	68.90 242	74.54 237	54.01 233	53.71 241	74.51 232	23.09 243	67.90 253	32.28 247	0.02 265	0.25 264

Gipuma		45.18 240	41.86 241	55.16 245	77.03 229	51.52 229	32.50 263	80.52 203	32.46 258	27.12 257	35.02 257	9.52 258	75.50 236	22.31 257	60.21 241	38.45 258
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 241	40.28 242	55.82 244	40.82 265	42.54 247	65.12 251	63.99 255	34.43 257	24.48 258	57.12 252	3.92 261	76.17 234	17.10 259	55.52 245	48.75 254
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 242	38.86 243	46.69 251	53.84 258	16.45 267	48.61 260	49.92 260	37.49 254	31.67 255	60.97 249	8.14 260	56.42 258	28.42 253	30.72 255	67.19 247

wuykxyi23d		39.76 243	33.18 245	59.51 241	46.98 263	44.01 244	57.70 257	67.74 249	24.13 262	13.98 264	34.33 258	1.27 264	71.33 248	34.23 244	18.23 259	63.18 251

PNet_i23d		38.26 244	35.42 244	46.79 250	58.74 255	35.48 257	59.65 255	51.25 259	32.45 259	23.44 260	47.53 255	2.04 263	58.96 256	25.60 255	18.09 261	45.92 257

E-PMN		31.77 245	30.64 246	35.15 253	52.87 260	27.67 263	57.09 258	47.86 261	24.64 261	16.40 262	33.05 259	11.23 256	54.90 259	14.46 261	18.15 260	22.87 260

EMVS		30.81 246	29.65 247	34.27 254	50.96 261	25.95 265	56.58 259	46.80 262	24.01 263	15.53 263	30.68 260	12.47 255	54.43 260	12.81 262	17.05 262	22.43 261

MVE	26.22 23	30.37 247	25.89 248	43.81 252	44.55 264	35.46 258	28.87 264	39.07 263	18.20 264	18.58 261	40.18 256	2.68 262	47.37 261	17.07 260	23.78 258	48.60 255
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		16.82 248	15.94 249	19.46 257	58.74 255	31.45 260	39.22 262	3.74 266	6.84 266	6.04 265	2.70 262	1.27 264	24.29 263	10.54 263	14.40 264	2.63 262

ab-mvs-re		7.23 249	9.64 250	0.00 260	0.00 269	0.00 271	0.00 265	0.00 269	0.00 269	0.00 268	86.72 136	0.00 268	0.00 266	0.00 266	0.00 268	0.00 266

test123		6.12 250	8.11 251	0.14 258	0.06 268	0.09 269	71.05 231	0.03 268	0.04 268	0.25 267	1.30 264	0.05 266	0.03 265	0.21 265	0.01 267	0.29 263

testmvs		6.04 251	8.02 252	0.10 259	0.08 267	0.03 270	69.74 235	0.04 267	0.05 267	0.31 266	1.68 263	0.02 267	0.04 264	0.24 264	0.02 265	0.25 264

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						72.60 18	89.01 59

test_prior2							88.85 62		75.41 60	84.91 18	93.54 20	74.28 11		83.31 18	95.86 4

test_prior				86.33 33	92.61 32	69.59 48		92.97 22					95.48 32			93.91 22

旧先验2							86.56 123		58.10 215	87.04 8			88.98 180	74.07 75

新几何2							86.29 130

新几何1				83.42 80	93.13 24	70.71 37		85.48 163	57.43 221	81.80 45	91.98 40	63.28 73	92.27 123	64.60 140	92.99 32	87.27 177

旧先验1					91.96 38	65.79 103		86.37 155			93.08 29	69.31 33			92.74 35	88.74 141

无先验							87.48 98	88.98 120	60.00 205				94.12 71	67.28 118		88.97 136

原ACMM2							86.86 116

原ACMM1				84.35 63	93.01 27	68.79 59		92.44 31	63.96 181	81.09 51	91.57 49	66.06 54	95.45 34	67.19 120	94.82 18	88.81 138

test222					91.50 43	68.26 72	84.16 161	83.20 182	54.63 231	79.74 57	91.63 47	58.97 132			91.42 45	86.77 188

testdata2													91.01 152	62.37 152

segment_acmp												73.08 16

testdata				79.97 154	90.90 50	64.21 135		84.71 169	59.27 210	85.40 14	92.91 30	62.02 106	89.08 177	68.95 108	91.37 46	86.63 192

testdata1							84.14 162		75.71 56

test12				86.80 26	92.63 31	70.70 38		91.79 51		82.71 41		71.67 23	96.16 21		94.50 21	93.54 36

plane_prior7					90.08 60	68.51 69

plane_prior6					89.84 64	68.70 66						60.42 126

plane_prior5								92.44 31					95.38 36	78.71 43	86.32 88	91.33 74

plane_prior4											91.00 58

plane_prior3						68.60 68			78.44 24	78.92 66

plane_prior2							91.25 24		79.12 17

plane_prior1					89.90 63

plane_prior						68.71 64	90.38 36		77.62 28						86.16 90

n2								0.00 269

nn								0.00 269

door-mid								69.98 245

lessismore_v0				78.97 168	81.01 208	57.15 196		65.99 251		61.16 225	82.82 182	39.12 222	91.34 141	59.67 171	46.92 252	88.43 157

LGP-MVS_train				84.50 58	89.23 78	68.76 61		91.94 47	75.37 62	76.64 107	91.51 50	54.29 154	94.91 49	78.44 45	83.78 104	89.83 120

test11								92.23 37

door								69.44 247

HQP5-MVS						66.98 89

HQP-NCC					89.33 70		89.17 53		76.41 47	77.23 97

ACMP_Plane					89.33 70		89.17 53		76.41 47	77.23 97

BP-MVS														77.47 51

HQP4-MVS										77.24 96			95.11 42			91.03 79

HQP3-MVS								92.19 40							85.99 92

HQP2-MVS												60.17 129

NP-MVS					89.62 65	68.32 71					90.24 67

MDTV_nov1_ep13_2view						37.79 255	75.16 221		55.10 229	66.53 207		49.34 180		53.98 199		87.94 165

MDTV_nov1_ep13			69.97 187		83.18 182	53.48 222	77.10 211	80.18 209	60.45 200	69.33 184	80.44 208	48.89 184	86.90 192	51.60 208	78.51 145

ACMMP++_ref															81.95 119

ACMMP++															81.25 124

Test By Simon												64.33 65

ITE_SJBPF				78.22 177	81.77 200	60.57 168		83.30 181	69.25 136	67.54 199	87.20 131	36.33 231	87.28 191	54.34 198	74.62 186	86.80 187

DeepMVS_CX				27.40 256	40.17 266	26.90 264		24.59 264	17.44 265	23.95 259	48.61 254	9.77 257	26.48 262	18.06 258	24.47 257	28.83 259

High-resolution multi-view benchmark