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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

DPE-MVS		95.53 3	96.13 3	94.82 1	96.81 22	98.05 3	97.42 1	93.09 1	94.31 8	91.49 6	97.12 1	95.03 3	93.27 3	95.55 5	94.58 11	96.86 6	98.25 3

SED-MVS		95.61 1	96.36 1	94.73 2	96.84 19	98.15 2	97.08 3	92.92 2	95.64 2	91.84 4	95.98 4	95.33 1	92.83 6	96.00 1	94.94 3	96.90 4	98.45 2

APDe-MVS		95.23 4	95.69 5	94.70 4	97.12 10	97.81 5	97.19 2	92.83 3	95.06 5	90.98 10	96.47 2	92.77 10	93.38 2	95.34 8	94.21 15	96.68 8	98.17 4

DVP-MVS		95.56 2	96.26 2	94.73 2	96.93 16	98.19 1	96.62 6	92.81 4	96.15 1	91.73 5	95.01 7	95.31 2	93.41 1	95.95 2	94.77 7	96.90 4	98.46 1

APD-MVS		94.37 11	94.47 15	94.26 6	97.18 8	96.99 16	96.53 7	92.68 5	92.45 24	89.96 17	94.53 11	91.63 20	92.89 5	94.58 20	93.82 22	96.31 17	97.26 18
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
xxxxxxxxxxxxxcwj		92.95 24	91.88 33	94.20 8	96.75 24	97.07 11	95.82 18	92.60 6	93.98 12	91.09 8	95.89 5	71.01 125	91.93 15	94.40 25	93.56 28	97.04 2	97.27 16

SF-MVS		94.61 7	94.96 9	94.20 8	96.75 24	97.07 11	95.82 18	92.60 6	93.98 12	91.09 8	95.89 5	92.54 12	91.93 15	94.40 25	93.56 28	97.04 2	97.27 16

HPM-MVS++		94.60 8	94.91 10	94.24 7	97.86 1	96.53 32	96.14 8	92.51 8	93.87 15	90.76 12	93.45 18	93.84 4	92.62 8	95.11 11	94.08 18	95.58 50	97.48 13

NCCC		93.69 19	93.66 22	93.72 16	97.37 5	96.66 29	95.93 16	92.50 9	93.40 19	88.35 25	87.36 35	92.33 14	92.18 12	94.89 13	94.09 17	96.00 23	96.91 26

CNVR-MVS		94.37 11	94.65 11	94.04 11	97.29 6	97.11 10	96.00 10	92.43 10	93.45 16	89.85 19	90.92 25	93.04 8	92.59 9	95.77 4	94.82 5	96.11 21	97.42 15

MSP-MVS		95.12 5	95.83 4	94.30 5	96.82 21	97.94 4	96.98 4	92.37 11	95.40 3	90.59 13	96.16 3	93.71 5	92.70 7	94.80 15	94.77 7	96.37 14	97.99 7

SD-MVS		94.53 9	95.22 7	93.73 15	95.69 36	97.03 14	95.77 22	91.95 12	94.41 7	91.35 7	94.97 8	93.34 7	91.80 20	94.72 18	93.99 19	95.82 34	98.07 6
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
SMA-MVS		94.70 6	95.35 6	93.93 12	97.57 2	97.57 7	95.98 11	91.91 13	94.50 6	90.35 14	93.46 17	92.72 11	91.89 18	95.89 3	95.22 1	95.88 27	98.10 5
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
SteuartSystems-ACMMP		94.06 13	94.65 11	93.38 19	96.97 15	97.36 8	96.12 9	91.78 14	92.05 28	87.34 30	94.42 12	90.87 24	91.87 19	95.47 7	94.59 10	96.21 19	97.77 10
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	87.86 3	92.26 31	91.86 34	92.73 25	96.18 29	96.87 19	95.19 28	91.76 15	92.17 27	86.58 35	81.79 50	85.85 50	90.88 29	94.57 21	94.61 9	95.80 35	97.18 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MCST-MVS		93.81 16	94.06 18	93.53 17	96.79 23	96.85 20	95.95 13	91.69 16	92.20 26	87.17 32	90.83 27	93.41 6	91.96 14	94.49 23	93.50 31	97.61 1	97.12 22

AdaColmap		90.29 43	88.38 57	92.53 26	96.10 31	95.19 53	92.98 46	91.40 17	89.08 47	88.65 23	78.35 71	81.44 69	91.30 28	90.81 82	90.21 76	94.72 88	93.59 87

ACMMP_NAP		93.94 15	94.49 14	93.30 20	97.03 13	97.31 9	95.96 12	91.30 18	93.41 18	88.55 24	93.00 19	90.33 28	91.43 26	95.53 6	94.41 13	95.53 52	97.47 14

DeepC-MVS_fast	88.76 1	93.10 22	93.02 29	93.19 22	97.13 9	96.51 33	95.35 26	91.19 19	93.14 21	88.14 26	85.26 41	89.49 35	91.45 23	95.17 9	95.07 2	95.85 32	96.48 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		93.80 17	93.45 25	94.20 8	97.53 3	96.43 36	95.88 17	91.12 20	94.09 11	92.74 3	87.68 33	90.77 25	92.04 13	94.74 17	93.56 28	95.91 26	96.85 27

DPM-MVS		91.72 35	91.48 35	92.00 31	95.53 37	95.75 45	95.94 14	91.07 21	91.20 34	85.58 41	81.63 54	90.74 26	88.40 45	93.40 38	93.75 24	95.45 56	93.85 81

SR-MVS					96.58 26			90.99 22				92.40 13

HFP-MVS		94.02 14	94.22 17	93.78 14	97.25 7	96.85 20	95.81 20	90.94 23	94.12 10	90.29 16	94.09 14	89.98 31	92.52 10	93.94 32	93.49 33	95.87 29	97.10 23

TSAR-MVS + MP.		94.48 10	94.97 8	93.90 13	95.53 37	97.01 15	96.69 5	90.71 24	94.24 9	90.92 11	94.97 8	92.19 15	93.03 4	94.83 14	93.60 26	96.51 13	97.97 8
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS		93.35 20	93.59 23	93.08 23	97.39 4	96.82 22	95.38 25	90.71 24	90.82 36	88.07 27	92.83 21	90.29 29	91.32 27	94.03 29	93.19 38	95.61 48	97.16 20
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMPR		93.72 18	93.94 19	93.48 18	97.07 11	96.93 17	95.78 21	90.66 26	93.88 14	89.24 21	93.53 16	89.08 38	92.24 11	93.89 34	93.50 31	95.88 27	96.73 31

CP-MVS		93.25 21	93.26 26	93.24 21	96.84 19	96.51 33	95.52 24	90.61 27	92.37 25	88.88 22	90.91 26	89.52 34	91.91 17	93.64 36	92.78 44	95.69 41	97.09 24

train_agg		92.87 25	93.53 24	92.09 30	96.88 18	95.38 49	95.94 14	90.59 28	90.65 38	83.65 51	94.31 13	91.87 19	90.30 31	93.38 39	92.42 46	95.17 69	96.73 31

X-MVS		92.36 30	92.75 30	91.90 33	96.89 17	96.70 25	95.25 27	90.48 29	91.50 33	83.95 48	88.20 31	88.82 40	89.11 36	93.75 35	93.43 34	95.75 39	96.83 29

TSAR-MVS + ACMM		92.97 23	94.51 13	91.16 37	95.88 34	96.59 30	95.09 29	90.45 30	93.42 17	83.01 53	94.68 10	90.74 26	88.74 40	94.75 16	93.78 23	93.82 128	97.63 11

PCF-MVS	84.60 6	88.66 54	87.75 67	89.73 48	93.06 62	96.02 40	93.22 44	90.00 31	82.44 76	80.02 73	77.96 74	85.16 53	87.36 58	88.54 109	88.54 113	94.72 88	95.61 50
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepPCF-MVS	88.51 2	92.64 29	94.42 16	90.56 41	94.84 44	96.92 18	91.31 61	89.61 32	95.16 4	84.55 46	89.91 29	91.45 21	90.15 33	95.12 10	94.81 6	92.90 147	97.58 12

ACMMP		92.03 33	92.16 31	91.87 34	95.88 34	96.55 31	94.47 35	89.49 33	91.71 31	85.26 42	91.52 24	84.48 55	90.21 32	92.82 48	91.63 52	95.92 25	96.42 36
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
MSLP-MVS++		92.02 34	91.40 37	92.75 24	96.01 32	95.88 44	93.73 40	89.00 34	89.89 44	90.31 15	81.28 56	88.85 39	91.45 23	92.88 47	94.24 14	96.00 23	96.76 30

LS3D		85.96 77	84.37 92	87.81 67	94.13 50	93.27 80	90.26 69	89.00 34	84.91 65	72.84 105	71.74 104	72.47 119	87.45 57	89.53 100	89.09 104	93.20 143	89.60 144

CPTT-MVS		91.39 37	90.95 40	91.91 32	95.06 39	95.24 51	95.02 30	88.98 36	91.02 35	86.71 34	84.89 43	88.58 43	91.60 22	90.82 81	89.67 92	94.08 115	96.45 35

CSCG		92.76 26	93.16 27	92.29 29	96.30 28	97.74 6	94.67 33	88.98 36	92.46 23	89.73 20	86.67 37	92.15 17	88.69 42	92.26 54	92.92 42	95.40 57	97.89 9

CDPH-MVS		91.14 39	92.01 32	90.11 43	96.18 29	96.18 39	94.89 31	88.80 38	88.76 48	77.88 84	89.18 30	87.71 47	87.29 60	93.13 42	93.31 36	95.62 46	95.84 45

OPM-MVS		87.56 67	85.80 83	89.62 50	93.90 53	94.09 69	94.12 36	88.18 39	75.40 126	77.30 87	76.41 79	77.93 91	88.79 39	92.20 56	90.82 63	95.40 57	93.72 85

EPNet		89.60 47	89.91 45	89.24 54	96.45 27	93.61 75	92.95 47	88.03 40	85.74 59	83.36 52	87.29 36	83.05 62	80.98 93	92.22 55	91.85 50	93.69 133	95.58 51
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		92.71 28	93.91 20	91.30 35	91.96 72	96.00 41	93.43 41	87.94 41	92.53 22	86.27 40	93.57 15	91.94 18	91.44 25	93.29 40	92.89 43	96.78 7	97.15 21

PGM-MVS		92.76 26	93.03 28	92.45 28	97.03 13	96.67 28	95.73 23	87.92 42	90.15 43	86.53 36	92.97 20	88.33 44	91.69 21	93.62 37	93.03 39	95.83 33	96.41 37

3Dnovator+	86.06 4	91.60 36	90.86 42	92.47 27	96.00 33	96.50 35	94.70 32	87.83 43	90.49 39	89.92 18	74.68 90	89.35 36	90.66 30	94.02 30	94.14 16	95.67 43	96.85 27

PHI-MVS		92.05 32	93.74 21	90.08 44	94.96 41	97.06 13	93.11 45	87.71 44	90.71 37	80.78 68	92.40 22	91.03 22	87.68 53	94.32 27	94.48 12	96.21 19	96.16 40

HQP-MVS		89.13 51	89.58 49	88.60 60	93.53 56	93.67 73	93.29 43	87.58 45	88.53 49	75.50 88	87.60 34	80.32 74	87.07 61	90.66 87	89.95 84	94.62 94	96.35 39

CANet		91.33 38	91.46 36	91.18 36	95.01 40	96.71 24	93.77 38	87.39 46	87.72 51	87.26 31	81.77 51	89.73 32	87.32 59	94.43 24	93.86 21	96.31 17	96.02 43

PLC	83.76 9	88.61 56	86.83 73	90.70 39	94.22 49	92.63 91	91.50 59	87.19 47	89.16 46	86.87 33	75.51 85	80.87 71	89.98 34	90.01 92	89.20 102	94.41 107	90.45 141
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
abl_6				90.66 40	94.65 47	96.27 37	92.21 50	86.94 48	90.23 41	86.38 37	85.50 40	92.96 9	88.37 46			95.40 57	95.46 53

ACMM	83.27 10	87.68 66	86.09 79	89.54 51	93.26 58	92.19 97	91.43 60	86.74 49	86.02 57	82.85 55	75.63 84	75.14 101	88.41 44	90.68 86	89.99 81	94.59 95	92.97 94
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_0304		90.88 40	91.35 38	90.34 42	93.91 52	96.79 23	94.49 34	86.54 50	86.57 55	82.85 55	81.68 53	89.70 33	87.57 55	94.64 19	93.93 20	96.67 10	96.15 41

MVS_111021_HR		90.56 41	91.29 39	89.70 49	94.71 46	95.63 47	91.81 57	86.38 51	87.53 52	81.29 62	87.96 32	85.43 52	87.69 52	93.90 33	92.93 41	96.33 15	95.69 48

OMC-MVS		90.23 44	90.40 43	90.03 45	93.45 57	95.29 50	91.89 56	86.34 52	93.25 20	84.94 45	81.72 52	86.65 49	88.90 37	91.69 62	90.27 75	94.65 92	93.95 79

DELS-MVS		89.71 46	89.68 48	89.74 47	93.75 54	96.22 38	93.76 39	85.84 53	82.53 73	85.05 44	78.96 68	84.24 56	84.25 75	94.91 12	94.91 4	95.78 38	96.02 43
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
CNLPA		88.40 57	87.00 71	90.03 45	93.73 55	94.28 65	89.56 79	85.81 54	91.87 29	87.55 29	69.53 118	81.49 68	89.23 35	89.45 101	88.59 112	94.31 111	93.82 82

MSDG		83.87 93	81.02 115	87.19 72	92.17 71	89.80 127	89.15 84	85.72 55	80.61 95	79.24 75	66.66 131	68.75 135	82.69 80	87.95 116	87.44 122	94.19 113	85.92 173

TSAR-MVS + COLMAP		88.40 57	89.09 52	87.60 70	92.72 67	93.92 72	92.21 50	85.57 56	91.73 30	73.72 98	91.75 23	73.22 117	87.64 54	91.49 64	89.71 91	93.73 131	91.82 122

3Dnovator	85.17 5	90.48 42	89.90 46	91.16 37	94.88 43	95.74 46	93.82 37	85.36 57	89.28 45	87.81 28	74.34 92	87.40 48	88.56 43	93.07 43	93.74 25	96.53 12	95.71 47

ACMP	83.90 8	88.32 60	88.06 60	88.62 59	92.18 70	93.98 71	91.28 62	85.24 58	86.69 54	81.23 63	85.62 39	75.13 102	87.01 63	89.83 94	89.77 89	94.79 82	95.43 54
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		88.25 61	88.55 54	87.89 66	92.84 66	93.66 74	93.35 42	85.22 59	85.77 58	74.03 97	86.60 38	76.29 98	86.62 65	91.20 68	90.58 71	95.29 65	95.75 46

QAPM		89.49 48	89.58 49	89.38 52	94.73 45	95.94 42	92.35 49	85.00 60	85.69 60	80.03 72	76.97 78	87.81 46	87.87 50	92.18 58	92.10 48	96.33 15	96.40 38

TAPA-MVS	84.37 7	88.91 53	88.93 53	88.89 55	93.00 63	94.85 59	92.00 53	84.84 61	91.68 32	80.05 71	79.77 63	84.56 54	88.17 48	90.11 91	89.00 108	95.30 64	92.57 109
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_111021_LR		90.14 45	90.89 41	89.26 53	93.23 59	94.05 70	90.43 66	84.65 62	90.16 42	84.52 47	90.14 28	83.80 59	87.99 49	92.50 52	90.92 61	94.74 86	94.70 67

UA-Net		86.07 75	87.78 65	84.06 97	92.85 65	95.11 54	87.73 103	84.38 63	73.22 146	73.18 101	79.99 62	89.22 37	71.47 167	93.22 41	93.03 39	94.76 85	90.69 136

UniMVSNet_NR-MVSNet		81.87 111	81.33 111	82.50 113	85.31 143	91.30 102	85.70 131	84.25 64	75.89 122	64.21 144	66.95 130	64.65 147	80.22 105	87.07 124	89.18 103	95.27 67	94.29 72

ACMH	78.52 14	81.86 112	80.45 123	83.51 106	90.51 89	91.22 103	85.62 134	84.23 65	70.29 162	62.21 158	69.04 122	64.05 149	84.48 74	87.57 120	88.45 115	94.01 119	92.54 111
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TranMVSNet+NR-MVSNet		80.52 123	79.84 132	81.33 127	84.92 152	90.39 112	85.53 136	84.22 66	74.27 135	60.68 173	64.93 144	59.96 170	77.48 134	86.75 132	89.28 99	95.12 74	93.29 89

Anonymous202405211			82.75 101		89.58 100	92.97 86	89.04 89	84.13 67	78.72 108		57.18 179	76.64 97	83.13 79	89.55 99	89.92 85	93.38 141	94.28 75

EPNet_dtu		81.98 110	83.82 94	79.83 143	94.10 51	85.97 168	87.29 110	84.08 68	80.61 95	59.96 175	81.62 55	77.19 95	62.91 190	87.21 122	86.38 141	90.66 173	87.77 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
casdiffmvs		87.45 68	87.15 70	87.79 69	90.15 96	94.22 66	89.96 72	83.93 69	85.08 63	80.91 65	75.81 83	77.88 92	86.08 67	91.86 61	90.86 62	95.74 40	94.37 70

PVSNet_BlendedMVS		88.19 62	88.00 62	88.42 62	92.71 68	94.82 60	89.08 86	83.81 70	84.91 65	86.38 37	79.14 65	78.11 89	82.66 81	93.05 44	91.10 56	95.86 30	94.86 63

PVSNet_Blended		88.19 62	88.00 62	88.42 62	92.71 68	94.82 60	89.08 86	83.81 70	84.91 65	86.38 37	79.14 65	78.11 89	82.66 81	93.05 44	91.10 56	95.86 30	94.86 63

baseline1		84.54 87	84.43 91	84.67 85	90.62 85	91.16 104	88.63 95	83.75 72	79.78 100	71.16 108	75.14 87	74.10 107	77.84 132	91.56 63	90.67 68	96.04 22	88.58 150

FC-MVSNet-train		85.18 82	85.31 86	85.03 83	90.67 84	91.62 101	87.66 104	83.61 73	79.75 101	74.37 95	78.69 69	71.21 123	78.91 124	91.23 66	89.96 83	94.96 77	94.69 68

UGNet		85.90 78	88.23 58	83.18 107	88.96 105	94.10 68	87.52 105	83.60 74	81.66 83	77.90 83	80.76 59	83.19 61	66.70 184	91.13 75	90.71 67	94.39 108	96.06 42
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
MAR-MVS		88.39 59	88.44 56	88.33 65	94.90 42	95.06 55	90.51 65	83.59 75	85.27 61	79.07 76	77.13 76	82.89 63	87.70 51	92.19 57	92.32 47	94.23 112	94.20 77
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
DU-MVS		81.20 120	80.30 124	82.25 116	84.98 150	90.94 106	85.70 131	83.58 76	75.74 123	64.21 144	65.30 140	59.60 175	80.22 105	86.89 127	89.31 98	94.77 84	94.29 72

NR-MVSNet		80.25 126	79.98 130	80.56 136	85.20 145	90.94 106	85.65 133	83.58 76	75.74 123	61.36 168	65.30 140	56.75 187	72.38 163	88.46 111	88.80 110	95.16 70	93.87 80

Baseline_NR-MVSNet		79.84 130	78.37 147	81.55 124	84.98 150	86.66 164	85.06 138	83.49 78	75.57 125	63.31 151	58.22 178	60.97 165	78.00 130	86.89 127	87.13 126	94.47 103	93.15 91

OpenMVS	82.53 11	87.71 65	86.84 72	88.73 57	94.42 48	95.06 55	91.02 63	83.49 78	82.50 75	82.24 59	67.62 128	85.48 51	85.56 70	91.19 69	91.30 55	95.67 43	94.75 65

ACMH+	79.08 13	81.84 113	80.06 128	83.91 99	89.92 98	90.62 108	86.21 126	83.48 80	73.88 139	65.75 135	66.38 132	65.30 145	84.63 73	85.90 145	87.25 125	93.45 139	91.13 134

ETV-MVS		89.22 50	89.76 47	88.60 60	91.60 73	94.61 63	89.48 81	83.46 81	85.20 62	81.58 60	82.75 46	82.59 64	88.80 38	94.57 21	93.28 37	96.68 8	95.31 55

PVSNet_Blended_VisFu		87.40 69	87.80 64	86.92 73	92.86 64	95.40 48	88.56 97	83.45 82	79.55 103	82.26 58	74.49 91	84.03 57	79.24 123	92.97 46	91.53 54	95.15 71	96.65 33

Anonymous20231211		84.42 91	83.02 97	86.05 77	88.85 106	92.70 89	88.92 92	83.40 83	79.99 98	78.31 79	55.83 183	78.92 86	83.33 78	89.06 104	89.76 90	93.50 138	94.90 61

COLMAP_ROB	76.78 15	80.50 124	78.49 143	82.85 109	90.96 82	89.65 133	86.20 127	83.40 83	77.15 116	66.54 130	62.27 153	65.62 144	77.89 131	85.23 155	84.70 162	92.11 155	84.83 177
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UniMVSNet (Re)		81.22 119	81.08 114	81.39 125	85.35 142	91.76 100	84.93 140	82.88 85	76.13 121	65.02 141	64.94 143	63.09 153	75.17 147	87.71 119	89.04 106	94.97 76	94.88 62

EIA-MVS		87.94 64	88.05 61	87.81 67	91.46 74	95.00 57	88.67 93	82.81 86	82.53 73	80.81 67	80.04 61	80.20 75	87.48 56	92.58 51	91.61 53	95.63 45	94.36 71

thres100view900		82.55 106	81.01 117	84.34 89	90.30 94	92.27 95	89.04 89	82.77 87	75.14 127	69.56 115	65.72 135	63.13 151	79.62 118	89.97 93	89.26 100	94.73 87	91.61 129

tfpn200view9		82.86 101	81.46 108	84.48 87	90.30 94	93.09 82	89.05 88	82.71 88	75.14 127	69.56 115	65.72 135	63.13 151	80.38 104	91.15 72	89.51 95	94.91 78	92.50 113

thres400		82.68 104	81.15 113	84.47 88	90.52 87	92.89 87	88.95 91	82.71 88	74.33 134	69.22 120	65.31 139	62.61 157	80.63 99	90.96 79	89.50 96	94.79 82	92.45 115

thres600view7		82.53 107	81.02 115	84.28 92	90.61 86	93.05 83	88.57 96	82.67 90	74.12 137	68.56 123	65.09 142	62.13 162	80.40 103	91.15 72	89.02 107	94.88 79	92.59 107

thres200		82.77 103	81.25 112	84.54 86	90.38 91	93.05 83	89.13 85	82.67 90	74.40 133	69.53 117	65.69 137	63.03 154	80.63 99	91.15 72	89.42 97	94.88 79	92.04 119

DI_MVS_plusplus_trai		86.41 73	85.54 85	87.42 71	89.24 101	93.13 81	92.16 52	82.65 92	82.30 77	80.75 69	68.30 125	80.41 73	85.01 72	90.56 88	90.07 79	94.70 90	94.01 78

TDRefinement		79.05 142	77.05 161	81.39 125	88.45 109	89.00 146	86.92 118	82.65 92	74.21 136	64.41 143	59.17 171	59.16 178	74.52 153	85.23 155	85.09 157	91.37 165	87.51 162

canonicalmvs		89.36 49	89.92 44	88.70 58	91.38 76	95.92 43	91.81 57	82.61 94	90.37 40	82.73 57	82.09 48	79.28 84	88.30 47	91.17 70	93.59 27	95.36 60	97.04 25

IS_MVSNet		86.18 74	88.18 59	83.85 100	91.02 80	94.72 62	87.48 106	82.46 95	81.05 90	70.28 112	76.98 77	82.20 67	76.65 139	93.97 31	93.38 35	95.18 68	94.97 59

CS-MVS		88.97 52	89.44 51	88.41 64	91.45 75	95.24 51	90.03 70	82.43 96	84.08 68	81.16 64	81.02 58	83.83 58	88.74 40	94.25 28	92.73 45	96.67 10	94.95 60

EPP-MVSNet		86.55 71	87.76 66	85.15 82	90.52 87	94.41 64	87.24 112	82.32 97	81.79 82	73.60 99	78.57 70	82.41 65	82.07 87	91.23 66	90.39 73	95.14 72	95.48 52

CLD-MVS		88.66 54	88.52 55	88.82 56	91.37 77	94.22 66	92.82 48	82.08 98	88.27 50	85.14 43	81.86 49	78.53 88	85.93 69	91.17 70	90.61 69	95.55 51	95.00 58
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Vis-MVSNet (Re-imp)		83.65 96	86.81 74	79.96 141	90.46 90	92.71 88	84.84 142	82.00 99	80.93 92	62.44 157	76.29 80	82.32 66	65.54 187	92.29 53	91.66 51	94.49 102	91.47 131

PatchMatch-RL		83.34 98	81.36 110	85.65 79	90.33 93	89.52 135	84.36 146	81.82 100	80.87 94	79.29 74	74.04 93	62.85 156	86.05 68	88.40 112	87.04 129	92.04 156	86.77 166

UniMVSNet_ETH3D		79.24 140	76.47 166	82.48 114	85.66 138	90.97 105	86.08 128	81.63 101	64.48 186	68.94 122	54.47 186	57.65 183	78.83 125	85.20 158	88.91 109	93.72 132	93.60 86

diffmvs		86.52 72	86.76 75	86.23 76	88.31 111	92.63 91	89.58 78	81.61 102	86.14 56	80.26 70	79.00 67	77.27 94	83.58 76	88.94 105	89.06 105	94.05 117	94.29 72

IB-MVS	79.09 12	82.60 105	82.19 103	83.07 108	91.08 79	93.55 76	80.90 173	81.35 103	76.56 118	80.87 66	64.81 145	69.97 129	68.87 174	85.64 148	90.06 80	95.36 60	94.74 66
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
tfpnnormal		77.46 158	74.86 183	80.49 137	86.34 131	88.92 147	84.33 147	81.26 104	61.39 194	61.70 165	51.99 193	53.66 198	74.84 150	88.63 108	87.38 124	94.50 100	92.08 117

TransMVSNet (Re)		76.57 165	75.16 182	78.22 157	85.60 139	87.24 160	82.46 158	81.23 105	59.80 197	59.05 181	57.07 180	59.14 179	66.60 185	88.09 114	86.82 131	94.37 109	87.95 159

test_part1		83.23 100	80.55 122	86.35 75	88.60 108	90.61 109	90.78 64	81.13 106	70.89 156	83.01 53	55.72 184	74.60 104	82.19 85	87.79 117	89.26 100	92.39 152	95.01 57

Vis-MVSNet		84.38 92	86.68 76	81.70 121	87.65 118	94.89 58	88.14 99	80.90 107	74.48 132	68.23 124	77.53 75	80.72 72	69.98 171	92.68 49	91.90 49	95.33 63	94.58 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DCV-MVSNet		85.88 79	86.17 77	85.54 81	89.10 104	89.85 125	89.34 82	80.70 108	83.04 71	78.08 82	76.19 81	79.00 85	82.42 84	89.67 97	90.30 74	93.63 136	95.12 56

WR-MVS		76.63 164	78.02 152	75.02 178	84.14 160	89.76 129	78.34 186	80.64 109	69.56 163	52.32 191	61.26 156	61.24 164	60.66 191	84.45 166	87.07 127	93.99 120	92.77 100

ET-MVSNet_ETH3D		84.65 86	85.58 84	83.56 104	74.99 204	92.62 93	90.29 68	80.38 110	82.16 78	73.01 104	83.41 44	71.10 124	87.05 62	87.77 118	90.17 77	95.62 46	91.82 122

GBi-Net		84.51 88	84.80 88	84.17 94	84.20 157	89.95 120	89.70 75	80.37 111	81.17 86	75.50 88	69.63 114	79.69 81	79.75 115	90.73 83	90.72 64	95.52 53	91.71 124

test1		84.51 88	84.80 88	84.17 94	84.20 157	89.95 120	89.70 75	80.37 111	81.17 86	75.50 88	69.63 114	79.69 81	79.75 115	90.73 83	90.72 64	95.52 53	91.71 124

FMVSNet3		84.44 90	84.64 90	84.21 93	84.32 156	90.13 118	89.85 74	80.37 111	81.17 86	75.50 88	69.63 114	79.69 81	79.62 118	89.72 96	90.52 72	95.59 49	91.58 130

CDS-MVSNet		81.63 117	82.09 104	81.09 130	87.21 123	90.28 114	87.46 108	80.33 114	69.06 166	70.66 109	71.30 105	73.87 109	67.99 177	89.58 98	89.87 86	92.87 148	90.69 136
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+		85.33 81	85.08 87	85.63 80	89.69 99	93.42 78	89.90 73	80.31 115	79.32 104	72.48 107	73.52 98	74.03 108	86.55 66	90.99 77	89.98 82	94.83 81	94.27 76

FMVSNet2		83.87 93	83.73 95	84.05 98	84.20 157	89.95 120	89.70 75	80.21 116	79.17 106	74.89 92	65.91 133	77.49 93	79.75 115	90.87 80	91.00 60	95.52 53	91.71 124

MVS_Test		86.93 70	87.24 69	86.56 74	90.10 97	93.47 77	90.31 67	80.12 117	83.55 70	78.12 80	79.58 64	79.80 79	85.45 71	90.17 90	90.59 70	95.29 65	93.53 88

thisisatest0530		85.15 83	85.86 81	84.33 90	89.19 103	92.57 94	87.22 113	80.11 118	82.15 79	74.41 94	78.15 72	73.80 111	79.90 111	90.99 77	89.58 93	95.13 73	93.75 84

tttt0517		85.11 84	85.81 82	84.30 91	89.24 101	92.68 90	87.12 117	80.11 118	81.98 80	74.31 96	78.08 73	73.57 113	79.90 111	91.01 76	89.58 93	95.11 75	93.77 83

DTE-MVSNet		75.14 181	75.44 180	74.80 180	83.18 169	87.19 161	78.25 188	80.11 118	66.05 178	48.31 198	60.88 161	54.67 194	64.54 188	82.57 178	86.17 144	94.43 106	90.53 140

PEN-MVS		76.02 174	76.07 170	75.95 173	83.17 170	87.97 154	79.65 177	80.07 121	66.57 176	51.45 193	60.94 160	55.47 192	66.81 183	82.72 176	86.80 132	94.59 95	92.03 120

MVSTER		86.03 76	86.12 78	85.93 78	88.62 107	89.93 123	89.33 83	79.91 122	81.87 81	81.35 61	81.07 57	74.91 103	80.66 98	92.13 59	90.10 78	95.68 42	92.80 99

v2v482		79.84 130	78.07 150	81.90 119	83.75 162	90.21 117	87.17 114	79.85 123	70.65 158	65.93 134	61.93 154	60.07 169	80.82 94	85.25 154	86.71 133	93.88 125	91.70 127

CP-MVSNet		76.36 171	76.41 167	76.32 170	82.73 179	88.64 149	79.39 180	79.62 124	67.21 172	53.70 187	60.72 162	55.22 193	67.91 179	83.52 172	86.34 142	94.55 98	93.19 90

FMVSNet1		81.64 116	80.61 120	82.84 110	82.36 182	89.20 141	88.67 93	79.58 125	70.79 157	72.63 106	58.95 174	72.26 120	79.34 121	90.73 83	90.72 64	94.47 103	91.62 128

PS-CasMVS		75.90 176	75.86 175	75.96 172	82.59 180	88.46 152	79.23 183	79.56 126	66.00 179	52.77 189	59.48 170	54.35 196	67.14 182	83.37 173	86.23 143	94.47 103	93.10 92

RPSCF		83.46 97	83.36 96	83.59 103	87.75 114	87.35 159	84.82 143	79.46 127	83.84 69	78.12 80	82.69 47	79.87 77	82.60 83	82.47 179	81.13 182	88.78 184	86.13 171

pm-mvs1		78.51 150	77.75 155	79.40 144	84.83 153	89.30 138	83.55 153	79.38 128	62.64 190	63.68 149	58.73 176	64.68 146	70.78 170	89.79 95	87.84 118	94.17 114	91.28 133

WR-MVS_H		75.84 177	76.93 163	74.57 183	82.86 176	89.50 136	78.34 186	79.36 129	66.90 174	52.51 190	60.20 166	59.71 172	59.73 192	83.61 171	85.77 151	94.65 92	92.84 97

v148		78.59 148	76.84 164	80.62 135	83.61 165	89.16 142	83.65 152	79.24 130	69.38 164	69.34 119	59.88 168	60.41 168	75.19 146	83.81 170	84.63 163	92.70 150	90.63 138

CHOSEN 1792x2688		82.16 108	80.91 118	83.61 102	91.14 78	92.01 98	89.55 80	79.15 131	79.87 99	70.29 111	52.51 192	72.56 118	81.39 89	88.87 107	88.17 116	90.15 177	92.37 116

baseline2		82.80 102	82.86 100	82.73 112	87.68 117	90.50 111	84.92 141	78.93 132	78.07 113	73.06 102	75.08 88	69.77 130	77.31 135	88.90 106	86.94 130	94.50 100	90.74 135

USDC		80.69 122	79.89 131	81.62 123	86.48 129	89.11 144	86.53 123	78.86 133	81.15 89	63.48 150	72.98 100	59.12 180	81.16 91	87.10 123	85.01 158	93.23 142	84.77 178

FC-MVSNet-test		76.53 167	81.62 107	70.58 191	84.99 149	85.73 171	74.81 194	78.85 134	77.00 117	39.13 209	75.90 82	73.50 114	54.08 198	86.54 137	85.99 149	91.65 161	86.68 167

pmmvs4		79.99 127	78.08 149	82.22 117	83.04 172	87.16 162	84.95 139	78.80 135	78.64 109	74.53 93	64.61 146	59.41 176	79.45 120	84.13 168	84.54 165	92.53 151	88.08 156

IterMVS-LS		83.28 99	82.95 99	83.65 101	88.39 110	88.63 150	86.80 121	78.64 136	76.56 118	73.43 100	72.52 103	75.35 100	80.81 95	86.43 140	88.51 114	93.84 127	92.66 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GA-MVS		79.52 135	79.71 135	79.30 145	85.68 137	90.36 113	84.55 144	78.44 137	70.47 161	57.87 183	68.52 124	61.38 163	76.21 141	89.40 102	87.89 117	93.04 146	89.96 143

v1144		79.38 139	77.83 153	81.18 129	83.62 164	90.23 115	87.15 116	78.35 138	69.13 165	64.02 147	60.20 166	59.41 176	80.14 109	86.78 130	86.57 137	93.81 129	92.53 112

HyFIR lowres test		81.62 118	79.45 138	84.14 96	91.00 81	93.38 79	88.27 98	78.19 139	76.28 120	70.18 113	48.78 196	73.69 112	83.52 77	87.05 125	87.83 120	93.68 134	89.15 147

TinyColmap		76.73 162	73.95 186	79.96 141	85.16 147	85.64 173	82.34 161	78.19 139	70.63 159	62.06 160	60.69 163	49.61 203	80.81 95	85.12 159	83.69 170	91.22 169	82.27 186

Effi-MVS+-dtu		82.05 109	81.76 105	82.38 115	87.72 115	90.56 110	86.90 120	78.05 141	73.85 140	66.85 129	71.29 106	71.90 121	82.00 88	86.64 135	85.48 154	92.76 149	92.58 108

test-LLR		79.47 137	79.84 132	79.03 147	87.47 119	82.40 193	81.24 170	78.05 141	73.72 141	62.69 154	73.76 95	74.42 105	73.49 158	84.61 164	82.99 174	91.25 167	87.01 164

test0.0.03 1		76.03 173	78.51 142	73.12 188	87.47 119	85.13 179	76.32 191	78.05 141	73.19 148	50.98 196	70.64 108	69.28 133	55.53 194	85.33 153	84.38 166	90.39 175	81.63 189

v1192		78.94 143	77.33 157	80.82 132	83.25 168	89.90 124	86.91 119	77.72 144	68.63 169	62.61 156	59.17 171	57.53 184	80.62 101	86.89 127	86.47 139	93.79 130	92.75 102

Fast-Effi-MVS+		83.77 95	82.98 98	84.69 84	87.98 112	91.87 99	88.10 100	77.70 145	78.10 112	73.04 103	69.13 120	68.51 136	86.66 64	90.49 89	89.85 87	94.67 91	92.88 96

v144192		78.81 144	77.22 159	80.67 134	82.95 173	89.79 128	86.40 124	77.42 146	68.26 171	63.13 152	59.50 169	58.13 181	80.08 110	85.93 144	86.08 146	94.06 116	92.83 98

v8		79.90 129	78.39 146	81.66 122	83.97 161	89.81 126	87.16 115	77.40 147	71.49 152	67.71 125	61.24 157	62.49 158	79.83 114	85.48 152	86.17 144	93.89 124	92.02 121

LTVRE_ROB	74.41 16	75.78 178	74.72 184	77.02 164	85.88 133	89.22 140	82.44 160	77.17 148	50.57 207	45.45 202	65.44 138	52.29 200	81.25 90	85.50 151	87.42 123	89.94 179	92.62 105
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
v1921920		78.57 149	76.99 162	80.41 139	82.93 174	89.63 134	86.38 125	77.14 149	68.31 170	61.80 164	58.89 175	56.79 186	80.19 108	86.50 139	86.05 148	94.02 118	92.76 101

pmmvs6		74.83 182	72.89 189	77.09 162	82.11 183	87.50 158	80.88 174	76.97 150	52.79 205	61.91 163	46.66 198	60.49 166	69.28 173	86.74 133	85.46 155	91.39 164	90.56 139

thisisatest0515		79.76 132	80.59 121	78.80 149	84.40 155	88.91 148	79.48 179	76.94 151	72.29 150	67.33 127	67.82 127	65.99 142	70.80 169	88.50 110	87.84 118	93.86 126	92.75 102

v10		79.62 133	78.19 148	81.28 128	83.73 163	89.69 131	87.27 111	76.86 152	70.50 160	65.46 136	60.58 164	60.47 167	80.44 102	86.91 126	86.63 136	93.93 121	92.55 110

v1240		78.15 151	76.53 165	80.04 140	82.85 177	89.48 137	85.61 135	76.77 153	67.05 173	61.18 171	58.37 177	56.16 190	79.89 113	86.11 143	86.08 146	93.92 122	92.47 114

V42		79.59 134	78.43 145	80.94 131	82.79 178	89.71 130	86.66 122	76.73 154	71.38 153	67.42 126	61.01 159	62.30 160	78.39 127	85.56 150	86.48 138	93.65 135	92.60 106

v7n		77.22 159	76.23 169	78.38 156	81.89 185	89.10 145	82.24 164	76.36 155	65.96 180	61.21 170	56.56 181	55.79 191	75.07 149	86.55 136	86.68 134	93.52 137	92.95 95

SixPastTwentyTwo		76.02 174	75.72 176	76.36 169	83.38 166	87.54 157	75.50 193	76.22 156	65.50 183	57.05 184	70.64 108	53.97 197	74.54 152	80.96 184	82.12 179	91.44 163	89.35 146

MDA-MVSNet-bldmvs		66.22 196	64.49 199	68.24 194	61.67 208	82.11 195	70.07 201	76.16 157	59.14 199	47.94 199	54.35 187	35.82 213	67.33 181	64.94 206	75.68 196	86.30 196	79.36 195

test20.03		68.31 194	70.05 195	66.28 198	82.41 181	80.84 197	67.35 203	76.11 158	58.44 200	40.80 208	53.77 189	54.54 195	42.28 205	83.07 174	81.96 181	88.73 185	77.76 199

pmmvs-eth3d		74.32 185	71.96 191	77.08 163	77.33 198	82.71 189	78.41 185	76.02 159	66.65 175	65.98 133	54.23 188	49.02 205	73.14 162	82.37 180	82.69 176	91.61 162	86.05 172

Anonymous20231206		70.80 190	70.59 194	71.04 190	81.60 187	82.49 192	74.64 195	75.87 160	64.17 187	49.27 197	44.85 202	53.59 199	54.68 197	83.07 174	82.34 178	90.17 176	83.65 181

baseline		84.89 85	86.06 80	83.52 105	87.25 122	89.67 132	87.76 102	75.68 161	84.92 64	78.40 78	80.10 60	80.98 70	80.20 107	86.69 134	87.05 128	91.86 159	92.99 93

CANet_DTU		85.43 80	87.72 68	82.76 111	90.95 83	93.01 85	89.99 71	75.46 162	82.67 72	64.91 142	83.14 45	80.09 76	80.68 97	92.03 60	91.03 58	94.57 97	92.08 117

MS-PatchMatch		81.79 114	81.44 109	82.19 118	90.35 92	89.29 139	88.08 101	75.36 163	77.60 114	69.00 121	64.37 148	78.87 87	77.14 138	88.03 115	85.70 152	93.19 144	86.24 170

CVMVSNet		76.70 163	78.46 144	74.64 182	83.34 167	84.48 181	81.83 166	74.58 164	68.88 167	51.23 195	69.77 113	70.05 128	67.49 180	84.27 167	83.81 168	89.38 181	87.96 158

testgi		71.92 189	74.20 185	69.27 193	84.58 154	83.06 185	73.40 196	74.39 165	64.04 188	46.17 201	68.90 123	57.15 185	48.89 202	84.07 169	83.08 173	88.18 187	79.09 197

pmmvs5		76.93 161	76.33 168	77.62 159	81.97 184	88.40 153	81.32 169	74.35 166	65.42 184	61.42 167	63.07 151	57.95 182	73.23 161	85.60 149	85.35 156	93.41 140	88.55 151

MIMVSNet1		65.00 197	66.24 198	63.55 200	58.41 211	80.01 199	69.00 202	74.03 167	55.81 203	41.88 206	36.81 207	49.48 204	47.89 203	81.32 183	82.40 177	90.08 178	77.88 198

EG-PatchMatch MVS		76.40 170	75.47 179	77.48 160	85.86 135	90.22 116	82.45 159	73.96 168	59.64 198	59.60 177	52.75 191	62.20 161	68.44 176	88.23 113	87.50 121	94.55 98	87.78 160

CMPMVS	56.49 17	73.84 187	71.73 192	76.31 171	85.20 145	85.67 172	75.80 192	73.23 169	62.26 191	65.40 137	53.40 190	59.70 173	71.77 166	80.25 187	79.56 185	86.45 195	81.28 190
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
anonymousdsp		77.94 153	79.00 139	76.71 166	79.03 193	87.83 155	79.58 178	72.87 170	65.80 181	58.86 182	65.82 134	62.48 159	75.99 142	86.77 131	88.66 111	93.92 122	95.68 49

Fast-Effi-MVS+-dtu		79.95 128	80.69 119	79.08 146	86.36 130	89.14 143	85.85 129	72.28 171	72.85 149	59.32 178	70.43 112	68.42 137	77.57 133	86.14 142	86.44 140	93.11 145	91.39 132

IterMVS-SCA-FT		79.41 138	80.20 126	78.49 154	85.88 133	86.26 166	83.95 149	71.94 172	73.55 144	61.94 161	70.48 111	70.50 126	75.23 145	85.81 147	84.61 164	91.99 158	90.18 142

PM-MVS		74.17 186	73.10 187	75.41 175	76.07 201	82.53 191	77.56 189	71.69 173	71.04 154	61.92 162	61.23 158	47.30 206	74.82 151	81.78 182	79.80 183	90.42 174	88.05 157

TAMVS		76.42 168	77.16 160	75.56 174	83.05 171	85.55 174	80.58 175	71.43 174	65.40 185	61.04 172	67.27 129	69.22 134	67.99 177	84.88 162	84.78 161	89.28 182	83.01 184

N_pmnet		66.85 195	66.63 196	67.11 197	78.73 194	74.66 205	70.53 200	71.07 175	66.46 177	46.54 200	51.68 194	51.91 201	55.48 195	74.68 200	72.38 201	80.29 206	74.65 202

new-patchmatchnet		63.80 198	63.31 200	64.37 199	76.49 199	75.99 203	63.73 206	70.99 176	57.27 201	43.08 204	45.86 200	43.80 207	45.13 204	73.20 201	70.68 204	86.80 194	76.34 201

CostFormer		80.94 121	80.21 125	81.79 120	87.69 116	88.58 151	87.47 107	70.66 177	80.02 97	77.88 84	73.03 99	71.40 122	78.24 128	79.96 188	79.63 184	88.82 183	88.84 148

tpm cat1		77.78 155	75.28 181	80.70 133	87.14 124	85.84 170	85.81 130	70.40 178	77.44 115	78.80 77	63.72 149	64.01 150	76.55 140	75.60 199	75.21 198	85.51 199	85.12 175

MDTV_nov1_ep13		79.14 141	79.49 137	78.74 151	85.40 141	86.89 163	84.32 148	70.29 179	78.85 107	69.42 118	75.37 86	73.29 116	75.64 144	80.61 185	79.48 186	87.36 190	81.91 187

FMVSNet5		75.50 180	76.07 170	74.83 179	76.16 200	81.19 196	81.34 168	70.21 180	73.20 147	61.59 166	58.97 173	68.33 138	68.50 175	85.87 146	85.85 150	91.18 170	79.11 196

dps		78.02 152	75.94 174	80.44 138	86.06 132	86.62 165	82.58 157	69.98 181	75.14 127	77.76 86	69.08 121	59.93 171	78.47 126	79.47 190	77.96 191	87.78 188	83.40 182

EU-MVSNet		69.98 192	72.30 190	67.28 196	75.67 203	79.39 200	73.12 197	69.94 182	63.59 189	42.80 205	62.93 152	56.71 188	55.07 196	79.13 193	78.55 189	87.06 193	85.82 174

IterMVS		78.79 145	79.71 135	77.71 158	85.26 144	85.91 169	84.54 145	69.84 183	73.38 145	61.25 169	70.53 110	70.35 127	74.43 154	85.21 157	83.80 169	90.95 171	88.77 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PMVS	50.48 18	55.81 203	51.93 205	60.33 202	72.90 205	49.34 211	48.78 210	69.51 184	43.49 210	54.25 186	36.26 208	41.04 212	39.71 207	65.07 205	60.70 206	76.85 208	67.58 206
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		63.63 199	60.08 204	67.78 195	80.01 191	71.50 207	72.88 198	69.41 185	61.82 193	53.11 188	45.12 201	42.11 210	50.86 200	66.69 204	63.84 205	80.41 205	69.46 205

SCA		79.51 136	80.15 127	78.75 150	86.58 128	87.70 156	83.07 155	68.53 186	81.31 85	66.40 131	73.83 94	75.38 99	79.30 122	80.49 186	79.39 187	88.63 186	82.96 185

CR-MVSNet		78.71 146	78.86 140	78.55 153	85.85 136	85.15 177	82.30 162	68.23 187	74.71 130	65.37 138	64.39 147	69.59 132	77.18 136	85.10 160	84.87 159	92.34 154	88.21 154

Patchmtry						85.54 175	82.30 162	68.23 187		65.37 138

PatchmatchNet		78.67 147	78.85 141	78.46 155	86.85 127	86.03 167	83.77 151	68.11 189	80.88 93	66.19 132	72.90 101	73.40 115	78.06 129	79.25 192	77.71 192	87.75 189	81.75 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		74.69 183	75.60 178	73.62 185	76.02 202	85.31 176	81.21 172	67.43 190	71.02 155	59.07 180	54.48 185	64.07 148	66.14 186	86.52 138	86.64 135	91.83 160	81.17 191

PMMVS		81.65 115	84.05 93	78.86 148	78.56 195	82.63 190	83.10 154	67.22 191	81.39 84	70.11 114	84.91 42	79.74 80	82.12 86	87.31 121	85.70 152	92.03 157	86.67 169

tpm		76.30 172	76.05 172	76.59 167	86.97 125	83.01 187	83.83 150	67.06 192	71.83 151	63.87 148	69.56 117	62.88 155	73.41 160	79.79 189	78.59 188	84.41 200	86.68 167

CHOSEN 280x420		80.28 125	81.66 106	78.67 152	82.92 175	79.24 201	85.36 137	66.79 193	78.11 111	70.32 110	75.03 89	79.87 77	81.09 92	89.07 103	83.16 172	85.54 198	87.17 163

EPMVS		77.53 157	78.07 150	76.90 165	86.89 126	84.91 180	82.18 165	66.64 194	81.00 91	64.11 146	72.75 102	69.68 131	74.42 155	79.36 191	78.13 190	87.14 192	80.68 193

MDTV_nov1_ep13_2view		73.21 188	72.91 188	73.56 186	80.01 191	84.28 183	78.62 184	66.43 195	68.64 168	59.12 179	60.39 165	59.69 174	69.81 172	78.82 194	77.43 193	87.36 190	81.11 192

tpmrst		76.55 166	75.99 173	77.20 161	87.32 121	83.05 186	82.86 156	65.62 196	78.61 110	67.22 128	69.19 119	65.71 143	75.87 143	76.75 197	75.33 197	84.31 201	83.28 183

gm-plane-assit		70.29 191	70.65 193	69.88 192	85.03 148	78.50 202	58.41 209	65.47 197	50.39 208	40.88 207	49.60 195	50.11 202	75.14 148	91.43 65	89.78 88	94.32 110	84.73 179

RPMNet		77.07 160	77.63 156	76.42 168	85.56 140	85.15 177	81.37 167	65.27 198	74.71 130	60.29 174	63.71 150	66.59 141	73.64 157	82.71 177	82.12 179	92.38 153	88.39 152

MVS-HIRNet		68.83 193	66.39 197	71.68 189	77.58 197	75.52 204	66.45 204	65.05 199	62.16 192	62.84 153	44.76 203	56.60 189	71.96 165	78.04 195	75.06 199	86.18 197	72.56 203

PatchT		76.42 168	77.81 154	74.80 180	78.46 196	84.30 182	71.82 199	65.03 200	73.89 138	65.37 138	61.58 155	66.70 140	77.18 136	85.10 160	84.87 159	90.94 172	88.21 154

gg-mvs-nofinetune		75.64 179	77.26 158	73.76 184	87.92 113	92.20 96	87.32 109	64.67 201	51.92 206	35.35 210	46.44 199	77.05 96	71.97 164	92.64 50	91.02 59	95.34 62	89.53 145

Gipuma		49.17 204	47.05 206	51.65 204	59.67 210	48.39 212	41.98 212	63.47 202	55.64 204	33.33 212	14.90 210	13.78 217	41.34 206	69.31 203	72.30 202	70.11 209	55.00 209
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ADS-MVSNet		74.53 184	75.69 177	73.17 187	81.57 188	80.71 198	79.27 182	63.03 203	79.27 105	59.94 176	67.86 126	68.32 139	71.08 168	77.33 196	76.83 194	84.12 203	79.53 194

TESTMET0.1,1		77.78 155	79.84 132	75.38 176	80.86 190	82.40 193	81.24 170	62.72 204	73.72 141	62.69 154	73.76 95	74.42 105	73.49 158	84.61 164	82.99 174	91.25 167	87.01 164

test-mter		77.79 154	80.02 129	75.18 177	81.18 189	82.85 188	80.52 176	62.03 205	73.62 143	62.16 159	73.55 97	73.83 110	73.81 156	84.67 163	83.34 171	91.37 165	88.31 153

new_pmnet		59.28 201	61.47 203	56.73 203	61.66 209	68.29 209	59.57 208	54.91 206	60.83 195	34.38 211	44.66 204	43.65 208	49.90 201	71.66 202	71.56 203	79.94 207	69.67 204

E-PMN		31.40 206	26.80 209	36.78 206	51.39 213	29.96 215	20.20 215	54.17 207	25.93 213	12.75 215	14.73 211	8.58 219	34.10 209	27.36 211	37.83 210	48.07 213	43.18 211

EMVS		30.49 208	25.44 210	36.39 207	51.47 212	29.89 216	20.17 216	54.00 208	26.49 212	12.02 216	13.94 213	8.84 218	34.37 208	25.04 212	34.37 211	46.29 214	39.53 212

pmmvs3		61.89 200	61.74 202	62.06 201	64.30 207	70.83 208	64.22 205	52.14 209	48.78 209	44.47 203	41.67 205	41.70 211	63.03 189	76.06 198	76.02 195	84.18 202	77.14 200

PMMVS2		41.68 205	44.74 207	38.10 205	46.97 214	52.32 210	40.63 213	48.08 210	35.51 211	7.36 217	26.86 209	24.64 215	16.72 211	55.24 208	59.03 207	68.85 210	59.59 208

MVE	30.17 19	30.88 207	33.52 208	27.80 210	23.78 216	39.16 214	18.69 217	46.90 211	21.88 214	15.39 214	14.37 212	7.31 220	24.41 210	41.63 210	56.22 208	37.64 215	54.07 210
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX						48.31 213	48.03 211	26.08 212	56.42 202	25.77 213	47.51 197	31.31 214	51.30 199	48.49 209		53.61 211	61.52 207

tmp_tt				32.73 208	43.96 215	21.15 217	26.71 214	8.99 213	65.67 182	51.39 194	56.01 182	42.64 209	11.76 212	56.60 207	50.81 209	53.55 212

testmvs		1.03 209	1.63 211	0.34 211	0.09 218	0.35 218	0.61 219	0.16 214	1.49 215	0.10 219	3.15 214	0.15 221	0.86 214	1.32 213	1.18 212	0.20 216	3.76 214

GG-mvs-BLEND		57.56 202	82.61 102	28.34 209	0.22 217	90.10 119	79.37 181	0.14 215	79.56 102	0.40 218	71.25 107	83.40 60	0.30 215	86.27 141	83.87 167	89.59 180	83.83 180

test123		0.87 210	1.40 212	0.25 212	0.03 219	0.25 219	0.35 220	0.08 216	1.21 216	0.05 220	2.84 215	0.03 222	0.89 213	0.43 214	1.16 213	0.13 217	3.87 213

uanet_test		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet-low-res		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

RE-MVS-def										56.08 185

9.14												92.16 16

our_test_3					81.81 186	83.96 184	76.61 190

ambc			61.92 201		70.98 206	73.54 206	63.64 207		60.06 196	52.23 192	38.44 206	19.17 216	57.12 193	82.33 181	75.03 200	83.21 204	84.89 176

MTAPA										92.97 2		91.03 22

MTMP										93.14 1		90.21 30

Patchmatch-RL test							8.55 218

XVS					93.11 60	96.70 25	91.91 54			83.95 48		88.82 40				95.79 36

X-MVStestdata					93.11 60	96.70 25	91.91 54			83.95 48		88.82 40				95.79 36

mPP-MVS					97.06 12							88.08 45

NP-MVS									87.47 53

High-resolution multi-view benchmark