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- Mes comptes
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- Nous contacter
Société Générale Madagasikara 14 Rue Général RABEHEVITRA –...
- Mes comptes
- Overview
- Models
- Experiments
- Acknowledgements
- Citation
Zhengxue Wang, Zhiqiang Yan✉, Jian Yang✉
PCA Lab, Nanjing University of Science and Technology, China
All pretrained models can be found here. Please note that some variable names in the initial pretrained .pth files are not consistent with those in the latest code. Therefore, we have reuploaded the new .pth files for completeness, named xxx_R.pth.
Visual comparison
Train & test on real-world RGB-D-D: Train & test on synthetic NYU-v2 (x16): Train on NYU-v2, test on RGB-D-D (x16):
We thank all reviewers for their professional and instructive suggestions.
We thank these repos sharing their codes: DKN and SUFT.
If our method proves to be of any assistance, please consider citing:
10 déc. 2023 · SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution. Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task.
In this paper, we propose structure guided network (SGNet), a method that pays more attention to gradient and frequency domains, both of which have the inherent ability to capture high-frequency structure. Specifically, we first introduce the gradient calibration module (GCM), which employs the accurate gradient prior of RGB to sharpen the LR ...
•We propose SGNet that consists of novel GCM and FAM, where GCM leverages the gradient prior to adap-tively calibrate and sharpen LR structure, whilst FAM employs recursive SDB to propagate the high-frequency components into LR for clear structure recovery. •SGNet achieves significantly superior performance on both real-world and synthetic ...
To address this issue, this paper proposes a sequence-based convolution and ligand graph network, called SGNet, to fuse the molecular graph information and the amino acid sequence information. This method integrates Conjoint Triad (CT) encoding of amino acid sequence and one-dimensional convolutional neural network module to extract protein ...
