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All Projects → Gorilla-Lab-SCUT → SkeletonBridgeRecon

Gorilla-Lab-SCUT / SkeletonBridgeRecon

Licence: other
The code for CVPR2019 Oral paper "A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images"

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Labels

skeletonmeshmesh-generation3d-reconstructionshape-generationmedial-axis-transformstructure-inferenceimage2skeleton

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This repository constains the codes and ShapeNet-Skeleton.zip datasets for the Paper.

This implementation uses Pytorch and TensorFlow.

Implementation details

For each stage, please follow the README.md under the Skeleton_inference/Volume_refinement/Mesh_refinement folder.

Fast demo

  • Skeleton inference from the RGB images, and then extract coarse meshes from refined volumes.
python demo_im2mesh.py
  • Reuse the input images to deform coarse meshes for surface fitting.
python demo_deform.py

Citing this work

If you find this work useful in your research, please consider citing:

@InProceedings{Tang_2019_CVPR,
author = {Tang, Jiapeng and Han, Xiaoguang and Pan, Junyi and Jia, Kui and Tong, Xin},
title = {A Skeleton-Bridged Deep Learning Approach for Generating Meshes of Complex Topologies From Single RGB Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Contact

If you have any questions, please feel free to contact with Tang Jiapeng [email protected].

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].