Deep Implicit Volume Compression

@article{Tang2020DeepIV,
  title={Deep Implicit Volume Compression},
  author={Danhang Tang and Saurabh Singh and Philip A. Chou and Christian Haene and Mingsong Dou and S. Fanello and Jonathan Taylor and Philip L. Davidson and Onur G. Guleryuz and Yinda Zhang and Shahram Izadi and Andrea Tagliasacchi and Sofien Bouaziz and Cem Keskin},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={1290-1300}
}
  • Danhang Tang, Saurabh Singh, +11 authors Cem Keskin
  • Published 18 May 2020
  • Engineering, Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method relies on a block-based neural network architecture trained end-to-end, achieving state-of-the-art rate-distortion trade-off. To prevent topological errors, we losslessly com- press the signs of the TSDF, which also upper bounds the reconstruction error by the voxel size. To compress the corresponding texture, we… Expand
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