PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression
<|reference_start|>PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression: The universality of the point cloud format enables many 3D applications, making the compression of point clouds a critical phase in practice. Sampled as discrete 3D points, a point cloud approximates 2D surface(s) embedded in 3D with a finite bit-depth. However, the point distribution of a practical point cloud changes drastically as its bit-depth increases, requiring different methodologies for effective consumption/analysis. In this regard, a heterogeneous point cloud compression (PCC) framework is proposed. We unify typical point cloud representations -- point-based, voxel-based, and tree-based representations -- and their associated backbones under a learning-based framework to compress an input point cloud at different bit-depth levels. Having recognized the importance of voxel-domain processing, we augment the framework with a proposed context-aware upsampling for decoding and an enhanced voxel transformer for feature aggregation. Extensive experimentation demonstrates the state-of-the-art performance of our proposal on a wide range of point clouds.<|reference_end|>
@article{pang2024pivot-net:,
    title={PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point
  Cloud Compression},
    author={Jiahao Pang, Kevin Bui, Dong Tian},
    journal={arXiv preprint arXiv:2402.07243},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07243},
    primaryClass={cs.CV eess.IV}
}