PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression
This addresses the critical need for efficient compression in 3D applications like VR and autonomous driving, representing an incremental improvement by integrating existing representations.
The paper tackles point cloud compression by proposing a heterogeneous framework that unifies point-based, voxel-based, and tree-based representations with learning-based methods, achieving state-of-the-art performance across various point clouds.
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.