PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression

• URL: http://arxiv.org/abs/2402.07243v1
• Date: Sun, 11 Feb 2024 16:57:08 GMT
• Title: PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression
• Authors: Jiahao Pang, Kevin Bui, Dong Tian
• Abstract summary: We propose a heterogeneous point cloud compression (PCC) framework. We unify typical point cloud representations -- point-based, voxel-based, and tree-based representations -- and their associated backbones. We augment the framework with a proposed context-aware upsampling for decoding and an enhanced voxel transformer for feature aggregation.
• Score: 8.778300313732027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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