Point Cloud Compression with Sibling Context and Surface Priors

TL;DR

This paper introduces a novel octree-based point cloud compression method that leverages sibling context and surface priors, achieving significant bitrate reductions and improved reconstruction quality for large-scale, unstructured point clouds.

Contribution

The paper proposes a new entropy model using hierarchical dependencies and surface priors within an octree framework, enhancing compression efficiency for large-scale point clouds.

Findings

Outperforms state-of-the-art methods with 11-16% bitrate reduction on KITTI and nuScenes datasets.

Uses sibling context and surface priors to improve entropy encoding.

Achieves better reconstruction quality with a two-step heuristic decoding strategy.

Abstract

We present a novel octree-based multi-level framework for large-scale point cloud compression, which can organize sparse and unstructured point clouds in a memory-efficient way. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree using the context of siblings' children, ancestors, and neighbors to encode the occupancy information of each non-leaf octree node into a bitstream. Moreover, we locally fit quadratic surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding. These strong priors empower our entropy framework to encode the octree into a more compact bitstream. In the decoding stage, we apply a two-step heuristic strategy to restore point clouds with better reconstruction quality. The quantitative evaluation shows that our method outperforms state-of-the-art baselines with a bitrate improvement of 11-16% and 12-14% on the KITTI Odometry and nuScenes datasets, respectively.