OctHilNet: Hilbert-Guided Hierarchical Geometry Codec for Octree-Structured LiDAR Point Clouds

Abstract

High-quality LiDAR point cloud (LPC) compression is essential for the storage and transmission of 3D data. The octree-structured entropy codec has emerged as the predominant method; however, previous methods do not fully utilize spatial contextual information, due to the loss of local features caused by uneven scanning density. To address this problem, we propose OctHilNet, a novel Hilbert-guided hierarchical framework for LPC compression that introduces the polarized octree for efficient node organization and the serialize-driven entropy model to strengthen the continuity of node contexts. Specifically, to counteract the inherent density imbalance, OctHilNet first transforms points into polar coordinates and applies a non-linear rebalancing to the radial distance. Then, we introduce the Hilbert space-filling curve to mitigate the impact of the decoupling between sequential adjacency and geometric proximity in octree node sequences. Finally, to better capture fine-grained spatial correlations, we propose LocAtten and NeighbConv modules in a hierarchical Transformer, which jointly strengthen local dependencies overlooked by standard self-attention. Compared to the previous state-of-the-art works, our method achieves 45.1%-50.1% and 51.9%-53.9% BD-Rate gains on the LPC benchmark SemanticKITTI and MPEG-specified Ford datasets, respectively. In particular, our OctHilNet allows for extension to downstream tasks (i.e., vehicle detection and semantic segmentation), further demonstrating the practicality of the method.