ELiC: Efficient LiDAR Geometry Compression via Cross-Bit-depth Feature Propagation and Bag-of-Encoders

TL;DR

ELiC introduces a real-time LiDAR geometry compression framework that leverages cross-bit-depth feature reuse, adaptive encoding, and a Morton-order hierarchy to achieve state-of-the-art results efficiently.

Contribution

The paper proposes ELiC, a novel LiDAR compression method combining feature propagation, a Bag-of-Encoders scheme, and Morton hierarchy to improve efficiency and accuracy.

Findings

Achieves state-of-the-art compression performance on Ford and SemanticKITTI datasets.

Operates in real-time, suitable for practical applications.

Reduces latency by eliminating per-level sorting through Morton hierarchy.

Abstract

Hierarchical LiDAR geometry compression encodes voxel occupancies from low to high bit-depths, yet prior methods treat each depth independently and re-estimate local context from coordinates at every level, limiting compression efficiency. We present ELiC, a real-time framework that combines cross-bit-depth feature propagation, a Bag-of-Encoders (BoE) selection scheme, and a Morton-order-preserving hierarchy. Cross-bit-depth propagation reuses features extracted at denser, lower depths to support prediction at sparser, higher depths. BoE selects, per depth, the most suitable coding network from a small pool, adapting capacity to observed occupancy statistics without training a separate model for each level. The Morton hierarchy maintains global Z-order across depth transitions, eliminating per-level sorting and reducing latency. Together these components improve entropy modeling and computation efficiency, yielding state-of-the-art compression at real-time throughput on Ford and SemanticKITTI. Code and pretrained models are available at https://github.com/moolgom/ELiCv1.