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Conference Paper
ELiC: Efficient LiDAR Geometry Compression via Cross-Bit-depth Feature Propagation and Bag-of-Encoders
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- Authors
- Junsik Kim, Gun Bang, Soowoong Kim
- Issue Date
- 2026-06
- Citation
- Conference on Computer Vision and Pattern Recognition (CVPR) 2026, pp.39011-39020
- Publisher
- Computer Vision Foundation
- Language
- English
- Type
- Conference Paper
- 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 http://github.com/moolgom/ELiCv1.
- KSP Keywords
- Computation efficiency, Depth feature, Depth propagation, Geometry compression, Local context, Order-preserving, Real-time, bit depth, compression efficiency, selection scheme, state-of-The-Art