Kavli Affiliate: Jing Wang
| First 5 Authors: Pengpeng Yu, Pengpeng Yu, , ,
| Summary:
LiDAR point clouds are fundamental to various applications, yet
high-precision scans incur substantial storage and transmission overhead.
Existing methods typically convert unordered points into hierarchical octree or
voxel structures for dense-to-sparse predictive coding. However, the extreme
sparsity of geometric details hinders efficient context modeling, thereby
limiting their compression performance and speed. To address this challenge, we
propose to generate compact features for efficient predictive coding. Our
framework comprises two lightweight modules. First, the Geometry
Re-Densification Module re-densifies encoded sparse geometry, extracts features
at denser scale, and then re-sparsifies the features for predictive coding.
This module avoids costly computation on highly sparse details while
maintaining a lightweight prediction head. Second, the Cross-scale Feature
Propagation Module leverages occupancy cues from multiple resolution levels to
guide hierarchical feature propagation. This design facilitates information
sharing across scales, thereby reducing redundant feature extraction and
providing enriched features for the Geometry Re-Densification Module. By
integrating these two modules, our method yields a compact feature
representation that provides efficient context modeling and accelerates the
coding process. Experiments on the KITTI dataset demonstrate state-of-the-art
compression ratios and real-time performance, achieving 26 FPS for
encoding/decoding at 12-bit quantization. Code is available at
https://github.com/pengpeng-yu/FastPCC.
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