Range Image-Based Implicit Neural Compression for LiDAR Point Clouds
Journal:
arXiv
Published Date:
Apr 24, 2025
Abstract
This paper presents a novel scheme to efficiently compress Light Detection
and Ranging~(LiDAR) point clouds, enabling high-precision 3D scene archives,
and such archives pave the way for a detailed understanding of the
corresponding 3D scenes. We focus on 2D range images~(RIs) as a lightweight
format for representing 3D LiDAR observations. Although conventional image
compression techniques can be adapted to improve compression efficiency for
RIs, their practical performance is expected to be limited due to differences
in bit precision and the distinct pixel value distribution characteristics
between natural images and RIs. We propose a novel implicit neural
representation~(INR)--based RI compression method that effectively handles
floating-point valued pixels. The proposed method divides RIs into depth and
mask images and compresses them using patch-wise and pixel-wise INR
architectures with model pruning and quantization, respectively. Experiments on
the KITTI dataset show that the proposed method outperforms existing image,
point cloud, RI, and INR-based compression methods in terms of 3D
reconstruction and detection quality at low bitrates and decoding latency.
