用隐式神经表示压缩激光雷达范围图像，提升3D场景重建精度
Range Image-Based Implicit Neural Compression for LiDAR Point Clouds
- 将范围图像拆分为深度与掩码图，分别用块级和像素级隐式网络压缩
- 在KITTI数据集上低比特率下重建与检测性能优于现有方法
- 适合需要高效存储与快速解码的自动驾驶3D感知系统
本文提出一种新型激光雷达点云压缩方案，通过2D范围图像（RIs）实现高精度3D场景存档，为深入理解3D场景提供支持。针对自然图像与范围图像在比特精度和像素值分布上的差异，传统图像压缩方法效率受限。本文设计基于隐式神经表示（INR）的范围图像压缩方法，将RIs分解为深度图与掩码图，分别采用块级和像素级INR结构，并结合模型剪枝与量化技术进行压缩。在KITTI数据集上的实验表明，该方法在低比特率和低解码延迟条件下，3D重建与目标检测质量均优于现有图像、点云、范围图像及基于INR的压缩方法。
原文摘要 · Abstract (English)
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.
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