针对点云压缩中丢失点导致失真问题，提出两种新型有损压缩方法。
Octree-based Learned Point Cloud Geometry Compression: A Lossy Perspective
- 对叶节点进行位级编码与二值预测实现有损压缩
- 在物体点云上性能超越传统方法，LiDAR点云误差仅1%无微调
- 专为不同点云特性设计，适合3D内容传输与存储场景
基于八叉树的上下文学习已成为点云压缩的主流方法，但在有损压缩方面的潜力尚未被发掘。传统采用无损八叉树加量化步长调整的有损压缩范式，因量化导致大量点缺失而引发严重失真。为此，本文分析了不同点云的数据特性，提出针对性的有损压缩方案：针对物体点云，提出一种新的叶节点有损压缩方法，通过在叶节点上进行位级编码与二值预测实现压缩；针对激光雷达（LiDAR）点云，探索可变率方法，并提出一种简单有效的码率控制策略。实验结果表明，所提叶节点有损压缩方法在物体点云上显著优于以往八叉树方法；所提码率控制方法在无需微调的情况下，使LiDAR点云的比特误差约为1%。
原文摘要 · Abstract (English)
Octree-based context learning has recently become a leading method in point cloud compression. However, its potential on lossy compression remains undiscovered. The traditional lossy compression paradigm using lossless octree representation with quantization step adjustment may result in severe distortions due to massive missing points in quantization. Therefore, we analyze data characteristics of different point clouds and propose lossy approaches specifically. For object point clouds that suffer from quantization step adjustment, we propose a new leaf nodes lossy compression method, which achieves lossy compression by performing bit-wise coding and binary prediction on leaf nodes. For LiDAR point clouds, we explore variable rate approaches and propose a simple but effective rate control method. Experimental results demonstrate that the proposed leaf nodes lossy compression method significantly outperforms the previous octree-based method on object point clouds, and the proposed rate control method achieves about 1% bit error without finetuning on LiDAR point clouds.
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