端到端联合优化采样与特征提取，提升大规模点云压缩率。
Compression of Large-Scale 3D Point Clouds Based on Joint Optimization of Point Sampling and Feature Extraction
- 采样模块可训练，通过可学习权重估计最优下采样点位置。
- 在SemanticKITTI和nuScenes上压缩率显著优于现有方法。
- 适合需要高效点云存储与传输的自动驾驶场景。
基于激光雷达扫描的大规模3D点云（LS3DPC）因数据量庞大，需占用巨大存储空间与传输带宽。现有压缩方法分别进行规则采样与可学习特征提取，导致压缩性能受限。本文提出一种全端到端训练框架，联合优化点采样与特征提取，以最小化率失真损失。首先使采样模块可训练，通过可学习权重聚合估计下采样点的最优位置；同时设计可靠的点重建方案，自适应聚合扩展候选点，以精修上采样点位置。在SemanticKITTI与nuScenes数据集上的实验表明，所提方法显著优于当前最先进压缩技术。
原文摘要 · Abstract (English)
Large-scale 3D point clouds (LS3DPC) obtained by LiDAR scanners require huge storage space and transmission bandwidth due to a large amount of data. The existing methods of LS3DPC compression separately perform rule-based point sampling and learnable feature extraction, and hence achieve limited compression performance. In this paper, we propose a fully end-to-end training framework for LS3DPC compression where the point sampling and the feature extraction are jointly optimized in terms of the rate and distortion losses. To this end, we first make the point sampling module to be trainable such that an optimal position of the downsampled point is estimated via aggregation with learnable weights. We also develop a reliable point reconstruction scheme that adaptively aggregates the expanded candidate points to refine the positions of upsampled points. Experimental results evaluated on the SemanticKITTI and nuScenes datasets show that the proposed method achieves significantly higher compression ratios compared with the existing state-of-the-art methods.
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