Increasing the density of the 3D LiDAR point cloud is appealing for many applications in robotics. However, high-density LiDAR sensors are usually costly and still limited to a level of coverage per scan (e.g., 128 channels). Meanwhile, denser point cloud scans and maps mean larger volumes to store and longer times to transmit. Existing works focus on either improving point cloud density or compressing its size. This paper aims to design a novel 3D point cloud representation that can continuously increase point cloud density while reducing its storage and transmitting size. The pipeline of the proposed Continuous, Ultra-compact Representation of LiDAR (CURL) includes four main steps: meshing, upsampling, encoding, and continuous reconstruction. It is capable of transforming a 3D LiDAR scan or map into a compact spherical harmonics representation which can be used or transmitted in low latency to continuously reconstruct a much denser 3D point cloud. Extensive experiments on four public datasets, covering college gardens, city streets, and indoor rooms, demonstrate that much denser 3D point clouds can be accurately reconstructed using the proposed CURL representation while achieving up to 80% storage space-saving. We open-source the CURL codes for the community.

翻译：增加 3D LiDAR 点云的密度正在吸引机器人的多种应用。 然而, 高密度的 LiDAR 传感器通常成本高昂, 并且仍然局限于每个扫描( 128 个频道) 的覆盖水平。 与此同时, 更稠密的点云扫描和地图意味着存储量和传送时间的更大。 现有的工程侧重于提高点云密度或压缩其尺寸。 本文旨在设计一个新的 3D 点云显示, 它可以不断增加点云密度, 同时减少其存储和传输大小。 拟议的 3DAR (CURL) 连续、 超复杂显示( CURL ) 的管道包括四个主要步骤: 网格、 取样、 编码和连续重建。 它能够将 3D LD 扫描或地图转换成一个压缩的球体调表示器, 可以在低薄度下使用或传输来持续重建一个更稠密的 3D 点云。 在四个公共数据集上进行广泛的实验, 覆盖大学花园、 城市街道和室内房间, 显示, 更密的 3D 点云可以精确地重建 C- L 将 C- slovereal 复制 C- smal 并同时实现 C- sloveilveal 的 C- smal 80 。