论文标题
卷发：LIDAR的连续，超紧凑的表示
CURL: Continuous, Ultra-compact Representation for LiDAR
论文作者
论文摘要
增加3D激光点云的密度对机器人技术中的许多应用都有吸引力。但是，高密度的LIDAR传感器通常成本高昂，并且仍然限于每扫描的覆盖率水平（例如128个通道）。同时，密集的点云扫描和地图意味着要存储更长的时间来传输。现有作品的重点是提高点云密度或压缩其大小。本文旨在设计一种新颖的3D点云表示，该表示可以不断地增加点云密度，同时降低其存储和传输大小。所提出的LIDAR（卷发）的连续超紧凑表示的管道包括四个主要步骤：网格划分，上采样，编码和连续重建。它能够将3D LiDAR扫描或映射转换为紧凑的球形谐波表示，可以在低潜伏期中使用或传播，以连续重建一个较密集的3D点云。在四个公共数据集上进行了广泛的实验，涵盖了大学花园，城市街道和室内房间，证明可以使用拟议的卷发代表能够准确地重建许多密集的3D点云，同时实现多达80％的存储空间空间。我们为社区开放卷曲代码。
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.
