Real-time light detection and ranging (LiDAR) perceptions, e.g., 3D object detection and simultaneous localization and mapping are computationally intensive to mobile devices of limited resources and often offloaded on the edge. Offloading LiDAR perceptions requires compressing the raw sensor data, and lossy compression is used for efficiently reducing the data volume. Lossy compression degrades the quality of LiDAR point clouds, and the perception performance is decreased consequently. In this work, we present an interpolation algorithm improving the quality of a LiDAR point cloud to mitigate the perception performance loss due to lossy compression. The algorithm targets the range image (RI) representation of a point cloud and interpolates points at the RI based on depth gradients. Compared to existing image interpolation algorithms, our algorithm shows a better qualitative result when the point cloud is reconstructed from the interpolated RI. With the preliminary results, we also describe the next steps of the current work.

翻译：摘要：实时光检测与测距（LiDAR）感知，例如三维物体检测和同步定位与建图，对资源受限的移动设备而言计算强度大，通常被卸载到边缘端。卸载LiDAR感知需要压缩原始传感器数据，而有损压缩用于高效减少数据量。有损压缩会降低LiDAR点云的质量，进而导致感知性能下降。在本研究中，我们提出一种插值算法，通过提升LiDAR点云质量来缓解因有损压缩造成的感知性能损失。该算法针对点云的距离图像（RI）表示，并基于深度梯度在RI上插值点。与现有图像插值算法相比，当从插值后的RI重建点云时，我们的算法展现出更好的定性结果。基于初步结果，我们还描述了当前工作的后续步骤。