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.

我们提出了一种基于深度梯度的插值算法，用于改善 LiDAR 点云的质量，以减轻由于有损压缩导致的感知性能损失。与现有图像插值算法相比，我们的算法在从插值后的范围图像重建点云时显示出更好的定性结果。