Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression
This work addresses a domain-specific issue for mobile and edge computing systems using LiDAR, but it is incremental as it builds on existing interpolation techniques.
The paper tackles the problem of performance degradation in edge-assisted LiDAR perceptions due to lossy compression by proposing an interpolation algorithm that improves point cloud quality, showing better qualitative results compared to existing methods.
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.