# Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression

arXiv: https://arxiv.org/abs/2309.04549 · HTML: https://mlsanity.com/paper/2309.04549/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation](https://mlsanity.com/paper/2003.14032/) (2020)
- [HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection](https://mlsanity.com/paper/2003.00186/) (2020)
- [Complex-YOLO: Real-time 3D Object Detection on Point Clouds](https://mlsanity.com/paper/1803.06199/) (2018)
- [Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds](https://mlsanity.com/paper/1904.07537/) (2019)
- [Real-Time Spatio-Temporal LiDAR Point Cloud Compression](https://mlsanity.com/paper/2008.06972/) (2020)
- [FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on Lossy RI](https://mlsanity.com/paper/2307.15005/) (2023)

## Abstract (arXiv)

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.