ALICE-LRI: A General Method for Lossless Range Image Generation for Spinning LiDAR Sensors without Calibration Metadata
ALICE-LRI: A General Method for Lossless Range Image Generation for Spinning LiDAR Sensors without Calibration Metadata
This paper introduces ALICE-LRI, a metadata-free method that generates lossless range images from spinning multi-beam LiDAR point clouds by estimating intrinsic calibration parameters including beam configuration and per-beam geometric corrections. Evaluated on the complete KITTI and DurLAR datasets, the method achieves zero lost points across all point clouds, maintains reconstruction deviations below manufacturer-reported range-performance specifications, and operates in real time. A compression case study further demonstrates improved reconstruction quality and a better quality-compression trade-off for downstream applications, enabling high-fidelity LiDAR processing without relying on factory calibration files or packet-level data.
The authors present a metadata-free method for generating lossless range images from spinning LiDAR point clouds, estimating intrinsic calibration parameters without factory files or beam identifiers; evaluation on KITTI and DurLAR datasets reports zero lost points and reconstruction deviations within manufacturer specifications while running in real time.
Source: arXiv Robotics — research abstracts · Read original article ↗
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beam configuration, angular distributions, and per-beam geometric corrections. Evaluation on the complete KITTI and DurLAR datasets yielded zero lost points across all point clouds. Reconstruction deviations remain below manufacturer-reported range-performance specifications, while the method runs in real time. A compression case study also shows improved reconstruction quality and a better quality-compression trade-
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Source：arXiv Robotics — research abstracts · arxiv.org