ALICE-LRI: A General Method for Lossless Range Image Generation for Spinning LiDAR Sensors without Calibration Metadata
Overview
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.
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- arXiv Robotics — research abstractsSignalALICE-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.
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