ALICE-LRI: A General Method for Lossless Range Image Generation for
  Spinning LiDAR Sensors without Calibration Metadata
3D LiDAR sensors are essential for autonomous navigation, environmental
monitoring, and precision mapping in remote sensing applications. To
efficiently process the massive point clouds generated by these sensors, LiDAR
data is often projected into 2D range images that organize points by their
angular positions and distances. While these range image representations enable
efficient processing, conventional projection methods suffer from fundamental
geometric inconsistencies that cause irreversible information loss,
compromising high-fidelity applications. We present ALICE-LRI (Automatic LiDAR
Intrinsic Calibration Estimation for Lossless Range Images), the first general,
sensor-agnostic method that achieves lossless range image generation from
spinning LiDAR point clouds without requiring manufacturer metadata or
calibration files. Our algorithm automatically reverse-engineers the intrinsic
geometry of any spinning LiDAR sensor by inferring critical parameters
including laser beam configuration, angular distributions, and per-beam
calibration corrections, enabling lossless projection and complete point cloud
reconstruction with zero point loss. Comprehensive evaluation across the
complete KITTI and DurLAR datasets demonstrates that ALICE-LRI achieves perfect
point preservation, with zero points lost across all point clouds. Geometric
accuracy is maintained well within sensor precision limits, establishing
geometric losslessness with real-time performance. We also present a
compression case study that validates substantial downstream benefits,
demonstrating significant quality improvements in practical applications. This
paradigm shift from approximate to lossless LiDAR projections opens new
possibilities for high-precision remote sensing applications requiring complete
geometric preservation.
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Samuel Soutullo
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2025-10-24T15:57:40.348Z
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Samuel Soutullo
ssoutullo
user
ssoutullo
68fb75884799a63578375e0c
Miguel Yermo
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David L. Vilariño
68fb75884799a63578375e0e
Óscar G. Lorenzo
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José C. Cabaleiro
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Francisco F. Rivera
ALICE-LRI: A General Method for Lossless Range Image Generation for
  Spinning LiDAR Sensors without Calibration Metadata
3D LiDAR sensors are essential for autonomous navigation, environmental
monitoring, and precision mapping in remote sensing applications. To
efficiently process the massive point clouds generated by these sensors, LiDAR
data is often projected into 2D range images that organize points by their
angular positions and distances. While these range image representations enable
efficient processing, conventional projection methods suffer from fundamental
geometric inconsistencies that cause irreversible information loss,
compromising high-fidelity applications. We present ALICE-LRI (Automatic LiDAR
Intrinsic Calibration Estimation for Lossless Range Images), the first general,
sensor-agnostic method that achieves lossless range image generation from
spinning LiDAR point clouds without requiring manufacturer metadata or
calibration files. Our algorithm automatically reverse-engineers the intrinsic
geometry of any spinning LiDAR sensor by inferring critical parameters
including laser beam configuration, angular distributions, and per-beam
calibration corrections, enabling lossless projection and complete point cloud
reconstruction with zero point loss. Comprehensive evaluation across the
complete KITTI and DurLAR datasets demonstrates that ALICE-LRI achieves perfect
point preservation, with zero points lost across all point clouds. Geometric
accuracy is maintained well within sensor precision limits, establishing
geometric losslessness with real-time performance. We also present a
compression case study that validates substantial downstream benefits,
demonstrating significant quality improvements in practical applications. This
paradigm shift from approximate to lossless LiDAR projections opens new
possibilities for high-precision remote sensing applications requiring complete
geometric preservation.
ALICE-LRI is a sensor-agnostic method that achieves lossless range image generation from spinning LiDAR point clouds, preserving all points and maintaining geometric accuracy in real-time.
LiDAR point clouds range images intrinsic calibration laser beam configuration angular distributions per-beam calibration corrections KITTI DurLAR geometric accuracy real-time performance