ALICE-LRI: A General Method for Lossless Range Image Generation for Spinning LiDAR Sensors without Calibration Metadata

TL;DR

ALICE-LRI tackles the problem of lossless range-image generation for spinning LiDARs without calibration metadata by automatically inferring intrinsic sensor parameters from calibrated point clouds. It combines a vertical-parameter stage (via Hough Transform, error-bounded point selection, and weighted least squares with fallbacks) and a horizontal-parameter stage (exhaustive H-search plus regression and heuristics) to recover $L$, $\,\varphi^{(l)}$, $o_y^{(l)}$, $H^{(l)}$, $o_x^{(l)}$, and $\theta_{off}^{(l)}$. With these parameters, ALICE-LRI enables bijective projection and lossless reconstruction of the original point cloud, outperforming standard projection baselines (e.g., PBEA) in Chamfer Distance, PSNR, and sampling preservation across KITTI and DurLAR datasets, while maintaining real-time per-frame performance. The work further demonstrates practical benefits through a compression case study, showing improved compression efficiency at equal reconstruction quality. Overall, ALICE-LRI establishes a sensor-agnostic framework for high-fidelity LiDAR processing by eliminating dependence on manufacturer calibration metadata.

Abstract

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.

Figures (12)

• Figure 1: LiDAR point cloud projection onto a 2D range image grid based on discretized angular coordinates.
• Figure 2: Illustration of geometric differences between the ideal sensor model (perfectly aligned beams from a common origin) and the real-world model (beams with spatial and angular offsets), highlighting the calibration parameters required for lossless projection.
• Figure 3: General outline of ALICE-LRI for estimating LiDAR intrinsic parameters and generating lossless range images. Gray diamond-shaped nodes represent data inputs and outputs; rectangular nodes represent processing steps. Solid lines denote execution flow, while dashed lines indicate data flow. The method first estimates sensor parameters from the input point cloud, then uses these parameters to generate range images from subsequent point clouds.
• Figure 4: Flowchart of the vertical parameter estimation algorithm (Sec. \ref{['sec:vertical_parameter_estimation']}). The algorithm iteratively identifies candidate scanline parameters using the Hough Transform, selects points consistent with these parameters, fits the scanline to refine parameter values, and resolves conflicts with previously accepted scanlines. The process continues until all points are assigned to scanlines or the search space is exhausted. Key processing steps are annotated with references to their corresponding subsections.
• Figure 5: Comparison of basic Hough Transform strategy versus vote-for-discontinuities strategy. The basic strategy (a) misses the intersection peak due to vote fragmentation across cells, while the discontinuities strategy (b) successfully detects it by incrementing intermediate cells when votes skip multiple bins due to steep gradients in the parameter mapping.