3D LiDAR sensors are widely used in autonomous navigation, environmental monitoring, and precision mapping. Their point clouds are often projected into 2D range images for efficient processing, but conventional projection methods can lose points because of geometric inconsistencies. We present ALICE-LRI (Automatic LiDAR Intrinsic Calibration Estimation for Lossless Range Images), a metadata-free method for lossless range-image generation from calibrated spinning multi-beam LiDAR point clouds. It requires neither factory calibration files, packet-level data, nor beam identifiers, and targets operating regimes where scanlines contain enough returns for reliable parameter estimation. The method estimates the intrinsic calibration parameters relevant to projection, including 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-off for a downstream application. These results enable range-image-based LiDAR processing in applications that require high measurement fidelity and preservation of acquired geometric information.

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