ALICE-LRI：一种无需标定元数据的旋转激光雷达传感器无损距离图像通用生成方法

摘要

三维激光雷达传感器在遥感应用中对于自主导航、环境监测和精密测绘至关重要。为高效处理这些传感器生成的海量点云数据，激光雷达数据常被投影为二维距离图像，通过角度位置和距离对点进行组织。虽然这种距离图像表征能实现高效处理，但传统投影方法存在固有几何不一致性，会导致不可逆的信息损失，影响高保真应用。我们提出ALICE-LRI（无损距离图像自动激光雷达内参标定估算）——首个通用、传感器无关的方法，无需制造商元数据或标定文件即可从旋转式激光雷达点云实现无损距离图像生成。该算法通过推断激光束配置、角度分布及每束光校准校正等关键参数，自动逆向计算任意旋转式激光雷达传感器的内在几何特性，实现无损投影和零点数损失的完整点云重建。在完整KITTI和DurLAR数据集上的综合评估表明，ALICE-LRI实现了完美点保存，所有点云均无点数损失。几何精度严格保持在传感器精度极限内，以实时性能确立几何无损特性。我们还通过压缩案例研究验证了显著的 downstream 效益，展示了实际应用中的重大质量提升。这种从近似到无损的激光雷达投影范式转变，为需要完整几何保存的高精度遥感应用开辟了新可能。

English

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：一种无需标定元数据的旋转激光雷达传感器无损距离图像通用生成方法

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

摘要

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