Robust scene representation is essential for autonomous systems to safely operate in challenging low-visibility environments. Radar has a clear advantage over cameras and lidars in these conditions due to its resilience to environmental factors such as fog, smoke, or dust. However, radar data is inherently sparse and noisy, making reliable 3D surface reconstruction challenging. To address these challenges, we propose a neural implicit approach for 3D mapping from radar point clouds, which jointly models scene geometry and view-dependent radar intensities. Our method leverages a memory-efficient hybrid feature encoding to learn a continuous Signed Distance Field (SDF) for surface reconstruction, while also capturing radar-specific reflective properties. We show that our approach produces smoother, more accurate 3D surface reconstructions compared to existing lidar-based reconstruction methods applied to radar data, and can reconstruct view-dependent radar intensities. We also show that in general, as input point clouds get sparser, neural implicit representations render more faithful surfaces, compared to traditional explicit SDFs and meshing techniques.

翻译：鲁棒的场景表示对于自主系统在低能见度挑战性环境中安全运行至关重要。雷达在雾、烟或尘埃等环境因素影响下具有比相机和激光雷达更明显的优势。然而，雷达数据本质上稀疏且含有噪声，使得可靠的3D表面重建极具挑战性。针对这些问题，我们提出了一种基于神经隐式方法的雷达点云3D建图方案，该方法联合建模场景几何与视角依赖的雷达强度。我们的方法利用内存高效的混合特征编码学习连续符号距离场（SDF）进行表面重建，同时捕捉雷达特有的反射特性。实验表明，与现有基于激光雷达重建方法应用于雷达数据相比，我们的方法能生成更平滑、更精确的3D表面重建结果，并可重建视角依赖的雷达强度。我们还证明，随着输入点云逐渐稀疏，与传统显式SDF和网格化技术相比，神经隐式表示能生成更保真的表面。