用神经隐式模型从稀疏雷达点云重建更精确的三维表面和反射特性。
Neural Surface and Reflectance Modelling from 3D Radar Data
- 融合雷达强度与几何信息，用混合编码学习连续符号距离场。
- 在稀疏点云下，重建表面比传统方法更平滑准确，误差降低约18%。
- 适合自动驾驶在雾、烟等低可见环境中的场景建模需求。
可靠场景表征对自主系统在恶劣低能见度环境下安全运行至关重要。雷达因对雾、烟、尘等环境因素具有强鲁棒性，相比相机和激光雷达更具优势。然而，雷达数据本身稀疏且噪声大，导致可靠的3D表面重建极具挑战。为此，我们提出一种基于神经隐式表示的雷达点云三维映射方法，联合建模场景几何结构与视角依赖的雷达强度。该方法采用内存高效的混合特征编码，学习连续的符号距离场（SDF）以实现表面重建，同时捕捉雷达特有的反射特性。实验表明，本方法在重建精度和表面平滑度上优于现有应用于雷达数据的激光雷达重建方法，并可有效恢复视角相关的雷达强度。此外，在输入点云逐渐稀疏时，神经隐式表示生成的表面比传统显式SDF和网格化技术更忠实于真实几何结构。
原文摘要 · Abstract (English)
Robust scene representation is essential for autonomous systems to safely operate in challenging low-visibility environments. In these conditions, radar has a clear advantage over cameras and lidars 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 this, we propose a neural implicit approach for 3D mapping from radar point clouds that 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 than traditional explicit SDFs and meshing techniques.
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