We present Neural Fields for LiDAR (NFL), a method to optimise a neural field
scene representation from LiDAR measurements, with the goal of synthesizing
realistic LiDAR scans from novel viewpoints. NFL combines the rendering power
of neural fields with a detailed, physically motivated model of the LiDAR
sensing process, thus enabling it to accurately reproduce key sensor behaviors
like beam divergence, secondary returns, and ray dropping. We evaluate NFL on
synthetic and real LiDAR scans and show that it outperforms explicit
reconstruct-then-simulate methods as well as other NeRF-style methods on LiDAR
novel view synthesis task. Moreover, we show that the improved realism of the
synthesized views narrows the domain gap to real scans and translates to better
registration and semantic segmentation performance.

本文提出了一种基于神经场的雷达（NFL）方法，该方法旨在从 LiDAR 测量中优化神经场场景表示，以便合成来自新视点的逼真的 LiDAR 扫描。该方法与 LiDAR 传感过程的详细、物理上可靠的模型相结合，能够准确地再现关键传感器行为，如光束发散、次级返回和光线丢失。我们在合成的和实际的 LiDAR 扫描上评估了 NFL，并表明它在 LiDAR 新视点合成任务上优于显式重建 - 模拟方法以及其他类似 NeRF 的方法。此外，我们展示了综合视图的改善逼真性将领域差距缩小到实际扫描，并转化为更好的注册和语义分割性能。