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.

翻译：我们提出神经激光雷达场（NFL），这是一种从激光雷达测量中优化神经场场景表示的方法，旨在从新视角合成逼真的激光雷达扫描。NFL将神经场的渲染能力与激光雷达传感过程的详细物理模型相结合，从而能够准确再现波束发散、二次回波和射线丢失等关键传感器行为。我们在合成和真实激光雷达扫描上评估了NFL，并表明在激光雷达新视角合成任务中，它优于显式的先重建后模拟方法以及其他NeRF风格的方法。此外，我们还表明，合成视角真实感的提升缩小了与真实扫描之间的域差距，并转化为更好的配准和语义分割性能。