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arXiv · 2305.01643

Neural LiDAR Fields for Novel View Synthesis

Shengyu Huang·Zan Gojcic·Zian Wang·Francis Williams·Yoni Kasten·Sanja Fidler·Konrad Schindler·Or Litany
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Abstract

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.

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Shengyu Huang, Zan Gojcic, Zian Wang, Francis Williams, Yoni Kasten, Sanja Fidler, Konrad Schindler, Or Litany. 2023-05-02. Neural LiDAR Fields for Novel View Synthesis. https://arxiv.org/abs/2305.01643

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