LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes

Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, Buyu Liu, Xiaohui Xie, Manmohan Chandraker; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19563-19572

Photorealistic simulation plays a crucial role in applications such as autonomous driving where advances in neural radiance fields (NeRFs) may allow better scalability through the automatic creation of digital 3D assets. However reconstruction quality suffers on street scenes due to largely collinear camera motions and sparser samplings at higher speeds. On the other hand the application often demands rendering from camera views that deviate from the inputs to accurately simulate behaviors like lane changes. In this paper we propose several insights that allow a better utilization of Lidar data to improve NeRF quality on street scenes. First our framework learns a geometric scene representation from Lidar which are fused with the implicit grid-based representation for radiance decoding thereby supplying stronger geometric information offered by explicit point cloud. Second we put forth a robust occlusion-aware depth supervision scheme which allows utilizing densified Lidar points by accumulation. Third we generate augmented training views from Lidar points for further improvement. Our insights translate to largely improved novel view synthesis under real driving scenes.