Neural Radiance Field with LiDAR maps

MingFang Chang, Akash Sharma, Michael Kaess, Simon Lucey; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 17914-17923

We address outdoor Neural Radiance Fields (NeRF) with LiDAR maps. Existing NeRF methods usually require specially collected hypersampled source views and do not perform well with the open source camera-LiDAR datasets - significantly limiting the approach's practical utility. In this paper, we demonstrate an approach that allows for these datasets to be utilized for high quality neural renderings. Our design leverages 1) LiDAR sensors for strong 3D geometry priors that significantly improve the ray sampling locality, and 2) Conditional Adversarial Networks (cGANs) to recover image details since aggregating embeddings from imperfect LiDAR maps causes artifacts in the synthesized images. Our experiments show that while NeRF baselines produce either noisy or blurry results on Argoverse 2, the images synthesized by our system not only outperform baselines in image quality metrics under both clean and noisy conditions, but also obtain closer Detectron2 results to the ground truth images. Furthermore, to show the substantial applicability of our system, we demonstrate that our system can be used in data augmentation for training a pose regression network and multi-season view synthesis. Our dataset and code will be released