@inproceedings{
xue2024geonlf,
title={Geo{NLF}: Geometry guided Pose-Free Neural Li{DAR} Fields},
author={Weiyi Xue and Zehan Zheng and Fan Lu and Haiyun Wei and Guang Chen and changjun jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=v3y785TN7B}
}
Although recent efforts have extended Neural Radiance Field (NeRF) into LiDAR point cloud synthesis, the majority of existing works exhibit a strong dependence on precomputed poses. However, point cloud registration methods struggle to achieve precise global pose estimation, whereas previous pose-free NeRFs overlook geometric consistency in global reconstruction. In light of this, we explore the geometric insights of point clouds, which provide explicit registration priors for reconstruction. Based on this, we propose Geometry guided Neural LiDAR Fields (GeoNLF), a hybrid framework performing alternately global neural reconstruction and pure geometric pose optimization. Furthermore, NeRFs tend to overfit individual frames and easily get stuck in local minima under sparse-view inputs. To tackle this issue, we develop a selective-reweighting strategy and introduce geometric constraints for robust optimization. Extensive experiments on NuScenes and KITTI-360 datasets demonstrate the superiority of GeoNLF in both novel view synthesis and multi-view registration of low-frequency large-scale point clouds.
GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields
Weiyi Xue Zehan Zheng Fan Lu Haiyun Wei Guang Chen changjun jiang