  Although recent efforts have extended Neural Radiance Fields (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.
