第二个工作，大家有时间可以去github点个小星星啦，and 希望大家不要喷啦，感谢感谢
摘要
Large-scale 3D scene reconstruction and novel view synthesis are vital for autonomous vehicles, especially utilizing temporally sparse LiDAR frames. However, conventional explicit representations remain a significant bottleneck towards representing the reconstructed and synthetic scenes at unlimited resolution. Although the recently developed neural radiance fields (NeRF) have shown compelling results in implicit representations, the problem of large-scale 3D scene reconstruction and novel view synthesis using sparse LiDAR frames remains unexplored. To bridge this gap, we propose a 3D scene reconstruction and novel view synthesis framework called parent-child neural radiance field (PC-NeRF). Based on its two modules, parent NeRF and child NeRF, the framework implements hierarchical spatial partitioning and multi-level scene representation, including scene, segment, and point levels. The multi-level scene representation enhances the efficient utilization of sparse LiDAR point cloud data and enables the rapid acquisition of an approximate volumetric scene representation. With extensive experiments, PC-NeRF is proven to achieve high-precision novel LiDAR view synthesis and 3D reconstruction in large-scale scenes. Moreover, PC-NeRF can effectively handle situations with sparse LiDAR frames and demonstrate high deployment efficiency with limited training epochs. Our approach implementation and the pre-trained models are available at https://github.com/biter0088/pc-nerf .
论文框架：
Our PC-NeRF framework: (a) The hierarchical spatial partition divides the entire large-scale scene into large blocks, referred to as parent NeRFs. After multi-frame point cloud fusing, ground filtering, and non-ground point cloud clustering, a large block is further divided into point cloud geometric segments represented by a child NeRF. The parent NeRF shares a network with the child NeRFs within it. (b) In the multi-level scene representation, the surface intersections of the LiDAR ray with the parent and child NeRF AABBs and the LiDAR origin are used to divide the entire LiDAR ray into different line segments. The three losses on these line segments concurrently optimize the scene representation at the scene level, segment level, and point level, effectively leveraging sparse LiDAR frames. © For depth inference of each LiDAR ray, PC-NeRF searches in the parent NeRF AABB to locate corresponding child NeRF AABBs and then refines its inference in the child NeRF AABBs for higher precision.
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