NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization
<|reference_start|>NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization: Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoints with remarkable fidelity. This is accomplished through the strategic utilization of object-centric camera poses characterized by significant inter-frame overlap. This paper explores a compelling, alternative utility of NeRF: the derivation of point clouds from aggregated urban landscape imagery. The transmutation of street-view data into point clouds is fraught with complexities, attributable to a nexus of interdependent variables. First, high-quality point cloud generation hinges on precise camera poses, yet many datasets suffer from inaccuracies in pose metadata. Also, the standard approach of NeRF is ill-suited for the distinct characteristics of street-view data from autonomous vehicles in vast, open settings. Autonomous vehicle cameras often record with limited overlap, leading to blurring, artifacts, and compromised pavement representation in NeRF-based point clouds. In this paper, we present NeRF2Points, a tailored NeRF variant for urban point cloud synthesis, notable for its high-quality output from RGB inputs alone. Our paper is supported by a bespoke, high-resolution 20-kilometer urban street dataset, designed for point cloud generation and evaluation. NeRF2Points adeptly navigates the inherent challenges of NeRF-based point cloud synthesis through the implementation of the following strategic innovations: (1) Integration of Weighted Iterative Geometric Optimization (WIGO) and Structure from Motion (SfM) for enhanced camera pose accuracy, elevating street-view data precision. (2) Layered Perception and Integrated Modeling (LPiM) is designed for distinct radiance field modeling in urban environments, resulting in coherent point cloud representations.<|reference_end|>
@article{tu2024nerf2points:,
    title={NeRF2Points: Large-Scale Point Cloud Generation From Street Views'
  Radiance Field Optimization},
    author={Peng Tu and Xun Zhou and Mingming Wang and Xiaojun Yang and Bo Peng
  and Ping Chen and Xiu Su and Yawen Huang and Yefeng Zheng and Chang Xu},
    journal={arXiv preprint arXiv:2404.04875},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04875},
    primaryClass={cs.CV}
}