Di-NeRF：用于协同学习与相对位姿精化的分布式神经辐射场
Di-NeRF: Distributed NeRF for Collaborative Learning with Relative Pose Refinement
AI总结：
提出一种完全分布式的Di-NeRF算法，使多机器人协同优化NeRF参数并联合精化相对位姿，实现低精度位姿下的稳健3D建图，实验验证其效率。
摘要：
未知环境的协同建图可以比单个机器人更快、更稳健地完成。然而，协同方法需要分布式范式以实现可扩展性并应对通信问题。本工作提出一种完全分布式的算法，使一组机器人能够共同优化神经辐射场（NeRF）的参数。该算法涉及通过网状网络通信每个机器人训练的NeRF参数，其中每个机器人仅使用自身的视觉数据训练其NeRF。此外，所有机器人的相对位姿与模型参数被联合优化，从而允许在相对相机位姿精度较低的情况下进行建图。我们展示了多机器人系统可以从多个NeRF优化的可微且稳健的3D重建中获益。在真实世界和合成数据上的实验证明了所提出算法的效率。项目网站提供了实验视频和补充材料（https://sites.google.com/view/di-nerf/home）。
英文摘要：
Collaborative mapping of unknown environments can be done faster and more robustly than a single robot. However, a collaborative approach requires a distributed paradigm to be scalable and deal with communication issues. This work presents a fully distributed algorithm enabling a group of robots to collectively optimize the parameters of a Neural Radiance Field (NeRF). The algorithm involves the communication of each robot's trained NeRF parameters over a mesh network, where each robot trains its NeRF and has access to its own visual data only. Additionally, the relative poses of all robots are jointly optimized alongside the model parameters, enabling mapping with less accurate relative camera poses. We show that multi-robot systems can benefit from differentiable and robust 3D reconstruction optimized from multiple NeRFs. Experiments on real-world and synthetic data demonstrate the efficiency of the proposed algorithm. See the website of the project for videos of the experiments and supplementary material (https://sites.google.com/view/di-nerf/home).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
