Di-NeRF: 相対姿勢推定を伴う協調学習のための分散NeRF
Di-NeRF: Distributed NeRF for Collaborative Learning with Relative Pose Refinement
複数ロボットがメッシュネットワーク上で各々のNeRFを訓練し、相対姿勢も同時に最適化することで、分散的に高精度な3Dマッピングを実現する手法を提案。
著者: Mahboubeh Asadi, Kourosh Zareinia, Sajad Saeedi
分類: cs.RO
原文アブストラクト
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).
