DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots
<|reference_start|>DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots: We present a framework, DISORF, to enable online 3D reconstruction and visualization of scenes captured by resource-constrained mobile robots and edge devices. To address the limited computing capabilities of edge devices and potentially limited network availability, we design a framework that efficiently distributes computation between the edge device and the remote server. We leverage on-device SLAM systems to generate posed keyframes and transmit them to remote servers that can perform high-quality 3D reconstruction and visualization at runtime by leveraging recent advances in neural 3D methods. We identify a key challenge with online training where naive image sampling strategies can lead to significant degradation in rendering quality. We propose a novel shifted exponential frame sampling method that addresses this challenge for online training. We demonstrate the effectiveness of our framework in enabling high-quality real-time reconstruction and visualization of unknown scenes as they are captured and streamed from cameras in mobile robots and edge devices.<|reference_end|>
@article{li2024disorf:,
    title={DISORF: A Distributed Online 3D Reconstruction Framework for Mobile
  Robots},
    author={Chunlin Li, Hanrui Fan, Xiaorui Huang, Ruofan Liang, Sankeerth
  Durvasula, Nandita Vijaykumar},
    journal={arXiv preprint arXiv:2403.00228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00228},
    primaryClass={cs.RO cs.CV}
}