DISORF: A Distributed Online NeRF Training and Rendering Framework for
  Mobile Robots
Chunlin Li Ruofan Liang Hanrui Fan Zhengen Zhang Sankeerth Durvasula Nandita Vijaykumar
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 compute capabilities of edge devices and
potentially limited network availability, we design a framework that
efficiently distributes computation between the edge device and 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 NeRF models. We identify a key challenge
with online NeRF 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 NeRF
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.