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.

翻译：本文提出DISORF框架，实现对资源受限的移动机器人和边缘设备所采集场景的在线三维重建与可视化。针对边缘设备计算能力有限及网络可能不稳定的问题，我们设计了在边缘设备与远程服务器间高效分配计算任务的框架。该框架利用设备端SLAM系统生成带位姿的关键帧，并将其传输至远程服务器，通过NeRF模型在运行时实现高质量三维重建与可视化。我们发现在线NeRF训练中存在一项关键挑战：朴素帧采样策略可能导致渲染质量显著下降。为此我们提出一种新颖的移位指数式帧采样方法，有效解决了在线NeRF训练中的这一难题。实验证明，该框架能够在移动机器人和边缘设备的摄像头实时采集并传输未知场景时，实现高质量的重建与可视化。