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.

翻译：本文提出一个框架DISORF，旨在为资源受限的移动机器人与边缘设备捕获的场景实现在线三维重建与可视化。为应对边缘设备有限的计算能力及潜在的网络可用性限制，我们设计了一个能高效分配边缘设备与远程服务器间计算任务的框架。我们利用设备端SLAM系统生成带位姿的关键帧，并将其传输至远程服务器；服务器端可借助神经三维方法的最新进展，在运行时执行高质量的三维重建与可视化。我们指出了在线训练中的一个关键挑战：朴素的图像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平移指数帧采样方法以应对在线训练中的这一挑战。实验表明，本框架能有效实现对未知场景的高质量实时重建与可视化，这些场景由移动机器人与边缘设备的摄像头实时捕获并流式传输。