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.

我们提出了一种名为 DISORF 的框架，用于实现资源有限的移动机器人和边缘设备捕捉场景的在线三维重建和可视化。该框架能够解决边缘设备计算能力有限和网络可用性不足的问题，并利用设备上的 SLAM 系统生成位姿关键帧，通过远程服务器在运行时利用 NeRF 模型实现高质量的三维重建和可视化。我们还提出了一种新的移动 NeRF 训练方法来解决在线 NeRF 训练中图像采样导致的渲染质量下降的挑战。我们演示了该框架在移动机器人和边缘设备的相机捕捉和流媒体传输的过程中实现高质量的实时重建和可视化的有效性。