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DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots | IEEE Journals & Magazine | IEEE Xplore

DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots


Abstract:

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...Show More

Abstract:

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.
Published in: IEEE Robotics and Automation Letters ( Volume: 10, Issue: 2, February 2025)
Page(s): 1329 - 1336
Date of Publication: 16 December 2024

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