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arXiv · 2403.00228

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

Chunlin Li·Hanrui Fan·Xiaorui Huang·Ruofan Liang·Sankeerth Durvasula·Nandita Vijaykumar
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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.

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Chunlin Li, Hanrui Fan, Xiaorui Huang, Ruofan Liang, Sankeerth Durvasula, Nandita Vijaykumar. 2024-03-01. DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots. https://arxiv.org/abs/2403.00228

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