DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909276985884672 |
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| author | Li, Chunlin Fan, Hanrui Huang, Xiaorui Liang, Ruofan Durvasula, Sankeerth Vijaykumar, Nandita |
| author_facet | Li, Chunlin Fan, Hanrui Huang, Xiaorui Liang, Ruofan Durvasula, Sankeerth Vijaykumar, Nandita |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00228 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots Li, Chunlin Fan, Hanrui Huang, Xiaorui Liang, Ruofan Durvasula, Sankeerth Vijaykumar, Nandita Robotics Computer Vision and Pattern Recognition 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. |
| title | DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.00228 |