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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2406.02972 |
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| _version_ | 1866910647654023168 |
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| author | Xiong, Tianyi Wu, Jiayi He, Botao Fermuller, Cornelia Aloimonos, Yiannis Huang, Heng Metzler, Christopher A. |
| author_facet | Xiong, Tianyi Wu, Jiayi He, Botao Fermuller, Cornelia Aloimonos, Yiannis Huang, Heng Metzler, Christopher A. |
| contents | By combining differentiable rendering with explicit point-based scene representations, 3D Gaussian Splatting (3DGS) has demonstrated breakthrough 3D reconstruction capabilities. However, to date 3DGS has had limited impact on robotics, where high-speed egomotion is pervasive: Egomotion introduces motion blur and leads to artifacts in existing frame-based 3DGS reconstruction methods. To address this challenge, we introduce Event3DGS, an {\em event-based} 3DGS framework. By exploiting the exceptional temporal resolution of event cameras, Event3GDS can reconstruct high-fidelity 3D structure and appearance under high-speed egomotion. Extensive experiments on multiple synthetic and real-world datasets demonstrate the superiority of Event3DGS compared with existing event-based dense 3D scene reconstruction frameworks; Event3DGS substantially improves reconstruction quality (+3dB) while reducing computational costs by 95\%. Our framework also allows one to incorporate a few motion-blurred frame-based measurements into the reconstruction process to further improve appearance fidelity without loss of structural accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02972 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion Xiong, Tianyi Wu, Jiayi He, Botao Fermuller, Cornelia Aloimonos, Yiannis Huang, Heng Metzler, Christopher A. Computer Vision and Pattern Recognition By combining differentiable rendering with explicit point-based scene representations, 3D Gaussian Splatting (3DGS) has demonstrated breakthrough 3D reconstruction capabilities. However, to date 3DGS has had limited impact on robotics, where high-speed egomotion is pervasive: Egomotion introduces motion blur and leads to artifacts in existing frame-based 3DGS reconstruction methods. To address this challenge, we introduce Event3DGS, an {\em event-based} 3DGS framework. By exploiting the exceptional temporal resolution of event cameras, Event3GDS can reconstruct high-fidelity 3D structure and appearance under high-speed egomotion. Extensive experiments on multiple synthetic and real-world datasets demonstrate the superiority of Event3DGS compared with existing event-based dense 3D scene reconstruction frameworks; Event3DGS substantially improves reconstruction quality (+3dB) while reducing computational costs by 95\%. Our framework also allows one to incorporate a few motion-blurred frame-based measurements into the reconstruction process to further improve appearance fidelity without loss of structural accuracy. |
| title | Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.02972 |