AIR-Embodied: An Efficient Active 3DGS-based Interaction and Reconstruction Framework with Embodied Large Language Model
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917784725749760 |
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| author | Qi, Zhenghao Yuan, Shenghai Liu, Fen Cao, Haozhi Deng, Tianchen Yang, Jianfei Xie, Lihua |
| author_facet | Qi, Zhenghao Yuan, Shenghai Liu, Fen Cao, Haozhi Deng, Tianchen Yang, Jianfei Xie, Lihua |
| contents | Recent advancements in 3D reconstruction and neural rendering have enhanced the creation of high-quality digital assets, yet existing methods struggle to generalize across varying object shapes, textures, and occlusions. While Next Best View (NBV) planning and Learning-based approaches offer solutions, they are often limited by predefined criteria and fail to manage occlusions with human-like common sense. To address these problems, we present AIR-Embodied, a novel framework that integrates embodied AI agents with large-scale pretrained multi-modal language models to improve active 3DGS reconstruction. AIR-Embodied utilizes a three-stage process: understanding the current reconstruction state via multi-modal prompts, planning tasks with viewpoint selection and interactive actions, and employing closed-loop reasoning to ensure accurate execution. The agent dynamically refines its actions based on discrepancies between the planned and actual outcomes. Experimental evaluations across virtual and real-world environments demonstrate that AIR-Embodied significantly enhances reconstruction efficiency and quality, providing a robust solution to challenges in active 3D reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16019 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIR-Embodied: An Efficient Active 3DGS-based Interaction and Reconstruction Framework with Embodied Large Language Model Qi, Zhenghao Yuan, Shenghai Liu, Fen Cao, Haozhi Deng, Tianchen Yang, Jianfei Xie, Lihua Robotics Recent advancements in 3D reconstruction and neural rendering have enhanced the creation of high-quality digital assets, yet existing methods struggle to generalize across varying object shapes, textures, and occlusions. While Next Best View (NBV) planning and Learning-based approaches offer solutions, they are often limited by predefined criteria and fail to manage occlusions with human-like common sense. To address these problems, we present AIR-Embodied, a novel framework that integrates embodied AI agents with large-scale pretrained multi-modal language models to improve active 3DGS reconstruction. AIR-Embodied utilizes a three-stage process: understanding the current reconstruction state via multi-modal prompts, planning tasks with viewpoint selection and interactive actions, and employing closed-loop reasoning to ensure accurate execution. The agent dynamically refines its actions based on discrepancies between the planned and actual outcomes. Experimental evaluations across virtual and real-world environments demonstrate that AIR-Embodied significantly enhances reconstruction efficiency and quality, providing a robust solution to challenges in active 3D reconstruction. |
| title | AIR-Embodied: An Efficient Active 3DGS-based Interaction and Reconstruction Framework with Embodied Large Language Model |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.16019 |