MetaScenes: Towards Automated Replica Creation for Real-world 3D Scans
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910928063168512 |
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| author | Yu, Huangyue Jia, Baoxiong Chen, Yixin Yang, Yandan Li, Puhao Su, Rongpeng Li, Jiaxin Li, Qing Liang, Wei Zhu, Song-Chun Liu, Tengyu Huang, Siyuan |
| author_facet | Yu, Huangyue Jia, Baoxiong Chen, Yixin Yang, Yandan Li, Puhao Su, Rongpeng Li, Jiaxin Li, Qing Liang, Wei Zhu, Song-Chun Liu, Tengyu Huang, Siyuan |
| contents | Embodied AI (EAI) research requires high-quality, diverse 3D scenes to effectively support skill acquisition, sim-to-real transfer, and generalization. Achieving these quality standards, however, necessitates the precise replication of real-world object diversity. Existing datasets demonstrate that this process heavily relies on artist-driven designs, which demand substantial human effort and present significant scalability challenges. To scalably produce realistic and interactive 3D scenes, we first present MetaScenes, a large-scale, simulatable 3D scene dataset constructed from real-world scans, which includes 15366 objects spanning 831 fine-grained categories. Then, we introduce Scan2Sim, a robust multi-modal alignment model, which enables the automated, high-quality replacement of assets, thereby eliminating the reliance on artist-driven designs for scaling 3D scenes. We further propose two benchmarks to evaluate MetaScenes: a detailed scene synthesis task focused on small item layouts for robotic manipulation and a domain transfer task in vision-and-language navigation (VLN) to validate cross-domain transfer. Results confirm MetaScene's potential to enhance EAI by supporting more generalizable agent learning and sim-to-real applications, introducing new possibilities for EAI research. Project website: https://meta-scenes.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MetaScenes: Towards Automated Replica Creation for Real-world 3D Scans Yu, Huangyue Jia, Baoxiong Chen, Yixin Yang, Yandan Li, Puhao Su, Rongpeng Li, Jiaxin Li, Qing Liang, Wei Zhu, Song-Chun Liu, Tengyu Huang, Siyuan Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics Embodied AI (EAI) research requires high-quality, diverse 3D scenes to effectively support skill acquisition, sim-to-real transfer, and generalization. Achieving these quality standards, however, necessitates the precise replication of real-world object diversity. Existing datasets demonstrate that this process heavily relies on artist-driven designs, which demand substantial human effort and present significant scalability challenges. To scalably produce realistic and interactive 3D scenes, we first present MetaScenes, a large-scale, simulatable 3D scene dataset constructed from real-world scans, which includes 15366 objects spanning 831 fine-grained categories. Then, we introduce Scan2Sim, a robust multi-modal alignment model, which enables the automated, high-quality replacement of assets, thereby eliminating the reliance on artist-driven designs for scaling 3D scenes. We further propose two benchmarks to evaluate MetaScenes: a detailed scene synthesis task focused on small item layouts for robotic manipulation and a domain transfer task in vision-and-language navigation (VLN) to validate cross-domain transfer. Results confirm MetaScene's potential to enhance EAI by supporting more generalizable agent learning and sim-to-real applications, introducing new possibilities for EAI research. Project website: https://meta-scenes.github.io/. |
| title | MetaScenes: Towards Automated Replica Creation for Real-world 3D Scans |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2505.02388 |