SceneMaker: Open-set 3D Scene Generation with Decoupled De-occlusion and Pose Estimation Model
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911314513756160 |
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| author | Shi, Yukai Li, Weiyu Wang, Zihao Li, Hongyang Chen, Xingyu Tan, Ping Zhang, Lei |
| author_facet | Shi, Yukai Li, Weiyu Wang, Zihao Li, Hongyang Chen, Xingyu Tan, Ping Zhang, Lei |
| contents | We propose a decoupled 3D scene generation framework called SceneMaker in this work. Due to the lack of sufficient open-set de-occlusion and pose estimation priors, existing methods struggle to simultaneously produce high-quality geometry and accurate poses under severe occlusion and open-set settings. To address these issues, we first decouple the de-occlusion model from 3D object generation, and enhance it by leveraging image datasets and collected de-occlusion datasets for much more diverse open-set occlusion patterns. Then, we propose a unified pose estimation model that integrates global and local mechanisms for both self-attention and cross-attention to improve accuracy. Besides, we construct an open-set 3D scene dataset to further extend the generalization of the pose estimation model. Comprehensive experiments demonstrate the superiority of our decoupled framework on both indoor and open-set scenes. Our codes and datasets is released at https://idea-research.github.io/SceneMaker/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SceneMaker: Open-set 3D Scene Generation with Decoupled De-occlusion and Pose Estimation Model Shi, Yukai Li, Weiyu Wang, Zihao Li, Hongyang Chen, Xingyu Tan, Ping Zhang, Lei Computer Vision and Pattern Recognition Artificial Intelligence We propose a decoupled 3D scene generation framework called SceneMaker in this work. Due to the lack of sufficient open-set de-occlusion and pose estimation priors, existing methods struggle to simultaneously produce high-quality geometry and accurate poses under severe occlusion and open-set settings. To address these issues, we first decouple the de-occlusion model from 3D object generation, and enhance it by leveraging image datasets and collected de-occlusion datasets for much more diverse open-set occlusion patterns. Then, we propose a unified pose estimation model that integrates global and local mechanisms for both self-attention and cross-attention to improve accuracy. Besides, we construct an open-set 3D scene dataset to further extend the generalization of the pose estimation model. Comprehensive experiments demonstrate the superiority of our decoupled framework on both indoor and open-set scenes. Our codes and datasets is released at https://idea-research.github.io/SceneMaker/. |
| title | SceneMaker: Open-set 3D Scene Generation with Decoupled De-occlusion and Pose Estimation Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.10957 |