Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866909001379217408 |
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| author | Zadaianchuk, Andrii Barcellona, Leonardo Schuenemann, Lennard Gumbsch, Christian Wang, Zehao Irshad, Muhammad Zubair Despinoy, Fabien Aljundi, Rahaf Gavves, Stratis Zakharov, Sergey |
| author_facet | Zadaianchuk, Andrii Barcellona, Leonardo Schuenemann, Lennard Gumbsch, Christian Wang, Zehao Irshad, Muhammad Zubair Despinoy, Fabien Aljundi, Rahaf Gavves, Stratis Zakharov, Sergey |
| contents | Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward scalable and reliable simulation for robotics. In this work, we introduce RecGen, a generative framework for probabilistic joint estimation of object and part shapes, as well as their pose under occlusion and partial visibility from one or multiple RGB-D images. By leveraging compositional synthetic scene generation and strong 3D shape priors, RecGen generalizes across diverse object types and real-world environments. RecGen achieves state-of-the-art performance on complex, heavily occluded datasets, robustly handling severe occlusions, symmetric objects, object parts, and intricate geometry and texture. Despite using nearly 80% fewer training meshes than the previous state of the art SAM3D, RecGen outperforms it by 30.1% in geometric shape quality, 9.1% in texture reconstruction, and 33.9% in pose estimation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_27106 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations Zadaianchuk, Andrii Barcellona, Leonardo Schuenemann, Lennard Gumbsch, Christian Wang, Zehao Irshad, Muhammad Zubair Despinoy, Fabien Aljundi, Rahaf Gavves, Stratis Zakharov, Sergey Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward scalable and reliable simulation for robotics. In this work, we introduce RecGen, a generative framework for probabilistic joint estimation of object and part shapes, as well as their pose under occlusion and partial visibility from one or multiple RGB-D images. By leveraging compositional synthetic scene generation and strong 3D shape priors, RecGen generalizes across diverse object types and real-world environments. RecGen achieves state-of-the-art performance on complex, heavily occluded datasets, robustly handling severe occlusions, symmetric objects, object parts, and intricate geometry and texture. Despite using nearly 80% fewer training meshes than the previous state of the art SAM3D, RecGen outperforms it by 30.1% in geometric shape quality, 9.1% in texture reconstruction, and 33.9% in pose estimation. |
| title | Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2604.27106 |