Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations

Andrii Zadaianchuk; Leonardo Barcellona; Lennard Schuenemann; Christian Gumbsch; Zehao Wang; Muhammad Zubair Irshad; Fabien Despinoy; Rahaf Aljundi; Stratis Gavves; Sergey Zakharov

arXiv · 2604.27106

Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations

Andrii Zadaianchuk·Leonardo Barcellona·Lennard Schuenemann·Christian Gumbsch·Zehao Wang·Muhammad Zubair Irshad·Fabien Despinoy·Rahaf Aljundi·Stratis Gavves·Sergey Zakharov

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Abstract

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.

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Andrii Zadaianchuk, Leonardo Barcellona, Lennard Schuenemann, Christian Gumbsch, Zehao Wang, Muhammad Zubair Irshad, Fabien Despinoy, Rahaf Aljundi, Stratis Gavves, Sergey Zakharov. 2026-04-29. Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations. https://arxiv.org/abs/2604.27106

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