Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations
Learn to reconstruct 3D multi-object scenes from sparse observations using a generative framework called RecGen, which enables probabilistic joint estimation of object and part shapes, and their pose under occlusion and partial visibility.
- Implement RecGen framework using Python and TensorFlow to reconstruct 3D scenes from RGB-D images
- Configure the framework to handle sparse observations and occlusion
- Train the model using a dataset of synthetic and real-world scenes
- Test the model on a variety of scenes with different levels of complexity and occlusion
- Compare the results with existing scene reconstruction methods to evaluate the accuracy and efficiency of RecGen
Computer vision engineers and researchers working on robotics and simulation can benefit from this framework to improve scene reconstruction accuracy and reliability.
💡 RecGen enables probabilistic joint estimation of object and part shapes, and their pose under occlusion and partial visibility, improving scene reconstruction accuracy and reliability.
🤖 RecGen: A generative framework for 3D multi-object scene reconstruction from sparse observations 📸💻
Key Takeaways
Learn to reconstruct 3D multi-object scenes from sparse observations using a generative framework called RecGen, which enables probabilistic joint estimation of object and part shapes, and their pose under occlusion and partial visibility.
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Abstract:
arXiv:2604.27106v1 Announce Type: cross 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
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