Unsupervised Discovery of Object-Centric Neural Fields
Abstract: We study inferring 3D object-centric scene representations from a single image. While recent methods have shown potential in unsupervised 3D object discovery, they are limited in generalizing to unseen spatial configurations. This limitation stems from the lack of translation invariance in their 3D object representations. Previous 3D object discovery methods entangle objects’ intrinsic attributes like shape and appearance with their 3D locations. This entanglement hinders learning generalizable 3D object representations. To tackle this bottleneck, we propose the unsupervised discovery of Object-Centric neural Fields (uOCF), which integrates translation invariance into the object representation. To allow learning object-centric representations from limited real-world images, we further introduce an object prior learning method that transfers object-centric prior knowledge from a synthetic dataset. To evaluate our approach, we collect four new datasets, including two real kitchen environments. Extensive experiments show that our approach significantly improves generalization and sample efficiency, and enables unsupervised 3D object discovery in real scenes. Notably, uOCF demonstrates zero-shot generalization to unseen objects from a single real image. We attach our code in the supplementary file and the project page is available at https://anonymous.4open.science/w/uOCF-TMLR25-58D0/
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Florian_Shkurti1
Submission Number: 3487
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