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A crucial ability of human intelligence is to build up models of individual3D objects from partial scene observations. Recent works achieve object-centricgeneration but without the ability to infer the representation, or achieve 3Dscene representation learning but without object-centric compositionality.Therefore, learning to represent and render 3D scenes with object-centriccompositionality remains elusive. In this paper, we propose a probabilisticgenerative model for learning to build modular and compositional 3D objectmodels from partial observations of a multi-object scene. The proposed modelcan (i) infer the 3D object representations by learning to search and groupobject areas and also (ii) render from an arbitrary viewpoint not onlyindividual objects but also the full scene by compositing the objects. Theentire learning process is unsupervised and end-to-end. In experiments, inaddition to generation quality, we also demonstrate that the learnedrepresentation permits object-wise manipulation and novel scene generation, andgeneralizes to various settings. Results can be found on our project website:https://sites.google.com/view/roots3d

ROOTS: Object-Centric Representation and Rendering of 3D Scenes