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We present ONeRF, a method that automatically segments and reconstructsobject instances in 3D from multi-view RGB images without any additional manualannotations. The segmented 3D objects are represented using separate NeuralRadiance Fields (NeRFs) which allow for various 3D scene editing and novel viewrendering. At the core of our method is an unsupervised approach using theiterative Expectation-Maximization algorithm, which effectively aggregates 2Dvisual features and the corresponding 3D cues from multi-views for joint 3Dobject segmentation and reconstruction. Unlike existing approaches that canonly handle simple objects, our method produces segmented full 3D NeRFs ofindividual objects with complex shapes, topologies and appearance. Thesegmented ONeRfs enable a range of 3D scene editing, such as objecttransformation, insertion and deletion.

ONeRF: Unsupervised 3D Object Segmentation from Multiple Views