We address the problem of learning to discover 3D parts for objects in unseen categories. Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches. Formulated as a contextual bandit problem, we propose a learning-based iterative grouping framework which learns a grouping policy to progressively merge small part proposals into bigger ones in a bottom-up fashion. At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to novel categories. On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples. Quantitative comparisons against four strong shape segmentation baselines show that we achieve the state-of-the-art performance.
A zero-shot segmentation framework for 3D object part segmentation. Model the segmentation as a decision-making process and solve as a contextual bandit problem. A method for segmenting 3D point clouds of objects into component parts, focused on generalizing part groupings to novel object categories unseen during training, that shows strong performance relative to baselines. This paper proposes a method for part segmentation in object point clouds.