  We address the problem of discovering 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 agglomerative clustering framework which learns a grouping
policy to progressively group 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
unseen categories. On the 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 shape segmentation
baselines shows that our approach achieve the state-of-the-art performance.
