Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen
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Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen
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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 …