论文标题
学习分组：在看不见类别中3D零件发现的自下而上的框架
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
论文作者
论文摘要
我们解决了在看不见类别中发现对象的3D零件的问题。能够学习零件的几何形状并在看不见的类别之前转移此几何形状，对数据驱动的形状分割方法提出了基本挑战。我们提出了一个基于学习的聚集聚类框架，该框架是作为上下文匪徒问题，该框架学习了一个分组策略，以逐渐以自下而上的方式将较小的建议逐步分组为更大的建议。我们方法的核心是限制提取零件级特征的本地环境，这鼓励人们普遍看不见类别。在大规模细粒度的3D零件数据集PartNet上，我们证明我们的方法可以将对零件从3个培训类别学习的知识转移到21个看不见的测试类别，而无需看到任何带注释的样本。与四个形状分割基线的定量比较表明，我们的方法达到了最新的性能。
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.
