Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. This assumption, however, is impractical for learning fine-grained segmentation. Although most off-the-shelf CAD models are, by construction, composed of fine-grained parts, they usually miss semantic tags and labeling those fine-grained parts is extremely tedious. We approach the problem with deep clustering, where the key idea is to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part segmentation through minimizing a novel low rank loss. To handle highly densely sampled point sets, we adopt a divide-and-conquer strategy. We partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network trained in a category-agnostic manner. We then train a graph convolution network to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.

翻译：基于学习的 3D 形状分解通常被设计成一个语义标签问题, 假设训练形状的所有部分都配有一组标签。 但是, 这个假设对于学习细微分解不切实际。 虽然大多数现成的 CAD 模型都是由细微分解组成, 但他们通常会错开语义标签, 并标记这些细微分化的部件。 我们用深层分组处理问题, 关键的想法是学习一个配有精细分解但无部分标签的形状数据集的部位前端。 鉴于点抽样的 3D 形状, 我们用相似的矩阵模型对点前端进行组合, 并通过尽量减少新的低级损失实现分解。 为了处理高度密集的抽样点组位, 我们通常会采取分和制式战略。 我们把大点分成成若干块。 每个区块都使用基于深层分组的先前网络部分进行分解, 以分类式分解方式训练。 我们然后训练一个具有挑战性的平面分解网络, 以图表的分块组合方法将所有州级分块合并为最后的分块。