Learning robust 3D shape segmentation functions with deep neural networks has emerged as a powerful paradigm, offering promising performance in producing a consistent part segmentation of each 3D shape. Generalizing across 3D shape segmentation functions requires robust learning of priors over the respective function space and enables consistent part segmentation of shapes in presence of significant 3D structure variations. Existing generalization methods rely on extensive training of 3D shape segmentation functions on large-scale labeled datasets. In this paper, we proposed to formalize the learning of a 3D shape segmentation function space as a meta-learning problem, aiming to predict a 3D segmentation model that can be quickly adapted to new shapes with no or limited training data. More specifically, we define each task as unsupervised learning of shape-conditioned 3D segmentation function which takes as input points in 3D space and predicts the part-segment labels. The 3D segmentation function is trained by a self-supervised 3D shape reconstruction loss without the need for part labels. Also, we introduce an auxiliary deep neural network as a meta-learner which takes as input a 3D shape and predicts the prior over the respective 3D segmentation function space. We show in experiments that our meta-learning approach, denoted as Meta-3DSeg, leads to improvements on unsupervised 3D shape segmentation over the conventional designs of deep neural networks for 3D shape segmentation functions.

翻译：利用深度神经网络学习鲁棒的三维形状分割函数已成为一种强大的范式，在实现每个三维形状的一致性部件分割方面展现出优越性能。跨三维形状分割函数的泛化需要对该函数空间中的先验进行鲁棒学习，从而在存在显著三维结构变化时实现形状的一致性部件分割。现有的泛化方法依赖于在大规模标注数据集上对三维形状分割函数进行广泛训练。本文提出将三维形状分割函数空间的学习形式化为元学习问题，旨在预测一个能够快速适应新形状（无需或仅需少量训练数据）的三维分割模型。具体而言，我们将每个任务定义为无监督学习形状条件化的三维分割函数，该函数以三维空间中的点作为输入并预测部件分割标签。该三维分割函数通过自监督的三维形状重建损失进行训练，无需部件标签。同时，我们引入一个辅助深度神经网络作为元学习器，其以三维形状为输入并预测相应三维分割函数空间的先验。实验表明，我们的元学习方法（记为Meta-3DSeg）在无监督三维形状分割任务中优于传统深度神经网络设计的三维形状分割函数。