Training a generalizable 3D part segmentation network is quite challenging but of great importance in real-world applications. To tackle this problem, some works design task-specific solutions by translating human understanding of the task to machine's learning process, which faces the risk of missing the optimal strategy since machines do not necessarily understand in the exact human way. Others try to use conventional task-agnostic approaches designed for domain generalization problems with no task prior knowledge considered. To solve the above issues, we propose AutoGPart, a generic method enabling training generalizable 3D part segmentation networks with the task prior considered. AutoGPart builds a supervision space with geometric prior knowledge encoded, and lets the machine to search for the optimal supervisions from the space for a specific segmentation task automatically. Extensive experiments on three generalizable 3D part segmentation tasks are conducted to demonstrate the effectiveness and versatility of AutoGPart. We demonstrate that the performance of segmentation networks using simple backbones can be significantly improved when trained with supervisions searched by our method.

我们提出了AutoGPart，一种通用的方法，它考虑任务优先，使用几何先验知识构建监督空间，并让机器自动从该空间中搜索特定分割任务的最佳监督，以训练具有一般化的3D部分分割网络。在三个3D部分分割任务上进行了实验，证明了AutoGPart的有效性和通用性，并表明使用简单backbones训练的分割网络的性能可以显著提高。

AutoGPart: 中间监督搜索通用的三维零件分割