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Training a generalizable 3D part segmentation network is quite challengingbut of great importance in real-world applications. To tackle this problem,some works design task-specific solutions by translating human understanding ofthe task to machine's learning process, which faces the risk of missing theoptimal strategy since machines do not necessarily understand in the exacthuman way. Others try to use conventional task-agnostic approaches designed fordomain generalization problems with no task prior knowledge considered. Tosolve the above issues, we propose AutoGPart, a generic method enablingtraining generalizable 3D part segmentation networks with the task priorconsidered. AutoGPart builds a supervision space with geometric prior knowledgeencoded, and lets the machine to search for the optimal supervisions from thespace for a specific segmentation task automatically. Extensive experiments onthree generalizable 3D part segmentation tasks are conducted to demonstrate theeffectiveness and versatility of AutoGPart. We demonstrate that the performanceof segmentation networks using simple backbones can be significantly improvedwhen trained with supervisions searched by our method.

AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation