Self-Supervision for 3D Real-World Challenges

Antonio Alliegro, Davide Boscaini, Tatiana Tommasi

Abstract: We consider several possible scenarios involving synthetic and real-world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation.