Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints

Abstract

Object geometry is key information for robot manipulation. Yet, object reconstruction is a challenging task because camera observations are partial due to occlusions. The scene may not offer the flexibility for a robot to alter its viewpoint to obtain a full observation of the object of interest. In this paper, we leverage two extra sources of information to reduce the ambiguity of vision signals under occlusion. First, generative models learn priors of the shapes of commonly seen objects, allowing us to make reasonable guesses of the unseen part of geometry. Second, contact information, which can be obtained from videos and physical interactions, provides sparse constraints on the boundary of the geometry. We combine the two sources of information through contact-guided 3D generation. The guidance formulation is inspired by drag-based generative image editing. We explore different guidance strategies and highlight the importance of short gradient paths for guided generation. Experiments on synthetic and real-world data show that our approach improves the object reconstruction compared to pure 3D generation and contact-based optimization methods.