3d Object Reconstruction of Unseen and Unlabeled Point Clouds

Abstract

In this paper we attempt to reconstruct 3D objects from unseen and unlabeled point clouds. Specifically, we explore generalization capabilities of neural Signed Distance Functions (SDF). 3D object reconstruction is becoming increasingly important for tasks such as self-driving and robotics manipulation, and the ability for a model to reconstruct objects from in-the-wild point clouds with unseen categories is crucial. Previous works using SDFs have achieved impressive results, but they either cannot operate on unlabeled data, or cannot generalize, making their applications limited. We propose a novel semi-supervised setting in which we train on non-overlapping labeled and unlabeled classes. We develop a 2- stage meta-learning approach as well as a self-supervised method to achieve both generalizability and scalability. We evaluate on synthetic and real-world datasets and show that our method outperforms SDF baselines and generalizes to unseen classes with favorable results.