HyperPocket: Generative Point Cloud Completion
This addresses the challenge of completing partial point clouds for computer vision applications, though it appears incremental as it builds on existing methods with a novel twist.
The paper tackles the problem of incomplete point cloud representations from real-life scans by reformulating completion as an object hallucination task, introducing HyperPocket, an autoencoder-based architecture that generates multiple plausible and geometrically consistent completed point clouds with competitive performance to state-of-the-art models.
Scanning real-life scenes with modern registration devices typically give incomplete point cloud representations, mostly due to the limitations of the scanning process and 3D occlusions. Therefore, completing such partial representations remains a fundamental challenge of many computer vision applications. Most of the existing approaches aim to solve this problem by learning to reconstruct individual 3D objects in a synthetic setup of an uncluttered environment, which is far from a real-life scenario. In this work, we reformulate the problem of point cloud completion into an object hallucination task. Thus, we introduce a novel autoencoder-based architecture called HyperPocket that disentangles latent representations and, as a result, enables the generation of multiple variants of the completed 3D point clouds. We split point cloud processing into two disjoint data streams and leverage a hypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the missing object parts. As a result, the generated point clouds are not only smooth but also plausible and geometrically consistent with the scene. Our method offers competitive performances to the other state-of-the-art models, and it enables a~plethora of novel applications.