HyperPocket: Generative Point Cloud Completion

• URL: http://arxiv.org/abs/2102.05973v1
• Date: Thu, 11 Feb 2021 12:30:03 GMT
• Title: HyperPocket: Generative Point Cloud Completion
• Authors: Przemys{\l}aw Spurek, Artur Kasymov, Marcin Mazur, Diana Janik, S{\l}awomir Tadeja, {\L}ukasz Struski, Jacek Tabor, Tomasz Trzci\'nski
• Abstract summary: We introduce a novel autoencoder-based architecture called HyperPocket that disentangles latent representations. We leverage a hypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the missing object parts. Our method offers competitive performances to the other state-of-the-art models.
• Score: 19.895219420937938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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