HyperPocket: Generative Point Cloud Completion
Przemys{\l}aw Spurek, Artur Kasymov, Marcin Mazur, Diana Janik,, S{\l}awomir Tadeja, {\L}ukasz Struski, Jacek Tabor, Tomasz Trzci\'nski

TL;DR
HyperPocket is a novel autoencoder-based method that generates multiple plausible completions for incomplete 3D point clouds, improving realism and consistency in real-world scene reconstructions.
Contribution
It introduces a hypernetwork-based architecture that disentangles latent representations to produce diverse and plausible 3D point cloud completions, addressing real-life scene challenges.
Findings
Achieves competitive performance with state-of-the-art models
Generates multiple diverse plausible completions
Ensures geometric consistency with scenes
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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques · Image Processing and 3D Reconstruction
MethodsHyperNetwork
