54 citations · 73 across the 9 of their papers we have counts for
7 papers · 1 filter
HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population
Ivan Kostiuk, Przemysław Stachura, Sławomir K. Tadeja +2
Designing a 3D game scene is a tedious task that often requires a substantial amount of work. Typically, this task involves synthesis, coloring, and placement of 3D models within t…
HyperPocket: Generative Point Cloud Completion
Przemysław Spurek, Artur Kasymov, Marcin Mazur +5
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 occlu…
Modeling 3D Surface Manifolds with a Locally Conditioned Atlas
Przemysław Spurek, Sebastian Winczowski, Maciej Zięba +3
Recently proposed 3D object reconstruction methods represent a mesh with an atlas - a set of planar patches approximating the surface. However, their application in a real-world sc…
Representing Point Clouds with Generative Conditional Invertible Flow Networks
Michał Stypułkowski, Kacper Kania, Maciej Zamorski +3
In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation match…
HyperFlow: Representing 3D Objects as Surfaces
Przemysław Spurek, Maciej Zięba, Jacek Tabor +1
In this work, we present HyperFlow - a novel generative model that leverages hypernetworks to create continuous 3D object representations in a form of lightweight surfaces (meshes)…
Hypernetwork approach to generating point clouds
Przemysław Spurek, Sebastian Winczowski, Jacek Tabor +3
In this work, we propose a novel method for generating 3D point clouds that leverage properties of hyper networks. Contrary to the existing methods that learn only the representati…
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly +1
In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of im…