54 citations · 73 across the 9 of their papers we have counts for
11 papers
Continual learning on 3D point clouds with random compressed rehearsal
Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski +2
Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype…
Non-Gaussian Gaussian Processes for Few-Shot Regression
Marcin Sendera, Jacek Tabor, Aleksandra Nowak +5
Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction…
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…
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Maciej Zięba, Marcin Przewięźlikowski, Marek Śmieja +3
Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenar…
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…
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Maciej Zamorski, Maciej Zięba, Piotr Klukowski +4
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtai…
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…