Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Zamorski, Maciej"'
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 for precise modeling of three-dimensional environments, but effective
Externí odkaz:
http://arxiv.org/abs/2205.08013
Autor:
Stypułkowski, Michał, Kania, Kacper, Zamorski, Maciej, Zięba, Maciej, Trzciński, Tomasz, Chorowski, Jan
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 matches intrinsic characteristics of point clouds: the number of points and
Externí odkaz:
http://arxiv.org/abs/2010.11087
Autor:
Spurek, Przemysław, Winczowski, Sebastian, Tabor, Jacek, Zamorski, Maciej, Zięba, Maciej, Trzciński, Tomasz
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 representation of a 3D object, our approach simultaneously finds a representation
Externí odkaz:
http://arxiv.org/abs/2003.00802
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a cloud-specific ne
Externí odkaz:
http://arxiv.org/abs/1910.07344
In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanatory factors that are hidden in the original data. With the invention o
Externí odkaz:
http://arxiv.org/abs/1903.12266
Publikováno v:
In Computer Vision and Image Understanding February 2023 228
Autor:
Zamorski, Maciej, Zięba, Maciej
Publikováno v:
Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science, vol 11431 (2019) 649-660
In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner. The presented method utilizes triplet loss function as an additional component of the objective function used to train
Externí odkaz:
http://arxiv.org/abs/1811.11426
Autor:
Zamorski, Maciej, Zięba, Maciej, Klukowski, Piotr, Nowak, Rafał, Kurach, Karol, Stokowiec, Wojciech, Trzciński, Tomasz
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 obtain meaningful representations of 3D shapes that can be used for challen
Externí odkaz:
http://arxiv.org/abs/1811.07605
Autor:
Zamorski, Maciej, Zięba, Maciej, Klukowski, Piotr, Nowak, Rafał, Kurach, Karol, Stokowiec, Wojciech, Trzciński, Tomasz
Publikováno v:
In Computer Vision and Image Understanding April 2020 193
Autor:
Stypułkowski, Michał, Kania, Kacper, Zamorski, Maciej, Zięba, Maciej, Trzciński, Tomasz, Chorowski, Jan
Publikováno v:
In Pattern Recognition Letters October 2021 150:26-32