Zobrazeno 1 - 10
of 149
pro vyhledávání: '"SPUREK, PRZEMYSŁAW"'
Autor:
Szymkowiak, Jakub, Jakubowska, Weronika, Malarz, Dawid, Smolak-Dyżewska, Weronika, Zięba, Maciej, Musialski, Przemysław, Pałubicki, Wojtek, Spurek, Przemysław
In computer graphics, there is a need to recover easily modifiable representations of 3D geometry and appearance from image data. We introduce a novel method for this task using 3D Gaussian Splatting, which enables intuitive scene editing through mes
Externí odkaz:
http://arxiv.org/abs/2411.18311
Autor:
Kaleta, Joanna, Smolak-Dyżewska, Weronika, Malarz, Dawid, Dall'Alba, Diego, Korzeniowski, Przemysław, Spurek, Przemysław
Endoscopic procedures are crucial for colorectal cancer diagnosis, and three-dimensional reconstruction of the environment for real-time novel-view synthesis can significantly enhance diagnosis. We present PR-ENDO, a framework that leverages 3D Gauss
Externí odkaz:
http://arxiv.org/abs/2411.12510
Autor:
Smolak-Dyżewska, Weronika, Malarz, Dawid, Howil, Kornel, Kaczmarczyk, Jan, Mazur, Marcin, Spurek, Przemysław
Implicit Neural Representations (INRs) employ neural networks to approximate discrete data as continuous functions. In the context of video data, such models can be utilized to transform the coordinates of pixel locations along with frame occurrence
Externí odkaz:
http://arxiv.org/abs/2411.11024
Implicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multilayer perceptrons (MLPs). However, MLPs are known to exhibit a low-fre
Externí odkaz:
http://arxiv.org/abs/2410.05050
Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the model to a specific domain, character, style, or concept. However, due
Externí odkaz:
http://arxiv.org/abs/2410.03941
In various scenarios motivated by real life, such as medical data analysis, autonomous driving, and adversarial training, we are interested in robust deep networks. A network is robust when a relatively small perturbation of the input cannot lead to
Externí odkaz:
http://arxiv.org/abs/2410.03373
Autor:
Waczyńska, Joanna, Szczepanik, Tomasz, Borycki, Piotr, Tadeja, Sławomir, Bohné, Thomas, Spurek, Przemysław
Implicit Neural Representations (INRs) approximate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neural networks to map pixel coordinates to RGB values, capturing shapes,
Externí odkaz:
http://arxiv.org/abs/2410.01521
Autor:
Borycki, Piotr, Smolak, Weronika, Waczyńska, Joanna, Mazur, Marcin, Tadeja, Sławomir, Spurek, Przemysław
Physics simulation is paramount for modeling and utilization of 3D scenes in various real-world applications. However, its integration with state-of-the-art 3D scene rendering techniques such as Gaussian Splatting (GS) remains challenging. Existing m
Externí odkaz:
http://arxiv.org/abs/2409.05819
Diffusion models are among the most effective methods for image generation. This is in particular because, unlike GANs, they can be easily conditioned during training to produce elements with desired class or properties. However, guiding a pre-traine
Externí odkaz:
http://arxiv.org/abs/2407.12889
One of the key advantages of 3D rendering is its ability to simulate intricate scenes accurately. One of the most widely used methods for this purpose is Gaussian Splatting, a novel approach that is known for its rapid training and inference capabili
Externí odkaz:
http://arxiv.org/abs/2405.18163