4 citations · 10 across the 16 of their papers we have counts for
5 papers · 1 filter
VeGaS: Video Gaussian Splatting
Weronika Smolak-Dyżewska, Dawid Malarz, Kornel Howil +3
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 tra…
GASP: Gaussian Splatting for Physic-Based Simulations
Piotr Borycki, Weronika Smolak, Joanna Waczyńska +3
Physics simulation is paramount for modeling and utilizing 3D scenes in various real-world applications. However, integrating with state-of-the-art 3D scene rendering techniques su…
PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification
Magdalena Trędowicz, Marcin Mazur, Szymon Janusz +3
Video processing is generally divided into two main categories: processing of the entire video, which typically yields optimal classification outcomes, and real-time processing, wh…
NegGS: Negative Gaussian Splatting
Artur Kasymov, Bartosz Czekaj, Marcin Mazur +2
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 nove…
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
Paweł Batorski, Dawid Malarz, Marcin Przewięźlikowski +3
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization pr…