1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2025
Learning Fine-to-Coarse Cuboid Shape Abstraction
Gregor Kobsik, Morten Henkel, Yanjiang He +4
The abstraction of 3D objects with simple geometric primitives like cuboids allows to infer structural information from complex geometry. It is important for 3D shape understanding…
cs.CV2024
Quantised Global Autoencoder: A Holistic Approach to Representing Visual Data
Tim Elsner, Paula Usinger, Victor Czech +4
In quantised autoencoders, images are usually split into local patches, each encoded by one token. This representation is redundant in the sense that the same number of tokens is s…
cs.CV2023★ 1 cited
Adaptive Voronoi NeRFs
Tim Elsner, Victor Czech, Julia Berger +3
Neural Radiance Fields (NeRFs) learn to represent a 3D scene from just a set of registered images. Increasing sizes of a scene demands more complex functions, typically represented…