5 citations · 6 across the 8 of their papers we have counts for
9 papers
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…
Multidimensional Byte Pair Encoding: Shortened Sequences for Improved Visual Data Generation
Tim Elsner, Paula Usinger, Julius Nehring-Wirxel +5
In language processing, transformers benefit greatly from text being condensed. This is achieved through a larger vocabulary that captures word fragments instead of plain character…
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…
Partial Symmetry Detection for 3D Geometry using Contrastive Learning with Geodesic Point Cloud Patches
Gregor Kobsik, Isaak Lim, Leif Kobbelt
Detecting partial extrinsic symmetry in 3D geometry is a fundamental yet persistent challenge in computer vision and graphics, critical for tasks ranging from shape completion to p…
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…
Localized Latent Updates for Fine-Tuning Vision-Language Models
Moritz Ibing, Isaak Lim, Leif Kobbelt
Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for imp…