55 citations · 161 across the 16 of their papers we have counts for
16 papers
Surface Reconstruction Using Rotation Systems
Ruiqi Cui, Emil Toftegaard Gæde, Eva Rotenberg +2
Inspired by the seminal result that a graph and an associated rotation system uniquely determine the topology of a closed manifold, we propose a combinatorial method for reconstruc…
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…
Neural Implicit Shape Editing using Boundary Sensitivity
Arturs Berzins, Moritz Ibing, Leif Kobbelt
Neural fields are receiving increased attention as a geometric representation due to their ability to compactly store detailed and smooth shapes and easily undergo topological chan…
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…
Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences
Moritz Ibing, Gregor Kobsik, Leif Kobbelt
Autoregressive models have proven to be very powerful in NLP text generation tasks and lately have gained popularity for image generation as well. However, they have seen limited u…
Intuitive Shape Editing in Latent Space
Tim Elsner, Moritz Ibing, Victor Czech +2
The use of autoencoders for shape editing or generation through latent space manipulation suffers from unpredictable changes in the output shape. Our autoencoder-based method enabl…
3D Shape Generation with Grid-based Implicit Functions
Moritz Ibing, Isaak Lim, Leif Kobbelt
Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortc…
Highly accurate digital traffic recording as a basis for future mobility research: Methods and concepts of the research project HDV-Mess
Laurent Kloeker, Fabian Thomsen, Lutz Eckstein +6
The research project HDV-Mess aims at a currently missing, but very crucial component for addressing important challenges in the field of connected and automated driving on public…
Fast Exact Booleans for Iterated CSG using Octree-Embedded BSPs
Julius Nehring-Wirxel, Philip Trettner, Leif Kobbelt
We present octree-embedded BSPs, a volumetric mesh data structure suited for performing a sequence of Boolean operations (iterated CSG) efficiently. At its core, our data structure…
SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform
Cheng Lin, Lingjie Liu, Changjian Li +4
Segmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications. Exis…
PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models
Lin Gao, Ling-Xiao Zhang, Hsien-Yu Meng +3
In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, ali…
Structured Discrete Shape Approximation: Theoretical Complexity and Practical Algorithm
Andreas M. Tillmann, Leif Kobbelt
We consider the problem of approximating a two-dimensional shape contour (or curve segment) using discrete assembly systems, which allow to build geometric structures based on limi…
A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization
Isaak Lim, Moritz Ibing, Leif Kobbelt
Automatic synthesis of high quality 3D shapes is an ongoing and challenging area of research. While several data-driven methods have been proposed that make use of neural networks…
A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes
Isaak Lim, Alexander Dielen, Marcel Campen +1
The question of representation of 3D geometry is of vital importance when it comes to leveraging the recent advances in the field of machine learning for geometry processing tasks.…
Sparse Data Driven Mesh Deformation
Lin Gao, Yu-Kun Lai, Jie Yang +3
Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can l…