30 citations · 31 across the 8 of their papers we have counts for
5 papers · 1 filter
Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models
Lisa Weijler, Irene Ballester, Guofeng Mei +2
Geometric foundation models, such as the Visual Geometry Grounded Transformer (VGGT), provide strong 3D priors from unposed images. However, such models operate purely in a feed-fo…
OpenHype: Hyperbolic Embeddings for Hierarchical Open-Vocabulary Radiance Fields
Lisa Weijler, Sebastian Koch, Fabio Poiesi +2
Modeling the inherent hierarchical structure of 3D objects and 3D scenes is highly desirable, as it enables a more holistic understanding of environments for autonomous agents. Acc…
Efficient Continuous Group Convolutions for Local SE(3) Equivariance in 3D Point Clouds
Lisa Weijler, Pedro Hermosilla
Extending the translation equivariance property of convolutional neural networks to larger symmetry groups has been shown to reduce sample complexity and enable more discriminative…
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data
Lisa Weijler, Michael Reiter, Pedro Hermosilla +2
This paper evaluates various deep learning methods for measurable residual disease (MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits of mode…
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick +2
Test-Time Training (TTT) proposes to adapt a pre-trained network to changing data distributions on-the-fly. In this work, we propose the first TTT method for 3D semantic segmentati…