11 citations · 12 across the 9 of their papers we have counts for
9 papers
Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships
Sebastian Koch, Narunas Vaskevicius, Mirco Colosi +2
Current approaches for 3D scene graph prediction rely on labeled datasets to train models for a fixed set of known object classes and relationship categories. We present Open3DSG,…
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…
Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs
Áron Samuel Kovács, Pedro Hermosilla, Renata G. Raidou
Mesh texture synthesis is a key component in the automatic generation of 3D content. Existing learning-based methods have drawbacks -- either by disregarding the shape manifold dur…
Attention-Guided Masked Autoencoders For Learning Image Representations
Leon Sick, Dominik Engel, Pedro Hermosilla +1
Masked autoencoders (MAEs) have established themselves as a powerful method for unsupervised pre-training for computer vision tasks. While vanilla MAEs put equal emphasis on recons…
PPSURF: Combining Patches and Point Convolutions for Detailed Surface Reconstruction
Philipp Erler, Lizeth Fuentes, Pedro Hermosilla +3
3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage, and engineering. Current approaches either try…
FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data
Lisa Weijler, Florian Kowarsch, Michael Reiter +3
While model architectures and training strategies have become more generic and flexible with respect to different data modalities over the past years, a persistent limitation lies…