229 citations · 1k across the 84 of their papers we have counts for
13 papers · 1 filter
DOS: Distilling Observable Softmaps of Zipfian Prototypes for Self-Supervised Point Representation
Mohamed Abdelsamad, Michael Ulrich, Bin Yang +3
Recent advances in self-supervised learning (SSL) have shown tremendous potential for learning 3D point cloud representations without human annotations. However, SSL for 3D point c…
Bridging Perspectives: Foundation Model Guided BEV Maps for 3D Object Detection and Tracking
Markus Käppeler, Özgün Çiçek, Daniele Cattaneo +3
Camera-based 3D object detection and tracking are essential for perception in autonomous driving. Current state-of-the-art approaches often rely exclusively on either perspective-v…
Hyperspectral Adapter for Semantic Segmentation with Vision Foundation Models
Juana Valeria Hurtado, Rohit Mohan, Abhinav Valada
Hyperspectral imaging (HSI) captures spatial information along with dense spectral measurements across numerous narrow wavelength bands. This rich spectral content has the potentia…
Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation
Julia Hindel, Ema Mekic, Enamundram Naga Karthik +4
Robot-assisted surgeries rely on accurate and real-time scene understanding to safely guide surgical instruments. However, segmentation models trained on static datasets face key l…
Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning
Rohit Mohan, Julia Hindel, Florian Drews +3
Autonomous vehicles that navigate in open-world environments may encounter previously unseen object classes. However, most existing LiDAR panoptic segmentation models rely on close…
Contour Errors: Ego-Centric Matching for 3D Multi-Object Tracking Performance Evaluation
Sharang Kaul, Simon Bultmann, Mario Berk +1
Open-loop performance evaluation of 3D multi-object tracking in autonomous driving requires matching criteria that effectively penalize translational, shape, and orientation errors…
Visual Loop Closure Detection Through Deep Graph Consensus
Martin Büchner, Liza Dahiya, Simon Dorer +4
Visual loop closure detection traditionally relies on place recognition methods to retrieve candidate loops that are validated using computationally expensive RANSAC-based geometri…
ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models
Jiarong Wei, Niclas Vödisch, Anna Rehr +2
Automated parking is a critical feature of Advanced Driver Assistance Systems (ADAS), where accurate trajectory prediction is essential to bridge perception and planning modules. D…
Learning Appearance and Motion Cues for Panoptic Tracking
Juana Valeria Hurtado, Sajad Marvi, Rohit Mohan +1
Panoptic tracking enables pixel-level scene interpretation of videos by integrating instance tracking in panoptic segmentation. This provides robots with a spatio-temporal understa…
Collaborative Dynamic 3D Scene Graphs for Open-Vocabulary Urban Scene Understanding
Tim Steinke, Martin Büchner, Niclas Vödisch +1
Mapping and scene representation are fundamental to reliable planning and navigation in mobile robots. While purely geometric maps using voxel grids allow for general navigation, o…
Label-Efficient LiDAR Semantic Segmentation with 2D-3D Vision Transformer Adapters
Julia Hindel, Rohit Mohan, Jelena Bratulic +3
LiDAR semantic segmentation models are typically trained from random initialization as universal pre-training is hindered by the lack of large, diverse datasets. Moreover, most poi…
Label-Efficient LiDAR Panoptic Segmentation
Ahmet Selim Çanakçı, Niclas Vödisch, Kürsat Petek +2
A main bottleneck of learning-based robotic scene understanding methods is the heavy reliance on extensive annotated training data, which often limits their generalization ability.…
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds
Mohamed Abdelsamad, Michael Ulrich, Claudius Gläser +1
Masked autoencoders (MAE) have shown tremendous potential for self-supervised learning (SSL) in vision and beyond. However, point clouds from LiDARs used in automated driving are p…