5 papers
Hyp2Former: Hierarchy-Aware Hyperbolic Embeddings for Open-Set Panoptic Segmentation
Yao Lu, Rohit Mohan, Florian Drews +2
Recognizing unknown objects is crucial for safety-critical applications such as autonomous driving and robotics. Open-Set Panoptic Segmentation (OPS) aims to segment known thing an…
Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking
Markus Käppeler, Özgün Çiçek, Yakov Miron +1
Camera-based 3D object detection and tracking are central to autonomous driving, yet precise 3D object localization remains fundamentally constrained by depth ambiguity when no exp…
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…
Pseudo-Simulation for Autonomous Driving
Wei Cao, Marcel Hallgarten, Tianyu Li +11
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibili…