10 citations · 12 across the 10 of their papers we have counts for
5 papers · 1 filter
Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu +4
Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribut…
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction
Severin Heidrich, Till Beemelmanns, Alexey Nekrasov +2
Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits. Ensuring robustness in these safety-critical systems is essential…
3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods
Till Beemelmanns, Yuchen Tao, Bastian Lampe +4
Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the…
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection
Till Beemelmanns, Quan Zhang, Christian Geller +1
Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes. However, their reliance on dense…
Explainable Multi-Camera 3D Object Detection with Transformer-Based Saliency Maps
Till Beemelmanns, Wassim Zahr, Lutz Eckstein
Vision Transformers (ViTs) have achieved state-of-the-art results on various computer vision tasks, including 3D object detection. However, their end-to-end implementation also mak…