Michael Fürst
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Learned Fusion: 3D Object Detection using Calibration-Free Transformer Feature Fusion Open
The state of the art in 3D object detection using sensor fusion heavily relies on calibration quality, which is difficult to maintain in large scale deployment outside a lab environment. We present the first calibration-free approach for 3…
SynPeDS: A Synthetic Dataset for Pedestrian Detection in Urban Traffic Scenes Open
We introduce the Synthetic Pedestrian Dataset (SynPeDS) which was designed to support a systematic safety analysis for pedestrian detection tasks in urban scenes. The dataset was generated synthetically with a real-time and a physically-ba…
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time Open
Object permanence is the concept that objects do not suddenly disappear in the physical world. Humans understand this concept at young ages and know that another person is still there, even though it is temporarily occluded. Neural network…
HPERL: 3D Human Pose Estimation from RGB and LiDAR Open
In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving. The current state-of-the-art is focused only on RGB an…
Automated Focal Loss for Image based Object Detection Open
Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced a new loss function called focal loss to mitigate this pro…
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB Open
While short range 3D pedestrian detection is sufficient for emergency breaking, long range detections are required for smooth breaking and gaining trust in autonomous vehicles. The current state-of-the-art on the KITTI benchmark performs s…
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths\n of LiDAR and RGB Open
While short range 3D pedestrian detection is sufficient for emergency\nbreaking, long range detections are required for smooth breaking and gaining\ntrust in autonomous vehicles. The current state-of-the-art on the KITTI\nbenchmark perform…