5 papers · 1 filter
Learned Fusion: 3D Object Detection using Calibration-Free Transformer Feature Fusion
Michael Fürst, Rahul Jakkamsetty, René Schuster +1
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 enviro…
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time
Michael Fürst, Priyash Bhugra, René Schuster +1
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 th…
HPERL: 3D Human Pose Estimation from RGB and LiDAR
Michael Fürst, Shriya T. P. Gupta, René Schuster +2
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 driv…
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB
Michael Fürst, Oliver Wasenmüller, Didier Stricker
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…
Automated Focal Loss for Image based Object Detection
Michael Weber, Michael Fürst, J. Marius Zöllner
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…