Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
Sangyun Shin,Stuart Golodetz,Madhu Vankadari,Kaichen Zhou,Andrew Markham,Niki Trigoni,Sangyun Shin,Stuart Golodetz,Madhu Vankadari,Kaichen Zhou,Andrew Markham,Niki Trigoni
Deep learning has led to great progress in the detection of mobile (i.e. movement-capable) objects in urban driving scenes in recent years. Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self- supervised methods to avoid this, with much success. Whilst weakly and semi-supervised methods require some a...
