Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. (arXiv:2112.15458v2 [cs.CV] UPDATED)

Efficiently and accurately detecting people from 3D point cloud data is of
great importance in many robotic and autonomous driving applications. This
fundamental perception task is still very challenging due to (i) significant
deformations of human body pose and gesture over time and (ii) point cloud
sparsity and scarcity for pedestrian class objects. Recent efficient 3D object
detection approaches rely on pillar features to detect objects from point cloud
data. However, these pillar features do not carry sufficient expressive
representations …

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