  Real-world scenarios pose several challenges to deep learning based computer
vision techniques despite their tremendous success in research. Deeper models
provide better performance, but are challenging to deploy and knowledge
distillation allows us to train smaller models with minimal loss in
performance. The model also has to deal with open set samples from classes
outside the ones it was trained on and should be able to identify them as
unknown samples while classifying the known ones correctly. Finally, most
existing image recognition research focuses only on using two-dimensional
snapshots of the real world three-dimensional objects. In this work, we aim to
bridge these three research fields, which have been developed independently
until now, despite being deeply interrelated. We propose a joint Knowledge
Distillation and Open Set recognition training methodology for
three-dimensional object recognition. We demonstrate the effectiveness of the
proposed method via various experiments on how it allows us to obtain a much
smaller model, which takes a minimal hit in performance while being capable of
open set recognition for 3D point cloud data.
