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Real-world scenarios pose several challenges to deep learning based computervision techniques despite their tremendous success in research. Deeper modelsprovide better performance, but are challenging to deploy and knowledgedistillation allows us to train smaller models with minimal loss inperformance. The model also has to deal with open set samples from classesoutside the ones it was trained on and should be able to identify them asunknown samples while classifying the known ones correctly. Finally, mostexisting image recognition research focuses only on using two-dimensionalsnapshots of the real world three-dimensional objects. In this work, we aim tobridge these three research fields, which have been developed independentlyuntil now, despite being deeply interrelated. We propose a joint KnowledgeDistillation and Open Set recognition training methodology forthree-dimensional object recognition. We demonstrate the effectiveness of theproposed method via various experiments on how it allows us to obtain a muchsmaller model, which takes a minimal hit in performance while being capable ofopen set recognition for 3D point cloud data.

Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification