  The detection of objects considering a 6DoF pose is a common requirement to
build virtual and augmented reality applications. It is usually a complex task
which requires real-time processing and high precision results for adequate
user experience. Recently, different deep learning techniques have been
proposed to detect objects in 6DoF in RGB images. However, they rely on high
complexity networks, requiring a computational power that prevents them from
working on mobile devices. In this paper, we propose an approach to reduce the
complexity of 6DoF detection networks while maintaining accuracy. We used
Knowledge Distillation to teach portables Convolutional Neural Networks (CNN)
to learn from a real-time 6DoF detection CNN. The proposed method allows
real-time applications using only RGB images while decreasing the hardware
requirements. We used the LINEMOD dataset to evaluate the proposed method, and
the experimental results show that the proposed method reduces the memory
requirement by almost 99\% in comparison to the original architecture with the
cost of reducing half the accuracy in one of the metrics. Code is available at
https://github.com/heitorcfelix/singleshot6Dpose.
