• 6D object pose estimation
• knowledge distillation
• model compression

Pose estimation, the task of determining the 3D location and orientation of an object
in an image, is a key component in many computer vision and robotics applications. However, existing methods often suffer from low accuracy and/or slow inference
times, making them unsuitable for real-time applications.
In this paper, the authors present a real-time, high-resolution 6D pose estimation
network that uses knowledge distillation to improve performance. The network is
trained on a large dataset of 3D models and corresponding 2D images and is able
to accurately estimate the 6D pose of objects in real-time.
To improve performance, the network based on well-known model PVNet
is compressed using masking and weight pruning techniques based on knowledge
distillation, resulting in a significant reduction in model size without sacrificing
accuracy. Additionally, knowledge distillation is used to transfer the knowledge of
a larger, more accurate model to the smaller, compressed model, further improving
performance.
The authors evaluate the proposed network on a LINEMOD’s benchmark and
show that it achieves state-of-the-art accuracy and real-time performance. They also
demonstrate the effectiveness of the proposed method on a real-world task, showing
that it can be used to enable precise objects detection in cluttered environments.
Overall, the proposed real-time, high-resolution 6D pose estimation network using
knowledge distillation is a significant advancement in the field and has the potential
to enable a wide range of applications in computer vision and robotics.