Efficient 3D object recognition in mobile edge environment

3D object recognition has great research and application value in the fields of automatic drive, virtual reality, and commercial manufacturing. Although various deep models have been exploited and achieved remarkable results for 3D object recognition, their computational cost is too high for most mobile applications. This paper combines edge computing and 3D object recognition into a powerful and efficient framework. It consists of a cloud-based rendering stage and a terminal-based recognition stage. In the first stage, inspired by the cloud-based rendering technique, we upload the 3D object data from the mobile device to the edge cloud server for multi-view rendering. The rendering stage utilizes the powerful computing resource in the edge cloud server to generate multiple view images of the given 3D object from different views by parallel high-quality rendering. During the terminal-based recognition stage, we integrate a lightweight CNN architecture and a neural network quantization technique into a 3D object recognition model based on the multiple images rendered in the edge cloud server, which can be executed fast in the mobile device. To reduce the cost of network training, we propose a novel semi-supervised 3D deep learning method with fewer labeled samples. Experiments demonstrate that our method achieves competitive performance compared to the state-of-the-art methods with low latency running in the mobile edge environment.