Toward real-time 3D object recognition: A lightweight volumetric CNN framework using multitask learning

Abstract

3D data are becoming increasingly popular and easier to access, making 3D information increasingly important for object recognition. Although volumetric convolutional neural networks (CNNs) have been exploited to recognize 3D objects and have achieved notable progress, their computational cost is too high for real-time applications. In this paper, we propose a lightweight volumetric CNN architecture (namely, LightNet) to address the real-time 3D object recognition problem leveraging on multitask learning. We use LightNet to simultaneously predict class and orientation labels from complete and partial shapes. In contrast to the earlier version of this method presented at 3DOR 2017, this extended version introduces batch normalization and better training strategies to improve the recognition accuracy, and also includes more experiments on the newly released large-scale ShapeNet Core55 dataset. Our model has been evaluated on three publicly available benchmarks of complete 3D CAD shapes and incomplete point clouds. Experimental results show that our model achieves the state-of-the-art 3D object recognition performance among shallow volumetric CNNs with the smallest number of training parameters. It is also demonstrated that our method can perform accurate object recognition in real time (less than 6 ms).