3D Capsule Networks for Object Classification With Weight Pruning

The proliferation of 3D sensors, due to the increased demand for 3D data, induced the 3D computer vision research in the last decade, and 3D data processing has gained a lot of interest. As in many other applications in computer vision, deep learning-based methods were quickly applied to 3D data classification and have become the state-of-the-art in this area. More recently, capsule networks, which are novel neural structures, have been introduced to enhance the ability of neural networks to better capture the parts-relationship, which yields more accurate classification with less training data. Moreover, deploying deep machine learning models on mobile platforms requires the models to be optimized due to limited memory and computational constraints. In this work, we propose methods to boost the accuracies of a standard 3D CNN-based and a Capsule Network-based classifier, help the training to better generalize the data distribution with limited data, and optimize the models for resource-constrained environments, such as mobile platforms. We also show that the introduction of capsules to 3D object classification pipeline improves the classification performance with limited training data, while a specifically optimized weight pruning method keeps the model compact enough for mobile deployment. Our broad spectrum of experiments show that proposed methods improve the performance of the base model while significantly reducing the memory and computation requirements.