Point cloud human pose estimation using capsule networks

Abstract

Human pose estimation based on points cloud is an emerging field that develops with 3D scanning devices’ popularity. Build-in LiDAR technology in mobile phones and a growing VR market creates a demand for lightweight and accurate models for 3D point cloud. Widely advanced deep learning tools are mainly used for structured data, and they face new challenges in unstructured 3D space. Recent research on capsule networks proves that this type of model outperforms classical CNN architectures in tasks that require viewpoint invariance from the model. Thus capsule networks challenge multiple issues of classic CNNs like preserving the orientation and spatial relationship of extracted features, which could significantly improve the 3D points cloud classification task’s performance. The project’s objective is to experimentally assess the applicability of capsule neural network architecture to the task of point cloud human pose estimation and measure performance on non-synthetic data. Additionally, measure noise sustainability of capsule networks for 3D data compared to regular models. Compare models’ performance with restricted amount of training data.