Toward Mobile 3D Vision

Abstract

In the past few years, the computer vision community has developed numerous novel technologies of 3D vision (e.g., 3D object detection and classification and 3D scene segmentation). In this work, we explore the opportunities brought by these innovations for enabling real-time 3D vision on mobile devices. Mobile 3D vision finds various use cases for emerging applications such as autonomous driving, drone navigation, and augmented reality (AR). The key differences between 3D vision and 2D vision mainly stem from the input data format (i.e., point clouds or 3D meshes vs. 2D images). Hence, the key challenge of 3D vision is that it is could be more computation intensive and memory hungry than 2D vision, due to the additional dimension of input data. For example, our preliminary measurement study of several state-of-the-art machine learning models for 3D vision shows that none of them can execute faster than one frame per second on smartphones. Motivated by these challenges, we present in this position paper a research agenda on offering systems support for real-time mobile 3D vision, focusing on improving its computation efficiency and memory utilization.