VPRNet: Voxel-based Efficient and Partial-to-Partial Point Cloud Registration on Mobile Devices

Abstract

With the popularity of embedded devices such as LIDAR sensors and depth cameras, the resulting point clouds become the main data format for representing the 3D world and spawn various smart mobile applications. A key technology for enabling these applications to furnish high-quality services is real-time point cloud registration on mobile devices, which synthesizes a complete model or a large-scale scene from multiple partial scans. It aims to deliver increasing sensing range, faster 3D reconstruction and more robust robot navigation. Unfortunately, the performance of these applications is limited by the scale and partial loss of raw point cloud frame. The existing solutions for point cloud registration are difficult to deploy on mobile devices due to their complex models and assumptions about point cloud pairs with large overlap, which cause significant delay and inaccuracy. This paper proposes VPRNet - the first voxel-based registration solution that can achieve real-time partial-to-partial registration with competitive registration quality while being more advantageous for large-scale point clouds on mobile devices. We conduct real-world experiments and extensive simulations cross various datasets and platforms to validate the efficacy of VPRNet and further compare the performance with state-of-the-art approaches.