We present a novel online 3D scanning system for high-quality object reconstruction with a mobile device, called Mobile3DScanner. Using a mobile device equipped with an embedded RGBD camera, our system provides online 3D object reconstruction capability for users to acquire high-quality textured 3D object models. Starting with a simultaneous pose tracking and TSDF fusion module, our system allows users to scan an object with a mobile device to get a 3D model for real-time preview. After the real-time scanning process is completed, the scanned 3D model is globally optimized and mapped with multi-view textures as an efficient postprocess to get the final textured 3D model on the mobile device. Unlike most existing state-of-the-art systems which can only scan homeware objects such as toys with small dimensions due to the limited computation and memory resources of mobile platforms, our system can reconstruct objects with large dimensions such as statues. We propose a novel visual-inertial ICP approach to achieve real-time accurate 6DoF pose tracking of each incoming frame on the front end, while maintaining a keyframe pool on the back end where the keyframe poses are optimized by local BA. Simultaneously, the keyframe depth maps are fused by the optimized poses to a TSDF model in real-time. Especially, we propose a novel adaptive voxel resizing strategy to solve the out-of-memory problem of large dimension TSDF fusion on mobile platforms. In the post-process, the keyframe poses are globally optimized and the keyframe depth maps are optimized and fused to obtain a final object model with more accurate geometry. The experiments with quantitative and qualitative evaluation demonstrate the effectiveness of the proposed 3D scanning system based on a mobile device, which can successfully achieve online high-quality 3D reconstruction of natural objects with larger dimensions for efficient AR content creation.

We present a real-time monocular 3D reconstruction system on a mobile phone, called Mobile3DRecon. Using an embedded monocular camera, our system provides an online mesh generation capability on back end together with real-time 6DoF pose tracking on front end for users to achieve realistic AR effects and interactions on mobile phones. Unlike most existing state-of-the-art systems which produce only point cloud based 3D models online or surface mesh offline, we propose a novel online incremental mesh generation approach to achieve fast online dense surface mesh reconstruction to satisfy the demand of real-time AR applications. For each keyframe of 6DoF tracking, we perform a robust monocular depth estimation, with a multi-view semi-global matching method followed by a depth refinement post-processing. The proposed mesh generation module incrementally fuses each estimated keyframe depth map to an online dense surface mesh, which is useful for achieving realistic AR effects such as occlusions and collisions. We verify our real-time reconstruction results on two mid-range mobile platforms. The experiments with quantitative and qualitative evaluation demonstrate the effectiveness of the proposed monocular 3D reconstruction system, which can handle the occlusions and collisions between virtual objects and real scenes to achieve realistic AR effects.