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This paper presents an accurate and robust dense 3D reconstruction system for detail preserving surface modeling of large-scale scenes from multi-view images, which we named DP-MVS. Our system performs high-quality large-scale dense reconstruction, which preserves geometric details for thin structures, especially for linear objects. Our framework begins with a sparse reconstruction carried out by an incremental Structure-from-Motion. Based on the reconstructed sparse map, a novel detail preserving PatchMatch approach is applied for depth estimation of each image view. The estimated depth maps of multiple views are then fused to a dense point cloud in a memory-efficient way, followed by a detail-aware surface meshing method to extract the final surface mesh of the captured scene. Experiments on ETH3D benchmark show that the proposed method outperforms other state-of-the-art methods on F1-score, with the running time more than 4 times faster. More experiments on large-scale photo collections demonstrate the effectiveness of the proposed framework for large-scale scene reconstruction in terms of accuracy, completeness, memory saving, and time efficiency.

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.

In view of the little research that has been conducted on the ride comfort of mini vehicles, an electric mini off-road vehicle was designed in this study and a 2 degree-of-freedom quarter car model was established to investigate the ride comfortability. The amplitude-frequency and vibration response characteristics of the suspension were analyzed with the natural frequencies of the front and rear suspensions selected in accordance with the required driving performance. A comprehensive objective function with respect to the safety and comfortability was established, and the damping ratio of the suspension was determined. The damping characteristics of the shock absorber were analyzed to derive an adjustment rule of the suspension damping ratio. The piecewise linear speed characteristics of the shock absorber were subsequently obtained, and suspension-parameter identification and ride comfort tests were conducted. The test results showed that the natural frequencies and damping ratios of the front and rear suspensions were 1.676 and 1.922 Hz, and 0.225 and 0.242, respectively. The results of a pulse input test and D-level road random running test also demonstrated the safety and good ride comfortability of the vehicle.