Efficient voxelization using projected optimal scanline

In the paper, we propose an efficient algorithm for the surface voxelization of 3D geometrically complex models. Unlike recent techniques relying on triangle-voxel intersection tests, our algorithm exploits the conventional parallel-scanline strategy. Observing that there does not exist an optimal scanline interval in general 3D cases if one wants to use parallel voxelized scanlines to cover the interior of a triangle, we subdivide a triangle into multiple axis-aligned slices and carry out the scanning within each polygonal slice. The theoretical optimal scanline interval can be obtained to maximize the efficiency of the algorithm without missing any voxels on the triangle. Once the collection of scanlines are determined and voxelized, we obtain the surface voxelization. We fine tune the algorithm so that it only involves a few operations of integer additions and comparisons for each voxel generated. Finally, we comprehensively compare our method with the state-of-the-art method in terms of theoretical complexity, runtime performance and the quality of the voxelization on both CPU and GPU of a regular desktop PC, as well as on a mobile device. The results show that our method outperforms the existing method, especially when the resolution of the voxelization is high

Intelligent Computational Transportation

Transportation is commonplace around our world. Numerous researchers dedicate great efforts to vast transportation research topics. The purpose of this dissertation is to investigate and address a couple of transportation problems with respect to geographic discretization, pavement surface automatic examination, and traffic ow simulation, using advanced computational technologies. Many applications require a discretized 2D geographic map such that local information can be accessed efficiently. For example, map matching, which aligns a sequence of observed positions to a real-world road network, needs to find all the nearby road segments to the individual positions. To this end, the map is discretized by cells and each cell retains a list of road segments coincident with this cell. An efficient method is proposed to form such lists for the cells without costly overlapping tests. Furthermore, the method can be easily extended to 3D scenarios for fast triangle mesh voxelization. Pavement surface distress conditions are critical inputs for quantifying roadway infrastructure serviceability. Existing computer-aided automatic examination techniques are mainly based on 2D image analysis or 3D georeferenced data set. The disadvantage of information losses or extremely high costs impedes their effectiveness iv and applicability. In this study, a cost-effective Kinect-based approach is proposed for 3D pavement surface reconstruction and cracking recognition. Various cracking measurements such as alligator cracking, traverse cracking, longitudinal cracking, etc., are identified and recognized for their severity examinations based on associated geometrical features. Smart transportation is one of the core components in modern urbanization processes. Under this context, the Connected Autonomous Vehicle (CAV) system presents a promising solution towards the enhanced traffic safety and mobility through state-of-the-art wireless communications and autonomous driving techniques. Due to the different nature between the CAVs and the conventional Human- Driven-Vehicles (HDVs), it is believed that CAV-enabled transportation systems will revolutionize the existing understanding of network-wide traffic operations and re-establish traffic ow theory. This study presents a new continuum dynamics model for the future CAV-enabled traffic system, realized by encapsulating mutually-coupled vehicle interactions using virtual internal and external forces. A Smoothed Particle Hydrodynamics (SPH)-based numerical simulation and an interactive traffic visualization framework are also developed