3D mesh voxelization

Treball final de Grau en Disseny i Desenvolupament de Videojocs. Codi: VJ1241. Curs acadèmic: 2018/2019This paper tries to make an approach to the use of voxels as a basic element of a videogame agent, both in terms of visualization and interaction through the use of a Unity game engine plug-in

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

Template-Cut: A Pattern-Based Segmentation Paradigm

We present a scale-invariant, template-based segmentation paradigm that sets up a graph and performs a graph cut to separate an object from the background. Typically graph-based schemes distribute the nodes of the graph uniformly and equidistantly on the image, and use a regularizer to bias the cut towards a particular shape. The strategy of uniform and equidistant nodes does not allow the cut to prefer more complex structures, especially when areas of the object are indistinguishable from the background. We propose a solution by introducing the concept of a "template shape" of the target object in which the nodes are sampled non-uniformly and non-equidistantly on the image. We evaluate it on 2D-images where the object's textures and backgrounds are similar, and large areas of the object have the same gray level appearance as the background. We also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning purposes.Comment: 8 pages, 6 figures, 3 tables, 6 equations, 51 reference

A fast voxelization algorithm for trilinearly interpolated isosurfaces

International audienceIn this work we propose a new method for a fast incremental voxelization of isosurfaces obtained by the trilinear interpolation of 3D data. Our objective consists in the fast generation of subvoxelized iso-surfaces extracted by a point-based technique similar to the Dividing Cubes algorithm. Our technique involves neither an exhaustive scan search process nor a graph-based search approach when generating iso-surface points. Instead an optimized incremental approach is adopted here for a rapid isosurface extraction. With a sufficient sampling subdivision criteria around critical points, the extracted isosurface is both correct and topologically consistent with respect to the piece-wise trilinear interpolant. Furthermore, the discretiza-tion scheme used in our method ensures obtaining thin-one voxel width-isosurfaces as compared to the given by the Dividing Cubes algorithm. The resultant sub-voxelized isosurfaces are efficiently tested against all possible configurations of the trilinear interpolant and real-world datasets

Mesh-based 3D Textured Urban Mapping

In the era of autonomous driving, urban mapping represents a core step to let vehicles interact with the urban context. Successful mapping algorithms have been proposed in the last decade building the map leveraging on data from a single sensor. The focus of the system presented in this paper is twofold: the joint estimation of a 3D map from lidar data and images, based on a 3D mesh, and its texturing. Indeed, even if most surveying vehicles for mapping are endowed by cameras and lidar, existing mapping algorithms usually rely on either images or lidar data; moreover both image-based and lidar-based systems often represent the map as a point cloud, while a continuous textured mesh representation would be useful for visualization and navigation purposes. In the proposed framework, we join the accuracy of the 3D lidar data, and the dense information and appearance carried by the images, in estimating a visibility consistent map upon the lidar measurements, and refining it photometrically through the acquired images. We evaluate the proposed framework against the KITTI dataset and we show the performance improvement with respect to two state of the art urban mapping algorithms, and two widely used surface reconstruction algorithms in Computer Graphics.Comment: accepted at iros 201

Hardware Acceleration of Progressive Refinement Radiosity using Nvidia RTX

A vital component of photo-realistic image synthesis is the simulation of indirect diffuse reflections, which still remain a quintessential hurdle that modern rendering engines struggle to overcome. Real-time applications typically pre-generate diffuse lighting information offline using radiosity to avoid performing costly computations at run-time. In this thesis we present a variant of progressive refinement radiosity that utilizes Nvidia's novel RTX technology to accelerate the process of form-factor computation without compromising on visual fidelity. Through a modern implementation built on DirectX 12 we demonstrate that offloading radiosity's visibility component to RT cores significantly improves the lightmap generation process and potentially propels it into the domain of real-time.Comment: 114 page

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

3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network

State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53 which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process