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"Texture mapping"
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3D-Visualization of Ultrasonic NDT Data Using Mixed Reality
In this paper we present an approach where ultrasonic testing data (UT) is linked with its spatial coordinates and direction vector to the examined specimen. Doing so, the processed nondestructive testing (NDT) results can be visualized directly on the sample in real-time using augmented or virtual reality. To enable the link between NDT data and physical object, a 3D-tracking system is used. Spatial coordinates and NDT sensor data are stored together. For visualization, texture mapping was applied on a 3D model. The testing process consists of data recording, processing and visualization. All three steps are performed in real-time. The data is recorded by an UT-USB interface, processed on a PC workstation and displayed using a Mixed-Reality-system (MR). Our system allows real-time 3D visualization of ultrasonic NDT data, which is directly drawn into the virtual representation. Therefore, the possibility arises to assist the operator during the manual testing process. This new approach results in a much more intuitive testing process and a data set optimally prepared to be saved in a digital twin environment. The size of the samples is not limited to a laboratory scale, but also works for larger objects, e.g. a helicopter fuselage. Our approach is inspired by concepts of NDE 4.0 to create a new kind of smart inspection systems.
Journal Article
3D engine design for virtual globes
\"The first half of the book details the design of a modern 3D graphics engine: a shader-based architecture with an abstract rendering API fed by hierarchical culling and state sorting. The second half of the book upgrades the generic 3D engine to a virtual globe engine by adding high precision rendering, accurate globe rendering, vector data rendering, out-of-core rendering, and terrain rendering. The algorithms and techniques in the book are not tied to any particular programming language or rendering API but they will provide concrete examples in C#, OpenGL, and GLSL\"--Provided by publisher.
Book
BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
We present a method for enhancing 3D Gaussian Splatting primitives with brushstroke-aware stylization. Previous approaches to 3D style transfer are typically limited to color or texture modifications, lacking an understanding of artistic shape deformation. In contrast, we focus on individual 3D Gaussian primitives, exploring their potential to enable style transfer that incorporates both color- and brushstroke-inspired local geometric stylization. Specifically, we introduce additional texture features for each Gaussian primitive and apply a texture mapping technique to achieve brushstroke-like geometric effects in a rendered scene. Furthermore, we propose an unsupervised clustering algorithm to efficiently prune redundant Gaussians, ensuring that our method seamlessly integrates with existing 3D Gaussian Splatting pipelines. Extensive evaluations demonstrate that our approach outperforms existing baselines by producing brushstroke-aware artistic renderings with richer geometric expressiveness and enhanced visual appeal.
Journal Article
Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms
This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise monitoring of abiotic and biotic stressors in crops. An innovative algorithm combining vegetation indices, path planning, and machine learning methods is introduced to enhance the efficiency of data collection and analysis. Experimental results demonstrate significant improvements in accuracy and operational efficiency, paving the way for real-time, data-driven decision-making in precision agriculture.
Journal Article
A user-friendly method for constructing realistic dental model based on two-dimensional/three-dimensional registration
Purpose
This paper aims to obtain a texture dental model with real images and improve the rendering effect of the dental model.
Design/methodology/approach
The paper proposes a semiautomatic method to construct a realistic dental model with real images based on two-dimensional/three-dimensional (2D/3D) registration. First, a 3D digital dental model and three intraoral images are obtained by a 3D scanner and digital single-lens reflex camera. Second, the camera projection poses for every intraoral images are calculated by using the single-objective optimization algorithm. Third, with camera poses, the preliminary projection texture mapping is performed; besides, the seam between two textures is marked. Finally, the marked regions are fused based on the image pyramid to eliminate obvious seams.
Findings
The paper provides a method to construct a realistic dental model. The method can map three intraoral images to the dental model. The experimental results show that the textured dental model without obvious distortion, dislocation and seams is constructed with simple interactions.
Originality/value
The proposed method can be applied to the digital smile design system to improve the communication efficiency between doctors, patients and technicians.
Journal Article
Statistical Modeling of Craniofacial Shape and Texture
We present a fully-automatic statistical 3D shape modeling approach and apply it to a large dataset of 3D images, the Headspace dataset, thus generating the first public shape-and-texture 3D morphable model (3DMM) of the full human head. Our approach is the first to employ a template that adapts to the dataset subject before dense morphing. This is fully automatic and achieved using 2D facial landmarking, projection to 3D shape, and mesh editing. In dense template morphing, we improve on the well-known Coherent Point Drift algorithm, by incorporating iterative data-sampling and alignment. Our evaluations demonstrate that our method has better performance in correspondence accuracy and modeling ability when compared with other competing algorithms. We propose a texture map refinement scheme to build high quality texture maps and texture model. We present several applications that include the first clinical use of craniofacial 3DMMs in the assessment of different types of surgical intervention applied to a craniosynostosis patient group.
Journal Article
AR image generation using view-dependent geometry modification and texture mapping
Augmented reality (AR) applications often require virtualized real objects, i.e., virtual objects that are built based on real objects and rendered from an arbitrary viewpoint. In this paper, we propose a method for real object virtualization and AR image generation based on view-dependent geometry modification and texture mapping. The proposed method is a hybrid of model- and image-based rendering techniques that uses multiple input images of the real object as well as the object’s three-dimensional (3D) model obtained by an automatic 3D reconstruction technique. Even with state-of-the-art technology, the reconstructed 3D model’s accuracy can be insufficient, resulting in such visual artifacts as false object boundaries. The proposed method generates a depth map from a 3D model of a virtualized real object and expands its region in the depth map to remove the false object boundaries. Since such expansion reveals the background pixels in the input images, which is particularly undesirable for AR applications, we preliminarily extract object regions and use them for texture mapping. With our GPU implementation for real-time AR image generation, we experimentally demonstrated that using expanded geometry reduces the number of required input images and maintains visual quality.
Journal Article
A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas
Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications.
Journal Article
Errors in soil maps: The need for better on-site estimates and soil map predictions
High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.
Journal Article