An overview of surface reconstruction using partial differential equation (PDE)

Surface reconstruction is the main process of reverse engineering where engineering model is reproduced in digital format. Surface reconstruction using PDE can be described as solving the PDE to generate the reconstructed surface of an object of interest. This paper provides a brief introduction to the process of surface reconstruction, partial differential equation and its application in surface reconstruction, as well as summarizing several works that utilize this approach. This paper also outlines the validation method used to assess a reconstruction model

Towards recovery of complex shapes in meshes using digital images for reverse engineering applications

When an object owns complex shapes, or when its outer surfaces are simply inaccessible, some of its parts may not be captured during its reverse engineering. These deficiencies in the point cloud result in a set of holes in the reconstructed mesh. This paper deals with the use of information extracted from digital images to recover missing areas of a physical object. The proposed algorithm fills in these holes by solving an optimization problem that combines two kinds of information: (1) the geometric information available on the surrounding of the holes, (2) the information contained in an image of the real object. The constraints come from the image irradiance equation, a first-order non-linear partial differential equation that links the position of the mesh vertices to the light intensity of the image pixels. The blending conditions are satisfied by using an objective function based on a mechanical model of bar network that simulates the curvature evolution over the mesh. The inherent shortcomings both to the current holefilling algorithms and the resolution of the image irradiance equations are overcom

Leverage of lidar point cloud for segmentation and shape reconstruction

Develop a method of annotating 3d sparse data (point cloud) in an efficient way with the help of deep neural network models and user corrections. Take the approach of human-in-the-loop to refine a AI generated fine annotation of the data. Focus on the task of self-driving cars and lidar sensor observations. The model generates a denser representation of the data and refines it by leveraging interactive human 2d annotations.Outgoin

Level set and PDE methods for visualization

Notes from IEEE Visualization 2005 Course #6, Minneapolis, MN, October 25, 2005. Retrieved 3/16/2006 from http://www.cs.drexel.edu/~david/Papers/Viz05_Course6_Notes.pdf.Level set methods, an important class of partial differential equation (PDE) methods, define dynamic surfaces implicitly as the level set (isosurface) of a sampled, evolving nD function. This course is targeted for researchers interested in learning about level set and other PDE-based methods, and their application to visualization. The course material will be presented by several of the recognized experts in the field, and will include introductory concepts, practical considerations and extensive details on a variety of level set/PDE applications. The course will begin with preparatory material that introduces the concept of using partial differential equations to solve problems in visualization. This will include the structure and behavior of several different types of differential equations, e.g. the level set, heat and reaction-diffusion equations, as well as a general approach to developing PDE-based applications. The second stage of the course will describe the numerical methods and algorithms needed to implement the mathematics and methods presented in the first stage, including information on implementing the algorithms on GPUs. Throughout the course the technical material will be tied to applications, e.g. image processing, geometric modeling, dataset segmentation, model processing, surface reconstruction, anisotropic geometric diffusion, flow field post-processing and vector visualization. Prerequisites: Knowledge of calculus, linear algebra, computer graphics, visualization, geometric modeling and computer vision. Some familiarity with differential geometry, differential equations, numerical computing and image processing is strongly recommended, but not required

Explorando ferramentas de modelação digital, aumentada e orientada por dados em engenharia e design de produto

Tools are indispensable for all diligent professional practice. New concepts and possibilities for paradigm shifting are emerging with recent computational technological developments in digital tools. However, new tools from key concepts such as “Big-Data”, “Accessibility” and “Algorithmic Design” are fundamentally changing the input and position of the Product Engineer and Designer. After the context introduction, this dissertation document starts by extracting three pivotal criteria from the Product Design Engineering's State of the Art analysis. In each one of those criteria the new emergent, more relevant and paradigmatic concepts are explored and later on are positioned and compared within the Product Lifecycle Management wheel scheme, where the potential risks and gaps are pointed to be explored in the experience part. There are two types of empirical experiences: the first being of case studies from Architecture and Urban Planning — from the student's professional experience —, that served as a pretext and inspiration for the experiments directly made for Product Design Engineering. First with a set of isolated explorations and analysis, second with a hypothetical experience derived from the latter and, finally, a deliberative section that culminate in a listing of risks and changes concluded from all the previous work. The urgency to reflect on what will change in that role and position, what kind of ethical and/or conceptual reformulations should exist for the profession to maintain its intellectual integrity and, ultimately, to survive, are of the utmost evidence.As ferramentas são indispensáveis para toda a prática diligente profissional. Novos conceitos e possibilidades de mudança de paradigma estão a surgir com os recentes progressos tecnológicos a nível computacional nas ferramentas digitais. Contudo, novas ferramentas originadas sobre conceitos-chave como “Big Data”, “Acessibilidade” e “Design Algorítmico” estão a mudar de forma fundamental o contributo e posição do Engenheiro e Designer de Produto. Esta dissertação, após uma primeira introdução contextual, começa por extrair três conceitos-eixo duma análise ao Estado da Arte actual em Engenharia e Design de Produto. Em cada um desses conceitos explora-se os novos conceitos emergentes mais relevantes e paradigmáticos, que então são comparados e posicionados no círculo de Gestão de Ciclo de Vida de Produto, apontando aí potenciais riscos e falhas que possam ser explorados em experiências. As experiências empíricas têm duas índoles: a primeira de projetos e casos de estudo de arquitetura e planeamento urbanístico — experiência em contexto de trabalho do aluno —, que serviu de pretexto e inspiração para as experiências relacionadas com Engenharia e Design de Produto. Primeiro com uma série de análises e experiências isoladas, segundo com uma formulação hipotética com o compêndio dessas experiências e, finalmente, com uma secção de reflexão que culmina numa série de riscos e mudanças induzidas do trabalho anterior. A urgência em refletir sobre o que irá alterar nesse papel e posição, que género de reformulações éticas e/ou conceptuais deverão existir para que a profissão mantenha a sua integridade intelectual e, em última instância, sobreviva, são bastante evidentes.Mestrado em Engenharia e Design de Produt

Feature preserving noise removal for binary voxel volumes using 3D surface skeletons

Skeletons are well-known descriptors that capture the geometry and topology of 2D and 3D shapes. We leverage these properties by using surface skeletons to remove noise from 3D shapes. For this, we extend an existing method that removes noise, but keeps important (salient) corners for 2D shapes. Our method detects and removes large-scale, complex, and dense multiscale noise patterns that contaminate virtually the entire surface of a given 3D shape, while recovering its main (salient) edges and corners. Our method can treat any (voxelized) 3D shapes and surface-noise types, is computationally scalable, and has one easy-to-set parameter. We demonstrate the added-value of our approach by comparing our results with several known 3D shape denoising methods

Designing an event display for the Transition Radiation Detector in ALICE

We document here a successful design study for an event display focused on the Transition Radiation Detector (TRD) within A Large Ion Collider Experiment (ALICE) at the European Organisation for Nuclear Research (CERN). Reviews of the fields of particle physics and visualisation are presented to motivate formally designing this display for two different audiences. We formulate a methodology, based on successful design studies in similar fields, that involves experimental physicists in the design process as domain experts. An iterative approach incorporating in-person interviews is used to define a series of visual components applying best practices from literature. Interactive event display prototypes are evaluated with potential users, and refined using elicited feedback. The primary artefact is a portable, functional, effective, validated event display – a series of case studies evaluate its use by both scientists and the general public. We further document use cases for, and hindrances preventing, the adoption of event displays, and propose novel data visualisations of experimental particle physics data. We also define a flexible intermediate JSON data format suitable for web-based displays, and a generic task to convert historical data to this format. This collection of artefacts can guide the design of future event displays. Our work makes the case for a greater use of high quality data visualisation in particle physics, across a broad spectrum of possible users, and provides a framework for the ongoing development of web-based event displays of TRD data

Assembling models of embryo development: Image analysis and the construction of digital atlases

Digital atlases of animal development provide a quantitative description of morphogenesis, opening the path toward processes modeling. Prototypic atlases offer a data integration framework where to gather information from cohorts of individuals with phenotypic variability. Relevant information for further theoretical reconstruction includes measurements in time and space for cell behaviors and gene expression. The latter as well as data integration in a prototypic model, rely on image processing strategies. Developing the tools to integrate and analyze biological multidimensional data are highly relevant for assessing chemical toxicity or performing drugs preclinical testing. This article surveys some of the most prominent efforts to assemble these prototypes, categorizes them according to salient criteria and discusses the key questions in the field and the future challenges toward the reconstruction of multiscale dynamics in model organisms