Visualización 3D de grandes cantidades de datos 3D para la prevención frente a desastres naturales: una revisión de la literatura

La visualización de datos 3D es un aspecto muy importante para varios campos de trabajo como la arquitectura, minería, videojuegos, diseño gráfico, geografía, etc. En especial en este último, la geografía, el cual a diario necesita visualizar información topográfica para hacer un estudio previo de los terrenos sin la necesidad de estar presente. En Perú, el uso información 3D por parte de los geógrafos para analizar terrenos a detalle, se ha vuelto una actividad de mucha importancia, debido a que el país es muy propenso a sufrir de fenómenos naturales como el fenómeno del Niño. Esta información topográfica suele ser de superficies de un gran tamaño que pueden llegar hasta un área de 7000 hectáreas, por lo que se necesita de procesar una inmensa cantidad de información 3D. Es por esto que el presente trabajo de investigación se centra en revisar la literatura para lograr una visualización de grandes cantidades de datos 3D. En primer lugar, se revisará cómo es que se deben guardar y organizar los datos 3D para que puedan ser fácilmente extraídos por el visualizador y qué tipo de estructura de datos es la mejor para este tipo de trabajos. En segundo lugar, se revisarán los métodos que existen actualmente para poder renderizar los datos 3D de forma fluida, con el objetivo de logar una visualización interactiva del usuario sin exigir tantos recursos. Por último, se presentarán las conclusiones de los dos puntos mencionados anteriormente y se explicarán cuáles son los mejores métodos para realizar el proyecto de visualización de grandes cantidades de datos 3D.Trabajo de investigació

Analysis and Manipulation of Repetitive Structures of Varying Shape

Self-similarity and repetitions are ubiquitous in man-made and natural objects. Such structural regularities often relate to form, function, aesthetics, and design considerations. Discovering structural redundancies along with their dominant variations from 3D geometry not only allows us to better understand the underlying objects, but is also beneficial for several geometry processing tasks including compact representation, shape completion, and intuitive shape manipulation. To identify these repetitions, we present a novel detection algorithm based on analyzing a graph of surface features. We combine general feature detection schemes with a RANSAC-based randomized subgraph searching algorithm in order to reliably detect recurring patterns of locally unique structures. A subsequent segmentation step based on a simultaneous region growing is applied to verify that the actual data supports the patterns detected in the feature graphs. We introduce our graph based detection algorithm on the example of rigid repetitive structure detection. Then we extend the approach to allow more general deformations between the detected parts. We introduce subspace symmetries whereby we characterize similarity by requiring the set of repeating structures to form a low dimensional shape space. We discover these structures based on detecting linearly correlated correspondences among graphs of invariant features. The found symmetries along with the modeled variations are useful for a variety of applications including non-local and non-rigid denoising. Employing subspace symmetries for shape editing, we introduce a morphable part model for smart shape manipulation. The input geometry is converted to an assembly of deformable parts with appropriate boundary conditions. Our method uses self-similarities from a single model or corresponding parts of shape collections as training input and allows the user also to reassemble the identified parts in new configurations, thus exploiting both the discrete and continuous learned variations while ensuring appropriate boundary conditions across part boundaries. We obtain an interactive yet intuitive shape deformation framework producing realistic deformations on classes of objects that are difficult to edit using repetition-unaware deformation techniques

Interactive Visual Analytics for Large-scale Particle Simulations

Particle based model simulations are widely used in scientific visualization. In cosmology, particles are used to simulate the evolution of dark matter in the universe. Clusters of particles (that have special statistical properties) are called halos. From a visualization point of view, halos are clusters of particles, each having a position, mass and velocity in three dimensional space, and they can be represented as point clouds that contain various structures of geometric interest such as filaments, membranes, satellite of points, clusters, and cluster of clusters. The thesis investigates methods for interacting with large scale data-sets represented as point clouds. The work mostly aims at the interactive visualization of cosmological simulation based on large particle systems. The study consists of three components: a) two human factors experiments into the perceptual factors that make it possible to see features in point clouds; b) the design and implementation of a user interface making it possible to rapidly navigate through and visualize features in the point cloud, c) software development and integration to support visualization

Pistepilvien visualisointi laitossuunnitteluohjelmistossa

Pistepilvet ovat useimmiten laserkeilaimilla mitattuja malleja jostakin tosimaailman esineestä tai maisemasta. Monimutkaisen geometrian sijaan kohdetta kuvataan valtavalla määrällä pisteitä, jotka visualisoimalla saadaan kuva yhtenäisistä pinnoista. Laserkeilaimella taltioituja pistepilviä käytetään esimerkiksi arkkitehtuurissa, rakentamisessa ja kuluttajatuotteiden suunnittelussa. Tässä tutkielmassa keskitytään pistepilvien käyttöön laitossuunnitteluohjelmistossa, jossa käyttäjä haluaa katsella suurista laitoksista keilattuja pistepilviä ja mallintaa geometriaa niiden avulla. Pistepilvien massiivinen koko aiheuttaa ongelmia niitä käsitteleville ja visualisoiville ohjelmistoille. Suuret pistepilvet eivät mahdu kerralla tietokoneen keskusmuistiin ja niiden visualisoiminen kestää kauan. Usein pistepilvi tallennetaan hierarkiseen tietorakenteeseen, joka mahdollistaa sen asteittaisen lataamisen kiintolevyltä ja sellaisen tarkkuustason valitseminen, joka mahdollistaa interaktiivisen ruudunpäivitystaajuuden. Tässä tutkielmassa perehdytään pistepilvien visualisoinnissa käytettyihin hierarkisiin tietorakenteisiin ja todetaan niin kutsuttujen sisäkkäispistepuiden soveltuvan laitossuunnitteluohjelmiston pistepilvivisualisoijassa käytettäväksi. Lisäksi esitellään sisäkkäispistepuille yksinkertainen kompressiotekniikka ja tärkeimpiä pisteitä priorisoiva visualisointialgoritmi. Lopuksi mitataan kompression ja visualisointialgoritmin vaikutusta suorituskykyyn. Pistedatan kompressointi pienentää tiedostokokoja ja lyhentää tietorakenteen rakennusaikaa, mutta kompression purkaminen vaatii laskenta-aikaa visualisointivaiheessa. Esitelty visualisointialgoritmi piirtää pisteitä ruudulle hitaammin kuin suoraviivainen toteutus, mutta katselijaa lähinnä olevat pisteet tulevat piirretyksi tarkemmalla resoluutiolla ja tyydyttävä tulos saavutetaan pienemmällä määrällä visualisoituja pisteitä

Algorithms for the reconstruction, analysis, repairing and enhancement of 3D urban models from multiple data sources

Over the last few years, there has been a notorious growth in the field of digitization of 3D buildings and urban environments. The substantial improvement of both scanning hardware and reconstruction algorithms has led to the development of representations of buildings and cities that can be remotely transmitted and inspected in real-time. Among the applications that implement these technologies are several GPS navigators and virtual globes such as Google Earth or the tools provided by the Institut Cartogràfic i Geològic de Catalunya. In particular, in this thesis, we conceptualize cities as a collection of individual buildings. Hence, we focus on the individual processing of one structure at a time, rather than on the larger-scale processing of urban environments. Nowadays, there is a wide diversity of digitization technologies, and the choice of the appropriate one is key for each particular application. Roughly, these techniques can be grouped around three main families: - Time-of-flight (terrestrial and aerial LiDAR). - Photogrammetry (street-level, satellite, and aerial imagery). - Human-edited vector data (cadastre and other map sources). Each of these has its advantages in terms of covered area, data quality, economic cost, and processing effort. Plane and car-mounted LiDAR devices are optimal for sweeping huge areas, but acquiring and calibrating such devices is not a trivial task. Moreover, the capturing process is done by scan lines, which need to be registered using GPS and inertial data. As an alternative, terrestrial LiDAR devices are more accessible but cover smaller areas, and their sampling strategy usually produces massive point clouds with over-represented plain regions. A more inexpensive option is street-level imagery. A dense set of images captured with a commodity camera can be fed to state-of-the-art multi-view stereo algorithms to produce realistic-enough reconstructions. One other advantage of this approach is capturing high-quality color data, whereas the geometric information is usually lacking. In this thesis, we analyze in-depth some of the shortcomings of these data-acquisition methods and propose new ways to overcome them. Mainly, we focus on the technologies that allow high-quality digitization of individual buildings. These are terrestrial LiDAR for geometric information and street-level imagery for color information. Our main goal is the processing and completion of detailed 3D urban representations. For this, we will work with multiple data sources and combine them when possible to produce models that can be inspected in real-time. Our research has focused on the following contributions: - Effective and feature-preserving simplification of massive point clouds. - Developing normal estimation algorithms explicitly designed for LiDAR data. - Low-stretch panoramic representation for point clouds. - Semantic analysis of street-level imagery for improved multi-view stereo reconstruction. - Color improvement through heuristic techniques and the registration of LiDAR and imagery data. - Efficient and faithful visualization of massive point clouds using image-based techniques.Durant els darrers anys, hi ha hagut un creixement notori en el camp de la digitalització d'edificis en 3D i entorns urbans. La millora substancial tant del maquinari d'escaneig com dels algorismes de reconstrucció ha portat al desenvolupament de representacions d'edificis i ciutats que es poden transmetre i inspeccionar remotament en temps real. Entre les aplicacions que implementen aquestes tecnologies hi ha diversos navegadors GPS i globus virtuals com Google Earth o les eines proporcionades per l'Institut Cartogràfic i Geològic de Catalunya. En particular, en aquesta tesi, conceptualitzem les ciutats com una col·lecció d'edificis individuals. Per tant, ens centrem en el processament individual d'una estructura a la vegada, en lloc del processament a gran escala d'entorns urbans. Avui en dia, hi ha una àmplia diversitat de tecnologies de digitalització i la selecció de l'adequada és clau per a cada aplicació particular. Aproximadament, aquestes tècniques es poden agrupar en tres famílies principals: - Temps de vol (LiDAR terrestre i aeri). - Fotogrametria (imatges a escala de carrer, de satèl·lit i aèries). - Dades vectorials editades per humans (cadastre i altres fonts de mapes). Cadascun d'ells presenta els seus avantatges en termes d'àrea coberta, qualitat de les dades, cost econòmic i esforç de processament. Els dispositius LiDAR muntats en avió i en cotxe són òptims per escombrar àrees enormes, però adquirir i calibrar aquests dispositius no és una tasca trivial. A més, el procés de captura es realitza mitjançant línies d'escaneig, que cal registrar mitjançant GPS i dades inercials. Com a alternativa, els dispositius terrestres de LiDAR són més accessibles, però cobreixen àrees més petites, i la seva estratègia de mostreig sol produir núvols de punts massius amb regions planes sobrerepresentades. Una opció més barata són les imatges a escala de carrer. Es pot fer servir un conjunt dens d'imatges capturades amb una càmera de qualitat mitjana per obtenir reconstruccions prou realistes mitjançant algorismes estèreo d'última generació per produir. Un altre avantatge d'aquest mètode és la captura de dades de color d'alta qualitat. Tanmateix, la informació geomètrica resultant sol ser de baixa qualitat. En aquesta tesi, analitzem en profunditat algunes de les mancances d'aquests mètodes d'adquisició de dades i proposem noves maneres de superar-les. Principalment, ens centrem en les tecnologies que permeten una digitalització d'alta qualitat d'edificis individuals. Es tracta de LiDAR terrestre per obtenir informació geomètrica i imatges a escala de carrer per obtenir informació sobre colors. El nostre objectiu principal és el processament i la millora de representacions urbanes 3D amb molt detall. Per a això, treballarem amb diverses fonts de dades i les combinarem quan sigui possible per produir models que es puguin inspeccionar en temps real. La nostra investigació s'ha centrat en les següents contribucions: - Simplificació eficaç de núvols de punts massius, preservant detalls d'alta resolució. - Desenvolupament d'algoritmes d'estimació normal dissenyats explícitament per a dades LiDAR. - Representació panoràmica de baixa distorsió per a núvols de punts. - Anàlisi semàntica d'imatges a escala de carrer per millorar la reconstrucció estèreo de façanes. - Millora del color mitjançant tècniques heurístiques i el registre de dades LiDAR i imatge. - Visualització eficient i fidel de núvols de punts massius mitjançant tècniques basades en imatges

Symmetry in 3D shapes - analysis and applications to model synthesis

Symmetry is an essential property of a shapes\u27 appearance and presents a source of information for structure-aware deformation and model synthesis. This thesis proposes feature-based methods to detect symmetry and regularity in 3D shapes and demonstrates the utilization of symmetry information for content generation. First, we will introduce two novel feature detection techniques that extract salient keypoints and feature lines for a 3D shape respectively. Further, we will propose a randomized, feature-based approach to detect symmetries and decompose the shape into recurring building blocks. Then, we will present the concept of docking sites that allows us to derive a set of shape operations from an exemplar and will produce similar shapes. This is a key insight of this thesis and opens up a new perspective on inverse procedural modeling. Finally, we will present an interactive, structure-aware deformation technique based entirely on regular patterns.Symmetrie ist eine essentielle Eigenschaft für das Aussehen eines Objekts und bietet eine Informationsquelle für strukturerhaltende Deformation und Modellsynthese. Diese Arbeit beschäftigt sich mit merkmalsbasierter Symmetrieerkennung in 3D-Objekten und der Synthese von 3D-Modellen mittels Symmetrieinformationen. Zunächst stellen wir zwei neue Verfahren zur Merkmalserkennung vor, die hervorstechende Punkte bzw. Linien in 3D-Objekten erkennen. Darauf aufbauend beschreiben wir einen randomisierten, merkmalsbasierten Ansatz zur Symmetrieerkennung, der ein Objekt in sich wiederholende Bausteine zerlegt. Des Weiteren führen wir ein Konzept zur Modifikation von Objekten ein, welches Andockstellen in Geometrie berechnet und zur Generierung von ähnlichen Objekten eingesetzt werden kann. Dieses Konzept eröffnet völlig neue Möglichkeiten für die Ermittlung von prozeduralen Regeln aus Beispielen. Zum Schluss präsentieren wir eine interaktive Technik zur strukturerhaltenden Deformation, welche komplett auf regulären Strukturen basiert

Interactive Editing of Large Point Clouds

This paper describes a new out-of-core multi-resolution data structure for real-time visualization and interactive editing of large point clouds. In addition, an editing system is discussed that makes use of the novel data structure to provide interactive editing tools for large scanner data sets. The new data structure provides efficient rendering and allows for handling very large data sets using out-of-core storage. Unlike related previous approaches, it also provides dynamic operations for online insertion, deletion and modification of points with time mostly independent of scene complexity. This permits local editing of huge models in real time while maintaining a full multi-resolution representation for visualization. The data structure is used to implement a prototypical editing system for large point clouds. It provides real-time local editing tools for huge data sets as well as a two-resolution scripting mode for planning large, non-local changes which are subsequently performed in an externally efficient offline computation. We evaluate our implementation on several synthetic and real-world examples of sizes up to 63GB