Lighting in the third dimension : laser scanning as an architectural survey and representation method

This paper proposes tridimensional (3D) laser scanning to architects and lighting designers as a lighting enquiry and visualization method for existing built environments. The method constitutes a complement to existing lighting methods by responding to limitations of photometric measurements, computer simulation and HDR imagery in surveying and visualizing light in actual buildings. The research explores advantages and limitations of 3D laser scanning in a case study addressing a vast, geometrically complex and fragmented naturally and artificially lit space. Lighting patterns and geometry of the case study are captured with a 3D laser scanner through a series of four scans. A single 3D model of the entire space is produced from the aligned and fused scans. Lighting distribution patterns are showcased in relation to the materiality, geometry and position of windows, walls, lighting fixtures and day lighting sources. Results and presented through images similar to architectural presentation drawings. More specifically, the lighting distribution patterns are illustrated in a floor plan, a reflected ceiling plan, an axonometry and a cross-section. The point cloud model of the case study is also generated into a video format representing the entire building as well as different viewpoints. The study shows that the proposed method provides powerful visualization results due to the unlimited number of images that can be generated from a point cloud and facilitates understanding of existing lighting conditions in spaces

Rapid Inspection of Pavement Markings Using Mobile Laser Scanning Point Clouds

Intelligent Transportation System (ITS) is the combination of information technology, sensors and communications for more efficient, safer, more secure and more eco-friendly surface transport. One of the most viable forms of ITS is the driverless car, which exist mainly as prototypes. Serval automobile manufacturers (e.g. Ford, GM, BMW, Toyota, Tesla, Honda) and non-automobile companies (e.g. Apple, Google, Nokia, Baidu, Huawei) have invested in this field, and wider commercialization of the driverless car is estimated in 2025 to 2030. Currently, the key elements of the driverless car are the sensors and a prior 3D map. The sensors mounted on the vehicle are the “eyes” of the driverless car to capture the 3D data of its environment. Comparing its environment and a pre-prepared prior known 3D map, the driverless car can distinguish moving targets (e.g. vehicles, pedestrians) and permanent surface features (e.g. buildings, trees, roads, traffic signs) and take relevant actions. With a centimetre-accuracy prior map, the intractable perception problem is transformed into a solvable localization task. The most important technology for generating the prior map is Mobile Laser Scanning (MLS). MLS technology can safely and rapidly acquire highly dense and accurate georeferenced 3D point clouds with the measurement of surface reflectivity. Therefore, the 3D point clouds with intensity data not only offer the detailed 3D surface of the road but also contains pavement marking information that are embedded in the prior map for automatic navigation. Relevant researches have been focused on the pavement marking extraction from MLS data to collect, update and maintain the 3D prior maps. However, the accuracy and efficiency of automatic extraction of pavement markings can be further improved by intensity correction and window-based enhancement. Thus, this study aims at building a robust method for semi-automated information extraction of pavement markings detected from MLS point clouds. The proposed workflow consists of three components: preprocessing, extraction, and classification. In preprocessing, the 3D MLS point clouds are converted into the radiometrically corrected and enhanced 2D intensity imagery of the road surface. Then the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu’s thresholding, neighbour-counting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters using a manually defined decision tree. Case studies are conducted using the MLS datasets acquired in Kingston (Ontario, Canada) and Xiamen (Fujian, China), respectively, with significantly different road environments by two RIEGL VMX-450 systems. The results demonstrated that the proposed workflow and method can achieve 93% in completeness, 95% in correctness, and 94% in F-score respectively when using Xiamen dataset, while 84%, 93%, 89% respectively when using Kingston dataset

Image-based recognition, 3D localization, and retro-reflectivity evaluation of high-quantity low-cost roadway assets for enhanced condition assessment

Systematic condition assessment of high-quantity low-cost roadway assets such as traffic signs, guardrails, and pavement markings requires frequent reporting on location and up-to-date status of these assets. Today, most Departments of Transportation (DOTs) in the US collect data using camera-mounted vehicles to filter, annotate, organize, and present the data necessary for these assessments. However, the cost and complexity of the collection, analysis, and reporting as-is conditions result in sparse and infrequent monitoring. Thus, some of the gains in efficiency are consumed by monitoring costs. This dissertation proposes to improve frequency, detail, and applicability of image-based condition assessment via automating detection, classification, and 3D localization of multiple types of high-quantity low-cost roadway assets using both images collected by the DOTs and online databases such Google Street View Images. To address the new requirements of US Federal Highway Administration (FHWA), a new method is also developed that simulates nighttime visibility of traffic signs from images taken during daytime and measures their retro-reflectivity condition. To initiate detection and classification of high-quantity low-cost roadway assets from street-level images, a number of algorithms are proposed that automatically segment and localize high-level asset categories in 3D. The first set of algorithms focus on the task of detecting and segmenting assets at high-level categories. More specifically, a method based on Semantic Texton Forest classifiers, segments each geo-registered 2D video frame at the pixel-level based on shape, texture, and color. A Structure from Motion (SfM) procedure reconstructs the road and its assets in 3D. Next, a voting scheme assigns the most observed asset category to each point in 3D. The experimental results from application of this method are promising, nevertheless because this method relies on using supervised ground-truth pixel labels for training purposes, scaling it to various types of assets is challenging. To address this issue, a non-parametric image parsing method is proposed that leverages lazy learning scheme for segmentation and recognition of roadway assets. The semi-supervised technique used in the proposed method does not need training and provides ground truth data in a more efficient manner. It is easily scalable to thousands of video frames captured during data collection. Once the high-level asset categories are detected, specific techniques needs to be exploited to detect and classify the assets at a higher level of granularity. To this end, performance of three computer vision algorithms are evaluated for classification of traffic signs in presence of cluttered backgrounds and static and dynamic occlusions. Without making any prior assumptions about the location of traffic signs in 2D, the best performing method uses histograms of oriented gradients and color together with multiple one-vs-all Support Vector Machines, and classifies these assets into warning, regulatory, stop, and yield sign categories. To minimize the reliance on visual data collected by the DOTs and improve frequency and applicability of condition assessment, a new end-to-end procedure is presented that applies the above algorithms and creates comprehensive inventory of traffic signs using Google Street View images. By processing images extracted using Google Street View API and discriminative classification scores from all images that see a sign, the most probable 3D location of each traffic sign is derived and is shown on the Google Earth using a dynamic heat map. A data card containing information about location, type, and condition of each detected traffic sign is also created. Finally, a computer vision-based algorithm is proposed that measures retro-reflectivity of traffic signs during daytime using a vehicle mounted device. The algorithm simulates nighttime visibility of traffic signs from images taken during daytime and measures their retro-reflectivity. The technique is faster, cheaper, and safer compared to the state-of-the-art as it neither requires nighttime operation nor requires manual sign inspection. It also satisfies measurement guidelines set forth by FHWA both in terms of granularity and accuracy. To validate the techniques, new detailed video datasets and their ground-truth were generated from 2.2-mile smart road research facility and two interstate highways in the US. The comprehensive dataset contains over 11,000 annotated U.S. traffic sign images and exhibits large variations in sign pose, scale, background, illumination, and occlusion conditions. The performance of all algorithms were examined using these datasets. For retro-reflectivity measurement of traffic signs, experiments were conducted at different times of day and for different distances. Results were compared with a method recommended by ASTM standards. The experimental results show promise in scalability of these methods to reduce the time and effort required for developing road inventories, especially for those assets such as guardrails and traffic lights that are not typically considered in 2D asset recognition methods and also multiple categories of traffic signs. The applicability of Google Street View Images for inventory management purposes and also the technique for retro-reflectivity measurement during daytime demonstrate strong potential in lowering inspection costs and improving safety in practical applications

Relevés lumineux tridimensionnels en architecture

Ce mémoire de maîtrise propose la numérisation laser tridimensionnelle LiDARcomme nouvelle méthode d'étude et de visualisation de l'éclairage naturel dans des environnements réels pour architectes et designers. Elle constitue un complément aux méthodes d'éclairage actuelles car elle répond aux limites de la méthode de mesure de l'éclairement, de la simulation numérique et de l'imagerie à haute dynamique(HDR)en ce qui concerne le relevé et la représentation des patterns lumineux. Il présente une étude de cas pour déterminer les avantages et les limites de la numérisation laser 3D dans une vaste cafétéria à éclairage naturel et artificiel, vaste, géométriquement complexe et fragmentée. Les patterns lumineuxet la géométrie del'espace sont capturés par un appareil à balayage laser 3D à travers une série de quatre numérisations. Les numérisations sont alignées et fusionnées pour former un seul modèle 3D de l'espace entier. Les patterns lumineuxsont présentés en relation avec la matérialité, la géométrie et la position des fenêtres, des murs, des appareils d'éclairage et des sources d'éclairage et présentés sous forme d'images semblables à des dessins de présentation architecturaux. Plus précisément, les patterns lumineuxsont illustrés dans un plan d'étage, un plan de plafond réfléchi, une axonométrie et une coupe transversale. La méthode fournit des résultats de visualisation percutants. Elle facilite leur compréhension des patterns lumineux, car un nombre illimité d'images peut être généré à partir d'un nuage de points. L'exactitude de la méthode de relevé des espaces éclairés naturellement estégalementvérifié pour desespacesrelevésen une et plusieurs numérisations en comparant les patterns lumineux des imageries HDR et des nuages de points. De plus, le mémoire explore le potentiel de la numérisation laser tridimensionnelle comme méthode pour simuler de nouvelles ambiances lumineuses dans des espaces existants.This master thesisproposesLiDARtridimensional laser scanning as a new daylighting enquiry and visualization method for real built environments for architects and designers. It constitutes a complement to actual lighting methods because it responds to the limitations of the illuminance measuring method, computer simulation and high dynamic rangeimagery concerning the survey and representation of lighting patterns. It presents a case study to determine the advantages and limitations of 3D laser scanning in a vast, geometrically complex and fragmented naturally and artificially lit cafeteria. Lighting patterns and the geometry of the space are captured with a 3D laser scanner through a series of four scans. The scans are aligned and fused to form a single 3D model of the entire space. The lighting distribution patterns are showcased in relation to the materiality, geometry and position of windows, walls, lighting fixtures and the lighting sources and presented through images similar to architectural presentation drawings. More precisely, the lighting distribution patterns are illustrated in a floor plan, a reflected ceiling plan, an axonometry and a cross-section. The method provides powerful visualization results and facilitates their understanding as an unlimited number of images can be generated from a point cloud.The precision of the method for surveying daylit environments surveyed through one and several scans is also verified by comparing lighting patterns between HDR and point cloud imageries. Moreover, it explores tridimensional laser scanning as a method for rendering new lighting ambiances in existing spaces

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