Variable Resolution & Dimensional Mapping For 3d Model Optimization

Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data

TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.Comment: Accepted to SIGGRAPH ASIA 202

Using Linear Features for Aerial Image Sequence Mosaiking

With recent advances in sensor technology and digital image processing techniques, automatic image mosaicking has received increased attention in a variety of geospatial applications, ranging from panorama generation and video surveillance to image based rendering. The geometric transformation used to link images in a mosaic is the subject of image orientation, a fundamental photogrammetric task that represents a major research area in digital image analysis. It involves the determination of the parameters that express the location and pose of a camera at the time it captured an image. In aerial applications the typical parameters comprise two translations (along the x and y coordinates) and one rotation (rotation about the z axis). Orientation typically proceeds by extracting from an image control points, i.e. points with known coordinates. Salient points such as road intersections, and building corners are commonly used to perform this task. However, such points may contain minimal information other than their radiometric uniqueness, and, more importantly, in some areas they may be impossible to obtain (e.g. in rural and arid areas). To overcome this problem we introduce an alternative approach that uses linear features such as roads and rivers for image mosaicking. Such features are identified and matched to their counterparts in overlapping imagery. Our matching approach uses critical points (e.g. breakpoints) of linear features and the information conveyed by them (e.g. local curvature values and distance metrics) to match two such features and orient the images in which they are depicted. In this manner we orient overlapping images by comparing breakpoint representations of complete or partial linear features depicted in them. By considering broader feature metrics (instead of single points) in our matching scheme we aim to eliminate the effect of erroneous point matches in image mosaicking. Our approach does not require prior approximate parameters, which are typically an essential requirement for successful convergence of point matching schemes. Furthermore, we show that large rotation variations about the z-axis may be recovered. With the acquired orientation parameters, image sequences are mosaicked. Experiments with synthetic aerial image sequences are included in this thesis to demonstrate the performance of our approach

Processing Camera-captured Document Images: Geometric Rectification, Mosaicing, and Layout Structure Recognition

This dissertation explores three topics: 1) geometric rectification of cameracaptured document images, 2) camera-captured document mosaicing, and 3) layout structure recognition. The first two topics pertain to camera-based document image analysis, a new trend within the OCR community. Compared to typical scanners,cameras offer convenient, flexible, portable, and non-contact image capture, which enables many new applications and breathes new life into existing ones. The third topic is related to the need for efficient metadata extraction methods, critical for managing digitized documents. The kernel of our geometric rectification framework is a novel method for estimating document shape from a single camera-captured image. Our method uses texture flows detected in printed text areas and is insensitive to occlusion. Classification of planar versus curved documents is done automatically. For planar pages, we obtain full metric rectification. For curved pages, we estimate a planar-strip approximation based on properties of developable surfaces. Our method can process any planar or smoothly curved document captured from an arbitrary position without requiring 3D data, metric data, or camera calibration. For the second topic, we design a novel registration method for document images, which produces good results in difficult situations including large displacements, severe projective distortion, small overlapping areas, and lack of distinguishable feature points. We implement a selective image composition method that outperforms conventional image blending methods in overlapping areas. It eliminates double images caused by mis-registration and preserves the sharpness in overlapping areas. We solve the third topic with a graph-based model matching framework. Layout structures are modeled by graphs, which integrate local and global features and are extensible to new features in the future. Our model can handle large variation within a class and subtle differences between classes. Through graph matching, the layout structure of a document is discovered. Our layout structure recognition technique accomplishes document classification and logical component labeling at the same time. Our model learning method enables a model to adapt to changes in classes over time

A unified framework for document image restoration

Ph.DDOCTOR OF PHILOSOPH

Surface Appearance Estimation from Video Sequences

The realistic virtual reproduction of real world objects using Computer Graphics techniques requires the accurate acquisition and reconstruction of both 3D geometry and surface appearance. Unfortunately, in several application contexts, such as Cultural Heritage (CH), the reflectance acquisition can be very challenging due to the type of object to acquire and the digitization conditions. Although several methods have been proposed for the acquisition of object reflectance, some intrinsic limitations still make its acquisition a complex task for CH artworks: the use of specialized instruments (dome, special setup for camera and light source, etc.); the need of highly controlled acquisition environments, such as a dark room; the difficulty to extend to objects of arbitrary shape and size; the high level of expertise required to assess the quality of the acquisition. The Ph.D. thesis proposes novel solutions for the acquisition and the estimation of the surface appearance in fixed and uncontrolled lighting conditions with several degree of approximations (from a perceived near diffuse color to a SVBRDF), taking advantage of the main features that differentiate a video sequences from an unordered photos collections: the temporal coherence; the data redundancy; the easy of the acquisition, which allows acquisition of many views of the object in a short time. Finally, Reflectance Transformation Imaging (RTI) is an example of widely used technology for the acquisition of the surface appearance in the CH field, even if limited to single view Reflectance Fields of nearly flat objects. In this context, the thesis addresses also two important issues in RTI usage: how to provide better and more flexible virtual inspection capabilities with a set of operators that improve the perception of details, features and overall shape of the artwork; how to increase the possibility to disseminate this data and to support remote visual inspection of both scholar and ordinary public

Towards Real-Time Novel View Synthesis Using Visual Hulls

This thesis discusses fast novel view synthesis from multiple images taken from different viewpoints. We propose several new algorithms that take advantage of modern graphics hardware to create novel views. Although different approaches are explored, one geometry representation, the visual hull, is employed throughout our work. First the visual hull plays an auxiliary role and assists in reconstruction of depth maps that are utilized for novel view synthesis. Then we treat the visual hull as the principal geometry representation of scene objects. A hardwareaccelerated approach is presented to reconstruct and render visual hulls directly from a set of silhouette images. The reconstruction is embedded in the rendering process and accomplished with an alpha map trimming technique. We go on by combining this technique with hardware-accelerated CSG reconstruction to improve the rendering quality of visual hulls. Finally, photometric information is exploited to overcome an inherent limitation of the visual hull. All algorithms are implemented on a distributed system. Novel views are generated at interactive or real-time frame rates.In dieser Dissertation werden mehrere Verfahren vorgestellt, mit deren Hilfe neue Ansichten einer Szene aus mehreren Bildströmen errechnet werden können. Die Bildströme werden hierzu aus unterschiedlichen Blickwinkeln auf die Szene aufgezeichnet. Wir schlagen mehrere Algorithmen vor, welche die Funktionen moderner Grafikhardware ausnutzen, um die neuen Ansichten zu errechnen. Obwohl die Verfahren sich methodisch unterscheiden, basieren sie auf der gleichen Geometriedarstellung, der Visual Hull. In der ersten Methode spielt die Visual Hull eine unterstützende Rolle bei der Rekonstruktion von Tiefenbildern, die zur Erzeugung neuer Ansichten verwendet werden. In den nachfolgend vorgestellten Verfahren dient die Visual Hull primär der Repräsentation von Objekten in einer Szene. Eine hardwarebeschleunigte Methode, um Visual Hulls direkt aus mehreren Silhouettenbildern zu rekonstruieren und zu rendern, wird vorgestellt. Das Rekonstruktionsverfahren ist hierbei Bestandteil der Renderingmethode und basiert auf einer Alpha Map Trimming Technik. Ein weiterer Algorithmus verbessert die Qualitaet der gerenderten Visual Hulls, indem das Alpha-Map-basierte Verfahren mit einer hardware-beschleunigten CSG Rekonstruktiontechnik kombiniert wird. Eine vierte Methode nutzt zusaetzlich photometrische Information aus, um eine grundlegende Beschraenkung des Visual-Hull-Ansatzes zu umgehen. Alle Verfahren ermoeglichen die interaktive oder Echtzeit- Erzeugung neuer Ansichten

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

Near real-time monitoring of buried oil pipeline right-of-way for third-party incursion

Many security systems employing different methods have been proposed to protect buried oil pipelines transporting petroleum products from the well head via the refinery to: depots and other receiving stations. Currently there is a security gap in the monitoring of these buried pipelines in real time and in keeping them protected from third party interference. This thesis addresses the problem of monitoring these systems by developing an automated image analysis system with the aid of a low-cost multisensory Unmanned Aerial Vehicle (UAV) for monitoring of buried pipeline right-of-way (ROW). The method used in this research is based on the identification of threat objects of interest from the video frame sequences of the pipeline right-of-way acquired by the UAV. This is achieved by training the system to recognise objects of interest using trained correlation filters. To determine the geographical location of detected objects, the Video frame sequences captured by the UAV platform were ortho-rectified to form ortho-images which were then mosaicked to form a seamless Digital Surface Model (DSM) covering the test area using a photogrammetry model. The DSM formed from the mosaicking of ortho-images is then emerged with a digital globe for geo-referencing of detected objects. Experiments were carried out on a test field located in United Kingdom and Nigeria, where video and telemetry data were collected, then processed using the techniques created in this research. The results demonstrated that the developed correlation filter was able to detect objects of interest despite the distortions that come with the object image, due to the fact that the expected distortion was compensated for using the training images. When compared with the 6 control points in the digital globe the accuracy of the two-dimension DSM gave a misalignment error of between 2 and 3 metres