Yes / Designed as a pragmatic approach that anticipates change to cultural heritage, this chapter discusses responses that encompass records for tangible cultural heritage (monuments, sites and landscapes) and the narratives that see the impact upon them. The Curious Travellers project provides a mechanism for digitally documenting heritage sites that have been destroyed or are under immediate threat from unsympathetic development, neglect, natural disasters, conflict and cultural vandalism. The project created and tested data-mining and crowd-sourced workflows that enable the accurate digital documentation and 3D visualisation of buildings, archaeological sites, monuments and heritage at risk. When combined with donated content, image data are used to recreate 3D models of endangered and lost monuments and heritage sites using a combination of open-source and proprietary methods. These models are queried against contextual information, helping to place and interrogate structures with relevant site and landscape data for the surrounding environment. Geospatial records such as aerial imagery and 3D mobile mapping laser scan data serve as a framework for adding new content and testing accuracy. In preserving time-event records, image metadata offers important information on visitor habits and conservation pressures, which can be used to inform measures for site management. / The Curious Travellers project was funded as a component of the AHRC Digital Transformations Theme Large Grant ‘Fragmented Heritage’ (AH/L00688X/1). AHRC Follow-on funding has seen this approach contribute to the BReaTHe project (AH/S005951/1) which seeks to Build Resilience Through Heritage for displaced communities and with a contribution to the BA Cities and Infrastructures Scheme project, ‘Reducing Disaster Risk to Life and Livelihoods by evaluating the seismic performance of retrofitted interventions within Kathmandu’s UNESCO World Heritage Site during the 2015 Earthquake’, with Durham University (KF1\100109).

This thesis covers the topic of depth- assisted Structure from Motion (SfM). When performing classic SfM, the goal is to reconstruct a 3D scene using only a set of unstructured RGB images. What is attempted to be achieved in this thesis is adding the depth dimension to the problem formulation, and consequently create a system that can receive a set of RGBD images. The problem has been addressed by modifying an already existing SfM pipeline and in particular, its Bundle Adjustment (BA) process. Comparisons between the modified framework and the baseline framework resulted in conclusions regarding the impact of the modifications. The results show mainly two things. First of all, the accuracy of the framework is increased in most situations. The difference is the most significant when the captured scene only is covered from a small sector. However, noisy data can cause the modified pipeline to decrease in performance. Secondly, the run time of the framework is significantly reduced. A discussion of how to modify other parts of the pipeline is covered in the conclusion of the report. / Följande examensarbete behandlar ämnet djupassisterad Struktur genom Rörelse (eng. SfM). Vid klassisk SfM är målet att återskapa en 3D scen, endast med hjälp av en sekvens av oordnade RGB bilder. I djupassiterad SfM adderas djupinformationen till problemformulering och följaktligen har ett system som kan motta RGBD bilder skapats. Problemet har lösts genom att modifiera en befintlig SfM- mjukvara och mer specifikt dess Buntjustering (eng. BA). Resultatet från den modifierade mjukvaran jämförs med resultatet av originalutgåvan för att dra slutsatser rådande modifikationens påverkan på prestandan. Resultaten visar huvudsakligen två saker. Först och främst, den modifierade mjukvaran producerar resultat med högre noggrannhet i de allra flesta fall. Skillnaden är som allra störst när bilderna är tagna från endast en liten sektor som omringar scenen. Data med brus kan dock försämra systemets prestanda aningen jämfört med orginalsystemet. För det andra, så minskar exekutionstiden betydligt. Slutligen diskuteras hur mjukvaran kan vidareutvecklas för att ytterligare förbättra resultaten.

Acquisition of 3D model of real objects and scenes is indispensable and useful in many practical applications, such as digital archives, game and entertainment industries, engineering, advertisement. There are 2 main methods for 3D acquisition : laser-based reconstruction (active method) and image-based reconstruction from multiple images of the scene in different points of view (passive method). While laser-based reconstruction achieves high accuracy, it is complex, expensive and difficult to set up for large-scale outdoor reconstruction. Image-based, or multi-view stereo methods are more versatile, easier, faster and cheaper. By the time we begin this thesis, most multi-view methods could handle only low resolution images under controlled environment. This thesis targets multi-view stereo both both in large scale and high accuracy issues. We significantly improve some previous methods and combine them into a remarkably effective multi-view pipeline with GPU acceleration. From high-resolution images, we produce highly complete and accurate meshes that achieve best scores in many international recognized benchmarks. Aiming even larger scale, on one hand, we develop Divide and Conquer approaches in order to reconstruct many small parts of a big scene. On the other hand, to combine separate partial results, we create a new merging method, which can merge automatically and quickly hundreds of meshes. With all these components, we are successful to reconstruct highly accurate water-tight meshes for cities and historical monuments from large collections of high-resolution images (around 1600 images of 5 M Pixel images)

Acquisition of 3D model of real objects and scenes is indispensable and useful in many practical applications, such as digital archives, game and entertainment industries, engineering, advertisement. There are 2 main methods for 3D acquisition : laser-based reconstruction (active method) and image-based reconstruction from multiple images of the scene in different points of view (passive method). While laser-based reconstruction achieves high accuracy, it is complex, expensive and difficult to set up for large-scale outdoor reconstruction. Image-based, or multi-view stereo methods are more versatile, easier, faster and cheaper. By the time we begin this thesis, most multi-view methods could handle only low resolution images under controlled environment. This thesis targets multi-view stereo both both in large scale and high accuracy issues. We significantly improve some previous methods and combine them into a remarkably effective multi-view pipeline with GPU acceleration. From high-resolution images, we produce highly complete and accurate meshes that achieve best scores in many international recognized benchmarks. Aiming even larger scale, on one hand, we develop Divide and Conquer approaches in order to reconstruct many small parts of a big scene. On the other hand, to combine separate partial results, we create a new merging method, which can merge automatically and quickly hundreds of meshes. With all these components, we are successful to reconstruct highly accurate water-tight meshes for cities and historical monuments from large collections of high-resolution images (around 1600 images of 5 M Pixel images)

A number of three-dimensional virtual environments are available to develop vision-based robotic capabilities. These have the advantage of repeated trials at low cost compared to field testing. However, they still suffer from a lack of realism and credibility for validation and verification.This work consists of the creation and validation of state of the art virtual terrains for research in Martian rover vision-based navigation algorithms. This Master's thesis focuses on the creation of virtual environments, which are the exact imitations of the planetary terrain testbed at the European Space Agency's ESTEC site. Two different techniques are used to recreate the Martian-like site in a simulator. The first method uses a novel multi-view stereo reconstruction technique. The second method uses a high precision laser scanning system to accurately map the terrain.Comparison of real environment to the virtual environments is done at exact same locations by making use of captured stereo camera images. Ultimately, the differences will be characterized by the main known feature detectors (e.g. SURF, and SIFT).The present work led to the creation and validation of a database containing highly realistic virtual terrains which can be found on Mars for the purpose of vision-based control algorithms verification. / <p>Validerat; 20120821 (anonymous)</p>

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23312号 / 情博第748号 / 新制||情||128(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 飯山 将晃, 教授 西野 恒, 教授 中村 裕一, 教授 美濃 導彦(京都大学 名誉教授) / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Nous étudions dans cette thèse le problème de reconstruction 3D multi-vue à partir d’une séquence d’images au sol acquises dans des environnements urbains ainsi que la prise en compte d’a priori permettant la préservation de la structure sous-jacente de la géométrie 3D observée, ainsi que le passage à l’échelle de tels processus de reconstruction qui est intrinsèquement délicat dans le contexte de l’imagerie urbaine. Bien que ces deux axes aient été traités de manière extensive dans la littérature, les méthodes de reconstruction 3D structurée souffrent d’une complexité en temps de calculs restreignant significativement leur intérêt. D’autre part, les approches de reconstruction 3D large échelle produisent généralement une géométrie simplifiée, perdant ainsi des éléments de structures qui sont importants dans le contexte urbain. L’objectif de cette thèse est de concilier les avantages des approches de reconstruction 3D structurée à celles des méthodes rapides produisant une géométrie simplifiée. Pour ce faire, nous présentons “Patchwork Stereo”, un framework qui combine stéréoscopie photométrique utilisant une poignée d’images issues de points de vue éloignés, et un nuage de point épars. Notre méthode intègre une analyse simultanée 2D-3D réalisant une extraction robuste de plans 3D ainsi qu’une segmentation d’images top-down structurée et repose sur une optimisation par champs de Markov aléatoires. Les contributions présentées sont évaluées via des expériences quantitatives et qualitatives sur des données d’imagerie urbaine complexes illustrant des performances tant quant à la fidélité structurelle des reconstructions 3D que du passage à l’échelle / In this thesis, we address the problem of 3D reconstruction from a sequence of calibrated street-level photographs with a simultaneous focus on scalability and the use of structure priors in Multi-View Stereo (MVS).While both aspects have been studied broadly, existing scalable MVS approaches do not handle well the ubiquitous structural regularities, yet simple, of man-made environments. On the other hand, structure-aware 3D reconstruction methods are slow and scale poorly with the size of the input sequences and/or may even require additional restrictive information. The goal of this thesis is to reconcile scalability and structure awareness within common MVS grounds using soft, generic priors which encourage : (i) piecewise planarity, (ii) alignment of objects boundaries with image gradients and (iii) with vanishing directions (VDs), and (iv) objects co-planarity. To do so, we present the novel “Patchwork Stereo” framework which integrates photometric stereo from a handful of wide-baseline views and a sparse 3D point cloud combining robust 3D plane extraction and top-down image partitioning from a unified 2D-3D analysis in a principled Markov Random Field energy minimization. We evaluate our contributions quantitatively and qualitatively on challenging urban datasets and illustrate results which are at least on par with state-of-the-art methods in terms of geometric structure, but achieved in several orders of magnitude faster paving the way for photo-realistic city-scale modeling

Computational stereo estimates 3D structure by analyzing visual changes between two or more passive images of a scene that are captured from different viewpoints. It is a key enabler for ubiquitous autonomous systems, large-scale surveying, virtual reality, and improved techniques for compression, tracking, and object recognition. The fact that computational stereo is an under-constrained inverse problem causes many challenges. Its computational and memory requirements are high. Typical heuristics and assumptions, used to constrain solutions or reduce computation, prevent treatment of key realities such as reflection, translucency, ambient lighting changes, or moving objects in the scene. As a result, a general solution is lacking. Stochastic models are common in computational stereo, but stochastic algorithms are severely under-represented. In this dissertation I present two stochastic algorithms and demonstrate their advantages over deterministic approaches. I first present the Quality-Efficient Stochastic Sampling (QUESS) approach. QUESS reduces the number of match quality function evaluations needed to estimate dense stereo correspondences. This facilitates the use of complex quality metrics or metrics that take unique values at non-integer disparities. QUESS is shown to outperform two competing approaches, and to have more attractive memory and scaling properties than approaches based on exhaustive sampling. I then present a second novel approach based on the Hough transform and extend it with distributed ray tracing (DRT). DRT is a stochastic anti-aliasing technique common to computer rendering but which has not been used in computational stereo. I demonstrate that the DRT-enhanced approach outperforms the unenhanced approach, a competing variation that uses re-accumulation in the Hough domain, and another baseline approach. DRT’s advantages are particularly strong for reduced image resolution and/or reduced accumulator matrix resolution. In support of this second approach, I develop two novel variations of the Hough transform that use DRT, and demonstrate that they outperform competing variations on a traditional line segment detection problem. I generalize these two examples to draw broader conclusions, suggest future work, and call for a deeper exploration by the community. Both practical and academic gaps in the state of the art can be reduced by a renewed exploration of stochastic computational stereo techniques. / text

The ability to automatically estimate the volume of timber is becoming increasingly important within the timber industry. The large number of timber trucks arriving each day at Swedish timber terminals fortifies the need for a volume estimation performed in real-time and on-the-go as the trucks arrive. This thesis investigates if a volumetric integration of disparity maps acquired from a Multi-View Stereo (MVS) system is a suitable approach for automatic volume estimation of timber loads. As real-time execution is preferred, efforts were made to provide a scalable method. The proposed method was quantitatively evaluated on datasets containing two geometric objects of known volume. A qualitative comparison to manual volume estimates of timber loads was also made on datasets recorded at a Swedish timber terminal. The proposed method is shown to be both accurate and precise under specific circumstances. However, robustness is poor to varying weather conditions, although a more thorough evaluation of this aspect needs to be performed. The method is also parallelizable, which means that future efforts can be made to significantly decrease execution time.

L'acquisition de modèles 3D des scènes réelles trouve son utilité dans de nombreuses applications pratiques, comme l'archivage numérique, les jeux vidéo, l'ingénierie, la publicité. Il existe principalement deux méthodes pour acquérir un modèle 3D: la reconstruction avec un scanner laser (méthode active) et la reconstruction à partir de plusieurs photographies d'une même scène prise dans des points de vues différentes (méthode passive). Si la méthode active permet d'acquérir des modèles avec une grande précision, il est cependant coûteux et difficile à mettre en place pour de grandes scènes extérieures. La méthode passive, ou la stéréo multi-vues est en revanche plus flexible, facile à mettre en oeuvre et surtout moins coûteuse que la méthode active. Cette thèse s'attaque au problème de la reconstruction de stéréo multi-vues à grande échelle et précise pour les scènes extérieures. Nous améliorons des méthodes précédentes et les assemblons pour créer une chaîne de stéréo multi-vues efficace tirant parti de l'accélération de cartes graphiques. La chaîne produit des maillages de qualité à partir d'images de haute résolution, ce qui permet d'atteindre les meilleurs scores dans de nombreuses évaluations. Aux plus grandes échelles, nous développons d'une part des techniques de type diviser-pour-régner pour reconstruire des morceaux partiaux de la scène. D'autre part, pour combiner ces résultats séparés, nous créons une nouvelle méthode qui fusionne rapidement des centaines de maillages. Nous réussissons à reconstruire de beaux maillages urbains et des monuments historiques précis à partir de grandes collections d'images (environ 1600 images de 5M Pixel) / Acquisition of 3D model of real objects and scenes is indispensable and useful in many practical applications, such as digital archives, game and entertainment industries, engineering, advertisement. There are 2 main methods for 3D acquisition : laser-based reconstruction (active method) and image-based reconstruction from multiple images of the scene in different points of view (passive method). While laser-based reconstruction achieves high accuracy, it is complex, expensive and difficult to set up for large-scale outdoor reconstruction. Image-based, or multi-view stereo methods are more versatile, easier, faster and cheaper. By the time we begin this thesis, most multi-view methods could handle only low resolution images under controlled environment. This thesis targets multi-view stereo both both in large scale and high accuracy issues. We significantly improve some previous methods and combine them into a remarkably effective multi-view pipeline with GPU acceleration. From high-resolution images, we produce highly complete and accurate meshes that achieve best scores in many international recognized benchmarks. Aiming even larger scale, on one hand, we develop Divide and Conquer approaches in order to reconstruct many small parts of a big scene. On the other hand, to combine separate partial results, we create a new merging method, which can merge automatically and quickly hundreds of meshes. With all these components, we are successful to reconstruct highly accurate water-tight meshes for cities and historical monuments from large collections of high-resolution images (around 1600 images of 5 M Pixel images)