Towards Robust Visual Localization in Challenging Conditions

Visual localization is a fundamental problem in computer vision, with a multitude of applications in robotics, augmented reality and structure-from-motion. The basic problem is to, based on one or more images, figure out the position and orientation of the camera which captured these images relative to some model of the environment. Current visual localization approaches typically work well when the images to be localized are captured under similar conditions compared to those captured during mapping. However, when the environment exhibits large changes in visual appearance, due to e.g. variations in weather, seasons, day-night or viewpoint, the traditional pipelines break down. The reason is that the local image features used are based on low-level pixel-intensity information, which is not invariant to these transformations: when the environment changes, this will cause a different set of keypoints to be detected, and their descriptors will be different, making the long-term visual localization problem a challenging one. In this thesis, four papers are included, which present work towards solving the problem of long-term visual localization. Three of the articles present ideas for how semantic information may be included to aid in the localization process: one approach relies only on the semantic information for visual localization, another shows how the semantics can be used to detect outlier feature correspondences, while the third presents a sequential localization algorithm which relies on the consistency of the reprojection of a semantic model, instead of traditional features. The final article is a benchmark paper, where we present three new benchmark datasets aimed at evaluating localization algorithms in the context of long-term visual localization

Robust and Accurate Camera Localisation at a Large Scale

The task of camera-based localization aims to quickly and precisely pinpoint at which location (and viewing direction) the image was taken, against a pre-stored large-scale map of the environment. This technique can be used in many 3D computer vision applications, e.g., AR/VR and autonomous driving. Mapping the world is the first step to enable camera-based localization since a pre-stored map serves as a reference for a query image/sequence. In this thesis, we exploit three readily available sources: (i) satellite images; (ii) ground-view images; (iii) 3D points cloud. Based on the above three sources, we propose solutions to localize a query camera both effectively and efficiently, i.e., accurately localizing a query camera under a variety of lighting and viewing conditions within a small amount of time. The main contributions are summarized as follows. In chapter 3, we separately present a minimal 4-point and 2-point solver to estimate a relative and absolute camera pose. The core idea is exploiting the vertical direction from IMU or vanishing point to derive a closed-form solution of a quartic equation and a quadratic equation for the relative and absolute camera pose, respectively. In chapter 4, we localize a ground-view query image against a satellite map. Inspired by the insight that humans commonly use orientation information as an important cue for spatial localization, we propose a method that endows deep neural networks with the 'commonsense' of orientation. We design a Siamese network that explicitly encodes each pixel's orientation of the ground-view and satellite images. Our method boosts the learned deep features' discriminative power, outperforming all previous methods. In chapter 5, we localize a ground-view query image against a ground-view image database. We propose a representation learning method having higher location-discriminating power. The core idea is learning discriminative image embedding. Similarities among intra-place images (viewing the same landmarks) are maximized while similarities among inter-place images (viewing different landmarks) are minimized. The method is easy to implement and pluggable into any CNN. Experiments show that our method outperforms all previous methods. In chapter 6, we localize a ground-view query image against a large-scale 3D points cloud with visual descriptors. To address the ambiguities in direct 2D--3D feature matching, we introduce a global matching method that harnesses global contextual information exhibited both within the query image and among all the 3D points in the map. The core idea is to find the optimal 2D set to 3D set matching. Tests on standard benchmark datasets show the effectiveness of our method. In chapter 7, we localize a ground-view query image against a 3D points cloud with only coordinates. The problem is also known as blind Perspective-n-Point. We propose a deep CNN model that simultaneously solves for both the 6-DoF absolute camera pose and 2D--3D correspondences. The core idea is extracting point-wise 2D and 3D features from their coordinates and matching 2D and 3D features effectively in a global feature matching module. Extensive tests on both real and simulated data have shown that our method substantially outperforms existing approaches. Last, in chapter 8, we study the potential of using 3D lines. Specifically, we study the problem of aligning two partially overlapping 3D line reconstructions in Euclidean space. This technique can be used for localization with respect to a 3D line database when query 3D line reconstructions are available (e.g., from stereo triangulation). We propose a neural network, taking Pluecker representations of lines as input, and solving for line-to-line matches and estimate a 6-DoF rigid transformation. Experiments on indoor and outdoor datasets show that our method's registration (rotation and translation) precision outperforms baselines significantly

Localisation and tracking of stationary users for extended reality

In this thesis, we investigate the topics of localisation and tracking in the context of Extended Reality. In many on-site or outdoor Augmented Reality (AR) applications, users are standing or sitting in one place and performing mostly rotational movements, i.e. stationary. This type of stationary motion also occurs in Virtual Reality (VR) applications such as panorama capture by moving a camera in a circle. Both applications require us to track the motion of a camera in potentially very large and open environments. State-of-the-art methods such as Structure-from-Motion (SfM), and Simultaneous Localisation and Mapping (SLAM), tend to rely on scene reconstruction from significant translational motion in order to compute camera positions. This can often lead to failure in application scenarios such as tracking for seated sport spectators, or stereo panorama capture where the translational movement is small compared to the scale of the environment. To begin with, we investigate the topic of localisation as it is key to providing global context for many stationary applications. To achieve this, we capture our own datasets in a variety of large open spaces including two sports stadia. We then develop and investigate these techniques in the context of these sports stadia using a variety of state-of-the-art localisation approaches. We cover geometry-based methods to handle dynamic aspects of a stadium environment, as well as appearance-based methods, and compare them to a state-of-the-art SfM system to identify the most applicable methods for server-based and on-device localisation. Recent work in SfM has shown that the type of stationary motion that we target can be reliably estimated by applying spherical constraints to the pose estimation. In this thesis, we extend these concepts into a real-time keyframe-based SLAM system for the purposes of AR, and develop a unique data structure for simplifying keyframe selection. We show that our constrained approach can track more robustly in these challenging stationary scenarios compared to state-of-the-art SLAM through both synthetic and real-data tests. In the application of capturing stereo panoramas for VR, this thesis demonstrates the unsuitability of standard SfM techniques for reconstructing these circular videos. We apply and extend recent research in spherically constrained SfM to creating stereo panoramas and compare this with state-of-the-art general SfM in a technical evaluation. With a user study, we show that the motion requirements of our SfM approach are similar to the natural motion of users, and that a constrained SfM approach is sufficient for providing stereoscopic effects when viewing the panoramas in VR

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Geometric Inference with Microlens Arrays

This dissertation explores an alternative to traditional fiducial markers where geometric information is inferred from the observed position of 3D points seen in an image. We offer an alternative approach which enables geometric inference based on the relative orientation of markers in an image. We present markers fabricated from microlenses whose appearance changes depending on the marker\u27s orientation relative to the camera. First, we show how to manufacture and calibrate chromo-coding lenticular arrays to create a known relationship between the observed hue and orientation of the array. Second, we use 2 small chromo-coding lenticular arrays to estimate the pose of an object. Third, we use 3 large chromo-coding lenticular arrays to calibrate a camera with a single image. Finally, we create another type of fiducial marker from lenslet arrays that encode orientation with discrete black and white appearances. Collectively, these approaches oer new opportunities for pose estimation and camera calibration that are relevant for robotics, virtual reality, and augmented reality

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Privacidade em comunicações de dados para ambientes contextualizados

Doutoramento em InformáticaInternet users consume online targeted advertising based on information collected about them and voluntarily share personal information in social networks. Sensor information and data from smart-phones is collected and used by applications, sometimes in unclear ways. As it happens today with smartphones, in the near future sensors will be shipped in all types of connected devices, enabling ubiquitous information gathering from the physical environment, enabling the vision of Ambient Intelligence. The value of gathered data, if not obvious, can be harnessed through data mining techniques and put to use by enabling personalized and tailored services as well as business intelligence practices, fueling the digital economy. However, the ever-expanding information gathering and use undermines the privacy conceptions of the past. Natural social practices of managing privacy in daily relations are overridden by socially-awkward communication tools, service providers struggle with security issues resulting in harmful data leaks, governments use mass surveillance techniques, the incentives of the digital economy threaten consumer privacy, and the advancement of consumergrade data-gathering technology enables new inter-personal abuses. A wide range of fields attempts to address technology-related privacy problems, however they vary immensely in terms of assumptions, scope and approach. Privacy of future use cases is typically handled vertically, instead of building upon previous work that can be re-contextualized, while current privacy problems are typically addressed per type in a more focused way. Because significant effort was required to make sense of the relations and structure of privacy-related work, this thesis attempts to transmit a structured view of it. It is multi-disciplinary - from cryptography to economics, including distributed systems and information theory - and addresses privacy issues of different natures. As existing work is framed and discussed, the contributions to the state-of-theart done in the scope of this thesis are presented. The contributions add to five distinct areas: 1) identity in distributed systems; 2) future context-aware services; 3) event-based context management; 4) low-latency information flow control; 5) high-dimensional dataset anonymity. Finally, having laid out such landscape of the privacy-preserving work, the current and future privacy challenges are discussed, considering not only technical but also socio-economic perspectives.Quem usa a Internet vê publicidade direccionada com base nos seus hábitos de navegação, e provavelmente partilha voluntariamente informação pessoal em redes sociais. A informação disponível nos novos telemóveis é amplamente acedida e utilizada por aplicações móveis, por vezes sem razões claras para isso. Tal como acontece hoje com os telemóveis, no futuro muitos tipos de dispositivos elecónicos incluirão sensores que permitirão captar dados do ambiente, possibilitando o surgimento de ambientes inteligentes. O valor dos dados captados, se não for óbvio, pode ser derivado através de técnicas de análise de dados e usado para fornecer serviços personalizados e definir estratégias de negócio, fomentando a economia digital. No entanto estas práticas de recolha de informação criam novas questões de privacidade. As práticas naturais de relações inter-pessoais são dificultadas por novos meios de comunicação que não as contemplam, os problemas de segurança de informação sucedem-se, os estados vigiam os seus cidadãos, a economia digital leva á monitorização dos consumidores, e as capacidades de captação e gravação dos novos dispositivos eletrónicos podem ser usadas abusivamente pelos próprios utilizadores contra outras pessoas. Um grande número de áreas científicas focam problemas de privacidade relacionados com tecnologia, no entanto fazem-no de maneiras diferentes e assumindo pontos de partida distintos. A privacidade de novos cenários é tipicamente tratada verticalmente, em vez de re-contextualizar trabalho existente, enquanto os problemas actuais são tratados de uma forma mais focada. Devido a este fraccionamento no trabalho existente, um exercício muito relevante foi a sua estruturação no âmbito desta tese. O trabalho identificado é multi-disciplinar - da criptografia à economia, incluindo sistemas distribuídos e teoria da informação - e trata de problemas de privacidade de naturezas diferentes. À medida que o trabalho existente é apresentado, as contribuições feitas por esta tese são discutidas. Estas enquadram-se em cinco áreas distintas: 1) identidade em sistemas distribuídos; 2) serviços contextualizados; 3) gestão orientada a eventos de informação de contexto; 4) controlo de fluxo de informação com latência baixa; 5) bases de dados de recomendação anónimas. Tendo descrito o trabalho existente em privacidade, os desafios actuais e futuros da privacidade são discutidos considerando também perspectivas socio-económicas

Learning-based depth and pose prediction for 3D scene reconstruction in endoscopy

Colorectal cancer is the third most common cancer worldwide. Early detection and treatment of pre-cancerous tissue during colonoscopy is critical to improving prognosis. However, navigating within the colon and inspecting the endoluminal tissue comprehensively are challenging, and success in both varies based on the endoscopist's skill and experience. Computer-assisted interventions in colonoscopy show much promise in improving navigation and inspection. For instance, 3D reconstruction of the colon during colonoscopy could promote more thorough examinations and increase adenoma detection rates which are associated with improved survival rates. Given the stakes, this thesis seeks to advance the state of research from feature-based traditional methods closer to a data-driven 3D reconstruction pipeline for colonoscopy. More specifically, this thesis explores different methods that improve subtasks of learning-based 3D reconstruction. The main tasks are depth prediction and camera pose estimation. As training data is unavailable, the author, together with her co-authors, proposes and publishes several synthetic datasets and promotes domain adaptation models to improve applicability to real data. We show, through extensive experiments, that our depth prediction methods produce more robust results than previous work. Our pose estimation network trained on our new synthetic data outperforms self-supervised methods on real sequences. Our box embeddings allow us to interpret the geometric relationship and scale difference between two images of the same surface without the need for feature matches that are often unobtainable in surgical scenes. Together, the methods introduced in this thesis help work towards a complete, data-driven 3D reconstruction pipeline for endoscopy

From the grassroots: regional film policy and practice in England

This thesis is an analysis of the development of regional film policy and practice in England. From the late 1960s regional film production sectors have gradually emerged from small-scale, under-resourced cottage industries to become significant areas of British film practice. By the mid-2000s the English regions were incorporated into a national film policy strategy based on a network of nine Regional Screen Agencies and centrally coordinated by the UK Film Council. Along with similar developments in Scotland, Wales and Northern Ireland, for many commentators the devolution of film production has questioned the traditional way that British cinema can be understood as a national cinema. This thesis aims to understand how regional film production sectors have developed, what filmmaking practices have characterised them and what these mean for British cinema. It is argued that the development of regional film policy and practice can be understood in terms of two distinct models: the regional workshop model and the regional “creative industries” model. Each was based on different systemic processes and ideological frameworks, and is best represented in institutions. The development of an institutional framework for regional film production is placed within the wider context of the trajectory of public policy in Britain in the post-War period; specifically the shifting boundaries between cultural policy and economic policy. The thesis employs a critical political economy approach to analyse the development of these policy frameworks and the filmmaking practices that have emerged from them, including detailed case studies of regional film practices, specifically regional documentary, regional short film and regionally-produced feature films