Leveraging Overhead Imagery for Localization, Mapping, and Understanding

Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis is a general framework for estimating geospatial functions, such as land cover or land use, which integrates visual evidence from both ground-level and overhead image viewpoints

Deep Probabilistic Models for Camera Geo-Calibration

The ultimate goal of image understanding is to transfer visual images into numerical or symbolic descriptions of the scene that are helpful for decision making. Knowing when, where, and in which direction a picture was taken, the task of geo-calibration makes it possible to use imagery to understand the world and how it changes in time. Current models for geo-calibration are mostly deterministic, which in many cases fails to model the inherent uncertainties when the image content is ambiguous. Furthermore, without a proper modeling of the uncertainty, subsequent processing can yield overly confident predictions. To address these limitations, we propose a probabilistic model for camera geo-calibration using deep neural networks. While our primary contribution is geo-calibration, we also show that learning to geo-calibrate a camera allows us to implicitly learn to understand the content of the scene

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Fusing Multimedia Data Into Dynamic Virtual Environments

In spite of the dramatic growth of virtual and augmented reality (VR and AR) technology, content creation for immersive and dynamic virtual environments remains a significant challenge. In this dissertation, we present our research in fusing multimedia data, including text, photos, panoramas, and multi-view videos, to create rich and compelling virtual environments. First, we present Social Street View, which renders geo-tagged social media in its natural geo-spatial context provided by 360° panoramas. Our system takes into account visual saliency and uses maximal Poisson-disc placement with spatiotemporal filters to render social multimedia in an immersive setting. We also present a novel GPU-driven pipeline for saliency computation in 360° panoramas using spherical harmonics (SH). Our spherical residual model can be applied to virtual cinematography in 360° videos. We further present Geollery, a mixed-reality platform to render an interactive mirrored world in real time with three-dimensional (3D) buildings, user-generated content, and geo-tagged social media. Our user study has identified several use cases for these systems, including immersive social storytelling, experiencing the culture, and crowd-sourced tourism. We next present Video Fields, a web-based interactive system to create, calibrate, and render dynamic videos overlaid on 3D scenes. Our system renders dynamic entities from multiple videos, using early and deferred texture sampling. Video Fields can be used for immersive surveillance in virtual environments. Furthermore, we present VRSurus and ARCrypt projects to explore the applications of gestures recognition, haptic feedback, and visual cryptography for virtual and augmented reality. Finally, we present our work on Montage4D, a real-time system for seamlessly fusing multi-view video textures with dynamic meshes. We use geodesics on meshes with view-dependent rendering to mitigate spatial occlusion seams while maintaining temporal consistency. Our experiments show significant enhancement in rendering quality, especially for salient regions such as faces. We believe that Social Street View, Geollery, Video Fields, and Montage4D will greatly facilitate several applications such as virtual tourism, immersive telepresence, and remote education

Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation

In this paper, we address panoramic semantic segmentation which is under-explored due to two critical challenges: (1) image distortions and object deformations on panoramas; (2) lack of semantic annotations in the 360-degree imagery. To tackle these problems, first, we propose the upgraded Transformer for Panoramic Semantic Segmentation, i.e., Trans4PASS+, equipped with Deformable Patch Embedding (DPE) and Deformable MLP (DMLPv2) modules for handling object deformations and image distortions whenever (before or after adaptation) and wherever (shallow or deep levels). Second, we enhance the Mutual Prototypical Adaptation (MPA) strategy via pseudo-label rectification for unsupervised domain adaptive panoramic segmentation. Third, aside from Pinhole-to-Panoramic (Pin2Pan) adaptation, we create a new dataset (SynPASS) with 9,080 panoramic images, facilitating Synthetic-to-Real (Syn2Real) adaptation scheme in 360-degree imagery. Extensive experiments are conducted, which cover indoor and outdoor scenarios, and each of them is investigated with Pin2Pan and Syn2Real regimens. Trans4PASS+ achieves state-of-the-art performances on four domain adaptive panoramic semantic segmentation benchmarks. Code is available at https://github.com/jamycheung/Trans4PASS.Comment: Extended version of CVPR 2022 paper arXiv:2203.01452. Code is available at https://github.com/jamycheung/Trans4PAS

Spherical perspective

We survey the present state of spherical perspective, regarding both mathematical structure and drawing practice, with a view to applications in the visual arts. We define a spherical perspective as the entailment of a conical anamorphosis with a compact flattening of the visual sphere. We examine a general framework for solving spherical perspectives, exemplified with the azimuthal equidistant (“fisheye”) and equirectangular cases. We consider the relation between spherical and curvilinear perspectives. We briefly discuss computer renderings but focus on methods adapted to freehand sketching or technical drawing with simple instruments such as ruler and compass. We discuss how handmade spherical perspective drawings can generate immersive anamorphoses, which can be rendered as virtual reality panoramas, leading to hybrid visual creations that bridge the gap between traditional drawing and digital environments.info:eu-repo/semantics/publishedVersio

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysisready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data

Viewpoint-Free Photography for Virtual Reality

Viewpoint-free photography, i.e., interactively controlling the viewpoint of a photograph after capture, is a standing challenge. In this thesis, we investigate algorithms to enable viewpoint-free photography for virtual reality (VR) from casual capture, i.e., from footage easily captured with consumer cameras. We build on an extensive body of work in image-based rendering (IBR). Given images of an object or scene, IBR methods aim to predict the appearance of an image taken from a novel perspective. Most IBR methods focus on full or near-interpolation, where the output viewpoints either lie directly between captured images, or nearby. These methods are not suitable for VR, where the user has significant range of motion and can look in all directions. Thus, it is essential to create viewpoint-free photos with a wide field-of-view and sufficient positional freedom to cover the range of motion a user might experience in VR. We focus on two VR experiences: 1) Seated VR experiences, where the user can lean in different directions. This simplifies the problem, as the scene is only observed from a small range of viewpoints. Thus, we focus on easy capture, showing how to turn panorama-style capture into 3D photos, a simple representation for viewpoint-free photos, and also how to speed up processing so users can see the final result on-site. 2) Room-scale VR experiences, where the user can explore vastly different perspectives. This is challenging: More input footage is needed, maintaining real-time display rates becomes difficult, view-dependent appearance and object backsides need to be modelled, all while preventing noticeable mistakes. We address these challenges by: (1) creating refined geometry for each input photograph, (2) using a fast tiled rendering algorithm to achieve real-time display rates, and (3) using a convolutional neural network to hide visual mistakes during compositing. Overall, we provide evidence that viewpoint-free photography is feasible from casual capture. We thoroughly compare with the state-of-the-art, showing that our methods achieve both a numerical improvement and a clear increase in visual quality for both seated and room-scale VR experiences

Deep Learning for 3D Visual Perception

La percepción visual 3D se refiere al conjunto de problemas que engloban la reunión de información a través de un sensor visual y la estimación la posición tridimensional y estructura de los objetos y formaciones al rededor del sensor. Algunas funcionalidades como la estimación de la ego moción o construcción de mapas are esenciales para otras tareas de más alto nivel como conducción autónoma o realidad aumentada. En esta tesis se han atacado varios desafíos en la percepción 3D, todos ellos útiles desde la perspectiva de SLAM (Localización y Mapeo Simultáneos) que en si es un problema de percepción 3D.Localización y Mapeo Simultáneos –SLAM– busca realizar el seguimiento de la posición de un dispositivo (por ejemplo de un robot, un teléfono o unas gafas de realidad virtual) con respecto al mapa que está construyendo simultáneamente mientras la plataforma explora el entorno. SLAM es una tecnología muy relevante en distintas aplicaciones como realidad virtual, realidad aumentada o conducción autónoma. SLAM Visual es el termino utilizado para referirse al problema de SLAM resuelto utilizando unicamente sensores visuales. Muchas de las piezas del sistema ideal de SLAM son, hoy en día, bien conocidas, maduras y en muchos casos presentes en aplicaciones. Sin embargo, hay otras piezas que todavía presentan desafíos de investigación significantes. En particular, en los que hemos trabajado en esta tesis son la estimación de la estructura 3D al rededor de una cámara a partir de una sola imagen, reconocimiento de lugares ya visitados bajo cambios de apariencia drásticos, reconstrucción de alto nivel o SLAM en entornos dinámicos; todos ellos utilizando redes neuronales profundas.Estimación de profundidad monocular is la tarea de percibir la distancia a la cámara de cada uno de los pixeles en la imagen, utilizando solo la información que obtenemos de una única imagen. Este es un problema mal condicionado, y por lo tanto es muy difícil de inferir la profundidad exacta de los puntos en una sola imagen. Requiere conocimiento de lo que se ve y del sensor que utilizamos. Por ejemplo, si podemos saber que un modelo de coche tiene cierta altura y también sabemos el tipo de cámara que hemos utilizado (distancia focal, tamaño de pixel...); podemos decir que si ese coche tiene cierta altura en la imagen, por ejemplo 50 pixeles, esta a cierta distancia de la cámara. Para ello nosotros presentamos el primer trabajo capaz de estimar profundidad a partir de una sola vista que es capaz de obtener un funcionamiento razonable con múltiples tipos de cámara; como un teléfono o una cámara de video.También presentamos como estimar, utilizando una sola imagen, la estructura de una habitación o el plan de la habitación. Para este segundo trabajo, aprovechamos imágenes esféricas tomadas por una cámara panorámica utilizando una representación equirectangular. Utilizando estas imágenes recuperamos el plan de la habitación, nuestro objetivo es reconocer las pistas en la imagen que definen la estructura de una habitación. Nos centramos en recuperar la versión más simple, que son las lineas que separan suelo, paredes y techo.Localización y mapeo a largo plazo requiere dar solución a los cambios de apariencia en el entorno; el efecto que puede tener en una imagen tomarla en invierno o verano puede ser muy grande. Introducimos un modelo multivista invariante a cambios de apariencia que resuelve el problema de reconocimiento de lugares de forma robusta. El reconocimiento de lugares visual trata de identificar un lugar que ya hemos visitado asociando pistas visuales que se ven en las imágenes; la tomada en el pasado y la tomada en el presente. Lo preferible es ser invariante a cambios en punto de vista, iluminación, objetos dinámicos y cambios de apariencia a largo plazo como el día y la noche, las estaciones o el clima.Para tener funcionalidad a largo plazo también presentamos DynaSLAM, un sistema de SLAM que distingue las partes estáticas y dinámicas de la escena. Se asegura de estimar su posición unicamente basándose en las partes estáticas y solo reconstruye el mapa de las partes estáticas. De forma que si visitamos una escena de nuevo, nuestro mapa no se ve afectado por la presencia de nuevos objetos dinámicos o la desaparición de los anteriores.En resumen, en esta tesis contribuimos a diferentes problemas de percepción 3D; todos ellos resuelven problemas del SLAM Visual.<br /

The Power of the In-Between

"The Power of the In-Between: Intermediality as a Tool for Aesthetic Analysis and Critical Reflection gathers fourteen individual case studies where intermedial issues—issues concerning that which takes place in between media—are explored in relation to a range of different cultural objects and contexts, different methodological approaches, and different disciplinary perspectives. The cases investigate the intermediality of such manifold objects and phenomena as contemporary installation art, twentieth-century geography books, renaissance sculpture, media theory, and public architecture of the 1970s. They also bring together scholars from the disciplines of art history, comparative literature, theatre studies, musicology, and the history of ideas. Starting out from an inclusive understanding of intermediality as “relations between media conventionally perceived as different,” each author specifies and investigates “intermediality” in their own particular case; that is, each examines how it is inflected by particular objects, methods, and research questions. “Intermediality” thus serves both as a concept employed to cover an inclusive range of cultural objects, cultural contexts, methodological approaches, and so on, and as a concept to be modelled out by the particular cases it is brought to bear on. Rather than merely applying a predefined concept, the objectives are experimental. The authors explore the concept of intermediality as a malleable tool of research. This volume further makes a point of transgressing the divide between media history and semiotically and/or aesthetically oriented intermedial studies. The former concerns the specificity of media technologies and media interrelations in socially, politically, and epistemologically defined space and time, and the latter targets formal considerations of media objects and its various meaning-making elements. These two conventionally separated fields of research are integrated in order to produce a richer understanding of the analytical and historical, as well as the aesthetic and technological, conditions and possibilities of intermedial phenomena.