Learning geometric and lighting priors from natural images

Comprendre les images est d’une importance cruciale pour une pléthore de tâches, de la composition numérique au ré-éclairage d’une image, en passant par la reconstruction 3D d’objets. Ces tâches permettent aux artistes visuels de réaliser des chef-d’oeuvres ou d’aider des opérateurs à prendre des décisions de façon sécuritaire en fonction de stimulis visuels. Pour beaucoup de ces tâches, les modèles physiques et géométriques que la communauté scientifique a développés donnent lieu à des problèmes mal posés possédant plusieurs solutions, dont généralement une seule est raisonnable. Pour résoudre ces indéterminations, le raisonnement sur le contexte visuel et sémantique d’une scène est habituellement relayé à un artiste ou un expert qui emploie son expérience pour réaliser son travail. Ceci est dû au fait qu’il est généralement nécessaire de raisonner sur la scène de façon globale afin d’obtenir des résultats plausibles et appréciables. Serait-il possible de modéliser l’expérience à partir de données visuelles et d’automatiser en partie ou en totalité ces tâches ? Le sujet de cette thèse est celui-ci : la modélisation d’a priori par apprentissage automatique profond pour permettre la résolution de problèmes typiquement mal posés. Plus spécifiquement, nous couvrirons trois axes de recherche, soient : 1) la reconstruction de surface par photométrie, 2) l’estimation d’illumination extérieure à partir d’une seule image et 3) l’estimation de calibration de caméra à partir d’une seule image avec un contenu générique. Ces trois sujets seront abordés avec une perspective axée sur les données. Chacun de ces axes comporte des analyses de performance approfondies et, malgré la réputation d’opacité des algorithmes d’apprentissage machine profonds, nous proposons des études sur les indices visuels captés par nos méthodes.Understanding images is needed for a plethora of tasks, from compositing to image relighting, including 3D object reconstruction. These tasks allow artists to realize masterpieces or help operators to safely make decisions based on visual stimuli. For many of these tasks, the physical and geometric models that the scientific community has developed give rise to ill-posed problems with several solutions, only one of which is generally reasonable. To resolve these indeterminations, the reasoning about the visual and semantic context of a scene is usually relayed to an artist or an expert who uses his experience to carry out his work. This is because humans are able to reason globally on the scene in order to obtain plausible and appreciable results. Would it be possible to model this experience from visual data and partly or totally automate tasks? This is the topic of this thesis: modeling priors using deep machine learning to solve typically ill-posed problems. More specifically, we will cover three research axes: 1) surface reconstruction using photometric cues, 2) outdoor illumination estimation from a single image and 3) camera calibration estimation from a single image with generic content. These three topics will be addressed from a data-driven perspective. Each of these axes includes in-depth performance analyses and, despite the reputation of opacity of deep machine learning algorithms, we offer studies on the visual cues captured by our methods

A Human-Centered Approach for the Design of Perimeter Office Spaces Based on Visual Environment Criteria

With perimeter office spaces with large glazing facades being an indisputable trend in modern architecture, human comfort has been in the scope of Building science; the necessity to improve occupants’ satisfaction, along with maintaining sustainability has become apparent, as productivity and even the well-being of occupants are connected with maintaining a pleasant environment in the interior. While thermal comfort has been extensively studied, the satisfaction with the visual environment has still aspects that are either inadequately explained, or even entirely absent from literature. This Thesis investigated most aspects of the visual environment, including visual comfort, lighting energy performance through the utilization of daylight and connection to the outdoors, using experimental studies, simulation studies and human subjects’ based experiments

Remote sensing of lunar aureole with a sky camera: Adding information in the nocturnal retrieval of aerosol properties with GRASP code

The use of sky cameras for nocturnal aerosol characterization is discussed in this study. Two sky cameras are configured to take High Dynamic Range (HDR) images at Granada and Valladolid (Spain). Some properties of the cameras, like effective wavelengths, sky coordinates of each pixel and pixel sensitivity, are characterized. After that, normalized camera radiances at lunar almucantar points (up to 20° in azimuth from the Moon) are obtained at three effective wavelengths from the HDR images. These normalized radiances are compared in different case studies to simulations fed with AERONET aerosol information, giving satisfactory results. The obtained uncertainty of normalized camera radiances is around 10% at 533 nm and 608 nm and 14% for 469 nm. Normalized camera radiances and six spectral aerosol optical depth values (obtained from lunar photometry) are used as input in GRASP code (Generalized Retrieval of Aerosol and Surface Properties) to retrieve aerosol properties for a dust episode over Valladolid. The retrieved aerosol properties (refractive indices, fraction of spherical particles and size distribution parameters) are in agreement with the nearest diurnal AERONET products. The calculated GRASP retrieval at night time shows an increase in coarse mode concentration along the night, while fine mode properties remained constant.This work was supported by the Andalusia Regional Government (project P12-RNM-2409) and by the “Consejería de Educación, Junta de Castilla y León” (project VA100U14).Spanish Ministry of Economy and Competitiveness and FEDER funds under the projects CGL2013-45410-R, CMT2015-66742-R, CGL2016-81092-R.“Juan de la Cierva-Formación” program (FJCI-2014-22052).European Union's Horizon 2020 research and innovation programme through project ACTRIS-2 (grant agreement No 654109)

Cloud cover detection combining high dymanics range sky images and ceilometer measurements

This paper presents a new algorithm for cloud detection based on high dynamic range images from a sky camera and ceilometer measurements. The algorithm is also able to detect the obstruction of the sun. This algorithm, called CPC (Camera Plus Ceilometer), is based on the assumption that under cloud-free conditions the sky field must show symmetry. The symmetry criteria are applied depending on ceilometer measurements of the cloud base height. CPC algorithm is applied in two Spanish locations (Granada and Valladolid). The performance of CPC retrieving the sun conditions (obstructed or unobstructed) is analyzed in detail using as reference pyranometer measurements at Granada. CPC retrievals are in agreement with those derived from the reference pyranometer in 85% of the cases (it seems that this agreement does not depend on aerosol size or optical depth). The agreement percentage goes down to only 48% when another algorithm, based on Red-Blue Ratio (RBR), is applied to the sky camera images. The retrieved cloud cover at Granada and Valladolid is compared with that registered by trained meteorological observers. CPC cloud cover is in agreement with the reference showing a slight overestimation and a mean absolute error around 1 okta. A major advantage of the CPC algorithm with respect to the RBR method is that the determined cloud cover is independent of aerosol properties. The RBR algorithm overestimates cloud cover for coarse aerosols and high loads. Cloud cover obtained only from ceilometer shows similar results than CPC algorithm; but the horizontal distribution cannot be obtained. In addition, it has been observed that under quick and strong changes on cloud cover ceilometers retrieve a cloud cover fitting worse with the real cloud cover.This work was supported by the Andalusia Regional Government (project P12-RNM-2409) and by the Consejería de Educación, Junta de Castilla y León (project VA100U14).Spanish Ministry of Economy and Competitiveness (CGL2013-45410-R, CMT2015-66742-R, CGL2016-81092-R, and FJCI-2014-22052).FEDER funds under the projects CGL2013-45410-R, CMT2015-66742-R, CGL2016-81092-R.“Juan de la Cierva-Formación” (FJCI-2014-22052) program.European Union H2020-INFRAIA-2014-2015 project ACTRIS-2 (grant agreement No. 654109