Rectification Strategies for a Binary Coded Structured Light 3D Scanner

Making a computer able to see exactly as a human being does was for many years one of the most interesting and challenging tasks involving lots of experts and pioneers in fields such as Computer Science and Artificial Intelligence. As a result, a whole field called Computer Vision has emerged becoming very soon a part of our daily life. The successful methodologies of this discipline have been applied in countless areas of application and their use is still in continuous expansion. On the other hand, in an increasing number of applications extracting information from simple 2D images is not enough and what is more requested instead is to use three-dimensional imaging techniques in order to reconstruct the 3D shape of the imaged objects and scene. The techniques developed in this context include both active systems, where some form of illumination is projected onto the scene, and passive systems, where the natural illumination of the scene is used. Among the active systems, one of the most reliable approaches for recovering the surface of objects is the use of structured light. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. In particular, the performances of this kind of 3D scanner are determined by two key aspects, the accuracy and the acquisition time. This thesis aims to design and experiment some rectification strategies for a prototype of binary coded structured light 3D scanner. The rectification is a commonly used technique for stereo vision systems which, in case of structured light, facilitates the establishment of correspondences across a projected pattern and an acquired image and reduces the number of pattern images to be projected, resulting finally in a speeding-up of the acquisition times.Making a computer able to see exactly as a human being does was for many years one of the most interesting and challenging tasks involving lots of experts and pioneers in fields such as Computer Science and Artificial Intelligence. As a result, a whole field called Computer Vision has emerged becoming very soon a part of our daily life. The successful methodologies of this discipline have been applied in countless areas of application and their use is still in continuous expansion. On the other hand, in an increasing number of applications extracting information from simple 2D images is not enough and what is more requested instead is to use three-dimensional imaging techniques in order to reconstruct the 3D shape of the imaged objects and scene. The techniques developed in this context include both active systems, where some form of illumination is projected onto the scene, and passive systems, where the natural illumination of the scene is used. Among the active systems, one of the most reliable approaches for recovering the surface of objects is the use of structured light. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. In particular, the performances of this kind of 3D scanner are determined by two key aspects, the accuracy and the acquisition time. This thesis aims to design and experiment some rectification strategies for a prototype of binary coded structured light 3D scanner. The rectification is a commonly used technique for stereo vision systems which, in case of structured light, facilitates the establishment of correspondences across a projected pattern and an acquired image and reduces the number of pattern images to be projected, resulting finally in a speeding-up of the acquisition times

Calibrage et modélisation d’un système de stéréovision hybride et panoramique

Dans cette thèse nos contributions à la résolution de deux problématiques rencontrées en vision numérique et en photogrammétrie, qui sont le calibrage de caméras et la stéréovision, sont présentées. Ces deux problèmes font l’objet de très nombreuses recherches depuis plusieurs années. Les techniques de calibrage existantes diffèrent beaucoup suivant le type de caméras à calibrer (classique ou panoramique, à focale fixe ou à focale variable, ..). Notre première contribution est un banc de calibrage, à l’aide des éléments d’optique diffractive, qui permet de calibrer avec une bonne précision une très grande partie des caméras existantes. Un modèle simple et précis qui décrit la projection de la grille formée sur l’image et une méthode de calibrage pour chaque type de caméras est proposé. La technique est très robuste et les résultats pour l’ensemble des caméras calibrées sont optimaux. Avec la multiplication des types de caméras et la diversité des modèles de projections, un modèle de formation d'image générique semble très intéressant. Notre deuxième contribution est un modèle de projection unifié pour plusieurs caméras classiques et panoramiques. Dans ce modèle, toute caméra est modélisée par une projection rectiligne et des splines cubiques composées permettant de représenter toutes sortes de distorsions. Cette approche permet de modéliser géométriquement les systèmes de stéréovision mixtes ou panoramiques et de convertir une image panoramique en une image classique. Par conséquent, le problème de stéréovision mixte ou panoramique est transformé en un problème de stéréovision conventionnelle. Mots clés : calibrage, vision panoramique, distorsion, fisheye, zoom, panomorphe, géométrie épipolaire, reconstruction tridimensionnelle, stéréovision hybride, stéréovision panoramique.This thesis aims to present our contributions to the resolution of two problems encountered in the field of computer vision and photogrammetry, which are camera calibration and stereovision. These two problems have been extensively studied in the last years. Different camera calibration techniques have been developed in the literature depending on the type of camera (classical or panoramic, with zoom lens or fixed lens..). Our first contribution is a compact and accurate calibration setup, based on diffractive optical elements, which is suitable for different kind of cameras. The technique is very robust and optimal results were achieved for different types of cameras. With the multiplication of camera types and the diversity of the projection models, a generic model has become very interesting. Our second contribution is a generic model, which is suitable for conventional and panoramic cameras. In this model, composed cubic splines functions provide more realistic model of both radial and tangential distortions. Such an approach allows to model either hybrid or panoramic stereovision system and to convert panoramic image to classical image. Consequently, the processing challenges of a hybrid stereovision system or a panoramic stereovision system are turned into simple classical stereovision problems. Keywords: Calibration, panoramic vision, distortions, fisheye, zoom, panomorph, epipolar geometry, three-dimensional reconstruction, hybrid stereovision, panoramic stereovision

Perceptual monocular depth estimation

Monocular depth estimation (MDE), which is the task of using a single image to predict scene depths, has gained considerable interest, in large part owing to the popularity of applying deep learning methods to solve “computer vision problems”. Monocular cues provide sufficient data for humans to instantaneously extract an understanding of scene geometries and relative depths, which is evidence of both the processing power of the human visual system and the predictive power of the monocular data. However, developing computational models to predict depth from monocular images remains challenging. Hand-designed MDE features do not perform particularly well, and even current “deep” models are still evolving. Here we propose a novel approach that uses perceptually-relevant natural scene statistics (NSS) features to predict depths from monocular images in a simple, scale-agnostic way that is competitive with state-of-the-art systems. While the statistics of natural photographic images have been successfully used in a variety of image and video processing, analysis, and quality assessment tasks, they have never been applied in a predictive end-to-end deep-learning model for monocular depth. Here we accomplish this by developing a new closed-form bivariate model of image luminances and use features extracted from this model and from other NSS models to drive a novel deep learning framework for predicting depth given a single image. We then extend our perceptually-based MDE model to fisheye images, which suffer from severe spatial distortions, and we show that our method that uses monocular cues performs comparably to our best fisheye stereo matching approach. Fisheye cameras have become increasingly popular in automotive applications, because they provide a wider (approximately 180 degrees) field-of-view (FoV), thereby giving drivers and driver assistance systems more visibility with minimal hardware. We explore fisheye stereo as it pertains to the problem of automotive surround-view (SV), specifically, which is a system comprising four fisheye cameras positioned on the front, right, rear, and left sides of a vehicle. The SV system perspectively transforms the images captured by these four cameras and stitches them together in a birdseye-view representation of the scene centered around the ego vehicle to display to the driver. With the camera axes oriented orthogonally away from each other and with each camera capturing approximately 180 degrees laterally, there exists an overlap in FoVs between adjacent cameras. It is within these regions where we have stereo vision, and can thus triangulate depths with an appropriate correspondence matching method. Each stereo system within the SV configuration has a wide baseline and two orthogonally-divergent camera axes, both of which make traditional methods for estimating stereo correspondences perform poorly. Our stereo pipeline, which relies on a neural network trained for predicting stereo correspondences, performs well even when the stereo system has limited overlap in FoVs and two dissimilar views. Our monocular approach, however, can be applied to entire fisheye images and does not rely on the underlying geometry of the stereo configuration. We compare these two depth-prediction methods in both performance and application. To explore stereo correspondence matching using fisheye images and MDE in non-fisheye images, we also generated a large-scale photorealistic synthetic database containing co-registered RGB images and depth maps using a simulated SV camera configuration. The database was first captured using fisheye cameras with known intrinsic parameters, and the fisheye distortions were then removed to create the non-fisheye portion of the database. We detail the process of creating the synthetic-but-realistic city scene in which we captured the images and depth maps along with the methodology for generating such a large, varied, and generalizable datasetElectrical and Computer Engineerin

Calibración y Segmentación de Imágenes en Cámaras con Distorsión Radial

La mayoría de los enfoques sobre el problema de la distorsión radial, asume la presencia de pixeles cuadrados, más no rectangulares, lo que complica el proceso de calibración en tiempo real. En este trabajo se propone una nueva metodología que permita tanto recalibrar la cámara, como analizar los efectos de la segmentación de imágenes compensadas con distorsión radial.&lt

3D panoramic imaging for virtual environment construction

The project is concerned with the development of algorithms for the creation of photo-realistic 3D virtual environments, overcoming problems in mosaicing, colour and lighting changes, correspondence search speed and correspondence errors due to lack of surface texture. A number of related new algorithms have been investigated for image stitching, content based colour correction and efficient 3D surface reconstruction. All of the investigations were undertaken by using multiple views from normal digital cameras, web cameras and a ”one-shot” panoramic system. In the process of 3D reconstruction a new interest points based mosaicing method, a new interest points based colour correction method, a new hybrid feature and area based correspondence constraint and a new structured light based 3D reconstruction method have been investigated. The major contributions and results can be summarised as follows: • A new interest point based image stitching method has been proposed and investigated. The robustness of interest points has been tested and evaluated. Interest points have been proved robust to changes in lighting, viewpoint, rotation and scale. • A new interest point based method for colour correction has been proposed and investigated. The results of linear and linear plus affine colour transforms have proved more accurate than traditional diagonal transforms in accurately matching colours in panoramic images. • A new structured light based method for correspondence point based 3D reconstruction has been proposed and investigated. The method has been proved to increase the accuracy of the correspondence search for areas with low texture. Correspondence speed has also been increased with a new hybrid feature and area based correspondence search constraint. • Based on the investigation, a software framework has been developed for image based 3D virtual environment construction. The GUI includes abilities for importing images, colour correction, mosaicing, 3D surface reconstruction, texture recovery and visualisation. • 11 research papers have been published.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Motorcycles that see: Multifocal stereo vision sensor for advanced safety systems in tilting vehicles

Advanced driver assistance systems, ADAS, have shown the possibility to anticipate crash accidents and effectively assist road users in critical traffic situations. This is not the case for motorcyclists, in fact ADAS for motorcycles are still barely developed. Our aim was to study a camera-based sensor for the application of preventive safety in tilting vehicles. We identified two road conflict situations for which automotive remote sensors installed in a tilting vehicle are likely to fail in the identification of critical obstacles. Accordingly, we set two experiments conducted in real traffic conditions to test our stereo vision sensor. Our promising results support the application of this type of sensors for advanced motorcycle safety applications