Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects

We introduce a method based on the deflectometry principle for the reconstruction of specular objects exhibiting significant size and geometric complexity. A key feature of our approach is the deployment of an Automatic Virtual Environment (CAVE) as pattern generator. To unfold the full power of this extraordinary experimental setup, an optical encoding scheme is developed which accounts for the distinctive topology of the CAVE. Furthermore, we devise an algorithm for detecting the object of interest in raw deflectometric images. The segmented foreground is used for single-view reconstruction, the background for estimation of the camera pose, necessary for calibrating the sensor system. Experiments suggest a significant gain of coverage in single measurements compared to previous methods. To facilitate research on specular surface reconstruction, we will make our data set publicly available

Specular surface recovery from reflections of a planar pattern undergoing an unknown pure translation

LNCS v. 6493 entitled: Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part 2This paper addresses the problem of specular surface recovery, and proposes a novel solution based on observing the reflections of a translating planar pattern. Previous works have demonstrated that a specular surface can be recovered from the reflections of two calibrated planar patterns. In this paper, however, only one reference planar pattern is assumed to have been calibrated against a fixed camera observing the specular surface. Instead of introducing and calibrating a second pattern, the reference pattern is allowed to undergo an unknown pure translation, and a closed form solution is derived for recovering such a motion. Unlike previous methods which estimate the shape by directly triangulating the visual rays and reflection rays, a novel method based on computing the projections of the visual rays on the translating pattern is introduced. This produces a depth range for each pixel which also provides a measure of the accuracy of the estimation. The proposed approach enables a simple auto-calibration of the translating pattern, and data redundancy resulting from the translating pattern can improve both the robustness and accuracy of the shape estimation. Experimental results on both synthetic and real data are presented to demonstrate the effectiveness of the proposed approach. © 2011 Springer-Verlag Berlin Heidelberg.postprintThe 10th Asian Conference on Computer Vision, Queenstown, New Zealand, 8-12 November 2010. In Lecture Notes in Computer Science, 2010, v. 6493, p. 137-14

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

Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking

Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática

Stereo Reconstruction using Induced Symmetry and 3D scene priors

Tese de doutoramento em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraRecuperar a geometria 3D a partir de dois vistas, conhecida como reconstrução estéreo, é um dos tópicos mais antigos e mais investigado em visão por computador. A computação de modelos 3D do ambiente é útil para uma grande número de aplicações, desde a robótica‎, passando pela sua utilização do consumidor comum, até a procedimentos médicos. O princípio para recuperar a estrutura 3D cena é bastante simples, no entanto, existem algumas situações que complicam consideravelmente o processo de reconstrução. Objetos que contêm estruturas pouco texturadas ou repetitivas, e superfícies com bastante inclinação ainda colocam em dificuldade os algoritmos state-of-the-art. Esta tese de doutoramento aborda estas questões e apresenta um novo framework estéreo que é completamente diferente das abordagens convencionais. Propomos a utilização de simetria em vez de foto-similaridade para avaliar a verosimilhança de pontos em duas imagens distintas serem uma correspondência. O framework é chamado SymStereo, e baseia-se no efeito de espelhagem que surge sempre que uma imagem é mapeada para a outra câmera usando a homografia induzida por um plano de corte virtual que intersecta a baseline. Experiências em estéreo denso comprovam que as nossas funções de custo baseadas em simetria se comparam favoravelmente com os custos baseados em foto-consistência de melhor desempenho. Param além disso, investigamos a possibilidade de realizar Stereo-Rangefinding, que consiste em usar estéreo passivo para recuperar exclusivamente a profundidade ao longo de um plano de varrimento. Experiências abrangentes fornecem evidência de que estéreo baseada em simetria induzida é especialmente eficaz para esta finalidade. Como segunda linha de investigação, propomos superar os problemas descritos anteriormente usando informação a priori sobre o ambiente 3D, com o objectivo de aumentar a robustez do processo de reconstrução. Para tal, apresentamos uma nova abordagem global para detectar pontos de desvanecimento e grupos de direcções de desvanecimento mutuamente ortogonais em ambientes Manhattan. Experiências quer em imagens sintéticas quer em imagens reais demonstram que os nossos algoritmos superaram os métodos state-of-the-art, mantendo a computação aceitável. Além disso, mostramos pela primeira vez resultados na detecção simultânea de múltiplas configurações de Manhattan. Esta informação a priori sobre a estrutura da cena é depois usada numa pipeline de reconstrução que gera modelos piecewise planares de ambientes urbanos a partir de duas vistas calibradas. A nossa formulação combina SymStereo e o algoritmo de clustering PEARL [3], e alterna entre um passo de otimização discreto, que funde hipóteses de superfícies planares e descarta detecções com pouco suporte, e uma etapa de otimização contínua, que refina as poses dos planos. Experiências com pares estéreo de ambientes interiores e exteriores confirmam melhorias significativas sobre métodos state-of-the-art relativamente a precisão e robustez. Finalmente, e como terceira contribuição para melhorar a visão estéreo na presença de superfícies inclinadas, estendemos o recente framework de agregação estéreo baseada em histogramas [4]. O algoritmo original utiliza janelas de suporte fronto-paralelas para a agregação de custo, o que leva a resultados imprecisos na presença de superfícies com inclinação significativa. Nós abordamos o problema considerando hipóteses de orientação discretas. Os resultados experimentais obtidos comprovam a eficácia do método, permitindo melhorar a precisção de correspondência, preservando simultaneamente uma baixa complexidade computacional.Recovering the 3D geometry from two or more views, known as stereo reconstruction, is one of the earliest and most investigated topics in computer vision. The computation of 3D models of an environment is useful for a very large number of applications, ranging from robotics, consumer utilization to medical procedures. The principle to recover the 3D scene structure is quite simple, however, there are some issues that considerable complicate the reconstruction process. Objects containing complicated structures, including low and repetitive textures, and highly slanted surfaces still pose difficulties to state-of-the-art algorithms. This PhD thesis tackles this issues and introduces a new stereo framework that is completely different from conventional approaches. We propose to use symmetry instead of photo-similarity for assessing the likelihood of two image locations being a match. The framework is called SymStereo, and is based on the mirroring effect that arises whenever one view is mapped into the other using the homography induced by a virtual cut plane that intersects the baseline. Extensive experiments in dense stereo show that our symmetry-based cost functions compare favorably against the best performing photo-similarity matching costs. In addition, we investigate the possibility of accomplishing Stereo-Rangefinding that consists in using passive stereo to exclusively recover depth along a scan plane. Thorough experiments provide evidence that Stereo from Induced Symmetry is specially well suited for this purpose. As a second research line, we propose to overcome the previous issues using priors about the 3D scene for increasing the robustness of the reconstruction process. For this purpose, we present a new global approach for detecting vanishing points and groups of mutually orthogonal vanishing directions in man-made environments. Experiments in both synthetic and real images show that our algorithms outperform the state-of-the-art methods while keeping computation tractable. In addition, we show for the first time results in simultaneously detecting multiple Manhattan-world configurations. This prior information about the scene structure is then included in a reconstruction pipeline that generates piece-wise planar models of man-made environments from two calibrated views. Our formulation combines SymStereo and PEARL clustering [3], and alternates between a discrete optimization step, that merges planar surface hypotheses and discards detections with poor support, and a continuous optimization step, that refines the plane poses. Experiments with both indoor and outdoor stereo pairs show significant improvements over state-of-the-art methods with respect to accuracy and robustness. Finally, and as a third contribution to improve stereo matching in the presence of surface slant, we extend the recent framework of Histogram Aggregation [4]. The original algorithm uses a fronto-parallel support window for cost aggregation, leading to inaccurate results in the presence of significant surface slant. We address the problem by considering discrete orientation hypotheses. The experimental results prove the effectiveness of the approach, which enables to improve the matching accuracy while preserving a low computational complexity

Stereo Reconstruction using Induced Symmetry and 3D scene priors

Tese de doutoramento em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraRecuperar a geometria 3D a partir de dois vistas, conhecida como reconstrução estéreo, é um dos tópicos mais antigos e mais investigado em visão por computador. A computação de modelos 3D do ambiente é útil para uma grande número de aplicações, desde a robótica‎, passando pela sua utilização do consumidor comum, até a procedimentos médicos. O princípio para recuperar a estrutura 3D cena é bastante simples, no entanto, existem algumas situações que complicam consideravelmente o processo de reconstrução. Objetos que contêm estruturas pouco texturadas ou repetitivas, e superfícies com bastante inclinação ainda colocam em dificuldade os algoritmos state-of-the-art. Esta tese de doutoramento aborda estas questões e apresenta um novo framework estéreo que é completamente diferente das abordagens convencionais. Propomos a utilização de simetria em vez de foto-similaridade para avaliar a verosimilhança de pontos em duas imagens distintas serem uma correspondência. O framework é chamado SymStereo, e baseia-se no efeito de espelhagem que surge sempre que uma imagem é mapeada para a outra câmera usando a homografia induzida por um plano de corte virtual que intersecta a baseline. Experiências em estéreo denso comprovam que as nossas funções de custo baseadas em simetria se comparam favoravelmente com os custos baseados em foto-consistência de melhor desempenho. Param além disso, investigamos a possibilidade de realizar Stereo-Rangefinding, que consiste em usar estéreo passivo para recuperar exclusivamente a profundidade ao longo de um plano de varrimento. Experiências abrangentes fornecem evidência de que estéreo baseada em simetria induzida é especialmente eficaz para esta finalidade. Como segunda linha de investigação, propomos superar os problemas descritos anteriormente usando informação a priori sobre o ambiente 3D, com o objectivo de aumentar a robustez do processo de reconstrução. Para tal, apresentamos uma nova abordagem global para detectar pontos de desvanecimento e grupos de direcções de desvanecimento mutuamente ortogonais em ambientes Manhattan. Experiências quer em imagens sintéticas quer em imagens reais demonstram que os nossos algoritmos superaram os métodos state-of-the-art, mantendo a computação aceitável. Além disso, mostramos pela primeira vez resultados na detecção simultânea de múltiplas configurações de Manhattan. Esta informação a priori sobre a estrutura da cena é depois usada numa pipeline de reconstrução que gera modelos piecewise planares de ambientes urbanos a partir de duas vistas calibradas. A nossa formulação combina SymStereo e o algoritmo de clustering PEARL [3], e alterna entre um passo de otimização discreto, que funde hipóteses de superfícies planares e descarta detecções com pouco suporte, e uma etapa de otimização contínua, que refina as poses dos planos. Experiências com pares estéreo de ambientes interiores e exteriores confirmam melhorias significativas sobre métodos state-of-the-art relativamente a precisão e robustez. Finalmente, e como terceira contribuição para melhorar a visão estéreo na presença de superfícies inclinadas, estendemos o recente framework de agregação estéreo baseada em histogramas [4]. O algoritmo original utiliza janelas de suporte fronto-paralelas para a agregação de custo, o que leva a resultados imprecisos na presença de superfícies com inclinação significativa. Nós abordamos o problema considerando hipóteses de orientação discretas. Os resultados experimentais obtidos comprovam a eficácia do método, permitindo melhorar a precisção de correspondência, preservando simultaneamente uma baixa complexidade computacional.Recovering the 3D geometry from two or more views, known as stereo reconstruction, is one of the earliest and most investigated topics in computer vision. The computation of 3D models of an environment is useful for a very large number of applications, ranging from robotics, consumer utilization to medical procedures. The principle to recover the 3D scene structure is quite simple, however, there are some issues that considerable complicate the reconstruction process. Objects containing complicated structures, including low and repetitive textures, and highly slanted surfaces still pose difficulties to state-of-the-art algorithms. This PhD thesis tackles this issues and introduces a new stereo framework that is completely different from conventional approaches. We propose to use symmetry instead of photo-similarity for assessing the likelihood of two image locations being a match. The framework is called SymStereo, and is based on the mirroring effect that arises whenever one view is mapped into the other using the homography induced by a virtual cut plane that intersects the baseline. Extensive experiments in dense stereo show that our symmetry-based cost functions compare favorably against the best performing photo-similarity matching costs. In addition, we investigate the possibility of accomplishing Stereo-Rangefinding that consists in using passive stereo to exclusively recover depth along a scan plane. Thorough experiments provide evidence that Stereo from Induced Symmetry is specially well suited for this purpose. As a second research line, we propose to overcome the previous issues using priors about the 3D scene for increasing the robustness of the reconstruction process. For this purpose, we present a new global approach for detecting vanishing points and groups of mutually orthogonal vanishing directions in man-made environments. Experiments in both synthetic and real images show that our algorithms outperform the state-of-the-art methods while keeping computation tractable. In addition, we show for the first time results in simultaneously detecting multiple Manhattan-world configurations. This prior information about the scene structure is then included in a reconstruction pipeline that generates piece-wise planar models of man-made environments from two calibrated views. Our formulation combines SymStereo and PEARL clustering [3], and alternates between a discrete optimization step, that merges planar surface hypotheses and discards detections with poor support, and a continuous optimization step, that refines the plane poses. Experiments with both indoor and outdoor stereo pairs show significant improvements over state-of-the-art methods with respect to accuracy and robustness. Finally, and as a third contribution to improve stereo matching in the presence of surface slant, we extend the recent framework of Histogram Aggregation [4]. The original algorithm uses a fronto-parallel support window for cost aggregation, leading to inaccurate results in the presence of significant surface slant. We address the problem by considering discrete orientation hypotheses. The experimental results prove the effectiveness of the approach, which enables to improve the matching accuracy while preserving a low computational complexity

Fehlerkaschierte Bildbasierte Darstellungsverfahren

Creating photo-realistic images has been one of the major goals in computer graphics since its early days. Instead of modeling the complexity of nature with standard modeling tools, image-based approaches aim at exploiting real-world footage directly,as they are photo-realistic by definition. A drawback of these approaches has always been that the composition or combination of different sources is a non-trivial task, often resulting in annoying visible artifacts. In this thesis we focus on different techniques to diminish visible artifacts when combining multiple images in a common image domain. The results are either novel images, when dealing with the composition task of multiple images, or novel video sequences rendered in real-time, when dealing with video footage from multiple cameras.Fotorealismus ist seit jeher eines der großen Ziele in der Computergrafik. Anstatt die Komplexität der Natur mit standardisierten Modellierungswerkzeugen nachzubauen, gehen bildbasierte Ansätze den umgekehrten Weg und verwenden reale Bildaufnahmen zur Modellierung, da diese bereits per Definition fotorealistisch sind. Ein Nachteil dieser Variante ist jedoch, dass die Komposition oder Kombination mehrerer Quellbilder eine nichttriviale Aufgabe darstellt und häufig unangenehm auffallende Artefakte im erzeugten Bild nach sich zieht. In dieser Dissertation werden verschiedene Ansätze verfolgt, um Artefakte zu verhindern oder abzuschwächen, welche durch die Komposition oder Kombination mehrerer Bilder in einer gemeinsamen Bilddomäne entstehen. Im Ergebnis liefern die vorgestellten Verfahren neue Bilder oder neue Ansichten einer Bildsammlung oder Videosequenz, je nachdem, ob die jeweilige Aufgabe die Komposition mehrerer Bilder ist oder die Kombination mehrerer Videos verschiedener Kameras darstellt

PatchMatch Belief Propagation for Correspondence Field Estimation and its Applications

Correspondence fields estimation is an important process that lies at the core of many different applications. Is it often seen as an energy minimisation problem, which is usually decomposed into the combined minimisation of two energy terms. The first is the unary energy, or data term, which reflects how well the solution agrees with the data. The second is the pairwise energy, or smoothness term, and ensures that the solution displays a certain level of smoothness, which is crucial for many applications. This thesis explores the possibility of combining two well-established algorithms for correspondence field estimation, PatchMatch and Belief Propagation, in order to benefit from the strengths of both and overcome some of their weaknesses. Belief Propagation is a common algorithm that can be used to optimise energies comprising both unary and pairwise terms. It is however computational expensive and thus not adapted to continuous spaces which are often needed in imaging applications. On the other hand, PatchMatch is a simple, yet very efficient method for optimising the unary energy of such problems on continuous and high dimensional spaces. The algorithm has two main components: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these components are related to the components of a specific form of Belief Propagation, called Particle Belief Propagation (PBP). PatchMatch however suffers from the lack of an explicit smoothness term. We show that unifying the two approaches yields a new algorithm, PMBP, which has improved performance compared to PatchMatch and is orders of magnitude faster than PBP. We apply our new optimiser to two different applications: stereo matching and optical flow. We validate the benefits of PMBP through series of experiments and show that we consistently obtain lower errors than both PatchMatch and Belief Propagation

Dense mirroring surface recovery from 1D homographies and sparse correspondences

In this work we recover the 3D shape of mirroring objects such as mirrors, sunglasses, and stainless steel objects. A computer monitor displays several images of parallel stripes, each image at a different angle. Reflections of these stripes in a mirroring surface are captured by the camera. For every image point, the directions of the displayed stripes and their reflections in the image are related by a 1D homography which can be computed robustly and using the statistically accurate heteroscedastic model, without monitor–image correspondence, which is generally required by other techniques. Focusing on a small set of image points for which monitor–image correspondence is computed, the depth and the local shape may be calculated relying on this homography. This is done by an optimization process which is related to the one proposed by Savarese, Chen and Perona[10], but is different and more stable. Then dense surface recovery is performed using constrained interpolation, which does not simply interpolate the surface depth values, but rather solves for the depth, the correspondence, and the local surface shape, simultaneously at each interpolated point. Consistency with the 1D homography is thus required. The proposed method as well as the method described in [10] are inherently unstable on a small part of the surface. We propose a method to detect these instabilities and correct them. The method was implemented and the shapes of a mirror, sunglasses, and a stainless steel ashtray were recovered at sub-millimeter accuracy. 1-4244-1180-7/07/$25.00 ©2007 IEEE