Photometric stereo and appearance capture

Ph.DDOCTOR OF PHILOSOPH

An interest point based illumination condition matching approach to photometric registration within augmented reality worlds

With recent and continued increases in computing power, and advances in the field of computer graphics, realistic augmented reality environments can now offer inexpensive and powerful solutions in a whole range of training, simulation and leisure applications. One key challenge to maintaining convincing augmentation, and therefore user immersion, is ensuring consistent illumination conditions between virtual and real environments, so that objects appear to be lit by the same light sources. This research demonstrates how real world lighting conditions can be determined from the two-dimensional view of the user. Virtual objects can then be illuminated and virtual shadows cast using these conditions. This new technique uses pairs of interest points from real objects and the shadows that they cast, viewed from a binocular perspective, to determine the position of the illuminant. This research has been initially focused on single point light sources in order to show the potential of the technique and has investigated the relationships between the many parameters of the vision system. Optimal conditions have been discovered by mapping the results of experimentally varying parameters such as FoV, camera angle and pose, image resolution, aspect ratio and illuminant distance. The technique is able to provide increased robustness where greater resolution imagery is used. Under optimal conditions it is possible to derive the position of a real world light source with low average error. An investigation of available literature has revealed that other techniques can be inflexible, slow, or disrupt scene realism. This technique is able to locate and track a moving illuminant within an unconstrained, dynamic world without the use of artificial calibration objects that would disrupt scene realism. The technique operates in real-time as the new algorithms are of low computational complexity. This allows high framerates to be maintained within augmented reality applications. Illuminant updates occur several times a second on an average to high end desktop computer. Future work will investigate the automatic identification and selection of pairs of interest points and the exploration of global illuminant conditions. The latter will include an analysis of more complex scenes and the consideration of multiple and varied light sources.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Shadow segmentation and tracking in real-world conditions

Visual information, in the form of images and video, comes from the interaction of light with objects. Illumination is a fundamental element of visual information. Detecting and interpreting illumination effects is part of our everyday life visual experience. Shading for instance allows us to perceive the three-dimensional nature of objects. Shadows are particularly salient cues for inferring depth information. However, we do not make any conscious or unconscious effort to avoid them as if they were an obstacle when we walk around. Moreover, when humans are asked to describe a picture, they generally omit the presence of illumination effects, such as shadows, shading, and highlights, to give a list of objects and their relative position in the scene. Processing visual information in a way that is close to what the human visual system does, thus being aware of illumination effects, represents a challenging task for computer vision systems. Illumination phenomena interfere in fact with fundamental tasks in image analysis and interpretation applications, such as object extraction and description. On the other hand, illumination conditions are an important element to be considered when creating new and richer visual content that combines objects from different sources, both natural and synthetic. When taken into account, illumination effects can play an important role in achieving realism. Among illumination effects, shadows are often integral part of natural scenes and one of the elements contributing to naturalness of synthetic scenes. In this thesis, the problem of extracting shadows from digital images is discussed. A new analysis method for the segmentation of cast shadows in still and moving images without the need of human supervision is proposed. The problem of separating moving cast shadows from moving objects in image sequences is particularly relevant for an always wider range of applications, ranging from video analysis to video coding, and from video manipulation to interactive environments. Therefore, particular attention has been dedicated to the segmentation of shadows in video. The validity of the proposed approach is however also demonstrated through its application to the detection of cast shadows in still color images. Shadows are a difficult phenomenon to model. Their appearance changes with changes in the appearance of the surface they are cast upon. It is therefore important to exploit multiple constraints derived from the analysis of the spectral, geometric and temporal properties of shadows to develop effective techniques for their extraction. The proposed method combines an analysis of color information and of photometric invariant features to a spatio-temporal verification process. With regards to the use of color information for shadow analysis, a complete picture of the existing solutions is provided, which points out the fundamental assumptions, the adopted color models and the link with research problems such as computational color constancy and color invariance. The proposed spatial verification does not make any assumption about scene geometry nor about object shape. The temporal analysis is based on a novel shadow tracking technique. On the basis of the tracking results, a temporal reliability estimation of shadows is proposed which allows to discard shadows which do not present time coherence. The proposed approach is general and can be applied to a wide class of applications and input data. The proposed cast shadow segmentation method has been evaluated on a number of different video data representing indoor and outdoor real-world environments. The obtained results have confirmed the validity of the approach, in particular its ability to deal with different types of content and its robustness to different physically important independent variables, and have demonstrated the improvement with respect to the state of the art. Examples of application of the proposed shadow segmentation tool to the enhancement of video object segmentation, tracking and description operations, and to video composition, have demonstrated the advantages of a shadow-aware video processing

Surface analysis and visualization from multi-light image collections

Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation

Multiplexed photography : single-exposure capture of multiple camera settings

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 115-124).The space of camera settings is large and individual settings can vary dramatically from scene to scene. This thesis explores methods for capturing and manipulating multiple camera settings in a single exposure. Multiplexing multiple camera settings in a single exposure can allow post-exposure control and improve the quality of photographs taken in challenging lighting environments (e.g. low light or high motion). We first describe the design and implementation of a prototype optical system and associated algorithms to capture four images of a scene in a single exposure, each taken with a different aperture setting. Our system can be used with commercially available DSLR cameras and photographic lenses without modification to either. We demonstrate several applications of our multi-aperture camera, such as post-exposure depth of field control, synthetic refocusing, and depth-guided deconvolution. Next we describe multiplexed flash illumination to recover both flash and ambient light information as well as extract depth information in a single exposure. Traditional photographic flashes illuminate the scene with a spatially-constant light beam. By adding a mask and optics to a flash, we can project a spatially varying illumination onto the scene which allows us to spatially multiplex the flash and ambient illuminations onto the imager. We apply flash multiplexing to enable single exposure flash/no-flash image fusion, in particular, performing flash/no-flash relighting on dynamic scenes with moving objects. Finally, we propose spatio-temporal multiplexing, a novel image sensor feature that enables simultaneous capture of flash and ambient illumination.(cont.) We describe two possible applications of spatio-temporal multiplexing: single-image flash/no-flash relighting and white balancing scenes containing two distinct illuminants (e.g. flash and fluorescent lighting).by Paul Elijah Green.Ph.D

Photometric Reconstruction from Images: New Scenarios and Approaches for Uncontrolled Input Data

The changes in surface shading caused by varying illumination constitute an important cue to discern fine details and recognize the shape of textureless objects. Humans perform this task subconsciously, but it is challenging for a computer because several variables are unknown and intermix in the light distribution that actually reaches the eye or camera. In this work, we study algorithms and techniques to automatically recover the surface orientation and reflectance properties from multiple images of a scene. Photometric reconstruction techniques have been investigated for decades but are still restricted to industrial applications and research laboratories. Making these techniques work on more general, uncontrolled input without specialized capture setups has to be the next step but is not yet solved. We explore the current limits of photometric shape recovery in terms of input data and propose ways to overcome some of its restrictions. Many approaches, especially for non-Lambertian surfaces, rely on the illumination and the radiometric response function of the camera to be known. The accuracy such algorithms are able to achieve depends a lot on the quality of an a priori calibration of these parameters. We propose two techniques to estimate the position of a point light source, experimentally compare their performance with the commonly employed method, and draw conclusions which one to use in practice. We also discuss how well an absolute radiometric calibration can be performed on uncontrolled consumer images and show the application of a simple radiometric model to re-create night-time impressions from color images. A focus of this thesis is on Internet images which are an increasingly important source of data for computer vision and graphics applications. Concerning reconstructions in this setting we present novel approaches that are able to recover surface orientation from Internet webcam images. We explore two different strategies to overcome the challenges posed by this kind of input data. One technique exploits orientation consistency and matches appearance profiles on the target with a partial reconstruction of the scene. This avoids an explicit light calibration and works for any reflectance that is observed on the partial reference geometry. The other technique employs an outdoor lighting model and reflectance properties represented as parametric basis materials. It yields a richer scene representation consisting of shape and reflectance. This is very useful for the simulation of new impressions or editing operations, e.g. relighting. The proposed approach is the first that achieves such a reconstruction on webcam data. Both presentations are accompanied by evaluations on synthetic and real-world data showing qualitative and quantitative results. We also present a reconstruction approach for more controlled data in terms of the target scene. It relies on a reference object to relax a constraint common to many photometric stereo approaches: the fixed camera assumption. The proposed technique allows the camera and light source to vary freely in each image. It again avoids a light calibration step and can be applied to non-Lambertian surfaces. In summary, this thesis contributes to the calibration and to the reconstruction aspects of photometric techniques. We overcome challenges in both controlled and uncontrolled settings, with a focus on the latter. All proposed approaches are shown to operate also on non-Lambertian objects

Advanced methods for relightable scene representations in image space

The realistic reproduction of visual appearance of real-world objects requires accurate computer graphics models that describe the optical interaction of a scene with its surroundings. Data-driven approaches that model the scene globally as a reflectance field function in eight parameters deliver high quality and work for most material combinations, but are costly to acquire and store. Image-space relighting, which constrains the application to create photos with a virtual, fix camera in freely chosen illumination, requires only a 4D data structure to provide full fidelity. This thesis contributes to image-space relighting on four accounts: (1) We investigate the acquisition of 4D reflectance fields in the context of sampling and propose a practical setup for pre-filtering of reflectance data during recording, and apply it in an adaptive sampling scheme. (2) We introduce a feature-driven image synthesis algorithm for the interpolation of coarsely sampled reflectance data in software to achieve highly realistic images. (3) We propose an implicit reflectance data representation, which uses a Bayesian approach to relight complex scenes from the example of much simpler reference objects. (4) Finally, we construct novel, passive devices out of optical components that render reflectance field data in real-time, shaping the incident illumination into the desired imageDie realistische Wiedergabe der visuellen Erscheinung einer realen Szene setzt genaue Modelle aus der Computergraphik für die Interaktion der Szene mit ihrer Umgebung voraus. Globale Ansätze, die das Verhalten der Szene insgesamt als Reflektanzfeldfunktion in acht Parametern modellieren, liefern hohe Qualität für viele Materialtypen, sind aber teuer aufzuzeichnen und zu speichern. Verfahren zur Neubeleuchtung im Bildraum schränken die Anwendbarkeit auf fest gewählte Kameras ein, ermöglichen aber die freie Wahl der Beleuchtung, und erfordern dadurch lediglich eine 4D - Datenstruktur für volle Wiedergabetreue. Diese Arbeit enthält vier Beiträge zu diesem Thema: (1) wir untersuchen die Aufzeichnung von 4D Reflektanzfeldern im Kontext der Abtasttheorie und schlagen einen praktischen Aufbau vor, der Reflektanzdaten bereits während der Messung vorfiltert. Wir verwenden ihn in einem adaptiven Abtastschema. (2) Wir führen einen merkmalgesteuerten Bildsynthesealgorithmus für die Interpolation von grob abgetasteten Reflektanzdaten ein. (3) Wir schlagen eine implizite Beschreibung von Reflektanzdaten vor, die mit einem Bayesschen Ansatz komplexe Szenen anhand des Beispiels eines viel einfacheren Referenzobjektes neu beleuchtet. (4) Unter der Verwendung optischer Komponenten schaffen wir passive Aufbauten zur Darstellung von Reflektanzfeldern in Echtzeit, indem wir einfallende Beleuchtung direkt in das gewünschte Bild umwandeln