Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging

A variety of techniques such as light field, structured illumination, and time-of-flight (TOF) are commonly used for depth acquisition in consumer imaging, robotics and many other applications. Unfortunately, each technique suffers from its individual limitations preventing robust depth sensing. In this paper, we explore the strengths and weaknesses of combining light field and time-of-flight imaging, particularly the feasibility of an on-chip implementation as a single hybrid depth sensor. We refer to this combination as depth field imaging. Depth fields combine light field advantages such as synthetic aperture refocusing with TOF imaging advantages such as high depth resolution and coded signal processing to resolve multipath interference. We show applications including synthesizing virtual apertures for TOF imaging, improved depth mapping through partial and scattering occluders, and single frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding, depth fields can improve depth sensing in the wild and generate new insights into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201

Aperture Supervision for Monocular Depth Estimation

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image.Comment: To appear at CVPR 2018 (updated to camera ready version

Light field image processing: an overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data

Superquadric representation of scenes from multi-view range data

Object representation denotes representing three-dimensional (3D) real-world objects with known graphic or mathematic primitives recognizable to computers. This research has numerous applications for object-related tasks in areas including computer vision, computer graphics, reverse engineering, etc. Superquadrics, as volumetric and parametric models, have been selected to be the representation primitives throughout this research. Superquadrics are able to represent a large family of solid shapes by a single equation with only a few parameters. This dissertation addresses superquadric representation of multi-part objects and multiobject scenes. Two issues motivate this research. First, superquadric representation of multipart objects or multi-object scenes has been an unsolved problem due to the complex geometry of objects. Second, superquadrics recovered from single-view range data tend to have low confidence and accuracy due to partially scanned object surfaces caused by inherent occlusions. To address these two problems, this dissertation proposes a multi-view superquadric representation algorithm. By incorporating both part decomposition and multi-view range data, the proposed algorithm is able to not only represent multi-part objects or multi-object scenes, but also achieve high confidence and accuracy of recovered superquadrics. The multi-view superquadric representation algorithm consists of (i) initial superquadric model recovery from single-view range data, (ii) pairwise view registration based on recovered superquadric models, (iii) view integration, (iv) part decomposition, and (v) final superquadric fitting for each decomposed part. Within the multi-view superquadric representation framework, this dissertation proposes a 3D part decomposition algorithm to automatically decompose multi-part objects or multiobject scenes into their constituent single parts consistent with human visual perception. Superquadrics can then be recovered for each decomposed single-part object. The proposed part decomposition algorithm is based on curvature analysis, and includes (i) Gaussian curvature estimation, (ii) boundary labeling, (iii) part growing and labeling, and (iv) post-processing. In addition, this dissertation proposes an extended view registration algorithm based on superquadrics. The proposed view registration algorithm is able to handle deformable superquadrics as well as 3D unstructured data sets. For superquadric fitting, two objective functions primarily used in the literature have been comprehensively investigated with respect to noise, viewpoints, sample resolutions, etc. The objective function proved to have better performance has been used throughout this dissertation. In summary, the three algorithms (contributions) proposed in this dissertation are generic and flexible in the sense of handling triangle meshes, which are standard surface primitives in computer vision and graphics. For each proposed algorithm, the dissertation presents both theory and experimental results. The results demonstrate the efficiency of the algorithms using both synthetic and real range data of a large variety of objects and scenes. In addition, the experimental results include comparisons with previous methods from the literature. Finally, the dissertation concludes with a summary of the contributions to the state of the art in superquadric representation, and presents possible future extensions to this research

Revealing the Invisible: On the Extraction of Latent Information from Generalized Image Data

The desire to reveal the invisible in order to explain the world around us has been a source of impetus for technological and scientific progress throughout human history. Many of the phenomena that directly affect us cannot be sufficiently explained based on the observations using our primary senses alone. Often this is because their originating cause is either too small, too far away, or in other ways obstructed. To put it in other words: it is invisible to us. Without careful observation and experimentation, our models of the world remain inaccurate and research has to be conducted in order to improve our understanding of even the most basic effects. In this thesis, we1 are going to present our solutions to three challenging problems in visual computing, where a surprising amount of information is hidden in generalized image data and cannot easily be extracted by human observation or existing methods. We are able to extract the latent information using non-linear and discrete optimization methods based on physically motivated models and computer graphics methodology, such as ray tracing, real-time transient rendering, and image-based rendering

Occluder-aided non-line-of-sight imaging

Non-line-of-sight (NLOS) imaging is the inference of the properties of objects or scenes outside of the direct line-of-sight of the observer. Such inferences can range from a 2D photograph-like image of a hidden area, to determining the position, motion or number of hidden objects, to 3D reconstructions of a hidden volume. NLOS imaging has many enticing potential applications, such as leveraging the existing hardware in many automobiles to identify hidden pedestrians, vehicles or other hazards and hence plan safer trajectories. Other potential application areas include improving navigation for robots or drones by anticipating occluded hazards, peering past obstructions in medical settings, or in surveying unreachable areas in search-and-rescue operations. Most modern NLOS imaging methods fall into one of two categories: active imaging methods that have some control of the illumination of the hidden area, and passive methods that simply measure light that already exists. This thesis introduces two NLOS imaging methods, one of each category, along with modeling and data processing techniques that are more broadly applicable. The methods are linked by their use of objects (‘occluders’) that reside somewhere between the observer and the hidden scene and block some possible light paths. Computational periscopy, a passive method, can recover the unknown position of an occluding object in the hidden area and then recover an image of the hidden scene behind it. It does so using only a single photograph of a blank relay wall taken by an ordinary digital camera. We develop also a framework using an optimized preconditioning matrix to improve the speed at which these reconstructions can be made and greatly improve the robustness to ambient light. Lastly, we develop tools necessary to demonstrate recovery of scenes at multiple unknown depths – paving the way towards three-dimensional reconstructions. Edge-resolved transient imaging, an active method, enables the formation of 2.5D representations – a plan view plus heights – of large-scale scenes. A pulsed laser illuminates spots along a small semi-circle on the floor, centered on the edge of a vertical wall such as in a doorway. The wall edge occludes some light paths, only allowing the laser light reflecting off of the floor to illuminate certain portions of the hidden area beyond the wall, depending on where along the semi-circle it is illuminating. The time at which photons return following a laser pulse is recorded. The occluding wall edge provides angular resolution, and time-resolved sensing provides radial resolution. This novel acquisition strategy, along with a scene response model and reconstruction algorithm, allow for 180° field of view reconstructions of large-scale scenes unlike other active imaging methods. Lastly, we introduce a sparsity penalty named mutually exclusive group sparsity (MEGS), that can be used as a constraint or regularization in optimization problems to promote solutions in which certain components are mutually exclusive. We explore how this penalty relates to other similar penalties, develop fast algorithms to solve MEGS-regularized problems, and demonstrate how enforcing mutual exclusivity structure can provide great utility in NLOS imaging problems

Computational See-Through Near-Eye Displays

See-through near-eye displays with the form factor and field of view of eyeglasses are a natural choice for augmented reality systems: the non-encumbering size enables casual and extended use and large field of view enables general-purpose spatially registered applications. However, designing displays with these attributes is currently an open problem. Support for enhanced realism through mutual occlusion and the focal depth cues is also not found in eyeglasses-like displays. This dissertation provides a new strategy for eyeglasses-like displays that follows the principles of computational displays, devices that rely on software as a fundamental part of image formation. Such devices allow more hardware simplicity and flexibility, showing greater promise of meeting form factor and field of view goals while enhancing realism. This computational approach is realized in two novel and complementary see-through near-eye display designs. The first subtractive approach filters omnidirectional light through a set of optimized patterns displayed on a stack of spatial light modulators, reproducing a light field corresponding to in-focus imagery. The design is thin and scales to wide fields of view; see-through is achieved with transparent components placed directly in front of the eye. Preliminary support for focal cues and environment occlusion is also demonstrated. The second additive approach uses structured point light illumination to form an image with a minimal set of rays. Each of an array of defocused point light sources is modulated by a region of a spatial light modulator, essentially encoding an image in the focal blur. See-through is also achieved with transparent components and thin form factors and wide fields of view (>= 100 degrees) are demonstrated. The designs are examined in theoretical terms, in simulation, and through prototype hardware with public demonstrations. This analysis shows that the proposed computational near-eye display designs offer a significantly different set of trade-offs than conventional optical designs. Several challenges remain to make the designs practical, most notably addressing diffraction limits.Doctor of Philosoph

Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning

The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper

Image-based appearance preservation

The three-dimensional digital preservation of real objects comprises two main aspects: the preservation of the shape of the object and the preservation of its appearance. This thesis focuses on the image-based appearance preservation of real objects and provides a set of contributions on the theme. The first contribution consists in two groups of experiments, where each one of them targets one different image-based appearance preservation approach. These experiments are based in fundamental concepts related to the behavior of light and in a compilation of works that aim to preserve the appearance of real objects using different types of images. The first group of experiments attempts to disregard as much as possible the influence of the environment light. The second one goes one step further and considers a single light source. These experiments were the basis and motivation for the development of the main contribution of this thesis, which is a novel image-based appearance preservation method that considers the whole environment as a source of light. It presents as novelty the fact that it estimates the incoming light from the whole environment to each point in an object surface patch. At the best knowledge of this work, none of the current existing methods adopts this approach. Considering the whole environment as source of light allows flexible acquisition setups and, as it reproduces what happens in reality, potentially retrieves more reliable information about the incident lighting. This thesis presents this method and its application on real and synthetic environments. Conclusions about this work are presented and future research directions are discussed._________________________________________________________________________________________ RESUMO: A preservação tridimensional digital de objetos reais compreende dois aspectos: a preservação da forma do objeto e a preservação de sua aparência. Esta tese tem como foco a preservação da aparência de objetos reais baseada em imagens e provê uma série de contribuições sobre o tema. A primeira contribuição consiste em dois grupos de experimentos, onde cada um trabalha uma abordagem diferente na preservação da aparência baseada em imagens. Esses experimentos são baseados em conceitos fundamentais relacionados ao comportamento da luz e em uma compilação de trabalhos que visam preservar a aparência de objetos reais usando diferentes tipos de imagens. O primeiro grupo de experimentos tenta desconsiderar ao máximo a inuência da luz. O segundo vai um passo além e considera uma única fonte de luz. Estes experimentos são a base e motivação para o desenvolvimento da principal contribuição desta tese, que é um novo método de preservação da aparência baseado em imagens que considera todo o ambiente como fonte de luz. Ele apresenta como novidade o fato de estimar a luz vinda de todo o ambiente para cada ponto em uma região na superfície de um objeto. Até onde foi pesquisado neste trabalho, nenhum método existente adota essa abordagem. Considerar todo o ambiente como fonte de luz permite configurações flexíveis durante a aquisição e, já que reproduz o que acontece na realidade, recupera informações potencialmente mais confiáveis sobre a iluminação incidente. Esta tese apresenta este método e sua aplicação em ambientes reais e sintéticos. Conclusões sobre este trabalho são apresentadas e direções futuras de pesquisa são discutidas