Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

In 1997, Azuma published a survey on augmented reality (AR). Our goal is to complement, rather than replace, the original survey by presenting representative examples of the new advances. We refer one to the original survey for descriptions of potential applications (such as medical visualization, maintenance and repair of complex equipment, annotation, and path planning); summaries of AR system characteristics (such as the advantages and disadvantages of optical and video approaches to blending virtual and real, problems in display focus and contrast, and system portability); and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.

https://www.cc.gatech.edu/~blair/papers/ARsurveyCGA.pdf

Recent advances in augmented reality

We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

Photo tourism: exploring photo collections in 3D

A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter. The method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.

/pdf/texture-synthesis-by-non-parametric-sampling-545ddgjo96.pdf

Texture synthesis by non-parametric sampling

We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Our algorithm uses these differently exposed photographs to recover the response function of the imaging process, up to factor of scale, using the assumption of reciprocity. With the known response function, the algorithm can fuse the multiple photographs into a single, high dynamic range radiance map whose pixel values are proportional to the true radiance values in the scene. We demonstrate our method on images acquired with both photochemical and digital imaging processes. We discuss how this work is applicable in many areas of computer graphics involving digitized photographs, including image-based modeling, image compositing, and image processing. Lastly, we demonstrate a few applications of having high dynamic range radiance maps, such as synthesizing realistic motion blur and simulating the response of the human visual system.

/pdf/recovering-high-dynamic-range-radiance-maps-from-photographs-zk2yr4fcgd.pdf

Recovering high dynamic range radiance maps from photographs

The light transport (LT) of a scene describes how it appears under different lighting conditions from different viewing directions, and complete knowledge of a scene’s LT enables the synthesis of novel views under arbitrary lighting. In this article, we focus on image-based LT acquisition, primarily for human bodies within a light stage setup. We propose a semi-parametric approach for learning a neural representation of the LT that is embedded in a texture atlas of known but possibly rough geometry. We model all non-diffuse and global LT as residuals added to a physically based diffuse base rendering. In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint. This strategy allows the network to learn complex material effects (such as subsurface scattering) and global illumination (such as diffuse interreflection), while guaranteeing the physical correctness of the diffuse LT (such as hard shadows). With this learned LT, one can relight the scene photorealistically with a directional light or an HDRI map, synthesize novel views with view-dependent effects, or do both simultaneously, all in a unified framework using a set of sparse observations. Qualitative and quantitative experiments demonstrate that our Neural Light Transport (NLT) outperforms state-of-the-art solutions for relighting and view synthesis, without requiring separate treatments for both problems that prior work requires. The code and data are available at http://nlt.csail.mit.edu.

Neural Light Transport for Relighting and View Synthesis

Overview In 2008, the "Digital Emily" project [Alexander et al. 2009] showed how a set of high-resolution facial expressions scanned in a light stage could be rigged into a real-time photoreal digital character and driven with video-based facial animation techniques. However, Digital Emily was rendered offline, involved just the front of the face, and was never seen in a tight closeup. In this collaboration between Activision and USC ICT shown at SIGGRAPH 2013's Real-Time Live venue, we endeavoured to create a real-time, photoreal digital human character which could be seen from any viewpoint, in any lighting, and could perform realistically from video performance capture even in a tight closeup. In addition, we wanted this to run in a real-time game-ready production pipeline, ultimately achieving 180 frames per second for a full-screen character on a two-year old graphics card.

Digital Ira: creating a real-time photoreal digital actor

We present a concept for a full-parallax light field display achieved by having users look directly into an array of video projectors. Each projector acts as one angularly-varying pixel, so the display's spatial resolution depends on the number of video projectors and the angular resolution depends on the pixel resolution of any one video projector. We prototype a horizontal-parallax-only arrangement by mechanically moving a single pico-projector to an array of positions, and use long-exposure photography to simulate video of a horizontal array. With this setup, we determine the minimal projector density required to produce a continuous image, and describe practical ways to achieve such density and to realize the resulting system. We finally show that if today's pico-projectors become sufficiently inexpensive, immersive full-parallax displays with arbitrarily high spatial and angular resolution will become possible.

https://ict.usc.edu/pubs/Prototyping%20a%20Light%20Field%20Display%20Involving%20Direct%20Observation%20of%20a%20Video%20Projector%20Array.pdf

Prototyping a light field display involving direct observation of a video projector array

We present a technique for generating microstructure-level facial geometry by augmenting a mesostructure-level facial scan with detail synthesized from a set of exemplar skin patches scanned at much higher resolution. Additionally, we make point-source reflectance measurements of the skin patches to characterize the specular reflectance lobes at this smaller scale and analyze facial reflectance variation at both the mesostructure and microstructure scales. We digitize the exemplar patches with a polarization-based computational illumination technique which considers specular reflection and single scattering. The recorded microstructure patches can be used to synthesize full-facial microstructure detail for either the same subject or to a different subject. We show that the technique allows for greater realism in facial renderings including more accurate reproduction of skin's specular reflection effects.

Measurement-based Synthesis of Facial Microgeometry

We present a near-instant method for acquiring facial geometry and reflectance using a set of commodity DSLR cameras and flashes. Our setup consists of twenty-four cameras and six flashes which are fired in rapid succession with subsets of the cameras. Each camera records only a single photograph and the total capture time is less than the 67ms blink reflex. The cameras and flashes are specially arranged to produce an even distribution of specular highlights on the face. We employ this set of acquired images to estimate diffuse color, specular intensity, specular exponent, and surface orientation at each point on the face. We further refine the facial base geometry obtained from multi-view stereo using estimated diffuse and specular photometric information. This allows final submillimeter surface mesostructure detail to be obtained via shape-from-specularity. The final system uses commodity components and produces models suitable for authoring high-quality digital human characters.

/pdf/near-instant-capture-of-high-resolution-facial-geometry-and-14ch36mpv9.pdf

Paul Debevec

Papers

Neural Light Transport for Relighting and View Synthesis

Digital Ira: creating a real-time photoreal digital actor

Prototyping a light field display involving direct observation of a video projector array

Measurement-based Synthesis of Facial Microgeometry

Near-instant capture of high-resolution facial geometry and reflectance