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Figure 1: From left to right: our projector-based display showing an HDR image; our LED-based HDR display showing a discrete and a smooth intensity ramp (the top half of the discrete ramp and the bottom half of the smooth ramp have each been covered by a 1% transparent filter to illustrate high luminance content on the left side of the image, which cannot be captured by the camera); a color-coded original HDR image; HDR photograph taken off the screen of our projector-based system; HDR photograph taken off a conventional monitor displaying the tone-mapped image. AbstractThe dynamic range of many real-world environments exceeds the capabilities of current display technology by several orders of magnitude. In this paper we discuss the design of two different display systems that are capable of displaying images with a dynamic range much more similar to that encountered in the real world. The first display system is based on a combination of an LCD panel and a DLP projector, and can be built from off-the-shelf components. While this design is feasible in a lab setting, the second display system, which relies on a custom-built LED panel instead of the projector, is more suitable for usual office workspaces and commercial applications. We describe the design of both systems as well as the software issues that arise. We also discuss the advantages and disadvantages of the two designs and potential applications for both systems.

We present a novel process for acquiring detailed facial geometry with high resolution diffuse and specular photometric information from multiple viewpoints using polarized spherical gradient illumination. Key to our method is a new pair of linearly polarized lighting patterns which enables multiview diffuse-specular separation under a given spherical illumination condition from just two photographs. The patterns -one following lines of latitude and one following lines of longitude -allow the use of fixed linear polarizers in front of the cameras, enabling more efficient acquisition of diffuse and specular albedo and normal maps from multiple viewpoints. In a second step, we employ these albedo and normal maps as input to a novel multi-resolution adaptive domain message passing stereo reconstruction algorithm to create high resolution facial geometry. To do this, we formulate the stereo reconstruction from multiple cameras in a commonly parameterized domain for multiview reconstruction. We show competitive results consisting of high-resolution facial geometry with relightable reflectance maps using five DSLR cameras. Our technique scales well for multiview acquisition without requiring specialized camera systems for sensing multiple polarization states.

Figure 1: An example of relit images of a scene generated from a reflectance field captured using just 1000 non-adaptive illumination patterns (emitted from the right onto the scene). The incident lighting resolution, and resolution of each reflectance function, is AbstractIn this paper we propose a new framework for capturing light transport data of a real scene, based on the recently developed theory of compressive sensing. Compressive sensing offers a solid mathematical framework to infer a sparse signal from a limited number of non-adaptive measurements. Besides introducing compressive sensing for fast acquisition of light transport to computer graphics, we develop several innovations that address specific challenges for image-based relighting, and which may have broader implications. We develop a novel hierarchical decoding algorithm that improves reconstruction quality by exploiting inter-pixel coherency relations. Additionally, we design new non-adaptive illumination patterns that minimize measurement noise and further improve reconstruction quality. We illustrate our framework by capturing detailed highresolution reflectance fields for image-based relighting.

We present a practical method for modeling layered facial reflectance consisting of specular reflectance, single scattering, and shallow and deep subsurface scattering. We estimate parameters of appropriate reflectance models for each of these layers from just 20 photographs recorded in a few seconds from a single viewpoint. We extract spatially-varying specular reflectance and single-scattering parameters from polarization-difference images under spherical and point source illumination. Next, we employ direct-indirect separation to decompose the remaining multiple scattering observed under cross-polarization into shallow and deep scattering components to model the light transport through multiple layers of skin. Finally, we match appropriate diffusion models to the extracted shallow and deep scattering components for different regions on the face. We validate our technique by comparing renderings of subjects to reference photographs recorded from novel viewpoints and under novel illumination conditions.

We present a novel technique for acquiring detailed facial geometry of a dynamic performance using extended spherical gradient illumination. Key to our method is a new algorithm for jointly aligning two photographs, under a gradient illumination condition and its complement, to a full-on tracking frame, providing dense temporal correspondences under changing lighting conditions. We employ a two-step algorithm to reconstruct detailed geometry for every captured frame. In the first step, we coalesce information from the gradient illumination frames to the full-on tracking frame, and form a temporally aligned photometric normal map, which is subsequently combined with dense stereo correspondences yielding a detailed geometry. In a second step, we propagate the detailed geometry back to every captured instance guided by the previously computed dense correspondences. We demonstrate reconstructed dynamic facial geometry, captured using moderate to video rates of acquisition, for every captured frame.

Photorealistic rendering of real world environments is important in a range of different areas; including Visual Special effects, Interior/Exterior Modelling, Architectural Modelling, Cultural Heritage, Computer Games and Automotive Design. Currently, rendering systems are able to produce photorealistic simulations of the appearance of many real‐world materials. In the real world, viewer perception of objects depends on the lighting and object/material/surface characteristics, the way a surface interacts with the light and on how the light is reflected, scattered, absorbed by the surface and the impact these characteristics have on material appearance. In order to re‐produce this, it is necessary to understand how materials interact with light. Thus the representation and acquisition of material models has become such an active research area. This survey of the state‐of‐the‐art of BRDF Representation and Acquisition presents an overview of BRDF (Bidirectional Reflectance Distribution Function) models used to represent surface/material reflection characteristics, and describes current acquisition methods for the capture and rendering of photorealistic materials.

This paper presents a novel method for estimating specular roughness and tangent vectors, per surface point, from polarized second order spherical gradient illumination patterns. We demonstrate that for isotropic BRDFs, only three second order spherical gradients are sufficient to robustly estimate spatially varying specular roughness. For anisotropic BRDFs, an additional two measurements yield specular roughness and tangent vectors per surface point. We verify our approach with different illumination configurations which project both discrete and continuous fields of gradient illumination. Our technique provides a direct estimate of the per-pixel specular roughness and thus does not require off-line numerical optimization that is typical for the measure-and-fit approach to classical BRDF modeling.