Abhijeet Ghosh

High dynamic range display systems

The 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.

Compressive light transport sensing

In this article 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 nonadaptive 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 interpixel coherency relations. Additionally, we design new nonadaptive illumination patterns that minimize measurement noise and further improve reconstruction quality. We illustrate our framework by capturing detailed high-resolution reflectance fields for image-based relighting.

Practical modeling and acquisition of layered facial reflectance

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.

Multiview face capture using polarized spherical gradient illumination

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.

Bidirectional importance sampling for direct illumination

Image-based representations for illumination can capture complex real-world lighting that is difficult to represent in other forms. Current importance sampling strategies for image-based illumination have difficulties in cases where both the illumination and the surface BRDF contain important high-frequency detail – for example, when a specular surface is illuminated by an environment map containing small light sources. We introduce the notion of bidirectional importance sampling, in which samples are drawn from the product distribution of both the surface reflectance and the light source energy. While this approach makes the sample selection process more expensive, we drastically reduce the number of visibility tests required to obtain good image quality. As a consequence, we achieve significant quality improvements over previous sampling strategies for the same compute time.

BRDF Acquisition with Basis Illumination

Realistic descriptions of surface reflectance have long been a topic of interest in both computer vision and computer graphics research. In this paper, we describe a novel and fast approach for the acquisition of bidirectional reflectance distribution functions (BRDFs). We develop a novel theory for directly measuring BRDFs in a basis representation by projecting incident light as a sequence of basis functions from a spherical zone of directions. We derive an orthonormal basis over spherical zones that is ideally suited for this task. BRDF values outside the zonal directions are extrapolated by re-projecting the zonal measurements into a spherical harmonics basis, or by fitting analytical reflection models to the data. We verify this approach with a compact optical setup that requires no moving parts and only a small number of image measurements. Using this approach, a BRDF can be measured in just a few minutes.

Estimating specular roughness and anisotropy from second order spherical gradient illumination

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.

Acquiring reflectance and shape from continuous spherical harmonic illumination

We present a novel technique for acquiring the geometry and spatially-varying reflectance properties of 3D objects by observing them under continuous spherical harmonic illumination conditions. The technique is general enough to characterize either entirely specular or entirely diffuse materials, or any varying combination across the surface of the object. We employ a novel computational illumination setup consisting of a rotating arc of controllable LEDs which sweep out programmable spheres of incident illumination during 1-second exposures. We illuminate the object with a succession of spherical harmonic illumination conditions, as well as photographed environmental lighting for validation. From the response of the object to the harmonics, we can separate diffuse and specular reflections, estimate world-space diffuse and specular normals, and compute anisotropic roughness parameters for each view of the object. We then use the maps of both diffuse and specular reflectance to form correspondences in a multiview stereo algorithm, which allows even highly specular surfaces to be corresponded across views. The algorithm yields a complete 3D model and a set of merged reflectance maps. We use this technique to digitize the shape and reflectance of a variety of objects difficult to acquire with other techniques and present validation renderings which match well to photographs in similar lighting.

Circularly polarized spherical illumination reflectometry

We present a novel method for surface reflectometry from a few observations of a scene under a single uniform spherical field of circularly polarized illumination. The method is based on a novel analysis of the Stokes reflectance field of circularly polarized spherical illumination and yields per-pixel estimates of diffuse albedo, specular albedo, index of refraction, and specular roughness of isotropic BRDFs. To infer these reflectance parameters, we measure the Stokes parameters of the reflected light at each pixel by taking four photographs of the scene, consisting of three photographs with differently oriented linear polarizers in front of the camera, and one additional photograph with a circular polarizer. The method only assumes knowledge of surface orientation, for which we make a few additional photometric measurements. We verify our method with three different lighting setups, ranging from specialized to off-the-shelf hardware, which project either discrete or continuous fields of spherical illumination. Our technique offers several benefits: it estimates a more detailed model of per-pixel surface reflectance parameters than previous work, it requires a relatively small number of measurements, it is applicable to a wide range of material types, and it is completely viewpoint independent.

Temporal upsampling of performance geometry using photometric alignment

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.