4,555 research outputs found
Single-shot layered reflectance separation using a polarized light field camera
We present a novel computational photography technique for single shot separation of diffuse/specular reflectance as well as novel angular domain separation of layered reflectance. Our solution consists of a two-way polarized light field (TPLF) camera which simultaneously captures two orthogonal states of polarization. A single photograph of a subject acquired with the TPLF camera under polarized illumination then enables standard separation of diffuse (depolarizing) and polarization preserving specular reflectance using light field sampling. We further demonstrate that the acquired data also enables novel angular separation of layered reflectance including separation of specular reflectance and single scattering in the polarization preserving component, and separation of shallow scattering from deep scattering in the depolarizing component. We apply our approach for efficient acquisition of facial reflectance including diffuse and specular normal maps, and novel separation of photometric normals into layered reflectance normals for layered facial renderings. We demonstrate our proposed single shot layered reflectance separation to be comparable to an existing multi-shot technique that relies on structured lighting while achieving separation results under a variety of illumination conditions
The Use of Separated Reflection Components in Estimating Geometrical Parameters of Curved Surface Elements
Iterative least-squares estimation, requires accurate reflectance models to retrieve geometrical parameters of curved surface elements from an image projection. We investigate the use of separating the diffuse (body) reflection from the specular (surface) reflection being responsible for image highlights. Experiments show that the (smooth) diffuse component yields the best convergence properties, while the (sharp) specular component can contribute to the improvement of the noise insensitivit
Recovery of surface orientation from diffuse polarization
When unpolarized light is reflected from a smooth dielectric surface, it becomes partially polarized. This is due to the orientation of dipoles induced in the reflecting medium and applies to both specular and diffuse reflection. This paper is concerned with exploiting polarization by surface reflection, using images of smooth dielectric objects, to recover surface normals and, hence, height. This paper presents the underlying physics of polarization by reflection, starting with the Fresnel equations. These equations are used to interpret images taken with a linear polarizer and digital camera, revealing the shape of the objects. Experimental results are presented that illustrate that the technique is accurate near object limbs, as the theory predicts, with less precise, but still useful, results elsewhere. A detailed analysis of the accuracy of the technique for a variety of materials is presented. A method for estimating refractive indices using a laser and linear polarizer is also given
Joint Material and Illumination Estimation from Photo Sets in the Wild
Faithful manipulation of shape, material, and illumination in 2D Internet
images would greatly benefit from a reliable factorization of appearance into
material (i.e., diffuse and specular) and illumination (i.e., environment
maps). On the one hand, current methods that produce very high fidelity
results, typically require controlled settings, expensive devices, or
significant manual effort. To the other hand, methods that are automatic and
work on 'in the wild' Internet images, often extract only low-frequency
lighting or diffuse materials. In this work, we propose to make use of a set of
photographs in order to jointly estimate the non-diffuse materials and sharp
lighting in an uncontrolled setting. Our key observation is that seeing
multiple instances of the same material under different illumination (i.e.,
environment), and different materials under the same illumination provide
valuable constraints that can be exploited to yield a high-quality solution
(i.e., specular materials and environment illumination) for all the observed
materials and environments. Similar constraints also arise when observing
multiple materials in a single environment, or a single material across
multiple environments. The core of this approach is an optimization procedure
that uses two neural networks that are trained on synthetic images to predict
good gradients in parametric space given observation of reflected light. We
evaluate our method on a range of synthetic and real examples to generate
high-quality estimates, qualitatively compare our results against
state-of-the-art alternatives via a user study, and demonstrate
photo-consistent image manipulation that is otherwise very challenging to
achieve
Photometric stereo for strong specular highlights
Photometric stereo (PS) is a fundamental technique in computer vision known
to produce 3-D shape with high accuracy. The setting of PS is defined by using
several input images of a static scene taken from one and the same camera
position but under varying illumination. The vast majority of studies in this
3-D reconstruction method assume orthographic projection for the camera model.
In addition, they mainly consider the Lambertian reflectance model as the way
that light scatters at surfaces. So, providing reliable PS results from real
world objects still remains a challenging task. We address 3-D reconstruction
by PS using a more realistic set of assumptions combining for the first time
the complete Blinn-Phong reflectance model and perspective projection. To this
end, we will compare two different methods of incorporating the perspective
projection into our model. Experiments are performed on both synthetic and real
world images. Note that our real-world experiments do not benefit from
laboratory conditions. The results show the high potential of our method even
for complex real world applications such as medical endoscopy images which may
include high amounts of specular highlights
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