6,614 research outputs found
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
Understanding the user - why, what and how?
Explains the need, importance, purposes and scope of user studies, discusses procedure for conducting sound user studies together with associated problems of research like selection of problem, formulation of hypothesis, design of study, sampling strategy, data collection methods, scaling techniques, pilot study, processing and analysis of data, testing of hypothesis, interpretation, drawing inferences, communication and dissemination of results and finally concludes by highlighting methodological flaws and gaps in user studies
First Class Call Stacks: Exploring Head Reduction
Weak-head normalization is inconsistent with functional extensionality in the
call-by-name -calculus. We explore this problem from a new angle via
the conflict between extensionality and effects. Leveraging ideas from work on
the -calculus with control, we derive and justify alternative
operational semantics and a sequence of abstract machines for performing head
reduction. Head reduction avoids the problems with weak-head reduction and
extensionality, while our operational semantics and associated abstract
machines show us how to retain weak-head reduction's ease of implementation.Comment: In Proceedings WoC 2015, arXiv:1606.0583
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