301 research outputs found
DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination
In this paper we are extracting surface reflectance and natural environmental
illumination from a reflectance map, i.e. from a single 2D image of a sphere of
one material under one illumination. This is a notoriously difficult problem,
yet key to various re-rendering applications. With the recent advances in
estimating reflectance maps from 2D images their further decomposition has
become increasingly relevant.
To this end, we propose a Convolutional Neural Network (CNN) architecture to
reconstruct both material parameters (i.e. Phong) as well as illumination (i.e.
high-resolution spherical illumination maps), that is solely trained on
synthetic data. We demonstrate that decomposition of synthetic as well as real
photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the
first time, on Low Dynamic Range (LDR) as well. Results are compared to
previous approaches quantitatively as well as qualitatively in terms of
re-renderings where illumination, material, view or shape are changed.Comment: Stamatios Georgoulis and Konstantinos Rematas contributed equally to
this wor
What Is Around The Camera?
How much does a single image reveal about the environment it was taken in? In
this paper, we investigate how much of that information can be retrieved from a
foreground object, combined with the background (i.e. the visible part of the
environment). Assuming it is not perfectly diffuse, the foreground object acts
as a complexly shaped and far-from-perfect mirror. An additional challenge is
that its appearance confounds the light coming from the environment with the
unknown materials it is made of. We propose a learning-based approach to
predict the environment from multiple reflectance maps that are computed from
approximate surface normals. The proposed method allows us to jointly model the
statistics of environments and material properties. We train our system from
synthesized training data, but demonstrate its applicability to real-world
data. Interestingly, our analysis shows that the information obtained from
objects made out of multiple materials often is complementary and leads to
better performance.Comment: Accepted to ICCV. Project:
http://homes.esat.kuleuven.be/~sgeorgou/multinatillum
BxDF material acquisition, representation, and rendering for VR and design
Photorealistic and physically-based rendering of real-world environments with high fidelity materials is important to a range of applications, including special effects, architectural modelling, cultural heritage, computer games, automotive design, and virtual reality (VR). Our perception of the world depends on lighting and surface material characteristics, which determine how the light is reflected, scattered, and absorbed. In order to reproduce appearance, we must therefore understand all the ways objects interact with light, and the acquisition and representation of materials has thus been an important part of computer graphics from early days. Nevertheless, no material model nor acquisition setup is without limitations in terms of the variety of materials represented, and different approaches vary widely in terms of compatibility and ease of use. In this course, we describe the state of the art in material appearance acquisition and modelling, ranging from mathematical BSDFs to data-driven capture and representation of anisotropic materials, and volumetric/thread models for patterned fabrics. We further address the problem of material appearance constancy across different rendering platforms. We present two case studies in architectural and interior design. The first study demonstrates Yulio, a new platform for the creation, delivery, and visualization of acquired material models and reverse engineered cloth models in immersive VR experiences. The second study shows an end-to-end process of capture and data-driven BSDF representation using the physically-based Radiance system for lighting simulation and rendering
On-site surface reflectometry
The rapid development of Augmented Reality (AR) and Virtual Reality (VR)
applications over the past years has created the need to quickly and accurately scan
the real world to populate immersive, realistic virtual environments for the end
user to enjoy. While geometry processing has already gone a long way towards that
goal, with self-contained solutions commercially available for on-site acquisition of
large scale 3D models, capturing the appearance of the materials that compose
those models remains an open problem in general uncontrolled environments.
The appearance of a material is indeed a complex function of its geometry,
intrinsic physical properties and furthermore depends on the illumination conditions
in which it is observed, thus traditionally limiting the scope of reflectometry
to highly controlled lighting conditions in a laboratory setup. With the rapid development
of digital photography, especially on mobile devices, a new trend in the
appearance modelling community has emerged, that investigates novel acquisition
methods and algorithms to relax the hard constraints imposed by laboratory-like
setups, for easy use by digital artists. While arguably not as accurate, we demonstrate
the ability of such self-contained methods to enable quick and easy solutions
for on-site reflectometry, able to produce compelling, photo-realistic imagery.
In particular, this dissertation investigates novel methods for on-site acquisition
of surface reflectance based on off-the-shelf, commodity hardware. We successfully
demonstrate how a mobile device can be utilised to capture high quality
reflectance maps of spatially-varying planar surfaces in general indoor lighting
conditions. We further present a novel methodology for the acquisition of highly
detailed reflectance maps of permanent on-site, outdoor surfaces by exploiting
polarisation from reflection under natural illumination.
We demonstrate the versatility of the presented approaches by scanning various
surfaces from the real world and show good qualitative and quantitative agreement
with existing methods for appearance acquisition employing controlled or
semi-controlled illumination setups.Open Acces
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