287 research outputs found
Minimal BRDF Sampling for Two-Shot Near-Field Reflectance Acquisition
We develop a method to acquire the BRDF of a homogeneous flat sample from only two images, taken by a near-field perspective camera, and lit by a directional light source. Our method uses the MERL BRDF database to determine the optimal set of lightview pairs for data-driven reflectance acquisition. We develop a mathematical framework to estimate error from a given set of measurements, including the use of multiple measurements in an image simultaneously, as needed for acquisition from near-field setups. The novel error metric is essential in the near-field case, where we show that using the condition-number alone performs poorly. We demonstrate practical near-field acquisition of BRDFs from only one or two input images. Our framework generalizes to configurations like a fixed camera setup, where we also develop a simple extension to spatially-varying BRDFs by clustering the materials.</jats:p
FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition
Efficient and accurate BRDF acquisition of real world materials is a
challenging research problem that requires sampling millions of incident light
and viewing directions. To accelerate the acquisition process, one needs to
find a minimal set of sampling directions such that the recovery of the full
BRDF is accurate and robust given such samples. In this paper, we formulate
BRDF acquisition as a compressed sensing problem, where the sensing operator is
one that performs sub-sampling of the BRDF signal according to a set of optimal
sample directions. To solve this problem, we propose the Fast and Robust
Optimal Sampling Technique (FROST) for designing a provably optimal
sub-sampling operator that places light-view samples such that the recovery
error is minimized. FROST casts the problem of designing an optimal
sub-sampling operator for compressed sensing into a sparse representation
formulation under the Multiple Measurement Vector (MMV) signal model. The
proposed reformulation is exact, i.e. without any approximations, hence it
converts an intractable combinatorial problem into one that can be solved with
standard optimization techniques. As a result, FROST is accompanied by strong
theoretical guarantees from the field of compressed sensing. We perform a
thorough analysis of FROST-BRDF using a 10-fold cross-validation with publicly
available BRDF datasets and show significant advantages compared to the
state-of-the-art with respect to reconstruction quality. Finally, FROST is
simple, both conceptually and in terms of implementation, it produces
consistent results at each run, and it is at least two orders of magnitude
faster than the prior art.Comment: Submitted to IEEE Transactions on Visualization and Computer Graphics
(IEEE TVCG
Practical SVBRDF Acquisition of 3D Objects with Unstructured Flash Photography
Capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) of 3D objects with just a single, hand-held camera (such as an off-the-shelf smartphone or a DSLR camera) is a difficult, open problem. Previous works are either limited to planar geometry, or rely on previously scanned 3D geometry, thus limiting their practicality. There are several technical challenges that need to be overcome: First, the built-in flash of a camera is almost colocated with the lens, and at a fixed position; this severely hampers sampling procedures in the light-view space. Moreover, the near-field flash lights the object partially and unevenly. In terms of geometry, existing multiview stereo techniques assume diffuse reflectance only, which leads to overly smoothed 3D reconstructions, as we show in this paper. We present a simple yet powerful framework that removes the need for expensive, dedicated hardware, enabling practical acquisition of SVBRDF information from real-world, 3D objects with a single, off-the-shelf camera with a built-in flash. In addition, by removing the diffuse reflection assumption and leveraging instead such SVBRDF information, our method outputs high-quality 3D geometry reconstructions, including more accurate high-frequency details than state-of-the-art multiview stereo techniques. We formulate the joint reconstruction of SVBRDFs, shading normals, and 3D geometry as a multi-stage, iterative inverse-rendering reconstruction pipeline. Our method is also directly applicable to any existing multiview 3D reconstruction technique. We present results of captured objects with complex geometry and reflectance; we also validate our method numerically against other existing approaches that rely on dedicated hardware, additional sources of information, or both
On-site example-based material appearance acquisition
We present a novel example-based material appearance modeling method suitable for rapid digital content creation. Our method only requires a single HDR photograph of a homogeneous isotropic dielectric exemplar object under known natural illumination. While conventional methods for appearance modeling require prior knowledge on the object shape, our method does not, nor does it recover the shape explicitly, greatly simplifying on-site appearance acquisition to a lightweight photography process suited for non-expert users. As our central contribution, we propose a shape-agnostic BRDF estimation procedure based on binary RGB profile matching. We also model the appearance of materials exhibiting a regular or stationary texture-like appearance, by synthesizing appropriate mesostructure from the same input HDR photograph and a mesostructure exemplar with (roughly) similar features. We believe our lightweight method for on-site shape-agnostic appearance acquisition presents a suitable alternative for a variety of applications that require plausible “rapid-appearance-modeling
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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