3,334 research outputs found
Embedded polarizing filters to separate diffuse and specular reflection
Polarizing filters provide a powerful way to separate diffuse and specular
reflection; however, traditional methods rely on several captures and require
proper alignment of the filters. Recently, camera manufacturers have proposed
to embed polarizing micro-filters in front of the sensor, creating a mosaic of
pixels with different polarizations. In this paper, we investigate the
advantages of such camera designs. In particular, we consider different design
patterns for the filter arrays and propose an algorithm to demosaic an image
generated by such cameras. This essentially allows us to separate the diffuse
and specular components using a single image. The performance of our algorithm
is compared with a color-based method using synthetic and real data. Finally,
we demonstrate how we can recover the normals of a scene using the diffuse
images estimated by our method.Comment: ACCV 201
CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
Most of the traditional work on intrinsic image decomposition rely on
deriving priors about scene characteristics. On the other hand, recent research
use deep learning models as in-and-out black box and do not consider the
well-established, traditional image formation process as the basis of their
intrinsic learning process. As a consequence, although current deep learning
approaches show superior performance when considering quantitative benchmark
results, traditional approaches are still dominant in achieving high
qualitative results. In this paper, the aim is to exploit the best of the two
worlds. A method is proposed that (1) is empowered by deep learning
capabilities, (2) considers a physics-based reflection model to steer the
learning process, and (3) exploits the traditional approach to obtain intrinsic
images by exploiting reflectance and shading gradient information. The proposed
model is fast to compute and allows for the integration of all intrinsic
components. To train the new model, an object centered large-scale datasets
with intrinsic ground-truth images are created. The evaluation results
demonstrate that the new model outperforms existing methods. Visual inspection
shows that the image formation loss function augments color reproduction and
the use of gradient information produces sharper edges. Datasets, models and
higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.Comment: CVPR 201
Statistical/Geometric Techniques for Object Representation and Recognition
Object modeling and recognition are key areas of research in computer vision and graphics with wide range of applications. Though research in these areas is not new, traditionally most of it has focused on analyzing problems under controlled environments. The challenges posed by real life applications demand for more general and robust solutions. The wide variety of objects with large intra-class variability makes the task very challenging. The difficulty in modeling and matching objects also vary depending on the input modality. In addition, the easy availability of sensors and storage have resulted in tremendous increase in the amount of data that needs to be processed which requires efficient algorithms suitable for large-size databases. In this dissertation, we address some of the challenges involved in modeling and matching of objects in realistic scenarios.
Object matching in images require accounting for large variability in the appearance due to changes in illumination and view point. Any real world object is characterized by its underlying shape and albedo, which unlike the image intensity are insensitive to changes in illumination conditions. We propose a stochastic filtering framework for estimating object albedo from a single intensity image by formulating the albedo estimation as an image estimation problem. We also show how this albedo estimate can be used for illumination insensitive object matching and for more accurate shape recovery from a single image using standard shape from shading formulation. We start with the simpler problem where the pose of the object is known and only the illumination varies. We then extend the proposed approach to handle unknown pose in addition to illumination variations. We also use the estimated albedo maps for another important application, which is recognizing faces across age progression.
Many approaches which address the problem of modeling and recognizing objects from images assume that the underlying objects are of diffused texture. But most real world objects exhibit a combination of diffused and specular properties. We propose an approach for separating the diffused and specular reflectance from a given color image so that the algorithms proposed for objects of diffused texture become applicable to a much wider range of real world objects.
Representing and matching the 2D and 3D geometry of objects is also an integral part of object matching with applications in gesture recognition, activity classification, trademark and logo recognition, etc. The challenge in matching 2D/3D shapes lies in accounting for the different rigid and non-rigid deformations, large intra-class variability, noise and outliers. In addition, since shapes are usually represented as a collection of landmark points, the shape matching algorithm also has to deal with the challenges of missing or unknown correspondence across these data points. We propose an efficient shape indexing approach where the different feature vectors representing the shape are mapped to a hash table. For a query shape, we show how the similar shapes in the database can be efficiently retrieved without the need for establishing correspondence making the algorithm extremely fast and scalable. We also propose an approach for matching and registration of 3D point cloud data across unknown or missing correspondence using
an implicit surface representation. Finally, we discuss possible future directions of this research
NeISF: Neural Incident Stokes Field for Geometry and Material Estimation
Multi-view inverse rendering is the problem of estimating the scene
parameters such as shapes, materials, or illuminations from a sequence of
images captured under different viewpoints. Many approaches, however, assume
single light bounce and thus fail to recover challenging scenarios like
inter-reflections. On the other hand, simply extending those methods to
consider multi-bounced light requires more assumptions to alleviate the
ambiguity. To address this problem, we propose Neural Incident Stokes Fields
(NeISF), a multi-view inverse rendering framework that reduces ambiguities
using polarization cues. The primary motivation for using polarization cues is
that it is the accumulation of multi-bounced light, providing rich information
about geometry and material. Based on this knowledge, the proposed incident
Stokes field efficiently models the accumulated polarization effect with the
aid of an original physically-based differentiable polarimetric renderer.
Lastly, experimental results show that our method outperforms the existing
works in synthetic and real scenarios
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