226 research outputs found
INVESTIGATING 3D RECONSTRUCTION OF NON-COLLABORATIVE SURFACES THROUGH PHOTOGRAMMETRY AND PHOTOMETRIC STEREO
Abstract. 3D digital reconstruction techniques are extensively used for quality control purposes. Among them, photogrammetry and photometric stereo methods have been for a long time used with success in several application fields. However, generating highly-detailed and reliable micro-measurements of non-collaborative surfaces is still an open issue. In these cases, photogrammetry can provide accurate low-frequency 3D information, whereas it struggles to extract reliable high-frequency details. Conversely, photometric stereo can recover a very detailed surface topography, although global surface deformation is often present. In this paper, we present the preliminary results of an ongoing project aiming to combine photogrammetry and photometric stereo in a synergetic fusion of the two techniques. Particularly, hereafter, we introduce the main concept design behind an image acquisition system we developed to capture images from different positions and under different lighting conditions as required by photogrammetry and photometric stereo techniques. We show the benefit of such a combination through some experimental tests. The experiments showed that the proposed method recovers the surface topography at the same high-resolution achievable with photometric stereo while preserving the photogrammetric accuracy. Furthermore, we exploit light directionality and multiple light sources to improve the quality of dense image matching in poorly textured surfaces
3D Reconstruction using Active Illumination
In this thesis we present a pipeline for 3D model acquisition. Generating 3D models of real-world objects is an important task in computer vision with many applications, such as in 3D design, archaeology, entertainment, and virtual or augmented reality.
The contribution of this thesis is threefold: we propose a calibration procedure for the cameras, we describe an approach for capturing and processing photometric normals using gradient illuminations in the hardware set-up, and finally we present a multi-view photometric stereo 3D reconstruction method.
In order to obtain accurate results using multi-view and photometric stereo reconstruction, the cameras are calibrated geometrically and photometrically.
For acquiring data, a light stage is used. This is a hardware set-up that allows to control the illumination during acquisition. The procedure used to generate appropriate illuminations and to process the acquired data to obtain accurate photometric normals is described.
The core of the pipeline is a multi-view photometric stereo reconstruction method. In this method, we first generate a sparse reconstruction using the acquired images and computed normals. In the second step, the information from the normal maps is used to obtain a dense reconstruction of an object’s surface. Finally, the reconstructed surface is filtered to remove artifacts introduced by the dense reconstruction step
Photometric reconstruction of a dynamic textured surface from just one color image acquisition
http://www.opticsinfobase.org/josaa/abstract.cfm?msid=85528 This article has been selected for inclusion in the Virtual Journal for Biomedical Optics (Vol. 3, Iss. 4)International audienceTextured surface analysis is essential for many applications. We present a three-dimensional recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to measure the textured surfaces with a high degree of accuracy. For this, we use a color digital sensor and principles of color photometric stereo. This method uses a single color image, instead of a sequence of gray-scale images, to recover the surface of the three dimensions. It can thus be integrated into dynamic systems where there is significant relative motion between the object and the camera. To evaluate the performances of our method, we compare it on real textured surfaces to traditional photometric stereo using three images. We show thus that it is possible to have similar results with just one color image
Ear-to-ear Capture of Facial Intrinsics
We present a practical approach to capturing ear-to-ear face models
comprising both 3D meshes and intrinsic textures (i.e. diffuse and specular
albedo). Our approach is a hybrid of geometric and photometric methods and
requires no geometric calibration. Photometric measurements made in a
lightstage are used to estimate view dependent high resolution normal maps. We
overcome the problem of having a single photometric viewpoint by capturing in
multiple poses. We use uncalibrated multiview stereo to estimate a coarse base
mesh to which the photometric views are registered. We propose a novel approach
to robustly stitching surface normal and intrinsic texture data into a
seamless, complete and highly detailed face model. The resulting relightable
models provide photorealistic renderings in any view
Multilinear methods for disentangling variations with applications to facial analysis
Several factors contribute to the appearance of an object in a visual scene, including pose,
illumination, and deformation, among others. Each factor accounts for a source of variability
in the data. It is assumed that the multiplicative interactions of these factors emulate the
entangled variability, giving rise to the rich structure of visual object appearance. Disentangling
such unobserved factors from visual data is a challenging task, especially when the data have
been captured in uncontrolled recording conditions (also referred to as “in-the-wild”) and label
information is not available. The work presented in this thesis focuses on disentangling the
variations contained in visual data, in particular applied to 2D and 3D faces. The motivation
behind this work lies in recent developments in the field, such as (i) the creation of large, visual
databases for face analysis, with (ii) the need of extracting information without the use of labels
and (iii) the need to deploy systems under demanding, real-world conditions.
In the first part of this thesis, we present a method to synthesise plausible 3D expressions
that preserve the identity of a target subject. This method is supervised as the model uses
labels, in this case 3D facial meshes of people performing a defined set of facial expressions, to
learn. The ability to synthesise an entire facial rig from a single neutral expression has a large
range of applications both in computer graphics and computer vision, ranging from the ecient
and cost-e↵ective creation of CG characters to scalable data generation for machine learning
purposes. Unlike previous methods based on multilinear models, the proposed approach is
capable to extrapolate well outside the sample pool, which allows it to accurately reproduce
the identity of the target subject and create artefact-free expression shapes while requiring
only a small input dataset. We introduce global-local multilinear models that leverage the
strengths of expression-specific and identity-specific local models combined with coarse motion
estimations from a global model. The expression-specific and identity-specific local models
are built from di↵erent slices of the patch-wise local multilinear model. Experimental results
show that we achieve high-quality, identity-preserving facial expression synthesis results that
outperform existing methods both quantitatively and qualitatively.
In the second part of this thesis, we investigate how the modes of variations from visual data
can be extracted. Our assumption is that visual data has an underlying structure consisting of
factors of variation and their interactions. Finding this structure and the factors is important
as it would not only help us to better understand visual data but once obtained we can edit the factors for use in various applications. Shape from Shading and expression transfer are just two
of the potential applications. To extract the factors of variation, several supervised methods
have been proposed but they require both labels regarding the modes of variations and the same
number of samples under all modes of variations. Therefore, their applicability is limited to
well-organised data, usually captured in well-controlled conditions. We propose a novel general
multilinear matrix decomposition method that discovers the multilinear structure of possibly
incomplete sets of visual data in unsupervised setting. We demonstrate the applicability of the
proposed method in several computer vision tasks, including Shape from Shading (SfS) (in the
wild and with occlusion removal), expression transfer, and estimation of surface normals from
images captured in the wild.
Finally, leveraging the unsupervised multilinear method proposed as well as recent advances in
deep learning, we propose a weakly supervised deep learning method for disentangling multiple
latent factors of variation in face images captured in-the-wild. To this end, we propose a deep
latent variable model, where we model the multiplicative interactions of multiple latent factors
of variation explicitly as a multilinear structure. We demonstrate that the proposed approach
indeed learns disentangled representations of facial expressions and pose, which can be used in
various applications, including face editing, as well as 3D face reconstruction and classification
of facial expression, identity and pose.Open Acces
Robust 3D face capture using example-based photometric stereo
We show that using example-based photometric stereo, it is possible to achieve realistic reconstructions of the human face. The method can handle non-Lambertian reflectance and attached shadows after a simple calibration step. We use spherical harmonics to model and de-noise the illumination functions from images of a reference object with known shape, and a fast grid technique to invert those functions and recover the surface normal for each point of the target object. The depth coordinate is obtained by weighted multi-scale integration of these normals, using an integration weight mask obtained automatically from the images themselves. We have applied these techniques to improve the PHOTOFACE system of Hansen et al. (2010). © 2013 Elsevier B.V. All rights reserved
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