6,818 research outputs found
A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data
In this paper we present a practical approach for generating an
occlusion-free textured 3D map of urban facades by the synergistic use of
terrestrial images, 3D point clouds and area-based information. Particularly in
dense urban environments, the high presence of urban objects in front of the
facades causes significant difficulties for several stages in computational
building modeling. Major challenges lie on the one hand in extracting complete
3D facade quadrilateral delimitations and on the other hand in generating
occlusion-free facade textures. For these reasons, we describe a
straightforward approach for completing and recovering facade geometry and
textures by exploiting the data complementarity of terrestrial multi-source
imagery and area-based information
Topological characterization of antireflective and hydrophobic rough surfaces: are random process theory and fractal modeling applicable?
The random process theory (RPT) has been widely applied to predict the joint
probability distribution functions (PDFs) of asperity heights and curvatures of
rough surfaces. A check of the predictions of RPT against the actual statistics
of numerically generated random fractal surfaces and of real rough surfaces has
been only partially undertaken. The present experimental and numerical study
provides a deep critical comparison on this matter, providing some insight into
the capabilities and limitations in applying RPT and fractal modeling to
antireflective and hydrophobic rough surfaces, two important types of textured
surfaces. A multi-resolution experimental campaign by using a confocal
profilometer with different lenses is carried out and a comprehensive software
for the statistical description of rough surfaces is developed. It is found
that the topology of the analyzed textured surfaces cannot be fully described
according to RPT and fractal modeling. The following complexities emerge: (i)
the presence of cut-offs or bi-fractality in the power-law power-spectral
density (PSD) functions; (ii) a more pronounced shift of the PSD by changing
resolution as compared to what expected from fractal modeling; (iii) inaccuracy
of the RPT in describing the joint PDFs of asperity heights and curvatures of
textured surfaces; (iv) lack of resolution-invariance of joint PDFs of textured
surfaces in case of special surface treatments, not accounted by fractal
modeling.Comment: 21 pages, 13 figure
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PDE Face: A Novel 3D Face Model
YesWe introduce a novel approach to face models, which
exploits the use of Partial Differential Equations (PDE) to
generate the 3D face. This addresses some common
problems of existing face models. The PDE face benefits
from seamless merging of surface patches by using only a
relatively small number of parameters based on boundary
curves. The PDE face also provides users with a great
degree of freedom to individualise the 3D face by
adjusting a set of facial boundary curves. Furthermore, we
introduce a uv-mesh texture mapping method. By
associating the texels of the texture map with the vertices
of the uv mesh in the PDE face, the new texture mapping
method eliminates the 3D-to-2D association routine in
texture mapping. Any specific PDE face can be textured
without the need for the facial expression in the texture
map to match exactly that of the 3D face model
3D Face Reconstruction by Learning from Synthetic Data
Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.Comment: The first two authors contributed equally to this wor
Mean value coordinates–based caricature and expression synthesis
We present a novel method for caricature synthesis based on mean value coordinates (MVC). Our method can be applied to any single frontal face image to learn a specified caricature face pair for frontal and 3D caricature synthesis. This technique only requires one or a small number of exemplar pairs and a natural frontal face image training set, while the system can transfer the style of the exemplar pair across individuals. Further exaggeration can be fulfilled in a controllable way. Our method is further applied to facial expression transfer, interpolation, and exaggeration, which are applications of expression editing. Additionally, we have extended our approach to 3D caricature synthesis based on the 3D version of MVC. With experiments we demonstrate that the transferred expressions are credible and the resulting caricatures can be characterized and recognized
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