14,119 research outputs found
A perceptual quality metric for 3D triangle meshes based on spatial pooling
© 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. In computer graphics, various processing operations are applied to 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In this context, perceptual quality assessment of 3D triangle meshes has become a crucial issue. In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh. Our analysis indicates that the overall quality of a distorted mesh is sensitive to the distortion distribution. The proposed metric is based on a spatial pooling strategy and statistical descriptors of the distortion distribution. We generate a perceptual distortion map for vertices in the reference mesh while taking into account the visual masking effect of the human visual system. The proposed metric extracts statistical descriptors from the distortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt a support vector regression model to predict the mesh quality score.We validate the performance of our method with three publicly available databases, and the comparison with state-of-the-art metrics demonstrates the superiority of our method. Experimental results show that our proposed method achieves a high correlation between objective assessment and subjective scores
Meshed Up: Learnt Error Correction in 3D Reconstructions
Dense reconstructions often contain errors that prior work has so far
minimised using high quality sensors and regularising the output. Nevertheless,
errors still persist. This paper proposes a machine learning technique to
identify errors in three dimensional (3D) meshes. Beyond simply identifying
errors, our method quantifies both the magnitude and the direction of depth
estimate errors when viewing the scene. This enables us to improve the
reconstruction accuracy.
We train a suitably deep network architecture with two 3D meshes: a
high-quality laser reconstruction, and a lower quality stereo image
reconstruction. The network predicts the amount of error in the lower quality
reconstruction with respect to the high-quality one, having only view the
former through its input. We evaluate our approach by correcting
two-dimensional (2D) inverse-depth images extracted from the 3D model, and show
that our method improves the quality of these depth reconstructions by up to a
relative 10% RMSE.Comment: Accepted for the International Conference on Robotics and Automation
(ICRA) 201
IGS: an IsoGeometric approach for Smoothing on surfaces
We propose an Isogeometric approach for smoothing on surfaces, namely
estimating a function starting from noisy and discrete measurements. More
precisely, we aim at estimating functions lying on a surface represented by
NURBS, which are geometrical representations commonly used in industrial
applications. The estimation is based on the minimization of a penalized
least-square functional. The latter is equivalent to solve a 4th-order Partial
Differential Equation (PDE). In this context, we use Isogeometric Analysis
(IGA) for the numerical approximation of such surface PDE, leading to an
IsoGeometric Smoothing (IGS) method for fitting data spatially distributed on a
surface. Indeed, IGA facilitates encapsulating the exact geometrical
representation of the surface in the analysis and also allows the use of at
least globally continuous NURBS basis functions for which the 4th-order
PDE can be solved using the standard Galerkin method. We show the performance
of the proposed IGS method by means of numerical simulations and we apply it to
the estimation of the pressure coefficient, and associated aerodynamic force on
a winglet of the SOAR space shuttle
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset
Assisting people in efficiently producing visually plausible 3D characters
has always been a fundamental research topic in computer vision and computer
graphics. Recent learning-based approaches have achieved unprecedented accuracy
and efficiency in the area of 3D real human digitization. However, none of the
prior works focus on modeling 3D biped cartoon characters, which are also in
great demand in gaming and filming. In this paper, we introduce 3DBiCar, the
first large-scale dataset of 3D biped cartoon characters, and RaBit, the
corresponding parametric model. Our dataset contains 1,500 topologically
consistent high-quality 3D textured models which are manually crafted by
professional artists. Built upon the data, RaBit is thus designed with a
SMPL-like linear blend shape model and a StyleGAN-based neural UV-texture
generator, simultaneously expressing the shape, pose, and texture. To
demonstrate the practicality of 3DBiCar and RaBit, various applications are
conducted, including single-view reconstruction, sketch-based modeling, and 3D
cartoon animation. For the single-view reconstruction setting, we find a
straightforward global mapping from input images to the output UV-based texture
maps tends to lose detailed appearances of some local parts (e.g., nose, ears).
Thus, a part-sensitive texture reasoner is adopted to make all important local
areas perceived. Experiments further demonstrate the effectiveness of our
method both qualitatively and quantitatively. 3DBiCar and RaBit are available
at gaplab.cuhk.edu.cn/projects/RaBit.Comment: CVPR 2023, Project page: https://gaplab.cuhk.edu.cn/projects/RaBit
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