8,417 research outputs found
Semantic 3D Reconstruction with Finite Element Bases
We propose a novel framework for the discretisation of multi-label problems
on arbitrary, continuous domains. Our work bridges the gap between general FEM
discretisations, and labeling problems that arise in a variety of computer
vision tasks, including for instance those derived from the generalised Potts
model. Starting from the popular formulation of labeling as a convex relaxation
by functional lifting, we show that FEM discretisation is valid for the most
general case, where the regulariser is anisotropic and non-metric. While our
findings are generic and applicable to different vision problems, we
demonstrate their practical implementation in the context of semantic 3D
reconstruction, where such regularisers have proved particularly beneficial.
The proposed FEM approach leads to a smaller memory footprint as well as faster
computation, and it constitutes a very simple way to enable variable, adaptive
resolution within the same model
A Study of Different Modeling Choices For Simulating Platelets Within the Immersed Boundary Method
The Immersed Boundary (IB) method is a widely-used numerical methodology for
the simulation of fluid-structure interaction problems. The IB method utilizes
an Eulerian discretization for the fluid equations of motion while maintaining
a Lagrangian representation of structural objects. Operators are defined for
transmitting information (forces and velocities) between these two
representations. Most IB simulations represent their structures with
piecewise-linear approximations and utilize Hookean spring models to
approximate structural forces. Our specific motivation is the modeling of
platelets in hemodynamic flows. In this paper, we study two alternative
representations - radial basis functions (RBFs) and Fourier-based
(trigonometric polynomials and spherical harmonics) representations - for the
modeling of platelets in two and three dimensions within the IB framework, and
compare our results with the traditional piecewise-linear approximation
methodology. For different representative shapes, we examine the geometric
modeling errors (position and normal vectors), force computation errors, and
computational cost and provide an engineering trade-off strategy for when and
why one might select to employ these different representations.Comment: 33 pages, 17 figures, Accepted (in press) by APNU
Multiframe Scene Flow with Piecewise Rigid Motion
We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formulate scene flow recovery as a global non-linear least
squares problem which is iteratively solved by a damped Gauss-Newton approach.
As a result, we obtain a qualitatively new level of accuracy in RGB-D based
scene flow estimation which can potentially run in real-time. Our method can
handle challenging cases with rigid, piecewise rigid, articulated and moderate
non-rigid motion, and does not rely on prior knowledge about the types of
motions and deformations. Extensive experiments on synthetic and real data show
that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
Multiframe Scene Flow with Piecewise Rigid Motion
We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formulate scene flow recovery as a global non-linear least
squares problem which is iteratively solved by a damped Gauss-Newton approach.
As a result, we obtain a qualitatively new level of accuracy in RGB-D based
scene flow estimation which can potentially run in real-time. Our method can
handle challenging cases with rigid, piecewise rigid, articulated and moderate
non-rigid motion, and does not rely on prior knowledge about the types of
motions and deformations. Extensive experiments on synthetic and real data show
that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
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