91 research outputs found
Implicit surfaces with globally regularised and compactly supported basis functions
We consider the problem of constructing a function whose zero set is to represent a surface, given sample points with surface normal vectors. The contributions include a novel means of regularising multi-scale compactly supported basis functions that leads to the desirable properties previously only associated with fully supported bases, and show equivalence to a Gaussian process with modified covariance function. We also provide a regularisation framework for simpler and more direct treatment of surface normals, along with a corresponding generalisation of the representer theorem. We demonstrate the techniques on 3D problems of up to 14 million data points, as well as 4D time series data
Local causal structures, Hadamard states and the principle of local covariance in quantum field theory
In the framework of the algebraic formulation, we discuss and analyse some
new features of the local structure of a real scalar quantum field theory in a
strongly causal spacetime. In particular we use the properties of the
exponential map to set up a local version of a bulk-to-boundary correspondence.
The bulk is a suitable subset of a geodesic neighbourhood of any but fixed
point p of the underlying background, while the boundary is a part of the
future light cone having p as its own tip. In this regime, we provide a novel
notion for the extended *-algebra of Wick polynomials on the said cone and, on
the one hand, we prove that it contains the information of the bulk counterpart
via an injective *-homomorphism while, on the other hand, we associate to it a
distinguished state whose pull-back in the bulk is of Hadamard form. The main
advantage of this point of view arises if one uses the universal properties of
the exponential map and of the light cone in order to show that, for any two
given backgrounds M and M' and for any two subsets of geodesic neighbourhoods
of two arbitrary points, it is possible to engineer the above procedure such
that the boundary extended algebras are related via a restriction homomorphism.
This allows for the pull-back of boundary states in both spacetimes and, thus,
to set up a machinery which permits the comparison of expectation values of
local field observables in M and M'.Comment: 42 pages, xy package is used, typos corrected, clarifications adde
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Implicit scene modelling from imprecise point clouds
In applying optical methods for automated 3D indoor modelling, the 3D reconstruction of objects and surfaces is very sensitive to both lighting conditions and the observed surface properties, which ultimately compromise the utility of the acquired 3D point clouds. This paper presents a robust scene reconstruction method which is predicated upon the observation that most objects contain only a small set of primitives. The approach combines sparse approximation techniques from the compressive sensing domain with surface rendering approaches from computer graphics. The amalgamation of these techniques allows a scene to be represented by a small set of geometric primitives and to generate perceptually appealing results. The resulting scene surface models are defined as implicit functions and may be processed using conventional rendering algorithms such as marching cubes, to deliver polygonal models of arbitrary resolution. It will also be shown that 3D point clouds with outliers, strong noise and varying sampling density can be reliably processed without manual intervention
Implicit reconstructions of thin leaf surfaces from large, noisy point clouds
Thin surfaces, such as the leaves of a plant, pose a significant challenge
for implicit surface reconstruction techniques, which typically assume a
closed, orientable surface. We show that by approximately interpolating a point
cloud of the surface (augmented with off-surface points) and restricting the
evaluation of the interpolant to a tight domain around the point cloud, we need
only require an orientable surface for the reconstruction. We use polyharmonic
smoothing splines to fit approximate interpolants to noisy data, and a
partition of unity method with an octree-like strategy for choosing subdomains.
This method enables us to interpolate an N-point dataset in O(N) operations. We
present results for point clouds of capsicum and tomato plants, scanned with a
handheld device. An important outcome of the work is that sufficiently smooth
leaf surfaces are generated that are amenable for droplet spreading
simulations
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A Knowledge Integration Framework for 3D Shape Reconstruction
The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points in sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment in which the robots operate with. This means unstructured 3D samples must be processed by application-specific models to enable a robot, for instance, to detect and identify objects and infer the scene geometry for path-planning more efficiently than by using raw 3D data. This thesis specifically focuses on the fundamental task of 3D shape reconstruction and modelling by presenting a new knowledge integration framework for unstructured 3D samples. The novelty lies in the representation of surfaces by algebraic functions with limited support, which enables the extraction of smooth consistent shapes from noisy samples with a heterogeneous density. Moreover, many surfaces in urban environments can reasonably be assumed to be planar, and the framework exploits this knowledge to enable effective noise suppression without loss of detail. This is achieved by using a convex optimization technique which has linear computational complexity. Thus is much more efficient than existing solutions. The new framework has been validated by critical experimental analysis and evaluation and has been shown to increase the accuracy of the reconstructed shape significantly compared to state-of-the-art methods. Applying this new knowledge integration framework means that less accurate, low-cost 3D sensors can be employed without sacrificing the high demands that 3D perception must achieve. This links well into the area of robotic inspection, as for example regarding small drones that use inaccurate and lightweight image sensors
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