Analysis of binning of normals for spherical harmonic cross-correlation

Spherical harmonic cross-correlation is a robust registration technique that uses the normals of two overlapping point clouds to bring them into coarse rotational alignment. This registration technique however has a high computational cost as spherical harmonics need to be calculated for every normal. By binning the normals, the computational efficiency is improved as the spherical harmonics can be pre-computed and cached at each bin location. In this paper we evaluate the efficiency and accuracy of the equiangle grid, icosahedron subdivision and the Fibonacci spiral, an approach we propose. It is found that the equiangle grid has the best efficiency as it can perform direct binning, followed by the Fibonacci spiral and then the icosahedron, all of which decrease the computational cost compared to no binning. The Fibonacci spiral produces the highest achieved accuracy of the three approaches while maintaining a low number of bins. The number of bins allowed by the equiangle grid and icosahedron are much more restrictive than the Fibonacci spiral. The performed analysis shows that the Fibonacci spiral can perform as well as the original cross-correlation algorithm without binning, while also providing a significant improvement in computational efficiency

Spiral Tessellation on the Sphere

In this paper we describe a tessellation of the unit sphere in the 3-dimensional space realized using a spiral joining the north and the south poles. This tiling yields to a one dimensional labeling of the tiles covering the whole sphere and to a 1-dimensional natural ordering on the set of tiles of the tessellation. The correspondence between a point on the sphere and the tile containing it is derived as an analytical function, allowing the direct computation of the tile. This tessellation exhibits some intrinsic features useful for general applications: absence of singular points and efficient tiles computation. Moreover, this tessellation can be parametrized to obtain additional features especially useful for spherical coordinate indexing: tiles with equal area and good shape uniformity of tiles. An application to spherical indexing of a database is presented, it shows an assessment of our spiral tiling for practical use

POTENT Reconstruction from Mark III Velocities

We present an improved POTENT method for reconstructing the velocity and mass density fields from radial peculiar velocities, test it with mock catalogs, and apply it to the Mark III Catalog. Method improvments: (a) inhomogeneous Malmquist bias is reduced by grouping and corrected in forward or inverse analyses of inferred distances, (b) the smoothing into a radial velocity field is optimized to reduce window and sampling biases, (c) the density is derived from the velocity using an improved nonlinear approximation, and (d) the computational errors are made negligible. The method is tested and optimized using mock catalogs based on an N-body simulation that mimics our cosmological neighborhood, and the remaining errors are evaluated quantitatively. The Mark III catalog, with ~3300 grouped galaxies, allows a reliable reconstruction with fixed Gaussian smoothing of 10-12 Mpc/h out to ~60 Mpc/h. We present maps of the 3D velocity and mass-density fields and the corresponding errors. The typical systematic and random errors in the density fluctuations inside 40 Mpc/h are \pm 0.13 and \pm 0.18. The recovered mass distribution resembles in its gross features the galaxy distribution in redshift surveys and the mass distribution in a similar POTENT analysis of a complementary velocity catalog (SFI), including the Great Attractor, Perseus-Pisces, and the void in between. The reconstruction inside ~40 Mpc/h is not affected much by a revised calibration of the distance indicators (VM2, tailored to match the velocities from the IRAS 1.2Jy redshift survey). The bulk velocity within the sphere of radius 50 Mpc/h about the Local Group is V_50=370 \pm 110 km/s (including systematic errors), and is shown to be mostly generated by external mass fluctuations. With the VM2 calibration, V_50 is reduced to 305 \pm 110 km/s.Comment: 60 pages, LaTeX, 3 tables and 27 figures incorporated (may print the most crucial figures only, by commenting out one line in the LaTex source

Computing Eigenmodes of Elliptic Operators on Manifolds Using Radial Basis Functions

In this work, a numerical approach based on meshless methods is proposed to obtain eigenmodes of Laplace-Beltrami operator on manifolds, and its performance is compared against existing alternative methods. Radial Basis Function (RBF)-based methods allow one to obtain interpolation and differentiation matrices easily by using scattered data points. We derive expressions for such matrices for the Laplace-Beltrami operator via so-called Reilly’s formulas and use them to solve the respective eigenvalue problem. Numerical studies of proposed methods are performed in order to demonstrate convergence on simple examples of one-dimensional curves and two-dimensional surfaces

Analysing and Enhancing the Coarse Registration Pipeline

The current and continual development of sensors and imaging systems capable of acquiring three-dimensional data provides a novel form in which the world can be expressed and examined. The acquisition process, however, is often limited by imaging systems only being able to view a portion of a scene or object from a single pose at a given time. A full representation can still be produced by shifting the system and registering subsequent acquisitions together. While many solutions to the registration problem have been proposed, there is no quintessential approach appropriate for all situations. This dissertation aims to coarsely register range images or point-clouds of a priori unknown pose by matching their overlapping regions. Using spherical harmonics to correlate normals in a coarse registration pipeline has been shown previously to be an effective means for registering partially overlapping point-clouds. The advantage of normals is their translation invariance, which permits the rotation and translation to be decoupled and determined separately. Examining each step of this pipeline in depth allows its registration capability to be quantified and identifies aspects which can be enhanced to further improve registration performance. The pipeline consists of three primary steps: identifying the rotation using spherical harmonics, identifying the translation in the Fourier domain, and automatically verifying if alignment is correct. Having achieved coarse registration, a fine registration algorithm can be used to refine and complete the alignment. Major contributions to knowledge are provided by this dissertation at each step of the pipeline. Point-clouds with known ground-truth are used to examine the pipeline's capability, allowing its limitations to be determined; an analysis which has not been performed previously. This examination allowed modifications to individual components to be introduced and measured, establishing their provided benefit. The rotation step received the greatest attention as it is the primary weakness of the pipeline, especially as the nature of the overlap between point-clouds is unknown. Examining three schemes for binning normals found that equiangular binning, when appropriately normalised, only had a marginal decrease in accuracy with respect to the icosahedron and the introduced Fibonacci schemes. Overall, equiangular binning was the most appropriate due to its natural affinity for fast spherical-harmonic conversion. Weighting normals was found to provide the greatest benefit to registration performance. The introduction of a straightforward method of combining two different weighting schemes using the orthogonality of complex values increased correct alignments by approximately 80% with respect to the next best scheme; additionally, point-cloud pairs with overlap as low as 5% were able to be brought into correct alignment. Transform transitivity, one of two introduced verification strategies, correctly classified almost 100% of point-cloud pair registrations when there are sufficient correct alignments. The enhancements made to the coarse registration pipeline throughout this dissertation provide significant improvements to its performance. The result is a pipeline with state-of-the-art capabilities that allow it to register point-cloud with minimal overlap and correct for alignments that are classified as misaligned. Even with its exceptional performance, it is unlikely that this pipeline has yet reached its pinnacle, as the introduced enhancements have the potential for further development

Meshfree Methods for PDEs on Surfaces

This dissertation focuses on meshfree methods for solving surface partial differential equations (PDEs). These PDEs arise in many areas of science and engineering where they are used to model phenomena ranging from atmospheric dynamics on earth to chemical signaling on cell membranes. Meshfree methods have been shown to be effective for solving surface PDEs and are attractive alternatives to mesh-based methods such as finite differences/elements since they do not require a mesh and can be used for surfaces represented only by a point cloud. The dissertation is subdivided into two papers and software. In the first paper, we examine the performance and accuracy of two popular meshfree methods for surface PDEs:generalized moving least squares (GMLS) and radial basis function-finite differences (RBF-FD). While these methods are computationally efficient and can give high orders of accuracy for smooth problems, there are no published works that have systematically compared their benefits and shortcomings. We perform such a comparison by examining their convergence rates for approximating the surface gradient, divergence, and Laplacian on the sphere and a torus as the resolution of the discretization increases. We investigate these convergence rates also as the various parameters of the methods are changed. We also compare the overall efficiencies of the methods in terms of accuracy per computation cost. The second paper is focused on developing a novel meshfree geometric multilevel (MGM) method for solving linear systems associated with meshfree discretizations of elliptic PDEs on surfaces represented by point clouds. Multilevel (or multigrid) methods are efficient iterative methods for solving linear systems that arise in numerical PDEs. The key components for multilevel methods: \grid coarsening, restriction/ interpolation operators coarsening, and smoothing. The first three components present challenges for meshfree methods since there are no grids or mesh structures, only point clouds. To overcome these challenges, we develop a geometric point cloud coarsening method based on Poisson disk sampling, interpolation/ restriction operators based on RBF-FD, and apply Galerkin projections to coarsen the operator. We test MGM as a standalone solver and preconditioner for Krylov subspace methods on various test problems using RBF-FD and GMLS discretizations, and numerically analyze convergence rates, scaling, and efficiency with increasing point cloud resolution. We finish with several application problems. We conclude the dissertation with a description of two new software packages. The first one is our MGM framework for solving elliptic surface PDEs. This package is built in Python and utilizes NumPy and SciPy for the data structures (arrays and sparse matrices), solvers (Krylov subspace methods, Sparse LU), and C++ for the smoothers and point cloud coarsening. The other package is the RBFToolkit which has a Python version and a C++ version. The latter uses the performance library Kokkos, which allows for the abstraction of parallelism and data management for shared memory computing architectures. The code utilizes OpenMP for CPU parallelism and can be extended to GPU architectures