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| • Research interests |
| As a former Computer Science PhD student of Neil Dodgson at the University of Cambridge Computer Laboratory, my specific interests include geometric data structures such as Voronoi diagrams and their generalisations, Fast Marching techniques for distance mapping across point clouds, farthest point sampling schemes for two- and higher-dimensional manifolds, and the intrinsic and meshless processing of point-based geometry. For more detail, see below. |
| • Publications - Journal papers |
| Carsten Moenning, Facundo Mémoli, Guillermo Sapiro, Nira Dyn and Neil A. Dodgson: Meshless Geometric Subdivision (05/07) |
Graphical Models: Point-based surface processing has developed into an attractive alternative to mesh-based processing tools for a number of geometric modeling applications. By working with point clouds directly, processing is based on the raw data and its underlying geometry rather than any arbitrary intermediate representations and generally artificial connectivity relations. We extend this principle into the area of subdivision surfaces by introducing the notion of meshless geometric subdivision. Our approach replaces the role of mesh connectivity with intrinsic point proximity thereby avoiding a number of limitations of mesh-based surface subdivision schemes. Apart from introducing this idea of meshless subdivision, we put forward a first intrinsic meshless subdivision scheme and present a new method for the computation of intrinsic means on Euclidean manifolds. |
| • Publications - Conference papers |
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Carsten Moenning and Neil A. Dodgson: Intrinsic point cloud simplification (07/04) |
GraphiCon '04: Modelling and visualisation methods working directly with point-sampled geometry have developed into attractive alternatives to more traditional mesh-based surface processing. In this paper, we consider a vital step in any point-based surface processing pipeline, point cloud simplification. Building upon the intrinsic point cloud simplification idea put forward in Moenning and Dodgson (2003), we obtain a simplification algorithm allowing for intuitive density control and satisfying a set of important requirements unsupported by existing simplification techniques. The algorithm operates efficiently and gives a point set density guarantee. It supports both sub- and resampling of the input point set and allows for uniform and user-controlled feature-sensitive simplification. It can further deal with non-uniformly distributed point sets and point-sampled geometry featuring illegitimate holes of simple complexity. The algorithm is inherently progressive and supports the generation of multiresolution representations in the form of levels of detail. We are primarily concerned with describing the conceptual framework of our intrinsic approach and show its viability by giving a number of application examples using massive data sets. |
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Carsten Moenning and Neil A. Dodgson: Fast Marching farthest point sampling (09/03) |
EUROGRAPHICS 2003 (poster paper): We introduce the Fast Marching farthest point sampling (FastFPS) approach for the progressive sampling of planar domains and curved manifolds in triangulated, point cloud or implicit form. By using Fast Marching methods for the incremental computation of distance maps across the sampling domain, we obtain a farthest point sampling technique superior to earlier point sampling principles in two important respects. Firstly, our method performs equally well in both the uniform and the adaptive case. Secondly, the algorithm is applicable to both images and higher dimensional surfaces in triangulated, point cloud or implicit form. This paper presents the methods underlying the algorithm and gives examples for the processing of images and triangulated surfaces. |
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Carsten Moenning and Neil A. Dodgson: A new point cloud simplification algorithm (09/03) |
The 3rd IASTED conference for Visualization, Imaging and Image Processing (VIIP 2003): We present a new technique for the simplification of point-sampled geometry without any prior surface reconstruction. Using Fast Marching farthest point sampling for implicit surfaces and point clouds, we devise a coarse-to-fine uniform or feature-sensitive simplification algorithm with user-controlled density guarantee. The algorithm is computationally and memory efficient, easy to implement and inherently allows for the generation of progressive and multiresolution representations of the input point set. |
| • Publications - Technical reports |
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Carsten Moenning and Neil A. Dodgson: Fast Marching farthest point sampling for implicit surfaces and point clouds (last updated 20/08/03) |
Computer Laboratory Technical Report No. 565: In this paper, we overcome the restrictions of FastFPS for triangulated surfaces by using Memoli and Sapiro's (2001, 2002) extension of the Fast Marching method to the handling of implicit surfaces and point clouds. We find that the extended FastFPS algorithm can be applied to surfaces in implicit or point cloud form without the loss of the original algorithm's computational optimality and without the need for any preprocessing. |
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Carsten Moenning and Neil A. Dodgson: Fast Marching farthest point sampling (last updated 20/08/03) |
Computer Laboratory Technical Report No. 562: We introduce the Fast Marching farthest point sampling (FastFPS) approach for the progressive sampling of planar domains and curved manifolds in triangulated, point cloud or implicit form. By using Fast Marching methods for the incremental computation of distance maps across the sampling domain, we extend earlier uniform farthest point sampling principles in two important respects. Firstly, we present a consistent extension of the uniform farthest point sampling concept to the non-uniform, adaptive case. Secondly, our FastFPS algorithm is applicable to the sampling of both images and higher dimensional surfaces in triangulated, point cloud or implicit form. |
| • Publications - Other |
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Carsten Moenning: Pesaran's bounds test, Johansen's system-rank test and the Engle-Granger two-step procedure: A comparative study(06/97) |
University of Cambridge - Dissertation: We present an investigation and application of Pesaran's bounds test procedure vs. both Johansen's system-rank test and the conventional Engle-Granger two-step procedure for long-run relationship testing. We discuss the various conceptual problems encountered and indicate which long-run testing procedure seems preferable given the state of theoretical knowledge. |
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Carsten Moenning: Integration, cointegration analysis and error-correction models (05/95) |
Universität Paderborn (Germany) - Diplomarbeit: We provide a relatively detailed discussion of the derivation of error-correction models (ECMs) with particular reference to the Granger Representation Theorem. These theoretical concepts and their implicit strengths and drawbacks are then illustrated by the reconsideration of the quarterly and annual versions of the Davidson et al. 1978 consumption ECM. We conclude with assessing the impact of both integration and cointegration analysis as well as error-correction modelling (on consumption modelling in the UK). |
© Carsten Moenning, last modified 19/04/2008.
