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PMR: Point to Mesh Rendering, A FeatureBased Approach
"... Within the field of computer graphics and visualization, it is often necessary to visualize polygonal models with large number of polygons. Display quality is mandatory, but it is also desirable to have the ability to rapidly update the display in order to facilitate interactive use. Point based ren ..."
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Cited by 21 (0 self)
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Within the field of computer graphics and visualization, it is often necessary to visualize polygonal models with large number of polygons. Display quality is mandatory, but it is also desirable to have the ability to rapidly update the display in order to facilitate interactive use. Point based rendering methods have been shown effective for this task. Building on this paradigm we introduce the PMR system which uses a hierarchy both in points and triangles for rendering. This hierarchy is fundamentally different from the ones used in existing methods. It is based on the feature geometry in the object space rather than its projection in the screen space. This provides certain advantages over the existing methods.
DISTANCE FUNCTIONS AND GEODESICS ON SUBMANIFOLDS OF R^d AND POINT CLOUDS
, 2005
"... A theoretical and computational framework for computing intrinsic distance functions and geodesics on submanifolds of Rd given by point clouds is introduced and developed in this paper. The basic idea is that, as shown here, intrinsic distance functions and geodesics on general codimension submanif ..."
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Cited by 20 (5 self)
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A theoretical and computational framework for computing intrinsic distance functions and geodesics on submanifolds of Rd given by point clouds is introduced and developed in this paper. The basic idea is that, as shown here, intrinsic distance functions and geodesics on general codimension submanifolds of Rd can be accurately approximated by extrinsic Euclidean ones computed inside a thin offset band surrounding the manifold. This permits the use of computationally optimal algorithms for computing distance functions in Cartesian grids. We use these algorithms, modified to deal with spaces with boundaries, and obtain a computationally optimal approach also for the case of intrinsic distance functions on submanifolds of Rd. For point clouds, the offset band is constructed without the need to explicitly find the underlying manifold, thereby computing intrinsic distance functions and geodesics on point clouds while skipping the manifold reconstruction step. The case of point clouds representing noisy samples of a submanifold of Euclidean space is studied as well. All the underlying theoretical results are presented along with experimental examples for diverse applications and comparisons to graphbased distance algorithms.
Distance functions and geodesics on point clouds
, 2003
"... An new paradigm for computing intrinsic distance functions and geodesics on submanifolds of given by point clouds is introduced in this paper. The basic idea is that, as shown here, intrinsic distance functions and geodesics on general codimension submanifolds of can be accurately approximated by ..."
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Cited by 16 (2 self)
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An new paradigm for computing intrinsic distance functions and geodesics on submanifolds of given by point clouds is introduced in this paper. The basic idea is that, as shown here, intrinsic distance functions and geodesics on general codimension submanifolds of can be accurately approximated by extrinsic Euclidean ones computed inside a thin offset band surrounding the manifold. This permits the use of computationally optimal algorithms for computing distance functions in Cartesian grids. We use these algorithms, modified to deal with spaces with boundaries, and obtain also for the case of intrinsic distance functions on submanifolds of, a computationally optimal approach. For point clouds, the offset band is constructed without the need to explicitly find the underlying manifold, thereby computing intrinsic distance functions and geodesics on point clouds while skipping the manifold reconstruction step. The case of point clouds representing noisy samples of a submanifold of Euclidean space is studied as well. All the underlying theoretical results are presented along with experimental examples for diverse applications and comparisons to graphbased distance algorithms.
Intrinsic point cloud simplification
 In Proc. 14th GraphiCon ’04. http://www.cl.cam.ac.uk/users/cm230/docs/pdfs/GraphiCon04.pdf
, 2004
"... Modelling and visualisation methods working directly with pointsampled geometry have developed into attractive alternatives to more traditional meshbased surface processing. In this paper, we consider a vital step in any pointbased surface processing pipeline, point cloud simplification. Building ..."
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Cited by 15 (1 self)
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Modelling and visualisation methods working directly with pointsampled geometry have developed into attractive alternatives to more traditional meshbased surface processing. In this paper, we consider a vital step in any pointbased surface processing pipeline, point cloud simplification. Building upon the intrinsic point cloud simplification idea put forward in [14], 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 usercontrolled featuresensitive simplification. It can further deal with nonuniformly distributed point sets and pointsampled 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. Keywords: Point cloud simplification, Pointsampled geometry processing, Fast Marching level set methods
A New Point Cloud Simplification Algorithm
 In Proceedings 3rd IASTED Conference on Visualization, Imaging and Image Processing
, 2003
"... We present a new technique for the simplification of pointsampled geometry without any prior surface reconstruction. Using Fast Marching farthest point sampling for implicit surfaces and point clouds [1], we devise a coarsetofine uniform or featuresensitive simplification algorithm with usercontr ..."
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Cited by 11 (2 self)
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We present a new technique for the simplification of pointsampled geometry without any prior surface reconstruction. Using Fast Marching farthest point sampling for implicit surfaces and point clouds [1], we devise a coarsetofine uniform or featuresensitive simplification algorithm with usercontrolled 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.
Convectiondriven dynamic surface reconstruction
 In Proc. Shape Modeling International
, 2005
"... Reconstruction of a simplified mesh Local dynamic reconstruction refinement Figure 1. Our reconstruction framework, illustrated on the TRIPLE HECATE model. Starting from a dense input point set, we reconstruct a simplified mesh (center). Benefiting from the connectivity of this initial reconstructio ..."
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Cited by 8 (3 self)
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Reconstruction of a simplified mesh Local dynamic reconstruction refinement Figure 1. Our reconstruction framework, illustrated on the TRIPLE HECATE model. Starting from a dense input point set, we reconstruct a simplified mesh (center). Benefiting from the connectivity of this initial reconstruction, we can make it to evolve dynamically so as to refine the approximation locally. This refinement can be achieved either in an automatic fashion, for example in order to improve the quality of the elements of the mesh, or interactively, in order to add or remove sample points. Here, the draped dress has been locally enhanced (right). In this paper, we introduce a flexible framework for the reconstruction of a surface from an unorganized point set, extending the geometric convection approach introduced by Chaine [9]. Given a dense input point cloud, we first extract a triangulated surface that interpolates a subset of the initial data. We compute this surface in an output sensitive manner by decimating the input point set onthefly during the reconstruction process. Our simplification procedure relies on a simple criterion that locally detects and reduces oversampling. If needed, we then operate in a dynamic fashion for local refinement or further simplification of the reconstructed surface. Our method allows to locally update the reconstructed surface by inserting or removing sample points without restarting the convection process from scratch. This iterative correction process can be controlled interactively by the user or automatized given some specific local sampling constraints.
Undersampling and Oversampling in Sample Based Shape Modeling
 in Proc. IEEE Visualization, vol.I,San
, 2001
"... Shape modeling is an integral part of many visualization problems. Recent advances in scanning technology and a number of surface reconstruction algorithms have opened up a new paradigm for modeling shapes from samples. Many of the problems currently faced in this modeling paradigm can be traced bac ..."
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Cited by 6 (0 self)
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Shape modeling is an integral part of many visualization problems. Recent advances in scanning technology and a number of surface reconstruction algorithms have opened up a new paradigm for modeling shapes from samples. Many of the problems currently faced in this modeling paradigm can be traced back to two anomalies in sampling, namely undersampling and oversampling. Boundaries, nonsmoothness and small features create undersampling problems, whereas oversampling leads to too many triangles. We use Voronoi cell geometry as a unified guide to detect undersampling and oversampling. We apply these detections in surface reconstruction and model simplification. Guarantees of the algorithms can be proved. In this paper we show the success of the algorithms empirically on a number of interesting data sets.
Pointbased simplification algorithm
 WSEAS Trans. Comp. Res
"... Abstract: This study presents a novel, rapid, and effective point simplification algorithm based on a point cloud without any normal and connectivity information. This study is initiated with a scattered sampled point set in three dimensions, and the final output is a triangular mesh model, which i ..."
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Abstract: This study presents a novel, rapid, and effective point simplification algorithm based on a point cloud without any normal and connectivity information. This study is initiated with a scattered sampled point set in three dimensions, and the final output is a triangular mesh model, which is simplified according to a restrictive criteria. The proposed method reduces the number of calculations required to establish the relation between triangulation and the connectivity. Due to the continuous development of computer graphics technology, diversified virtual reality applications are being increasingly adopted. Recently, the efficient and vivid portrayal of 3D objects in the real world in virtual scenes has become a crucial issue in computer graphics. A triangular mesh is one of the most popular data structures for representing 3D models in applications. Numerous methods currently exist for constructing objects using surface reconstruction. The data required for the sampled points are generally obtained from a laser scanner. However, the extracted sampled points are frequently affected by shape variation. The number of triangles created increases with the number of points sampled from the surface of a 3D object, which helps in the reconstruction of the correct model. Nevertheless, subsequent graphics applications, such as morphing or rendering, increase the computation costs. Appropriate relevant points should be chosen so as to retain the object features and reduce the storage space and calculation costs. KeyWords: point simplification, discrete shape operator, feature extraction, curvature, torsion. 1
Study of Registration and Fusion of Multistation Terrestrial Laser Scan Point Cloud
"... Abstract3Dlaser scan has been used for collecting accurate and compressed coordinates of objects for many years, and has been regarded as a new revolution in the field of survey after the Global Positioning System (GPS). Laser scanner can obtain a large amount of points which are called as point c ..."
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Abstract3Dlaser scan has been used for collecting accurate and compressed coordinates of objects for many years, and has been regarded as a new revolution in the field of survey after the Global Positioning System (GPS). Laser scanner can obtain a large amount of points which are called as point cloud in a few minutes, including 3D coordinate, intensity and color. However, these point cloud are recorded in the scanner’s own coordinate system, in order to process the data to get more information, algorithms about translating these data from different stations into a unified coordinate system should be adopted, which are named as registration. This paper describes three main methods of registration, and the data are acquired in different stations. The first algorithm is widely used in most canner software. The second algorithm regard laser scanner as total station which is set on known points. The third algorithm is the main study method which has been proved effective. ICP method is adopted after a little betterment for the initial parameters. A fusion method is also provided to simplify the point cloud, and figures in this paper proved that this method is viable adequately.