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Smooth surface extraction from unstructured pointbased volume data using pdes
 IEEE Transactions on Visualization and Computer Graphics
"... Abstract—Smooth surface extraction using partial differential equations (PDEs) is a wellknown and widely used technique for visualizing volume data. Existing approaches operate on gridded data and mainly on regular structured grids. When considering unstructured pointbased volume data where sampl ..."
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Abstract—Smooth surface extraction using partial differential equations (PDEs) is a wellknown and widely used technique for visualizing volume data. Existing approaches operate on gridded data and mainly on regular structured grids. When considering unstructured pointbased volume data where sample points do not form regular patterns nor are they connected in any form, one would typically resample the data over a grid prior to applying the known PDEbased methods. We propose an approach that directly extracts smooth surfaces from unstructured pointbased volume data without prior resampling or mesh generation. When operating on unstructured data one needs to quickly derive neighborhood information. The respective information is retrieved by partitioning the 3D domain into cells using a kdtree and operating on its cells. We exploit neighborhood information to estimate gradients and mean curvature at every sample point using a fourdimensional leastsquares fitting approach. Gradients and mean curvature are required for applying the chosen PDEbased method that combines hyperbolic advection to an isovalue of a given scalar field and mean curvature flow. Since we are using an explicit timeintegration scheme, time steps and neighbor locations are bounded to ensure convergence of the process. To avoid small global time steps, one can use asynchronous local integration. We extract a smooth surface by successively fitting a smooth auxiliary function to the data set. This auxiliary function is initialized as a signed distance function. For each sample and for every time step we compute the respective gradient, the mean curvature, and a stable time step. With these informations the auxiliary function is manipulated using an explicit Euler time integration. The process successively continues with the next sample point in time. If the norm of the auxiliary function gradient in a sample exceeds a given
Georeferenced Point Clouds: A Survey of Features and Point Cloud Management
, 2013
"... Abstract: This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point ..."
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Abstract: This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features.
Direct Surface Extraction from Smoothed Particle Hydrodynamics Simulation Data
"... Smoothed particle hydrodynamics is a completely meshfree method to simulate fluid flow. Rather than representing the physical variables on a fixed grid, the fluid is represented by freely moving interpolation centers (”particles”). Apart from their position and velocity these particles carry inform ..."
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Smoothed particle hydrodynamics is a completely meshfree method to simulate fluid flow. Rather than representing the physical variables on a fixed grid, the fluid is represented by freely moving interpolation centers (”particles”). Apart from their position and velocity these particles carry information about the physical quantities of the considered fluid, such as temperature, composition, chemical potentials etc. Being completely Lagrangian and following the motion of the flow, these particles represent an unstructured data set at each point in time, i.e. the particles do not exhibit a spatial arrangement nor a fixed connectivity. To visualize the simulated particle data at a certain point in time, we propose a method that extracts surfaces segmenting the domain of the particles with respect to some scalar field. For scalar volume data, isosurface extraction is a standard visualization method and has been subject to research for decades. We propose a method that directly extracts surfaces from smoothed particle hydrodynamics simulation data without 3D mesh generation or 1 reconstruction over a structured grid. It is based on spatial domain partitioning using a kdtree and an indexing scheme for efficient neighbor search. A geometry extraction step computes points on the surface by linearly interpolating between neighbored pairs of sample points. Its output is a point cloud representation of the surface. The final rendering step uses pointbased rendering techniques to visualize the point cloud. A levelset approach can be applied for smoother segmentation results when extracting the geometry of the zero level set. 1
Onthefly Luminance Correction for Rendering of Inconsistently Lit Point Clouds
"... Scanning 3D objects has become a valuable asset to many applications. For larger objects such as buildings or bridges, a scanner is positioned at several locations and the scans are merged to one representation. Nowadays, such scanners provide, beside geometry, also color information. The different ..."
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Scanning 3D objects has become a valuable asset to many applications. For larger objects such as buildings or bridges, a scanner is positioned at several locations and the scans are merged to one representation. Nowadays, such scanners provide, beside geometry, also color information. The different lighting conditions present when taking the various scans lead to severe luminance artifacts, where scans come together. We present an approach to remove such luminance inconsistencies during rendering. Our approach is based on imagespace operations for both luminance correction and pointcloud rendering. It produces smoothlooking surface renderings at interactive rates without any preprocessing steps. The quality of our results is similar to the results obtained with an objectspace luminance correction. In contrast to such an objectspace technique the presented imagespace approach allows for instantaneous rendering of scans, e.g. for immediate onsite checks of scanning quality.
0SmoothViz: Visualization of Smoothed Particles Hydrodynamics Data
"... Smoothed particle hydrodynamics (SPH) is a completely meshfree method to simulate fluid flow (Gingold & Monaghan, 1977; Lucy, 1977). Rather than representing the physical variables on a fixed grid, the fluid is represented by freely moving interpolation centers (“particles”). Apart from their p ..."
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Smoothed particle hydrodynamics (SPH) is a completely meshfree method to simulate fluid flow (Gingold & Monaghan, 1977; Lucy, 1977). Rather than representing the physical variables on a fixed grid, the fluid is represented by freely moving interpolation centers (“particles”). Apart from their position and velocity these particles carry information about
Algebraic Splats Representation for Point Based Models
 SIXTH INDIAN CONFERENCE ON COMPUTER VISION, GRAPHICS & IMAGE PROCESSING
, 2008
"... The primitives of pointbased representations are independent but are rendered using surfels, which approximate the immediate neighborhood of each point linearly. A large number of surfels are needed to convey the exact shape. Higherorder approximations of the local neighborhood have the potential ..."
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The primitives of pointbased representations are independent but are rendered using surfels, which approximate the immediate neighborhood of each point linearly. A large number of surfels are needed to convey the exact shape. Higherorder approximations of the local neighborhood have the potential to represent the shape using fewer primitives,simultaneously achieving higher rendering speeds. In this paper, we propose algebraic splats as a basic primitive of representation for point based models. An algebraic splat based representation can be computed using a moving least squares procedure. We specifically study low order polynomial splats in this paper. Quadratic and cubic splats provide good quality and high rendering speed using far fewer primitives on a wide range of models. They can also be rendered fast using ray tracing on modern GPUs. We also present an algorithm to construct a representation of a model with a userspecified number of primitives. Our method to generates a holefree representation parametrized by a smoothing radius. The holefree representation reduces the number of primitives needed by a factor 20 to 30 on most models and by a factor of over 100 on dense models like David with little or no drop in visual quality. We also present a twopass GPU algorithm that raytraces the algebraic splats and blends them using a Gaussian weighting scheme for smooth appearance. We are able to render models like David at upwards of 200 fps on a commodity GPU using algebraic splats.
PANORAMIC RENDERINGBASED POLYGON EXTRACTION FROM INDOOR MOBILE LIDAR DATA
"... In this paper, we propose a method for panoramic pointcloud renderingbased polygon extraction from indoor mobile LiDAR data. Our aim was to improve regionbased pointcloud clustering in modeling after pointcloud registration. First, we propose a pointcloud clustering methodology for polygon ext ..."
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In this paper, we propose a method for panoramic pointcloud renderingbased polygon extraction from indoor mobile LiDAR data. Our aim was to improve regionbased pointcloud clustering in modeling after pointcloud registration. First, we propose a pointcloud clustering methodology for polygon extraction on a panoramic range image generated with pointbased rendering from a massive point cloud. Next, we describe an experiment that was conducted to verify our methodology with an indoor mobile mapping system in an indoor environment. This experiment was wallsurface extraction using a rendered pointcloud from 64 viewpoints over a wide indoor area. Finally, we confirmed that our proposed methodology could achieve polygon extraction through pointcloud clustering from a complex indoor environment. 1.
E MINES ParisTech
"... pour obtenir le grade de docteur délivré par l’École nationale supérieure des mines de Paris Spécialité « Mathématique et Automatique » présentée et soutenue publiquement par Philippe MOULIN ..."
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pour obtenir le grade de docteur délivré par l’École nationale supérieure des mines de Paris Spécialité « Mathématique et Automatique » présentée et soutenue publiquement par Philippe MOULIN
(2008 IEEE Visualization Design Contest Winner) Linking Multidimensional Feature Space Cluster Visualization to Surface Extraction from Multifield Volume Data
"... Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multifield volume data rather than concentrating on one variable. We present a visualization approach based on surfa ..."
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Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multifield volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multifield volume data. The extracted surfaces segment the data with respect to an underlying multivariate function. Decisions on segmentation properties are based on the analysis of a multidimensional feature space. The feature space exploration is performed using an automated multidimensional hierarchical clustering method. The hierarchical clusters are shown as a cluster tree in a 2D radial layout. In the cluster tree layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. Based on the segmentation property