scholarly journals Point Set Surface Reconstruction for VTK

2011 ◽  
Author(s):  
David Doria
Keyword(s):  
Surface Reconstruction ◽  
Visualization Toolkit ◽  
Point Set

This document presents a set of classes (vtkPointSetSurfaceReconstruction, vtkVoxelizePolyData) to produce a surface from an oriented point set. These classes are implemented as VTK filters. A Paraview plugin interface is provided to allow extremely easy experimentation with the new functionality. We propose these classes as an addition to the Visualization Toolkit. The code is available here: https://github.com/daviddoria/PointSetSurfaceReconstruction

scholarly journals Poisson Surface Reconstruction for VTK

2010 ◽  
Author(s):  
David Doria ◽  
Arnaud Gelas
Keyword(s):  
Surface Reconstruction ◽  
Reconstruction Algorithm ◽  
Visualization Toolkit ◽  
Point Set

This document presents an implementation of the Poisson surface reconstruction algorithm in the VTK framework. (This code was, with permission, adapted directly from the original implementation by Kazhdan, Bolitho, and Hugues. The original implementation can be found here http://www.cs.jhu.edu/~misha/Code/IsoOctree/). We present a class, vtkPoissonReconstruction, which produces a surface from an oriented point set. A Paraview plugin interface is provided to allow extremely easy experimentation with the new functionality. We propose these classes as an addition to the Visualization Toolkit.

A Polygonal Surface Reconstruction from Oriented Point Set

2020 ◽  
Vol 25 (4) ◽  
pp. 455-461
Author(s):  
Sangkun Park
Keyword(s):  
Surface Reconstruction ◽  
Polygonal Surface ◽  
Point Set

AN APPROACH TO SURFACE RECONSTRUCTION USING UNCERTAIN DATA

2007 ◽  
Vol 07 (01) ◽  
pp. 177-194
Author(s):  
LAURA PAPALEO
Keyword(s):  
Surface Reconstruction ◽  
Critical Points ◽  
Uncertain Data ◽  
Complex Object ◽  
Sampled Data ◽  
Reconstruction Algorithms ◽  
Additional Information ◽  
Multiple Sensors ◽  
Generic Framework ◽  
Visualization Toolkit

In a research context in which multiple and well-behaved Surface Reconstruction algorithms already exist, the main goal is not to implement a visualization toolkit able render complex object, but the implementation of methods which can improve our knowledge on the observed world. This work presents a general Surface Reconstruction framework which encapsulates the uncertainty of the sampled data, making no assumption on the shape of the surface to be reconstructed. Starting from the input points (either points clouds or multiple range images), an Estimated Existence Function (EEF) is built which models the space in which the desired surface could exist and, by the extraction of EEF critical points, the surface is reconstructed. The final goal is the development of a generic framework that is able to adapt the result to different kinds of additional information coming that is from multiple sensors.

scholarly journals Surface Reconstruction through Point Set Structuring

2013 ◽  
Vol 32 (2pt2) ◽  
pp. 225-234 ◽  
Author(s):  
Florent Lafarge ◽  
Pierre Alliez
Keyword(s):  
Surface Reconstruction ◽  
Point Set

Surface Reconstruction of In Vivo Geometry Based on Medical Images Using Multilevel Radial Basis Functions

2004 ◽  
Author(s):  
Yoke Kong Kuan ◽  
Paul F. Fischer ◽  
Francis Loth
Keyword(s):  
Radial Basis Functions ◽  
Surface Reconstruction ◽  
Medical Images ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Basis Functions ◽  
Radial Basis ◽  
Point Set ◽  
Three Dimensional Space

Compactly supported radial basis functions (RBFs) were used for surface reconstruction of in vivo geometry, translated from two dimensional (2D) medical images. RBFs provide a flexible approach to interpolation and approximation for problems featuring unstructured data in three-dimensional space. Point-set data are obtained from the contour of segmented 2-D slices. Multilevel RBFs allow smoothing and fill in missing data of the original geometry while maintaining the overall structure shape.

scholarly journals Point Set Processing for VTK - Outlier Removal, Curvature Estimation, Normal Estimation, Normal Orientation

2011 ◽  
Author(s):  
David Doria
Keyword(s):  
Source Code ◽  
Point Sets ◽  
Outlier Removal ◽  
Normal Estimation ◽  
Normal Orientation ◽  
Visualization Toolkit ◽  
Point Set ◽  
Curvature Estimation

This document presents a set of classes (vtkPointSetOutlierRemoval, vtkPointSetNormalEstimation, vtkPointSetNormalOrientation, vtkPointSetCurvatureEstimation, vtkEuclideanMinimumSpanningTree, and vtkRiemannianGraphFilter) to enable several basic operations on point sets. These classes are implemented as VTK filters. Paraview plugin interfaces to the filters are also provided to allow extremely easy experimentation with the new functionality. We propose these classes as an addition to the Visualization Toolkit.The latest source code can be found here: https://github.com/daviddoria/PointSetProcessing

Surface reconstruction from point set using projection operator

2008 ◽  
Author(s):  
Ly Phan ◽  
Lu Liu ◽  
Sasakthi Abeysinghe ◽  
Tao Ju ◽  
Cindy M. Grimm
Keyword(s):  
Surface Reconstruction ◽  
Projection Operator ◽  
Point Set

scholarly journals Multisource Point Clouds, Point Simplification and Surface Reconstruction

2019 ◽  
Vol 11 (22) ◽  
pp. 2659 ◽  
Author(s):  
Zhu ◽  
Kukko ◽  
Virtanen ◽  
Hyyppä ◽  
Kaartinen ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Surface Reconstruction ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Algebraic Point ◽  
Point Reduction ◽  
Alpha Shapes ◽  
Reconstruction Methods ◽  
Point Set ◽  
Different Levels

As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for surface reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for surface reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for surface reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known surface reconstruction methods, i.e., Alpha shapes, Screened Poisson reconstruction (SPR), the Crust, and Algebraic point set surfaces (APSS Marching Cubes), were utilized for object reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for surface reconstruction. These reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that i) the capacity of surface reconstruction in dealing with diverse objects needs to be improved; ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the reconstruction methods might fail; iii) for some reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; iv) all reconstruction methods are beneficial from the reduction of running time; and v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics.

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

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