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Streaming Surface Reconstruction Using Wavelets
Author(s) -
Manson J.,
Petrova G.,
Schaefer S.
Publication year - 2008
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2008.01281.x
Subject(s) - wavelet , octree , computer science , convolution (computer science) , representation (politics) , surface (topology) , multiresolution analysis , surface reconstruction , function (biology) , algorithm , artificial intelligence , computer vision , range (aeronautics) , wavelet transform , mathematics , geometry , discrete wavelet transform , artificial neural network , materials science , evolutionary biology , politics , political science , law , composite material , biology
We present a streaming method for reconstructing surfaces from large data sets generated by a laser range scanner using wavelets. Wavelets provide a localized, multiresolution representation of functions and this makes them ideal candidates for streaming surface reconstruction algorithms. We show how wavelets can be used to reconstruct the indicator function of a shape from a cloud of points with associated normals. Our method proceeds in several steps. We first compute a low‐resolution approximation of the indicator function using an octree followed by a second pass that incrementally adds fine resolution details. The indicator function is then smoothed using a modified octree convolution step and contoured to produce the final surface. Due to the local, multiresolution nature of wavelets, our approach results in an algorithm over 10 times faster than previous methods and can process extremely large data sets in the order of several hundred million points in only an hour.