consolidated visualization of enormous 3d scan point clouds with scanopy
DESCRIPTION
Consolidated Visualization of Enormous 3D Scan Point Clouds with Scanopy. Claus Scheiblauer 1 Michael Pregesbauer 2. 1 Institute of Computer Graphics and Algorithms, Vienna University of Technology, Austria 2 Government of Lower Austria, Austria. Screenshot. Scanning Project Area. - PowerPoint PPT PresentationTRANSCRIPT
Consolidated Visualization of Enormous 3D Scan Point Clouds
with Scanopy
Claus Scheiblauer1
Michael Pregesbauer2
1Institute of Computer Graphics and Algorithms, Vienna University of Technology, Austria
2Government of Lower Austria, Austria
Screenshot
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Scanning Project Area
Amphitheater 1, Bad Deutsch-Altenburg
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Motivation
Excavation accompanying data acquisition Documentation of the ancient amphitheatreCreation of a 3D model of the whole amphitheatre
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Scanning Project
Data aquisition between 2008 and 2010Laserscanner system Riegl LMS 420i120 scan positions106M points
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Data Aquisition
Geocoding within a national global reference systemCoarse registration with tie pointsFine registration by using identical patches for a multi station adjustmentScan position accuracy 1 - 2cm
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Point Cloud
Rendering with weighted point sizeOne color per splat
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Point Cloud
Rendering with weighted point sizeOne color per pixel
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Averaging Colors
One color per splatArbitrary color bordersColor noise due to
Overlapping splatsPoints from scan positions far away
One color per pixelPixel color is averaged from contributing splatsReduced color noise
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Gaussian Splats
Splat size is knowScreen aligned splatsPixels are weighted accordingto distance from center
Gaussian distributionAt each pixel the colors from different splats are blended
According to their weight
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0.4
Gaussian Splats Blending
Only splats up to a certain depth distance should be blendedSome heuristicUniform sampled point clouds without noise
Distance = splat radiusNon uniform sampled with noise
Distance = some constant
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Gaussian Splats Multipass
Splatting is divided into 3 passesDepth pass
First a depth image is createdAttribute pass
Only visible points contribute color valuesColors are weighted and blended
Normalization passThe colors are normalized at each pixel
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Gaussian Splats Properties
+ Splat sizes that are “too big” give better result+ Color noise is reduced+ Features become more visible - Increased rendering time
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Acknowledgements
FFG FIT-IT Projekt “Terapoints”Government of Lower AustriaImagination Computer Services
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Live Demo
Amphitheatre 1 in Bad Deutsch-Altenburg106M points1.6 GB data on disk
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