Consolidated Visualization of Enormous 3D Scan Point Clouds with Scanopy

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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 Presentation

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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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