6533b873fe1ef96bd12d4e5e
RESEARCH PRODUCT
Novel algorithms for 3D surface point cloud boundary detection and edge reconstruction
Rahul SummanStephen PierceCarmelo Mineosubject
PolynomialBoundary detection Edge reconstruction Point-cloudComputer scienceTKFast Fourier transformComputational MechanicsPoint cloudBoundary (topology)02 engineering and technologySettore ING-IND/14 - Progettazione Meccanica E Costruzione Di Macchine0203 mechanical engineeringlcsh:TA1740202 electrical engineering electronic engineering information engineeringEngineering (miscellaneous)Function (mathematics)lcsh:Engineering designComputer Graphics and Computer-Aided DesignHuman-Computer InteractionComputational MathematicsNoise020303 mechanical engineering & transportsModeling and SimulationCurve fittingArtificial noise020201 artificial intelligence & image processingAlgorithmdescription
Abstract Tessellated surfaces generated from point clouds typically show inaccurate and jagged boundaries. This can lead to tolerance errors and problems such as machine judder if the model is used for ongoing manufacturing applications. This paper introduces a novel boundary point detection algorithm and spatial FFT-based filtering approach, which together allow for direct generation of low noise tessellated surfaces from point cloud data, which are not based on pre-defined threshold values. Existing detection techniques are optimized to detect points belonging to sharp edges and creases. The new algorithm is targeted at the detection of boundary points and it is able to do this better than the existing methods. The FFT-based edge reconstruction eliminates the problem of defining a specific polynomial function order for optimum polynomial curve fitting. The algorithms were tested to analyse the results and measure the execution time for point clouds generated from laser scanned measurements on a turbofan engine turbine blade with varying numbers of member points. The reconstructed edges fit the boundary points with an improvement factor of 4.7 over a standard polynomial fitting approach. Furthermore, through adding artificial noise it has been demonstrated that the detection algorithm is very robust for out-of-plane noise lower than 25% of the cloud resolution and it can produce satisfactory results when the noise is lower than 75%. Highlights Introducing novel boundary point detection algorithm and spatial FFT-based filtering approach. Enabling reliable detection of boundary points and smooth boundary reconstruction. FFT-based edge reconstruction works better than polynomial curve fitting. Optimally smoothed edges facilitate the direct use of tessellated models for CAM tasks. The new detection algorithm tolerates out-of-plane noise. The reconstructed edges fit well the detected boundary points.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 | Journal of Computational Design and Engineering |