Point Cloud Modeling Based on Compactly Supported Radial Basis Function under KD Tree Index Strategy
Vol: 28, Issue: 9, Page: 2154-2158
2020
- 54Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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- Usage54
- Downloads45
- Abstract Views9
Artifact Description
Abstract: Modeling constructed by Compactly Supported Radial Basis Function (CSRBF) and visualization will fail. The main reason is that exhaustive search results in out of memory. KD tree can void exhaustive search due to the advantage of quickly searching. CSRBF combined KD tree was used to construct point cloud model. Modeling approach of CSRBF based on KD tree was proposed. KD tree index of the point cloud was constructed. CSRBF interpolation method was used to construct implicit function model of point cloud. Marching Cubes algorithm was to display model in 3D manner. The experimental results with rabbit point cloud show that it is feasible for point cloud modeling with CSRBF based on KD tree.
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