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Efficient Feature-preserving Local Projection Operator for Geometry Reconstruction
| Content Provider | CiteSeerX |
|---|---|
| Author | Liaoa, Bin Xiaoa, Chunxia Jina, Liqiang Fuc, Hongbo |
| Abstract | This paper proposes an efficient and Feature-preserving Locally Optimal Pro-jection operator (FLOP) for geometry reconstruction. Our operator is bilat-eral weighted, taking both spatial and geometric feature information into consideration for feature-preserving approximation. We then present an ac-celerated FLOP operator based on the random sampling of the Kernel Den-sity Estimate (KDE), which produces reconstruction results close to those generated using the complete point set data, to within a given accuracy. Ad-ditionally, we extend our approach to time-varying data reconstruction, called Spatial-Temporal Locally Optimal Projection operator (STLOP), which effi-ciently generates temporally coherent and stable features-preserving results. The experimental results show that the proposed algorithms are efficient and robust for feature-preserving geometry reconstruction on both static models and time-varying data sets. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Geometry Reconstruction Efficient Feature-preserving Local Projection Operator Feature-preserving Locally Optimal Pro-jection Operator Time-varying Data Reconstruction Complete Point Ac-celerated Flop Operator Static Model Feature-preserving Geometry Reconstruction Stable Features-preserving Result Time-varying Data Set Geometric Feature Information Feature-preserving Approximation Spatial-temporal Locally Optimal Projection Operator Experimental Result Kernel Den-sity Estimate Reconstruction Result Random Sampling |
| Content Type | Text |
| Resource Type | Article |
