Type of Document Thesis Author Mhatre, Amit Narendra Author's Email Address firstname.lastname@example.org URN etd-04102006-172149 Title Normal Estimation and Surface Reconstruction of Large Point Clouds Degree Master of Science Department Computer Science, Department of Advisory Committee
Advisor Name Title Piyush Kumar Committee Chair Ashok Srinivasan Committee Member Xiuwen Liu Committee Member Keywords
- Computational Geometry
- Normal Estimation
- Surface Reconstruction
Date of Defense 2006-04-05 Availability unrestricted AbstractEstimating normals for 3D point clouds and reconstructing a surface interpolating the points are important problems in Computational Geometry and Computer Graphics. Massive point clouds and surfaces with sharp features are active areas of research for these problems. This thesis provides a fast and accurate algorithm for normal estimation and surface reconstruction which can handle large datasets as well as sharp edges and corners. We were successfully able to compute accurate normals for all the points on a cube including corners and edges and reconstruct the cube. We use several techniques to make the implementation fast and external-memory efficient.
The implementation uses multiple threads operating in parallel and hence performs faster
on a multiprocessor system. We use a technique called fast projective clustering for fitting
multiple planes through the neighborhood of a point. We use a sliding window type streaming
algorithm that uses a dynamic data structure for nearest neighbor search. We also develop
a simplification algorithm that handles large datasets with sharp edges and corners and
enables us to render these datasets using in-core rendering softwares.
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