An adaptive learning approach for 3-D surface reconstruction from point clouds.
In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.
Department of Computer and Automation Engineering, Universidade Federal do Rio Grande do Norte, DCA-CT-UFRN, 59056-270, Natal, RN, Brasil. ambj@dca.ufrn.br
Agostinho de Medeiros Brito
Ade M
Junior
Adrião Duarte Dória
AD
Neto
Jorge Dantas
JD
de Melo
Luiz Marcos Garcia
LM
Goncalves
D000222:Adaptation, Physiological; D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*