2134/11745
Qinggang Meng
Qinggang
Meng
Baihua Li
Baihua
Li
Horst Holstein
Horst
Holstein
Yonghuai Liu
Yonghuai
Liu
Parameterization of point-cloud freeform surfaces using adaptive sequential learning RBFnetworks
Loughborough University
2013
Surface parameterization
Point clouds
Adaptive sequential learning
Artificial Intelligence and Image Processing
Information and Computing Sciences not elsewhere classified
2013-02-22 10:06:09
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Parameterization_of_point-cloud_freeform_surfaces_using_adaptive_sequential_learning_RBFnetworks/9401840
We propose a self-organizing Radial Basis Function (RBF) neural network method for parameterization of freeform surfaces from larger, noisy and unoriented point clouds. In particular, an adaptive sequential learning algorithm is presented for network construction from a single instance of point set. The adaptive learning allows neurons to be dynamically inserted and fully adjusted (e.g. their locations, widths and weights), according to mapping residuals and data point novelty associated to underlying geometry. Pseudo-neurons, exhibiting very limited contributions, can be removed through a pruning procedure. Additionally, a neighborhood extended Kalman filter (NEKF) was developed to significantly accelerate parameterization. Experimental results show that this adaptive learning enables effective capture of global low-frequency variations while preserving sharp local details, ultimately leading to accurate and compact parameterization, as characterized by a small number of neurons. Parameterization using the proposed RBF network provides simple, low cost and low storage solutions to many problems such as surface construction, re-sampling, hole filling, multiple level-of-detail meshing and data compression from unstructured and incomplete range data. Performance results are also presented for comparison.