Autonomic point cloud-based surface reconstruction using SVR

ABSTRACT

A surface reconstruction framework based on support vector regression (SVR) to generate a three-dimensional (3D) model is proposed in this paper. It can reduce the noise in sampled data as well as repair the holes by handling the missing data during the acquisition phase. SVR is quite efficient for surface reconstruction using parameter tuning and selective data sampling. Automatic parameter tuning of SVR is proposed using two techniques: particle swarm optimization (PSO) and genetic algorithm (GA). Independent component analysis (ICA) is a feature-preserved non-uniform simplification method which is applied to simplify point set by optimal attribute selection. First, under-sample the data, remove the redundancy, reduce the features using ICA and construct the surface using SVR. Both theoretical analysis and experimental results show that the performance of the proposed method yields an average SVR error ≈ 3% on the publicly available datasets. For majority of standard datasets, PSO–SVR is found superior to GA–SVR in convergence speed. Details of the surface are also preserved well which makes it suitable for 3D surface reconstruction.

KEYWORDS:

• SVR
• surface reconstruction
• point cloud
• particle swarm optimization
• genetic algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).