Camera calibration and 3d reconstruction using RBF network in stereovision system

Authors:
Hai-feng Hu
Affiliations:
Department of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 3
Cited 0

Camera Calibration with Distortion Models and Accuracy Evaluation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint

Three-dimensional computer vision: a geometric viewpoint
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, RBF network (RBFN) is used to provide effective methodologies for solving difficult computational problems in camera calibration and 3D reconstruction process. RBFN works in three aspects: Firstly, a RBFN is adopted to learn and memorize the nonlinear relationship in stereovision system. Secondly, another RBFN is trained to search the correspondent lines in two images such that stereo matching is performed in one dimension. Finally, the trained network in the first stage is used to reconstruct the object’s 3D figuration and surface. The technique avoids the complicated and large calculation in conventional methods. Experiments have been performed on common stereo pairs and the results are accurate and convincing.