A Realistic 3-D Reverse Modeling System Based on Real-World Sampling Dataset

Abstract

This paper presents an image-based three-dimensioal (3-D) reverse modeling system. We take advantage of stereo vision-based method to acquire geometric information through sampling the surface of a physical object mounted on end effectors of a 4-DOF planar robot. Based on real-world sampling datasets, a realistic 3-D graphical model can be automatically constructed. We provide a new approach to system calibration, geometric information acquisition, and surface parameterization, and also implement a prototype system of rapidly establishing textured model that is available to virtual reality application.