Efficient generation for 3D printing model based on image self-calibration

In order to improve the performance of three-dimensional reconstruction and overcome the highly dependent relationship on calibration board, an efficient generation 3D printing model method was proposed. Without using the calibration board to calculate the camera parameters in this method,the image captured by single camera can be used to generate 3D model. However, this self-calibration method is deeply influenced by the image quality and point matching accuracy, which limits the 3D printing model generation with high efficiency. In order to overcome these effects, firstly the background area and restrain noise are removed by interactive and graph partition to enhance the images region-of-tnterest(ROI).Second feature point is extracted from sequences pictures by improved speed-up robust features (SURF) algorithm and is matched based on its matching factor, which shows faster than the previous program. Then camera model parameters are calculated by self-calibration matching information. Finally, dense 3D object is reconstructed by combining camera model and feature point matching information. A series of experiments show the proposed method is characterized by effectiveness, convenience and wide application.