Clustering and selecting vantage images in a low-cost system for 3D reconstruction of texture-less objects

Abstract

Abstract Structure from Motion is one of the image-based methods for 3D reconstruction. This technique coupled with Dense Multi-View Matching can provide an accurate and dense point cloud from texture-full objects. Although the procedure in these techniques is now fully automatic, capturing images of the object in suitable position is difficult, especially when the object is texture-less and needs projectors to provide a pattern on the object. Even though the images can be captured, the processing time in Dense Matching software is directly related to the number of images. A package including hardware and software is presented in this paper for solving these issues. Regarding the hardware, a low-cost and portable system for 3D modeling of texture-less objects is proposed. This system includes a rotation table designed and developed by using a stepper motor and a light circular plate. The system also has eight laser light sources with very dense and strong beams which provide a relatively appropriate pattern on texture-less objects. Regarding the software, Imaging Network Designer (IND), recently developed by the first author, was adjusted to be exploited by the proposed system. IND can both design a complete imaging network and cluster and select vantages images in a dense imaging network. In this system, regarding to the step of stepper motor, images are semi automatically taken by a digital camera. The images were imported in Structure From Motion (SFM) procedures implemented in Agisoft Photoscan to align images and provide a rough model. IND then used this information, to cluster and select vantage images for dense image matching. The comparison between normal procedure and using IND in the proposed system showed the importance of IND in reducing the elapsed time by 30 percentages. To evaluate the accuracy of the proposed system, two dark objects were considered and the point clouds of these objects were obtained by both the proposed system and a structured light system called GOM ATOS Compact Scan 2M laser scanner with an accuracy of 0.02 mm. The comparison between point clouds in three different tests showed the ability of the proposed system in delivering a 3D model for the texture-less objects with an accuracy of 0.3 mm.