Close-range image stitching based on depth information and moving DLT

In the image-based industrial inspection field, the imaging distance between the scene and the camera is relatively short, and the field-of-view of the imaging system is too small to meet the requirements of detection. So a close-range image stitching method is needed to get high quality and large field-of-view images. The traditional image stitching method uses a global homography transformation matrix for image stitching, which is stable, but only suitable for flat scenes, remote scenes or the scenes which are captured by the camera with rotation only. The As-Projective-As-Possible and Content- Preserving-Warping methods, which are realized by mesh optimization, improve the stitching result to a certain degree, but there will still have obvious ghost for the close-range scenes and images which have relatively large parallax. In this paper, an image stitching method which utilizes depth information and mesh optimization is proposed. The feature points are detected and clustered, and the depth information and grouping points are used to assign weights to each mesh to compute homography for each mesh respectively. Other state-of-the-art methods are compared with our method, it can be seen that the proposed method can get a better result.