This paper presents a new approach to full automatic relative orientation of several digital images taken with a calibrated camera. This approach uses new algorithms for feature extraction and relative orientation developed in the last few years. There is no need for special markers in the scene nor for approximate values for the parameters of the exterior orientation. We use the point operator developed by D. G. Lowe (Lowe, 2004), which extracts points with scale- and rotation-invariant descriptors (SIFT-features). These descriptors allow a successful matching of image points even in situations with highly convergent images. The approach consists of the following steps: After extracting image points on all images each image pair is matched using the SIFT parameters only. No prior information about the pose of the images or the overlapping parts of the images is needed. For every image pair a relative orientation is computed using a RANSAC procedure. Here we use the new 5-point algorithm developed by D. Nister (Nister, 2004). Based on these orientations approximate values for the orientation parameters and the object coordinates are calculated. This is achieved by computing the relative scale and transforming into a common coordinate system. Several tests are carried out to ensure reliable inputs for the currently final step: a bundle block adjustment. The paper discusses the practical impacts of the algorithms involved. Examples of different indoor- and outdoor-scenes including a dataset of tilted aerial images are presented and the results of the approach are evaluated. These results show that the approach can be used for a wide range of scenes with different types of the image geometry and taken with different types of cameras including inexpensive consumer cameras. In particular we investigate in the robustness of the algorithms, e.g. in geometric tests on image triplets. In the outlook further developments like the use of image pyramids with a modified matching are discussed.
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