Automatic 3D model acquisition from uncalibrated images

This work address all of the stages required to take a sequence of images of an object and recover a 3D model in order to produce as system that maximises automation and minimises the demands placed on the user. To that end we present a practical implementation of an automatic method for recovering the positions and properties of the cameras used to take a series of images using a textured ground-plane. We then offer two contributions to simplify the task of segmentation an object observed in multiple images. The first, applicable to more simple scenes, automatically segments the object fixated upon by the camera. We achieve this by iteratively exploiting the rigid structure of the scene, to perform the segmentation in 3D across all the images simultaneously, and the consistent appearance of the object. For more complex scenes we move to our second algorithm that allows the user to select the required object in an interaction manner whilst minimising demands on their time. We combine the different appearance and spatial constraints to produce a clustering problem to group regions across images that allows the user to label many images at the same time. Finally we present an automatic reconstruction algorithm that improve the performance of existing state-of-the-art methods to allow accurate models to be obtained from smaller image sequences. This takes the form of a filtering process that rejects erroneous depth estimates by considering multiple depth hypotheses and identifying the true depth or an unknown state using a 2D Markov Random Field framework.EThOS - Electronic Theses Online ServiceGBUnited Kingdo