In this work, image based estimation methods, also known as direct methods, are studied which avoid feature extraction and matching completely. Cost functions use raw pixels as measurements and the goal is to produce precise 3D pose and structure estimates. The cost functions presented minimize the sensor error, because measurements are not transformed or modified. In photometric camera pose estimation, 3D rotation and translation parameters are estimated by minimizing a sequence of image based cost functions, which are non-linear due to perspective projection and lens distortion. In image based structure refinement, on the other hand, 3D structure is refined using a number of additional views and an image based cost metric. Image based estimation methods are usable whenever the Lambertian illumination assumption holds, where 3D points have constant color despite viewing angle. The main application domains in this work are indoor 3D reconstructions, robotics and augmented reality. The overall project goal is to improve image based estimation methods, and to produce computationally efficient methods which can be accomodated into real applications. The main questions for this work are : What is an efficient formulation for an image based 3D pose estimation and structure refinement task ? How to organize computation to enable an efficient real-time implementation ? What are the practical considerations of using image based estimation methods in applications such as augmented reality and 3D reconstruction ?
