We propose a fast approach to 3D object detection and pose estimation that owes its robustness to a training phase during which the target object slowly moves with respect to the camera. No additional information is provided to the system, save a very rough initialization in the first frame of the training sequence. It can be used to detect the target object in each video frame independently. Our approach relies on a Randomized Tree-based approach to wide baseline feature matching. Unlike previous classification-based approaches to 3-D pose estimation, we do not require an a priori 3-D model. Instead, our algorithm learns both geometry and appearance. In the process, it collects, or harvests, a list of features that can be reliably recognized even when large motions and aspect changes cause complex variations of feature appearances. This is made possible by the great fl exibility of Randomized Trees, which lets us add and remove feature points to our list as needed with a minimum amount of extra computation.