Fast 6D pose from a single RGB image using Cascaded Forests Templates

This paper presents a method for 6D pose estimation from a single RGB image for complex texture-less objects. This class of objects are common in any environment but still challenging to deal with. This is due to the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. Here we propose a novel part-based method using an efficient template matching approach where each template independently encodes the similarity function using a Forest trained over the templates. Moreover, accuracy is even more incremented by using a cascade of the learned forest. These templates forests together with the simplicity of the computed image features allow a quick estimate of the pose achieving real-time performance. Performance are demonstrated both on synthetic and real images with known ground truth.