Online learning of visibility and appearance for object pose estimation
This paper presents an online self-supervised approach to improve the quality and relevance of input point cloud to a 3D registration algorithm. The suggested method considers the visibility of the model points and learns discriminative appearance of the object under gradual changes. It selectively reduces the amount of information to process by excluding non-visible points of the model and removing outliers from data stream, which results in better alignment between the input data and the model. Thus, by providing a good initial pose, it speeds up the iterative procedure of EM-like optimization for pose estimation (i.e., ICP) to achieve better efficiency and robustness. We compiled a new object dataset of RGBD images under camera motion with ground truth poses of the camera and the objects. We have performed experiments on this dataset and obtained promising results.