Deep Quaternion Pose Proposals for 6D Object Pose Tracking

Mateusz Majcher, Bogdan Kwolek; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 243-251

In this work we study quaternion pose distributions for tracking in RGB image sequences the 6D pose of an object selected from a set of objects, for which common models were trained in advance. We propose an unit quaternion representation of the rotational state space for a particle filter, which is then integrated with the particle swarm optimization to shift samples toward local maximas. Owing to k-means++ we better maintain multimodal probability distributions. We train convolutional neural networks to estimate the 2D positions of fiducial points and then to determine PnP-based object pose hypothesis. A CNN is utilized to estimate the positions of fiducial points in order to calculate PnP-based object pose hypothesis. A common Siamese neural network for all objects, which is trained on keypoints from current and previous frame is employed to guide the particles towards predicted pose of the object. Such a keypoint based pose hypothesis is injected into the probability distribution that is recursively updated in a Bayesian framework. The 6D object pose tracker is evaluated on Nvidia Jetson AGX Xavier both on synthetic and real sequences of images acquired from a calibrated RGB camera.