PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning

TitlePoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning
Publication TypeConference Paper
Year of Publication2017
AuthorsKrull, A, Brachmann, E, Nowozin, S, Michel, F, Shotton, J, Rother, C
Conference NameProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Date Publisheddec
ISBN Number9781538604571

State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.

Citation KeyKrull2017