Pose Guided RGBD Feature Learning for 3D Object Pose Estimation

In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation. Previous works have focused on learning feature embeddings based on metric learning with triplet comparisons and rely only on the qualitative distinction of similar and dissimilar pose labels. In contrast, we consider the exact pose differences between the training samples, and aim to learn embeddings such that the distances in the pose label space are proportional to the distances in the feature space. However, since it is less desirable to force the pose-feature correlation when objects are symmetric, we discuss the use of weights that reflect object symmetry when measuring the pose distances. Furthermore, end-to-end pose regression is investigated and is shown to further boost the discriminative power of feature learning, improving pose recognition accuracies. Experimental results show that the features that are learnt guided by poses, are significantly more discriminative than the ones learned in the traditional way, outperforming state-of-the-art works. Finally, we measure the generalisation capacity of pose guided feature learning in previously unseen scenes containing objects under different occlusion levels, and we show that it adapts well to novel tasks.