Abstract
Estimation of six degrees of freedom (6DoF) attitude of rigid bodies is an essential issue in such fields as robotics and virtual reality. This paper proposed a method that could accurately estimate 6DoF attitude of known rigid bodies with RGB and RGB-D input image data. Additionally, the method could also work well in the scenes in which objects exhibit mutual similarities and occlusion. As one of the contributions made by this paper, a modularized assembly line method was proposed, which integrated deep learning and multi-view geometry method. At first, a neural network for instance segmentation was used to identify the general locations of known objects in the images and give the bounding boxes and masks. Then 6DoF attitude was estimated roughly according to the local features of RGB-D images and templates. Finally, purely geometric method was used to refine the estimation. Another contribution of this paper was the correction of misclassification with the help of some information reserved in the process of training the network. The proposed method achieves a superior performance on a challenging public dataset.
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Chen, T., Sun, L. (2019). 6D Pose Estimation of 3D Objects in Scenes with Mutual Similarities and Occlusions. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_93
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DOI: https://doi.org/10.1007/978-3-030-00665-5_93
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