Deep learning based pose estimation method using 3D point clouds

Abstract

Pose estimation refers to the acquisition of a rigid transformation of an object relative to its original model coordinate system. This paper proposes a deep learning based approach for pose estimation with point clouds of textureless objects. The contribution of this paper can be summarized as follows: (1) A multi-scale local feature aggregation strategy for emphasizing the neighbor region of interest points. (2) The extension of the original spatial transformer network to point clouds, pose estimation and object classification as the output in a single proposed network. (3) A deep learning model combined with the symmetric function and the multi-scale features to improve the accuracy and robustness of the network model, and a new defined joint loss function by considering the objectives of pose estimation and classification. The experiments are conducted to verify the performance of pose estimation when the point clouds of textureless objects are taken as the input data, which shows that the proposed deep learning framework effectively performs on the pose estimation for both synthetic and real point clouds according to the experimental results.