Accurate 3d object recognition and 6-dOF pose estimation have been pervasively applied to a variety of applications, such as unmanned warehouse, cooperative robots, and manufacturing industry. How to extract a robust ...
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Accurate 3d object recognition and 6-dOF pose estimation have been pervasively applied to a variety of applications, such as unmanned warehouse, cooperative robots, and manufacturing industry. How to extract a robust and representative feature from the point clouds is an inevitable and important issue. In this paper, an unsupervisedfeature learning network is introduced to extract 3d keypoint features from point clouds directly, rather than transforming point clouds to voxel grids or projected RGB images, which saves computational time while preserving the object geometric information as well. Specifically, the proposed network features in a stacked point featureencoder, which can stack the local discriminative features within its neighborhoods to the original point-wise feature counterparts. The main framework consists of both offline training phase and online testing phase. In the offline training phase, the stacked point featureencoder is trained first and then generate featuredatabase of all keypoints, which are sampled from synthetic point clouds of multiple model views. In the online testing phase, each feature extracted from the unknown testing scene is matched among the database by using the K-d tree voting strategy. Afterwards, the matching results are achieved by using the hypothesis & verification strategy. The proposed method is extensively evaluated on four public datasets and the results show that ours deliver comparable or even superior performances than the state-of-the-arts in terms of F1-score, Average of the 3ddistance (Add) and Recognition rate. (C) 2019 Elsevier Ltd. All rights reserved.
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