Efficient 3D object recognition via geometric information preservation. (August 2019)

• Record Type: Journal Article
• Authors: Liu, Hongsen; Cong, Yang; Yang, Chenguang; Tang, Yandong
• Is Part Of: Pattern recognition. Volume 92(2019:Aug.)
• Page Start: 135
• Page End: 145
• Publication Date: 2019-08
• Subjects: Stacked 3D feature encoder -- 3D object recognition -- 6-DOF pose estimation -- Geometric information preservation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.03.025
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new unsupervised encoder layer is able to extract 3D point-wise features directly. Improving feature discrimination performance via geometric information preservation. Improving robustness by stacking locally aggregated features on point-wise features. Using 3D point cloud features better resists occlusion and background clutter. Abstract: 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 unsupervised feature 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 feature encoder, 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 feature encoder is trained first and then generate feature database 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 3D distance (ADD) and Recognition rate.