期刊
IEEE ACCESS
卷 8, 期 -, 页码 44335-44345出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2978255
关键词
Three-dimensional displays; Feature extraction; Solid modeling; Object recognition; Pose estimation; Observability; Computational modeling; 3D object recognition; 3D pose estimation; point cloud; point pair feature
资金
- National Natural Science Foundation of China [61775172]
- Natural Science Foundation of Guangdong Province, China [2018030310482]
- Jinan University Special Fund for Science and Technology Platform Construction [17817003]
Recognition and pose estimation from 3D free-form objects is a key step for autonomous robotic manipulation. Recently, the point pair features (PPF) voting approach has been shown to be effective for simultaneous object recognition and pose estimation. However, the global model descriptor (e.g., PPF and its variants) that contained some unnecessary point pair features decreases the recognition performance and increases computational efficiency. To address this issue, in this paper, we introduce a novel strategy for building a global model descriptor using stably observed point pairs. The stably observed point pairs are calculated from the partial view point clouds which are rendered by the virtual camera from various viewpoints. The global model descriptor is extracted from the stably observed point pairs and then stored in a hash table. Experiments on several datasets show that our proposed method reduces redundant point pair features and achieves better compromise of speed vs accuracy.
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