Prof. Jie Zhang Profile

Prof. Jie Zhang

at Beijing Technology and Business Univ

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Publications (2)

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Proceedings Article | 24 November 2021 Paper

3D small-scale object recognition network in cluttered point cloud scenes

Zhengmao Sun, Junhua Sun, Jie Zhang

Proceedings Volume 12061, 120610R (2021) https://doi.org/10.1117/12.2605034

KEYWORDS: 3D image processing, Image segmentation, Object recognition, Network architectures, Inspection, 3D vision, 3D surface sensing

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This paper proposes a recognition network for small-scale objects in cluttered point clouds. The network consists of two components: improved semantic segmentation for large-scale 3D point clouds and an adaptive instantiation algorithm. In semantic segmentation, based on the backbone, we introduce the grid sampling module and the normal-angle feature to improve the efficiency and accuracy of segmentation respectively. Then the network outputs point-wise semantic labels. After that, we propose an adaptive instantiation algorithm to group points that are closely packed together and obtain the objects. In this way, our network completes the recognition of the small-scale objects. We conducted experiments on real aero-engine datasets and the results reveal that the proposed network can recognize a small-sized component in the cluttered point cloud scene of aero-engine.

Proceedings Article | 24 October 2017 Paper

3D discriminative feature selection for mid-level representation

Jie Zhang, Junhua Sun

Proceedings Volume 10462, 104620X (2017) https://doi.org/10.1117/12.2282948

KEYWORDS: Feature selection, Shape analysis, Rigid registration

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Discriminative feature representation is significant for boosting the performance of computer vision tasks covering different levels. Traditional low-level feature representation exhibits good generalization and robustness while lacks of enough discriminant ability. In this paper, we focus on 3D local shape features, proposing a discriminative feature selection method, which is also closely related with mid-level 3D shape representation. We firstly design a histogram-signature hybrid 3D local shape descriptor using 3D geometrical information from the 3D point cloud of a tested object. Then, we propose a discrimination power metric to automatically select a collection of discriminative local shapes from a candidate set, resulting in a mid-level shape feature representation. The proposed algorithm is applied in the task of multi-view 2.5D scan registration. The performance was verified on public and popular instance-level 3D object datasets. Both qualitative and quantitative results demonstrate the effectiveness and robustness of the proposed algorithm on different 3D objects. Compared with low-level 3D object representation, the discriminative feature selection for 3D shape feature representation allows for superior performance with higher precision and recall rate.

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