Point distance mask attention network for learning on point cloud

YueHui Bao; Yu Gong; ShuKai Duan

doi:10.1117/12.2685007

19 October 2023 Point distance mask attention network for learning on point cloud

YueHui Bao, Yu Gong, ShuKai Duan

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127091W (2023) https://doi.org/10.1117/12.2685007
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Point cloud data has been widely used in many fields, and processing them using deep learning methods has become a popular research topic. However, the irregularity and unordered data structure of point cloud makes it necessary to design neural networks that are different from those used in image and natural language processing to accommodate its characteristics. In this paper, we propose a novel network called the Point Distance Mask Attention Network. This network utilizes the position relationship between the centroid and the generated neighborhood to weight the features of each point, making the network more focused on the features close to the centroid. Additionally, we use a masking operation to ensure the permutation invariance of the network when using the ball query method to find the neighborhood. Furthermore, we propose a residual-like connection in the network architecture, which achieves better results without changing the network feature extractor and depth. We evaluated the network model on the ModelNet40 dataset and achieved an accuracy of 93.7%. Our experiments show that our network achieves better results than the original baseline network by 1.1%, with almost the same parameters and inference time.

Citation Download Citation

YueHui Bao, Yu Gong, and ShuKai Duan "Point distance mask attention network for learning on point cloud", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127091W (19 October 2023); https://doi.org/10.1117/12.2685007

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KEYWORDS

Point clouds

Network architectures

Data modeling

Feature extraction

3D modeling

Performance modeling

Solid modeling

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