An efficient point cloud semantic segmentation framework based on superpoint fusion and joint feature learning

Yifeng Wang; Zefei Yang; Nan Luo; Zhenfeng Huo

doi:10.1117/12.3009558

16 October 2023 An efficient point cloud semantic segmentation framework based on superpoint fusion and joint feature learning

Yifeng Wang, Zefei Yang, Nan Luo, Zhenfeng Huo

Author Affiliations +

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030N (2023) https://doi.org/10.1117/12.3009558
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Extensive progress has been made in point cloud semantic segmentation, yet the heavy preprocessing and complex design of network model still drag the training efficiency. In view of this, this paper proposes an optimized graph convolutional framework model PM-SPG based on superpoint fusion to efficiently perform point cloud. After the input cloud is pre-segmented into superpoints, a fusion algorithm is designed to merge the superpoints with similar geometric structures, which enhances the sparse representation of point clouds, and greatly improves the efficiency for extracting better local features. Meanwhile, a feature joint learning module is introduced to extract point cloud features in 3D and 2D perspectives, respectively by PointNet and multi-view feature embedding network, forming 3D-2D joint features to make full use of the local geometric information in the point cloud. The experimental evaluations on datasets S3DIS and Semantic3D show that the proposed framework significantly improves the model training efficiency while achieving competitive accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yifeng Wang, Zefei Yang, Nan Luo, and Zhenfeng Huo "An efficient point cloud semantic segmentation framework based on superpoint fusion and joint feature learning", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030N (16 October 2023); https://doi.org/10.1117/12.3009558

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KEYWORDS

Point clouds

Education and training

Semantics

3D modeling

Feature extraction

Feature fusion

Vegetation

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