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|a 10.1109/TIP.2024.3372446
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|a eng
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1 |
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|a Han, Jiawei
|e verfasserin
|4 aut
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| 245 |
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|a A Large-Scale Network Construction and Lightweighting Method for Point Cloud Semantic Segmentation
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 20.03.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a To significantly enhance the performance of point cloud semantic segmentation, this manuscript presents a novel method for constructing large-scale networks and offers an effective lightweighting technique. First, a latent point feature processing (LPFP) module is utilized to interconnect base networks such as PointNet++ and Point Transformer. This intermediate module serves both as a feature information transfer and a ground truth supervision function. Furthermore, in order to alleviate the increase in computational costs brought by constructing large-scale networks and better adapt to the demand for terminal deployment, a novel point cloud lightweighting method for semantic segmentation network (PCLN) is proposed to compress the network by transferring multidimensional feature information of large-scale networks. Specifically, at different stages of the large-scale network, the structure and attention information of the point features are selectively transferred to guide the compressed network to train in the direction of the large-scale network. This paper also solves the problem of representing global structure information of large-scale point clouds through feature sampling and aggregation. Extensive experiments on public datasets and real-world data demonstrate that the proposed method can significantly improve the performance of different base networks and outperform the state-of-the-art
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|a Journal Article
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1 |
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|a Liu, Kaiqi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Guangzhi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Wenguang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Feng
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 20., Seite 2004-2017
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|x 1941-0042
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|g volume:33
|g year:2024
|g day:20
|g pages:2004-2017
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|u http://dx.doi.org/10.1109/TIP.2024.3372446
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