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Mining local geometric structure for large-scale 3D point clouds semantic segmentation
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DOI：10.1016/j.neucom.2022.05.060.png)
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Abstract
En 中文
In this paper, we concentrate on how to preserve fine-grained geometric structure information when extracting local contextual features for efficient large-scale point clouds semantic segmentation. Firstly, the Local Geometric Structure Representation Block is proposed to model fine-grained geometric structures for individual points by fully utilizing relative and global geometric relationships in the neigh-borhood. Then, we design a Parallel Attentive Fusion Module focusing on geometric structure and seman-tic information respectively, which reduces the feature's ambiguity and preserves local geometric structure information. Furthermore, thanks to these two modules, we present a lightweight Local Contextual Features Extractor through the bilateral structure to obtain more discriminate local contex-tual features. Finally, a deep network named LGS-Net is introduced to predict point's classes. Extensive experiment shows that our network surpasses the state-of-the-art approaches for semantic segmentation on two public large-scale point clouds datasets Semantic3D, SensatUrban, and achieve competitive per-formance on S3DIS. Especially, our LGS-Net with minimal model size outperforms the state-of-the-art network by 1.2% on the Semantic3D dataset. Thorough ablation studies and visualizations are presented to understand our network.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
3D point clouds
Semantic segmentation
Deep learning