RAAFNet: Reverse Attention Adaptive Fusion Network for Large-Scale Point Cloud Semantic Segmentation

Kai Wang and Huanhuan Zhang ()
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Kai Wang: School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China
Huanhuan Zhang: School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China

Mathematics, 2024, vol. 12, issue 16, 1-12

Abstract: Point cloud semantic segmentation is essential for comprehending and analyzing scenes. However, performing semantic segmentation on large-scale point clouds presents challenges, including demanding high memory requirements, a lack of structured data, and the absence of topological information. This paper presents a novel method based on the Reverse Attention Adaptive Fusion network (RAAFNet) for segmenting large-scale point clouds. RAAFNet consists of a reverse attention encoder–decoder module, an adaptive fusion module, and a local feature aggregation module. The reverse attention encoder–decoder module is applied to extract point cloud features at different scales. The adaptive fusion module enhances fine-grained representation within multi-resolution feature maps. Furthermore, a local aggregation classifier is introduced, which aggregates the features of neighboring points to the center point in order to leverage contextual information and enhance the classifier’s perceptual capability. Finally, the predicted labels are generated. Notably, our method excels at extracting point cloud features across different dimensions and produces highly accurate segmentation results. Experimental results on the Semantic3D dataset achieved an overall accuracy of 89.9% and a mIoU of 74.4%.

Keywords: semantic segmentation; reverse attention; adaptive fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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