Abstract
This paper introduces a framework for large-scale 3D point cloud semantic segmentation - the MLEF-Net model. The model aims to improve the segmentation accuracy of large-scale point clouds by innovatively combining Manhattan distance-based KNN neighborhood search with feature aggregation techniques. This approach uniquely handles spatial, color, and normal vector attributes, thereby improving the segmentation results. The superiority of the model is validated through comprehensive testing on the SemanticKITTI and nuScenes datasets, demonstrating its potential to enhance point cloud segmentation through advanced feature fusion strategies.
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