A Local-Global Feature Fusing Method for Point Clouds Semantic Segmentation

A Local-Global Feature Fusing Method for Point Clouds Semantic Segmentation

Blog Article

In recent years, the abundance of information in 3D data has made the semantic segmentation of 3D point clouds a topic of great interest.However, current methods often rely solely on the original three-dimensional coordinates of the point cloud as input geometric features, Gift Items » Bedding leading to poor generalization performance.Additionally, occlusion of the point cloud data can negatively impact segmentation accuracy when only local information is considered.

To address these issues, this paper proposes a network named LGFF-Net.To fully utilize the original information of point clouds, we designed a Local Feature Aggregation (LFA) module that treats geometric and semantic information equally and preserves the 7-Keto original properties while cross-augmenting them.On the other hand, we proposed a simple and effective Global Feature Extraction (GFE) module to extract global features.

Finally, we hierarchically fuse local and global features using a U-shaped segmentation structure.Compared to state-of-the-art networks, our method achieves competitive results on several benchmark datasets, including Semantic Topographic Point Labeling-Synthetic 3D, Toronto_3D, Stanford Large 3-D Indoor Space, and ScanNet.We also conduct multiple ablation experiments to validate the efficacy of LGFF-Net.