Enhanced 3D Semantic Segmentation via Local Polar Encoding and Attention Fusion

As a fundamental task in fields such as remote sensing, autonomous vehicles, augmented reality, and robotic navigation, Point-cloud semantic segmentation is critical for interpreting 3D environments, as it involves classifying point cloud data to assign semantic labels to points within a 3D scene. Leveraging spatial positions and other attributes, 3D semantic segmentation enables accurate object representation and categorization. While deep learning has advanced feature extraction from point clouds, developing efficient and accurate segmentation networks remains challenging. To address this, we propose an encoder-decoder framework that integrates local polar embedding and attention fusion. The method enhances local geometric feature extraction and reduces the semantic gap between encoded and decoded features. For enhancing the network's ability to segment complex-shaped point clouds, we first employ polar encoding and offset updating to redefine neighborhood coordinates. Then, a hybrid pooling module is introduced to improve local feature sensing. Finally, we integrate attention feature fusion between encoding and decoding layers to minimize semantic discrepancies and optimize feature mapping. Both qualitative and quantitative experiments demonstrate the effectiveness of our approach, showcasing its competitiveness with state-of-the-art methods in 3D semantic segmentation.