In this paper, a Backward Attentive Fusing Network with Local Aggregation Classifier (BAF-LAC) is proposed to improve the performance of 3D point cloud semantic segmentation. It consists of a Backward Attentive Fusing Encoder-Decoder (BAF-ED) to learn semantic features and a Local Aggregation Classifier (LAC) to maintain the context-awareness of points. BAF-ED narrows the semantic gap between the encoder and the decoder via fusing multi-layer encoder features with the decoder features. High-level encoder features are transformed into an attention map to modulate low-level encoder features backward. LAC adaptively enhances the intermediate features in point-wise MLPs via aggregating the features of neighboring points into the center point. It takes the place of commonly used post-processing techniques and retains context consistency into the classifier. Equipped with these modules, BAF-LAC can extract discriminative semantic features and predict smoother results. Extensive experiments on Semantic3D, SemanticKITTI, and S3DIS demonstrate that the proposed method can achieve competitive results against the state-of-the-art methods.

中文翻译：

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用于 3D 点云语义分割的具有本地聚合分类器的后向关注融合网络


本文提出了一种带有局部聚合分类器的后向注意融合网络（BAF-LAC）来提高3D点云语义分割的性能。它由用于学习语义特征的后向注意融合编码器-解码器（BAF-ED）和用于维护点的上下文感知的局部聚合分类器（LAC）组成。 BAF-ED 通过融合多层编码器特征与解码器特征来缩小编码器和解码器之间的语义差距。高级编码器特征被转换为注意力图以向后调制低级编码器特征。 LAC 通过将相邻点的特征聚合到中心点来自适应增强逐点 MLP 中的中间特征。它取代了常用的后处理技术，并保留了分类器的上下文一致性。配备这些模块，BAF-LAC 可以提取有区别的语义特征并预测更平滑的结果。在 Semantic3D、SemanticKITTI 和 S3DIS 上进行的大量实验表明，所提出的方法可以取得与最先进的方法相媲美的结果。