A Dual-Branch Network Leveraging Heterogeneous Features for Semantic Segmentation of Large-Scale Point Clouds

With the advancement of point cloud technology, its significance in fields like remote sensing has escalated, especially in tasks like 3-D terrain modeling and environmental monitoring. Except for positional information, point clouds also contain diverse information like color and intensity. Effectively utilizing these heterogeneous point features from large-scale point clouds to attain precise semantic segmentation is a considerable challenge. To alleviate the feature conflict problem in semantic segmentation, the heterogeneous feature alignment network (HFA-Net) is proposed. This method employs two network branches to align heterogeneous features and facilitate information exchange. Initially, a dual-branch architecture is designed to independently process heterogeneous features, aiming to extract unique knowledge from these features. Furthermore, a homogeneous feature aggregation module is established to aggregate features from the decoders of both network branches into a unified space. To enhance feature discrimination, constraints are imposed within this module. These constraints aim to reduce the distance between features of the same class while increasing the distance between those of different classes, thereby enhancing semantic-level information exchange. In addition, a pointwise alignment module is proposed to enhance the similarity between pointwise predictions generated by the dual-branch network, promoting information exchange at the prediction level. Experimental results, obtained by integrating the benchmark network for point cloud semantic segmentation into our proposed framework, reveal that the network trained within this framework exhibits superior performance compared to existing networks.