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Semantic segmentation is key in analyzing urban mobile laser scanning (MLS) point clouds. In recent years, the Transformer mechanism, known for capturing long-range contextual relationships, has attracted significant research attention in the field of 3D vision. However, computing global self-attention for 3D scenes incurs high computational costs and the loss of local details. Our work introduces a Dual Domain Transformer network (D2T-Net), adept at processing complex urban MLS point clouds. It operates efficiently in spatial and channel dimensions, enabling efficient semantic segmentation while maintaining detailed scene elements like small urban objects. We introduce a Local Spatial-wise Transformer (LST) block to enrich local semantics through an improved self-attention mechanism that incorporates relative embeddings and transfers spatial information across various representation subspaces in parallel. Based on a feature pyramid framework that fuses and refines features from LST, we introduce a Global Channel-wise Transformer (GCT) block, which efficiently captures global context by focusing on feature channel inter-relationships with a controlled flow gate employed for selective information transfer. D2T-Net utilizes Transformers in both spatial and channel domains to review and fuse features from multiple layers, effectively summarizing semantic contexts and enriching spatial details with multi-scale information. Experiments conducted on three challenging benchmark MLS datasets, the Oakland 3-D, Toronto-3D, and Paris-Lille-3D have confirmed D2T-Net's accuracy, achieving 98.2%, 83.9%, and 83.8% mIoU respectively.
Luo et al. (Wed,) studied this question.