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Semantic segmentation of building facade is significant in variousapplications, such as urban building reconstruction and damage assessment. Asthere is a lack of 3D point clouds datasets related to the fine-grainedbuilding facade, we construct the first large-scale building facade pointclouds benchmark dataset for semantic segmentation. The existing methods ofsemantic segmentation cannot fully mine the local neighborhood information ofpoint clouds. Addressing this problem, we propose a learnable attention modulethat learns Dual Local Attention features, called DLA in this paper. Theproposed DLA module consists of two blocks, including the self-attention blockand attentive pooling block, which both embed an enhanced position encodingblock. The DLA module could be easily embedded into various networkarchitectures for point cloud segmentation, naturally resulting in a new 3Dsemantic segmentation network with an encoder-decoder architecture, calledDLA-Net in this work. Extensive experimental results on our constructedbuilding facade dataset demonstrate that the proposed DLA-Net achieves betterperformance than the state-of-the-art methods for semantic segmentation.

DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds