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