Roof Classification From 3-D LiDAR Point Clouds Using Multiview CNN With Self-Attention

Classification of light detection and ranging (LiDAR) point clouds of building roofs plays a vital role in various urban management applications and is significant in geographic information systems (GISs) and remote sensing. In this letter, a novel deep learning-based method is proposed for classifying roof point clouds, which outperforms the state-of-the-art methods. We use a view-based method called a multiview convolutional neural network with self-attention (MVCNN-SA), which takes the multiple views of a roof point cloud as input and outputs the category of the roof. Current view-based approaches treat all views equally and simply combine the view features into a single compact 3-D descriptor. Our adaptive weight-learning algorithm, which uses the SA block, discovers the relative importance of each view, thus assigning relative weights to the views. This enhances the shape descriptor, resulting in better classification performance. The effectiveness of the proposed method is then verified on the publicly available data set - RoofN3D - by comparing it with the current state-of-the-art methods.