Roof Classification from 3D LiDAR Point Clouds using Multi-view CNN with Self Attention - Shajahan, Dimple; Nayel, Vaibhav; Muthuganapathy, Ramanathan (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI); 2019: Anais da XXXII Conference on Graphics, Patterns and Images, 2019)
Classification of LiDAR point clouds of building roofs play a vital role in various urban management applications and is significant in GIS 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 multi-view 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 3D descriptor. Our adaptive weight learning algorithm, which uses the self-attention (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 dataset: RoofN3D by comparing it with the current state-of-the-art method.