EDGE-CONVOLUTION POINT NET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE POINT CLOUDS

In this paper, we propose a deep learning-based framework which can manage large-scale point clouds of outdoor scenes with high spatial resolution. For large and high-resolution outdoor scenes, point-wise classification approaches are often an intractable problem. Analogous to Object-Based Image Analysis (OBIA), our approach segments the scene by grouping similar points together to generate meaningful objects. Later, our net classifies segments instead of individual points using an architecture inspired by PointNet, which applies Edge convolutions. This approach is trained using both visual and geometrical information. Experiments show the potential of this task even for small training sets. Furthermore, we can show competitive performance on a Large-scale Point Cloud Classification Benchmark.