An efficient and general framework for aerial point cloud classification in urban scenarios

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification has been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while other are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art meth-ods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.