  Many point-based semantic segmentation methods have been designed for indoor
scenarios, but they struggle if they are applied to point clouds that are
captured by a LiDAR sensor in an outdoor environment. In order to make these
methods more efficient and robust such that they can handle LiDAR data, we
introduce the general concept of reformulating 3D point-based operations such
that they can operate in the projection space. While we show by means of three
point-based methods that the reformulated versions are between 300 and 400
times faster and achieve a higher accuracy, we furthermore demonstrate that the
concept of reformulating 3D point-based operations allows to design new
architectures that unify the benefits of point-based and image-based methods.
As an example, we introduce a network that integrates reformulated 3D
point-based operations into a 2D encoder-decoder architecture that fuses the
information from different 2D scales. We evaluate the approach on four
challenging datasets for semantic LiDAR point cloud segmentation and show that
leveraging reformulated 3D point-based operations with 2D image-based
operations achieves very good results for all four datasets.
