Semantic segmentation is used to enable a computer to understand its surrounding environment. In image processing, images are partitioned into segments for this purpose. State-of-the-art methods make use of Convolutional Neural Networks to segment a 2D image. Compared to that, 3D approaches suffer from computational cost and are not applicable without any further steps. In this work, we focus on semantic segmentation based on 3D point clouds. We use the idea to project the 3D data into a 2D image to accelerate the segmentation process. Afterward, the processed image gets re-projected to receive the desired result. We investigate different projection views and compare them to clarify their strengths and weaknesses. To compensate for projection errors and the loss of geometrical information, we evolve the approach and show how to fuse different views. We have decided to fuse the bird's-eye and the spherical projection as each of them achieves reasonable results, and the two perspectives complement each other best. For training and evaluation, we use the real-world datasets SemanticKITTI. Further, we use the ParisLille and synthetic data generated by the simulation framework Carla to analyze the approaches in more detail and clarify their strengths and weaknesses. Although these methods achieve reasonable and competitive results, they lack flexibility. They depend on the sensor used and the setup in which the sensor is used.
Keywords: point cloud projection; point cloud segmentation; supervised learning.