PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data

Semantic understanding of the surrounding environment is essential for automated vehicles. The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios. While most existing approaches predict sparse pointwise semantic classes for the sparse input LiDAR scan, we propose PillarSegNet to be able to output a dense semantic grid map. In contrast to a previously proposed grid map method, PillarSegNet uses PointNet to learn features directly from the 3D point cloud and then conducts 2D semantic segmentation in the top view. To train and evaluate our approach, we use both sparse and dense ground truth, where the dense ground truth is obtained from multiple superimposed scans. Experimental results on the SemanticKITTI dalaset show that PillarSegNet achieves a performance gain of about 10% mIou over the state-of-the-art grid map method.

Automated summary

Semantic understanding is crucial for automated vehicles, and the SemanticKITTI dataset has spurred research on semantic segmentation of LiDAR point clouds in urban scenarios. PillarSegNet, a new approach, achieves a 10% performance gain over the state-of-the-art grid map method by outputting dense semantic grid maps through PointNet feature learning and 2D semantic segmentation in top view.