An Efficient Point Cloud Semantic Segmentation Method Based on Bilateral Enhancement and Random Sampling

Point cloud semantic segmentation is of utmost importance in practical applications. However, most existing methods have evolved to be incredibly intricate, leading to a rise in complexity that has made them increasingly impractical for real-world utilization. The escalating complexity of these methods has resulted in a deterioration in their efficiency and ease of implementation, making them less suitable for use in time-sensitive and resource-constrained environments. Towards this issue, we propose an efficient and lightweight segmentation method, able to achieve a remarkable performance in terms of both segmentation accuracy, training speed, and space consumption. Specifically, we first propose to adopt random sampling to replace the original one to obtain more efficiency. Moreover, a lightweight decoding module and an improved bilateral enhancement (BAE) module are developed to further improve the performance. The proposed method achieved a 73.6% and 60.7% mIoU on the S3DIS and Semantickitti datasets, respectively. In the future, the random sampling and the proposed BAE module can be adopted in a more concise and lightweight network to achieve faster and more-accurate point cloud segmentation.