TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation

Rong Li, Shijie Li, Xieyuanli Chen, Teli Ma, Juergen Gall, Junwei Liang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4547-4556

LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist within this domain including point-based range-image-based polar-coordinate-based and hybrid strategies. Among these range-image-based techniques have gained widespread adoption in practical applications due to their efficiency. However they face a significant challenge known as the "many-to-one" problem caused by the range image's limited horizontal and vertical angular resolution. As a result around 20% of the 3D points can be occluded. In this paper we present TFNet a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue. Specifically we incorporate a temporal fusion layer to extract useful information from previous scans and integrate it with the current scan. We then design a max-voting-based post-processing technique to correct false predictions particularly those caused by the "many-to-one" issue. We evaluated the approach on two benchmarks and demonstrated that the plug-in post-processing technique is generic and can be applied to various networks.