LEST: Large-scale LiDAR Semantic Segmentation with Transformer
Chuanyu Luo Nuo Cheng Sikun Ma Han Li Xiaohan Li Shengguang Lei Pu Li
Large-scale LiDAR-based point cloud semantic segmentation is a critical task
in autonomous driving perception. Almost all of the previous state-of-the-art
LiDAR semantic segmentation methods are variants of sparse 3D convolution.
Although the Transformer architecture is becoming popular in the field of
natural language processing and 2D computer vision, its application to
large-scale point cloud semantic segmentation is still limited. In this paper,
we propose a LiDAR sEmantic Segmentation architecture with pure Transformer,
LEST. LEST comprises two novel components: a Space Filling Curve (SFC) Grouping
strategy and a Distance-based Cosine Linear Transformer, DISCO. On the public
nuScenes semantic segmentation validation set and SemanticKITTI test set, our
model outperforms all the other state-of-the-art methods.