  3D Transformers have achieved great success in point cloud understanding and
representation. However, there is still considerable scope for further
development in effective and efficient Transformers for large-scale LiDAR point
cloud scene segmentation. This paper proposes a novel 3D Transformer framework,
named 3D Learnable Supertoken Transformer (3DLST). The key contributions are
summarized as follows. Firstly, we introduce the first Dynamic Supertoken
Optimization (DSO) block for efficient token clustering and aggregating, where
the learnable supertoken definition avoids the time-consuming pre-processing of
traditional superpoint generation. Since the learnable supertokens can be
dynamically optimized by multi-level deep features during network learning,
they are tailored to the semantic homogeneity-aware token clustering. Secondly,
an efficient Cross-Attention-guided Upsampling (CAU) block is proposed for
token reconstruction from optimized supertokens. Thirdly, the 3DLST is equipped
with a novel W-net architecture instead of the common U-net design, which is
more suitable for Transformer-based feature learning. The SOTA performance on
three challenging LiDAR datasets (airborne MultiSpectral LiDAR (MS-LiDAR)
(89.3% of the average F1 score), DALES (80.2% of mIoU), and Toronto-3D dataset
(80.4% of mIoU)) demonstrate the superiority of 3DLST and its strong
adaptability to various LiDAR point cloud data (airborne MS-LiDAR, aerial
LiDAR, and vehicle-mounted LiDAR data). Furthermore, 3DLST also achieves
satisfactory results in terms of algorithm efficiency, which is up to 5x faster
than previous best-performing methods.
