Recently, point cloud processing and analysis have made great progress due to
the development of 3D Transformers. However, existing 3D Transformer methods
usually are computationally expensive and inefficient due to their huge and
redundant attention maps. They also tend to be slow due to requiring
time-consuming point cloud sampling and grouping processes. To address these
issues, we propose an efficient point TransFormer with Dynamic Token
Aggregating (DTA-Former) for point cloud representation and processing.
Firstly, we propose an efficient Learnable Token Sparsification (LTS) block,
which considers both local and global semantic information for the adaptive
selection of key tokens. Secondly, to achieve the feature aggregation for
sparsified tokens, we present the first Dynamic Token Aggregating (DTA) block
in the 3D Transformer paradigm, providing our model with strong aggregated
features while preventing information loss. After that, a dual-attention
Transformer-based Global Feature Enhancement (GFE) block is used to improve the
representation capability of the model. Equipped with LTS, DTA, and GFE blocks,
DTA-Former achieves excellent classification results via hierarchical feature
learning. Lastly, a novel Iterative Token Reconstruction (ITR) block is
introduced for dense prediction whereby the semantic features of tokens and
their semantic relationships are gradually optimized during iterative
reconstruction. Based on ITR, we propose a new W-net architecture, which is
more suitable for Transformer-based feature learning than the common U-net
design. Extensive experiments demonstrate the superiority of our method. It
achieves SOTA performance with up to 30$\times$ faster than prior point
Transformers on ModelNet40, ShapeNet, and airborne MultiSpectral LiDAR
(MS-LiDAR) datasets.

提出了一种高效的基于 DTA 的前向（DTA-Former）方法，用于点云表示和处理，通过逐渐优化令牌的语义特征和语义关系，实现了在模型 Net40，ShapeNet 和航空多光谱 LiDAR（MS-LiDAR）数据集上超越先前点变换器的 SOTA 性能 30 倍更快。

高效的点云处理动态令牌聚合点变换器

Efficient Point Transformer with Dynamic Token Aggregating for Point  Cloud Processing

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.

3DLST 是一种新颖的 3D Transformer 框架，通过引入 DSO 块实现高效的令牌聚类和聚合，提出了高效的 CAU 块用于令牌重构，并采用 W-net 架构进行基于 Transformer 的特征学习，其在多个具有挑战性的 LiDAR 数据集上表现出优越性能和适应性。