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。