  Point cloud segmentation is one of the most important tasks in computer
vision with widespread scientific, industrial, and commercial applications. The
research thereof has resulted in many breakthroughs in 3D object and scene
understanding. Previous methods typically utilized hierarchical architectures
for feature representation. However, the commonly used sampling and grouping
methods in hierarchical networks are only based on point-wise three-dimensional
coordinates, ignoring local semantic homogeneity of point clusters.
Additionally, the prevalent Farthest Point Sampling (FPS) method is often a
computational bottleneck. To address these issues, we propose a novel 3D point
cloud representation network, called Dynamic Clustering Transformer Network
(DCTNet). It has an encoder-decoder architecture, allowing for both local and
global feature learning. Specifically, we propose novel semantic feature-based
dynamic sampling and clustering methods in the encoder, which enables the model
to be aware of local semantic homogeneity for local feature aggregation.
Furthermore, in the decoder, we propose an efficient semantic feature-guided
upsampling method. Our method was evaluated on an object-based dataset
(ShapeNet), an urban navigation dataset (Toronto-3D), and a multispectral LiDAR
dataset, verifying the performance of DCTNet across a wide variety of practical
engineering applications. The inference speed of DCTNet is 3.8-16.8$\times$
faster than existing State-of-the-Art (SOTA) models on the ShapeNet dataset,
while achieving an instance-wise mIoU of $86.6\%$, the current top score. Our
method similarly outperforms previous methods on the other datasets, verifying
it as the new State-of-the-Art in point cloud segmentation.
