Point cloud segmentation, which helps us understand the environment of
specific structures and objects, can be performed in class-specific and
class-agnostic ways. We propose a novel region-based transformer model called
Region-Transformer for performing class-agnostic point cloud segmentation. The
model utilizes a region-growth approach and self-attention mechanism to
iteratively expand or contract a region by adding or removing points. It is
trained on simulated point clouds with instance labels only, avoiding semantic
labels. Attention-based networks have succeeded in many previous methods of
performing point cloud segmentation. However, a region-growth approach with
attention-based networks has yet to be used to explore its performance gain. To
our knowledge, we are the first to use a self-attention mechanism in a
region-growth approach. With the introduction of self-attention to
region-growth that can utilize local contextual information of neighborhood
points, our experiments demonstrate that the Region-Transformer model
outperforms previous class-agnostic and class-specific methods on indoor
datasets regarding clustering metrics. The model generalizes well to
large-scale scenes. Key advantages include capturing long-range dependencies
through self-attention, avoiding the need for semantic labels during training,
and applicability to a variable number of objects. The Region-Transformer model
represents a promising approach for flexible point cloud segmentation with
applications in robotics, digital twinning, and autonomous vehicles.

基于区域转换器的自我注意力机制的增长式点云分割模型优于以往的通用类和特定类方法，通过自我注意力捕捉远距离依赖关系，在训练过程中避免了对语义标签的需求，适用于灵活的点云分割，并可应用于机器人技术、数字化双生和自动驾驶等领域。