  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.
