Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation
<|reference_start|>Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation: 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.<|reference_end|>
@article{gyawali2024region-transformer:,
    title={Region-Transformer: Self-Attention Region Based Class-Agnostic Point
  Cloud Segmentation},
    author={Dipesh Gyawali, Jian Zhang, BB Karki},
    journal={19th International Joint Conference on Computer Vision, Imaging
  and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,
  341-348, 2024 , Rome, Italy},
    year={2024},
    doi={10.5220/0012424500003660},
    archivePrefix={arXiv},
    eprint={2403.01407},
    primaryClass={cs.CV cs.AI cs.RO}
}