Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation

Author:

Dipesh Gyawali, Jian Zhang, BB Karki

Keyword:

Computer Science, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Robotics (cs.RO)

journal:

19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2024

date:

2024-03-03 00:00:00

Abstract

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.

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