The author proposes the Region-Transformer model, combining self-attention with region-growing for class-agnostic point cloud segmentation, demonstrating superior performance over existing methods.
Region-Transformer combines self-attention and region-growth for class-agnostic point cloud segmentation, outperforming previous methods.
This research paper introduces Subspace Prototype Guidance (SPG), a novel method designed to enhance point cloud semantic segmentation by effectively addressing the challenge of class imbalance, thereby improving accuracy, particularly for minority categories.
Inaccurate color information, particularly similar but incorrect color shades, significantly degrades the accuracy of point cloud semantic segmentation, even when geometric information is incorporated.