Enhancing primitive segmentation through transformer-based cross-task interaction

Point cloud primitive segmentation, which divides a point cloud into surface patches of distinct primitive types, is fundamental to three-dimensional objects processing and recognition. However, existing deep learning methods for primitive segmentation lack the capability to capture global spatial relationships across tasks, limiting the exploitation of inter-task consistency. To address this issue, we propose a novel transformer-based cross-task interaction primitive segmentation (TCIPS) method that models global spatial relationships between all tasks, leading to improved segmentation accuracy. Specifically, TCIPS leverages center offset and regional purity prediction as auxiliary tasks, providing supplementary supervision to facilitate the learning of spatial and boundary information, thereby promoting richer and more generalized feature learning. Furthermore, we design a cross-task transformer fusion module that fuses and refines features from task-specific decoders using two types of transformer blocks: the feature fusion block and the task query block. Extensive experiments and comparisons with state-of-the-art methods demonstrate the effectiveness and robustness of our approach. Codes and models are publicly available at https: //github. com/MingFengHill/TCIPS.

Authors

• Tao Wang
• Weibin Xi
• Yong Cheng
• Jun Zhang
• Ruochen Yin
• Yang Yang

Keywords

• Primitive segmentation
• Multi-task learning
• Transformer
• Point cloud processing