Hybrid Cross-Transformer-KPConv for Point Cloud Segmentation

Point cloud segmentation is one of the challenging areas due to its disorder and irregularity. Currently, a lot of work utilising Transformer instead of conventional convolution methods has been proposed, which can well cope with these difficulties and is suitable for point cloud segmentation tasks. However, most existing Transformer methods extract global or local features in isolation, failing to obtain rich contextual information. In this letter, a cross-scale Transformer network for feature extration is proposed. Multi-level contextual information is captured appllying FPS algorithm. Integrated with point cloud convolution method, achieving excellent segmentation performance. Extensive experiments on SemanticKITTI dataset demonstrate the superior performance of the proposed method on mIoU.