Suyi Liu1,*, Jianning Chi1, Chengdong Wu1, Fang Xu2,3,4, Xiaosheng Yu1
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4471-4489, 2024, DOI:10.32604/cmc.2024.049450
Abstract In recent years, semantic segmentation on 3D point cloud data has attracted much attention. Unlike 2D images where pixels distribute regularly in the image domain, 3D point clouds in non-Euclidean space are irregular and inherently sparse. Therefore, it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space. Most current methods either focus on local feature aggregation or long-range context dependency, but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks. In this paper, we propose a Transformer-based… More >
Login
Register
