Integrating Contextual Information and Attention Mechanisms with Sparse Convolution for the Extraction of Internal Objects within Buildings from Three-Dimensional Point Clouds

Integrating Contextual Information and Attention Mechanisms with Sparse Convolution for the Extraction of Internal Objects within Buildings from Three-Dimensional Point Clouds

Deep learning-based point cloud semantic segmentation has gained popularity over time, with sparse convolution being the most prominent example. Although sparse convolution is more efficient than regular convolution, it comes with the drawback of sacrificing global context information. To solve this...

Full description

Bibliographic Details
Main Authors: Mingyang Yu, Zhongxu Li, Qiuxiao Xu, Fei Su, Xin Chen, Weikang Cui, Qingrui Ji
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Buildings
Subjects:
point cloud semantic segmentation
sparse convolution
offset attention
context aggregation module
Online Access:https://www.mdpi.com/2075-5309/14/3/636

Internet

https://www.mdpi.com/2075-5309/14/3/636

Similar Items