Abstract:
With the advent of the era of Industry 4.0 and the continuous development of point cloud data acquisition technology, point cloud data has been widely used in unmanned di...Show MoreMetadata
Abstract:
With the advent of the era of Industry 4.0 and the continuous development of point cloud data acquisition technology, point cloud data has been widely used in unmanned distribution of intelligent logistics. This paper designs a 3D point cloud classification model with coordinate attention, blueprint separation involution neural network (BICANet). Firstly, the combination of 2D features and 3D features is adopted to maintain the spatial structure of the point cloud. Secondly, the Involution network is introduced to reduce the amount of redundant data for neural network computation and improve the whole network computation efficiency. At the same time, to further enhance the network feature learning capability, the blueprint separation convolution is combined with coordinate attention. The experimental results prove that the overall accuracy of BICANet in Vaihingen and GML B datasets reaches 86.0% and 98.8%, respectively. It is highly competitive with the currently available methods.
Published in: 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Date of Conference: 24-26 May 2023
Date Added to IEEE Xplore: 22 June 2023
ISBN Information: