公开(公告)号：US20240020970A1

公开(公告)日：2024-01-18

申请号：US18352246

申请日：2023-07-14

Applicant: GUILIN UNIVERSITY OF TECHNOLOGY

Inventor： Guoqing ZHOU ,  Yue JIANG ,  Haoyu WANG

IPC: G06V20/10 ,  G06T7/11 ,  G06V10/80 ,  G06V10/94 ,  G06V10/764 ,  G06V10/77

CPC classification number: G06V20/176 ,  G06T7/11 ,  G06V10/806 ,  G06V10/95 ,  G06V10/764 ,  G06V10/7715 ,  G06T2207/10028 ,  G06T2207/30181 ,  G06T2207/20081

Abstract: The present disclosure relates to a PointEFF method for urban object classification with LiDAR point cloud data, and belongs to the field of LiDAR point cloud classification. The method comprises: point cloud data segmentation; End-to-end feature extraction layer construction; External feature fusion layer construction; and precision evaluation. The PointEFF method for urban object classification with LiDAR point cloud data fuses point cloud hand-crafted descriptors with End-to-end features obtained from a network at an up-sampling stage of a model by constructing an External Feature Fusion module, which improves a problem of local point cloud information loss caused by interpolation operation in the up-sampling process of domain feature pooling methods represented by PointNet and PointNet++, greatly improves classification precision of the model in complex ground features, especially in rough surface ground features, and is capable of being better applied to the classification of urban ground features with complex ground feature types.