  In point cloud analysis tasks, the existing local feature aggregation
descriptors (LFAD) are unable to fully utilize information in the neighborhood
of central points. Previous methods rely solely on Euclidean distance to
constrain the local aggregation process, which can be easily affected by
abnormal points and cannot adequately fit with the original geometry of the
point cloud. We believe that fine-grained geometric information (FGGI) is
significant for the aggregation of local features. Therefore, we propose a
gradient-based local attention module, termed as Gradient Attention Module
(GAM), to address the aforementioned problem. Our proposed GAM simplifies the
process that extracts gradient information in the neighborhood and uses the
Zenith Angle matrix and Azimuth Angle matrix as explicit representation, which
accelerates the module by 35X. Comprehensive experiments were conducted on five
benchmark datasets to demonstrate the effectiveness and generalization
capability of the proposed GAM for 3D point cloud analysis. Especially on S3DIS
dataset, GAM achieves the best performance among current point-based models
with mIoU/OA/mAcc of 74.4%/90.6%/83.2%, respectively.
