GAT3D: Attention-Based Graph Neural Network for Efficient 3D Object Detection

Le résumé fourni par la source

This paper proposes a 3D object detection method using graph neural networks to process LiDAR point clouds. The raw data is voxel-sampled into a multi-layer graph, with each node representing local geometric features. A graph attention mechanism (GAT) is employed to adaptively weight neighboring nodes during message passing, enhancing spatial context capture and focus on key points. An alignment module addresses spatial displacement issues, while bounding box merging and score refinement improve detection outcomes. Experiments on the KITTI dataset show that the method achieves competitive accuracy using only point cloud data, outperforming several existing approaches and validating the effectiveness of GAT in 3D detection.

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