SPBA-Net: Point Cloud Object Detection with Sparse Attention and Box Aligning

Abstract Object detection in point clouds is essential for various applications, including autonomous navigation, household robots,and augmented/virtual reality. However, during voxelization and Bird’s Eye View transformation, local point cloud data often remains sparse due to non-target areas and noise points, posing a significant challenge for feature extraction. In this paper,we propose a novel mechanism named Sparse Attention to enhance the semantic information of point clouds by calculating Euclidean distances between selected keypoints and others. Additionally, we introduce Instance-wise Box Aligning, a method for expanding predicted boxes and clustering the points within them to achieve precise alignment between predicted bounding boxes and ground-truth targets. Experimental results demonstrate the superiority of our proposed SPBA-Net in 3D object detection on point clouds compared to other state-of-the-art methods.