SPBA-Net point cloud object detection with sparse attention and box aligning

• Published in: Scientific reports 2024-11, Vol.14 (1), p.28420-10, Article 28420
• Main Authors: Sha, Haojie, Gao, Qingrui, Zeng, Hao, Li, Kai, Li, Wang, Zhang, Xuande, Wang, Xiaohui
• Format: Article
• Language: English
• Subjects: 3D object detection; 639/705/117; 639/705/258; Accuracy; Boxes; Computer applications; Datasets; Humanities and Social Sciences; Instance-wise box aligning; Keypoint guided sparse attention; Methods; multidisciplinary; Science; Science (multidisciplinary); Semantics; Sparsity
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-77097-z

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 Keypoint Guided Sparse Attention (KGSA) 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.The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.