sianms non maximum suppression with siamese networks for multi camera 3d object detection

• DOI: 10.48550/arxiv.2002.08239
• Publication Date: 2020-10-19
• Authors: Jorge Beltran; Arturo de la Esca...; Irene Cortes; Fernando Checa Ga...
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

The rapid development of embedded hardware in autonomous vehicles broadens their computational capabilities, thus bringing the possibility to mount more complete sensor setups able to handle driving scenarios of higher complexity. As a result, new challenges such as multiple detections of the same object have to be addressed. In this work, a siamese network is integrated into the pipeline of a well-known 3D object detector approach to suppress duplicate proposals coming from different cameras via re-identification. Additionally, associations are exploited to enhance the 3D box regression of the object by aggregating their corresponding LiDAR frustums. The experimental evaluation on the nuScenes dataset shows that the proposed method outperforms traditional NMS approaches.<br/>Submitted to IEEE Intelligent Vehicles Symposium 2020 (IV2020)<br/>