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The rapid development of embedded hardware in autonomous vehicles broadenstheir computational capabilities, thus bringing the possibility to mount morecomplete sensor setups able to handle driving scenarios of higher complexity.As a result, new challenges such as multiple detections of the same object haveto be addressed. In this work, a siamese network is integrated into thepipeline of a well-known 3D object detector approach to suppress duplicateproposals coming from different cameras via re-identification. Additionally,associations are exploited to enhance the 3D box regression of the object byaggregating their corresponding LiDAR frustums. The experimental evaluation onthe nuScenes dataset shows that the proposed method outperforms traditional NMSapproaches.

siaNMS: Non-Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection