SRCN3D: Sparse R-CNN 3D Surround-View Camera Object Detection and Tracking for Autonomous Driving
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Detection And Tracking of Moving Objects (DATMO) is an essential component in environmental perception for autonomous driving. While 3D detectors using surround-view cameras are just flourishing, there is a growing tendency of using different transformer-based methods to learn queries in 3D space from 2D feature maps of perspective view. This paper proposes Sparse R-CNN 3D (SRCN3D), a novel two-stage fully-convolutional mapping pipeline for surround-view camera detection and tracking. SRCN3D adopts a cascade structure with twin-track update of both fixed number of proposal boxes and proposal latent features. Proposal boxes are projected to perspective view so as to aggregate Region of Interest (RoI) local features. Based on that, proposal features are refined via a dynamic instance interactive head, which then generates classification and the offsets applied to original bounding boxes. Compared to prior arts, our sparse feature sampling module only utilizes local 2D features for adjustment of each corresponding 3D proposal box, leading to a complete sparse paradigm. The proposal features and appearance features are both taken in data association process in a multi-hypotheses 3D multi-object tracking approach. Extensive experiments on nuScenes dataset demonstrate the effectiveness of our proposed SRCN3D detector and tracker. Code is available at https://github.com/synsin0/SRCN3D.
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