3M3D: Multi-view, Multi-path, Multi-representation for 3D Object Detection

3D visual perception tasks based on multi-camera images are essential for autonomous driving systems. The latest work in this field performs 3D object detection by leveraging multi-view images as input and iteratively enhancing object queries (object proposals) by cross-attending multi-view features. However, individual backbone features are not updated with multi-view features, and it stays as a mere collection of the output of the single-image backbone network. Therefore we propose 3M3D: A Multi-view, Multi-path, Multi-representation for 3D Object Detection where we update both multi-view features and query features to enhance the representation of the scene in both fine panoramic view and coarse global view. Firstly, we update multi-view features by multi-view axis self-attention. It will incorporate panoramic information in the multi-view features and enhance understanding of the global scene. Secondly, we update multi-view features by self-attention of the Region of Interest (ROI) windows which encodes local finer details. It will help exchange the information not only along the multi-view axis but also along the other spatial dimension. Lastly, we leverage the fact of the multi-representation of queries (MRQ) in different domains to further boost performance.