CrossInteraction: Multi-Modal Interaction and Alignment Strategy for 3D Perception

Cameras and LiDAR are the primary sensors utilized in contemporary 3D object perception, leading to the development of various multi-modal detection algorithms for images, point clouds, and their fusion. Given the demanding accuracy requirements in autonomous driving environments, traditional multi-modal fusion techniques often overlook critical information from individual modalities and struggle to effectively align transformed features. In this paper, we introduce an improved modal interaction strategy, called CrossInteraction. This method enhances the interaction between modalities by using the output of the first modal representation as the input for the second interaction enhancement, resulting in better overall interaction effects. To further address the challenge of feature alignment errors, we employ a graph convolutional network. Finally, the prediction process is completed through a cross-attention mechanism, ensuring more accurate detection out- comes.