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Multi-modal 3D object detection has received growing attention as theinformation from different sensors like LiDAR and cameras are complementary.Most fusion methods for 3D detection rely on an accurate alignment andcalibration between 3D point clouds and RGB images. However, such an assumptionis not reliable in a real-world self-driving system, as the alignment betweendifferent modalities is easily affected by asynchronous sensors and disturbedsensor placement. We propose a novel {F}usion network by {B}ox {M}atching(FBMNet) for multi-modal 3D detection, which provides an alternative way forcross-modal feature alignment by learning the correspondence at the boundingbox level to free up the dependency of calibration during inference. With thelearned assignments between 3D and 2D object proposals, the fusion fordetection can be effectively performed by combing their ROI features. Extensiveexperiments on the nuScenes dataset demonstrate that our method is much morestable in dealing with challenging cases such as asynchronous sensors,misaligned sensor placement, and degenerated camera images than existing fusionmethods. We hope that our FBMNet could provide an available solution to dealingwith these challenging cases for safety in real autonomous driving scenarios.Codes will be publicly available at https://github.com/happinesslz/FBMNet.

Multi-Modal 3D Object Detection by Box Matching