  Object detection through either RGB images or the LiDAR point clouds has been
extensively explored in autonomous driving. However, it remains challenging to
make these two data sources complementary and beneficial to each other. In this
paper, we propose \textit{AutoAlign}, an automatic feature fusion strategy for
3D object detection. Instead of establishing deterministic correspondence with
camera projection matrix, we model the mapping relationship between the image
and point clouds with a learnable alignment map. This map enables our model to
automate the alignment of non-homogenous features in a dynamic and data-driven
manner. Specifically, a cross-attention feature alignment module is devised to
adaptively aggregate \textit{pixel-level} image features for each voxel. To
enhance the semantic consistency during feature alignment, we also design a
self-supervised cross-modal feature interaction module, through which the model
can learn feature aggregation with \textit{instance-level} feature guidance.
Extensive experimental results show that our approach can lead to 2.3 mAP and
7.0 mAP improvements on the KITTI and nuScenes datasets, respectively. Notably,
our best model reaches 70.9 NDS on the nuScenes testing leaderboard, achieving
competitive performance among various state-of-the-arts.
