  Existing LiDAR-based 3D object detection methods for autonomous driving
scenarios mainly adopt the training-from-scratch paradigm. Unfortunately, this
paradigm heavily relies on large-scale labeled data, whose collection can be
expensive and time-consuming. Self-supervised pre-training is an effective and
desirable way to alleviate this dependence on extensive annotated data. In this
work, we present BEV-MAE, an efficient masked autoencoder pre-training
framework for LiDAR-based 3D object detection in autonomous driving.
Specifically, we propose a bird's eye view (BEV) guided masking strategy to
guide the 3D encoder learning feature representation in a BEV perspective and
avoid complex decoder design during pre-training. Furthermore, we introduce a
learnable point token to maintain a consistent receptive field size of the 3D
encoder with fine-tuning for masked point cloud inputs. Based on the property
of outdoor point clouds in autonomous driving scenarios, i.e., the point clouds
of distant objects are more sparse, we propose point density prediction to
enable the 3D encoder to learn location information, which is essential for
object detection. Experimental results show that BEV-MAE surpasses prior
state-of-the-art self-supervised methods and achieves a favorably pre-training
efficiency. Furthermore, based on TransFusion-L, BEV-MAE achieves new
state-of-the-art LiDAR-based 3D object detection results, with 73.6 NDS and
69.6 mAP on the nuScenes benchmark. The source code will be released at
https://github.com/VDIGPKU/BEV-MAE
