  Current outdoor LiDAR-based 3D object detection methods 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. Recently, masked modeling has
become a successful self-supervised learning approach for point clouds.
However, current works mainly focus on synthetic or indoor datasets. When
applied to large-scale and sparse outdoor point clouds, they fail to yield
satisfactory results. In this work, we present BEV-MAE, a simple masked
autoencoder pre-training framework for 3D object detection on outdoor point
clouds. Specifically, we first 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. Besides, 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. Finally,
based on the property of outdoor point clouds, 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 achieves new state-of-the-art
self-supervised results on both Waymo and nuScenes with diverse 3D object
detectors. Furthermore, with only 20% data and 7% training cost during
pre-training, BEV-MAE achieves comparable performance with the state-of-the-art
method ProposalContrast. The source code and pre-trained models will be made
publicly available.

Computer Vision and Pattern Recognition