Back to Search
Start Over
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
- Publication Year :
- 2022
-
Abstract
- Current perception models in autonomous driving rely heavily on large-scale labeled LiDAR data, which is costly and time-consuming to annotate. In this work, we aim to facilitate research on self-supervised masked learning using the vast amount of unlabeled LiDAR data available in autonomous driving. However, existing masked point autoencoding methods only focus on small-scale indoor point clouds and struggle to adapt to outdoor scenes, which usually have a large number of non-evenly distributed LiDAR points. To address these challenges, we propose a new self-supervised masked learning method named Occupancy-MAE, specifically designed for large-scale outdoor LiDAR points. We leverage the gradually sparse occupancy structure of large-scale outdoor LiDAR point clouds and introduce a range-aware random masking strategy and a pretext task of occupancy prediction. Occupancy-MAE randomly masks voxels of LiDAR point clouds based on their distance to LiDAR and predicts the masked occupancy structure of the whole 3D scene. This simple occupancy prediction objective encourages Occupancy-MAE to extract high-level semantic information to recover the masked voxel from only a small amount of visible voxels. Extensive experiments demonstrate the effectiveness of Occupancy-MAE across several downstream tasks. For the 3D object detection task, Occupancy-MAE reduces the labeled data required for car detection on KITTI by half and boosts small object detection by around 2% mAP on Waymo. For the 3D semantic segmentation task, Occupancy-MAE outperforms training from scratch by around 2% mIOU on nuScenes. For the unsupervised domain adaptation task, Occupancy-MAE improves the performance by about 0.5\% ~ 1% mAP. Our results show that it is feasible to pre-train unlabeled large-scale LiDAR point clouds with masked autoencoding to enhance the 3D perception ability of autonomous driving.<br />10 pages, 4 figures
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....1511f958e9dc456b11adb8de8f6f5c44