  The scarcity of annotated data in LiDAR point cloud understanding hinders
effective representation learning. Consequently, scholars have been actively
investigating efficacious self-supervised pre-training paradigms. Nevertheless,
temporal information, which is inherent in the LiDAR point cloud sequence, is
consistently disregarded. To better utilize this property, we propose an
effective pre-training strategy, namely Temporal Masked Auto-Encoders (T-MAE),
which takes as input temporally adjacent frames and learns temporal dependency.
A SiamWCA backbone, containing a Siamese encoder and a windowed cross-attention
(WCA) module, is established for the two-frame input. Considering that the
movement of an ego-vehicle alters the view of the same instance, temporal
modeling also serves as a robust and natural data augmentation, enhancing the
comprehension of target objects. SiamWCA is a powerful architecture but heavily
relies on annotated data. Our T-MAE pre-training strategy alleviates its demand
for annotated data. Comprehensive experiments demonstrate that T-MAE achieves
the best performance on both Waymo and ONCE datasets among competitive
self-supervised approaches. Codes will be released at
https://github.com/codename1995/T-MAE
