T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning

The scarcity of annotated data in outdoor point cloud segmentation poses a significant obstacle in harnessing the modeling capabilities of advanced networks like transformers. Consequently, scholars have been actively investigating efficacious self-supervised pre-training strategies, e.g. contrasting learning and reconstruction-based pretext tasks. 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 AutoEncoders (T-MAE), which takes as input temporally adjacent frames and learns temporal dependency. A SiamWCA backbone, containing a Siamese encoder and a window-based cross-attention (WCA) module, is established for the two-frame input. Taking into account that the motion of an ego-vehicle alters the illumination angles of the same instance, temporal modeling also serves as a robust and natural data augmentation, enhancing the comprehension of target objects. Moreover, instead of utilizing consecutive frames, it is more cost-effective and powerful by using distant historical frames. SiamWCA is a powerful architecture but heavily relies on annotated data. With our T-MAE pre-training strategy, we achieve the best performance on the Waymo dataset among self-supervised learning methods. Comprehensive experiments are conducted to validate all components of our proposal. Upon acceptance, the source code will be made accessible.