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

• URL: http://arxiv.org/abs/2312.10217v3
• Date: Mon, 22 Jul 2024 08:30:53 GMT
• Title: T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning
• Authors: Weijie Wei, Fatemeh Karimi Nejadasl, Theo Gevers, Martin R. Oswald,
• Abstract summary: We propose an effective pre-training strategy, namely Temporal Masked Auto-Encoders (T-MAE) T-MAE takes as input temporally adjacent frames and learns temporal dependency. Our T-MAE pre-training strategy alleviates its demand for annotated data.
• Score: 22.002220932086693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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