Abstract
Recent advancements in self-supervised learning in the point cloud domain have demonstrated significant potential. However, these methods often suffer from drawbacks, including lengthy pre-training time, the necessity of reconstruction in the input space, or the necessity of additional modalities. In order to address these issues, we introduce Point-JEPA, a joint embedding predictive architecture designed specifically for point cloud data. To this end, we introduce a sequencer that orders point cloud tokens to efficiently compute and utilize tokens proximity based on their indices during target and context selection. The sequencer also allows shared computations of the tokens proximity between context and target selection, further improving the efficiency. Experimentally, our method achieves competitive results with state-of-the-art methods while avoiding the reconstruction in the input space or additional modality.
Abstract (translated)
近年来，在点云领域自监督学习的进展已经展示了很大的潜力。然而，这些方法通常存在缺点，包括漫长的预训练时间、在输入空间进行重建的必要性，或者需要额外的模块。为了应对这些问题，我们引入了点JEPA，一种专门针对点云数据的联合嵌入预测架构。为此，我们引入一个序列器，对点云令牌进行排序，以在目标和上下文选择期间基于其索引计算并利用令牌的接近性。序列器还允许在上下文和目标选择之间共享计算令牌接近性，从而进一步提高效率。实验证明，我们的方法在获得与最先进方法竞争力的结果的同时，避免了在输入空间进行重建或添加额外模块。
URL
https://arxiv.org/abs/2404.16432
