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.

近期在点云领域中自监督学习方面的最新进展表现出显著的潜力。然而，这些方法常常面临一些问题，包括长时间的预训练时间、对输入空间的重构以及需要额外的模态。为了解决这些问题，我们介绍了一种专门针对点云数据设计的联合嵌入预测架构 ——Point-JEPA。为此，我们引入了一种排序器，它能够对点云标记进行排序，以在目标和上下文选择期间高效计算和利用标记的接近性。排序器还允许在上下文和目标选择之间共享标记的接近性计算，从而进一步提高效率。在实验证明，我们的方法在避免了对输入空间进行重构或使用额外模态的同时，取得了与最先进的方法相竞争的结果。