Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud

arXiv:2404.16432v1 Announce Type: new
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 …

abstract architecture arxiv cloud cs.cv domain embedding however jepa joint embedding predictive architecture predictive pre-training self-supervised learning space supervised learning training type