Self Supervised 3D

Self-supervised 3D representation learning aims to train powerful 3D models without relying on extensive, manually labeled datasets. Current research focuses on developing novel architectures, such as joint embedding predictive architectures (JEPAs), masked autoencoders (MAEs), and contrastive learning frameworks, often incorporating implicit surface representations or generative models to overcome challenges posed by the inherent sparsity and irregularity of 3D data. These advancements improve the efficiency and generalizability of 3D scene understanding for tasks like object detection, semantic segmentation, and pose estimation, impacting fields ranging from robotics to augmented reality. The ultimate goal is to enable robust and data-efficient 3D perception in various applications.