DeFM: Learning Foundation Representations from Depth for Robotics
DeFM is a self-supervised foundation model trained entirely on 60 million depth images to learn robust geometric and semantic representations for robotics. It uses a DINOv2 style self-distillation objective to achieve generalization across diverse tasks, eliminating the need for task-specific fine-tuning. This allows for off-the-shelf deployment of powerful depth encoders on resource-constrained robotic systems.