JEPADepth: Self-Supervised Monocular Depth Estimation via Joint-Embedding Predictive Architectures

We propose JEPADepth, a self-supervised monocular depth estimation framework that augments photometric view synthesis with an auxiliary masked-prediction loss computed in the representation space of a pretrained DINOv3 Vision Transformer (ViT), following the Image Joint-Embedding Predictive Architecture (I-JEPA) principle. A predictor infers target-region embeddings from visible context-region embeddings under structured masking; the predictor and target encoder are discarded at inference, so the deployed 24 M-parameter model costs no more than the photometric-only baseline. On the KITTI Eigen split, adding the JEPA objective to an identical DINOv3 photometric baseline lowers absolute relative error from 0.105 to 0.101 and raises δ1 from 0.891 to 0.898, a gain roughly four times the run-to-run standard deviation measured over five random seeds. On the improved ground truth, it reaches 0.075 absolute relative error, matching the best transformer competitor while using 11% fewer inference parameters. Under zero-shot transfer without fine-tuning, it obtains the best absolute relative error on Make3D (0.275) and ties the best on Cityscapes (0.143) while leading on five of seven metrics. Training overhead is ∼16%; inference cost is unchanged.