The geometric understanding of a scene is a crucial component for safe and efficient robotic operations in novel and unstructured environments. The recent emergence of foundation models for Monocular Depth Estimation (MDE) has provided an opportunity to enrich robotic perception capabilities with dense long-range depth predictions, replacing expensive onboard hardware (e.g., LiDARs and stereo cameras). However, these offline models are primarily trained on affine-invariant depth representations, and their generalization to novel scenes remains unclear. In this work we present EM-ViT, a monocular depth estimation model that learns to adapt online relative depth inputs from a foundation model prior to the metric scale with observations collected during robotic operations. Motivated by the stereo camera parameterization, our method decomposes depth prediction via dense disparity refinement to recover the scene's relative structure and an effective baseline scalar prediction to determine the missing metric scale. The learning process leverages sparse LiDAR points and, optionally, the robot's Ego-Motion (EM) to rapidly train a lightweight Vision Transformer (ViT), while an internal gradient matching loss preserves the fine-grained semantic structure of the input disparity in real-time. We demonstrate the efficacy of our method on publicly available sequences, such as KITTI, and show that our online learning pipeline can adapt to novel scenes faster and with higher accuracy than existing MDE foundation models. The code and supplementary materials will be publicly available at https://emvit-mde.github.io.

翻译：暂无翻译