DeFM: Learning Foundation Representations from Depth for Robotics
Read the original on arXiv Computer Vision →DeFM is a self‑supervised foundation model trained solely on depth images for robotics. It uses a DINO‑style self‑distillation objective on 60 M curated depth images to learn geometric and semantic representations that generalize across environments, tasks, and sensors. The model incorporates a novel input normalization strategy for metric awareness, can be distilled into compact versions for resource‑constrained systems, and achieves state‑of‑the‑art performance on depth‑based classification, segmentation, navigation, locomotion, and manipulation benchmarks, with strong sim‑to‑real transfer.
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