DeFM: Learning Foundation Representations from Depth for Rob
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: DeFM: Learning Foundation Representations from Depth for Robotics. DeFM is a self-supervised foundation model trained entirely on depth images for robotic applications. Using a DINO-style self-distillation objective on a curated dataset of 60M depth images, DeFM learns geometric and sem…
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- arXiv Robotics — research abstractsDeFM: Learning Foundation Representations from Depth for Robotics
DeFM is a self-supervised foundation model trained entirely on depth images for robotic applications. Using a DINO-style self-distillation objective on a curated dataset of 60M depth images, DeFM learns geometric and semantic representations that generalize to diverse environments, tasks, and sensors. It achieves state-of-the-art performance on depth-based benchmarks and is released for direct use in robotic learning without task-specific fine-tuning.
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