Masked boundary modeling improves vision pretraining for spatial perception
Vision Pretraining for Dense Spatial Perception
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Summary
Researchers propose boundary-centric vision pretraining through masked boundary modeling, a self-supervised paradigm that learns sub-pixel boundary representations. LingBot-Vision, developed using this framework, advances depth estimation tasks essential for embodied AI and outperforms DINOv3 across diverse vision tasks.
#vision#pretraining#depth estimation#self-supervised learning#spatial perception