Vision Pretraining for Dense Spatial Perception

2026-07-06

AI Summary

Current vision foundation models emphasize semantic invariance, which falls short of meeting the metric-level dense spatial understanding required by embodied intelligence. This work proposes Mask Boundary Modeling—a self-supervised approach that leverages a boundary-centric perspective to dynamically learn sub-pixel accurate boundary representations and guide the learning of dense visual tokens. By elevating boundary modeling from simple line segments to a scalable pretraining paradigm, the method effectively drives dense spatial representation learning. Built upon this framework, LingBot-Vision significantly outperforms the DINOv3 baseline on downstream tasks such as depth estimation, enabling the upgrade of LingBot-Depth from version 1.0 to 2.0 and substantially improving performance in depth completion and estimation.

Abstract

Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structured, metric, and actionable representations from pixel observations. Modern visual foundation models tend to prioritize semantic invariance, often at the expense of detailed spatial understanding. In this work, we study vision pretraining through a boundary-centric lens, motivated by the premise that boundaries and shape discontinuities offer essential cues for perceiving geometric properties. Concretely, we propose masked boundary modeling, a self-supervised paradigm that dynamically learns sub-pixel boundary representations and subsequently leverages the discovered boundary-bearing tokens as masked targets to facilitate dense visual token learning. By scaling this framework, we develop LingBot-Vision and demonstrate its efficacy across a diverse set of downstream vision tasks with DINOv3 as a strong baseline. Remarkably, LingBot-Vision drives the progression from LingBot-Depth 1.0 to LingBot-Depth 2.0 for depth completion, and thereby yields enhanced depth estimation, a key pillar for embodied artificial intelligence. Our findings reveal that boundary modeling goes beyond simple line segments and instead serves as a scalable pretraining principle for learning spatially structured visual representations.

Problem

• dense spatial perception
• vision pretraining
• boundary modeling
• spatial understanding
• visual foundation models

Innovation

• masked boundary modeling
• dense spatial perception
• self-supervised pretraining
• sub-pixel boundary representation
• visual foundation models