AI学会看边界，空间感知能力飙升
现在的视觉AI擅长认东西（这是猫、那是车），但不太会看空间——比如物体离你多远、形状的棱角在哪。这篇论文发现，如果让AI专门去学图像里的“边界”（物体轮廓、棱线），它的空间感知能力会大幅提升。研究者设计了一种自监督方法，让AI自己找出哪些像素点构成了边界，然后用这些边界信息作为训练目标，强迫模型学会更精细的空间表示。他们训练出的模型LingBot-Vision在深度估计（判断距离）任务上显著超越了当前最强的DINOv3，并推动了深度补全模型从1.0升级到2.0。这背后是给机器人、自动驾驶等需要“看懂物理世界”的AI装上更准的尺子。它不是你明天就能用的工具，但指明了视觉AI从“认东西”进化到“懂空间”的关键路径。
📄 原文摘要(英文)
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.