面向密集空间感知的视觉预训练

摘要

密集空间感知是物理智能的核心要素，视觉系统需要从像素观测中恢复出结构化、具备度量属性且可操作的表示。现代视觉基础模型往往优先保证语义不变性，却常以牺牲细致的空间理解为代价。本研究从边界感知的视角重新审视视觉预训练，其核心理念在于：边界与形状不连续性为几何特性感知提供了关键线索。具体而言，我们提出掩膜边界建模——一种自监督范式，该范式动态学习亚像素级边界表示，进而将所发现的边界标记作为掩膜目标，推动密集视觉标记的学习。通过扩展这一框架，我们构建了LingBot-Vision，并以DINOv3作为强基线，验证了其在多种下游视觉任务中的有效性。尤为值得关注的是，LingBot-Vision推动了深度补全任务从LingBot-Depth 1.0到LingBot-Depth 2.0的演进，从而显著提升了深度估计精度——这正是具身人工智能的关键支柱。本研究表明，边界建模的意义远超简单线段提取，它可作为学习空间结构化视觉表示的可扩展预训练原则。

English

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.