Ant LingBot Releases Spatial Native Vision Foundation Model LingBot-Vision, Enhancing Robot Spatial Perception with Boundary Modeling

On June 8, Ant LingBot released the next-generation spatial perception model LingBot-Depth 2.0 and open-sourced the visual foundation model LingBot-Vision for embodied intelligence. This model is the world's first spatial native vision foundation model, using an innovative "boundary-centric masked modeling" method to embed spatial structure into the training objective during pre-training, enabling robots to more accurately understand distance, boundaries, and spatial relationships.

Technical Core: Boundary-Forced Masked Modeling

Traditional visual foundation models (e.g., DINOv3) use random masking during masked modeling. LingBot-Vision's key insight is that object boundary regions carry the most information and should be forcibly masked. The model uses a teacher model to predict boundary fields online, adding boundary patches to the masked set, forcing the model to reconstruct geometric structures. To solve the bootstrapping problem of "training from scratch without knowing boundaries," the team uses sparse corner points to anchor the decoding process, ensuring coherent decoded line segments even when boundary fields are randomly generated. Additionally, boundary prediction is converted into a classification problem, and a-contrario testing is introduced to filter noise, ensuring clean training targets.

Training Efficiency and Performance

LingBot-Vision (approximately 1.1B parameters, ViT-g/16) uses only 161 million images (1/10 of DINOv3) and less than one-third of DINOv3's training cost. In depth estimation tasks, it achieves an NYUv2 RMSE of 0.296, outperforming DINOv3 (0.309) with 7B parameters; on KITTI, it is the strongest among models with less than 2B parameters. In segmentation and video tasks, it matches DINOv3 ViT-H+ (0.8B) and surpasses DINOv2 by over 4 percentage points. Classification tasks are slightly weaker, consistent with the "spatial-first" design goal. The distilled 0.3B student model matches the 7B DINOv3 on NYUv2, with a parameter difference of about 23 times.

Practical Application: LingBot-Depth 2.0

The depth model based on LingBot-Vision performs stably on transparent/reflective objects, small targets, long distances, complex indoor scenes, and low-light occlusion scenarios. For example, the depth map of a transparent champagne tower has complete contours, and objects as small as tennis balls are clearly distinguishable. It achieves leading results on 12 depth completion benchmarks, with advantages increasing as downstream data grows.

Open Source and Deployment

LingBot-Vision is open-sourced, offering ViT-G/L/B/S model sizes. The technical report, code, and weights are available on platforms such as GitHub and Hugging Face.