Show HN: Lingbot Vision – Self-Supervised Dense Perception (0.296 NYUv2 RMSE) (technology.robbyant.com)

LingBot-Vision has introduced a groundbreaking approach to dense spatial perception in AI, utilizing masked boundary modeling to improve visual understanding. This innovative technique allows the model to learn sub-pixel boundary representations in real-time, without relying on pre-trained backbones or external annotations. By effectively capturing object boundaries, LingBot-Vision outperforms existing models such as DINOv3, achieving a remarkable RMSE score of 0.296 on the NYUv2 dataset with a 1B-parameter Vision Transformer (ViT-g/16). The significance of LingBot-Vision lies in its ability to deliver high-resolution feature inference and enhance depth completion tasks through its robust boundary-anchored features. As it powers LingBot-Depth 2.0, depth modeling is achieved with striking accuracy, halving RMSE rates on complex surfaces like transparent or reflective materials. This innovative method not only improves performance across 14 benchmarks but also showcases the model's scalability and efficiency, as it operates effectively with fewer parameters while maintaining competitive accuracy in depth estimation and semantic segmentation. LingBot-Vision's advancements could shape the future of AI applications, making them more perceptive and capable of real-world navigation and interaction.