VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale Recovery

We propose a robust method for monocular depth scale recovery. Monocular depth estimation can be divided into two main directions: (1) relative depth estimation, which provides normalized or inverse depth without scale information, and (2) metric depth estimation, which involves recovering depth with absolute scale. To obtain absolute scale information for practical downstream tasks, utilizing textual information to recover the scale of a relative depth map is a highly promising approach. However, since a single image can have multiple descriptions from different perspectives or with varying styles, it has been shown that different textual descriptions can significantly affect the scale recovery process. To address this issue, our method, VGLD, stabilizes the influence of textual information by incorporating high-level semantic information from the corresponding image alongside the textual description. This approach resolves textual ambiguities and robustly outputs a set of linear transformation parameters (scalars) that can be globally applied to the relative depth map, ultimately generating depth predictions with metric-scale accuracy. We validate our method across several popular relative depth models(MiDas, DepthAnything), using both indoor scenes (NYUv2) and outdoor scenes (KITTI). Our results demonstrate that VGLD functions as a universal alignment module when trained on multiple datasets, achieving strong performance even in zero-shot scenarios. Code is available at: https://github.com/pakinwu/VGLD.

Visually-Guided Linguistic Disambiguation for Monocular Depth Scale Recovery

We propose a robust method for monocular depth scale recovery. Monocular depth estimation can be divided into two main directions: (1) relative depth estimation, which provides normalized or inverse depth without scale information, and (2) metric depth estimation, which involves recovering depth with absolute scale. To obtain absolute scale information for practical downstream tasks, utilizing textual information to recover the scale of a relative depth map is a highly promising approach. However, since a single image can have multiple descriptions from different perspectives or with varying styles, it has been shown that different textual descriptions can significantly affect the scale recovery process. To address this issue, our method, VGLD, stabilizes the influence of textual information by incorporating high-level semantic information from the corresponding image alongside the textual description. This approach resolves textual ambiguities and robustly outputs a set of linear transformation parameters (scalars) that can be globally applied to the relative depth map, ultimately generating depth predictions with metric-scale accuracy. We validate our method across several popular relative depth models(MiDas, DepthAnything), using both indoor scenes (NYUv2) and outdoor scenes (KITTI). Our results demonstrate that VGLD functions as a universal alignment module when trained on multiple datasets, achieving strong performance even in zero-shot scenarios. Code is available at: https://github.com/pakinwu/VGLD.