{"ID":2843282,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08036","arxiv_id":"2511.08036","title":"WEDepth: Efficient Adaptation of World Knowledge for Monocular Depth Estimation","abstract":"Monocular depth estimation (MDE) has widely applicable but remains highly challenging due to the inherently ill-posed nature of reconstructing 3D scenes from single 2D images. Modern Vision Foundation Models (VFMs), pre-trained on large-scale diverse datasets, exhibit remarkable world understanding capabilities that benefit for various vision tasks. Recent studies have demonstrated significant improvements in MDE through fine-tuning these VFMs. Inspired by these developments, we propose WEDepth, a novel approach that adapts VFMs for MDE without modi-fying their structures and pretrained weights, while effec-tively eliciting and leveraging their inherent priors. Our method employs the VFM as a multi-level feature en-hancer, systematically injecting prior knowledge at differ-ent representation levels. Experiments on NYU-Depth v2 and KITTI datasets show that WEDepth establishes new state-of-the-art (SOTA) performance, achieving competi-tive results compared to both diffusion-based approaches (which require multiple forward passes) and methods pre-trained on relative depth. Furthermore, we demonstrate our method exhibits strong zero-shot transfer capability across diverse scenarios.","short_abstract":"Monocular depth estimation (MDE) has widely applicable but remains highly challenging due to the inherently ill-posed nature of reconstructing 3D scenes from single 2D images. Modern Vision Foundation Models (VFMs), pre-trained on large-scale diverse datasets, exhibit remarkable world understanding capabilities that be...","url_abs":"https://arxiv.org/abs/2511.08036","url_pdf":"https://arxiv.org/pdf/2511.08036v1","authors":"[\"Gongshu Wang\",\"Zhirui Wang\",\"Kan Yang\"]","published":"2025-11-11T09:41:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}