{"ID":5551764,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T10:10:07.702510095Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00736","arxiv_id":"2607.00736","title":"Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation","abstract":"Self-Supervised Monocular Depth Estimation (MDE) has garnered attention in recent years due to its independence from ground truth. However, most existing models are limited to a single scale and exhibit considerable performance degradation in complex driving environments. Networks specifically designed to handle dynamic traffic participants tend to be overly complex, hindering their deployment on resource-constrained automotive edge devices. To address these limitations and move towards robust driving perception, we propose FlexDepth, a scale-driven and flexible family of self-supervised MDE models tailored for challenging road scenarios. FlexDepth employs a two-stage static-dynamic decoupled training strategy, enabling the independent assessment of confidence for both static backgrounds and dynamic road objects. Furthermore, it introduces a meticulously designed Scale-Driven Decoder (SDD) to dynamically select components based on scale size, facilitating efficient feature fusion and the output of high-precision depth maps. Extensive experiments on standard driving benchmarks demonstrate that without any auxiliary information, our model achieves state-of-the-art performance across arbitrary scales with minimal computational overhead. Our smallest model, Flex-Nano, requires only 0.7 GFLOPs and achieves 37.6 FPS on mobile platforms, ensuring reliable real-time perception while maintaining excellent zero-shot generalization.Our source code is avalible: https://github.com/startnew/flexdepth","short_abstract":"Self-Supervised Monocular Depth Estimation (MDE) has garnered attention in recent years due to its independence from ground truth. However, most existing models are limited to a single scale and exhibit considerable performance degradation in complex driving environments. Networks specifically designed to handle dynami...","url_abs":"https://arxiv.org/abs/2607.00736","url_pdf":"https://arxiv.org/pdf/2607.00736v1","authors":"[\"Zhaowen Zhu\",\"Li Zhang\",\"Yujie Chen\",\"Tian Zhang\",\"Yingjie Wang\",\"Mingxia Zhan\"]","published":"2026-07-01T10:18:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":613841,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-02T01:54:51.863792489Z","DeletedAt":null,"paper_id":5551764,"paper_url":"https://arxiv.org/abs/2607.00736","paper_title":"Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation","repo_url":"https://github.com/startnew/flexdepth","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}