Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation

arXiv:2607.00736 · cs.CV · Submitted 2026-07-01 · Read on arXiv

cs.CV

Submitted: 2026-07-01

Updated: 2026-09-04

Comments: Accepted by ECCV2026. Code is available at https://github.com/startnew/flexdepth

Journal ref: Computer Vision - ECCV 2026, Lecture Notes in Computer Science, vol. 17003, Springer, Cham, 2026

DOI: 10.1007/978-3-032-36846-1_7

Code: https://github.com/startnew/flexdepth

License: http://creativecommons.org/licenses/by/4.0/

The gist: Self-Supervised Monocular Depth Estimation (MDE) has garnered attention in recent years due to its independence from ground truth.

Terminology

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 available: https://github.com/startnew/flexdepth

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