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

2026-07-01 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

FlexDepth, a family of self-supervised Monocular Depth Estimation (MDE) models, addresses limitations of existing single-scale MDE solutions in complex driving environments. Designed for robust driving perception, FlexDepth employs a two-stage static-dynamic decoupled training strategy to independently assess confidence for static backgrounds and dynamic objects. It also introduces a meticulously designed Scale-Driven Decoder (SDD) for dynamic component selection, enabling efficient feature fusion and high-precision depth maps. Experiments show FlexDepth achieves state-of-the-art performance across arbitrary scales with minimal computational overhead, exemplified by its smallest model, Flex-Nano, requiring only 0.7 GFLOPs and reaching 37.6 FPS on mobile platforms, ensuring reliable real-time perception and excellent zero-shot generalization.

Key takeaway

For Machine Learning Engineers deploying monocular depth estimation on resource-constrained automotive edge devices, you should evaluate FlexDepth. Its two-stage static-dynamic decoupled training and Scale-Driven Decoder offer state-of-the-art performance across scales with minimal overhead, like Flex-Nano's 0.7 GFLOPs and 37.6 FPS. This approach ensures robust, real-time perception and strong zero-shot generalization, critical for challenging driving scenarios.

Key insights

FlexDepth improves monocular depth estimation for driving by decoupling static/dynamic training and using a scale-driven decoder.

Principles

• Self-supervised MDE can achieve SOTA without auxiliary data.
• Dynamic component selection enhances efficiency and precision.

Method

FlexDepth uses a two-stage static-dynamic decoupled training strategy and a Scale-Driven Decoder (SDD) to dynamically select components based on scale size for feature fusion.

In practice

• Implement decoupled training for static and dynamic scene elements.
• Design decoders that adapt components based on input scale.

Topics

• Monocular Depth Estimation
• Self-Supervised Learning
• Driving Perception
• Automotive Edge Devices
• FlexDepth
• Scale-Driven Decoder

Code references

• startnew/flexdepth

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.