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

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

Terminology

• Accuracy — 26× in this paper · explained in 1 episode
• Ablation — 19× in this paper · explained in 1 episode
• Decoupling — 19× in this paper · explained in 2 episodes
• Generalization — 19× in this paper · explained in 13 episodes
• Self-Supervised Monocular Depth Estimation — 19× in this paper
• Foundation Model — 15× in this paper · explained in 3 episodes
• Robustness — 15× in this paper · explained in 5 episodes
• Foundation Models — 14× in this paper · explained in 7 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 10× in this paper · explained in 5 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Consistency — 9× in this paper · explained in 3 episodes
• Computational overhead — 8× in this paper · explained in 2 episodes
• Scale-Driven Decoder — 8× in this paper
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation study — 7× in this paper · explained in 1 episode
• Edge Devices — 6× in this paper · explained in 2 episodes
• Two-stage training — 6× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Adaptability — 4× in this paper · explained in 1 episode
• Adaptation — 4× in this paper · explained in 1 episode
• Ablation Studies — 3× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Self-Supervised Learning — 3× in this paper · explained in 7 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Mask Generation — 2× in this paper · explained in 2 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• Self-distillation — 2× in this paper · explained in 6 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Mechanism — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Cross Stage Partial — 1× in this paper
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• High-Efficiency Bottleneck — 1× in this paper
• High-Performance Bottleneck — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• PuriLight: A Lightweight Shuffle and Purification Framework for Monocular Depth Estimation
• DINOv2: Learning Robust Visual Features without Supervision

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