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Di Rao

2 accepted papers

2025

Lightweight Self-Supervised Monocular Depth Estimation for All-Day Scenes Using Generative Adversarial Network

ICASSP 2025accepted

Self-supervised monocular depth estimation (MDE) has achieved performance levels comparable to supervised methods in well-lit environments. However, current methods struggle particularly with challenging nighttime scenes. Existing all-day self-supervised MDE methods often rely on specialized nightti…

Cited by 0SourceScholar
2023

Edge Devices Friendly Self-Supervised Monocular Depth Estimation via Knowledge Distillation

RA-L 2023

Self-supervised monocular depth estimation (MDE) has great potential for deployment in a wide range of applications, including virtual reality, autonomous driving, and robotics. Nevertheless, most previous studies focused on complex architectures to pursue better performance in MDE. In this letter,

Cited by 11SourceScholar