DepthDark: Robust Monocular Depth Estimation for Low-Light Environments

• URL: http://arxiv.org/abs/2507.18243v1
• Date: Thu, 24 Jul 2025 09:32:53 GMT
• Title: DepthDark: Robust Monocular Depth Estimation for Low-Light Environments
• Authors: Longjian Zeng, Zunjie Zhu, Rongfeng Lu, Ming Lu, Bolun Zheng, Chenggang Yan, Anke Xue,
• Abstract summary: We present DepthDark, a robust foundation model for low-light monocular depth estimation.<n>We first introduce a flare-simulation module and a noise-simulation module to accurately simulate the imaging process under nighttime conditions.<n>We then present an effective low-light PEFT strategy that utilizes illumination guidance and multiscale feature fusion to enhance the model's capability in low-light environments.
• Score: 28.45734920093837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, foundation models for monocular depth estimation have received increasing attention. Current methods mainly address typical daylight conditions, but their effectiveness notably decreases in low-light environments. There is a lack of robust foundational models for monocular depth estimation specifically designed for low-light scenarios. This largely stems from the absence of large-scale, high-quality paired depth datasets for low-light conditions and the effective parameter-efficient fine-tuning (PEFT) strategy. To address these challenges, we propose DepthDark, a robust foundation model for low-light monocular depth estimation. We first introduce a flare-simulation module and a noise-simulation module to accurately simulate the imaging process under nighttime conditions, producing high-quality paired depth datasets for low-light conditions. Additionally, we present an effective low-light PEFT strategy that utilizes illumination guidance and multiscale feature fusion to enhance the model's capability in low-light environments. Our method achieves state-of-the-art depth estimation performance on the challenging nuScenes-Night and RobotCar-Night datasets, validating its effectiveness using limited training data and computing resources.

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