针对夜间低光环境，提出高效深度估计模型DepthDark。
DepthDark: Robust Monocular Depth Estimation for Low-Light Environments
- 通过模拟眩光和噪声生成高质量低光配对数据集。
- 在nuScenes-Night与RobotCar-Night上达到顶尖性能。
- 仅用少量数据和算力即实现强鲁棒性，适合边缘部署。
近年来，单目深度估计的基础模型受到越来越多关注。现有方法主要针对典型日间条件设计，但在低光环境下性能显著下降。目前缺乏专为低光场景构建的鲁棒基础模型，主要原因在于缺少大规模、高质量的低光配对深度数据集，以及高效的参数高效微调（PEFT）策略。为此，我们提出DepthDark，一种面向低光环境的鲁棒单目深度估计基础模型。首先引入耀斑模拟模块和噪声模拟模块，精准模拟夜间成像过程，生成高质量低光配对数据集。此外，提出一种有效的低光PEFT策略，利用光照引导与多尺度特征融合，增强模型在低光环境下的表现。该方法在具有挑战性的nuScenes-Night和RobotCar-Night数据集上取得当前最优性能，且在有限训练数据与计算资源下仍具有效性。
原文摘要 · Abstract (English)
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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