用红外图像提升热成像深度估计，靠自信度筛选知识迁移。
MonoTher-Depth: Enhancing Thermal Depth Estimation via Confidence-Aware Distillation
- 用RGB深度模型的预测置信度筛选知识，只传有效信息给热成像模型。
- 无标签数据情况下，热成像深度误差降低22.88%。
- 适合夜间、雾天等恶劣环境下机器人感知系统使用。
单目热成像深度估计对在雾、烟及低光照条件下运行的机器人系统至关重要。由于标注热成像数据稀缺，热成像单目深度估计模型的泛化能力远逊于受益于数百万张图像的主流RGB单目深度估计模型。为此，我们提出一种新颖的增强流程，通过从通用的RGB单目深度估计模型中进行知识蒸馏来提升热成像深度估计性能。该方法采用置信度感知的蒸馏机制，利用RGB模型的预测置信度选择性强化热成像模型，充分发挥其优势并规避其不足。实验表明，该方法在无需标注深度监督的情况下显著提升热成像深度估计精度，并大幅拓展其在新场景中的适用性。在无标签深度的新场景测试中，相比无蒸馏基线，绝对相对误差降低22.88%。
原文摘要 · Abstract (English)
Monocular depth estimation (MDE) from thermal images is a crucial technology for robotic systems operating in challenging conditions such as fog, smoke, and low light. The limited availability of labeled thermal data constrains the generalization capabilities of thermal MDE models compared to foundational RGB MDE models, which benefit from datasets of millions of images across diverse scenarios. To address this challenge, we introduce a novel pipeline that enhances thermal MDE through knowledge distillation from a versatile RGB MDE model. Our approach features a confidence-aware distillation method that utilizes the predicted confidence of the RGB MDE to selectively strengthen the thermal MDE model, capitalizing on the strengths of the RGB model while mitigating its weaknesses. Our method significantly improves the accuracy of the thermal MDE, independent of the availability of labeled depth supervision, and greatly expands its applicability to new scenarios. In our experiments on new scenarios without labeled depth, the proposed confidence-aware distillation method reduces the absolute relative error of thermal MDE by 22.88\% compared to the baseline without distillation.
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