Monocular 3D object detection is an important challenging task in autonomous driving. Existing methods mainly focus on performing 3D detection in ideal weather conditions, characterized by scenarios with clear and optimal visibility. However, the challenge of autonomous driving requires the ability to handle changes in weather conditions, such as foggy weather, not just clear weather. We introduce MonoWAD, a novel weather-robust monocular 3D object detector with a weather-adaptive diffusion model. It contains two components: (1) the weather codebook to memorize the knowledge of the clear weather and generate a weather-reference feature for any input, and (2) the weather-adaptive diffusion model to enhance the feature representation of the input feature by incorporating a weather-reference feature. This serves an attention role in indicating how much improvement is needed for the input feature according to the weather conditions. To achieve this goal, we introduce a weather-adaptive enhancement loss to enhance the feature representation under both clear and foggy weather conditions. Extensive experiments under various weather conditions demonstrate that MonoWAD achieves weather-robust monocular 3D object detection. The code and dataset are released at https://github.com/VisualAIKHU/MonoWAD.

翻译：单目三维目标检测是自动驾驶领域一项重要且具有挑战性的任务。现有方法主要集中于在理想天气条件下执行三维检测，其特征是场景清晰且能见度最佳。然而，自动驾驶的挑战要求其具备处理天气条件变化（例如雾天，而不仅仅是晴天）的能力。我们提出了MonoWAD，一种新颖的、具有天气自适应扩散模型的天气鲁棒单目三维目标检测器。它包含两个组件：（1）天气码本，用于记忆晴天知识并为任何输入生成天气参考特征；（2）天气自适应扩散模型，通过融入天气参考特征来增强输入特征的特征表示。这起到一种注意力机制的作用，根据天气条件指示输入特征需要多大程度的改进。为实现这一目标，我们引入了一种天气自适应增强损失，以增强在晴天和雾天条件下的特征表示。在各种天气条件下的大量实验表明，MonoWAD实现了天气鲁棒的单目三维目标检测。代码和数据集发布于 https://github.com/VisualAIKHU/MonoWAD。