RIDERS：用于鲁棒感知的雷达-红外深度估计
RIDERS: Radar-Infrared Depth Estimation for Robust Sensing
AI总结：
提出RIDERS方法，融合毫米波雷达与红外热像仪，通过三阶段处理实现恶劣天气下鲁棒的密集深度估计，在烟雾场景中表现出前所未有的鲁棒性。
摘要：
密集深度恢复在自动驾驶中至关重要，是避障、三维目标检测和局部路径规划的基础要素。雾霾、灰尘、雨、雪和黑暗等恶劣天气条件给精确的密集深度估计带来了重大挑战，从而在自动驾驶中构成了显著的安全风险。这些挑战对于依赖短电磁波传感器（如可见光谱相机和近红外激光雷达）的传统深度估计方法尤为突出，因为此类传感器在这些环境中易受衍射噪声和遮挡的影响。为从根本上解决这一问题，我们提出了一种新颖的方法，通过融合毫米波雷达和单目红外热像仪来实现鲁棒的度量深度估计，这两种传感器能够穿透大气颗粒物且不受光照条件影响。我们提出的雷达-红外融合方法通过三个阶段实现高精度且细节丰富的密集深度估计，包括具有全局尺度对齐的单目深度预测、通过学习雷达-像素对应关系进行准密集雷达增强，以及使用尺度图学习器对密集深度进行局部尺度细化。我们的方法通过解决直接融合多模态长波特征时出现的模糊性和错位问题，实现了卓越的视觉质量和准确的度量估计。我们在NTU4DRadLM数据集以及我们自行收集的具有挑战性的ZJU-Multispectrum数据集上评估了我们的方法性能。尤其值得关注的是，我们提出的方法在烟雾场景中展现出了前所未有的鲁棒性。我们的代码将在\url{https://github.com/MMOCKING/RIDERS}发布。
英文摘要：
Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning. Adverse weather conditions, including haze, dust, rain, snow, and darkness, introduce significant challenges to accurate dense depth estimation, thereby posing substantial safety risks in autonomous driving. These challenges are particularly pronounced for traditional depth estimation methods that rely on short electromagnetic wave sensors, such as visible spectrum cameras and near-infrared LiDAR, due to their susceptibility to diffraction noise and occlusion in such environments. To fundamentally overcome this issue, we present a novel approach for robust metric depth estimation by fusing a millimeter-wave Radar and a monocular infrared thermal camera, which are capable of penetrating atmospheric particles and unaffected by lighting conditions. Our proposed Radar-Infrared fusion method achieves highly accurate and finely detailed dense depth estimation through three stages, including monocular depth prediction with global scale alignment, quasi-dense Radar augmentation by learning Radar-pixels correspondences, and local scale refinement of dense depth using a scale map learner. Our method achieves exceptional visual quality and accurate metric estimation by addressing the challenges of ambiguity and misalignment that arise from directly fusing multi-modal long-wave features. We evaluate the performance of our approach on the NTU4DRadLM dataset and our self-collected challenging ZJU-Multispectrum dataset. Especially noteworthy is the unprecedented robustness demonstrated by our proposed method in smoky scenarios. Our code will be released at \url{https://github.com/MMOCKING/RIDERS}.
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