Using Stereo Depth Estimation Network and LiDAR-Assisted Camera for Dehazing

Shih-Li Lu, S. Miaou, Shyang-En Weng, Ying-Cheng Lin
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Abstract

Dehazing research is crucial to ensuring the safety of autonomous driving. To estimate the scattering coefficient of the scene, we use the point cloud produced by LiDAR. To acquire a more precise scene depth, we employ a stereo depth network. Finally, we dehaze the image using the transmission map of the atmospheric scattering model and the atmospheric light value. Experimental results show that the proposed dehazing method works better in object detection than previous dehazing methods.
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利用立体深度估计网络和激光雷达辅助相机进行去雾
除雾研究对于确保自动驾驶的安全性至关重要。为了估计场景的散射系数,我们使用激光雷达产生的点云。为了获得更精确的场景深度,我们采用了立体深度网络。最后,利用大气散射模型的透射图和大气光值对图像进行去雾处理。实验结果表明,所提出的除雾方法比以往的除雾方法具有更好的目标检测效果。
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