mmWave radars offer excellent depth resolution owing to their high bandwidth at mmWave radio frequencies. Yet, they suffer intrinsically from poor angular resolution, that is an order-of-magnitude worse than camera systems, and are therefore not a capable 3-D imaging solution in isolation. We propose Metamoran, a system that combines the complimentary strengths of radar and camera systems to obtain depth images at high azimuthal resolutions at distances of several tens of meters with high accuracy, all from a single fixed vantage point. Metamoran enables rich long-range depth imaging outdoors with applications to roadside safety infrastructure, surveillance and wide-area mapping. Our key insight is to use the high azimuth resolution from cameras using computer vision techniques, including image segmentation and monocular depth estimation, to obtain object shapes and use these as priors for our novel specular beamforming algorithm. We also design this algorithm to work in cluttered environments with weak reflections and in partially occluded scenarios. We perform a detailed evaluation of Metamoran's depth imaging and sensing capabilities in 200 diverse scenes at a major U.S. city. Our evaluation shows that Metamoran estimates the depth of an object up to 60~m away with a median error of 28~cm, an improvement of 13$\times$ compared to a naive radar+camera baseline and 23$\times$ compared to monocular depth estimation.

翻译：闪电雷达因其在毫米Wave无线电频率上的高带宽度而提供极好的深度分辨率。然而,它们本身就因角分辨率不高而深受其害,即磁度比摄像系统更差,因此无法孤立地使用3D成像解决方案。我们提出Metamoran,这是一个将雷达和相机系统的互补优势结合起来的系统,以便在远方数万米的高正方形分辨率上以高精度获得深度图像,全部来自一个固定的偏移点。Metamoran使得大量长距离室室外图像能够应用公路安全基础设施、监视和广域绘图。我们的关键洞察力是利用相机的高方位分辨率,使用计算机视觉技术,包括图像分解和单心深度估计。我们提出Metamran的算法,将雷达和摄像仪的深度分别用高方形图来获取对象形状,并将这些形状作为我们新的光谱缩缩影算法。我们还设计了这种算法,在模糊的环境中工作,而且部分是隐蔽的情景。我们详细评估了Metmoran的深度和感测能力,在200美元不同的图像中位图像上,比取了摄氏28号的图像的深度,比中程的深度为一个比。我们城市的深度为23号。我们对一个直径的中程的中程的深度比。我们的评价显示了比一个直径一个比。