Lo Chen-Chou, Vandewalle Patrick
Processing Speech and Images (PSI), Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium.
Sensors (Basel). 2024 Mar 14;24(6):1864. doi: 10.3390/s24061864.
Radar data can provide additional depth information for monocular depth estimation. It provides a cost-effective solution and is robust in various weather conditions, particularly when compared with lidar. Given the sparse and limited vertical field of view of radar signals, existing methods employ either a vertical extension of radar points or the training of a preprocessing neural network to extend sparse radar points under lidar supervision. In this work, we present a novel radar expansion technique inspired by the joint bilateral filter, tailored for radar-guided monocular depth estimation. Our approach is motivated by the synergy of spatial and range kernels within the joint bilateral filter. Unlike traditional methods that assign a weighted average of nearby pixels to the current pixel, we expand sparse radar points by calculating a confidence score based on the values of spatial and range kernels. Additionally, we propose the use of a range-aware window size for radar expansion instead of a fixed window size in the image plane. Our proposed method effectively increases the number of radar points from an average of 39 points in a raw radar frame to an average of 100 K points. Notably, the expanded radar exhibits fewer intrinsic errors when compared with raw radar and previous methodologies. To validate our approach, we assess our proposed depth estimation model on the nuScenes dataset. Comparative evaluations with existing radar-guided depth estimation models demonstrate its state-of-the-art performance.
雷达数据可为单目深度估计提供额外的深度信息。它提供了一种经济高效的解决方案，并且在各种天气条件下都很稳健，尤其是与激光雷达相比时。鉴于雷达信号的垂直视野稀疏且有限，现有方法要么采用雷达点的垂直扩展，要么训练一个预处理神经网络，在激光雷达监督下扩展稀疏的雷达点。在这项工作中，我们提出了一种受联合双边滤波器启发的新型雷达扩展技术，专为雷达引导的单目深度估计量身定制。我们的方法受到联合双边滤波器中空间和距离内核协同作用的启发。与传统方法不同，传统方法将附近像素的加权平均值分配给当前像素，我们通过基于空间和距离内核的值计算置信度得分来扩展稀疏雷达点。此外，我们建议使用距离感知窗口大小进行雷达扩展，而不是图像平面中的固定窗口大小。我们提出的方法有效地将原始雷达帧中雷达点的平均数量从39个增加到平均100K个。值得注意的是，与原始雷达和先前方法相比，扩展后的雷达表现出更少的固有误差。为了验证我们的方法，我们在nuScenes数据集上评估了我们提出的深度估计模型。与现有雷达引导深度估计模型的比较评估证明了其领先的性能。
