Radar and camera fusion yields robustness in perception tasks by leveraging
the strength of both sensors. The typical extracted radar point cloud is 2D
without height information due to insufficient antennas along the elevation
axis, which challenges the network performance. This work introduces a
learning-based approach to infer the height of radar points associated with 3D
objects. A novel robust regression loss is introduced to address the sparse
target challenge. In addition, a multi-task training strategy is employed,
emphasizing important features. The average radar absolute height error
decreases from 1.69 to 0.25 meters compared to the state-of-the-art height
extension method. The estimated target height values are used to preprocess and
enrich radar data for downstream perception tasks. Integrating this refined
radar information further enhances the performance of existing radar camera
fusion models for object detection and depth estimation tasks.

雷达和相机融合在感知任务中具有鲁棒性，通过充分利用两种传感器的优势。本文介绍了一种基于学习的方法来推断与三维物体相关的雷达点的高度，通过引入新的鲁棒回归损失来解决稀疏目标的挑战，并采用多任务训练策略强调重要特征。与最先进的高度扩展方法相比，雷达的平均绝对高度误差从 1.69 米降低到 0.25 米。估计的目标高度值用于预处理和丰富雷达数据，以用于后续的感知任务。集成这些精细的雷达信息进一步提升了现有雷达相机融合模型在物体检测和深度估计任务中的性能。