论文标题
雷达可以导致深度估计模型有多少深度信息？
How Much Depth Information can Radar Contribute to a Depth Estimation Model?
论文作者
论文摘要
最近，一些作品提出了将雷达数据作为额外的感知信号融合到单眼深度估计模型中的额外信号，因为雷达数据在不同的光和天气条件下具有鲁棒性。尽管在先前的工作中报告了改善的性能，但仍然很难说出深度信息雷达有多少有助于深度估计模型。在本文中，我们提出了雷达推理和监督实验，以使用Nuscenes数据集上的最新深度估计模型来研究雷达数据的内在深度潜力。在推论实验中，该模型仅将雷达作为输入来预测深度，以证明使用雷达数据的推理能力。在监督实验中，在雷达监督下对单眼深度估计模型进行了训练，以显示雷达可以贡献的内在深度信息。我们的实验表明，仅使用稀疏雷达作为输入的模型可以在预测的深度中在一定程度上检测周围环境的形状。此外，与经过稀疏激光监督训练的基线模型相比，预处理雷达监督的单眼深度估计模型可以达到良好的性能。
Recently, several works have proposed fusing radar data as an additional perceptual signal into monocular depth estimation models because radar data is robust against varying light and weather conditions. Although improved performances were reported in prior works, it is still hard to tell how much depth information radar can contribute to a depth estimation model. In this paper, we propose radar inference and supervision experiments to investigate the intrinsic depth potential of radar data using state-of-the-art depth estimation models on the nuScenes dataset. In the inference experiment, the model predicts depth by taking only radar as input to demonstrate the inference capability using radar data. In the supervision experiment, a monocular depth estimation model is trained under radar supervision to show the intrinsic depth information that radar can contribute. Our experiments demonstrate that the model using only sparse radar as input can detect the shape of surroundings to a certain extent in the predicted depth. Furthermore, the monocular depth estimation model supervised by preprocessed radar achieves a good performance compared to the baseline model trained with sparse lidar supervision.
