This paper presents a novel self-supervised two-frame multi-camera metric depth estimation network, termed M${^2}$Depth, which is designed to predict reliable scale-aware surrounding depth in autonomous driving. Unlike the previous works that use multi-view images from a single time-step or multiple time-step images from a single camera, M${^2}$Depth takes temporally adjacent two-frame images from multiple cameras as inputs and produces high-quality surrounding depth. We first construct cost volumes in spatial and temporal domains individually and propose a spatial-temporal fusion module that integrates the spatial-temporal information to yield a strong volume presentation. We additionally combine the neural prior from SAM features with internal features to reduce the ambiguity between foreground and background and strengthen the depth edges. Extensive experimental results on nuScenes and DDAD benchmarks show M${^2}$Depth achieves state-of-the-art performance. More results can be found in https://heiheishuang.xyz/M2Depth .

该研究论文提出了一种新颖的自监督双帧多摄像头度量深度估计网络(M^2Depth)，旨在预测自动驾驶中可靠的具有尺度感知的周围深度。与以往使用单个时间步的多视图图像或单个相机的多个时间步图像不同，M^2Depth采用多个相机的时间相邻的两帧图像作为输入，并生成高质量的周围深度。我们首先在空间和时间域中构造成本体积，并提出一个融合空间-时间信息的模块来生成强大的体积表示。我们还将SAM特征的神经先验与内部特征相结合，以减少前景和背景之间的模糊性并加强深度边缘。在nuScenes和DDAD基准测试上的大量实验证明，M^2Depth实现了最先进的性能。更多结果可以在此https URL中找到。

M${^2}$Depth: 自监督的双帧多摄像头度量深度估计