M${^2}$Depth: Self-supervised Two-Frame Multi-camera Metric Depth Estimation
This work addresses depth estimation for autonomous driving, offering a novel approach that combines spatial and temporal information from multiple cameras, though it appears incremental by building on existing multi-camera and temporal methods.
The paper tackles the problem of predicting scale-aware surrounding depth for autonomous driving by introducing M²Depth, a self-supervised two-frame multi-camera network that achieves state-of-the-art performance on nuScenes and DDAD benchmarks.
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 .