Depth estimation is a critical technology in autonomous driving, and
multi-camera systems are often used to achieve a 360{\deg} perception. These
360{\deg} camera sets often have limited or low-quality overlap regions, making
multi-view stereo methods infeasible for the entire image. Alternatively,
monocular methods may not produce consistent cross-view predictions. To address
these issues, we propose the Stereo Guided Depth Estimation (SGDE) method,
which enhances depth estimation of the full image by explicitly utilizing
multi-view stereo results on the overlap. We suggest building virtual pinhole
cameras to resolve the distortion problem of fisheye cameras and unify the
processing for the two types of 360{\deg} cameras. For handling the varying
noise on camera poses caused by unstable movement, the approach employs a
self-calibration method to obtain highly accurate relative poses of the
adjacent cameras with minor overlap. These enable the use of robust stereo
methods to obtain high-quality depth prior in the overlap region. This prior
serves not only as an additional input but also as pseudo-labels that enhance
the accuracy of depth estimation methods and improve cross-view prediction
consistency. The effectiveness of SGDE is evaluated on one fisheye camera
dataset, Synthetic Urban, and two pinhole camera datasets, DDAD and nuScenes.
Our experiments demonstrate that SGDE is effective for both supervised and
self-supervised depth estimation, and highlight the potential of our method for
advancing downstream autonomous driving technologies, such as 3D object
detection and occupancy prediction.

360 度摄像机的深度估计中，使用 Stereo Guided Depth Estimation (SGDE) 方法，通过利用重叠区域的多视图立体结果来提高整个图像的深度估计，解决了传统多视图立体方法和单目方法的局限性。