  Surround-view depth estimation is a crucial task aims to acquire the depth
maps of the surrounding views. It has many applications in real world scenarios
such as autonomous driving, AR/VR and 3D reconstruction, etc. However, given
that most of the data in the autonomous driving dataset is collected in daytime
scenarios, this leads to poor depth model performance in the face of
out-of-distribution(OoD) data. While some works try to improve the robustness
of depth model under OoD data, these methods either require additional training
data or lake generalizability. In this report, we introduce the DINO-SD, a
novel surround-view depth estimation model. Our DINO-SD does not need
additional data and has strong robustness. Our DINO-SD get the best performance
in the track4 of ICRA 2024 RoboDepth Challenge.
