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.

翻译：环视深度估计是一项旨在获取周围视角深度图的关键任务。它在自动驾驶、AR/VR和三维重建等现实场景中具有广泛应用。然而，鉴于自动驾驶数据集中大部分数据采集于白天场景，这导致深度模型在面对分布外数据时性能不佳。尽管已有一些工作尝试提升深度模型在分布外数据下的鲁棒性，但这些方法要么需要额外的训练数据，要么缺乏泛化能力。在本报告中，我们介绍了DINO-SD，一种新颖的环视深度估计模型。我们的DINO-SD无需额外数据且具备强鲁棒性。我们的DINO-SD在ICRA 2024 RoboDepth挑战赛的赛道4中取得了最佳性能。