DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge

TL;DR

DINO-SD is a robust surround-view depth estimation model that excels in out-of-distribution scenarios without requiring extra data, achieving top performance in the ICRA 2024 RoboDepth Challenge.

Contribution

The paper introduces DINO-SD, a novel depth estimation model that is data-efficient and highly robust, specifically designed for autonomous driving scenarios.

Findings

Achieved best performance in ICRA 2024 RoboDepth Challenge track 4.

Does not require additional training data for robustness.

Outperforms existing methods in out-of-distribution scenarios.

Abstract

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.