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.

DINO-SD 是一种新颖的周围环视深度估计模型，可以用于自动驾驶中的深度图获取，具有强鲁棒性，在 2024 年 ICRA RoboDepth 挑战赛的第四项赛道上取得了最佳表现。

DINO-SD 解决方案：2024 年 ICRA RoboDepth 挑战赛冠军

DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as
adverse weather conditions, sensor failure, and noise contamination, is
desirable for safety-critical applications. Existing depth estimation systems,
however, suffer inevitably from real-world corruptions and perturbations and
are struggled to provide reliable depth predictions under such cases. In this
paper, we summarize the winning solutions from the RoboDepth Challenge -- an
academic competition designed to facilitate and advance robust OoD depth
estimation. This challenge was developed based on the newly established KITTI-C
and NYUDepth2-C benchmarks. We hosted two stand-alone tracks, with an emphasis
on robust self-supervised and robust fully-supervised depth estimation,
respectively. Out of more than two hundred participants, nine unique and
top-performing solutions have appeared, with novel designs ranging from the
following aspects: spatial- and frequency-domain augmentations, masked image
modeling, image restoration and super-resolution, adversarial training,
diffusion-based noise suppression, vision-language pre-training, learned model
ensembling, and hierarchical feature enhancement. Extensive experimental
analyses along with insightful observations are drawn to better understand the
rationale behind each design. We hope this challenge could lay a solid
foundation for future research on robust and reliable depth estimation and
beyond. The datasets, competition toolkit, workshop recordings, and source code
from the winning teams are publicly available on the challenge website.

这篇论文总结了 RoboDepth 挑战中的获胜解决方案，重点关注鲁棒的自监督和全监督深度估计，涉及了深度估计、挑战赛、深度预测、超分辨率和降噪等方面的创新设计。