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.

翻译：在分布外场景下（如恶劣天气条件、传感器故障和噪声污染）实现准确深度估计，对于安全关键型应用至关重要。然而，现有深度估计系统在现实世界的退化与扰动中不可避免会出现性能下降，难以在此类情况下提供可靠的深度预测。本文总结了RoboDepth挑战赛的优胜方案——该学术竞赛旨在推动和促进鲁棒分布外深度估计的发展。赛事基于新构建的KITTI-C和NYUDepth2-C基准数据集，分别设立独立的自监督鲁棒深度估计与全监督鲁棒深度估计两个赛道。在两百余名参赛者中，涌现出九种独特且性能领先的方案，其创新设计涵盖以下方面：空间域与频率域数据增强、掩码图像建模、图像复原与超分辨率、对抗训练、基于扩散模型的噪声抑制、视觉-语言预训练、学习型模型集成以及层级特征增强。我们通过大量实验分析与深入见解，揭示了每种设计背后的原理机制。期望本次挑战赛能为鲁棒可靠深度估计及其他相关领域的未来研究奠定坚实基础。数据集、竞赛工具包、研讨会记录及优胜队伍源代码均已公开于赛事官网。