  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.
