  Different environments pose a great challenge to the outdoor robust visual
perception for long-term autonomous driving, and the generalization of
learning-based algorithms on different environments is still an open problem.
Although monocular depth prediction has been well studied recently, few works
focus on the robustness of learning-based depth prediction across different
environments, e.g. changing illumination and seasons, owing to the lack of such
a multi-environment real-world dataset and benchmark. To this end, the first
cross-season monocular depth prediction dataset and benchmark, SeasonDepth, is
introduced to benchmark the depth estimation performance under different
environments. We investigate several state-of-the-art representative
open-source supervised and self-supervised depth prediction methods using
newly-formulated metrics. Through extensive experimental evaluation on the
proposed dataset and cross-dataset evaluation with current autonomous driving
datasets, the performance and robustness against the influence of multiple
environments are analyzed qualitatively and quantitatively. We show that
long-term monocular depth prediction is still challenging and believe our work
can boost further research on the long-term robustness and generalization for
outdoor visual perception. The dataset is available on
https://seasondepth.github.io, and the benchmark toolkit is available on
https://github.com/ SeasonDepth/SeasonDepth.
