SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
This work addresses the generalization challenge for autonomous driving perception across different seasons, providing a new dataset and benchmark to boost research in this area, though it is incremental as it builds on existing depth prediction methods.
The authors tackled the problem of monocular depth prediction across varying outdoor environments like changing seasons and illumination, by introducing the SeasonDepth dataset and benchmark, and found that current state-of-the-art methods still struggle with long-term robustness, showing significant performance drops in cross-environment evaluations.
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.