SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
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by
Hanjiang Hu, Baoquan Yang, Zhijian Qiao, Ding Zhao, Hesheng Wang
2021
Abstract
Different environments pose a great challenge on the outdoor robust visual
perception for long-term autonomous driving and the generalization of
learning-based algorithms on different environmental effects is still an open
problem. Although monocular depth prediction has been well studied recently,
there is few work focusing on the robust 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 (available on https://seasondepth.github.io) is built based on CMU
Visual Localization dataset. To benchmark the depth estimation performance
under different environments, we investigate representative and recent
state-of-the-art open-source supervised, self-supervised and domain adaptation
depth prediction methods from KITTI benchmark using several newly-formulated
metrics. Through extensive experimental evaluation on the proposed dataset, the
influence of multiple environments on performance and robustness is analyzed
both qualitatively and quantitatively, showing that the long-term monocular
depth prediction is still challenging even with fine-tuning. We further give
promising avenues that self-supervised training and stereo geometry constraint
help to enhance the robustness to changing environments.
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