236 research outputs found
Spatially regularized T1 estimation from variable flip angles MRI
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134899/1/mp2747.pd
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
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 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 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 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. The dataset is available on
https://seasondepth.github.io, and benchmark toolkit is available on
https://github.com/SeasonDepth/SeasonDepth.Comment: 19 pages, 13 figure
- …