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Deep auxiliary learning for visual localization using colorization task
[article]
2021
arXiv
pre-print
Visual localization is one of the most important components for robotics and autonomous driving. Recently, inspiring results have been shown with CNN-based methods which provide a direct formulation to end-to-end regress 6-DoF absolute pose. Additional information like geometric or semantic constraints is generally introduced to improve performance. Especially, the latter can aggregate high-level semantic information into localization task, but it usually requires enormous manual annotations.
arXiv:2107.00222v1
fatcat:wv3m72tyorbxjngf6e36277diu