Corpus ID: 220936447

Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data

@article{Rodriguez2020ProjectTA,
  title={Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data},
  author={Adrian Lopez Rodriguez and Benjamin Busam and K. Mikolajczyk},
  journal={ArXiv},
  year={2020},
  volume={abs/2008.01034}
}
  • Adrian Lopez Rodriguez, Benjamin Busam, K. Mikolajczyk
  • Published 2020
  • Computer Science
  • ArXiv
  • Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or… CONTINUE READING
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