Adversarial Learning for Joint Optimization of Depth and Ego-Motion

IEEE Trans. Image Processing, pp. 4130-4142, 2020.

Cited by: 0|Bibtex|Views79|DOI:https://doi.org/10.1109/TIP.2020.2968751
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com|dblp.uni-trier.de

Abstract:

In recent years, supervised deep learning methods have shown a great promise in dense depth estimation. However, massive high-quality training data are expensive and impractical to acquire. Alternatively, self-supervised learning-based depth estimators can learn the latent transformation from monocular or binocular video sequences by mini...More

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