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|Title:||T2Net : synthetic-to-realistic translation for solving single-image depth estimation tasks||Authors:||Zheng, Chuanxia
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2018||Source:||Zheng, C., Cham, T.-J., & Cai, J. (2018). T2Net : synthetic-to-realistic translation for solving single-image depth estimation tasks. Computer Vision – ECCV 2018, 798-814. doi:10.1007/978-3-030-01234-2_47||Abstract:||Current methods for single-image depth estimation use training datasets with real image-depth pairs or stereo pairs, which are not easy to acquire. We propose a framework, trained on synthetic image-depth pairs and unpaired real images, that comprises an image translation network for enhancing realism of input images, followed by a depth prediction network. A key idea is having the first network act as a wide-spectrum input translator, taking in either synthetic or real images, and ideally producing minimally modified realistic images. This is done via a reconstruction loss when the training input is real, and GAN loss when synthetic, removing the need for heuristic self-regularization. The second network is trained on a task loss for synthetic image-depth pairs, with extra GAN loss to unify real and synthetic feature distributions. Importantly, the framework can be trained end-to-end, leading to good results, even surpassing early deep-learning methods that use real paired data.||URI:||https://hdl.handle.net/10356/138497||ISBN:||9783030012335||DOI:||10.1007/978-3-030-01234-2_47||Rights:||© 2018 Springer Nature Switzerland AG. All rights reserved. This paper was published in Computer Vision – ECCV 2018 and is made available with permission of Springer Nature Switzerland AG.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||IMI Conference Papers|
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