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Depth estimation on synthesized stereo image-pairs using a generative adversarial network

Boer, S.L. (2021) Depth estimation on synthesized stereo image-pairs using a generative adversarial network.

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Abstract:This research presents a novel method for depth estimation on synthesized stereo image-pairs. The goal of this research is to explore the possibilities of generative adversarial networks and improve the quality of existing depth estimation networks. This is done by synthesizing a stereo image-pair from a single-view image and using this stereo image-pair image, the depth is estimated. For both actions, i.e. the synthesis and the depth estimation, a generative adversarial network is trained. The method is mainly based on building a cycle consistency generative adversarial network and finding the most optimal network architecture and training methods, for the synthesis network and for the depth estimation network. In the conducted experiments; the influence of the identity loss function is measured, as well as various network architectural changes in both the generator- and discriminator model and the discriminator's ability to learn is restricted. We extracted the four most promising model configurations and trained a full-scale models. The dataset that was used to train our models, contained ground-truth depth maps that have been estimated by other depth estimation networks. Those have been evaluated using the FID score, RMSE metric and visual inspection. The main findings were that the stereo image-pair synthesis network performed better than expected, because it was able to quite successfully transform the single-view image's perspective. An improvement to this network would be to improve the quality of the synthesized image. The depth estimation network was able achieve fairly okay results. The per-pixel quality of the depth estimation can be improve quite a lot. Nonetheless, interesting to see was that our model outperformed the ground-truth depth maps that were estimated by state-of-the-art depth estimation networks: where the ground truth depth map was wrong, our depth prediction was more correct.
Item Type:Essay (Master)
Clients:
Info Support B.B., Veenendaal, Nederland
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Interaction Technology MSc (60030)
Link to this item:http://purl.utwente.nl/essays/88650
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