Publikation: Light Field Intrinsics With a Deep Encoder-Decoder Network
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We present a fully convolutional autoencoder for light fields, which jointly encodes stacks of horizontal and vertical epipolar plane images through a deep network of residual layers. The complex structure of the light field is thus reduced to a comparatively low-dimensional representation, which can be decoded in a variety of ways. The different pathways of upconvolution we currently support are for disparity estimation and separation of the lightfield into diffuse and specular intrinsic components. The key idea is that we can jointly perform unsupervised training for the autoencoder path of the network, and supervised training for the other decoders. This way, we find features which are both tailored to the respective tasks and generalize well to datasets for which only example light fields are available. We provide an extensive evaluation on synthetic light field data, and show that the network yields good results on previously unseen real world data captured by a Lytro Illum camera and various gantries.
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ALPEROVICH, Anna, Ole JOHANNSEN, Michael STRECKE, Bastian GOLDLÜCKE, 2018. Light Field Intrinsics With a Deep Encoder-Decoder Network. CVPR 2018 : IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah, 18. Juni 2018 - 22. Juni 2018. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition. The Computer Vision Foundation, 2018, pp. 9145-9154. Available under: doi: 10.1109/CVPR.2018.00953BibTex
@inproceedings{Alperovich2018Light-44382, year={2018}, doi={10.1109/CVPR.2018.00953}, title={Light Field Intrinsics With a Deep Encoder-Decoder Network}, url={http://openaccess.thecvf.com/content_cvpr_2018/html/Alperovich_Light_Field_Intrinsics_CVPR_2018_paper.html}, publisher={The Computer Vision Foundation}, booktitle={2018 IEEE Conference on Computer Vision and Pattern Recognition}, pages={9145--9154}, author={Alperovich, Anna and Johannsen, Ole and Strecke, Michael and Goldlücke, Bastian} }
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