Light Field Intrinsics with a Deep Encoder-Decoder Network
| Autor: | Ole Johannsen, Anna Alperovich, Michael Strecke, Bastian Goldluecke |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Epipolar geometry 020206 networking & telecommunications 02 engineering and technology Intrinsics Autoencoder Path (graph theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Specular reflection Artificial intelligence ddc:004 business Light field Decoding methods |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2018.00953 |
| Popis: | 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. published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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