@InProceedings{8553464,
  author = {A. Alperovich and O. Johannsen and B. Goldluecke},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Intrinsic Light Field Decomposition and Disparity Estimation with Deep Encoder-Decoder Network},
  year = {2018},
  pages = {2165-2169},
  abstract = {We present an encoder-decoder deep neural network that solves non-Lambertian intrinsic light field decomposition, where we recover all three intrinsic components: albedo, shading, and specularity. We learn a sparse set of features from 3D epipolar volumes and use them in separate decoder pathways to reconstruct intrinsic light fields. While being trained on synthetic data generated with Blender, our model still generalizes to real world examples captured with a Lytro Illum plenoptic camera. The proposed method outperforms state-of-the-art approaches for single images and achieves competitive accuracy with recent modeling methods for light fields.},
  keywords = {cameras;computer vision;image reconstruction;image representation;image sensors;learning (artificial intelligence);neural nets;deep encoder-decoder network;encoder-decoder deep neural network;solves nonLambertian intrinsic light field decomposition;intrinsic components;3D epipolar volumes;separate decoder pathways;intrinsic light fields;disparity estimation;Decoding;Estimation;Three-dimensional displays;Convolution;Two dimensional displays;Tensile stress;Cameras},
  doi = {10.23919/EUSIPCO.2018.8553464},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438941.pdf},
}

{"_id":"BktmGzMNMgbiwZekD","bibbaseid":"alperovich-johannsen-goldluecke-intrinsiclightfielddecompositionanddisparityestimationwithdeepencoderdecodernetwork-2018","authorIDs":[],"author_short":["Alperovich, A.","Johannsen, O.","Goldluecke, B."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["A."],"propositions":[],"lastnames":["Alperovich"],"suffixes":[]},{"firstnames":["O."],"propositions":[],"lastnames":["Johannsen"],"suffixes":[]},{"firstnames":["B."],"propositions":[],"lastnames":["Goldluecke"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Intrinsic Light Field Decomposition and Disparity Estimation with Deep Encoder-Decoder Network","year":"2018","pages":"2165-2169","abstract":"We present an encoder-decoder deep neural network that solves non-Lambertian intrinsic light field decomposition, where we recover all three intrinsic components: albedo, shading, and specularity. We learn a sparse set of features from 3D epipolar volumes and use them in separate decoder pathways to reconstruct intrinsic light fields. While being trained on synthetic data generated with Blender, our model still generalizes to real world examples captured with a Lytro Illum plenoptic camera. The proposed method outperforms state-of-the-art approaches for single images and achieves competitive accuracy with recent modeling methods for light fields.","keywords":"cameras;computer vision;image reconstruction;image representation;image sensors;learning (artificial intelligence);neural nets;deep encoder-decoder network;encoder-decoder deep neural network;solves nonLambertian intrinsic light field decomposition;intrinsic components;3D epipolar volumes;separate decoder pathways;intrinsic light fields;disparity estimation;Decoding;Estimation;Three-dimensional displays;Convolution;Two dimensional displays;Tensile stress;Cameras","doi":"10.23919/EUSIPCO.2018.8553464","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438941.pdf","bibtex":"@InProceedings{8553464,\n author = {A. Alperovich and O. Johannsen and B. Goldluecke},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Intrinsic Light Field Decomposition and Disparity Estimation with Deep Encoder-Decoder Network},\n year = {2018},\n pages = {2165-2169},\n abstract = {We present an encoder-decoder deep neural network that solves non-Lambertian intrinsic light field decomposition, where we recover all three intrinsic components: albedo, shading, and specularity. We learn a sparse set of features from 3D epipolar volumes and use them in separate decoder pathways to reconstruct intrinsic light fields. While being trained on synthetic data generated with Blender, our model still generalizes to real world examples captured with a Lytro Illum plenoptic camera. The proposed method outperforms state-of-the-art approaches for single images and achieves competitive accuracy with recent modeling methods for light fields.},\n keywords = {cameras;computer vision;image reconstruction;image representation;image sensors;learning (artificial intelligence);neural nets;deep encoder-decoder network;encoder-decoder deep neural network;solves nonLambertian intrinsic light field decomposition;intrinsic components;3D epipolar volumes;separate decoder pathways;intrinsic light fields;disparity estimation;Decoding;Estimation;Three-dimensional displays;Convolution;Two dimensional displays;Tensile stress;Cameras},\n doi = {10.23919/EUSIPCO.2018.8553464},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438941.pdf},\n}\n\n","author_short":["Alperovich, A.","Johannsen, O.","Goldluecke, B."],"key":"8553464","id":"8553464","bibbaseid":"alperovich-johannsen-goldluecke-intrinsiclightfielddecompositionanddisparityestimationwithdeepencoderdecodernetwork-2018","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438941.pdf"},"keyword":["cameras;computer vision;image reconstruction;image representation;image sensors;learning (artificial intelligence);neural nets;deep encoder-decoder network;encoder-decoder deep neural network;solves nonLambertian intrinsic light field decomposition;intrinsic components;3D epipolar volumes;separate decoder pathways;intrinsic light fields;disparity estimation;Decoding;Estimation;Three-dimensional displays;Convolution;Two dimensional displays;Tensile stress;Cameras"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2018url.bib","creationDate":"2021-02-13T15:38:40.451Z","downloads":0,"keywords":["cameras;computer vision;image reconstruction;image representation;image sensors;learning (artificial intelligence);neural nets;deep encoder-decoder network;encoder-decoder deep neural network;solves nonlambertian intrinsic light field decomposition;intrinsic components;3d epipolar volumes;separate decoder pathways;intrinsic light fields;disparity estimation;decoding;estimation;three-dimensional displays;convolution;two dimensional displays;tensile stress;cameras"],"search_terms":["intrinsic","light","field","decomposition","disparity","estimation","deep","encoder","decoder","network","alperovich","johannsen","goldluecke"],"title":"Intrinsic Light Field Decomposition and Disparity Estimation with Deep Encoder-Decoder Network","year":2018,"dataSources":["yiZioZximP7hphDpY","iuBeKSmaES2fHcEE9"]}