@inproceedings{f1b07186c08c4133b38cf26afdadfdea,
title = "Learning Lightprobes for Mixed Reality Illumination",
abstract = "This paper presents the first photometric registration pipeline for Mixed Reality based on high quality illumination estimation using convolutional neural networks (CNNs). For easy adaptation and deployment of the system, we train the CNNs using purely synthetic images and apply them to real image data. To keep the pipeline accurate and efficient, we propose to fuse the light estimation results from multiple CNN instances and show an approach for caching estimates over time. For optimal performance, we furthermore explore multiple strategies for the CNN training. Experimental results show that the proposed method yields highly accurate estimates for photo-realistic augmentations.",
keywords = "Artificial, augmented, Virtual Realities, Image Processing and Computer Vision, Photometric registration, 3D Reconstruction",
author = "David Mandl and Yi, {Kwang Moo} and Peter Mohr and Roth, {Peter M.} and Pascal Fua and Vincent Lepetit and Dieter Schmalstieg and Denis Kalkofen",
year = "2017",
month = nov,
day = "23",
doi = "10.1109/ISMAR.2017.25",
language = "English",
series = "Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2017",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "82--89",
editor = "Wolfgang Broll and Holger Regenbrecht and Swan, {J Edward}",
booktitle = "Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2017",
address = "United States",
note = "16th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2017 ; Conference date: 09-10-2017 Through 13-10-2017",
}