“DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality” by LeGendre, Ma, Fyffe, Flynn, Charbonnel, et al. …
Conference:
Type:
Entry Number: 46
Title:
- DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality
Presenter(s)/Author(s):
Abstract:
We present a learning-based method to infer plausible high dynamic range (HDR), omnidirectional illumination given an unconstrained, low dynamic range (LDR) image from a mobile phone camera with a limited field of view (FOV). For training data, we collect videos of various reflective spheres placed within the camera’s FOV, leaving most of the background unoccluded, leveraging that materials with diverse reflectance functions reveal different lighting cues in a single exposure. We train a deep neural network to regress from the LDR background image to HDR lighting by matching the LDR ground truth sphere images to those rendered with the predicted illumination using image-based relighting, which is differentiable. Our inference runs at interactive frame rates on a mobile device, enabling realistic rendering of virtual objects into real scenes for mobile mixed reality. Training on auto-exposed and white-balanced videos, we improve the realism of rendered objects compared to the state-of-the art methods for both indoor and outdoor scenes.
References:
Paul Debevec. 1998. Rendering synthetic objects into real scenes: Bridging traditional and image-based graphics with global illumination and high dynamic range pho tography. In Proceedings of the 25th annual conference on Computer graphics and interactive techniques. ACM, 189–198.
Paul Debevec, Tim Hawkins, Chris Tchou, Haarm-Pieter Duiker, Westley Sarokin, and Mark Sagar. 2000. Acquiring the reflectance field of a human face. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., 145–156.
Marc-André Gardner, Kalyan Sunkavalli, Ersin Yumer, Xiaohui Shen, Emiliano Gam baretto, Christian Gagné, and Jean-François Lalonde. 2017. Learning to Predict Indoor Illumination from a Single Image. ACM Trans. Graph. 36, 6, Article 176 (Nov. 2017), 14 pages. https://doi.org/10.1145/3130800.3130891
Yannick Hold-Geoffroy, Kalyan Sunkavalli, Sunil Hadap, Emiliano Gambaretto, and Jean-François Lalonde. 2017. Deep outdoor illumination estimation. In IEEE Inter national Conference on Computer Vision and Pattern Recognition, Vol. 2.
