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We propose a universal image reconstruction method to represent detailed images purely from binary sparse edge and flat color domain.
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function.
Per-pixel ground-truth depth data is challenging to acquire at scale.
#7 best model for Monocular Depth Estimation on KITTI Eigen split
A combination of Deep CNNs and Skip connection layers is used as a feature extractor for image features on both local and global area.
The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.
We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing.
Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train.