Deepview: View synthesis with learned gradient descent
Proceedings of the IEEE/CVF Conference on Computer Vision and …, 2019•openaccess.thecvf.com
We present a novel approach to view synthesis using multiplane images (MPIs). Building on
recent advances in learned gradient descent, our algorithm generates an MPI from a set of
sparse camera viewpoints. The resulting method incorporates occlusion reasoning,
improving performance on challenging scene features such as object boundaries, lighting
reflections, thin structures, and scenes with high depth complexity. We show that our method
achieves high-quality, state-of-the-art results on two datasets: the Kalantari light field …
recent advances in learned gradient descent, our algorithm generates an MPI from a set of
sparse camera viewpoints. The resulting method incorporates occlusion reasoning,
improving performance on challenging scene features such as object boundaries, lighting
reflections, thin structures, and scenes with high depth complexity. We show that our method
achieves high-quality, state-of-the-art results on two datasets: the Kalantari light field …
Abstract
We present a novel approach to view synthesis using multiplane images (MPIs). Building on recent advances in learned gradient descent, our algorithm generates an MPI from a set of sparse camera viewpoints. The resulting method incorporates occlusion reasoning, improving performance on challenging scene features such as object boundaries, lighting reflections, thin structures, and scenes with high depth complexity. We show that our method achieves high-quality, state-of-the-art results on two datasets: the Kalantari light field dataset, and a new camera array dataset, Spaces, which we make publicly available.
openaccess.thecvf.com
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