Dense Light Field Reconstruction From Sparse Sampling Using Residual Network
This addresses the challenge of time-consuming dense light field capture for applications in computer vision and graphics, though it is incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of reconstructing dense light fields from sparse sampling by presenting a learning-based method that reconstructs 2 to 4 novel light fields between two independent inputs, achieving better structure similarity and occlusion relationships compared to alternatives.
A light field records numerous light rays from a real-world scene. However, capturing a dense light field by existing devices is a time-consuming process. Besides, reconstructing a large amount of light rays equivalent to multiple light fields using sparse sampling arises a severe challenge for existing methods. In this paper, we present a learning based method to reconstruct multiple novel light fields between two mutually independent light fields. We indicate that light rays distributed in different light fields have the same consistent constraints under a certain condition. The most significant constraint is a depth related correlation between angular and spatial dimensions. Our method avoids working out the error-sensitive constraint by employing a deep neural network. We solve residual values of pixels on epipolar plane image (EPI) to reconstruct novel light fields. Our method is able to reconstruct 2 to 4 novel light fields between two mutually independent input light fields. We also compare our results with those yielded by a number of alternatives elsewhere in the literature, which shows our reconstructed light fields have better structure similarity and occlusion relationship.