Light field (LF) cameras record both intensity and directions of light rays, and encode 3D
scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have …

We present a practical and robust deep learning solution for capturing and rendering novel
views of complex real world scenes for virtual exploration. Previous approaches either …

Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results, but are slow
to render, requiring hundreds of network evaluations per pixel to approximate a volume …

Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce
beneficial angular information for image super-resolution (SR). However, it is challenging to …

Light field (LF) images acquired by hand-held devices usually suffer from low spatial
resolution as the limited sampling resources have to be shared with the angular dimension …

We consider the problem of high-dimensional light field reconstruction and develop a
learning-based framework for spatial and angular super-resolution. Many current …

Compared to 2D imaging data, the 4D light field (LF) data retains richer scene's structure
information, which can significantly improve the computer's perception capability, including …

The acquisition of light field images with high angular resolution is costly. Although many
methods have been proposed to improve the angular resolution of a sparsely-sampled light …

We present a novel neural representation for light field content that enables compact storage
and easy local reconstruction with high fidelity. We use a fully-connected neural network to …

Spatial-angular separable convolution (SAS-conv) has been widely used for efficient and
effective 4D light field (LF) feature embedding in different tasks, which mimics a 4D …