Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a
scene as a continuous volumetric function, parameterized by multilayer perceptrons that …

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 …

Abstract Neural Radiance Fields (NeRF) has achieved unprecedented view synthesis
quality using coordinate-based neural scene representations. However, NeRF's view …

We propose a method for converting a single RGB-D input image into a 3D photo, ie, a multi-
layer representation for novel view synthesis that contains hallucinated color and depth …

There has recently been great interest in neural rendering methods. Some approaches use
3D geometry reconstructed with Multi‐View Stereo (MVS) but cannot recover from the errors …

We present a novel algorithm for view synthesis that utilizes a soft 3D reconstruction to
improve quality, continuity and robustness. Our main contribution is the formulation of a soft …

View-dependent effects such as reflections pose a substantial challenge for image-based
and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they …

High-quality large-scale scene rendering requires a scalable representation and accurate
camera poses. This research combines tile-based hybrid neural fields with parallel …

The reflection superposition phenomenon is complex and widely distributed in the real
world, which derives various simplified linear and nonlinear formulations of the problem. In …

This paper proposes a deep neural network structure that exploits edge information in
addressing representative low-level vision tasks such as layer separation and image …