Image-based view synthesis techniques are widely applied to both computer graphics and
computer vision. One of the key issues is how to use the information from the input image to …

The problem of novel view synthesis, a significant area of research in computer vision and
graphics, involves the generation of an image from a novel viewpoint, utilizing a set of 2D …

Abstract Neural Radiance Fields (NeRF) can achieve state-of-the-art new view results when
given a sufficient number of training views. However, NeRF's rendering is based on …

Neural radiance fields (NeRF) synthesize realistic novel views by estimating point attributes
(density and color), followed by the volume rendering method. However, accurately …

Neural Radiance Field (NeRF) is a framework that represents a 3D scene in the weights of a
fully connected neural network, known as the Multi-Layer Perception (MLP). The method …

The recently proposed neural radiance fields (NeRF) use a continuous function formulated
as a multi-layer perceptron (MLP) to model the appearance and geometry of a 3D scene …

View synthesis methods using implicit continuous shape representations learned from a set
of images, such as the Neural Radiance Field (NeRF) method, have gained increasing …

Novel view synthesis aims at rendering any posed images from sparse observations of the
scene. Recently, neural radiance fields (NeRF) have demonstrated their effectiveness in …

Neural Radiance Fields (NeRF) have significantly impacted the novel view synthesis task.
While it can render realistic images from unseen viewpoints, the boundaries of the object or …

Traditional neural radiance fields for rendering novel views require intensive input images
and pre-scene optimization, which limits their practical applications. We propose a …