Novel view synthesis is a classical research problem in computer vision and has been well
studied in various ways. While recently proposed NeRF model achieves photo-realistic …

Neural radiance fields (NeRF) have demonstrated a promising research direction for novel
view synthesis. However, the existing approaches either require per‐scene optimization that …

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

By modelling complex scenes via a continuous volumetric scene function, neural radiance
fields (NeRF) achieves state-of-the-art results in novel view synthesis. However, since NeRF …

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

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 …

Compared with the traditional light field rendering, the neural radiance field can use the
neural network to fit the ray samples of the scene, and implicitly encode the light field of the …

In this paper, we present a new framework for view synthesis of novel view based on Neural
Radiance Fields (NeRF). We aim to address two main limitations of NeRF. Firstly, we …

We present CG-NeRF, a cascade and generalizable neural radiance fields method for view
synthesis. Recent generalizing view synthesis methods can render high-quality novel views …

In this paper, we introduce a novel approach to improve the performance of Neural
Radiance Fields (NeRF) from limited input views. NeRF has exhibited impressive …