We present HRF-Net, a novel view synthesis method based on holistic radiance fields that
renders novel views using a set of sparse inputs. Recent generalizing view synthesis …

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 …

Abstract Neural Radiance Fields (NeRF) have emerged as a powerful representation for the
task of novel view synthesis due to their simplicity and state-of-the-art performance. Though …

We present a method that achieves state-of-the-art results for synthesizing novel views of
complex scenes by optimizing an underlying continuous volumetric scene function using a …

In this paper, we present an efficient and robust deep learning solution for novel view
synthesis of complex scenes. In our approach, a 3D scene is represented as a light field, ie …

Neural radiance fields enable state-of-the-art photorealistic view synthesis. However,
existing radiance field representations are either too compute-intensive for real-time …

We present PVSeRF, a learning framework that reconstructs neural radiance fields from
single-view RGB images, for novel view synthesis. Previous solutions, such as pixelNeRF …

We study the problem of novel view synthesis from sparse source observations of a scene
comprised of 3D objects. We propose a simple yet effective approach that is neither …

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 …

Although neural radiance fields (NeRF) have shown impressive advances in novel view
synthesis, most methods require multiple input images of the same scene with accurate …