Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its
state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to …

Recent neural rendering methods have demonstrated accurate view interpolation by
predicting volumetric density and color with a neural network. Although such volumetric …

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 …

Novel view synthesis using implicit neural functions such as Neural Radiance Field (NeRF)
has achieved significant progress recently. However, it is very computationally expensive to …

In this paper, we propose a method to segment and recover a static, clean background and
360$^{\circ} $ objects from multiple scene observations. Recent works have used neural …

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 …

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 …

Deep learning is providing a wealth of new approaches to the old problem of novel view
synthesis, from Neural Radiance Field (NeRF) based approaches to end-to-end style …

Abstract Neural Radiance Fields (NeRF) series are impressive in representing scenes and
synthesizing high-quality novel views. However, most previous works fail to preserve texture …

We present a large-scale synthetic dataset for novel view synthesis consisting of~ 300k
images rendered from nearly 2000 complex scenes using high-quality ray tracing at high …