In the area of neural rendering-based novel view synthesis, illumination is important since
shadows cast by objects under various light sources provide indications about their …

We present MMNeRF, a simple yet powerful learning framework for highly photo-realistic
novel view synthesis by learning Multi-modal and Multi-view features to guide neural …

Novel view synthesis is a long-standing problem that revolves around rendering frames of
scenes from novel camera viewpoints. Volumetric approaches provide a solution for …

With dense inputs, Neural Radiance Fields (NeRF) is able to render photo-realistic novel
views under static conditions. Although the synthesis quality is excellent, existing NeRF …

Neural radiance fields have made a remarkable breakthrough in the novel view synthesis
task at the 3D static scene. However, for the 4D circumstance (eg, dynamic scene), the …

In this paper, we explore the use of low-light image enhancement as a preprocessing step to
improve the quality of novel view synthesis by Neural Light Fields (NeLF). NeLF is a 3D …

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

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 …

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 …

Abstract Neural Radiance Field (NeRF) has broken new ground in the novel view synthesis
due to its simple concept and state-of-the-art quality. However, it suffers from severe …