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 …

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

Although many recent works have investigated generalizable NeRF-based novel view
synthesis for unseen scenes, they seldom consider the synthetic-to-real generalization …

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

We propose a Transformer-based NeRF (TransNeRF) to learn a generic neural radiance
field conditioned on observed-view images for the novel view synthesis task. By contrast …

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 …

Abstract Neural Radiance Field (NeRF) has been a mainstream in novel view synthesis with
its remarkable quality of rendered images and simple architecture. Although NeRF has been …

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 …

Abstract Neural Radiance Field (NeRF) has shown impressive performance in novel view
synthesis via implicit scene representation. However, it usually suffers from poor scalability …