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

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

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 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 …

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 …

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 …

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

The emergence of the generalized neural radiance field models has greatly expanded the
applicability of novel view synthesis task. However, existing methods primarily rely on 2D …