Although Neural Radiance Fields (NeRF) has achieved impressive 3D reconstruction with
dense view images, its performance degrades significantly when the training views are …

Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruction
capabilities with dense view images. However, its performance significantly deteriorates …

The recent progress in novel view synthesis is attributed to the Neural Radiance Field
(NeRF), which requires plenty of images with precise camera poses. However, collecting …

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 …

Although the Neural Radiance Fields (NeRF) has been shown to achieve high-quality novel
view synthesis, existing models still perform poorly in some scenarios, particularly …

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 …

The recently proposed neural radiance fields (NeRF) use a continuous function formulated
as a multi-layer perceptron (MLP) to model the appearance and geometry of a 3D scene …

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 …

The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired
researchers to propose neural implicit scene reconstruction. However, most existing neural …

Neural Radiance Fields (NeRF) have led to breakthroughs in the novel view synthesis
problem. Positional Encoding (PE) is a critical factor that brings the impressive performance …