Neural radiance fields enable state-of-the-art photorealistic view synthesis. However,
existing radiance field representations are either too compute-intensive for real-time …

Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results, but are slow
to render, requiring hundreds of network evaluations per pixel to approximate a volume …

We present a method that achieves state-of-the-art results for synthesizing novel views of
complex scenes by optimizing an underlying continuous volumetric scene function using a …

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 …

Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged
as a compelling technique for learning to represent 3D scenes from images with the goal of …

Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual
quality. However, to render photorealistic images, NeRFs require hundreds of deep …

Novel view synthesis has recently been revolutionized by learning neural radiance fields
directly from sparse observations. However, rendering images with this new paradigm is …

Learning radiance fields has shown remarkable results for novel view synthesis. The
learning procedure usually costs lots of time, which motivates the latest methods to speed up …

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 …

Neural radiance fields (NeRF) have achieved impressive performances in view synthesis by
encoding neural representations of a scene. However, NeRFs require hundreds of images …