The neural radiance field (NeRF) has emerged as a prominent methodology for synthesizing
realistic images of novel views. While neural radiance representations based on voxels or …

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 …

With the emergence of neural radiance fields (NeRFs), view synthesis quality has reached
an unprecedented level. Compared to traditional mesh-based assets, this volumetric …

Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and
synthesizing novel views, but the computational overhead of NeRF at the inference stage is …

Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to
render. One reason is that they make use of volume rendering thus requiring many samples …

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 …

Neural radiance fields (NeRFs) have shown impressive results for novel view synthesis.
However, they depend on the repetitive use of a single-input single-output multilayer …

We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on
neural radiance fields. Our approach consists of two main stages: a geometry reasoner and …

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

Neural radiance fields have revolutionized the field of novel view synthesis, achieving
remarkable results. However, traditional approaches based on implicit representations …