Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed,
and modern methods are capable of rendering near-photorealistic scenes at interactive …

Real-time novel-view image synthesis on mobile devices is prohibitive due to the limited
computational power and storage. Using volumetric rendering methods, such as NeRF and …

Recent diffusion models have demonstrated impressive capabilities for text-based 2D image
editing. Applying similar ideas to edit a NeRF scene remains challenging as editing 2D …

In recent years, Neural Radiance Fields (NeRF) has made remarkable progress in the field
of computer vision and graphics, providing strong technical support for solving key tasks …

In this study we propose two novel input processing paradigms for novel view synthesis
(NVS) methods based on layered scene representations that significantly improve their …

Abstract Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes.
However rendering dynamic long-duration radiance fields on ubiquitous devices remains …

Volumetric video based on Neural Radiance Field (NeRF) holds vast potential for various
3D applications, but its substantial data volume poses significant challenges for …

Although 3D Gaussian Splatting (3DGS) has recently made progress in 3D human
reconstruction, it primarily relies on 2D pixel-level supervision, overlooking the geometric …

Reconstructing real-world 3D objects has numerous applications in computer vision, such
as virtual reality, video games, and animations. Ideally, 3D reconstruction methods should …