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

Abstract Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in
image-based 3D reconstruction. However, their implicit volumetric representations differ …

Existing NeRF-based methods for large scene reconstruction often have limitations in visual
quality and rendering speed. While the recent 3D Gaussian Splatting works well on small …

Capturing and re-animating the 3D structure of articulated objects present significant
barriers. On one hand methods requiring extensively calibrated multi-view setups are …

We present SPAD a novel approach for creating consistent multi-view images from text
prompts or single images. To enable multi-view generation we repurpose a pretrained 2D …

In this paper, we propose the Masked Space-Time Hash encoding (MSTH), a novel method
for efficiently reconstructing dynamic 3D scenes from multi-view or monocular videos. Based …

Neural radiance fields achieve unprecedented quality for novel view synthesis, but their
volumetric formulation remains expensive, requiring a huge number of samples to render …

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 …

Super-resolution (SR) techniques have recently been proposed to upscale the outputs of
neural radiance fields (NeRF) and generate high-quality images with enhanced inference …