Abstract 3D-aware Generative Adversarial Networks (GANs) have shown remarkable
progress in learning to generate multi-view-consistent images and 3D geometries of scenes …

We propose a neural reflectance model (NeuBRDF) that offers highly versatile material
representation yet with light memory and neural computation consumption towards …

Abstract We present Lagrangian Hashing, a representation for neural fields combining the
characteristics of fast training NeRF methods that rely on Eulerian grids (ie InstantNGP), with …

Neural Radiance Fields (NeRF) have achieved huge success in effectively capturing and
representing 3D objects and scenes. However, to establish a ubiquitous presence in …

Deep implicit functions have been found to be an effective tool for efficiently encoding all
manner of natural signals. Their attractiveness stems from their ability to compactly represent …

The goal of this paper is to encode a 3D scene into an extremely compact representation
from 2D images and to enable its transmittance, decoding and rendering in real-time across …

Previous local guiding methods used 3D data structures to model spatial radiance variations
but struggled with additional dimensions in the path integral, such as temporal changes in …

Block compression is a widely used technique to compress textures in real-time graphics
applications, offering a reduction in storage size. However, their storage efficiency is …

Abstract Interest in leveraging Augmented Reality (AR) and Virtual Reality (VR) for
educational enhancement is growing rapidly, but widespread adoption faces significant …