Tag: neural radiance fields

AI Machine Learning & Data Science Research

Alibaba’s VQRF Realizes a 100x Compression Rate, Reducing Volumetric Radiance Files to 1 MB

In the new paper Compressing Volumetric Radiance Fields to 1 MB, an Alibaba Group research team proposes vector quantized radiance fields (VQRF), a simple yet efficient framework for compressing volumetric radiance fields that achieves up to 100x storage reduction, reducing original grid model size to around 1 MB with negligible loss on rendering quality.

AI Computer Vision & Graphics Machine Learning & Data Science Research

IITM & UT Austin’s Generalizable NeRF Transformer Demonstrates Transformers’ Capabilities for Graphical Rendering

In the new paper Is Attention All NeRF Needs?, a research team from the Indian Institute of Technology Madras and the University of Texas at Austin proposes Generalizable NeRF Transformer (GNT), a pure and universal transformer-based architecture for efficient on-the-fly reconstruction of NeRFs. The work demonstrates that a pure attention mechanism suffices for learning a physically-grounded rendering process.