Tag: neural radiance fields

AI Computer Vision & Graphics Machine Learning & Data Science Research

UC Berkeley’s Instruct-NeRF2NeRF Edits 3D Scenes With Text Instructions

In the new paper Instruct-NeRF2NeRF: Editing 3D Scenes With Instructions, a UC Berkeley research team presents Instruct-NeRF2NeRF, an approach for editing 3D NeRF scenes through natural language text instructions. The proposed method can edit large-scale, real-world 3D scenes with improved ease of use and realism.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Oxford U Presents RealFusion: 360° Reconstructions of Any Object from a Single Image

In the new paper RealFusion: 360° Reconstruction of Any Object from a Single Image, an Oxford University research team leverages a diffusion model to generate 360° reconstructions of objects from a single image. Their RealFusion approach achieves state-of-the-art performance on monocular 3D reconstruction benchmarks.

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.