#Radiance Fields

This collection offers insights into the advancements in Radiance Fields, particularly NeRF and 3D Gaussian Splatting, serving as a comprehensive guide for mastering 3D editing techniques. Access key papers, relevant surveys, and the latest research developments, providing a thorough understanding of radiance field-based 3D editing. Connect with the research community through continuous updates and collaborative openings, simplifying your journey in this evolving discipline.

Discover MuRF's advancements in multi-baseline radiance fields, delivering top performance in various evaluations. Utilizing PyTorch and CUDA, it provides comprehensive scripts for installation, training, evaluation, and rendering. Models are available on Hugging Face with detailed datasets to thoroughly explore MuRF's capabilities. Suitable for researchers and developers looking to improve radiance field representations. Consult the documentation for easy integration and enhance computer vision projects with MuRF.

Discover a cutting-edge radiance field reconstruction method that significantly enhances speed and quality. It supports half-precision training, reducing convergence time by over 100% and improving results by approximately 1.5dB. LaRa efficiently handles multi-view, text, and single-view inputs using datasets like GObjaverse. The updated scripts facilitate seamless dataset downloads and preprocessing, while intuitive training and evaluation guides ensure ease of use. This solution is perfect for applications requiring comprehensive baseline processing.

TRIPS presents a method for real-time radiance field rendering that combines Gaussian Splatting and ADOP methodologies. This efficient technique uses trilinear rasterization and neural networks to render detailed, complete images at 60 FPS on typical hardware. It handles complex scenes and allows for automatic optimization in rendering.