#Neural Fields

Factor Fields offers a framework for neural field modeling that enhances approximation quality, compactness, and training speed. With Dictionary Fields, it generalizes well to new images and 3D scenes. This PyTorch-based project includes installation guides and usage instructions for scenarios like image regression. It supports multiple basis configurations for broad applicability. Explore training scripts and data resources for thorough implementation insights.

NeuRBF represents an innovative approach to neural fields using Adaptive Radial Basis Functions for high precision and compactness. Unveiled at ICCV 2023, it offers PyTorch implementation for image and SDF fitting, alongside neural radiance field tasks. Key features include adjustable model sizing, cross-environment compatibility, and utilization of datasets such as Synthetic NeRF and LLFF. Discover our setup guide for insights into this pioneering computational methodology.

This repository provides a comprehensive collection of research and resources on Implicit Representations and Neural Radiance Fields (NeRFs) in the context of Robotics and Reinforcement Learning (RL). It features a curated selection of key papers, relevant workshops, and progress updates on integrating NeRFs into areas such as object reconstruction, pose estimation, SLAM, and manipulation. Regular updates make it a central resource for researchers interested in exploring these technologies. Learn about significant features, recent developments, and practical applications illustrating the impact of NeRFs on robotic systems.