Awesome Implicit Neural Representations
A curated list of key papers and resources on implicit neural representations, a novel approach to parameterizing signals as continuous functions.
What is Awesome Implicit Neural Representations?
Awesome Implicit Neural Representations is a curated GitHub repository listing essential papers, tutorials, and resources on implicit neural representations (INRs). INRs are a novel way to parameterize signals—such as images, 3D geometry, and audio—as continuous functions mapped by neural networks, enabling resolution-independent and memory-efficient representations. The repository serves as an educational entry point for researchers and students exploring this intersection of neural networks, computer vision, and graphics.
Graduate students, researchers, and practitioners in computer vision, graphics, and machine learning who want to understand the fundamentals and state-of-the-art in implicit neural representations and neural rendering.
It provides a carefully selected, foundational set of resources curated by an active researcher, helping newcomers avoid information overload and quickly grasp key concepts and papers that have shaped the field.
Overview
A curated list of resources on implicit neural representations.
Use Cases
Best For
- Graduate students starting research in neural implicit representations
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