A curated collection of resources, papers, tutorials, and tools for learning and working with Signed Distance Fields (SDF) in computer graphics.
Signed Distance Field (SDF) is a curated GitHub repository that aggregates resources related to Signed Distance Fields, a mathematical representation used in computer graphics for defining shapes and surfaces. It compiles tutorials, research papers, code examples, and tool references to help developers understand and implement SDF-based techniques like ray marching, distance functions, and volumetric rendering.
Graphics programmers, shader developers, researchers, and hobbyists interested in real-time rendering, procedural content generation, or advanced computer graphics algorithms.
It saves significant research time by providing a single, organized source for high-quality SDF materials, from foundational tutorials to cutting-edge papers and practical tools, all maintained by the community.
Collection of resources (papers, links, discussions, shadertoys,...) related to Signed Distance Field
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Aggregates tutorials, papers, and code examples from across the web, serving as a one-stop shop that saves significant research time for developers.
Includes direct links to tools like MagicaCSG and Clayxels, providing actionable access to software that implements SDF techniques in real-world scenarios.
Encourages pull requests to grow the collection, ensuring it stays updated with new developments and maintains relevance through community contributions.
Covers core to advanced topics like sphere tracing and interval arithmetic, with links to papers and talks that offer deep dives into specialized areas.
The repository is a dense list of links without prioritization or difficulty ratings, making it hard for beginners to know where to start or which resources are most effective.
It functions as a bibliography rather than an interactive learning platform, requiring users to actively seek out and apply resources without built-in guidance or exercises.
Since it relies on community submissions, the quality and relevance of linked materials can be inconsistent, with some links potentially outdated or broken over time.