This directory contains interactive lesson documents designed to be loaded by an LLM coding agent (Claude Code, Cursor, or similar) to teach a user the fVDB API interactively.
The easiest way to use these lessons is with Claude Code (CLI or IDE extension) from the root of this repository. Claude Code can read the lesson files and the fvdb source code, so it can verify API details and help you debug exercises in real time.
Example prompt to get started:
Read docs/TEACHME and teach me how to use fvdb-core.
You can also give the LLM context about your background so it can tailor the lesson:
Read docs/TEACHME and teach me how to use fvdb-core. I have basic knowledge
of computer graphics and introductory experience with deep learning / PyTorch.
Keep fvdb_core_cheatsheet.md open in your editor while working through exercises — it's a quick API reference for the concepts covered in the lesson.
Each lesson is a self-contained markdown file that serves as both a curriculum and instructor prompt. The LLM acts as an interactive instructor, teaching concepts module by module, quizzing the student, and adapting to their responses.
Each lesson includes:
- Teacher instructions (persona, pacing, scope)
- Module-by-module curriculum with embedded concepts and code examples
- Quiz questions and answer key
- Exercises with progressive difficulty
- A capstone project
| Lesson | Cheat Sheet | Covers |
|---|---|---|
| fvdb_core_lesson.md | fvdb_core_cheatsheet.md | GridBatch, JaggedTensor, sampling/splatting, sparse convolution, grid hierarchy, U-Net backbone |
- Gaussian Splatting (
fvdb_reality_capture.GaussianSplat3d) — provided by fVDB Reality Capture - Visualization (
fvdb.viz) — not yet included - Volume rendering and ray marching — not yet included