# QianMo/PBR-White-Paper

⚡️基于物理的渲染（PBR）白皮书 |  White Paper of Physically Based Rendering(PBR)

Repository: https://github.com/QianMo/PBR-White-Paper
Canonical: https://ross.abutalabs.com/products/pbr-white-paper
License: MIT
License Family: permissive
Topics: pbr, ebook, physically-based-rendering, game-development, white-paper, shader, real-time-rendering
Last push: 2019-10-12T11:59:23+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2810, "days_push": 2517, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2018, forks 378 (observed 2026-08-28T04:06:06.159294+00:00)

## What it is
A Chinese-language white paper (ebook) summarizing the core knowledge system of Physically Based Rendering (PBR), covering BRDF/BSDF, normal distribution functions, and geometry functions. It includes knowledge architecture diagrams and a material F0 quick-reference chart.

## Use cases
- learn physically based rendering fundamentals
- understand Disney principled BRDF
- study normal distribution functions for shaders
- find reference values for material F0
- review PBR theory for real-time game rendering

## When to choose
- you want a structured, chapter-based summary of PBR theory
- you need visual knowledge maps of rendering concepts
- you are a graphics or game developer learning PBR

## When to avoid
- you need runnable code or a rendering engine
- you require up-to-date content with recent releases
- you need English-only documentation

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: graphics, documentation
- domain: graphics, tutorials
- platform: cross-platform
- tags: pbr, rendering, shader, ebook, white-paper, real-time-rendering, game-development

## Member repositories
- QianMo/PBR-White-Paper (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:06.159294+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:00:27.703782+00:00, confidence not recorded.
  - readme: https://github.com/QianMo/PBR-White-Paper (fetched 2026-08-28T04:06:06.159294+00:00, sha 601cddffc206)
- Data as of 2026-08-30T08:39:29.467469+00:00.