# yangjiheng/nerf_and_beyond_docs

Repository: https://github.com/yangjiheng/nerf_and_beyond_docs
Canonical: https://ross.abutalabs.com/products/nerf_and_beyond_docs
License Family: other
Last push: 2024-12-26T10:00:42+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 86
- inputs: {"age_days": 1214, "days_push": 615, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1166, forks 50 (observed 2026-08-28T04:03:50.249287+00:00)

## What it is
A curated, actively maintained collection of papers, notes, and documentation on NeRF and 3D Gaussian Splatting research, maintained by a Chinese-language community. It serves as a reading list and reference handbook companion to a published NeRF/3DGS book.

## Use cases
- find papers on neural radiance fields
- learn 3d gaussian splatting from scratch
- catch up on latest nerf research
- find reading notes on 3d reconstruction papers
- get a reference list for a nerf book
- track new 3dgs papers daily

## When to choose
- you want a curated, regularly updated paper list for NeRF/3DGS
- you are a beginner looking for guided reading material on 3D reconstruction
- you want community discussion notes linked to papers

## When to avoid
- you need runnable code or a software library
- you want a formal textbook rather than a paper index
- you need English-only resources since much of the community is Chinese-language

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: computer-vision, graphics, tutorials
- platform: cross-platform
- tags: nerf, 3dgs, 3d-reconstruction, paper-collection, awesome-list

## Member repositories
- yangjiheng/nerf_and_beyond_docs (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:50.249287+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 2026-08-30T06:29:19.444139+00:00, confidence not recorded.
  - readme: https://github.com/yangjiheng/nerf_and_beyond_docs (fetched 2026-08-28T04:03:50.249287+00:00, sha 8d9086e96970)
- Data as of 2026-08-30T08:39:29.467469+00:00.