Home / jaccen / awesome-gaussian-skills · skills/3dgs-compression-deploy/SKILL.md · GitHub

3dgs-compression-deploy skillA

3dgs-compression-deploy is agent-read markdown (skill) from jaccen/awesome-gaussian-skills: 3DGS compression-to-deployment pipeline: quantization (scalar/VQ/mixed-precision), pruning (coreset/adaptive/variational/merge/Bayesian), progressive streaming & LoD, Web/WebGPU/mobile deployment, hardware acceleration (Tensor Core/GEMM/FPGA/ASIC), training-free semantic compression. Covers 53+ methods across 6 compression categories. Use when: compressing 3DGS models, deploying 3DGS to web/mobile/edge, selecting quantization bit-width, designing streaming pipelines, 3DGS压缩/量化/剪枝/部署/流式传输/移动端/硬件加.

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

# 3DGS Compression & Deployment

> End-to-end pipeline from raw 3DGS model to deployed application. Covers 6 compression categories + 4 deployment targets + hardware acceleration.

## Capabilities

- Analyze 3DGS model attributes (position, SH, opacity, scale, rotation) and recommend compression strategy
- Select quantization method and bit-width per attribute (scalar, VQ, mixed-precision)
- Design pruning pipeline (coreset, adaptive, variational, merge-based)
- Plan VQ codebook architecture and residual coding
- Architect progressive streaming and LoD systems (static and 4D dynamic)
- Guide platform-specific deployment (WebGL, WebGPU, iOS/Android, desktop)
- Evaluate hardware acceleration paths (Tensor Core, GEMM, FPGA, ASIC)
- Estimate compression ratio, quality loss, and rendering speed for each method combination

## Compression Pipeline

```
Raw 3DGS Model
    │
    ▼
[Step 1] Analysis ── attribute profiling, bottleneck identification
    │
    ▼
[Step 2] Strategy Selection ── target platform → compression recipe
    │
    ▼
[Step 3] Pruning ── reduce Gaussian count (coreset / adaptive / variational / merge)
    │
    ▼
…

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How to install

Latest version
mdr add jaccen/awesome-gaussian-skills/3dgs-compression-deploy@v1.1.1
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mdr add jaccen/awesome-gaussian-skills/3dgs-compression-deploy@sha256:7952bcf57af4004a

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Versions

versioncommittedcommitsizeaudit
v1.1.1 latest2026-09-22 de154fa 23,341 BA view · diff
v1.1.12026-08-07 896e6a8 22,913 BA view · diff
v1.1.12026-07-23 3749223 22,867 BA view · diff
v1.1.02026-07-14 a2f8ab8 22,612 BA view · diff
v1.0.02026-07-09 c71345c 22,251 BA view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
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  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (23341 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
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Source

GitHub

jaccen/awesome-gaussian-skills · 157 stars · license Apache-2.0 · pushed 2026-09-22 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_jme3vzyx3btpi255
GET https://markdownregistry.com/api/v1/resolve?ref=jaccen/awesome-gaussian-skills/3dgs-compression-deploy
GET https://markdownregistry.com/api/v1/blob/7952bcf57af4004a7f661af9c2c783c24882d314478cb6ecc584cffb80b533c9

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