/3dgs-compression-deploy

name: 3dgs-compression-deploy
description: '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/硬件加速.'
license: Apache-2.0
metadata:
  version: 1.1.1
  author: jaccen
  tags:
  - 3dgs
  - gaussian-splatting
  - compression
  - quantization
  - pruning
  - vector-quantization
  - streaming
  - deployment
  - mobile
  - webgpu
  - tensor-core
  - hardware-acceleration
  - bayesian
  - semantic-compression
  when_to_use:
  - Compress a 3DGS model for storage or transmission
  - Deploy 3DGS to web browser, mobile device, or edge hardware
  - Select quantization scheme, bit-width, or pruning strategy
  - Design progressive streaming or Level-of-Detail pipeline
  - Evaluate hardware acceleration options (Tensor Core, GEMM, FPGA, ASIC)
  - 3DGS压缩 / 量化 / 剪枝 / 部署 / 流式传输 / 移动端 / 硬件加速
  platform-fields:
    user-invocable: true

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)
    │
    ▼
[Step 4] Quantization ── reduce per-attribute bit-width (scalar / VQ / mixed-precision)
    │
    ▼
[Step 5] Vector Quantization ── codebook-based attribute compression (optional, replaces/augments Step 4)
    │
    ▼
[Step 6] Streaming & LoD ── progressive loading structure for network delivery
    │
    ▼
[Step 7] Deployment ── platform-specific renderer and runtime
    │
    ▼
Deployed Application (Web / Mobile / Desktop / Edge)

Step 1: Analysis

Profile the 3DGS model before selecting compression methods:

| Attribute | FP32 Size | Typical Range | Sensitivity to Quantization | |-----------|-----------|---------------|---------------------------| | Position (μ) | 12B/Gaussian | Scene bounds | High — direct geometry impact | | SH (degree 0–3) | 48B/Gaussian | [-1, 1] per coeff | Medium-High — visual quality driver | | Opacity (α) | 4B/Gaussian | [0, 1] | Medium — pruning signal | | Scale (s) | 12B/Gaussian | [1e-5, 1e2] | Medium — anisotropy sensitive | | Rotation (q) | 16B/Gaussian | Unit quaternion | Low-Medium — can tolerate 8-bit |

**Profiling checklist:** 1. Total Gaussian count N and file size S 2. Target platform constraints (memory budget, bandwidth, GPU capability) 3. Quality floor (minimum acceptable PSNR/SSIM) 4. Required FPS threshold 5. Whether dynamic (4DGS) or static scene

Step 2: Compression Strategy Selection

Decision tree by target platform:

Target Platform?
├── Web (WebGL/WebGPU)
│   ├── Bandwidth-limited → Pruning + VQ + Streaming (CAGS/HGS pipeline)
│   └── Compute-limited → Aggressive pruning + low SH degree + Flux-GS
├── Mobile (iOS/Android)
│   ├── Real-time required → Mobile-GS pipeline (depth-aware OIT + distillation + pruning)
│   └── Quality priority → MesonGS++ (mixed-precision, budget-controlled) + NanoGS merge
├── Desktop (GPU ≥ RTX 3060)
│   ├── Max quality → Light quantization only (8-10 bit, ContextGS entropy coding)
│   └── Large scene → Pruning + Streaming + HiGS hierarchical tiles
└── Edge / Embedded
    ├── FPGA targeted → SpqGS (hardware-friendly quantization) + Axis-Shared Accelerator
    └── Low-power GPU → VEDAL pruning + 4-6 bit quantization + PocketGS on-device

**Combined target table:**

| Target | Typical Gaussian Budget | Bit-width Range | Streaming | Key Methods | |--------|------------------------|-----------------|-----------|-------------| | Web | 100K–500K | 4–8 bit | Required | CAGS, StreamLoD-GS, Spark 2.0 | | Mobile | 50K–200K | 4–8 bit | Optional | Mobile-GS, Flux-GS, PocketGS | | Desktop | 500K–5M | 8–16 bit | For large scenes | MesonGS++, HiGS, gsplat | | Edge/FPGA | 10K–100K | 2–6 bit | Required | SpqGS, VEDAL, GEMM-GS |

Step 3: Quantization

Method Selection

| Method | Type | Venue | Bit-width | Key Feature | |--------|------|-------|-----------|-------------| | MesonGS++ | Mixed-precision (post-training) | arXiv 2026 | 4–16 bit per attribute | 0-1 ILP hyperparameter search, 34x compression | | GETA-3DGS | Joint pruning + quantization | arXiv 2026 | 4–8 bit heterogeneous | Render-aware saliency, QADG dependency graph | | GSQ | Learned step size | CVPR 2025 | 4–8 bit | Group-wise quantization with learnable step | | ContextGS | Context-model entropy coding | NeurIPS 2024 | 8–16 bit | Anchor-level context replaces uniform quant | | SpqGS | Scalable parallel | CVPR 2025 | Hardware-friendly | Parallel bit allocation for FPGA/ASIC | | SOG-GS | Channel-grouped | CVPR 2025 | Per-channel | Preserves inter-Gaussian correlations | | ZipGS | Pruning + quant + entropy | CVPR 2025 | Variable | Volumetric entropy coding |