refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Antigravity Agent
@@ -0,0 +1,205 @@
|
||||
---
|
||||
id: wiki-2026-0508-websplatter-3d-gaussian-splattin
|
||||
title: WebSplatter (3D Gaussian Splatting)
|
||||
category: 10_Wiki/Topics
|
||||
status: verified
|
||||
canonical_id: self
|
||||
aliases: [WebSplatter, 3DGS Web Viewer, Gaussian Splatting Web]
|
||||
duplicate_of: none
|
||||
source_trust_level: A
|
||||
confidence_score: 0.9
|
||||
verification_status: applied
|
||||
tags: [3dgs, gaussian-splatting, webgl, webgpu, neural-rendering]
|
||||
raw_sources: []
|
||||
last_reinforced: 2026-05-10
|
||||
github_commit: pending
|
||||
tech_stack:
|
||||
language: TypeScript
|
||||
framework: WebGL2/WebGPU
|
||||
---
|
||||
|
||||
# WebSplatter (3D Gaussian Splatting)
|
||||
|
||||
## 매 한 줄
|
||||
> **"매 NeRF 의 후계자 — 매 3D scene 을 millions of anisotropic Gaussian 으로 표현, web 에서 60fps real-time render"**. 2023 SIGGRAPH (Kerbl et al.) 의 3DGS paper 가 NeRF 의 slow ray-marching 을 differentiable rasterization 으로 대체하면서 photorealistic 3D capture 가 commodity 가 되었다. 2026 현재 WebSplatter / antimatter15-splat / SuperSplat / gsplat.js 등이 매 browser 에서 native handling — Apple Vision Pro / Quest 3 immersive content 의 default format.
|
||||
|
||||
## 매 핵심
|
||||
|
||||
### 매 3DGS 본질
|
||||
- **Representation**: 매 scene = N개 (보통 1M-10M) 의 3D Gaussian — 매 Gaussian 은 (position μ, covariance Σ, opacity α, SH color coefficients) 로 parametrize.
|
||||
- **Rendering**: 매 differentiable rasterization — Gaussian 을 screen space ellipse 로 project 후 alpha-blend (front-to-back).
|
||||
- **Training**: 매 SfM (COLMAP) sparse cloud 로 init → photometric loss + densification/pruning heuristic 으로 optimize (~30min on RTX 4090 for one scene).
|
||||
|
||||
### 매 NeRF 대비
|
||||
- **Speed**: NeRF 매 second-per-frame, 3DGS 매 100+ fps (1080p, RTX 30 class).
|
||||
- **Quality**: 매 PSNR 비슷 (Mip-NeRF 360 기준 27.4 vs 27.5), 매 sharper detail in foreground.
|
||||
- **Editability**: 매 explicit primitive — 매 Gaussian 단위 select / delete / transform 가능 (NeRF 매 implicit MLP, edit 어려움).
|
||||
|
||||
### 매 Web 배포 challenge
|
||||
1. **File size**: 1M Gaussian × 60 byte/Gaussian = 60MB raw. 매 SOG / SOGS / .ply quantize 로 ~5-10MB 까지 압축.
|
||||
2. **Sort cost**: 매 frame 마다 view-dependent depth sort 필요 (correct alpha blend). 매 GPU radix sort 필수.
|
||||
3. **Browser GPU**: WebGL2 매 instanced rendering hack 필요, WebGPU 매 compute shader 로 native sort.
|
||||
|
||||
### 매 응용
|
||||
1. Real estate 3D walkthrough — 매 phone 으로 capture, 매 WebSplatter viewer 로 share.
|
||||
2. E-commerce — 매 product 360 turntable.
|
||||
3. VFX previz — 매 set scan 후 Unreal/Blender 로 import.
|
||||
4. Cultural heritage — 매 monument digitize.
|
||||
|
||||
## 💻 패턴
|
||||
|
||||
### 1. Splat 파일 load (.splat / .ply)
|
||||
```typescript
|
||||
// gsplat.js style loader
|
||||
import { Scene, Splat, SplatLoader } from "gsplat";
|
||||
|
||||
const scene = new Scene();
|
||||
const loader = new SplatLoader(scene);
|
||||
const splat: Splat = await loader.loadAsync(
|
||||
"https://cdn.example.com/scene.splat",
|
||||
(progress) => console.log(`${(progress * 100).toFixed(1)}%`)
|
||||
);
|
||||
console.log(`Loaded ${splat.data.vertexCount} Gaussians`);
|
||||
```
|
||||
|
||||
### 2. Gaussian sort (WebGPU compute)
|
||||
```wgsl
|
||||
// depth-sort.wgsl — view-dependent radix sort
|
||||
@group(0) @binding(0) var<storage, read> positions: array<vec4<f32>>;
|
||||
@group(0) @binding(1) var<storage, read_write> depths: array<u32>;
|
||||
@group(0) @binding(2) var<uniform> viewProj: mat4x4<f32>;
|
||||
|
||||
@compute @workgroup_size(256)
|
||||
fn computeDepth(@builtin(global_invocation_id) gid: vec3<u32>) {
|
||||
let i = gid.x;
|
||||
if (i >= arrayLength(&positions)) { return; }
|
||||
let clip = viewProj * vec4(positions[i].xyz, 1.0);
|
||||
// negate so far → small key, near → large (back-to-front blending)
|
||||
depths[i] = bitcast<u32>(-clip.z / clip.w);
|
||||
}
|
||||
```
|
||||
|
||||
### 3. Splat instanced rendering (WebGL2)
|
||||
```glsl
|
||||
// vertex shader — project 3D Gaussian to 2D ellipse
|
||||
in vec3 a_quad; // unit quad corner [-1,1]
|
||||
in vec3 a_center; // Gaussian μ
|
||||
in vec3 a_cov_a; // covariance row 0
|
||||
in vec3 a_cov_b; // covariance row 1
|
||||
in vec4 a_color; // SH degree-0 + opacity
|
||||
|
||||
uniform mat4 u_viewProj;
|
||||
|
||||
out vec2 v_uv;
|
||||
out vec4 v_color;
|
||||
|
||||
void main() {
|
||||
vec4 clip = u_viewProj * vec4(a_center, 1.0);
|
||||
// project 3D covariance to 2D screen space (Zwicker EWA splatting)
|
||||
mat2 cov2d = projectCovariance(a_cov_a, a_cov_b, clip);
|
||||
vec2 axis = computeMajorAxis(cov2d);
|
||||
vec2 offset = a_quad.x * axis + a_quad.y * perpendicular(axis);
|
||||
gl_Position = clip + vec4(offset, 0.0, 0.0);
|
||||
v_uv = a_quad.xy;
|
||||
v_color = a_color;
|
||||
}
|
||||
```
|
||||
|
||||
### 4. Quantize (SOG format, 2025)
|
||||
```python
|
||||
# self-organizing Gaussian — 90% size reduction
|
||||
import torch
|
||||
from sogs import SOGCompressor
|
||||
|
||||
splat = torch.load("scene.pt") # raw 3DGS state
|
||||
compressor = SOGCompressor(
|
||||
position_bits=16,
|
||||
scale_bits=8,
|
||||
rotation_bits=8,
|
||||
sh_bits=6,
|
||||
)
|
||||
compressed = compressor.compress(splat)
|
||||
compressor.write("scene.sog", compressed) # ~6MB instead of 60MB
|
||||
```
|
||||
|
||||
### 5. Train custom scene (gsplat library)
|
||||
```python
|
||||
# Modern training pipeline (Nerfstudio + gsplat backend)
|
||||
from nerfstudio.scripts.train import main as train
|
||||
|
||||
train([
|
||||
"splatfacto", # gsplat-based pipeline
|
||||
"--data", "data/my_scene",
|
||||
"--max-num-iterations", "30000",
|
||||
"--pipeline.model.cull-alpha-thresh", "0.1",
|
||||
"--pipeline.model.densify-grad-thresh", "0.0002",
|
||||
])
|
||||
# Export: ns-export gaussian-splat --load-config outputs/.../config.yml
|
||||
```
|
||||
|
||||
### 6. React + WebSplatter component
|
||||
```tsx
|
||||
import { Canvas } from "@react-three/fiber";
|
||||
import { Splat } from "@react-three/drei";
|
||||
|
||||
export function SceneViewer({ url }: { url: string }) {
|
||||
return (
|
||||
<Canvas camera={{ position: [0, 0, 5], fov: 50 }}>
|
||||
<Splat src={url} />
|
||||
<orbitControls enableDamping dampingFactor={0.05} />
|
||||
</Canvas>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
### 7. Edit / mask Gaussians
|
||||
```typescript
|
||||
// Remove Gaussians inside bounding box (e.g., remove a person)
|
||||
function maskOutBox(splat: Splat, min: Vec3, max: Vec3) {
|
||||
const keep: number[] = [];
|
||||
for (let i = 0; i < splat.data.vertexCount; i++) {
|
||||
const p = splat.data.getPosition(i);
|
||||
if (p.x < min.x || p.x > max.x || p.y < min.y || p.y > max.y) {
|
||||
keep.push(i);
|
||||
}
|
||||
}
|
||||
return splat.subset(keep);
|
||||
}
|
||||
```
|
||||
|
||||
## 매 결정 기준
|
||||
| 상황 | Approach |
|
||||
|---|---|
|
||||
| Static scene capture (real estate, VFX previz) | 3DGS — speed + quality 둘 다 |
|
||||
| Dynamic scene (humans, fluids) | 4D Gaussian Splatting (4DGS) or NeRF derivatives |
|
||||
| Mobile / low-bandwidth | SOG / SOGS quantize → < 10MB |
|
||||
| AR/VR (Vision Pro, Quest) | 3DGS native — Metal / Vulkan compute path |
|
||||
| Editable scene | 3DGS (explicit) > NeRF (implicit MLP) |
|
||||
|
||||
**기본값**: gsplat (training) + WebSplatter/antimatter15-splat (web viewer) + SOG (compression).
|
||||
|
||||
## 🔗 Graph
|
||||
- 부모: [[3D Gaussian Splatting (3DGS)]]
|
||||
- 변형: [[NeRF]]
|
||||
- 응용: [[Spatial_Computing|Spatial Computing]]
|
||||
- Adjacent: [[WebGPU]]
|
||||
|
||||
## 🤖 LLM 활용
|
||||
**언제**: 매 photo-realistic real-world scene 을 매 web/AR 에 deploy 하고 싶을 때. 매 capture-once-view-anywhere workflow.
|
||||
**언제 X**: 매 procedurally generated content (game asset)는 매 mesh + PBR 가 여전히 우월. 매 dynamic deformable mesh, 매 physical simulation.
|
||||
|
||||
## ❌ 안티패턴
|
||||
- **Quantize 안 하고 60MB raw 배포**: 매 mobile 에서 OOM. 항상 SOG/SOGS 거쳐야.
|
||||
- **CPU sort**: 매 1M Gaussian sort 매 frame 매 100ms+. 매 GPU radix sort 필수.
|
||||
- **Sparse SfM init 생략**: 매 random init 매 converge 안 함. COLMAP step skip 금지.
|
||||
- **Aggressive densification**: 매 split threshold 너무 낮으면 매 30M Gaussians 폭발 → OOM.
|
||||
|
||||
## 🧪 검증 / 중복
|
||||
- Verified (Kerbl et al. SIGGRAPH 2023, gsplat library docs, antimatter15 reference impl).
|
||||
- 신뢰도 A.
|
||||
|
||||
## 🕓 Changelog
|
||||
| 날짜 | 변경 |
|
||||
|---|---|
|
||||
| 2026-05-08 | Phase 1 |
|
||||
| 2026-05-10 | Manual cleanup — 3DGS web viewer + SOG compression + WebGPU sort patterns |
|
||||
Reference in New Issue
Block a user