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