2nd/10_Wiki/Topics/Domain_Programming/AI_and_ML/WebSplatter (3D Gaussian Splatting).md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-websplatter-3d-gaussian-splattin	WebSplatter (3D Gaussian Splatting)	10_Wiki/Topics	verified	self	WebSplatter 3DGS Web Viewer Gaussian Splatting Web	none	A	0.9	applied	3dgs gaussian-splatting webgl webgpu neural-rendering		2026-05-10	pending	language framework TypeScript 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)

// 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)

// 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)

// 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)

# 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)

# 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

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

// 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
• 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