Splat Lab
Train and compare four radiance-field primitives in the browser, side by side, with Babylon.js and WebGPU:
| Tab | Technique | Paper | How it's drawn here |
|---|
| 3DGS | 3D Gaussian splatting | Kerbl et al., SIGGRAPH 2023 (project) | Babylon's built-in GaussianSplattingMesh, sorted on a worker. The baseline. |
| Points | Gaussian point splatting | Rijsdijk et al., ACM TOG 2026 (doi) | Babylon 9's built-in WebGPU compute implementation (pointSplattingRenderMode). WebGL2 fallback included. |
| Triangle | Triangle splatting / Triangle Splatting+ | Held et al. 2025 (arXiv, +) | Custom shaders (WGSL + GLSL), screen-space window identical to the trainer's. Opaque results draw as a plain mesh. |
| Convex | 3D convex splatting | Held et al., CVPR 2025 (arXiv) | Custom shaders; 2D hull built in the vertex shader. |
Live demo: https://<your-user>.github.io/splat-lab/ (after enabling GitHub Pages, see below). Needs a WebGPU browser for training; rendering also works on WebGL2.
What it does
- Load a source. Meshes (
.glb .gltf .obj .stl), trained Gaussian splats (.ply .splat .spz), point clouds (.ply), or a sculpt written in a small SDF language with primitives, smooth booleans, domain repetition, twist, noise and per-shape color. Try the files in examples/.
- Convert. Every source becomes the same set of oriented elements (position, three axes, three radii, color, opacity). Untrained, each renderer simply converts those elements into its own primitive. This looks rough, on purpose: it is the starting point the papers optimize from, not their result.
- Train.
splat-train.js renders 32–96 views of a dense "teacher" version of the source, then optimizes the current tab's primitives against them with Adam:
- tile-based differentiable rasterizer in WGSL compute (project → prefix sum → key duplication → 4-bit LSD radix sort → per-tile front-to-back compositing);
- analytic backward pass with float atomics, chained to 3D parameters (Gaussian mean/scale/rotation, triangle corners, all six convex points, colors, opacities);
- L1 + L2 loss, MCMC-style relocation of dead primitives;
- optional opaque finish for triangles (anneal σ → 0 and opacity → 1, the Triangle Splatting+ recipe), after which triangles render as an ordinary depth-tested mesh with no sorting or blending.
Run locally
Any static server works. WebGPU requires a secure context, which localhost counts as.
git clone https://github.com/<your-user>/splat-lab.git
cd splat-lab
python3 -m http.server 8000 # or: npx serve .
# open http://localhost:8000/
Babylon.js 9.32.0 and its loaders load from jsDelivr; there is no build step.
Tests
tests/gradcheck.html (open it through the same server) runs:
- Gradient check: analytic gradients vs. central finite differences for all three primitive types. Last run: Gaussians 69/72, triangles 64/66, convexes 73/84 within 10%. Remaining misses come from discontinuities (tile edges, opacity cutoffs, points crossing the convex hull).
- Forward render: teacher views of
examples/helix.splat.
- Train test: 2,000 Gaussians and 2,000 triangles for 400 steps. Loss drops from ~0.10 to ~0.012 (Gaussians) and ~0.025 (triangles, opaque).
tests/bench.html trains all three primitive types at the app's default sizes against a procedural teacher and reports steps per second, the loss curve and a render of each result. Profile reports GPU time per kernel for one training step. Query parameters change the sizes, for example tests/bench.html?types=1&steps=500.
Status and known limits
This is a research playground, not a production renderer. Open questions and good first issues:
- Benchmarked on one GPU so far. On an RTX 5080 at the defaults (3,000 steps, 256², 64 views), training runs at about 137 steps/s for 200k Gaussians, 280 for 120k triangles and 77 for 60k convexes, so a default run takes 11–40 seconds. Numbers from other GPUs (
tests/bench.html) and screenshots are very welcome.
- View-independent color (no spherical harmonics) and a plain L1/L2 loss (no D-SSIM).
- Memory caps on training: 800k Gaussians, 400k triangles, 250k convexes. Train lowers a larger count to the cap (a loaded mesh shows 2M Gaussians by default).
- Convex training treats hull membership as fixed per step (as the paper does); gradients are approximate where points cross the hull.
- The GaussianSplattingMesh point mode is Babylon's code; issues there belong upstream.
Repository layout
index.html the app (UI, sources, SDF language, renderers, training UI)
splat-train.js standalone WebGPU trainer / differentiable rasterizer
examples/ small test assets (mesh, textured glTF, splats, point cloud)
tests/ gradient check, forward render and train tests
tools/ script that regenerates examples/
AGENTS.md handoff notes for coding agents and contributors
Publishing with GitHub Pages
Repository Settings → Pages → Build and deployment → Deploy from a branch → main / root. The site appears at https://<your-user>.github.io/splat-lab/ within a minute or two.
License
MIT, see LICENSE. The papers cited above belong to their authors; this repository is an independent implementation for learning and comparison.
