shramko-sog-lod-converter

Shramko SOG LOD Converter — giant PLY → Streamed SOG with real LODs for SuperSplat, in your browser

Convert huge 3D Gaussian Splatting .ply files (hundreds of millions of splats, 10+ GB) into a SuperSplat-ready Streamed SOG .zip with real levels of detail (LOD) and SOG compression — on your own GPU and CPU, in the browser. Nothing is uploaded, nothing to install.

Live tool: https://sog.flyreelstudio.eu · Author: Andrii Shramko · License: MIT

Status (2 Oct 2026): live at https://sog.flyreelstudio.eu. Verified end to end in Chrome on a 259-million-splat / 17.6 GB drone city scan: 10 levels of detail, 5.9 GB archive, 1 h 53 min on an RTX 4090 workstation, archive checked by three independent readers. See Measured.

New in 1.2: a queue — add file after file (each with its own orientation and preset), choose an output folder once, and they are converted one by one, e.g. overnight; a failed file does not stop the queue, and a closed tab resumes with one click.

New in 1.1: a preview to set the scene's orientation before converting (scans from different tools come out on their side or upside down — SuperSplat cannot rotate an uploaded streamed scene), and levels down to ≤ 100K splats by default, so city-scale scenes start light on VR headsets and phones (0.5–1M splat budgets).

Why this exists

superspl.at accepts at most 10 GB per upload. City-scale drone scans are bigger than that: the test scan for this tool is 258,951,032 splats / 17.6 GB. SuperSplat streams large scenes as Streamed SOG — a lod-meta.json spatial tree plus many small SOG chunks at several levels of detail — and it publishes a pre-built Streamed SOG .zip exactly as uploaded.

Two problems kept coming up:

1. The PLY is too big to upload, and building Streamed SOG yourself means Node.js, the splat-transform CLI and a two-step command per level.
2. "Streamed SOG" built from one PLY without decimated levels has only ONE level. The scene is cut into chunks, but there is nothing coarser to show — the viewer still has to fetch full detail. (This is exactly the bug that started this project: a hand-made bundle with lodLevels: 1.)

This tool fixes both, in a browser tab.

How it works

1. Plan the levels for every device. Each level keeps half of the previous one until a level holds at most 100,000 splats (default All devices preset) — 13 levels for 259M splats, so even a VR headset with a 0.5M budget starts light. The SuperSplat standard preset stops at ≤ 1M — 1 + ⌈log₂(N / 1M)⌉ levels, the policy superspl.at uses when it generates LODs on its servers (10 levels for 259M).
2. Set the orientation on a preview. The page reads a light sample of the file (~0.15% of a 259M scan in ~2 s, spread over the whole file) and draws it the way SuperSplat will show it: Y up, the grid is the ground. Turn it with buttons or exact angles; the rotation (and optional centring) is baked into the archive.
3. Cut the scene into tiles that fit a browser tab. Chrome caps a single ArrayBuffer at ~2 GiB and a tab at ~16 GB (measured in Chrome 154). The scene is split with a k-d tree into spatial tiles sized to the RAM you allow; a few sequential passes over the file load them. Each tile is Z-order (Morton) sorted in memory.
4. Simplify and compress on your hardware. For every tile, PlayCanvas splat-transform (MIT) decimates (merges neighbouring splats) to build each coarser level — on your GPU via WebGPU — and its LOD writer compresses every streaming chunk to SOG (quantised attributes in lossless WebP textures, k-means palettes for spherical harmonics).
5. Stream everything into one archive, then check it. Chunk files stream straight to disk into a ZIP64 archive (over 4 GB is normal) with lod-meta.json at the root; the tile trees are joined under the same k-d split. When done, the archive is read back from disk and verified: every level, every chunk and texture, splat counts, and the file names superspl.at's upload form accepts.

Your file never leaves your computer. The server only delivers the static page (plus a tiny API for feedback and anonymous error reports).

Use it

1. Open https://sog.flyreelstudio.eu in desktop Chrome or Edge (WebGPU + saving straight to disk).
2. Drop your .ply. The page reads the header instantly and shows the level plan, estimated time and archive size.
3. Look at the preview: if the ground is not at the bottom, turn the scene (Auto suggests an axis, Flip upside down, ±90° turns, or exact angles). The choice is remembered per file.
4. Pick a scene preset (default All devices) or open Advanced settings.
5. Click Convert, choose where to save <name>-SSOG.zip, keep the tab open — or click Add to queue and go on with the next file (see Queue). Progress, ETA, the current step and a live log are shown; if something fails, the error and its step are shown and (if you allow) an anonymous report is sent so it can be fixed.
6. Upload the .zip at superspl.at → Upload.

Settings

Scene presets

Pick the kind of scene instead of guessing settings. A preset sets only what the file controls; the splat budget, LOD distances and penalties belong to the viewer (SuperSplat uses 1M splats on phones and Quest/Pico in XR, 2–4M on desktops; standalone headsets run smoothest at ~0.5M).

Every level keeps 50% of the previous one in all presets. Why these values (measured 2026-10-02 with the PlayCanvas engine 2.22.6 LOD classes on public superspl.at manifests and on a 259M-splat drone city scan):

• The coarsest level is a floor. The viewer starts every node at its coarsest level and never draws fewer splats than that level's total; SuperSplat downloads all of it before the first frame. With SuperSplat's default (stop at ≤ 1M), the floor is 0.5–1.0M — 50–100% of a phone's or headset's budget, so they can barely refine. Stopping at ≤ 100K leaves a 0.5M headset ~80–90% of its budget for detail near the viewer and makes the first frame ~8–10× lighter. Each extra coarse level costs only +0.1–0.6% of the archive. The page shows the floor for your file as a percentage of a 0.5M VR budget (259M-splat city: 63,221 splats, 13%).
• Chunk size = download granularity. A unit file is downloaded whole; at 512K splats per unit the viewer keeps ~8× more splats resident than it draws. 128K units cut resident data by ~58–61%, at 4× more files.
• Aerial scans: fewer, larger nodes. In dense drone scans node count is set by chunkMin/chunkExtent; 32 m / 32K gives 4× fewer nodes (manifest 31 → 7.9 MB, LOD pass ~5× cheaper) with identical views, flying down to street level included.
• Eye level: 16 m nodes. 8 m gave no visible gain at 3× the nodes; 32 m made the near field coarser.
• Adaptive simplification is better on real scans: level 2 (25% of the splats) rendered against the source from the same camera — 34.4 dB PSNR adaptive vs 28.0 dB uniform on a 2M-splat sample of that scan. It needs about twice the working memory (more, smaller tiles).
• Dropping SH (view-dependent colour) saves ~37% of the archive and removes a colour re-render pass in the viewer; keep it for interiors and objects with reflections.
• Not changed: keep-per-level 0.5 (0.6 was worse in every simulated view and +25% data; 0.4's visual quality is not measured yet).

Queue — many files, one after another (overnight)

1. Pick a file, check its preview and preset, press Add to queue. Pick the next one, and so on — or select several files at once: they open one by one for checking. Files can be added while the queue runs.
2. Press Start the queue and choose an output folder once. Each result is saved there as <name>-SSOG.zip without further dialogs; an existing file is never overwritten (-2, -3 … are added).
3. One file at a time, each in a fresh worker (memory is released between files). The list shows every file's state, the time left and the bytes still to write; Copy the summary gives a text report.
4. A failed file does not stop the queue. Out of memory → one automatic retry with 60% of the RAM setting; GPU failure → one retry without the GPU; anything else → marked failed with its error report, and the queue goes on.
5. A closed or crashed tab — the queue (settings, orientation, file and folder handles) is kept in the browser's IndexedDB. Reopen the page and press Resume: Chrome asks once for access to the files and the folder; a file picked through the plain file dialog is matched again by name and size when you pick it again. The file that was converting starts over.
6. Keep the computer awake — and the window visible for full speed. The page holds a screen wake lock while it is visible; for overnight runs set the power plan so the PC does not sleep (a sleeping PC pauses the work). A hidden page (minimized window or a background tab) keeps converting but Chrome lowers its priority: measured 90 s instead of 65 s for the same 2M-splat file (≈ 1.4× slower, tools/bg-test.mjs, plain Chrome without test switches).

Verified (tools/queue.mjs): 4 files (two presets, two orientations, one broken file) with a page reload after the first — statuses done, done, failed, done, names -SSOG.zip, -SSOG-2.zip, -SSOG-3.zip, every archive passes verify-zip, and the queued result is identical to a single conversion of the same file (every entry's CRC equal; only the generator string differs).

Orientation

Scans come out of different tools in different orientations (Z up, Y down, …) — there is no single right rotation, so it is chosen per file on the preview, and remembered in the browser by file name and size.

• What the preview shows. A sample of opaque splats read from ~1,000 places spread over the file (400K points on desktops, 150K on phones; ~2 s for 17.6 GB), drawn with WebGL2 exactly as SuperSplat will place them: world = R(you) · Rz(180°) · file, Y up, a grid at ground level. Orbit, zoom, side and top views.
• Turning. Auto finds the scan's thinnest axis (for aerial and street scans that is "up") from the sample's covariance and suggests it; when its cues disagree about which side is the top, it says so and Flip upside down fixes it. ±90° buttons turn around the screen axes; exact angles use the same Euler convention as splat-transform -r.
• What is written. The rotation (and, if ticked, a shift that puts the scene's centre at the origin and the ground at height 0) is applied to every splat — positions, orientations and spherical harmonics — before simplification, so all levels are upright.
• Verified: the site's output for a 2M sample turned −90° about X matches splat-transform -r -90,0,0 to 0.01 m in every position quantile (apart from the centring shift), and a top-down render shows roofs, the render from below shows only undersides. The Euler math matches the PlayCanvas engine to 1e-8 (tools/test-orientation.ts).

Measured

Per-level counts of the 259M run: 258,951,032 / 129,475,520 / 64,737,762 / 32,368,885 / 16,184,446 / 8,092,227 / 4,046,119 / 2,023,064 / 1,011,536 / 505,772 — 12.2 bytes per splat over all levels; lod-meta.json 6.35 MB with 9,976 leaves. The time estimate shown before the run was 2 h 06 min.

Round-trip quality on the 2M sample: rendering LOD 0 decoded from the archive vs the source PLY from the same camera — PSNR 49.9 dB (negative control: the same render shifted by 40 px — 16.7 dB).

Comparison (as of 2 October 2026)

Questions people ask

How can I upload a Gaussian splat PLY larger than 10 GB to SuperSplat? Convert it here into a Streamed SOG .zip (typically 5–10% of the PLY size) and upload the .zip. The levels of detail are already inside, so SuperSplat publishes it as-is.

Is there a way to make Streamed SOG with LODs without installing Node.js? Yes — this page runs splat-transform in your browser. Everything happens locally.

Why does my Streamed SOG have no levels of detail? It was built from one PLY without decimated levels: splat-transform scene.ply lod-meta.json writes a single level cut into chunks. Check lodLevels in lod-meta.json — real LODs need it to be > 1. npx tsx tools/verify-zip.ts your.zip reports it.

How many LOD levels should a 3DGS scene have for SuperSplat? SuperSplat halves the count per level until a level holds ≤ 1M splats: 1 + ⌈log₂(N / 1M)⌉. 100M splats → 8 levels; 259M → 10. For phones and VR headsets go deeper — to ≤ 100K: 259M → 13 levels, a first view of 63K splats.

Can a 250-million-splat Gaussian splat city run on a Quest / VR headset? Yes, if the levels go deep enough: the viewer always draws at least the coarsest level, and headsets run at 0.5–1M splats. With the default All devices preset the coarsest level is ≤ 100K, so the headset starts light and refines only near you.

Can I batch-convert several Gaussian splat PLY files overnight? Yes: add them to the queue one by one (each with its own orientation and preset), press Start the queue, choose a folder once — they are converted one after another, a failure does not stop the rest, and a closed tab resumes with one click.

My Gaussian splat scene is sideways or upside down on SuperSplat. How do I rotate it? Drop the PLY here, turn it on the preview until the ground is at the bottom, convert. The rotation is baked into the Streamed SOG, so SuperSplat shows it upright (its editor cannot rotate an uploaded streamed scene).

Can I convert a 250-million-splat city scan in a browser? Yes — the scene is processed in spatial tiles, so memory stays bounded. It takes hours, not minutes: 259M splats took 1 h 53 min on an RTX 4090 workstation (see Measured).

Is my scan uploaded anywhere? No. Only optional feedback and anonymous error reports (no file, no file name) reach the server.

Can I use the result outside SuperSplat? Yes: unzip it and load lod-meta.json with the PlayCanvas engine's LOD streaming, or read it back with splat-transform.

Status — what works, what's in progress, what's next

Works (verified): binary PLY (little/big-endian, SH 0–3) → multi-LOD Streamed SOG .zip; spatial tiling beyond one tab's memory; ZIP64 archives > 4 GB (read back by our verifier and by Python zipfile with CRC check); WebGPU acceleration in Chrome; read-back verification; live progress with ETA; visible errors and anonymous error reports; feedback (bug / idea / cooperation); 4 languages (EN, ES, PL, RU).

Also works (1.2): queue of many files into one folder, auto-retries, resume after a closed tab.

Also works (1.1): orientation preview and per-file rotation baked into the archive; levels down to ≤ 100K (≤ 50K Light) for VR headsets and phones; 7 scene presets.

In progress: publishing the upright 13-level conversion of the 259M-splat test scan on superspl.at (the archive is built and verified).

Next: resume a single file mid-way after a crash (the queue restarts that file today) · optional direct upload to SuperSplat with your API token · separate sky/environment layer · compressed PLY and SPZ input · processing several tiles in parallel on many-core CPUs.

Limits and risks

• Desktop Chrome or Edge recommended. Firefox/Safari have no save-to-disk API: the archive is built in browser storage (quota-limited) and then downloaded.
• Long runs. Hundreds of millions of splats take hours; keep the tab open (a wake lock keeps the PC awake). A closed tab or a crash stops the run; it cannot resume yet.
• Memory. A tab can hold about 16 GB. With 16 GB of RAM or less, set RAM to use to 4–6 GB.
• Archive size. About 12 bytes per splat over all levels for SH0 scans; spherical harmonics add more. Over 10 GB will not upload to superspl.at — drop SH bands.
• Third-party changes. superspl.at's upload rules or the Streamed SOG format may change; the tool pins splat-transform 3.8.0. See docs/warnings.md and SECURITY.md.

Self-host / develop

git clone https://github.com/AndriiShramko/shramko-sog-lod-converter
cd shramko-sog-lod-converter
npm install          # Node.js 22+
npm run dev          # http://localhost:5180/en/
npm run build        # static site → dist/

Tests:

npx tsx tools/node-convert.ts input.ply out.zip   # the same engine under Node (CPU), then verification
npx tsx tools/verify-zip.ts out.zip               # check any Streamed SOG zip (exit 1 on problems)
npx tsx tools/test-zip64.ts <dir> && python tools/test-zip64.py <dir>   # ZIP64 > 4 GiB, 70k entries
node tools/e2e.mjs input.ply out.zip --mem 12     # real Chrome end to end (Playwright)
python -m unittest discover -s api/tests          # feedback / report API

Production runs as two small Docker containers behind nginx-proxy (deploy/): nginx:alpine for the static site and a stdlib-only Python API for feedback and error reports, with tight memory/CPU limits — the conversion never runs on the server. Runbook: deploy/README.md.

Repository layout: web/src/engine/ (pipeline, tiling, Morton sort, ZIP64 writer, LOD merge, verifier, worker), web/src/ui/ (page), api/ (feedback API), deploy/ (Docker, nginx, deploy/rollback scripts), tools/ (tests, reports reader). For AI coding agents: AGENTS.md, AGENT_SETUP.md, SKILL.md, llms.txt.

Author & working together

Andrii Shramko — I scan whole cities and buildings as 3D/4D Gaussian splats with drones and 360° cameras, and build tools around them. I made this converter because my own scans (250M+ splats) did not fit SuperSplat's upload and the existing tools didn't build real LODs.

Open to interesting people and ready to help teams build ambitious projects — scanning, splat pipelines, viewers, VR, partnerships, licensing or funding. Get in touch:

• 📅 Book a call: https://calendar.app.google/Ff729HqGk4RpzPNDA
• 💼 LinkedIn: https://www.linkedin.com/in/andrii-shramko/
• ✉️ Email: zmei116@gmail.com
• 🐙 GitHub: https://github.com/AndriiShramko

Credits & license

• Conversion core: @playcanvas/splat-transform 3.8.0 (MIT) and the PlayCanvas engine (MIT) for WebGPU. See NOTICE.
• This project: MIT © 2026 Andrii Shramko. If you use it in research or a product, please credit it — see CITATION.cff.
• Independent tool: SuperSplat and PlayCanvas are trademarks of their owners; not affiliated.