pretty_name: Splataverse (index)
license: other
license_name: per-source
configs:
- config_name: v5
default: true
data_files:
- split: train
path: splits/v5.parquet
- config_name: v4
data_files:
- split: train
path: splits/v4.parquet
- config_name: conversion_v1
data_files:
- split: test
path: splits/conversion_v1/objects.parquet
Splataverse: cross-dataset splits
This repo holds only split manifests. The Gaussian splats themselves live in the per-source repos, each under its own
licence. Every row of a manifest names the repo (hf_repo) and file (path) it comes from.
Where the data comes from
| source | repo | licence / notes |
|---|---|---|
| Sketchfab (Objaverse) | maxwildersmith/splataverse-sketchfab | cc-by / cc-by-sa / cc-by-nc(-sa), per object (license column); quality-filtered |
| Smithsonian 3D | maxwildersmith/splataverse-smithsonian | CC0; ~88% of val/test are bone specimens |
| TableVerse (tabletop scenes) | maxwildersmith/splataverse-tableverse | licence unknown; identical duplicate instances are ranked last |
| GitHub, textured (Objaverse-XL) | maxwildersmith/splataverse-github | per-repo open licence (MIT, Apache-2.0, GPL, BSD, CC0, ...); kept objects only |
| GitHub, untextured fallback (Objaverse-XL) | maxwildersmith/splataverse-github-untextured | same licensed repos, but geometry-only (grey) objects; training only, always the last pool to be used |
Split v5
splits/v5.parquet (config v5) has one row per object: uid, hf_source, hf_repo, path, license, split, tier, is_bone, group, reasons.
split is val, test, train (in a tier), train_unused, flagged (failed the splat quality check; reasons says why) or unscored.
- Training tiers are nested (1k is inside 5k is inside 10k ...): tier T is
split == "train" and tier <= T. Small tiers are Sketchfab plus ~10% Smithsonian; TableVerse and GitHub ramp in from 50k up (GitHub 5% at 50k, 8% at 100k, 12% at 250k, 20% at 1M; TableVerse 5% at 100k, 10% at 250k, 30% at 1M). Sketchfab fills the remainder, and once its clean pool is used up the other sources top the tier up. - The untextured GitHub fallback is used only after every other pool is exhausted, so it appears only in
train_all. Filter onhf_sourceto drop it. - val / test are fixed. Sketchfab 1,000 + 1,000 (licence-stratified), Smithsonian's frozen 150 + 150, TableVerse whole scenes (~600 each, so a scene never straddles train and test), and a seeded random 500 + 500 from textured GitHub (not yet grouped by repo, so forks can make GitHub a little optimistic). The untextured fallback is never in val/test.
| list | objects | Sketchfab | Smithsonian 3D | TableVerse | GitHub | GitHub, untextured fallback |
|---|---|---|---|---|---|---|
| val | 2,257 | 1,000 | 150 | 607 | 500 | - |
| test | 2,255 | 1,000 | 150 | 605 | 500 | - |
| train_1k | 1,000 | 900 | 100 | - | - | - |
| train_5k | 5,000 | 4,500 | 500 | - | - | - |
| train_10k | 10,000 | 9,000 | 1,000 | - | - | - |
| train_25k | 25,000 | 22,908 | 2,092 | - | - | - |
| train_50k | 50,000 | 45,408 | 2,092 | - | 2,500 | - |
| train_100k | 100,000 | 84,908 | 2,092 | 5,000 | 8,000 | - |
| train_250k | 250,000 | 192,908 | 2,092 | 25,000 | 30,000 | - |
| train_1m | 1,000,000 | 459,890 | 2,092 | 450,478 | 87,540 | - |
| train_all | 1,924,723 | 459,890 | 2,092 | 966,284 | 87,540 | 408,917 |
The 2M tier is not fillable from licensed data: the training pool is 1,924,723 objects, so train_all is the largest list. Unlicensed GitHub is deliberately not included.
splits/v5/<list>.txt has one <repo> <path> line per object. The previous split (v4, Sketchfab + Smithsonian only) is kept under splits/v4/.
ArtVIP and HOI are not in v5 yet. TableVerse paths may be <scene>/objects.tar::<member> (bundled in a tar).
Conversion benchmark (conversion_v1)
A fixed set of source meshes for evaluating mesh -> 3D Gaussian splat conversion (not our splats: you convert the
original mesh with your method and score it). splits/conversion_v1/objects.parquet (config conversion_v1) has one row
per object: uid, tier, source, hf_repo, path, license, source_url, sha256, file_type, is_bone, note; source_url and
sha256 are the objaverse-xl manifest entries of the original mesh, hf_repo/path point to our converted splat for reference.
bench: the exact objects of the Splataverse paper's conversion table (Sketchfab, Smithsonian scans, the Khronos SciFiHelmet). 1 object can no longer be downloaded from the source (957028a3-c370-5c5d-9615-962c8ec5ae37: removed at source (Smithsonian API 404, 2026-10-26)), so the paper reports 20 of 21; it stays listed with anote(also where it appears infull). Cameras ship insplits/conversion_v1/cameras.json.full: a seeded, stratified set drawn from the v5 test lists: Sketchfab (25 per licence), Smithsonian (25 bone + 17 other: all non-bone test objects, the test list is mostly bones), textured GitHub (100), and untextured GitHub geometry (50.stl+ 50 other). The untextured pool has no val/test in v5, so these 100 are also in v5'strain_allfallback pool; exclude them (hf_source == "github_untextured"anduidin this list) if you train ontrain_alland evaluate here.
| tier | objects | github | github_untextured | khronos | sketchfab | smithsonian |
|---|---|---|---|---|---|---|
| bench | 21 | - | - | 1 | 10 | 10 |
| full | 342 | 100 | 100 | - | 100 | 42 |
Protocol (paper, Evaluation protocol): ground truth is the textured mesh rendered unlit (emission = base colour) by Blender Cycles (16 spp, transparent film) from 16 held-out views (8 azimuths x elevations 15/45 deg, 512 px, 40 deg FoV, bounding sphere fitted with 10% margin); every method is rendered by the same gsplat rasterizer (antialiased, 4x4 supersampled, black background). Ranked on LPIPS (AlexNet), LPIPS after gain matching, structure and gradient correlation over the object mask; masked PSNR is reported but not ranked on; geometry is the Chamfer distance between Gaussian centres and 100k area samples (% of bbox diagonal).
pip install huggingface_hub objaverse trimesh pandas torch gsplat lpips plyfile pillow tyro
python eval_conversion.py --stage fetch --tier bench --out-dir conv_eval # source meshes + cameras
blender -b -P eval_conversion.py -- gt --out-dir conv_eval # unlit GT renders
python eval_conversion.py --stage score --out-dir conv_eval --splats my_method # my_method/<uid>.ply|.npz
Download a split
pip install huggingface_hub tyro
python fetch_split.py --split train_5k --out-dir splataverse_data
fetch_split.py (in this repo) skips files already downloaded and pauses whenever the Hugging Face API rate limit
(1,000 requests per 5 minutes) is reached, so large tiers can be fetched unattended. Use --version v4 for the old split.
Complete split and protocol catalog
The split catalog documents scientific partitions, nested tiers, historical versions, duplicate exclusions, the matched 600-object training and160-candidate paired-evaluation lists, the 1,119-object confirmatory test, retrieval galleries, and the new frozen balanced-profile cohorts. Validation checks and artifact hashes make these metadata additions auditable. Existing v4/v5 assignments are preserved.
Loader distinction: load_dataset(..., "v5")["train"] loads the metadata table. Select rows with split == "train" and the desired tier for scientific training; its val and test rows are evaluation data. UID-only lists and repo path lists are different formats.
Additional sources: ArtVIP, HOI, cities, ortho/Berlin, SimReady, and Splataverse-T have source-specific manifests and access rules; they are not included in v5 train/validation/test. Source-level inventory or conversion completion is not a benchmark split assignment. Workshop leaderboard splits will be versioned separately once frozen.
New conversion jobs use balanced by default. Existing split manifests continue pointing to the original payloads; they must not be relabeled as balanced measurements. See the catalog for channel/provenance availability and the limitations of historical duplicate controls.
Classic reference objects
CV-classic contains eleven canonical graphics/vision meshes as balanced and uncapped Gaussian PLYs, with PBR attributes, source geometry and source correspondence. It is a public manually gated reference collection outside the existing train/validation/test assignments. See its per-asset source terms and material provenance.