mad-bot/ommatidia

Ommatidium dataset

Synthetic renderer-native training and validation data for Ommatidium, generated with Blade.

• Checkpoint: https://huggingface.co/mad-bot/ommatidia
• Format and training code: https://github.com/kvark/ommatidia
• Generator: the ommatidia-data workspace crate

Contents

File	Input source	Samples	Purpose
data/blade-restir/train.omd	raw Blade ReSTIR	2,400	2,040 train / 360 held out
benchmarks/matched-raw.omd	raw Blade ReSTIR	128	matched replacement evaluation
benchmarks/matched-svgf.omd	Blade variance-guided SVGF	128	matched Blade baseline

Every sample contains low-resolution radiance, depth, world-space normals, diffuse albedo, specular F0, and roughness, plus a high-resolution canonical path-traced reference. The primary set uses 128×128 inputs and 256×256 references. Dataset-format version 2 records whether the low-resolution source is raw ReSTIR or SVGF-filtered.

The two benchmark captures use the same 128 procedural scenes and seed 10000; their canonical record payloads are byte-identical. They differ only in whether Blade's three-pass variance-guided filter was enabled for the low-resolution input.

Loading

.omd is Ommatidium's little-endian, contiguous f16 training format. It is designed for the Rust trainer's direct batch reads rather than the Hugging Face Dataset Viewer, which is disabled for this custom binary representation.

hf download mad-bot/ommatidia \
  data/blade-restir/train.omd \
  --repo-type dataset --revision v0.1.0 --local-dir data/hf

cargo run --release -p ommatidia-train -- \
  --data data/hf/data/blade-restir/train.omd \
  --steps 20000 --batch 8 --out runs/ommatidia

See docs/design.md for the semantic contract and the source tree for the authoritative parser.

Generation provenance

• Ommatidium: 7f08f025a3150c355643af513bc2825d88441520
• Blade upstream integration: 3a8895a
• Meganeura upstream integration: 256b906
• Primary generation seed: 1
• Matched validation seed: 10000

Future render estimators belong in this repository as separately identified sources rather than changing the dataset's identity. Training recipes should pin a Hub revision and enumerate the source names they consume.

Limitations

The first release contains small procedural scenes from one renderer and a narrow resolution distribution. It should not be treated as representative of production game content. The canonical targets reduce Monte Carlo noise but are finite-sample path traces rather than analytic ground truth.