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ommatidia

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Image-to-Image
Size:
1K<n<10K
Tags:
Synthetic
ray-tracing
denoising
upscaling
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Document v0.3 HR-guided dataset
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metadata 
license: cc
pretty_name: Ommatidium synthetic ray-tracing reconstruction data
size_categories:
  - 1K<n<10K
task_categories:
  - image-to-image
tags:
  - synthetic
  - ray-tracing
  - denoising
  - upscaling
viewer: false

Ommatidium dataset

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

Contents

File Input source Samples Purpose
data/blade-path-trace/4spp-hr-gbuffer-train.omd independent path trace, 4 spp + HR primary surfaces 2,400 v0.3 spatial training set
benchmarks/path-trace-4spp-hr-gbuffer-validation.omd independent path trace, 4 spp + HR primary surfaces 128 v0.3 separate seed-10000 validation
ablations/path-trace-4spp-static-train.omd independent path trace, 4 spp 2,400 v0.2 low-resolution-guide training set
benchmarks/path-trace-4spp-static-validation.omd independent path trace, 4 spp 128 separate seed-10000 validation
benchmarks/path-reference-svgf-validation.omd Blade ReSTIR+SVGF 128 comparison control with matched references
data/blade-path-trace/1spp-train.omd independent path trace, 1 spp 2,400 lower-ray-budget experiment
benchmarks/path-trace-1spp-validation.omd independent path trace, 1 spp 128 lower-ray-budget validation
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 v0.3 files additionally contain output-resolution depth, world normal, diffuse albedo, specular F0, and roughness from the same primary-surface pass. The primary set uses 128×128 inputs and 256×256 references. Dataset-format version 2 records both plane sets and whether the low-resolution source is an independent path trace, raw ReSTIR, or SVGF-filtered.

Each benchmark family uses the same 128 procedural scenes and seed 10000; its canonical record payloads are byte-identical. The path-first references use 4,096 spp, eight-bounce targets. The 1/4-spp and SVGF files differ only in the low-resolution evidence paired with those records. The 4-spp files accumulate four independent paths within one static frame. At quarter resolution that is one traced path per output pixel, before accounting for the cheaper low-resolution G-buffer. It is spatial, not motion-aware temporal, data.

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-path-trace/4spp-hr-gbuffer-train.omd \
  --repo-type dataset --revision v0.3.0 --local-dir data/hf

cargo run --release -p ommatidia-train -- \
  --data data/hf/data/blade-path-trace/4spp-hr-gbuffer-train.omd \
  --reconstruction-base hr-guided --steps 2000 --batch 8 --out runs/ommatidia

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

Generation provenance

  • Ommatidium: d416f7abdf01e761b7601d597dd0eacfec7a8157
  • Blade: 101c3abd283ddcff858671f3405f041e8d87b782
  • Meganeura: c3159e830d84be920fc41ecf13b690973e8732a6
  • Primary generation seed: 0
  • 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.

The old nearest-base spatial experiment gained only 0.22 dB at 1 spp and 0.08 dB at 4 spp. Re-evaluating deterministic reconstruction exposed that the baseline, rather than the backbone, was the limiting factor: low-resolution guidance reaches 34.08 dB / 0.9473 SSIM, while output-resolution primary surfaces raise the selected 5×5 reconstruction to 34.75 dB / 0.9545 before the learned correction. See the full result.

Limitations

The repository 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. No uploaded path dataset contains motion vectors, animation, or disocclusions yet.