OptaGen is a tool that helps you organize training datasets for deep-learning based Monte Carlo image denoisers.

This work has started on top of a great Optix-based renderer.

The code and document are not polished yet. I will continue to update the code to make it more clean, maintainable and robust.

Our lab repository offers 25 different 3D scenes and 30 HDRIs. Most of these scenes were made by artists on Blend Swap. Then they were cleaned up by Benedikt Bitterli. On top of that, I massaged some geometries, textures, and OBJ so that the scenes are compatible with the Optix-based renderer.

Train/Validation data Test data Origins of our test data HDRIs

Please take a look at how-to-build.txt.

Please take a look at the main data generator code and the feature visualizer code.

*I will write more detailed comments soon.

24 scenes = 15 outdoor + 9 indoor = 17 training + 7 test

30 HDRIs = 10 city theme outdoor + 10 nature theme outdoor + 10 indoor

The star sign(*) indicates the original implementation of the author of the Optix-based renderer.

• Microfacet models (GGX/Phong/Beckmann) for reflection and refraction

• Multiple importance sampling for HDR environmental map

• *Disney BRDF

• *Simple glass

• opacity (mask): 반투명 물체 구현이 시급

• conductor => eta, k 값 적용 가능하도록 (그래야 알루미늄, 철 등을 구분 가능) (혹시 eta, k 필요 없이도 sheen, clearcoat 로 구현 가능?) (color를 강제로 넣는 것도 괜찮을 듯)

• thindielectric => car, car2 scene에서 crucial

• coating: (for car, car2 scenes) IOR, thickness

• Multichannel rendering (.npy)

• Randomization of materials, camera parameters, HDR environmental maps, and lighting

• xml2scene parser for Mitsuba-oriented scenes

• drag-drop input file

• (not physically-based) tinted glass

• camera config. preset