👺 By the way, we spent a lot of effort curating and will release a large-scale indoor dataset (∼14k scenes) with diverse floorplans, geometries, and textures, serving as a benchmark for learning light transport.

👾 We demonstrate the versatility of our framework by reusing a shared pre-trained encoder while introducing task-specific decoders.

It takes as input the 3D scene assets—geometry, materials, and lighting represented as a point cloud—and encodes the light interaction into a 3D latent space, with codes anchored at the sampled scene points. The model is independent of both viewpoint and resolution!

Building on the analogy of light transport operator and Attention, our fully scalable model learns a generalizable light transport 3D embedding that captures global illumination.

🎄 Introducing our paper A Generalizable Light Transport 3D Embedding for Global Illumination lnkd.in/gQUMSAyV .
🙈 Just as Transformers learn long-range relationships between words or pixels, our new paper shows they can also learn how light interacts and bounces around a 3D scene.