DiffusionShadow: Diffusion-based Shadow Caching for Neural Volume Rendering

arXiv:2609.30658 · cs.GR, cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.GR, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/NVlabs/tiny-cuda-nn

Terminology

• Diffusion Model — 70× in this paper · explained in 8 episodes
• Diffusion Models — 34× in this paper · explained in 6 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Fidelity — 10× in this paper · explained in 2 episodes
• Hypernetwork — 7× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Generative Model — 5× in this paper · explained in 2 episodes
• Deep Volumetric Ambient Occlusion — 4× in this paper
• Generative Models — 4× in this paper · explained in 2 episodes
• ACM Transactions on Graphics — 3× in this paper
• Ablation study — 3× in this paper · explained in 1 episode
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Adaptive Sampling — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Two-stage training — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Learned Perceptual Image Patch Similarity — 1× in this paper
• Peak Signal-to-Noise Ratio — 1× in this paper
• Two-Stage Training Strategy — 1× in this paper · explained in 3 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation
• Real-time Neural Radiance Caching for Path Tracing
• DreamFusion: Text-to-3D using 2D Diffusion
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
• HyperNetworks
• Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network
• Learning to Learn with Generative Models of Neural Network Checkpoints
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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