# Deep Shading: Convolutional Neural Networks for Screen-Space Shading

arXiv: https://arxiv.org/abs/1603.06078 · HTML: https://mlsanity.com/paper/1603.06078/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2016.

## Built on by (most cited first, 30 of 46)

- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion models](https://mlsanity.com/paper/2405.00666/) (2024)
- [Real-time Neural Radiance Caching for Path Tracing](https://mlsanity.com/paper/2106.12372/) (2021)
- [Enhancing Photorealism Enhancement](https://mlsanity.com/paper/2105.04619/) (2021)
- [DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models](https://mlsanity.com/paper/2501.18590/) (2025)
- [BokehMe: When Neural Rendering Meets Classical Rendering](https://mlsanity.com/paper/2206.12614/) (2022)
- [Relighting Neural Radiance Fields with Shadow and Highlight Hints](https://mlsanity.com/paper/2308.13404/) (2023)
- [OutCast: Outdoor Single-image Relighting with Cast Shadows](https://mlsanity.com/paper/2204.09341/) (2022)
- [Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering](https://mlsanity.com/paper/2508.14461/) (2025)
- [NeuMIP: Multi-Resolution Neural Materials](https://mlsanity.com/paper/2104.02789/) (2021)
- [Active Exploration for Neural Global Illumination of Variable Scenes](https://mlsanity.com/paper/2203.08272/) (2022)
- [Plateau-reduced Differentiable Path Tracing](https://mlsanity.com/paper/2211.17263/) (2022)
- [Generative World Renderer](https://mlsanity.com/paper/2604.02329/) (2026)
- [Inverse Rendering of Translucent Objects using Physical and Neural Renderers](https://mlsanity.com/paper/2305.08336/) (2023)
- [DeRenderNet: Intrinsic Image Decomposition of Urban Scenes with Shape-(In)dependent Shading Rendering](https://mlsanity.com/paper/2104.13602/) (2021)
- [Photo-realistic Neural Domain Randomization](https://mlsanity.com/paper/2210.12682/) (2022)
- [Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics](https://mlsanity.com/paper/2308.05739/) (2023)
- [Light Sampling Field and BRDF Representation for Physically-based Neural Rendering](https://mlsanity.com/paper/2304.05472/) (2023)
- [Neural Rendering and Its Hardware Acceleration: A Review](https://mlsanity.com/paper/2402.00028/) (2024)
- [Generative World Renderer at the Speed of Play](https://mlsanity.com/paper/2607.18703/) (2026)
- [Z2P: Instant Visualization of Point Clouds](https://mlsanity.com/paper/2105.14548/) (2021)
- [Bokeh-Loss GAN: Multi-Stage Adversarial Training for Realistic Edge-Aware Bokeh](https://mlsanity.com/paper/2208.12343/) (2022)
- [Deep and Fast Approximate Order Independent Transparency](https://mlsanity.com/paper/2305.10197/) (2023)
- [Teamwork: Collaborative Diffusion with Low-rank Coordination and Adaptation](https://mlsanity.com/paper/2510.05532/) (2025)
- [A Generalizable Light Transport 3D Embedding for Global Illumination](https://mlsanity.com/paper/2510.18189/) (2025)
- [RenderFlow: Single-Step Neural Rendering via Flow Matching](https://mlsanity.com/paper/2601.06928/) (2026)
- [Neural BRDFs: Representation and Operations](https://mlsanity.com/paper/2111.03797/) (2021)
- [Generative Adversarial Shaders for Real-Time Realism Enhancement](https://mlsanity.com/paper/2306.04629/) (2023)
- [Neural Cone Radiosity for Interactive Global Illumination with Glossy Materials](https://mlsanity.com/paper/2509.07522/) (2025)
- [Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction](https://mlsanity.com/paper/2601.15098/) (2026)

## Abstract (arXiv)

In computer vision, convolutional neural networks (CNNs) have recently achieved new levels of performance for several inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance. In computer graphics, screen-space shading has recently increased the visual quality in interactive image synthesis, where per-pixel attributes such as positions, normals or reflectance of a virtual 3D scene are converted into RGB pixel appearance, enabling effects like ambient occlusion, indirect light, scattering, depth-of-field, motion blur, or anti-aliasing. In this paper we consider the diagonal problem: synthesizing appearance from given per-pixel attributes using a CNN. The resulting Deep Shading simulates various screen-space effects at competitive quality and speed while not being programmed by human experts but learned from example images.