# Deep and Fast Approximate Order Independent Transparency

arXiv: https://arxiv.org/abs/2305.10197 · HTML: https://mlsanity.com/paper/2305.10197/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [Deep Shading: Convolutional Neural Networks for Screen-Space Shading](https://mlsanity.com/paper/1603.06078/) (2016)
- [Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network](https://mlsanity.com/paper/1710.09834/) (2017)
- [Neural Shadow Mapping](https://mlsanity.com/paper/2301.05262/) (2023)
- [Learning Multiple-Scattering Solutions for Sphere-Tracing of Volumetric Subsurface Effects](https://mlsanity.com/paper/2011.03082/) (2020)

## Built on by (most cited first, 3 of 3)

- [StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splatting](https://mlsanity.com/paper/2503.24366/) (2025)
- [Moment-Based 3D Gaussian Splatting: Resolving Volumetric Occlusion with Order-Independent Transmittance](https://mlsanity.com/paper/2512.11800/) (2025)
- [STAR-NT: Spatiotemporal Acceleration of Real-Time Neural Transparency Rendering](https://mlsanity.com/paper/2606.16747/) (2026)

## Abstract (arXiv)

We present a machine learning approach for efficiently computing order independent transparency (OIT). Our method is fast, requires a small constant amount of memory (depends only on the screen resolution and not on the number of triangles or transparent layers), is more accurate as compared to previous approximate methods, works for every scene without setup and is portable to all platforms running even with commodity GPUs. Our method requires a rendering pass to extract all features that are subsequently used to predict the overall OIT pixel color with a pre-trained neural network. We provide a comparative experimental evaluation and shader source code of all methods for reproduction of the experiments.