# Automatic Testing and Validation of Level of Detail Reductions Through Supervised Learning

arXiv: https://arxiv.org/abs/2208.12674 · HTML: https://mlsanity.com/paper/2208.12674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [PieAPP: Perceptual Image-Error Assessment through Pairwise Preference](https://mlsanity.com/paper/1806.02067/) (2018)
- [Understanding SSIM](https://mlsanity.com/paper/2006.13846/) (2020)
- [Augmenting Automated Game Testing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.15819/) (2021)
- [Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics](https://mlsanity.com/paper/1802.06881/) (2018)
- [Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents](https://mlsanity.com/paper/2103.13798/) (2021)
- [Appearance-Driven Automatic 3D Model Simplification](https://mlsanity.com/paper/2104.03989/) (2021)
- [Subjective and Objective Visual Quality Assessment of Textured 3D Meshes](https://mlsanity.com/paper/2102.03982/) (2021)

## Built on by (most cited first, 2 of 2)

- [Weak Supervision for Label Efficient Visual Bug Detection](https://mlsanity.com/paper/2309.11077/) (2023)
- [A Hybrid Co-Finetuning Approach for Visual Bug Detection in Video Games](https://mlsanity.com/paper/2510.03591/) (2025)

## Abstract (arXiv)

Modern video games are rapidly growing in size and scale, and to create rich and interesting environments, a large amount of content is needed. As a consequence, often several thousands of detailed 3D assets are used to create a single scene. As each asset's polygon mesh can contain millions of polygons, the number of polygons that need to be drawn every frame may exceed several billions. Therefore, the computational resources often limit how many detailed objects that can be displayed in a scene. To push this limit and to optimize performance one can reduce the polygon count of the assets when possible. Basically, the idea is that an object at farther distance from the capturing camera, consequently with relatively smaller screen size, its polygon count may be reduced without affecting the perceived quality. Level of Detail (LOD) refers to the complexity level of a 3D model representation. The process of removing complexity is often called LOD reduction and can be done automatically with an algorithm or by hand by artists. However, this process may lead to deterioration of the visual quality if the different LODs differ significantly, or if LOD reduction transition is not seamless. Today the validation of these results is mainly done manually requiring an expert to visually inspect the results. However, this process is slow, mundane, and therefore prone to error. Herein we propose a method to automate this process based on the use of deep convolutional networks. We report promising results and envision that this method can be used to automate the process of LOD reduction testing and validation.