# CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry

arXiv: https://arxiv.org/abs/2211.14054 · HTML: https://mlsanity.com/paper/2211.14054/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1812.11788/) (2018)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes](https://mlsanity.com/paper/1708.01566/) (2017)
- [Understanding data augmentation for classification: when to warp?](https://mlsanity.com/paper/1609.08764/) (2016)
- [A Survey on Simulators for Testing Self-Driving Cars](https://mlsanity.com/paper/2101.05337/) (2021)
- [Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation](https://mlsanity.com/paper/1601.03651/) (2016)
- [How useful is photo-realistic rendering for visual learning?](https://mlsanity.com/paper/1603.08152/) (2016)
- [PeRFception: Perception using Radiance Fields](https://mlsanity.com/paper/2208.11537/) (2022)
- [Play and Learn: Using Video Games to Train Computer Vision Models](https://mlsanity.com/paper/1608.01745/) (2016)
- [Dataset Augmentation for Pose and Lighting Invariant Face Recognition](https://mlsanity.com/paper/1704.04326/) (2017)
- [Real-time Detection of 2D Tool Landmarks with Synthetic Training Data](https://mlsanity.com/paper/2210.11991/) (2022)

## Built on by (most cited first, 10 of 10)

- [Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment](https://mlsanity.com/paper/2311.11039/) (2023)
- [Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data Generation](https://mlsanity.com/paper/2504.08473/) (2025)
- [Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization](https://mlsanity.com/paper/2410.18490/) (2024)
- [SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection](https://mlsanity.com/paper/2410.14844/) (2024)
- [Fully-Synthetic Training for Visual Quality Inspection in Automotive Production](https://mlsanity.com/paper/2503.09354/) (2025)
- [Synthetic Similarity Search in Automotive Production](https://mlsanity.com/paper/2505.07256/) (2025)
- [Hybrid Synthetic Data Generation with Domain Randomization Enables Zero-Shot Vision-Based Part Inspection Under Extreme Class Imbalance](https://mlsanity.com/paper/2512.00125/) (2025)
- [CRISTAL: Real-time Camera Registration in Static LiDAR Scans using Neural Rendering](https://mlsanity.com/paper/2511.16349/) (2025)
- [A Comparative Study of 3D Model Acquisition Methods for Synthetic Data Generation of Agricultural Products](https://mlsanity.com/paper/2601.03784/) (2026)
- [The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data -- a Systematic Review](https://mlsanity.com/paper/2602.06609/) (2026)

## Abstract (arXiv)

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render.