# Blazer: Laser Scanning Simulation using Physically Based Rendering

arXiv: https://arxiv.org/abs/2104.05430 · HTML: https://mlsanity.com/paper/2104.05430/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 12 Apr 2021.

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

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views](https://mlsanity.com/paper/1505.05641/) (2015)
- [CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering](https://mlsanity.com/paper/1808.08601/) (2018)
- [BlenderProc](https://mlsanity.com/paper/1911.01911/) (2019)
- [Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve](https://mlsanity.com/paper/1912.02908/) (2019)
- [Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop](https://mlsanity.com/paper/2012.03806/) (2020)
- [BlendTorch: A Real-Time, Adaptive Domain Randomization Library](https://mlsanity.com/paper/2010.11696/) (2020)

## Built on by (most cited first, 1 of 1)

- [Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap](https://mlsanity.com/paper/2203.03833/) (2022)

## Abstract (arXiv)

Line laser scanners are a sub-type of structured light 3D scanners that are relatively common devices to find within the industrial setting, typically in the context of assembly, process control, and welding. Despite its extensive use, scanning of some materials remain a difficult or even impossible task without additional pre-processing. For instance, materials which are shiny, or transparent. In this paper, we present a Blazer, a virtual line laser scanner that, combined with physically based rendering, produces synthetic data with a realistic light-matter interaction, and hence realistic appearance. This makes it eligible for the use as a tool in the development of novel algorithms, and in particular as a source of synthetic data for training of machine learning models. Similar systems exist for synthetic RGB-D data generation, but to our knowledge this the first publicly available implementation for synthetic line laser data. We release this implementation under an open-source license to aid further research on line laser scanners.