# Structured light 3D scanner simulation dataset

## TL;DR

This paper introduces a simulated 3D scanner dataset to help test and improve 3D imaging algorithms without real-world complications.

## Contribution

The novelty is a simulated 3D structured light scanner dataset with controlled conditions for algorithm testing.

## Key findings

- The simulated scanner can project sinusoidal and Gray code patterns onto 3D objects.
- Surface materials and lighting can be controlled to test algorithms systematically.
- The dataset removes real-world errors for reliable algorithm evaluation.

## Abstract

Computer simulation of optical measurement systems plays a crucial role in the design, analysis, and optimization of real-world measurement solutions. However, acquiring real measurement data often involves challenges such as a complex experimental setup, sensitivity to environmental conditions, and the presence of noise and calibration errors, which can hinder controlled algorithm evaluation. To address this issue, in this data article, we introduce datasets generated using a simulated 3D structured light scanner. These samples are produced by projecting a series of sinusoidal and Gray code patterns onto various 3D objects using a simulated projector. The simulated environment also provides the flexibility to apply different surface materials and controlled illumination conditions, enabling systematic testing of algorithms under diverse yet precisely defined scenarios. This approach eliminates real-world uncertainties and errors associated with scene setup and environmental factors, supporting the development and evaluation of point cloud processing and surface reconstruction algorithms in controlled conditions.