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<p>Structured light-based 3D scanning systems are used for non-contact digitization of complex surfaces, with applications in quality control, reverse engineering, and biomedical engineering. The availability of low-cost sensors has expanded access to high-resolution data capture, enabling their use in applications with moderate accuracy requirements, such as capturing anatomical details for custom prostheses and diagnostics. However, the reliability of these low-cost systems for accurate surface geometry reconstruction remains uncertain. This study evaluates the performance of Apple’s TrueDepth sensor, focusing on the impact of stand-off distance variations on geometric reconstruction accuracy. Test specimens including planar, parallel planes, and hemispheric geometries were digitized using a semi-automatic test bench, and compared with reference values obtained with a coordinate measurement machine (CMM). The resulting point clouds were processed to evaluate the reliability of dimensional and geometric measurements. Key findings include significant point cloud dispersion as the sensor-to-object distance increases; i.e., the standard deviation of point-to-plane distances rose from 0.291 to 0.739 mm as the sensor-to-object distance increased from 175 to 450 mm. Similarly, the standard deviation of point-to-sphere distances range from 0.265 to 0.785 mm for the same stand-off interval. However, measurements of features derived from average values benefit from the statistical compensation of errors due to the Gaussian distribution of point dispersion. Thus, distance between planes error ranges from 0.053 to 0.355 mm within the experimental limits. These results suggest that TrueDepth may be a suitable option for fields like biomedicine, where precision requirements are less strict than those in industrial applications.</p>

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Performance analysis of Apple’s TrueDepth sensor for surface reconstruction of regular geometries across varying stand-off distances

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月24日

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  • Natalia Beltrán,
  • Julián Álvarez,
  • Alejandro Fernández,
  • Braulio J. Álvarez,
  • David Blanco

摘要

Structured light-based 3D scanning systems are used for non-contact digitization of complex surfaces, with applications in quality control, reverse engineering, and biomedical engineering. The availability of low-cost sensors has expanded access to high-resolution data capture, enabling their use in applications with moderate accuracy requirements, such as capturing anatomical details for custom prostheses and diagnostics. However, the reliability of these low-cost systems for accurate surface geometry reconstruction remains uncertain. This study evaluates the performance of Apple’s TrueDepth sensor, focusing on the impact of stand-off distance variations on geometric reconstruction accuracy. Test specimens including planar, parallel planes, and hemispheric geometries were digitized using a semi-automatic test bench, and compared with reference values obtained with a coordinate measurement machine (CMM). The resulting point clouds were processed to evaluate the reliability of dimensional and geometric measurements. Key findings include significant point cloud dispersion as the sensor-to-object distance increases; i.e., the standard deviation of point-to-plane distances rose from 0.291 to 0.739 mm as the sensor-to-object distance increased from 175 to 450 mm. Similarly, the standard deviation of point-to-sphere distances range from 0.265 to 0.785 mm for the same stand-off interval. However, measurements of features derived from average values benefit from the statistical compensation of errors due to the Gaussian distribution of point dispersion. Thus, distance between planes error ranges from 0.053 to 0.355 mm within the experimental limits. These results suggest that TrueDepth may be a suitable option for fields like biomedicine, where precision requirements are less strict than those in industrial applications.