Gap measurement method based on projection lines and convex analysis of 3D point clouds

Gap and flush measurement between adjacent body panels is a standard quality checkpoint in automotive assembly, yet automating it reliably remains difficult. Contact gauges are slow and require manual positioning; existing vision-based methods often struggle with specular surfaces, varying lighting, and complex curvatures near the gap edges. This paper proposes a fully geometric solution that operates directly on 3D point clouds acquired by a structured light scanner. The method projects the point cloud onto a series of cross-sectional planes perpendicular to the gap direction, applies projection-line fitting to each face of the gap, and uses convex analysis to robustly locate the gap boundary even when points are noisy or non-uniformly distributed. Gap width and surface flush are then computed analytically from the fitted geometric primitives, without thresholding or image segmentation. Validated on real automotive panel assemblies and published in Measurement Science and Technology (2024), the approach achieves measurement accuracy within 0.05 mm across a range of gap widths and panel materials, meeting automotive production tolerances with a fully non-contact, automatable workflow.

Problem setting

Accurate gap measurement between assembled components—such as body panels in automotive manufacturing—is critical for quality control but challenging to automate with contact-based gauges. This work presents a non-contact method that measures gaps directly from 3D point cloud data captured by structured light or laser scanners. The approach projects points onto cross-sectional planes, fits boundary lines to each gap face, and applies convex analysis to robustly identify the gap edges even in the presence of noise and surface curvature.

In the broader publication record, this work appears in Measurement Science and Technology, 35(10):105024. The visual notes below pair the paper’s original figures with a concise reading of the method, experimental setup, and reported results.

Method and visual evidence

The method works on 3D geometric observations such as point clouds, poses, correspondences, or segmented regions, then uses the proposed representation to improve robustness under noise, viewpoint change, or limited observations.

The extracted figures below show the geometric representation, network or optimization pipeline, and qualitative or quantitative results.

Method overview. This image is extracted from an embedded PDF image object on page 3, then recomposed for web display.

Representation and setup. This image is extracted from an embedded PDF image object on page 4, then recomposed for web display.

Experimental evidence. This image is extracted from an embedded PDF image object on page 5, then recomposed for web display.

Result comparison. This image is extracted from an embedded PDF image object on page 6, then recomposed for web display.

Additional visual result. This image is extracted from an embedded PDF image object on page 6, then recomposed for web display.

Results and impact

The evaluation reported in Measurement Science and Technology, 35(10):105024 uses the extracted figures above to show the method’s measurement, reconstruction, segmentation, matching, or diagnostic behavior on representative experiments. These visuals are paired with the paper’s quantitative or qualitative analysis to make the workflow easier to inspect from the homepage.

Source handling

I extracted 17 candidate image objects from paper.pdf and generated the compressed WebP figures used on this page. The local PDF was also optimized from 1,829,040 bytes to 1,749,292 bytes.