FPD: Fringe Photometric Deflectometry when Fringe Meets Photometric Stereo

Fringe Photometric Deflectometry (FPD) is built for industrial surfaces that are neither purely diffuse nor purely specular. A phone back plate, for example, may contain shiny logo regions, matte body regions, scratches, and fingerprint smudges in the same field of view. FPD treats PMD and PS as complementary measurement branches, then fuses them according to local reflectance.

The work appears in ICIVC 2025 and connects optical 3D reconstruction with deployable defect inspection. The project resources include the paper source, system diagrams, reconstruction comparisons, Smart3 interface screenshots, and the final inspection outputs.

Problem setting

Fringe Photometric Deflectometry (FPD) combines PMD phase-gradient cues with PS normal and curvature cues for non-Lambertian industrial surfaces. PMD provides reliable fringe-based gradients on shiny regions, while PS preserves fine surface details on diffuse regions. The method estimates specular and diffuse reflection components, derives adaptive PMD/PS confidence weights, and fuses the two modalities for more complete surface reconstruction.

In the broader publication record, this work appears in 2025 10th International Conference on Image, Vision and Computing (ICIVC), 408-413. 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 follows an optical 3D measurement pipeline: acquire coded images, recover phase or geometric cues, compensate the dominant error source, and reconstruct a reliable 3D result.

The extracted figures below show the sensing setup, algorithmic signal flow, and representative reconstruction or calibration results.

Method overview. This image is extracted from an embedded PDF image object on page 2, then recomposed for web display.

Representation and setup. This image is extracted from an embedded PDF image object on page 3, then recomposed for web display.

Experimental evidence. This image is extracted from an embedded PDF image object on page 4, then recomposed for web display.

Result comparison. This image is extracted from an embedded PDF image object on page 4, then recomposed for web display.

Additional visual result. This image is extracted from an embedded PDF image object on page 5, then recomposed for web display.

Results and impact

The evaluation reported in 2025 10th International Conference on Image, Vision and Computing (ICIVC), 408-413 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 13 candidate image objects from paper.pdf and generated the compressed WebP figures used on this page.