3D reconstruction of moving object by double sampling based on phase shifting profilometry

Phase shifting profilometry (PSP) is a high-accuracy structured light technique that computes surface depth from the phase of sinusoidal fringe patterns. Its main limitation for dynamic scenes is that each depth map requires multiple fringe captures, and any object movement between captures creates phase inconsistencies that produce artifacts or missing depth regions. This paper proposes a double-sampling strategy that captures two interleaved temporal sequences of fringe images—each a complete phase-shifted set—offset by a small time interval. The difference between corresponding images in the two sequences encodes the object motion, which is estimated and used to warp the second fringe set into alignment with the first before standard phase computation. Because both fringe sets are captured with the same hardware, no additional sensors or synchronisation equipment are required. The method is straightforward to integrate into existing PSP systems as a software update. Presented at the SPIE Symposium on Novel Photoelectronic Detection Technology and Applications (2023), the approach demonstrates measurable reduction in motion-induced reconstruction error on controlled moving-object experiments, establishing double sampling as a cost-effective approach to extending PSP to dynamic measurement tasks.

Problem setting

Phase shifting profilometry (PSP) achieves high accuracy 3D reconstruction by capturing multiple phase-shifted fringe images, but any object motion between captures corrupts the phase calculation and introduces reconstruction errors. This work proposes a double sampling strategy that acquires two interleaved sets of fringe images with a temporal offset, using the two sets to estimate inter-frame object motion and compensate for it before phase recovery. The motion estimate is obtained by comparing the two sampling sets, requiring no additional sensors or hardware beyond a standard structured light setup.

In the broader publication record, this work appears in Ninth Symposium on Novel Photoelectronic Detection Technology and Applications, vol. 12617 (SPIE), 1950–1958. 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 4, then recomposed for web display.

Representation and setup. This image is extracted from an embedded PDF image object on page 6, then recomposed for web display.

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

Result comparison. This image is extracted from an embedded PDF image object on page 7, then recomposed for web display.

Additional visual result. This image is extracted from an embedded PDF image object on page 7, then recomposed for web display.

Results and impact

The evaluation reported in Ninth Symposium on Novel Photoelectronic Detection Technology and Applications, vol. 12617 (SPIE), 1950–1958 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 19 candidate image objects from paper.pdf and generated the compressed WebP figures used on this page.