Diagnosing and Repairing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry
arXiv:2606.17093v1 Announce Type: new Abstract: Learning-based single-shot fringe projection profilometry (FPP) has been studied mostly at close range. The long-range regime (standoff beyond 1 m) remains largely unaddressed: inverse-square intensity falloff lowers fringe signal-to-noise ratio and degrades physical ground truth, the single-shot problem is ill-posed because fringe-order information is absent from one image, and these architectures have not been studied mechanistically. We present a diagnose-repair-verify study using mechanistic interpretability (MI) and conformal uncertainty qua
This research addresses limitations in learning-based 3D profilometry, specifically for long-range applications, which has previously been a significant challenge due to signal degradation and inherent ill-posedness.
Improved long-range 3D sensing capabilities are crucial for applications in industrial automation, robotics, and advanced manufacturing, where precise spatial data collection at a distance is often required.
The ability to diagnose and repair shape-prior shortcuts in single-shot fringe projection profilometry could lead to more robust and accurate 3D sensing systems, expanding the range and reliability of these technologies.
- · Industrial Automation Sector
- · Robotics Companies
- · Metrology Equipment Manufacturers
More accurate and reliable long-range object recognition and dimensioning become feasible for industrial applications.
This could enable new forms of automated inspection, quality control, and robot guidance in large-scale manufacturing or hazardous environments.
Broader adoption of long-range 3D sensing may reduce manual labor in inspection processes, potentially impacting labor allocation in manufacturing.
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