Diagnosing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry
Learning-based single-shot fringe projection profilometry (FPP) has been studied almost entirely at close range, and the networks used are evaluated only on aggregate error, leaving open whether they recover depth from fringe phase or from object-level shape cues that correlate with depth. This paper diagnoses that question mechanistically in the long-range regime (standoff beyond 1 m). Using FPP-ML-Bench, an open photorealistic synthetic benchmark (15,600 fringe images, 50 objects at 1.5--2.1 m), we first formalize why the single-shot fringe-to-depth mapping is more severely ill-posed at long range: it is non-injective without fringe-order information, and the depth error from an incorre...
| Authors | Adam Haroon, Anush Lakshman, Cody Fleming et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 June 2026 |
| Last updated at source | 9 July 2026 |
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