Hevolve AI: Self-Evolving Multimodal AI Agents

Turn your domain expertise into AI agents that keep learning. Hevolve AI lets experts build multimodal AI systems by talking to them and correcting them in real time, with no code to write.

Key Features

Quick Links

© 2024 Hevolve AI Pvt Ltd. All rights reserved.

← All research
Artificial Intelligence
arXiv (cs.CV) · July 16, 2026

Ray-based phase error correction for miniaturized DOE projector-based FPP under single-directional hyperbolic projection

Seung-Jae Son, Yatong An, Jae-Sang Hyun

Fringe Projection Profilometry measures 3D shape by projecting striped light patterns onto an object and reading how the stripes bend. When you shrink the projector down to a tiny diffractive optical element, though, the patterns come out distorted in nonlinear ways, and the depth reconstruction picks up bad phase errors.

The authors correct this by modeling the errors along the rays leaving the projector's pinhole, using the projector's geometry rather than smoothing things in the image itself. They add a way to estimate that pinhole from a single hyperbolic fringe pattern, so no stereo calibration is needed, and they build the correction from just one calibration pose to keep it data-light. Experiments on miniaturized DOE systems show clearer improvements in accuracy under these distorted conditions.

This is a plain read of a fairly technical abstract, so see the paper for the geometry and validation details.

From the arXiv (cs.CV) abstract

Fringe Projection Profilometry (FPP) systems using miniaturized DOE pro-jectors often suffer from severe phase artifacts due to nonlinear projection characteristics and limited pattern controllability. We propose a ray-based phase error correction framework that models phase artifacts along projection rays from the projector pinhole, incorporating projector geometry without re-lying on image-domain processing or neighboring pixels. A projector pinhole estimation method based…


More Artificial Intelligence papers