High-Precision 6DOF Pose Estimation via Global Phase Retrieval in Fringe Projection Profilometry for 3D Mapping

2026-03-11

AI Summary

This work addresses the challenge of achieving high-precision pose estimation in large-scale 3D metrology, where conventional methods such as ICP fall short of matching the micrometer-level reconstruction accuracy of digital fringe projection (DFP) systems due to their reliance on point cloud downsampling or feature extraction. To overcome this limitation, the authors propose a global phase-constrained pose estimation framework that eliminates the need for explicit feature extraction. By leveraging phase information from a fixed and calibrated global projector, pixel-level constraints are established and integrated with PnP-based reprojection optimization to solve for the pose of a moving DFP system within a global coordinate frame. The method demonstrates strong robustness against point cloud downsampling, low view overlap, and textureless surfaces, achieving sub-millimeter pose accuracy in practice, effectively suppressing trajectory drift while enabling quantifiable uncertainty bounds.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Digital fringe projection (DFP) enables micrometer-level 3D reconstruction, yet extending it to large-scale mapping remains challenging because six-degree-of-freedom pose estimation often cannot match the reconstruction's precision. Conventional iterative closest point (ICP) registration becomes inefficient on multi-million-point clouds and typically relies on downsampling or feature-based selection, which can reduce local detail and degrade pose precision. Drift-correction methods improve long-term consistency but do not resolve sampling sensitivity in dense DFP point clouds.We propose a high-precision pose estimation method that augments a moving DFP system with a fixed, intrinsically calibrated global projector. Using the global projector's phase-derived pixel constraints and a PnP-style reprojection objective, the method estimates the DFP system pose in a fixed reference frame without relying on deterministic feature extraction, and we experimentally demonstrate sampling invariance under coordinate-preserving subsampling. Experiments demonstrate sub-millimeter pose accuracy against a reference with quantified uncertainty bounds, high repeatability under aggressive subsampling, robust operation on homogeneous surfaces and low-overlap views, and reduced error accumulation when used to correct ICP-based trajectories. The method extends DFP toward accurate 3D mapping in quasi-static scenarios such as inspection and metrology, with the trade-off of time-multiplexed acquisition for the additional projector measurements.

Problem

• 6DOF pose estimation
• fringe projection profilometry
• 3D mapping
• point cloud registration
• sampling sensitivity

Innovation

• 6DOF pose estimation
• fringe projection profilometry
• global phase retrieval
• sampling invariance
• dense point cloud registration