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

Sehoon Tak, Keunhee Cho, Sangpil Kim, Jae-Sang Hyun

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.

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

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.
Paper Structure (25 sections, 16 equations, 6 figures, 7 tables)

This paper contains 25 sections, 16 equations, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Principles
  3. Absolute Phase Retrieval
  4. Pinhole Projection Model and Phase-to-Pixel Mapping
  5. The Optimization Model
  6. System Configuration and Measurement-Validity Filtering
  7. Batching and Sampling Protocol
  8. Camera Pose Optimization
  9. Implementation and Computational Profile
  10. Metrological Characterization and Sensitivity
  11. Metrics and Reporting Conventions
  12. Calibration Dataset for Hardware
  13. 3D Reconstruction Accuracy and Repeatability
  14. Calibration and Measurement Error Propagation
  15. Estimator Characterization
  16. ...and 10 more sections

Figures (6)

  • Figure 1: Illustration of system setup.
  • Figure 2: Pose drift under injected spatially correlated noise. Median (IQR) drift vs perturbation magnitude $\sigma$ for (a) translation $\delta_t$ (mm) and (b) rotation $\delta_R$ (mrad) over $K=20$ trials per $\sigma$. (Sec. \ref{['subsec:propagation']}).
  • Figure 3: Sampling repeatability on fixed 3D data over $K=400$ trials. ECDF of (a) translation repeatability $d_t$ (mm) and (b) rotation repeatability $d_R$ (mrad), defined relative to the trial-median pose. Dashed and dotted vertical lines indicate the median and IQR. (Sec. \ref{['subsubsec:precision']}).
  • Figure 4: Minimum-overlap robustness. Median (IQR) repeatability vs overlap ratio $r$ for (a) $d_t$ (mm) and (b) $d_R$ (mrad), over $K = 120$ trials per $r$. (Sec. \ref{['subsubsec:overlap']})
  • Figure 5: Featureless plane registration. (a) Estimated trajectory vs GT in the global reference frame. (b) Per-pose pose error relative to GT: translation $e_t$ (mm, left axis) and rotation $e_R$ (mrad, right axis). (Sec. \ref{['subsec:featureless']})
  • ...and 1 more figures