# Sensor-Informed Per-Point Covariance for Structured-Light 3D Imaging

arXiv: https://arxiv.org/abs/2608.10888 · HTML: https://mlsanity.com/paper/2608.10888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 11 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [A comprehensive survey on point cloud registration](https://mlsanity.com/paper/2103.02690/) (2021)
- [Quasi-calibration method for structured light system with auxiliary camera](https://mlsanity.com/paper/2403.01119/) (2024)
- [Spatially Coupled Phase-to-Depth Calibration for Fringe Projection Profilometry](https://mlsanity.com/paper/2606.11601/) (2026)

## Abstract (arXiv)

Per-point uncertainty models are important in structured-light 3D reconstruction for probabilistic registration, fusion, and quality assessment. In practice, however, point-cloud covariances are often modeled as isotropic constants or inferred from local surface geometry and therefore do not explicitly reflect the measurement process. This is a limitation in fringe projection profilometry (FPP), where phase noise propagates through calibrated reconstruction and produces strongly anisotropic 3D uncertainty. This paper presents a sensor-informed first-order method for constructing a per-point 3 x 3 covariance field from experimentally measured phase precision and calibrated phase-to-depth and phase-to-3D mappings. The formulation separates a rank-1 phase-induced covariance from an effective full-rank completion obtained by incorporating fitted lateral image-space perturbation scales. Repeated-plane experiments under fixed imaging conditions show close alignment of the dominant covariance direction with the viewing ray, and consistency between the dominant phase-induced uncertainty scale and scalar depth uncertainty. In G-ICP registration, the proposed covariance substantially improves over a constant isotropic model while providing a sensor-derived uncertainty representation complementary to conventional geometry-based covariances.