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标题: "Sensor-Informed Per-Point Covariance for Structured-Light 3D Imaging"
原文链接: "https://arxiv.org/abs/2608.10888"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.10888"
---

## Computer Science > Computer Vision and Pattern Recognition

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

Authors:[Sehoon Tak](https://arxiv.org/search/cs?searchtype=author&query=Tak,+S), [Jae-Sang Hyun](https://arxiv.org/search/cs?searchtype=author&query=Hyun,+J)

[View PDF](https://arxiv.org/pdf/2608.10888) [HTML (experimental)](https://arxiv.org/html/2608.10888v1)

> Abstract: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.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| --- | --- |
| Cite as: | [arXiv:2608.10888](https://arxiv.org/abs/2608.10888) \[cs.CV\] |
|  | (or [arXiv:2608.10888v1](https://arxiv.org/abs/2608.10888v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10888](https://doi.org/10.48550/arXiv.2608.10888) |

## Submission history

From: Jae-Sang Hyun \[[view email](https://arxiv.org/show-email/378f91a6/2608.10888)\]  
**\[v1\]** Tue, 11 Aug 2026 13:05:22 UTC (5,098 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10888) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
