English(EN) Solving Minimal Problems Without Matrix Inversion Using FFT-Based Interpolation

新方法使用FFT插值在没有矩阵求逆的情况下解决最小问题

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-07 17:03

研究人员开发了一种新颖的方法来解决相机几何估计中的最小问题，该方法避免了矩阵求逆。这种新方法利用稀疏隐藏变量结果式，并通过逆快速傅里叶变换插值来重建行列式多项式。与传统的Gröbner基求解器相比，该方法在数值稳定性和运行时性能方面表现出色，为小规模问题提供了一个实用的替代方案。 AI

影响为计算机视觉中求解复杂多项式系统引入了一种更具数值稳定性和效率的方法，可能对实时应用产生影响。

排序理由该集群包含一篇详细介绍计算机视觉任务新计算方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.CV TIER_1 English(EN) · Haidong Wu, Snehal Bhayani, Janne Heikkil\"a · 2026-05-08 04:00

使用基于FFT的插值在不进行矩阵求逆的情况下解决最小问题

arXiv:2605.06572v1 Announce Type: new Abstract: Estimating camera geometry typically involves solving minimal problems formulated as systems of multivariate polynomial equations, which often pose computational challenges when using existing Gr\"obner-basis or resultant-based meth…
arXiv cs.CV TIER_1 English(EN) · Janne Heikkilä · 2026-05-07 17:03

使用基于FFT的插值在不进行矩阵求逆的情况下解决最小问题

Estimating camera geometry typically involves solving minimal problems formulated as systems of multivariate polynomial equations, which often pose computational challenges when using existing Gröbner-basis or resultant-based methods due to matrix inversion needed in the online s…