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A Minimum Error Vanishing Point Detection Approach for Uncalibrated Monocular Images of Man-Made Environments

机译:人造环境未经校准的单目图像的最小误差消失点检测方法

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We present a novel vanishing point detection algorithm for uncalibrated monocular images of man-made environments. We advance the state-of-the-art by a new model of measurement error in the line segment extraction and minimizing its impact on the vanishing point estimation. Our contribution is twofold: 1) Beyond existing hand-crafted models, we formally derive a novel consistency measure, which captures the stochastic nature of the correlation between line segments and vanishing points due to the measurement error, and use this new consistency measure to improve the line segment clustering. 2) We propose a novel minimum error vanishing point estimation approach by optimally weighing the contribution of each line segment pair in the cluster towards the vanishing point estimation. Unlike existing works, our algorithm provides an optimal solution that minimizes the uncertainty of the vanishing point in terms of the trace of its covariance, in a closed-form. We test our algorithm and compare it with the state-of-the-art on two public datasets: York Urban Dataset and Eurasian Cities Dataset. The experiments show that our approach outperforms the state-of-the-art.
机译:我们提出了一种新的人造环境未校准单眼图像的消失点检测算法。我们通过线段提取中的新测量误差模型推进最先进的,并最大限度地减少其对消失点估计的影响。我们的贡献是双重的:1)除了现有的手工制作模型之外,我们正式推出了一种新的一致性测量,它由于测量误差而捕获了线段和消失点之间的相关性的随机性质,并使用这种新的一致性措施来改善线段群集。 2)我们通过最佳地称量在集群中朝向消失点估计的每个线段对的贡献来提出新的最小误差消失点估计方法。与现有的作品不同,我们的算法提供了最佳解决方案,以使其协方差迹线的消失点的不确定性最小化,以闭合形式最小化。我们测试我们的算法,并将其与最先进的两个公共数据集进行比较:York Urban DataSet和欧亚城市数据集。实验表明,我们的方法优于现有技术。

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