Towards Pointless Structure from Motion: 3D Reconstruction and Camera Parameters from General 3D Curves

Irina Nurutdinova, A. Fitzgibbon
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引用次数: 39

Abstract

Modern structure from motion (SfM) remains dependent on point features to recover camera positions, meaning that reconstruction is severely hampered in low-texture environments, for example scanning a plain coffee cup on an uncluttered table. We show how 3D curves can be used to refine camera position estimation in challenging low-texture scenes. In contrast to previous work, we allow the curves to be partially observed in all images, meaning that for the first time, curve-based SfM can be demonstrated in realistic scenes. The algorithm is based on bundle adjustment, so needs an initial estimate, but even a poor estimate from a few point correspondences can be substantially improved by including curves, suggesting that this method would benefit many existing systems.
从运动走向无意义的结构:从一般3D曲线的3D重建和相机参数
现代运动结构(SfM)仍然依赖于点特征来恢复相机位置,这意味着重建在低纹理环境中严重受阻,例如扫描整洁桌子上的普通咖啡杯。我们展示了如何在具有挑战性的低纹理场景中使用3D曲线来改进相机位置估计。与之前的工作相反,我们允许在所有图像中部分观察曲线,这意味着第一次可以在现实场景中展示基于曲线的SfM。该算法基于束平差,因此需要一个初始估计,但即使是几个点对应的差估计也可以通过包含曲线而得到很大的改善,这表明该方法将使许多现有系统受益。
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