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Improving three-dimensional point reconstruction from image correspondences using surface curvatures

机译：使用曲面曲率从图像对应关系改进三维点重建

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摘要

Recovering three-dimensional (3D) points from image correspondences is an important and fundamental task in computer vision. Traditionally, the task is completed by tri-angulation whose accuracy has its limitation in some applications. In this paper, we present a framework that incorporates surface characteristics such as Gaussian and mean curvatures into 3D point reconstruction to enhance the reconstruction accuracy. A Gaussian and mean curvature estimation scheme suitable to the proposed framework is also introduced in this paper. Based on this estimation scheme and the proposed framework, the 3D point recovery from image correspondences is formulated as an optimization problem with the surface curvatures modeled as soft constraints. To analyze the performance of proposed 3D reconstruction approach, we generated some synthetic data, including the points on the surfaces of a plane, a cylinder and a sphere, to test the approach. The experimental results demonstrated that the proposed framework can indeed improve the accuracy of 3D point reconstruction. Some real-image data were also tested and the results also confirm this point.
机译：从图像对应中恢复三维（3D）点是计算机视觉中的重要且基本的任务。传统上，该任务是通过三角测量来完成的，其精度在某些应用中受到限制。在本文中，我们提出了一个框架，该框架将诸如高斯和平均曲率之类的表面特征合并到3D点重建中，以提高重建精度。本文还介绍了适用于所提出框架的高斯和平均曲率估计方案。基于该估计方案和所提出的框架，将根据图像对应关系进行的3D点恢复公式化为优化问题，并将表面曲率建模为软约束。为了分析建议的3D重建方法的性能，我们生成了一些综合数据，包括平面，圆柱体和球体表面上的点，以测试该方法。实验结果表明，提出的框架确实可以提高3D点重建的准确性。还测试了一些真实图像数据，结果也证实了这一点。

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