多视角大尺度场景重建

Shi Limin, Zhang Feng, Zhenhui Xu, Zhanyi Hu
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引用次数: 0

摘要

本文提出了一种从多幅标定图像中重建大尺度场景的新方法。它首先通过匹配图像上的关键点生成场景的准密集3D点云。然后通过计算三维点集的三维Delaunay三角剖分,构建空间四面体分解;最后,通过将Delaunay四面体标记为内部或外部来提取场景的三角形网格。在图中有效地找到全局最优标签分配作为最小割解。实验结果证明了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large Scale Scenes Reconstruction from Multiple Views
In this paper, we present a novel method to reconstruct the large scale scenes from multiple calibrated images. It first generates a quasi-dense 3D point cloud of the scene by matching key points across images. Then it builds a tetrahedral decomposition of space by computing the 3D Delaunay triangulation of the 3D point set. Finally, a triangular mesh of the scene is extracted by labeling Delaunay tetrahedra as inside or outside. A globally optimal label assignment is efficiently found as a minimum cut solution in a graph. Experimental results demonstrate the effectiveness of the proposed algorithm.
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