物体的三维重建技术一直是计算机视觉领域研究的热点问题，提出一种利用Kinect传感器获取的深度图像实现多幅深度图像融合完成物体三维重建的方法。在图像空间中对深度图像进行三角化，然后在尺度空间中融合所有三角化的深度图像构建分层有向距离场(hierarchical signed distance field)，对距离场中所有的体素应用整体Delaunay三角剖分算法产生一个涵盖所有体素的凸包，并利用Marching Tetrahedra算法构造等值面，完成物体表面重建。实验结果表明，该方法利用Kinect传感器采集的不同方向37幅分辨率为640×480的深度图像完成目标物体的三维重建，仅需要48 s，并且得到非常精细的重建效果。

3D reconstruction of object is an interest subject in computer vision. A method for 3D reconstruction of object was proposed by integrating a set of depth maps obtained by Kinect sensor. To aggregate the contributions of the depth images at their corresponding scale, the depth images were triangulated in image space firstly, and the next step was to insert the triangulated depth images into the hierarchical signed distance field, then the global Delaunay tetrahedralization was applied to all the voxel positions yielding a convex hull that covers all the voxels, and the marching tetrahedra algorithm was applied to the resulting tetrahedral mesh for extracting the surface. Experimental results show that this method can make use of 37 depth images by Kinect sensor at different directions with the resolution of 640×480 to extract high-quality surfaces, which only costs 48 s.