中文摘要：

自然场景的多视图三维重建一直是计算机视觉领域的基本问题, 有着广泛的应用. 随着深度获取设备的日益普及, 如何有效地利用多视点的深度图信息重建场景的三维模型已成为一个重要的研究课题. 为了自动剔除输入深度图中的错误深度信息, 恢复高质量的场景模型, 提出一种多视图深度采样方法来实现自然场景的三维几何重建.首先对深度图进行非均匀采样以获得每帧的三维点集, 并剔除部分深度误差较大的三维点; 然后通过深度置信度估计的方法对多帧的三维点集进行融合并剔除多帧之间重复冗余的三维点, 从而获得整体场景的三维点云; 最后基于融合后的三维点云生成完整的场景几何模型. 一系列复杂的自然场景实例证明了该方法的正确性和鲁棒性, 其不仅能够重建小规模的物体,也同样适用于大规模场景的三维几何重建.

英文摘要：

Multi-view 3D reconstruction of natural scenes has long been a standard topic in computer vision with various applications. With the prevalence of depth capturing devices, how to effectively use multiple depth maps for 3D scene reconstruction becomes an important problem. This paper proposes a multi-view depth map sampling method for 3D reconstruction of natural scenes to automatically eliminate depth errors in the input depth maps, so that high-quality scene models can be recovered. We first do non-uniform sam-pling on depth maps to obtain a set of 3D points for each frame, and eliminate the 3D points with severe depth errors. Then, a depth confidence estimation algorithm is employed to integrate the sampled 3D points among multiple frames and eliminate redundant ones, so as to obtain the complete 3D point cloud of the scene. Finally, the complete scene geometry model can be generated based on the fused 3D point cloud. A variety of complicated natural scenes are experimented to demonstrate the accuracy and robustness of the proposed method, which can well handle both small objects and large-scale scenes.