We propose a new strategy to bridge point cloud denoising and surface reconstruction by alternately updating the denoised point clouds and the reconstructed surfaces. In Poisson surface reconstruction, the implicit function is generated by a set of smooth basis functions centered at the octnodes. When the octree depth is properly selected, the reconstructed surface is a good smooth approximation of the noisy point set. Our method projects the noisy points onto the surface and alternately reconstructs and projects the point set. We use the iterative Poisson surface reconstruction (iPSR) to support unoriented surface reconstruction. Our method iteratively performs iPSR and acts as an outer loop of iPSR. Considering that the octree depth significantly affects the reconstruction results, we propose an adaptive depth selection strategy to ensure an appropriate depth choice. To manage the oversmoothing phenomenon near the sharp features, we propose a $\lambda$-projection method, which means to project the noisy points onto the surface with an individual control coefficient $\lambda_{i}$ for each point. The coefficients are determined through a Voronoi-based feature detection method. Experimental results show that our method achieves high performance in point cloud denoising and unoriented surface reconstruction within different noise scales, and exhibits well-rounded performance in various types of inputs.

翻译：我们提出了一种新策略，通过交替更新去噪点云与重建曲面，将点云去噪与曲面重建相衔接。在泊松曲面重建中，隐式函数由一组以八叉树节点为中心的平滑基函数生成。当八叉树深度选择适当时，重建曲面可视为含噪点集的良好平滑逼近。我们的方法将含噪点投影至曲面，并交替执行点集的曲面重建与投影操作。采用迭代泊松曲面重建（iPSR）支持无定向曲面重建，该方法迭代执行iPSR并作为其外循环。鉴于八叉树深度显著影响重建结果，我们提出自适应深度选择策略以确保合理深度选取。针对尖锐特征附近的过平滑现象，提出$\lambda$投影法，即为每个点赋予独立控制系数$\lambda_{i}$，将含噪点投影至曲面。该系数通过基于Voronoi的特征检测方法确定。实验结果表明，本方法在不同噪声尺度下均能实现高效的点云去噪与无定向曲面重建，并在多种输入类型中展现出全面性能。