论文标题
迭代泊松表面重建（IPSR）
Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points
论文作者
论文摘要
泊松表面重建（PSR）仍然是一种流行技术，用于从3D点样品中重建水密表面，这要归功于其效率，简单性和稳健性。但是，现有的PSR方法和后续变体仅适用于定向点。本文打算验证改进的PSR（称为IPSR）可以完全消除点正常的要求并以迭代方式进行。在每次迭代中，IPSR作为输入点样品，其直接从前迭代中获得的表面直接计算出来的输入点样本，然后生成质量更高的新表面。广泛的定量评估证实，即使使用随机初始化的正常质量，新的IPR算法也会在5-30次迭代中收敛。如果以简单的基于可见的启发式初始化，IPSR可以进一步减少迭代次数。我们与PSR和其他强大的基于隐式功能的方法进行了全面的比较。最后，我们确认IPSR对AIM@Shape数据集以及挑战性（室内和室外）场景的有效性和可扩展性。本文的代码和数据在https://github.com/houfei0801/ipsr上。
Poisson surface reconstruction (PSR) remains a popular technique for reconstructing watertight surfaces from 3D point samples thanks to its efficiency, simplicity, and robustness. Yet, the existing PSR method and subsequent variants work only for oriented points. This paper intends to validate that an improved PSR, called iPSR, can completely eliminate the requirement of point normals and proceed in an iterative manner. In each iteration, iPSR takes as input point samples with normals directly computed from the surface obtained in the preceding iteration, and then generates a new surface with better quality. Extensive quantitative evaluation confirms that the new iPSR algorithm converges in 5-30 iterations even with randomly initialized normals. If initialized with a simple visibility based heuristic, iPSR can further reduce the number of iterations. We conduct comprehensive comparisons with PSR and other powerful implicit-function based methods. Finally, we confirm iPSR's effectiveness and scalability on the AIM@SHAPE dataset and challenging (indoor and outdoor) scenes. Code and data for this paper are at https://github.com/houfei0801/ipsr.
