论文标题
随机泊松表面重建
Stochastic Poisson Surface Reconstruction
论文作者
论文摘要
我们引入了经典泊松表面重建算法的统计扩展，用于从3D点云中恢复形状。我们没有输出隐式函数，而是将重建的形状表示为修改的高斯过程，这使我们能够进行统计查询（例如，空间点位于表面上或固体内部的可能性）。我们表明，这种观点：改善PSR在在线扫描过程中的集成，扩大其应用领域，并为其他研究范围打开大门，例如应用特定于任务的先验。
We introduce a statistical extension of the classic Poisson Surface Reconstruction algorithm for recovering shapes from 3D point clouds. Instead of outputting an implicit function, we represent the reconstructed shape as a modified Gaussian Process, which allows us to conduct statistical queries (e.g., the likelihood of a point in space being on the surface or inside a solid). We show that this perspective: improves PSR's integration into the online scanning process, broadens its application realm, and opens the door to other lines of research such as applying task-specific priors.
