Reconstructing a surface from a point cloud is an underdetermined problem. We use a neural network to study and quantify this reconstruction uncertainty under a Poisson smoothness prior. Our algorithm addresses the main limitations of existing work and can be fully integrated into the 3D scanning pipeline, from obtaining an initial reconstruction to deciding on the next best sensor position and updating the reconstruction upon capturing more data.

翻译：从点云重建表面是一个欠定问题。我们使用神经网络在泊松平滑先验下研究并量化这种重建不确定性。我们的算法解决了现有工作的主要局限性，并且可以完全集成到三维扫描流程中，从获取初始重建、决定下一个最佳传感器位置，到捕获更多数据后更新重建结果。