论文标题
带有可见性信息的点云的深层重建
Deep Surface Reconstruction from Point Clouds with Visibility Information
论文作者
论文摘要
大多数用于从点云重建表面的神经网络忽略传感器姿势，仅在原始点位置工作。但是，传感器可见性具有有关空间占用和表面取向的有意义的信息。在本文中，我们提供了两种简单的方法，可以通过可见性信息来增强原始点云，因此可以通过最小的适应性表面重建网络直接利用它。我们提出的修改一致地提高了生成的表面的准确性以及网络无法看到形状域的概括能力。我们的代码和数据可在https://github.com/raphaelsulzer/dsrv-data上找到。
Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations. Sensor visibility, however, holds meaningful information regarding space occupancy and surface orientation. In this paper, we present two simple ways to augment raw point clouds with visibility information, so it can directly be leveraged by surface reconstruction networks with minimal adaptation. Our proposed modifications consistently improve the accuracy of generated surfaces as well as the generalization ability of the networks to unseen shape domains. Our code and data is available at https://github.com/raphaelsulzer/dsrv-data.
