Implicit neural networks have been successfully used for surface
reconstruction from point clouds. However, many of them face scalability issues
as they encode the isosurface function of a whole object or scene into a single
latent vector. To overcome this limitation, a few approaches infer latent
vectors on a coarse regular 3D grid or on 3D patches, and interpolate them to
answer occupancy queries. In doing so, they loose the direct connection with
the input points sampled on the surface of objects, and they attach information
uniformly in space rather than where it matters the most, i.e., near the
surface. Besides, relying on fixed patch sizes may require discretization
tuning. To address these issues, we propose to use point cloud convolutions and
compute latent vectors at each input point. We then perform a learning-based
interpolation on nearest neighbors using inferred weights. Experiments on both
object and scene datasets show that our approach significantly outperforms
other methods on most classical metrics, producing finer details and better
reconstructing thinner volumes. The code is available at
this https URL

研究提出了一种使用点云卷积和在每个输入点计算潜在向量的方法，然后进行最近邻的基于学习的插值。实验结果表明，该方法在大多数经典指标上明显优于其他方法，生成更精细的细节并更好地重建了更薄的体积。