GridPull: Towards Scalability in Learning Implicit Representations from
  3D Point Clouds
Chao Chen Yu-Shen Liu Zhizhong Han
Learning implicit representations has been a widely used solution for surface
reconstruction from 3D point clouds. The latest methods infer a distance or
occupancy field by overfitting a neural network on a single point cloud.
However, these methods suffer from a slow inference due to the slow convergence
of neural networks and the extensive calculation of distances to surface
points, which limits them to small scale points. To resolve the scalability
issue in surface reconstruction, we propose GridPull to improve the efficiency
of learning implicit representations from large scale point clouds. Our novelty
lies in the fast inference of a discrete distance field defined on grids
without using any neural components. To remedy the lack of continuousness
brought by neural networks, we introduce a loss function to encourage
continuous distances and consistent gradients in the field during pulling
queries onto the surface in grids near to the surface. We use uniform grids for
a fast grid search to localize sampled queries, and organize surface points in
a tree structure to speed up the calculation of distances to the surface. We do
not rely on learning priors or normal supervision during optimization, and
achieve superiority over the latest methods in terms of complexity and
accuracy. We evaluate our method on shape and scene benchmarks, and report
numerical and visual comparisons with the latest methods to justify our
effectiveness and superiority. The code is available at
https://github.com/chenchao15/GridPull.