Surface Reconstruction based on Self-Merging Octree with Deep Learning
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:883-896, 2019.
A model segment method called Octree Subdivision has been presented for long years, which allows any three-dimensional point cloud object to be subdivided into infinitesimals so that it can be approximated by a particular surface function. In this paper, we proposed a new method named self-merging octree to reconstruct the surface of 3D Point Cloud which can be obtained by laser scanners or generated by some 3D modeling software. Different from any other surface reconstruction algorithms such as local property-based or specific type-based, a function pool-based was introduced in our research because it can express many different types of surfaces. We subdivide point cloud model by self-merging octree and categorize it by the neuro-network. In this idea, it is easy for us to find a proper surface function to present the subsurface of the model. What‘s more, while we extend the function pool, we can indicate far more style models. We have tried to reconstruct many point cloud models‘ surfaces in this way, and it works well and also shows its potential ability to build a bridge in the fields of model editing, model splicing, and model deformation.