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CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
[article]
2021
arXiv
pre-print
Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem. This paper presents CSG-Stump Net, an unsupervised end-to-end network for learning shapes from point clouds and discovering the underlying constituent modeling primitives and operations as well. At the core is a three-level structure called CSG-Stump, consisting of a complement layer at the bottom, an intersection layer in the middle, and a union layer at the top.
arXiv:2108.11305v1
fatcat:4565b6wyhnarlgaq3dfkdjvtd4