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We present a framework to generate watertight mesh representations in anunsupervised manner from noisy point clouds of complex, heterogeneous objectswith free-form surfaces. The resulting meshes are ready to use in applicationslike kinematics and dynamics simulation where watertightness and fastprocessing are the main quality criteria. This works with no necessity of userinteraction, mainly by utilizing a modified Growing Neural Gas technique forsurface reconstruction combined with several post-processing steps. In contrastto existing methods, the proposed framework is able to cope with input pointclouds generated by consumer-grade RGBD sensors and works even if the inputdata features large holes, e.g. a missing bottom which was not covered by thesensor. Additionally, we explain a method to unsupervisedly optimize theparameters of our framework in order to improve generalization quality and, atthe same time, keep the resulting meshes as coherent as possible to theoriginal object regarding visual and geometric properties.

Unsupervised Watertight Mesh Generation for Physics Simulation Applications Using Growing Neural Gas on Noisy Free-Form Object Models