Before revision:  These requirements are captured by persistence diagrams , and these are used to inform the optimization process that learns the parameters. We obtain parameters to build a likelihood function over the reconstruction domain. This novel topology-aware technique is useful to weed out topological noise from point scans, apart from capturing certain nuanced properties of the underlying shape , that could otherwise be lost while performing direct surface reconstruction.
After revision:  These requirements are captured by persistence diagrams which are incorporated in the optimization algorithm promote the correct topology. The result is a novel topology-aware technique is useful to weed out topological noise from point scans, apart from capturing certain nuanced properties of the underlying shape , that could otherwise be lost while performing direct surface reconstruction.