Improving Efficiency of Iso-Surface Extraction on Implicit Neural Representations Using Uncertainty Propagation

arXiv:2402.13861v1 Announce Type: cross
Abstract: Implicit Neural representations (INRs) are widely used for scientific data reduction and visualization by modeling the function that maps a spatial location to a data value. Without any prior knowledge about the spatial distribution of values, we are forced to sample densely from INRs to perform visualization tasks like iso-surface extraction which can be very computationally expensive. Recently, range analysis has shown promising results in improving the efficiency of geometric queries, such as ray casting …

abstract arxiv cs.gr cs.lg data data reduction data value distribution efficiency extraction function implicit neural representations iso knowledge location maps modeling prior propagation sample spatial surface type uncertainty value values visualization