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Volume Rendering of Neural Implicit Surfaces

Neural Information Processing Systems

We achieve that by modeling the volume density as a function of the geometry. This is in contrast to previous work modeling the geometry as a function of the volume density.



SequentialBayesianExperimentalDesignwith VariableCostStructure

Neural Information Processing Systems

While theoretically appealing, MIevaluation poses asignificant computational burden for most real world applications. As a result, many algorithms utilize MI bounds as proxies that lack regret-style guarantees. Here, we utilize two-sided bounds to provide such guarantees.