Browsing by Author "Rubab, Fizza"

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    Interactive Stroke-based Neural SDF Sculpting
    (The Eurographics Association, 2025) Rubab, Fizza; Tong, Yiying; Knoll, Aaron; Peters, Christoph
    Recent advances in implicit neural representations have made them a popular choice for modeling 3D geometry. However, directly editing these representations presents challenges due to the complex relationship between model weights and surface geometry, as well as the slow optimization required to update neural fields. Among various editing tools, sculpting stands out as a valuable operation for the graphics and modeling community. While traditional mesh-based tools like ZBrush enable intuitive edits, a comparable high-performance toolkit for sculpting neural SDFs is currently lacking. We introduce a framework that enables interactive surface sculpting directly on neural implicit representations with optimized performance. Unlike previous methods, which are limited to spot edits, our approach allows users to perform stroke-based modifications on the fly, ensuring intuitive shape manipulation without switching representations. By employing tubular neighborhoods to sample strokes and customizable brush profiles, we achieve smooth deformations along user-defined curves, providing intuitive control over the sculpting process. Our method demonstrates that versatile edits can be achieved while preserving the smooth nature of implicit representations, all without compromising interactive performance.
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    Learning Neural Antiderivatives
    (The Eurographics Association, 2025) Rubab, Fizza; Nsampi, Ntumba Elie; Balint, Martin; Mujkanovic, Felix; Seidel, Hans-Peter; Ritschel, Tobias; Leimkühler, Thomas; Egger, Bernhard; Günther, Tobias
    Neural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representations of repeated antiderivatives directly from a function, a continuous analogue of summed-area tables. Although widely used in discrete domains, such cumulative schemes rely on grids, which prevents their applicability in continuous neural contexts. We introduce and analyze a range of neural methods for repeated integration, including both adaptations of prior work and novel designs. Our evaluation spans multiple input dimensionalities and integration orders, assessing both reconstruction quality and performance in downstream tasks such as filtering and rendering. These results enable integrating classical cumulative operators into modern neural systems and offer insights into learning tasks involving differential and integral operators.