mlsanityData to 13 Oct 2026

Interactive Stroke-based Neural SDF Sculpting

arXiv 2502.02891 ↗First posted 5 Feb 2025cs.GR cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 5 Feb 2025.

Builds on

16 of 35 references.

References · 16

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4 more
  1. 2020Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondencecited by 119
  2. 2020SALD: Sign Agnostic Learning with Derivativescited by 115
  3. 2022Differentiable Rendering of Neural SDFs through Reparameterizationcited by 26
  4. 20223D Neural Sculpting (3DNS): Editing Neural Signed Distance Functionscited by 4

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