mlsanityData to 14 Oct 2026

Neural Implicit Shape Editing using Boundary Sensitivity

arXiv 2304.12951 ↗First posted 24 Apr 2023cs.CV cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Neural fields are receiving increased attention as a geometric representation due to their ability to compactly store detailed and smooth shapes and easily undergo topological changes. Compared to classic geometry representations, however, neural representations do not allow the user to exert intuitive control over the shape. Motivated by this, we leverage boundary sensitivity to express how perturbations in parameters move the shape boundary. This allows to interpret the effect of each learnable parameter and study achievable deformations. With this, we perform geometric editing: finding a parameter update that best approximates a globally prescribed deformation. Prescribing the deformation only locally allows the rest of the shape to change according to some prior, such as semantics or deformation rigidity. Our method is agnostic to the model its training and updates the NN in-place. Furthermore, we show how boundary sensitivity helps to optimize and constrain objectives (such as surface area and volume), which are difficult to compute without first converting to another representation, such as a mesh.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 48 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

21 of 48 references.

References · 21

  1. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  5. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  6. 2020Convolutional Occupancy Networkscited by 761
  7. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 695
  8. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 390
  9. 2020Local Implicit Grid Representations for 3D Scenescited by 373
  10. 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
  11. 2018Recent Advances in Autoencoder-Based Representation Learningcited by 133
  12. 2020MeshSDF: Differentiable Iso-Surface Extractioncited by 112
9 more
  1. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
  2. 2019Controlling Neural Level Setscited by 76
  3. 2021Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketchescited by 51
  4. 20213D Shape Generation with Grid-based Implicit Functionscited by 48
  5. 2022A Level Set Theory for Neural Implicit Evolution under Explicit Flowscited by 42
  6. 2022Exploring Differential Geometry in Neural Implicitscited by 26
  7. 2021Augmenting Implicit Neural Shape Representations with Explicit Deformation Fieldscited by 19
  8. 2021Intuitive Shape Editing in Latent Spacecited by 7
  9. 2022Local Distance Preserving Auto-encoders using Continuous k-Nearest Neighbours Graphscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Geometry-Informed Neural Networkscited by 12
  2. 2024Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshingcited by 5
  3. 2025Diverse Topology Optimization using Modulated Neural Fieldscited by 1
  4. 2026Refining 3D Medical Segmentation with Verbal Instructioncited by 0