mlsanityData to 14 Oct 2026

As-Rigid-As-Possible Regularization for Implicit Surfaces

arXiv 2608.15933 ↗First posted 16 Aug 2026cs.GR cs.CVcited by 0

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Abstract (arXiv)

Implicit surface representations have regained popularity because of their use in machine learning. A common component in optimization is regularization, penalizing the deviation of the surface from its original shape. The popular as-rigid-aspossible (ARAP) energy strikes a good compromise between realistic deformation behavior and efficient computation, at least for piecewise linear meshes. We develop an approach for computing the ARAP energy of a deformation function based on point sampling of the surface. The implicit representation is exploited to provide differentials in each sample. The evaluation is efficient and exact in each sample (up to numerical precision). We demonstrate the general applicability of the method to neural shape processing in several applications and contrast its properties with alternatives from the literature.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 16 Aug 2026.

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24 of 45 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  5. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  6. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  7. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  8. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  9. 2018Invertible Residual Networkscited by 365
  10. 2019Neural Cages for Detail-Preserving 3D Deformationscited by 106
  11. 2020Deep Implicit Templates for 3D Shape Representationcited by 86
  12. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
12 more
  1. 20193DN: 3D Deformation Networkcited by 60
  2. 2022Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshescited by 28
  3. 2022Neural Shape Deformation Priorscited by 27
  4. 2019Cubic Stylizationcited by 14
  5. 2023Accurate Differential Operators for Hybrid Neural Fieldscited by 9
  6. 2023Self-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objectscited by 8
  7. 2021Normal-Driven Spherical Shape Analogiescited by 5
  8. 2024Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshingcited by 5
  9. 2025ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Priorcited by 5
  10. 2023Developability Approximation for Neural Implicits through Rank Minimizationcited by 3
  11. 2025Uniform Sampling of Surfaces by Casting Rayscited by 2
  12. 2025Higher Order Continuity for Smooth As-Rigid-As-Possible Shape Modelingcited by 1

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