mlsanityData to 7 Oct 2026

Concise Plane Arrangements for Low-Poly Surface and Volume Modelling

arXiv 2404.06154 ↗First posted 9 Apr 2024cs.CG cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Plane arrangements are a useful tool for surface and volume modelling. However, their main drawback is poor scalability. We introduce two key novelties that enable the construction of plane arrangements for complex objects and entire scenes: (i) an ordering scheme for the plane insertion and (ii) the direct use of input points during arrangement construction. Both ingredients reduce the number of unwanted splits, resulting in improved scalability of the construction mechanism by up to two orders of magnitude compared to existing algorithms. We further introduce a remeshing and simplification technique that allows us to extract low-polygon surface meshes and lightweight convex decompositions of volumes from the arrangement. We show that our approach leads to state-of-the-art results for the aforementioned tasks by comparing it to learning-based and traditional approaches on various different datasets. Our implementation is available at https://github.com/raphaelsulzer/compod .

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 36 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

9 of 36 references.

References · 9

  1. 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 205
  2. 2016Thingi10K: A Dataset of 10,000 3D-Printing Modelscited by 182
  3. 2019CvxNet: Learnable Convex Decompositioncited by 173
  4. 2020Points2Surf: Learning Implicit Surfaces from Point Cloud Patchescited by 122
  5. 2022Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Searchcited by 61
  6. 2021CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsingcited by 51
  7. 2023D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropoutscited by 39
  8. 2021Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fieldscited by 20
  9. 2023A Survey and Benchmark of Automatic Surface Reconstruction from Point Cloudscited by 10

Built on by

Cited by year (2026 to 7 Oct)

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

4 papers.