mlsanityData to 14 Oct 2026

Sketch2PQ: Freeform Planar Quadrilateral Mesh Design via a Single Sketch

arXiv 2201.09367 ↗First posted 23 Jan 2022cs.GR cs.CV cs.LGcited by 6

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Abstract (arXiv)

The freeform architectural modeling process often involves two important stages: concept design and digital modeling. In the first stage, architects usually sketch the overall 3D shape and the panel layout on a physical or digital paper briefly. In the second stage, a digital 3D model is created using the sketch as a reference. The digital model needs to incorporate geometric requirements for its components, such as the planarity of panels due to consideration of construction costs, which can make the modeling process more challenging. In this work, we present a novel sketch-based system to bridge the concept design and digital modeling of freeform roof-like shapes represented as planar quadrilateral (PQ) meshes. Our system allows the user to sketch the surface boundary and contour lines under axonometric projection and supports the sketching of occluded regions. In addition, the user can sketch feature lines to provide directional guidance to the PQ mesh layout. Given the 2D sketch input, we propose a deep neural network to infer in real-time the underlying surface shape along with a dense conjugate direction field, both of which are used to extract the final PQ mesh. To train and validate our network, we generate a large synthetic dataset that mimics architect sketching of freeform quadrilateral patches. The effectiveness and usability of our system are demonstrated with quantitative and qualitative evaluation as well as user studies.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 11 of its 65 references that are in the map. First posted on arXiv 23 Jan 2022.

Builds on

11 of 65 references.

References · 11

  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. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  5. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  6. 2017MarrNet: 3D Shape Reconstruction via 2.5D Sketchescited by 108
  7. 2021Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketchescited by 54
  8. 20173D Shape Reconstruction from Sketches via Multi-view Convolutional Networkscited by 45
  9. 20173D Sketching using Multi-View Deep Volumetric Predictioncited by 37
  10. 2020Better Patch Stitching for Parametric Surface Reconstructioncited by 21
  11. 2013On Linear Spaces of Polyhedral Meshescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1; 2026: 2.

6 papers.