mlsanityData to 14 Oct 2026

TreeSketchNet: From Sketch To 3D Tree Parameters Generation

arXiv 2207.12297 ↗First posted 25 Jul 2022cs.CV cs.AI cs.GRcited by 2

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Abstract (arXiv)

3D modeling of non-linear objects from stylized sketches is a challenge even for experts in Computer Graphics (CG). The extrapolation of objects parameters from a stylized sketch is a very complex and cumbersome task. In the present study, we propose a broker system that mediates between the modeler and the 3D modelling software and can transform a stylized sketch of a tree into a complete 3D model. The input sketches do not need to be accurate or detailed, and only need to represent a rudimentary outline of the tree that the modeler wishes to 3D-model. Our approach is based on a well-defined Deep Neural Network (DNN) architecture, we called TreeSketchNet (TSN), based on convolutions and able to generate Weber and Penn parameters that can be interpreted by the modelling software to generate a 3D model of a tree starting from a simple sketch. The training dataset consists of Synthetically-Generated revision(SG) sketches that are associated with Weber-Penn parameters generated by a dedicated Blender modelling software add-on. The accuracy of the proposed method is demonstrated by testing the TSN with both synthetic and hand-made sketches. Finally, we provide a qualitative analysis of our results, by evaluating the coherence of the predicted parameters with several distinguishing features.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 32 of its 112 references that are in the map. First posted on arXiv 25 Jul 2022.

Builds on

32 of 112 references.

References · 32

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20 more
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Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025Demeter: A Parametric Model of Crop Plant Morphology from the Real Worldcited by 3
  2. 2026Deep Sketch-Based 3D Modeling: A Surveycited by 3