Shapes In A Box -- Disassembling 3D objects for efficient packing and fabrication
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Abstract (arXiv)
Modern 3D printing technologies and the upcoming mass-customization paradigm call for efficient methods to produce and distribute arbitrarily-shaped 3D objects. This paper introduces an original algorithm to split a 3D model in parts that can be efficiently packed within a box, with the objective of reassembling them after delivery. The first step consists in the creation of a hierarchy of possible parts that can be tightly packed within their minimum bounding boxes. In a second step, the hierarchy is exploited to extract the (single) segmentation whose parts can be most tightly packed. The fact that shape packing is an NP-complete problem justifies the use of heuristics and approximated solutions whose efficacy and efficiency must be assessed. Extensive experimentation demonstrates that our algorithm produces satisfactory results for arbitrarily-shaped objects while being comparable to ad-hoc methods when specific shapes are considered.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Apr 2021.
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Citing papers per year: 2023: 3; 2024: 0; 2025: 1; 2026: 1.
5 papers.
- 2023Learning based 2D Irregular Shape Packingcited by 5
- 2023Learning Gradient Fields for Scalable and Generalizable Irregular Packingcited by 3
- 2023An overview of some mathematical techniques and problems linking 3D vision to 3D printingcited by 0
- 2025Introducing Resizable Region Packing Problem in Image Generation, with a Heuristic Solutioncited by 0
- 2026Differentiable Packing of Irregular 3D Objects with Adaptive Container Estimationcited by 0
- 2026Differentiable Packing of Irregular 3D Objects with Adaptive Container Estimation15 Jun 2026
- 2025Introducing Resizable Region Packing Problem in Image Generation, with a Heuristic Solution19 Sep 2025
- 2023Learning Gradient Fields for Scalable and Generalizable Irregular Packing18 Oct 2023
- 2023An overview of some mathematical techniques and problems linking 3D vision to 3D printing19 Sep 2023
- 2023Learning based 2D Irregular Shape Packing19 Sep 2023