# Fabrication-Aware Reverse Engineering for Carpentry

arXiv: https://arxiv.org/abs/2107.09965 · HTML: https://mlsanity.com/paper/2107.09965/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 59 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Learning Shape Abstractions by Assembling Volumetric Primitives](https://mlsanity.com/paper/1612.00404/) (2016)
- [Scan2CAD: Learning CAD Model Alignment in RGB-D Scans](https://mlsanity.com/paper/1811.11187/) (2018)
- [Supervised Fitting of Geometric Primitives to 3D Point Clouds](https://mlsanity.com/paper/1811.08988/) (2018)
- [Learning to Infer and Execute 3D Shape Programs](https://mlsanity.com/paper/1901.02875/) (2019)
- [Im2Struct: Recovering 3D Shape Structure from a Single RGB Image](https://mlsanity.com/paper/1804.05469/) (2018)
- [Deep Parametric Shape Predictions using Distance Fields](https://mlsanity.com/paper/1904.08921/) (2019)
- [Deformation-Aware 3D Model Embedding and Retrieval](https://mlsanity.com/paper/2004.01228/) (2020)
- [ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis](https://mlsanity.com/paper/2009.08026/) (2020)
- [Parsing Geometry Using Structure-Aware Shape Templates](https://mlsanity.com/paper/1808.01337/) (2018)
- [Towards Zero-Waste Furniture Design](https://mlsanity.com/paper/1604.00047/) (2016)
- [Surface2Volume: Surface Segmentation Conforming Assemblable Volumetric Partition](https://mlsanity.com/paper/1904.10213/) (2019)

## Built on by (most cited first, 1 of 1)

- [CGGT: Curve-Grounded Geometry Transformer for 3D Parametric Curve Reconstruction](https://mlsanity.com/paper/2609.14521/) (2026)

## Abstract (arXiv)

We propose a novel method to generate fabrication blueprints from images of carpentered items. While 3D reconstruction from images is a well-studied problem, typical approaches produce representations that are ill-suited for computer-aided design and fabrication applications. Our key insight is that fabrication processes define and constrain the design space for carpentered objects, and can be leveraged to develop novel reconstruction methods. Our method makes use of domain-specific constraints to recover not just valid geometry, but a semantically valid assembly of parts, using a combination of image-based and geometric optimization techniques. We demonstrate our method on a variety of wooden objects and furniture, and show that we can automatically obtain designs that are both easy to edit and accurate recreations of the ground truth. We further illustrate how our method can be used to fabricate a physical replica of the captured object as well as a customized version, which can be produced by directly editing the reconstructed model in CAD software.