# A decision-support method for information inconsistency resolution in direct modeling of CAD models

arXiv: https://arxiv.org/abs/1906.08433 · HTML: https://mlsanity.com/paper/1906.08433/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings](https://mlsanity.com/paper/2209.02692/) (2022)
- [XVoxel-Based Parametric Design Optimization of Feature Models](https://mlsanity.com/paper/2303.15316/) (2023)

## Abstract (arXiv)

Direct modeling is a very recent CAD paradigm that can provide unprecedented modeling flexibility. It, however, lacks the parametric capability, which is indispensable to modern CAD systems. For direct modeling to have this capability, an additional associativity information layer in the form of geometric constraint systems needs to be incorporated into direct modeling. This is no trivial matter due to the possible inconsistencies between the associativity information and geometry information in a model after direct edits. The major issue of resolving such inconsistencies is that there often exist many resolution options. The challenge lies in avoiding invalid resolution options and prioritizing valid ones. This paper presents an effective method to support the user in making decisions among the resolution options. In particular, the method can provide automatic information inconsistency reasoning, avoid invalid resolution options completely, and guide the choice among valid resolution options. Case studies and comparisons have been conducted to demonstrate the effectiveness of the method.