Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910619827961856 |
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| author | Khan, Mohammad Sadil Sinha, Sankalp Sheikh, Talha Uddin Stricker, Didier Ali, Sk Aziz Afzal, Muhammad Zeshan |
| author_facet | Khan, Mohammad Sadil Sinha, Sankalp Sheikh, Talha Uddin Stricker, Didier Ali, Sk Aziz Afzal, Muhammad Zeshan |
| contents | Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using designer-friendly instructions for all skill levels. Furthermore, we introduce a data annotation pipeline for generating text prompts based on natural language instructions for the DeepCAD dataset using Mistral and LLaVA-NeXT. The dataset contains $\sim170$K models and $\sim660$K text annotations, from abstract CAD descriptions (e.g., generate two concentric cylinders) to detailed specifications (e.g., draw two circles with center $(x,y)$ and radius $r_{1}$, $r_{2}$, and extrude along the normal by $d$...). Within the Text2CAD framework, we propose an end-to-end transformer-based auto-regressive network to generate parametric CAD models from input texts. We evaluate the performance of our model through a mixture of metrics, including visual quality, parametric precision, and geometrical accuracy. Our proposed framework shows great potential in AI-aided design applications. Our source code and annotations will be publicly available. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_17106 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts Khan, Mohammad Sadil Sinha, Sankalp Sheikh, Talha Uddin Stricker, Didier Ali, Sk Aziz Afzal, Muhammad Zeshan Computer Vision and Pattern Recognition Graphics Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using designer-friendly instructions for all skill levels. Furthermore, we introduce a data annotation pipeline for generating text prompts based on natural language instructions for the DeepCAD dataset using Mistral and LLaVA-NeXT. The dataset contains $\sim170$K models and $\sim660$K text annotations, from abstract CAD descriptions (e.g., generate two concentric cylinders) to detailed specifications (e.g., draw two circles with center $(x,y)$ and radius $r_{1}$, $r_{2}$, and extrude along the normal by $d$...). Within the Text2CAD framework, we propose an end-to-end transformer-based auto-regressive network to generate parametric CAD models from input texts. We evaluate the performance of our model through a mixture of metrics, including visual quality, parametric precision, and geometrical accuracy. Our proposed framework shows great potential in AI-aided design applications. Our source code and annotations will be publicly available. |
| title | Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2409.17106 |