Text2CAD introduces a novel data annotation pipeline that leverages open-source LLMs (Large Language Models) and VLMs (Vision-Language Models) to annotate the DeepCAD dataset with text prompts containing varying levels of complexities and parametric details. This pipeline is divided into two stages:
Shape Description Generation: Using VLM (LlaVA-NeXT), the system generates a basic shape description.
Multi-Level Textual Annotation Generation: Utilizing LLM (Mixtral-50B), the system creates detailed parametric instructions.
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Text2CAD Transformer
The Text2CAD Transformer is an end-to-end Transformer-based autoregressive architecture designed to generate CAD design history from input text prompts. The model operates as follows:
Input: A text prompt \(T\) and a CAD subsequence \(\mathbf{C}_{1:t-1}\) of length \({t-1}\).
Text Embedding: The text embedding \(T_{adapt}\) is extracted from \(T\) using a pretrained BeRT Encoder followed by a trainable Adaptive layer.
CAD Sequence Embedding: The resulting embedding \(T_{adapt}\) and the CAD sequence embedding \(F^0_{t-1}\) are passed through \(\mathbf{L}\) decoder blocks to generate the full CAD sequence in an autoregressive manner.
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Visual Results
Visual examples demonstrate the effectiveness of Text2CAD in generating 3D CAD models from varied prompts. For instance:
Ring-like Model: Three different prompts yield the same ring-like model, some without explicitly mentioning 'ring'.
Star-shaped Model: Three diverse prompts result in the same star-shaped model, each emphasizing different star characteristics.
Quantitative Results
The performance of Text2CAD was evaluated using two strategies:
CAD Sequence Evaluation: This assesses the parametric correspondence between the generated CAD sequences and the input texts using the following metrics:
- F1 Scores: For Line, Arc, Circle, and Extrusion using the method proposed in CAD-SIGNet.
- Chamfer Distance (CD): Measures geometric alignment between the ground truth and reconstructed CAD models of Text2CAD and DeepCAD.
- Invalidity Ratio (IR): Measures the invalidity of the reconstructed CAD models.
Visual Inspection: The performance of Text2CAD and DeepCAD was compared with GPT-4 and human evaluation.
Video Acknowledgement
This work was partially supported by the EU Horizon Europe Framework under grant agreement 101135724 (LUMINOUS).
Remember these 3 key ideas for your startup:
Leverage AI for Efficiency: Text2CAD demonstrates how AI can streamline the design process by converting text prompts into detailed CAD models. This can significantly reduce the time and effort required for product design, allowing startups to focus on innovation and market entry.
Adopt Advanced Data Annotation Techniques: The novel data annotation pipeline used by Text2CAD leverages both LLMs and VLMs to generate multi-level text prompts. Startups can adopt similar techniques to enhance their data processing capabilities, leading to more accurate and efficient outcomes.
Utilize Transformer Architectures: The Text2CAD Transformer showcases the power of Transformer-based architectures in generating complex outputs from simple inputs. Startups can explore Transformer models to automate and improve various aspects of their operations, from customer service to product development.
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