PPI-NET: End-to-End Parametric Primitive Inference

• URL: http://arxiv.org/abs/2308.01521v1
• Date: Thu, 3 Aug 2023 03:50:49 GMT
• Title: PPI-NET: End-to-End Parametric Primitive Inference
• Authors: Liang Wang and Xiaogang Wang
• Abstract summary: In engineering applications, line, circle, arc, and point are collectively referred to as primitives. We propose an efficient and accurate end-to-end method to infer parametric primitives from hand-drawn sketch images.
• Score: 24.31083483088741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In engineering applications, line, circle, arc, and point are collectively referred to as primitives, and they play a crucial role in path planning, simulation analysis, and manufacturing. When designing CAD models, engineers typically start by sketching the model's orthographic view on paper or a whiteboard and then translate the design intent into a CAD program. Although this design method is powerful, it often involves challenging and repetitive tasks, requiring engineers to perform numerous similar operations in each design. To address this conversion process, we propose an efficient and accurate end-to-end method that avoids the inefficiency and error accumulation issues associated with using auto-regressive models to infer parametric primitives from hand-drawn sketch images. Since our model samples match the representation format of standard CAD software, they can be imported into CAD software for solving, editing, and applied to downstream design tasks.

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