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

TL;DR

PPI-NET is an end-to-end method for accurately inferring parametric primitives from sketches, streamlining CAD design by reducing repetitive tasks and error accumulation.

Contribution

It introduces a novel end-to-end approach that directly infers primitives from sketches, avoiding auto-regressive models and improving efficiency and accuracy in CAD modeling.

Findings

High accuracy in primitive inference from sketches

Reduced error accumulation compared to previous methods

Compatible with standard CAD software for downstream tasks

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.