Parametric cad modeling method, device and equipment of fusion view and storage medium

By extracting features and predicting sequences from multi-view images, an editable CAD model sequence is generated, solving the adaptation problem of multi-view engineering drawing input in existing technologies and realizing an efficient and reliable CAD modeling process.

CN122174297APending Publication Date: 2026-06-09SHENZHEN TIANHAI CHENGUANG TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN TIANHAI CHENGUANG TECH CO LTD
Filing Date
2026-01-20
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing deep learning-driven CAD generation methods struggle to directly handle multi-view engineering drawings commonly found in industrial practice, resulting in insufficient convenience and compatibility in modeling applications, and the output models lack editability and traceability.

Method used

By acquiring and aligning multi-view images, visual feature labels are generated using view feature extraction and pooling. Autoregressive modeling is then performed using a sequence prediction model to output an editable CAD model sequence that is compatible with multi-view engineering drawing input.

Benefits of technology

It enables end-to-end generation directly from multi-view drawings to structured CAD modeling instructions, improving the compatibility and reliability of the modeling process. The output model is interpretable and editable, meeting the traceability and version iteration needs of industrial design.

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Abstract

This application relates to the technical field of CAD modeling, and more particularly to a parametric CAD modeling method, apparatus, device, and storage medium for fused views. It includes: acquiring multi-view images after alignment processing of the modeling target; extracting features from each view image to obtain view feature representations corresponding to each viewpoint, and performing pooling processing on all view feature representations to obtain visual feature labels; inputting the visual feature labels as keys into a preset sequence prediction model, whereby the sequence prediction model, during autoregressive generation, uses the currently generated modeling label sequence as a query to progressively predict the next modeling label until a complete modeling label sequence is generated as the prediction model sequence; and performing deserialization processing on the prediction model sequence to obtain the CAD model corresponding to the modeling target. This application can be adapted to common multi-view engineering drawing input scenarios, improving the convenience of CAD modeling applications.
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