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Spectral Labs releases SGS-1: the first generative model for structured CAD

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Your browser does not support the video tag. SGS-1 in Fusion360 CAD software creating brackets for a roller assembly.

Today we are announcing SGS-1, a foundation model that can generate fully manufacturable and parametric 3D geometry. You can try a research preview of SGS-1 here.

Given an image or a 3D mesh, SGS-1 can generate CAD B-Rep parts in STEP format. Unlike all other existing generative models, SGS-1 outputs are accurate and can be edited easily in traditional CAD software.

Overview of SGS-1 - users can provide an image or “dumb” 3D file, and get back a parametric B-Rep file that can be easily edited to match specific dimensions

SGS-1 shows strong general results, producing much more complex and diverse CAD shapes than existing methods.

Illustrative results from SGS-1

SGS-1 can be used for real-world engineering tasks. In the below example, SGS-1 is used to design a bracket for a roller assembly from partial context and a text description (additional details below in Generating Parametric Geometry in Assembly Context section).

Your browser does not support the video tag. Bracket designed by SGS-1 as part of a simple roller assembly Your browser does not support the video tag. Bracket designed by SGS-1 as part of a complex conveyor assembly

Results and comparing SGS-1 to prior models

We compare SGS-1 to SOTA multimodal reasoning LLMs and open-source image-to-CAD models: GPT-5 thinking, a large reasoning model by OpenAI that can produce CadQuery code to represent parametric geometry, and HoLa, a 205M parameter latent diffusion model with 181M parameter VAE that generate B-Rep geometry conditioned on a single input image. We develop a benchmark set of 75 images depicting medium to high complexity parametric geometry, sourced from CAD image renders of various styles, engineering sketches, and images generated by generative AI models. Model performance is evaluated by successful/failed creation of a single valid watertight solid that is an accurate representation of the input image using distance metrics (Success Ratio).

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