Shape: A Self-Supervised 3D Geometry Foundation Model for Industrial CAD Analysis
Industrial CAD workflows require robust, generalizable 3D geometric representations supporting accuracy and explainability. We introduce Shape, a self-supervised foundation model converting surface meshes into dense per-token embeddings. Shape combines a structured 3D latent grid, a multi-scale geometry-aware tokenizer (MAGNO) with cross-attention, and a transformer processor using grouped-query attention and RMSNorm. A learned reconstruction prior enables per-region attribution for explainable predictions. Pretraining uses masked-token reconstruction of normalized geometry statistics and multi-resolution contrastive consistency. The 10.9M-parameter backbone is pretrained on 61,052 CAD me...
| Authors | Bayangmbe Mounmo, Sam Chien, Mile Mitrovic |
|---|---|
| Primary category | cs.CV |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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