Shape: A Self-Supervised 3D Geometry Foundation Model for Industrial CAD Analysis

TL;DR

Shape is a self-supervised 3D geometry foundation model for industrial CAD analysis that learns dense surface mesh embeddings for accurate, explainable, and generalizable 3D representations.

Contribution

It introduces a novel self-supervised model combining a structured 3D latent grid, multi-scale geometry-aware tokenizer, and transformer for CAD surface understanding.

Findings

Achieves 98.1% top-1 retrieval accuracy on CAD meshes.

Pretraining on 61,052 CAD meshes yields high reconstruction quality.

Per-dimension normalization is critical for model performance.

Abstract

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 meshes from Thingi10K, MFCAD, and Fusion360. On a held-out split of 2,983 meshes, Shape achieves reconstruction R2 = 0.729 and 98.1% top-1 retrieval under the Wang-Isola protocol, with near-zero reconstruction train/val gap (contrastive scores use a larger evaluation pool). A 2x2 ablation on loss type and target-space normalization shows per-dimension normalization is critical: without it, performance collapses (R2 < 0.14, top-1 < 88%); with it, both losses succeed (R2 > 0.70, top-1 > 96%). Smooth-L1 offers secondary stability. Code, embeddings, and an interactive demo are released at https://github.com/simd-ai/shape.