CADPalette: Geometric Primitive Segmentation of CAD Models via Palette-Coded Structured 3D Latents

Geometric primitive segmentation of CAD surfaces assigns each face a geometric type while preserving the boundaries of individual surface components. This task is difficult when discretization varies widely, because evidence learned from one mesh domain can change substantially across native CAD tessellations, regularized remeshes, and scan reconstructions, where altered sampling, resolution, and noise can destabilize boundaries. We introduce CADPalette, an instance-aware geometric primitive segmentation framework that carries primitive types and component membership along one structured 3D generative trajectory. Each surface is converted to Shape O-Voxel and Shape SLAT conditions, while a Palette O-Voxel stores fixed primitive bits and within-shape randomized instance colors in a shared carrier. A fine-tuned flow predicts the corresponding Palette SLAT, and one frozen material SC-VAE decodes both fields for face-level primitive-type and instance readout. Explicit RGB separation, a frozen SC-VAE response-interpolator gate, and latent relation supervision constrain the instance field from target construction through decoding. In the common validation protocol, CADPalette surpasses the baselines by 13.3% points in primitive-type Prim-mIoU and 14.9% points in primitive-type Prim-wIoU, while also surpassing the baselines by 21.5% points in Inst-mIoU, 7.3% points in ARI, and 5.3% points in Pair-F1. Extensive experiments show that CADPalette achieves stronger geometric primitive segmentation performance and better generalization than the existing methods.