LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation
arXiv:2510.22491v3 Announce Type: replace Abstract: Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide reliable control beyond the training distribution. We introduce LAMP, a data-efficient framework for controllable and interpretable 3D generation that aligns signed distance function (SDF) decoders by overfitting each exemplar from a shared initialization, then generates new designs by solving a parameter-constrained affine mixing problem in the aligned weight space.
The continuous drive for more efficient and controllable AI models in engineering design necessitates innovations like LAMP, addressing limitations of current data-intensive and less precise generation methods.
This development allows for high-fidelity 3D geometry generation with precise parameter control using significantly less data, crucial for rapid prototyping and specialized manufacturing without extensive training datasets.
The ability to generate complex 3D designs from limited data with explicit parameter constraints reduces the barrier to entry for highly specialized manufacturing and design processes, accelerating product development cycles.
- · Manufacturing sector
- · Engineering design firms
- · Robotics companies
- · AI model developers
- · Companies reliant on large-scale 3D data acquisition
- · Traditional CAD/CAM software lagging in AI integration
More complex and customized physical products can be designed and manufactured with greater efficiency.
This could lead to a decentralization of advanced manufacturing capabilities, as specialized designs become more accessible.
The reduced data requirements might enable AI-driven design in niche industries that traditionally lack large datasets, fostering new markets for highly customized goods.
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Read at arXiv cs.LG