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. To improve reliability, we propose a linearity-mismatch safety metric that detects when mixed decoders leave the valid local regime. We evaluate LAMP on DrivAerNet++, BlendedNet, and additional industry-level vehicle families, including sports cars, SUVs, and convertibles. LAMP enables controlled interpolation with as few as 50 samples, safe extrapolation up to 100% beyond training ranges, and performance-guided optimization under fixed parameters, outperforming conditional autoencoder and Deep Network Interpolation (DNI) baselines in extrapolation, data efficiency, and parameter fidelity. Our results demonstrate that LAMP advances controllable, data-efficient, and safe 3D generation for design exploration, dataset generation, and performance-driven optimization.

翻译：在工程设计中，于显式参数约束下生成高保真三维几何结构至关重要，然而现有方法通常需要大规模数据集，且无法在训练分布之外提供可靠控制。我们提出LAMP——一种数据高效的框架，用于可控且可解释的三维生成。该方法通过从共享初始化阶段对每个样本进行过拟合，对齐有符号距离函数（SDF）解码器，随后在对齐的权空间中通过求解参数约束仿射混合问题生成新设计。为提升可靠性，我们提出线性失配安全度量，用于检测混合解码器何时偏离有效局部区域。我们在DrivAerNet++、BlendedNet以及包含跑车、SUV和敞篷车在内的工业级车型系列上评估LAMP。LAMP仅需50个样本即可实现受控插值，在训练范围外达到100%的安全外推，并在固定参数下完成性能导向优化，在外推能力、数据效率与参数保真度方面均优于条件自编码器与深度网络插值（DNI）基线。实验结果表明，LAMP推动了面向设计探索、数据集生成及性能驱动优化的可控、数据高效且安全的三维生成技术发展。