用少量数据实现可控3D形状生成与安全外推，适合工程设计场景。
LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation
- 通过权重空间仿射混合实现参数约束下的3D生成
- 仅需50样本即可完成可控插值，外推范围达训练区间的100%
- 支持性能导向优化，适合工业级车辆设计场景
在明确参数约束下生成高保真3D几何结构是工程设计的核心挑战，现有方法通常需要大量数据，且难以可靠控制超出训练分布的区域。我们提出LAMP，一种数据高效的可控可解释3D生成框架：通过过拟合共享初始化中的每个样例来对齐符号距离函数（SDF）解码器，再在对齐的权重空间中求解参数约束的仿射混合问题以生成新设计。为提升可靠性，我们引入线性不匹配安全度量，检测混合解码器是否离开有效局部区域。我们在DrivAerNet++、BlendedNet及多个工业级车辆族（包括跑车、SUV和敞篷车）上评估LAMP，结果表明其在仅50个样本下即可实现可控插值，外推范围可达训练区间的100%，并在固定参数下实现性能导向优化，优于条件自编码器和深度网络插值（DNI）基线，在外推能力、数据效率和参数保真度方面均有提升。结果证明LAMP推动了可控、高效且安全的3D生成，适用于设计探索、数据集生成和性能驱动优化。
原文摘要 · Abstract (English)
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.
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