发表机构
Bilkent University; École Polytechnique Fédérale de Lausanne (EPFL)（比尔肯大学; 洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
StyleFields提出一种基于DeepSDF的多尺度AdaIN调制隐式SDF架构，实现从粗到细的三维形状重建与可控风格混合，并在汽车空气动力学优化中验证了其有效性。
AI 中文摘要
我们引入了StyleFields，一种基于DeepSDF的高保真三维重建架构，它实现了可控的几何风格混合：一个物体的粗略结构可以与另一个物体的精细细节相结合。核心思想是深度感知调制：我们不是使用单一的全局编码，而是在多个解码器深度处通过多级自适应实例归一化（AdaIN）注入潜在变量，并在逐步增加网络深度的同时，用从粗到细的调度来监督匹配的辅助头。这使得早期层与全局形状对齐，后期层与高频细节对齐，实现了无需部件标签或对抗训练的内容-风格解耦。StyleFields提供了忠实的重建、令人信服的跨实例混合，以及在注入深度和监督粒度上的消融实验中一致的性能提升。我们进一步展示了在汽车空气动力学中的一个实际应用：一个学习得到的代理阻力预测器作为可微目标函数，用于优化重建的汽车，通过冻结互补的潜在流，可以对全局形状或表面细节进行有针对性的编辑。StyleFields为可控隐式重建和下游性能驱动设计提供了一种简单而有效的方案。
英文摘要
We introduce StyleFields, a DeepSDF-based architecture for high-fidelity 3D reconstruction that enables controllable geometric style mixing: the coarse structure of one object can be combined with the fine-scale details of another. The core idea is depth-aware modulation: instead of a single global code, we inject latents via multi-level Adaptive Instance Normalization at several decoder depths, and supervise matching auxiliary heads with a coarse-to-fine schedule while gradually growing network depth. This aligns early layers with global shape and later layers with high-frequency detail, achieving content-style decoupling without part labels or adversarial training. StyleFields delivers faithful reconstructions, convincing cross-instance hybrids, and consistent gains in ablations over injection depth and supervision granularity. We further demonstrate a practical application in automotive aerodynamics: a learned surrogate drag predictor serves as a differentiable objective to optimize reconstructed cars, allowing targeted edits of global form or surface details by freezing the complementary latent stream. StyleFields offers a simple, effective recipe for controllable implicit reconstruction and downstream performance-driven design.
Comments39 pages, 20 figures, 3 tables. Includes supplementary material
