用神经广义圆柱实现可直观编辑的三维形状建模
Controllable Shape Modeling with Neural Generalized Cylinder
- 以中心曲线加神经特征定义形状，通过相对坐标显式控制
- 支持复杂弯曲、局部缩放与扭转等非刚性变形，效果优于现有方法
- 适合需要精细编辑的3D建模场景，如动画角色或工业设计
神经形状表示（如神经符号距离场，NSDF）因其能处理复杂拓扑和任意分辨率，在三维建模中日益流行。然而，由于其隐式特征表示方式，形状编辑缺乏直观性。本文提出神经广义圆柱（NGC），是对传统广义圆柱（GC）的扩展。首先定义一条中心曲线，并沿曲线分配神经特征以表示截面轮廓；随后在具有椭圆截面的专用广义圆柱坐标系中定义NSDF。通过相对坐标，可直接操控广义圆柱来显式调节形状。实验表明，NGC在复杂弯曲、局部缩放与扭转等非刚性变形任务中表现优异，且计算效率高。此外，通过简单的神经特征插值即可实现形状融合。
原文摘要 · Abstract (English)
Neural shape representation, such as neural signed distance field (NSDF), becomes more and more popular in shape modeling as its ability to deal with complex topology and arbitrary resolution. Due to the implicit manner to use features for shape representation, manipulating the shapes faces inherent challenge of inconvenience, since the feature cannot be intuitively edited. In this work, we propose neural generalized cylinder (NGC) for explicit manipulation of NSDF, which is an extension of traditional generalized cylinder (GC). Specifically, we define a central curve first and assign neural features along the curve to represent the profiles. Then NSDF is defined on the relative coordinates of a specialized GC with oval-shaped profiles. By using the relative coordinates, NSDF can be explicitly controlled via manipulation of the GC. To this end, we apply NGC to many non-rigid deformation tasks like complex curved deformation, local scaling and twisting for shapes. The comparison on shape deformation with other methods proves the effectiveness and efficiency of NGC. Furthermore, NGC could utilize the neural feature for shape blending by a simple neural feature interpolation.
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