We present FrameTwin, a curve-anchored Gaussian alignment framework that uses sparse-view images to close the control loop for adaptive wireframe 3D printing. Our key idea is to capture the deformation of thin wireframe structures from sparse-view images using Gaussian kernels anchored to parametric curves, yielding a compact and geometry-aware encoding that explicitly captures strut topology. Driven by a differentiable rendering pipeline, FrameTwin estimates a neural deformation field that aligns the partially printed target model with the deformed structure observed during fabrication, where the optimized curve-Gaussian representation serves as a digital twin of the evolving wireframe. Unlike general Gaussian-splatting approaches, our formulation constrains kernel placement along parametric curves, substantially reducing the ambiguity inherent in sparse-view observations of thin structures. The resultant deformation-field alignment enforces global consistency across all struts. By using the estimated deformation field to blend the distorted printed geometry with the remaining unprinted geometry, FrameTwin enables adaptive updates to future printing trajectories. We demonstrate that FrameTwin can robustly capture and compensate for deformation in wireframe models fabricated using a robotized 3D printing system.

翻译：我们提出FrameTwin——一种以曲线锚定高斯对齐框架，利用稀疏视角图像为自适应线框3D打印闭合控制回路。核心思想在于：通过将高斯核锚定在参数曲线上，从稀疏视角图像中捕捉薄壁线框结构的变形，从而生成紧凑且几何感知的编码，显式捕获支柱拓扑。凭借可微分渲染管线的驱动，FrameTwin估计一个神经变形场，将部分打印的目标模型与制造过程中观测到的变形结构对齐，其中优化后的曲线-高斯表示可作为演化线框的数字孪生体。与通用高斯溅射方法不同，本构型将核定位约束在参数曲线上，大幅降低了稀疏视角观测薄壁结构固有的模糊性。由此产生的变形场对齐强制所有支柱保持全局一致性。通过利用估计的变形场融合畸变的打印几何体与剩余未打印几何体，FrameTwin能够自适应更新未来的打印轨迹。实验证明，FrameTwin可在机器人化3D打印系统中稳健捕捉并补偿线框模型的变形。