发表机构
Technical University of Munich; Munich Center for Machine Learning (MCML)（慕尼黑工业大学; 慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ODeform将神经常微分方程扩展用于3D可变形物体连续4D动力学，把3D点云和物理条件转换到潜在空间，通过求解常微分方程对变形建模为连续流，在未见物理参数配置上评估，提升运动预测精度，能转移到新形状真实物体，可有效插值和外推动力学。
AI 中文摘要
对许多计算机视觉和机器人任务（如操纵和模拟）来说，对连续物体变形建模很重要。现有方法依赖基于学习的方法或物理模拟器来对形状变形建模，但要么使用离散时间步长，要么对实时应用计算量过大。我们提出ODeform，这是神经常微分方程对3D空间中可变形物体连续4D动力学的新扩展。该方法将3D点云和物理条件（如材料属性）转换到统一潜在空间，通过求解常微分方程对变形建模为学习嵌入中的连续流，消除离散时间步长需求并保持计算效率。我们在未见物理参数配置上评估方法，显示出比基线方法更高的运动预测精度，还成功转移到新形状的真实3D捕获物体上，且能有效插值和外推学习到的动力学。代码和数据将公开。
英文摘要
Modeling continuous object deformation is important for many computer vision and robotics tasks, such as manipulation and simulation. Existing approaches rely on learning-based methods or physics simulators to model shape deformations. However, these approaches either use discrete time steps or are too computationally intensive for real-time applications. We present ODeform, a novel extension of Neural Ordinary Differential Equations to continuous 4D dynamics of deformable objects in 3D space. Our method transforms 3D point clouds and physical conditions (like material properties) into a unified latent space. By solving the resulting ordinary differential equations over time, we model deformations as continuous flows within this learned embedding, eliminating the need for discrete time steps while maintaining computational efficiency. We evaluate our approach on unseen physical parameter configurations, showing improved motion prediction accuracy over baseline methods. Our experiments further demonstrate a successful transfer to real 3D captured objects with novel shapes, along with effective interpolation and extrapolation of the learned dynamics. Our code and data will be made publicly available.
CommentsAccepted at IROS 2026
