用平滑滤波引导神经微分方程，实现旋转运动的高精度长期预测。
Continuous-Time SO(3) Forecasting with Savitzky--Golay Neural Controlled Differential Equations
- 基于萨维茨基-戈拉耶路径约束神经微分方程，建模连续时间旋转动态。
- 在真实数据上实现比现有方法更优的长期旋转预测性能。
- 适合需要高精度轨迹预测的机器人与视觉系统开发者。
跟踪和预测物体旋转在计算机视觉与机器人领域至关重要，但SO(3)外推仍具挑战性，主要源于（1）传感器观测存在噪声且稀疏，（2）运动模式受复杂动力学支配，（3）应用场景常需长期预测。本文提出使用由萨维茨基-戈拉耶路径引导的神经控制微分方程，对SO(3)上的连续时间旋转动态进行建模。与依赖简化运动假设的现有方法不同，该方法在保持旋转几何结构的前提下，学习物体轨迹的通用潜在动力系统。在真实世界数据上的实验表明，其预测能力显著优于现有方法。
原文摘要 · Abstract (English)
Tracking and forecasting the rotation of objects is fundamental in computer vision and robotics, yet SO(3) extrapolation remains challenging as (1) sensor observations can be noisy and sparse, (2) motion patterns can be governed by complex dynamics, and (3) application settings can demand long-term forecasting. This work proposes modeling continuous-time rotational object dynamics on $SO(3)$ using Neural Controlled Differential Equations guided by Savitzky-Golay paths. Unlike existing methods that rely on simplified motion assumptions, our method learns a general latent dynamical system of the underlying object trajectory while respecting the geometric structure of rotations. Experimental results on real-world data demonstrate compelling forecasting capabilities compared to existing approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。