用3D高斯点云重建高速抛体运动，兼顾物理一致性与长时序稳定性。
PMGS: Reconstruction of Projectile Motion Across Large Spatiotemporal Spans via 3D Gaussian Splatting
- 通过动态分解与密度控制实现物体中心化建模
- 在高速非线性刚体运动上优于主流动态方法
- 适合需要物理一致性的高速运动重建任务
在大时空跨度下建模复杂刚体运动仍是动态重建中的未解难题。现有方法多局限于短期、小范围形变，且对物理一致性考虑不足。本文提出PMGS，基于3D高斯点云实现抛体运动的重建。流程分为两阶段：1）目标建模：通过动态场景分解和改进的点密度控制实现物体中心化重建；2）运动恢复：学习每帧的SE(3)位姿以还原完整运动序列。引入加速度一致性约束，连接牛顿力学与位姿估计，并设计基于运动状态自适应调度学习率的动态模拟退火策略。此外，采用卡尔曼融合方案优化多源观测误差累积，缓解干扰。实验表明，相比主流动态方法，PMGS在高精度重建高速非线性刚体运动方面表现更优。
原文摘要 · Abstract (English)
Modeling complex rigid motion across large spatiotemporal spans remains an unresolved challenge in dynamic reconstruction. Existing paradigms are mainly confined to short-term, small-scale deformation and offer limited consideration for physical consistency. This study proposes PMGS, focusing on reconstructing Projectile Motion via 3D Gaussian Splatting. The workflow comprises two stages: 1) Target Modeling: achieving object-centralized reconstruction through dynamic scene decomposition and an improved point density control; 2) Motion Recovery: restoring full motion sequences by learning per-frame SE(3) poses. We introduce an acceleration consistency constraint to bridge Newtonian mechanics and pose estimation, and design a dynamic simulated annealing strategy that adaptively schedules learning rates based on motion states. Furthermore, we devise a Kalman fusion scheme to optimize error accumulation from multi-source observations to mitigate disturbances. Experiments show PMGS's superior performance in reconstructing high-speed nonlinear rigid motion compared to mainstream dynamic methods.
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