PMGS：通过3D高斯点划法重建大时空跨度的抛体运动
PMGS: Reconstruction of Projectile Motion Across Large Spatiotemporal Spans via 3D Gaussian Splatting
AI总结：
PMGS通过3D高斯点划法实现大时空跨度抛体运动的高效重建，引入加速度一致性约束和动态模拟退火策略，提升复杂运动序列的恢复能力。
摘要：
在动态重建中，对大时空跨度的复杂刚体运动建模仍是一个未解决的挑战。现有方法主要局限于短时、小尺度变形，对物理一致性考虑有限。本文提出PMGS，专注于通过3D高斯点划法重建抛体运动。流程分为两个阶段：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：通过学习每帧SE(3)姿态恢复完整运动序列。我们引入了加速度一致性约束，将牛顿力学与姿态估计联系起来，并设计了一种动态模拟退火策略，根据运动状态自适应地调度学习率。此外，我们设计了一种卡尔曼融合方案，通过优化多源观测中的误差累积来减轻干扰。实验表明，PMGS在重建高速非线性刚体运动方面相比主流动态方法具有更优的性能。
英文摘要：
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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