用正弦几何先验提升动态3D场景重建精度
Learning Dynamic Scene Reconstruction with Sinusoidal Geometric Priors
- 结合正弦网络周期性与关键点几何约束
- 60万标注样本训练，显著提升时空一致性
- 适合快速运动和多目标复杂场景重建
我们提出SirenPose，一种新型损失函数，将正弦表示网络的周期激活特性与关键点结构的几何先验相结合，以提升动态3D场景重建的准确性。现有方法在快速运动和多目标场景中常难以保持运动建模精度与时空一致性。通过引入受物理启发的约束机制，SirenPose在空间和时间维度上强制关键点预测的一致性。我们进一步将训练数据集扩展至60万标注实例，以支持稳健学习。实验结果表明，使用SirenPose训练的模型在时空一致性指标上显著优于先前方法，在处理快速运动和复杂场景变化时表现更优。
原文摘要 · Abstract (English)
We propose SirenPose, a novel loss function that combines the periodic activation properties of sinusoidal representation networks with geometric priors derived from keypoint structures to improve the accuracy of dynamic 3D scene reconstruction. Existing approaches often struggle to maintain motion modeling accuracy and spatiotemporal consistency in fast moving and multi target scenes. By introducing physics inspired constraint mechanisms, SirenPose enforces coherent keypoint predictions across both spatial and temporal dimensions. We further expand the training dataset to 600,000 annotated instances to support robust learning. Experimental results demonstrate that models trained with SirenPose achieve significant improvements in spatiotemporal consistency metrics compared to prior methods, showing superior performance in handling rapid motion and complex scene changes.
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