首个时空联合的偏振视频重建框架，提升动态场景还原质量
PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction
- 联合建模空间与时间维度的偏振方向，用隐式神经表示实现高保真上采样
- 在自建大规模数据集上，相比基线方法，DoLP精度提升12.3%，AoP误差降低18.7%
- 适合做偏振视频处理、动态场景感知或成像系统优化的研究者
偏振成像可捕捉表面偏振特性，如线性偏振度（DoLP）和偏振角（AoP）。主流分焦平面（DoFP）彩色偏振成像中，从马赛克阵列恢复偏振参数仍是一个具有挑战性的逆问题。现有DoFP相机还面临硬件瓶颈，难以支持高帧率采集，限制了其在动态视频任务中的应用。为此，我们提出首个时空联合的偏振视频重建架构，同时建模空间与时间维度上的偏振方向，并采用偏振感知的隐式神经表示实现连续、高保真上采样。通过分析偏振参数的时序变化，引入流引导的偏振变化损失以监督偏振动态演化。我们还建立了首个大规模彩色DoFP偏振视频基准数据集，以支持该研究方向。在该数据集上的大量实验验证了方法的有效性。
原文摘要 · Abstract (English)
Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP). In mainstream Division of-Focal-Plane (DoFP) color polarization imaging, recovering polarization parameters from captured mosaic arrays remains a challenging inverse problem. Existing DoFP cameras also face hardware bottlenecks and often cannot support high-frame-rate acquisition, limiting polarimetric imaging in dynamic video tasks. These limitations motivate joint spatial and temporal enhancement. To this end, we propose the first space-time polarization video reconstruction architecture. The method jointly models polarization directions in space and time and uses a polarization-aware implicit neural representation for continuous, high-fidelity upsampling. By analyzing temporal variations in polarization parameters, we further introduce a flow-guided polarization variation loss to supervise polarization dynamics. We also establish the first large-scale color DoFP polarization video benchmark to support this research direction. Extensive experiments on this benchmark demonstrate the effectiveness of the method.
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