PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction
2026-05-11 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address the challenge of capturing detailed color polarization information in videos using special cameras called Division-of-Focal-Plane (DoFP) sensors. They created a new method that improves how these cameras process polarization data over both space and time, allowing for smoother and more accurate video results. Their approach uses a neural network that understands how polarization changes frame by frame and helps reconstruct higher-quality polarization videos. They also built a large video dataset to test and support their method. Their experiments show this method works well for enhancing polarimetric video reconstruction.
Polarimetric imagingDegree of Linear Polarization (DoLP)Angle of Polarization (AoP)Division-of-Focal-Plane (DoFP) cameraImplicit neural representationVideo reconstructionPolarization dynamicsFlow-guided lossUpsamplingBenchmark dataset
Authors
Chenggong Li, Yidong Luo, Junchao Zhang, Boxin Shi, Degui Yang
Abstract
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.