Polarimetric Imagery for Object Pose Estimation

Date of Award


Degree Name

M.S. in Electrical and Computer Engineering


Department of Electrical and Computer Engineering


Bradley Ratliff


Polarization imaging is a rich modality that describes the orientation of reflected optical radiance in a scene. Polarization has been shown to be useful for computer vision tasks by improving robustness to low visibility conditions, improving contrast between polarized and non-polarized objects, and providing shape information about polarized objects. However, properly applying polarimetric information to convolutional neural networks (CNNs) is an ongoing area of research. In this work, our goal is to explore new and existing methods of introducing polarimetric imagery to pretrained RGB intensity CNNs for the purpose of object pose estimation. As part of our research, we design and execute a controlled data collection where we measure the linear Stokes parameters at each point in a well-lit image. For each well-lit image, we generate a synthetic low-light image. We then develop a pipeline to generate 3D bounding box parameters for objects of interest in a semi-automated manner. Lastly, we use our dataset to create several deep-learning-based pose estimation models which utilize polarization information in differing ways. We compare the pose estimation performance of each network under varying illumination conditions.


Electrical Engineering, Optics, Polarimetric Imagery, visible-spectrum, deep-learning, object pose estimation, CNN, late-fusion, Stokes-products dataset

Rights Statement

Copyright © 2023, author