Polarized Computational Imaging and Beyond

Agastya Kalra


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Date: Oct 13, 2021


Is computational imaging the next frontier in computer vision? As materials, lighting, and geometry become more complex an ordinary camera starts to be the limiting step in the vision pipeline. A case in point are transparent objects which cannot be seen by an ordinary camera (which mimics the human eye). However, a plenoptic camera that captures polarization spawns a unique texture to previously invisible objects. The polarized texture results from complex light-matter interactions in shape and refractive index. Such texture can be subsequently modeled and “baked” into neural pipelines to enable inference on transparent objects, a decades-open task in traditional computer vision. WIth polarization simply scratching the surface of what computational imaging can do, we end with a discussion of how broader forms of plenoptic data can be leveraged in future neural pipelines.

Further Information:

Agastya Kalra is currently a Principal Engineer at Akasha Imaging, where he is also a founding team member. His research interests lie at the intersection of computational imaging, multi-view geometry, and machine learning. He has published in NeuriPS, ICCV, and CVPR and is a co-inventor on more than a dozen patents in computational imaging. His website can be found at kalraa.github.io

Created: Wednesday, October 13th, 2021