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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Event-Based Shape from Polarization |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 979-8-3503-0129-8 |
| ISSN | 1063-6919 |
| Page Range | 1547 - 1556 |
| Event Title | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 |
| Event Type | conference |
| Event Location | Vancouver, BC, Canada |
| Event Start Date | June 18 - 2023 |
| Event End Date | June 22 - 2023 |
| Series Name | IEEE Conference on Computer Vision and Pattern Recognition. Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers |
| Abstract Text | State-of-the-art solutions for Shape-from-Polarization (SfP) suffer from a speed-resolution tradeoff: they either sacrifice the number of polarization angles measured or necessitate lengthy acquisition times due to framerate constraints, thus compromising either accuracy or latency. We tackle this tradeoff using event cameras. Event cameras operate at microseconds resolution with negligible motion blur, and output a continuous stream of events that precisely measures how light changes over time asynchronously. We propose a setup that consists of a linear polarizer rotating at high speeds in front of an event camera. Our method uses the continuous event stream caused by the rotation to reconstruct relative intensities at multiple polarizer angles. Experiments demonstrate that our method outperforms physics-based baselines using frames, reducing the MAE by 25% in synthetic and real-world datasets. In the real world, we observe, however, that the challenging conditions (i.e., when few events are generated) harm the performance of physics-based solutions. To overcome this, we propose a learning-based approach that learns to estimate surface normals even at low event-rates, improving the physics-based approach by 52% on the real world dataset. The proposed system achieves an acquisition speed equivalent to 50 fps (>twice the framerate of the commercial polarization sensor) while retaining the spatial resolution of 1 MP. Our evaluation is based on the first large-scale dataset for event-based SfP. |
| Digital Object Identifier | 10.1109/CVPR52729.2023.00155 |
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| Additional Information | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |