Polarized Optical-Flow Gyroscope

Tzabari, Masada; Schechner, Yoav Y.

doi:10.1007/978-3-030-58517-4_22

Cited by 5 publications

(2 citation statements)

References 81 publications

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“…Then, the features are fused at different levels, and given to the decoder. Tzabari and Schechner 54 present a static approach in which they use the AoLP and the DoLP to expand the optical flow theory. This new component accounts for the rotation speed estimation, which cannot be done with the classical optical-flow approach.…”

Section: Related Workmentioning

confidence: 99%

Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization

Rodriguez,

Lew-Yan-Voon,

Martins

et al. 2024

J. Electron. Imag.

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Polarization information of the light can provide rich cues for computer vision and scene understanding tasks, such as the type of material, pose, and shape of the objects. With the advent of new and cheap polarimetric sensors, this imaging modality is becoming accessible to a wider public for solving problems, such as pose estimation, 3D reconstruction, underwater navigation, and depth estimation. However, we observe several limitations regarding the usage of this sensorial modality, as well as a lack of standards and publicly available tools to analyze polarization images. Furthermore, although polarization camera manufacturers usually provide acquisition tools to interface with their cameras, they rarely include processing algorithms that make use of the polarization information. In this work, we review recent advances in applications that involve polarization imaging, including a comprehensive survey of recent advances on polarization for vision and robotics perception tasks. We also introduce a complete software toolkit that provides common standards to communicate with and process information from most of the existing micro-grid polarization cameras on the market. The toolkit also implements several image processing algorithms for this modality, and it is publicly available on GitHub.

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Section: Related Workmentioning

confidence: 99%

Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization

Rodriguez,

Lew-Yan-Voon,

Martins

et al. 2024

J. Electron. Imag.

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“…Polarization cues have been used in many applications across computer vision, including image dehazing [43], panorama stitching and mosaicing [44], reflection removal [21], image segmentation [26], optical flow gyroscope, [49] and material classification [5]. Among these, Shape-from-Polarization (SfP) methods exploit changes in polariza- The proposed eventbased method uses the continuous event stream to reconstruct relative intensities at multiple polarizer angles which is used to estimate surface normals using physics-based and learning-based method.…”

Section: Introductionmentioning

confidence: 99%

Event-based Shape from Polarization

Muglikar¹,

Bauersfeld²,

Moeys³

et al. 2023

Preprint

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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 dataset. 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 physicsbased 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 1MP. Our evaluation is based on the first large-scale dataset for event-based SfP.

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