mlsanityData to 14 Oct 2026

A Practical Calibration Method for RGB Micro-Grid Polarimetric Cameras

arXiv 2208.13485 ↗First posted 29 Aug 2022cs.CVcited by 1

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Abstract (arXiv)

Polarimetric imaging has been applied in a growing number of applications in robotic vision (ex. underwater navigation, glare removal, de-hazing, object classification, and depth estimation). One can find on the market RGB Polarization cameras that can capture both color and polarimetric state of the light in a single snapshot. Due to the sensor's characteristic dispersion, and the use of lenses, it is crucial to calibrate these types of cameras so as to obtain correct polarization measurements. The calibration methods that have been developed so far are either not adapted to this type of cameras, or they require complex equipment and time consuming experiments in strict setups. In this paper, we propose a new method to overcome the need for complex optical systems to efficiently calibrate these cameras. We show that the proposed calibration method has several advantages such as that any user can easily calibrate the camera using a uniform, linearly polarized light source without any a priori knowledge of its polarization state, and with a limited number of acquisitions. We will make our calibration code publicly available.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 40 references that are in the map. First posted on arXiv 29 Aug 2022.

Builds on

4 of 40 references.

References · 4

  1. 2021Polarimetric Pose Predictioncited by 16
  2. 2022CroMo: Cross-Modal Learning for Monocular Depth Estimationcited by 10
  3. 2021Polarimetric Monocular Dense Mapping Using Relative Deep Depth Priorcited by 8
  4. 2020P2D: a self-supervised method for depth estimation from polarimetrycited by 7

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