# Standardized spectral and radiometric calibration of consumer cameras

arXiv: https://arxiv.org/abs/1906.04155 · HTML: https://mlsanity.com/paper/1906.04155/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation](https://mlsanity.com/paper/2306.15662/) (2023)
- [Spectral Sensitivity Estimation Without a Camera](https://mlsanity.com/paper/2304.11549/) (2023)
- [Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks](https://mlsanity.com/paper/2103.08482/) (2021)

## Abstract (arXiv)

Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database.