# OSPC: Online Sequential Photometric Calibration

arXiv: https://arxiv.org/abs/2305.17673 · HTML: https://mlsanity.com/paper/2305.17673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 28 May 2023.

## Builds on (most cited first, 4 of 4)

- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [A Photometrically Calibrated Benchmark For Monocular Visual Odometry](https://mlsanity.com/paper/1607.02555/) (2016)
- [Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM](https://mlsanity.com/paper/1710.02081/) (2017)

## Built on by (most cited first, 1 of 1)

- [Inline Photometrically Calibrated Hybrid Visual SLAM](https://mlsanity.com/paper/2409.16810/) (2024)

## Abstract (arXiv)

Photometric calibration is essential to many computer vision applications. One of its key benefits is enhancing the performance of Visual SLAM, especially when it depends on a direct method for tracking, such as the standard KLT algorithm. Another advantage could be in retrieving the sensor irradiance values from measured intensities, as a pre-processing step for some vision algorithms, such as shape-from-shading. Current photometric calibration systems rely on a joint optimization problem and encounter an ambiguity in the estimates, which can only be resolved using ground truth information. We propose a novel method that solves for photometric parameters using a sequential estimation approach. Our proposed method achieves high accuracy in estimating all parameters; furthermore, the formulations are linear and convex, which makes the solution fast and suitable for online applications. Experiments on a Visual Odometry system validate the proposed method and demonstrate its advantages.