# Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration

arXiv: https://arxiv.org/abs/2309.10314 · HTML: https://mlsanity.com/paper/2309.10314/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Pothole Detection Based on Disparity Transformation and Road Surface Modeling](https://mlsanity.com/paper/1908.00894/) (2019)
- [Road surface 3d reconstruction based on dense subpixel disparity map estimation](https://mlsanity.com/paper/1807.01874/) (2018)
- [PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching](https://mlsanity.com/paper/2103.07094/) (2021)
- [Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation](https://mlsanity.com/paper/2012.10802/) (2020)
- [Computer Stereo Vision for Autonomous Driving](https://mlsanity.com/paper/2012.03194/) (2020)
- [Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms](https://mlsanity.com/paper/2109.02711/) (2021)
- [High-Precision Online Markerless Stereo Extrinsic Calibration](https://mlsanity.com/paper/1903.10705/) (2019)

## Built on by (most cited first, 4 of 4)

- [Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching](https://mlsanity.com/paper/2404.18083/) (2024)
- [SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning](https://mlsanity.com/paper/2407.05283/) (2024)
- [A Birotation Solution for Relative Pose Problems](https://mlsanity.com/paper/2505.02025/) (2025)
- [Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching](https://mlsanity.com/paper/2506.22784/) (2025)

## Abstract (arXiv)

Accurate estimation of stereo camera extrinsic parameters is the key to guarantee the performance of stereo matching algorithms. In prior arts, the online self-calibration of stereo cameras has commonly been formulated as a specialized visual odometry problem, without taking into account the principles of stereo rectification. In this paper, we first delve deeply into the concept of rectifying homography, which serves as the cornerstone for the development of our novel stereo camera online self-calibration algorithm, for cases where only a single pair of images is available. Furthermore, we introduce a simple yet effective solution for global optimum extrinsic parameter estimation in the presence of stereo video sequences. Additionally, we emphasize the impracticality of using three Euler angles and three components in the translation vectors for performance quantification. Instead, we introduce four new evaluation metrics to quantify the robustness and accuracy of extrinsic parameter estimation, applicable to both single-pair and multi-pair cases. Extensive experiments conducted across indoor and outdoor environments using various experimental setups validate the effectiveness of our proposed algorithm. The comprehensive evaluation results demonstrate its superior performance in comparison to the baseline algorithm. Our source code, demo video, and supplement are publicly available at mias.group/StereoCalibrator.