端到端标定双目相机，无需标定板也能准确估计内外参。
StereoGeo: an end-to-end stereo camera calibration method
- 用深度网络+可微优化联合估计双目相机内参与外参。
- 在真实场景中表现优于仅支持单目校准的现有方法。
- 适合无标定板、快速部署的双目系统开发场景。
本文提出 StereoGeo，一种基于端到端神经网络的双目相机标定方法。该方法同时估计左右相机的焦距和重力方向，以及二者间的相对外参。现有方法多依赖结构化环境中的标定板，或仅处理单个相机配置，且通常只估计内参或外参，依赖多视角设置。StereoGeo 在 GeoCalib 算法基础上，融合深度神经网络特征提取与可微优化器。在真实世界基准上的大量实验表明，StereoGeo 在内参标定上达到竞争力水平，并实现高精度的双目外参估计，优于仅限单目设置的现有方法。本研究使用的数据集部分公开于 https://github.com/meddourimane/StereoGeo-dataset。
原文摘要 · Abstract (English)
In this work, we propose StereoGeo, an end-to-end network-based approach for stereo camera calibration. Our method estimates the focal lengths and gravity directions of the left and right cameras, as well as the relative extrinsic transformation relating them. Existing methods often rely on calibration patterns in structured environments or address only a single camera configuration, being limited to either intrinsic or extrinsic estimation, and depending on a multi-view setups. StereoGeo extends the GeoCalib algorithm, integrating deep neural network feature extraction with a differentiable optimizer. Extensive experiments on real-world benchmarks demonstrate that StereoGeo achieves competitive performance for intrinsic calibration and provides accurate stereo extrinsic estimation, outperforming existing methods that are limited to monocular settings. The dataset used in this work is partially publicly available at https://github.com/meddourimane/StereoGeo-dataset.
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