用场景数据自动校准大阵列相机，无需额外标定图。
Multi-Cali Anything: Dense Feature Multi-Frame Structure-from-Motion for Large-Scale Camera Array Calibration
- 基于密集特征和多帧数据优化相机内参
- 精度接近专用标定，3D重建更准确
- 可无缝接入现有SfM流程，适合大阵列部署
大尺度相机阵列（如穹顶式布置）的标定耗时且通常需专用标定图案。虽然外参因物理结构固定，但内参可能因镜头调整或温度变化而变动。本文提出一种基于密集特征的多帧标定方法，直接从场景数据中优化内参，无需额外标定采集。通过在传统SfM中引入外参正则化项、密集特征重投影项及内参方差项，实现多帧联合优化。在Multiface数据集上的实验表明，该方法达到与专用标定相近的精度，显著提升内参估计和3D重建质量。完全兼容现有SfM流程，提供高效实用的即插即用解决方案。代码已公开：https://github.com/YJJfish/Multi-Cali-Anything
原文摘要 · Abstract (English)
Calibrating large-scale camera arrays, such as those in dome-based setups, is time-intensive and typically requires dedicated captures of known patterns. While extrinsics in such arrays are fixed due to the physical setup, intrinsics often vary across sessions due to factors like lens adjustments or temperature changes. In this paper, we propose a dense-feature-driven multi-frame calibration method that refines intrinsics directly from scene data, eliminating the necessity for additional calibration captures. Our approach enhances traditional Structure-from-Motion (SfM) pipelines by introducing an extrinsics regularization term to progressively align estimated extrinsics with ground-truth values, a dense feature reprojection term to reduce keypoint errors by minimizing reprojection loss in the feature space, and an intrinsics variance term for joint optimization across multiple frames. Experiments on the Multiface dataset show that our method achieves nearly the same precision as dedicated calibration processes, and significantly enhances intrinsics and 3D reconstruction accuracy. Fully compatible with existing SfM pipelines, our method provides an efficient and practical plug-and-play solution for large-scale camera setups. Our code is publicly available at: https://github.com/YJJfish/Multi-Cali-Anything
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