Multi-Cali Anything: Dense Feature Multi-Frame Structure-from-Motion for Large-Scale Camera Array Calibration
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
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
