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IROS 2025

Multi-Cali Anything: Dense Feature Multi-Frame Structure-from-Motion for Large-Scale Camera Array Calibration

Conference Paper Accepted Paper Artificial Intelligence ยท Robotics

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

Authors

Keywords

  • Three-dimensional displays
  • Temperature
  • Pipelines
  • Robot vision systems
  • Cameras
  • Distortion
  • Calibration
  • Optimization
  • Intelligent robots
  • Lenses
  • Feature Space
  • 3D Reconstruction
  • Calibration Method
  • Multiple Frames
  • Ground Truth Values
  • Large-scale Array
  • Scene Data
  • Scalable
  • Complex Models
  • Convolutional Neural Network
  • Total Loss
  • Density Map
  • Focal Length
  • Image Space
  • Mean Vector
  • 3D Point
  • Cyanoacrylate
  • Intrinsic Parameters
  • Camera Calibration
  • Joint Estimation
  • Reprojection Error
  • Bundle Adjustment
  • Sparse Reconstruction
  • 2D Keypoints
  • Pinhole Camera Model
  • Camera Intrinsics
  • Multi-view Stereo

Context

Venue
IEEE/RSJ International Conference on Intelligent Robots and Systems
Archive span
1988-2025
Indexed papers
26578
Paper id
868616723460942804
v2026.09.13