About

Overview

  • Build an automated calibration system based on an efficient Structure-from-Motion (SfM) pipeline to estimate intrinsic camera parameters.
  • Enhance accuracy by applying featuremetric refinement to detected keypoints and triangulated 3D points.
  • Incorporate known ground-truth extrinsic in order to refine intrinsic estimates.

Background

  • Human face data captured by hundreds of cameras across hundreds of frames require precise calibration.
  • Extrinsics remain stable over a short period (e.g. 1 day ~ 1 week).
  • Intrinsics fluctuate frequently due to environmental factors.

  • Traditional calibration using regular patterns achieves high precision but demands an additional capture process, which is time-consuming and inefficient.
  • An efficient SfM pipeline is proposed to directly calibrate cameras using human face data, aligning with the system’s ultimate purpose of facial data capture.

Dataset

  • The Multiface dataset from Meta Reality Labs is used for our calibration process. This dataset is designed for large-scale multi-view codec avatar tasks, particularly neural face rendering.
  • The dataset includes 10+ identities recorded with various facial expressions, raw images, tracked meshes, unwrapped textures, headposes, phonetically balanced sentences for each identity, and ground-truth camera parameters.