Superaccurate camera calibration via inverse rendering

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The most prevalent routine for camera calibration is based on the detection of well-defined feature points on a purpose-made calibration artifact. These could be checkerboard saddle points, circles, rings or triangles, often printed on a planar structure. The feature points are first detected and then used in a nonlinear optimization to estimate the internal camera parameters. We propose a new method for camera calibration using the principle of inverse rendering. Instead of relying solely on detected feature points, we use an estimate of the internal parameters and the pose of the calibration object to implicitly render a non-photorealistic equivalent of the optical features. This enables us to compute pixel-wise differences in the image domain without interpolation artifacts. We can then improve our estimate of the internal parameters by minimizing pixel-wise least-squares differences. In this way, our model optimizes a meaningful metric in the image space assuming normally distributed noise characteristic for camera sensors. We demonstrate using synthetic and real camera images that our method improves the accuracy of estimated camera parameters as compared with current state-of-the-art calibration routines. Our method also estimates these parameters more robustly in the presence of noise and in situations where the number of calibration images is limited.
Original languageEnglish
Title of host publicationProceedings of SPIE
Number of pages10
PublisherSPIE - International Society for Optical Engineering
Publication date2019
Article number1105717
ISBN (Print)9781510627932
Publication statusPublished - 2019
EventSPIE Optical Metrology 2019 - Internationales Congress Center, Munich, Germany
Duration: 24 Jun 201927 Jun 2019


ConferenceSPIE Optical Metrology 2019
LocationInternationales Congress Center
SeriesProceedings of SPIE - The International Society for Optical Engineering


  • Camera calibration
  • Inverse rendering
  • Camera intrinsics


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