Detection of Critical Camera Configurations for Structure from Motion Using Random Forest

  • Mario MicheliniEmail author
  • Helmut Mayer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12046)


This paper presents an approach for the detection of critical camera configurations in unorganized image sets with (approximately) known internal camera parameters. Critical configurations are caused by an insufficient distance between cameras compared to the distance of the observed scene and can cause problems in triangulation-based structure from motion application.

We give a summary of existing techniques and propose a new approach for the detection of image pairs with a critical camera configuration based on classification using a random forest. To this end, several features characterizing the quality of the reconstructed 3D points as well as the estimated camera poses have been defined and evaluated for various configurations. The proposed approach is integrated into the structure from motion framework COLMAP demonstrating its potential on an independent real-world dataset.


Structure from motion Camera configuration Classification Random forest Pure rotation Weak baseline 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Applied Computer ScienceBundeswehr University MunichNeubibergGermany

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