Robust Key Frame Extraction for 3D Reconstruction from Video Streams

Automatic reconstruction of 3D models from video sequences requires selection of appropriate video frames for performing the reconstruction. We introduce a complete method for key frame selection that automatically avoids degeneracies and is robust to inaccurate correspondences caused by motion blur. Our method combines selection criteria based on the number of frame-to-frame point correspondences, Torr’s geometrical robust information criterion (GRIC) scores for the frame-to-frame homography and fundamental matrix, and the point-to-epipolar line cost for the frame-to-frame point correspondence set. In a series of experiments with real and synthetic data sets, we show that our method achieves robust 3D reconstruction in the presence of noise and degenerate motion.