From Projective to Euclidean Reconstruction

To make a Euclidean reconstruction of the world seen through a stereo rig, we can either use a calibration grid, and the results will rely on the precision of the grid and the extracted points of interest, or use self-calibration. Past work on self-calibration is focussed on the use of only one camera, and gives sometimes very unstable results.

In this paper, we use a stereo rig which is supposed to be weakly calibrated using a method such as the one described in [1]. Then, by matching two sets of points of the same scene reconstructed from different points of view, we try to find both the homography that maps the projective reconstruction [2] to the Euclidean space and the displacement from the first set of points to the second set of points.

We present results of the Euclidean reconstruction of a whole object from uncalibrated cameras using the method proposed here.

And you can also take a look at the VRML model (337Kb + 332Kb textures) of the full textured 3-D reconstruction (turn off lighting when texture is on), a model made from one stereo pair, and a images (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) from the stereo pairs.

If you do not have a VRML viewers, try these images: model with texture model without texture model from one pair of images.


Frederic Devernay