Computer Vision Meets Geometric Modeling: Multi-view Reconstruction of Surface Points and Normals Using Affine Correspondences

Abstract

A novel surface normal estimator is introduced using affine-invariant features extracted and tracked across multiple views. Normal estimation is robustified and integrated into our reconstruction pipeline that has increased accuracy compared to the State-of-the-Art. Parameters of the views and the obtained spatial model, including surface normals, are refined by a novel bundle adjustment-like numerical optimization. The process is an alternation with a novel robust view-dependent consistency check for surface normals, removing normals inconsistent with the multiple-view track. Our algorithms are quantitatively validated on the reverse engineering of geometrical elements such as planes, spheres, or cylinders. It is shown here that the accuracy of the estimated surface properties is appropriate for object detection. The pipeline is also tested on the reconstruction of man-made and free-form objects.