3D Object Reconstruction using Point Pair Features
I finally finished my bachelor’s thesis. Here is the final thesis as well as the final presentation slides. All the code I developed during research is open source and hosted in the ppf-reconstruction repository.
This work aims at reconstructing 3D objects by robustly and accurately registering multiple range images of an object from different viewpoints. An initial alignment between any two overlapping scans is obtained via a voting scheme which matches similar point pair features and thus constrains the relative 6DoF rigid body motion between the poses of two viewpoints. This initial alignment is then refined using pairwise point-to-plane ICP. The result of this step is a tree of relative pose constraints. In a subsequent global optimization step, we build up a graph of absolute poses, our vertices, from the tree of initial relative pose estimates by adding further edges. We add edges for the k-nearest-neighbors of a vertex, taking the translational difference of the corresponding poses as a distance measure. Constraints between two vertices are added for each closest point correspondence in their respective point clouds. The global point-toplane energy is then minimized iteratively using the nonlinear least-squares method called Multiview Levenberg-Marquardt ICP. This refined registration of all the scans used may now be integrated and their corresponding point clouds fused and then meshed to obtain the final reconstructed 3D object mesh.