S K Savant
Organization: Point Cloud Library (PCL)
Assigned mentors: Emanuele Rodolà
Abstract: The project aims to extend the functionality of pcl::registration by adding algorithms for automatic multiview registration from unordered views.
The project consists of 2 parts:
1. Port multiview registration via graph diffusion of dual quaternions to PCL. and
2. Implement global alignment algorithm from unordered views and integrate the multiview registration from .
Surface registration is a fundamental step in the reconstruction of three-dimensional objects. However, while PCL already includes several fast and reliable methods to align two surfaces, the tools available to align multiple surfaces are relatively limited. We aim to extend the functionalities of pcl::registration by adding efficient algorithms for automatic multiview registration from unordered views. In particular, we will consider the dual quaternion algorithm for multiple view surface reconstruction from point cloud data as described in the following paper:
 A. Torsello, E. Rodolà, and A. Albarelli. "Multiview registration via graph diffusion of dual quaternions". Proc. IEEE Intl. Conf. on Computer Vision and Pattern Recognition (2011)
The approach allows for a completely generic topology over which the pairwise motions are diffused. It is both orders of magnitude faster than the state of the art, and more robust to extreme positional noise and outliers. The dramatic speedup of the approach allows it to be alternated with pairwise alignment resulting in a smoother energy profile, reducing the risk of getting stuck at local minima, and enabling the integration of the algorithm within a global alignment framework, namely:
 D. F. Huber and M. Hebert. "Fully automatic registration of multiple 3D data sets". Image and Vision Computing 21 (2003) 637-650
I will thus port the original code of  to PCL and implement a basic version of the global alignment algorithm in . Also, I will evaluate a full multiview registration pipeline built by assembling the various tools available in PCL.
More info at http://pcl-gsoc-blog.sksavant.net