Robust framework for 3D synchronization

The 3D synchronization task stands for weaving a set of 3D objects and their representations into a self-consistent global configuration. With an increasing number of large 3D datasets becoming accessible, efficient synchronization methods are required for applications in 3D vision. In this work, we propose a simple and efficient framework for solving synchronization tasks over a set of 3D objects. We start with a general map synchronization over symmetric 3D objects and present a joint map representation for solving a globally consistent map. Next we propagate to the 3D registration problem and propose a discretized spectral method to compute consistent global poses of symmetric partial objects. Next, we handle the setting with multiple estimation over a single edge through our diffusion-and-clustering method over continuous pose distributions. After the global phase, we shift our focus to the quality of multi-view representation and pairwise maps. For the quality of multi-view representation of 3D objects, we present a learning based method which efficiently solves the view selection problem for covering indoor scenes. Finally, we introduce a data-driven method to improve the accuracy of pairwise transformations over the transformation graph for the multi-piece 3D assembly task with small overlaps between pieces, which extracts shape features from local geometry for surface classification and piece matching.