3D Reconstruction Completion Using Per-Object Priors

Knowledge of 3D scene geometry, including the structure of unobserved regions, is critically important to robotics, with applications in manipulator grasp planning and free space mapping. This thesis proposes novel approaches on using learning-based priors for per-object reconstructions, allowing for scalable multi-view reconstructions of entire scenes.


computer vision