  In this paper, we propose an efficient method for robust 3D self-portraits
using a single RGBD camera. Benefiting from the proposed PIFusion and
lightweight bundle adjustment algorithm, our method can generate detailed 3D
self-portraits in seconds and shows the ability to handle subjects wearing
extremely loose clothes. To achieve highly efficient and robust reconstruction,
we propose PIFusion, which combines learning-based 3D recovery with volumetric
non-rigid fusion to generate accurate sparse partial scans of the subject.
Moreover, a non-rigid volumetric deformation method is proposed to continuously
refine the learned shape prior. Finally, a lightweight bundle adjustment
algorithm is proposed to guarantee that all the partial scans can not only
"loop" with each other but also remain consistent with the selected live key
observations. The results and experiments show that the proposed method
achieves more robust and efficient 3D self-portraits compared with
state-of-the-art methods.
