Adversarial 3D Human Pointcloud Completion from Limited Angle Depth Data

Most research in 3D objects and its occluded region reconstruction from a single perspective focuses on object completion from a synthetically generated dataset. This leaves a major knowledge gap when morphing 3D object reconstruction from an imperfect real-world frame. As a solution to this problem, we propose a three-stage deep auto-refining adversarial neural network capable of denoising and refining real-world depth data for a full human body posture shape completion. The proposed solution achieves results which are on par with other state-of-the-art approaches in both EarthMover's and Chamfer distances, 0.059 and 0.079, respectively, while having the benefit of reconstructing from mask-less depth frames. Visual inspection of reconstructed pointcloud suggests great adaptation capabilities to the majority of real-world depth sensor noise deformities for both LiDAR and structured light depth sensors.