Auto-Refining 3D Mesh Reconstruction Algorithm From Limited Angle Depth Data

3D object reconstruction is a very rapidly developing field, especially from a single perspective. Yet the majority of modern research is focused on developing algorithms around a single static object reconstruction and in most of the cases derived from synthetically generated datasets, failing or at least working insufficiently accurately in real-world data scenarios, regarding morphing the 3D object’s restoration from a deficient real world frame. For solving that problem, we introduce an extended version of the three-staged deep auto-refining adversarial neural network architecture that can denoise and refine real-world depth sensor data current methods for a full human body pose reconstruction, in both
Earth Mover’s (0.059)
and
Chamfer (0.079)
distances. Visual inspection of the reconstructed point-cloud proved future adaptation potential to most of depth sensor noise defects for both structured light depth sensors and
LiDAR
sensors.