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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. [ABSTRACT FROM AUTHOR]

Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions. [ABSTRACT FROM AUTHOR]