3D real human reconstruction via multiple low-cost depth cameras

In traditional human-centered games and virtual reality applications, a skeleton is commonly tracked using consumer-level cameras or professional motion capture devices to animate an avatar. In this paper, we propose a novel application that automatically reconstructs a real 3D moving human captured by multiple RGB-D cameras in the form of a polygonal mesh, and which may help users to actually enter a virtual world or even a collaborative immersive environment. Compared with 3D point clouds, a 3D polygonal mesh is commonly adopted to represent objects or characters in games and virtual reality applications. A vivid 3D human mesh can hugely promote the feeling of immersion when interacting with a computer. The proposed method includes three key steps for realizing dynamic 3D human reconstruction from RGB images and noisy depth data captured from a distance. First, we remove the static background to obtain a 3D partial view of the human from the depth data with the help of calibration parameters, and register two neighboring partial views. The whole 3D human is globally registered using all the partial views to obtain a relatively clean 3D human point cloud. A complete 3D mesh model is constructed from the point cloud using Delaunay triangulation and Poisson surface reconstruction. Finally, a series of experiments demonstrates the reconstruction quality of the 3D human meshes. Dynamic meshes with different poses are placed in a virtual environment, which can be used to provide personalized avatars for everyday users, and enhance the interactive experience in games and virtual reality environments.