Dense 3D reconstruction using RGB-D cameras

The generation of fine 3D models from RGB-D (color plus depth) measurements is of great interest for the computer vision community. Although the 3D reconstruction pipeline has been widely studied in the last decades, a new era has started recently with the advent of low cost consumer depth cameras (called RGB-D cameras) that capture RGB-D images at a video rate (e.g., Microsoft Kinect or Asus Xtion Pro). The introduction to the public of 3D measurements has brought its own revolution to the scientific community with many projects and applications using RGB-D cameras.

In this tutorial, we will give an overview of the existing 3D reconstruction methods using a single RGB-D camera using various 3D representations, including point based representations (SURFELS), implicit volumetric representations (TSDF), patch based representations and parametric representations. These different 3D scene representations give us powerful tools to build virtual representations of the real world in real-time from RGB-D cameras. We can not only reconstruct small-scale static scenes but also large-scale scenes and dynamic scenes. We will also discuss about current trend in depth sensing and future challenges for 3D scene reconstruction.