Kinect-Based Data-Driven 3D Modeling

This paper presents a fast modeling method based on the highly noisy and incomplete scanned data from Kinect in a single view.We take full advantage of the abundant models of the same semantic class on the Internet to build a heuristic model based on the 3D point clouds and the corresponding RGB images.Our method includes three major phases:Firstly, we analyze the structure of the 3Dpoint clouds and the RGB images based on the semantic segmentation of the candidate model to label the components of the scanned target;secondly, we search for candidate parts for the labeled parts in the target to get matched component information;lastly, we assemble the candidate parts to create the final 3Dmodel.We demonstrate the efficiency and speed of our method in building models based on incomplete point clouds data from Kinect.