Reconstructing three-dimensional models of objects using a Kinect sensor

Abstract

Advanced sensor technology has allowed us to acquire three-dimensional (3D) information from a scene using a low-cost RGB-D sensor such as Kinect. Although this sensor can recover the 3D structure of a scene, it cannot distinguish a target object from the background. In view of this, we incorporate an interactive 3D segmentation algorithm with a well-known Kinect scene reconstruction system, the KinectFusion, to effectively extract an object from the scene, and hence obtain a 3D point cloud of the object. With this system, a user can freely move the Kinect sensor to reconstruct the scene and then select the foreground/background seeds from the reconstructed point cloud. The system can take over the following tasks to complete the 3D reconstruction of the selected object. The advantage of this system is that users need not select the foreground/background seeds very carefully, which greatly reduces the operational complexity. Moreover, previous segmentation results are inherited to the next phase as new foreground/background seeds, which minimizes the required user intervention. With a simple seed selection, the point cloud of the selected object can be gradually recovered when a user moves the sensor to different viewpoints. Several experiments were conducted, and the results confirmed the effectiveness of the proposed system. The 3D structures of objects with complex shapes are well reconstructed by our system.