KinectFusion: Real time dense surface mapping and tracking
We present a system for accurate real time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse to fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame to frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR) in particular, it allows dense surfaces to be reconstructed in real time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.
2011 10th IEEE International Symposium on Mixed and Augmented Reality