In this paper, we study the problem of 3D deformable surface tracking with RGBD cameras, specifically Microsofts Kinect. In order to achieve this we introduce a fully automated framework that includes several components: automatic initialization based on segmentation of the object of interest, then robust range flow that guides deformations of the object of interest and finally representation of the results using mass-spring model. The key contribution is extension of the range flow work of Spies and J¨ahne [1] that combines Lucas-Kanade [2] and Horn and Shunk [3] approaches for RGB-D data, makes it to converge faster and incorporates color information with multichannel formulation. We also introduced a pipeline for generating synthetic data and performed error analysis and comparison to original range flow approach. The results show that our method is accurate and is precise enough to track significant deformation smoothly at near real-time run times.     «

In this paper, we study the problem of 3D deformable surface tracking with RGBD cameras, specifically Microsofts Kinect. In order to achieve this we introduce a fully automated framework that includes several components: automatic initialization based on segmentation of the object of interest, then robust range flow that guides deformations of the object of interest and finally representation of the results using mass-spring model. The key contribution is extension of the range flow work of Spie...     »