Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation

This paper presents different application scenarios for which the registration of sub-sequence reconstructions or multi-camera reconstructions is essential for successful camera motion estimation and 3D reconstruction from video. The registration is achieved by merging unconnected feature point tracks between the reconstructions. One application is drift removal for sequential camera motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent camera motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential camera motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. In the third scenario image sequences of the same scene, which are captured under different illuminations, are registered. Several experiments with challenging real video sequences demonstrate that the presented techniques work in practice.

Real-time Human Motion Capture with Simple Marker Sets and Monocular Video

In this paper we present a hybrid method to track human motions in real-time. With simplified marker sets and monocular video input, the strength of both marker-based and marker-free motion capturing are utilized: A cumbersome marker calibration is avoided while the robustness of the marker-free tracking is enhanced by referencing the tracked marker positions. An improved inverse kinematics solver is employed for real-time pose estimation. A computer-visionbased approach is applied to refine the pose estimation and reduce the ambiguity of the inverse kinematics solutions. We use this hybrid method to capture typical table tennis upper body movements in a real-time virtual reality application.