Differential Camera Tracking through Linearizing the Local Appearance Manifold

The appearance of a scene is a function of the scene contents, the lighting, and the camera pose. A set of n-pixel images of a non-degenerate scene captured from different perspectives lie on a 6D nonlinear manifold in n . In general, this nonlinear manifold is complicated and numerous samples are required to learn it globally. In this paper, we present a novel method and some preliminary results for incrementally tracking camera motion through sampling and linearizing the local appearance manifold. At each frame time, we use a cluster of calibrated and synchronized small baseline cameras to capture scene appearance samples at different camera poses. We compute a first-order approximation of the appearance manifold around the current camera pose. Then, as new cluster samples are captured at the next frame time, we estimate the incremental camera motion using a linear solver. By using intensity measurements and directly sampling the appearance manifold, our method avoids the commonly-u...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Hua Yang, Marc Pollefeys, Greg Welch, Jan-Michael Frahm, Adrian Ilie
• Keywords: Appearance Manifold, Computer Vision, Current Camera Pose, CVPR 2007, Local Appearance Manifold, Nonlinear Manifold, Scene Appearance Samples