  Estimating motion from images is a well-studied problem in computer vision
and robotics. Previous work has developed techniques to estimate the motion of
a moving camera in a largely static environment (e.g., visual odometry) and to
segment or track motions in a dynamic scene using known camera motions (e.g.,
multiple object tracking).
  It is more challenging to estimate the unknown motion of the camera and the
dynamic scene simultaneously. Most previous work requires a priori object
models (e.g., tracking-by-detection), motion constraints (e.g., planar motion),
or fails to estimate the full SE(3) motions of the scene (e.g., scene flow).
While these approaches work well in specific application domains, they are not
generalizable to unconstrained motions.
  This paper extends the traditional visual odometry (VO) pipeline to estimate
the full SE(3) motion of both a stereo/RGB-D camera and the dynamic scene. This
multimotion visual odometry (MVO) pipeline requires no a priori knowledge of
the environment or the dynamic objects. Its performance is evaluated on a
real-world dynamic dataset with ground truth for all motions from a motion
capture system.
