  Visual motion estimation is a well-studied challenge in autonomous
navigation. Recent work has focused on addressing multimotion estimation in
highly dynamic environments. These environments not only comprise multiple,
complex motions but also tend to exhibit significant occlusion.
  Estimating third-party motions simultaneously with the sensor egomotion is
difficult because an object's observed motion consists of both its true motion
and the sensor motion. Most previous works in multimotion estimation simplify
this problem by relying on appearance-based object detection or
application-specific motion constraints. These approaches are effective in
specific applications and environments but do not generalize well to the full
multimotion estimation problem (MEP).
  This paper presents Multimotion Visual Odometry (MVO), a multimotion
estimation pipeline that estimates the full SE(3) trajectory of every motion in
the scene, including the sensor egomotion, without relying on appearance-based
information. MVO extends the traditional visual odometry (VO) pipeline with
multimotion segmentation and tracking techniques. It uses physically founded
motion priors to extrapolate motions through temporary occlusions and identify
the reappearance of motions through motion closure. Evaluations on real-world
data from the Oxford Multimotion Dataset (OMD) and the KITTI Vision Benchmark
Suite demonstrate that MVO achieves good estimation accuracy compared to
similar approaches and is applicable to a variety of multimotion estimation
challenges.
