{"id":2553,"date":"2014-01-20T09:51:00","date_gmt":"2014-01-20T09:51:00","guid":{"rendered":"https:\/\/mapir.isa.uma.es\/mapirwebsite_wordpress\/?p=2553"},"modified":"2022-01-20T10:01:09","modified_gmt":"2022-01-20T10:01:09","slug":"scene-flow","status":"publish","type":"post","link":"https:\/\/mapir.isa.uma.es\/mapirwebsite\/?p=2553","title":{"rendered":"Scene Flow"},"content":{"rendered":"\n
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Scene flow is the semi-dense or dense 3D motion field of a scene that moves completely of partially with respect to a camera.<\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n\n\n

The potential applications of scene flow are numerous. In robotics, it can be used for autonomous navigation and\/or manipulation in dynamic environments where the future distribution of the surrounding objects need to be predicted. Besides, it could complement and improve state-of-the-art Visual Odometry and SLAM algorithms which typically assume to work in rigid or quasi-rigid enviroments. On the other hand, it could be employed for human-robot or human-computer interaction, as well as for virtual and augmented reality. In general, it would be useful at any case where motion capture \/ analysis were required.  <\/p>\n\n\n\n

Traditionally, scene flow has been computed from image pairs coming from stereo cameras, where both the disparity and the motion need to be estimated. However, the new RGB-D cameras changed this trend since they provide registered intensity and depth images which can be employed to estimate 3D motions accurately (for short-medium distances < 5 meters). In our work, we propose several algorithms to estimate scene flow with RGB-D cameras. <\/p>\n\n\n\n

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