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 \begin{abstract}
-  Dies ist der Abstract der Arbeit.
-  Er gibt wertungsfrei, kurz und prägnant den Inhalt der wissenschaftlichen Arbeit wieder.
-\end{abstract}  
 
+Many state of the art energy-minimization approaches to optical flow and scene flow estimation
+rely on a rigid scene model, where the scene is represented as an ensemble of distinct,
+rigidly moving objects, a static background and a moving camera.
+By using a physical scene model, the search space of the optimization problem is significantly
+reduced, enabling higly accurate motion estimation.
+
+With the advent of deep learning methods, it has become popular to re-purpose generic deep networks
+for classical computer vision problems involving pixel-wise estimation.
+
+Following this trend, many recent end-to-end deep learning approaches to optical flow
+and scene flow directly predict full resolution
+depth and flow fields with a generic network for dense, pixel-wise prediction,
+thereby ignoring the inherent structure of the underlying motion estimation problem
+and any physical constraints within the scene.
+
+We introduce an end-to-end deep learning approach for dense motion estimation
+that respects the structure of the scene as being composed of distinct objects,
+thus unifying end-to-end deep networks and a strong physical scene model.
+
+Building on recent advanced in region-based convolutional networks (R-CNNs), we integrate motion
+estimation with instance segmentation.
+Given two consecutive frames from a monocular RGBD camera,
+our resulting end-to-end deep network detects objects with accurate per-pixel masks
+and estimates the 3d motion of each detected object between the frames.
+By additionally estimating a global camera motion in the same network, we compose a dense
+optical flow field based on instance-level motion predictions.
+
+We demonstrate the effectiveness of our approach on the KITTI 2015 optical flow benchmark.
+\end{abstract}