edit abstract

abstract.tex

@@ -1,10 +1,10 @@
 \begin{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.
+rely on a (piecewise) rigid scene model, where the scene is represented as an ensemble of distinct,
+rigidly moving components, a static background and a moving camera.
+By constraining the optimization problem with a physically sound scene model,
+these approaches enable 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.
@@ -17,7 +17,8 @@ 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.
+thus combining the representation learning benefits of end-to-end deep networks
+with a physically plausible scene model.
 
 Building on recent advanced in region-based convolutional networks (R-CNNs), we integrate motion
 estimation with instance segmentation.