The three R’s of computer vision: Recognition, reconstruction and reorganization

• Bidirectional interactions between recognition, reconstruction and re-organization are very important.
• Bottom-up grouping generates object candidates, which can be classified top down, following which the segmentations are refined again.
• Recognition of 3D objects benefits from a reconstruction of 3D structure.
• 3D reconstruction benefits from object category-specific priors.
• Reconstruction of 3D structure from video data goes hand in hand with the reorganization of the scene.

We argue for the importance of the interaction between recognition, reconstruction and re-organization, and propose that as a unifying framework for computer vision. In this view, recognition of objects is reciprocally linked to re-organization, with bottom-up grouping processes generating candidates, which can be classified using top down knowledge, following which the segmentations can be refined again. Recognition of 3D objects could benefit from a reconstruction of 3D structure, and 3D reconstruction can benefit from object category-specific priors. We also show that reconstruction of 3D structure from video data goes hand in hand with the reorganization of the scene. We demonstrate pipelined versions of two systems, one for RGB-D images, and another for RGB images, which produce rich 3D scene interpretations in this framework.