text: In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. To encode such a function, we opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-of-the-art on several problems and benchmark datasets.
  @cite_7: We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to compute the stereo matching cost. The cost is refined by cross-based cost aggregation and semiglobal matching, followed by a left-right consistency check to eliminate errors in the occluded regions. Our stereo method achieves an error rate of 2.61 on the KITTI stereo dataset and is currently (August 2014) the top performing method on this dataset.
summary: Zbontar and LeCun in @cite_7 have recently proposed a CNN-based approach to compare patches for computing cost in small baseline stereo problem and shown the best performance in KITTI dataset. However, the focus of that work was only on comparing pairs that consist of very small patches like the ones in narrow baseline stereo. In contrast, here we aim for a similarity function that can account for a broader set of appearance changes and can be used in a much wider and more challenging set of applications, including, , wide baseline stereo, feature matching and image retrieval.