Using CNN features in feature matching problems? I am looking online for using the features from the first layers of a CNN for multi view methods instead of using hand methods like SIFT. I cannot seem to find many papers on this, most people seem to focus on harder problems like learning the feature matching on the way to learning a depth map such as in deep stereo, or single image based 3d reconstruction networks, for example. I am just wondering about using a network for the features, and then doing traditional feature matching afterwards on these features for multi frame problems. I imagine a quantized resnet backbone would rival SIFT in speed. What is the consensus on this?