  We present a point tracking system powered by two deep convolutional neural
networks. The first network, MagicPoint, operates on single images and extracts
salient 2D points. The extracted points are "SLAM-ready" because they are by
design isolated and well-distributed throughout the image. We compare this
network against classical point detectors and discover a significant
performance gap in the presence of image noise. As transformation estimation is
more simple when the detected points are geometrically stable, we designed a
second network, MagicWarp, which operates on pairs of point images (outputs of
MagicPoint), and estimates the homography that relates the inputs. This
transformation engine differs from traditional approaches because it does not
use local point descriptors, only point locations. Both networks are trained
with simple synthetic data, alleviating the requirement of expensive external
camera ground truthing and advanced graphics rendering pipelines. The system is
fast and lean, easily running 30+ FPS on a single CPU.
