YOLOPoint Joint Keypoint and Object Detection
Anton Backhaus Thorsten Luettel Hans-Joachim Wuensche
Intelligent vehicles of the future must be capable of understanding and
navigating safely through their surroundings. Camera-based vehicle systems can
use keypoints as well as objects as low- and high-level landmarks for
GNSS-independent SLAM and visual odometry. To this end we propose YOLOPoint, a
convolutional neural network model that simultaneously detects keypoints and
objects in an image by combining YOLOv5 and SuperPoint to create a single
forward-pass network that is both real-time capable and accurate. By using a
shared backbone and a light-weight network structure, YOLOPoint is able to
perform competitively on both the HPatches and KITTI benchmarks.