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We present a novel approach to the detection and 3D pose estimation ofobjects in color images. Its main contribution is that it does not require anytraining phases nor data for new objects, while state-of-the-art methodstypically require hours of training time and hundreds of training registeredimages. Instead, our method relies only on the objects' geometries. Our methodfocuses on objects with prominent corners, which covers a large number ofindustrial objects. We first learn to detect object corners of various shapesin images and also to predict their 3D poses, by using training images of asmall set of objects. To detect a new object in a given image, we firstidentify its corners from its CAD model; we also detect the corners visible inthe image and predict their 3D poses. We then introduce a RANSAC-like algorithmthat robustly and efficiently detects and estimates the object's 3D pose bymatching its corners on the CAD model with their detected counterparts in theimage. Because we also estimate the 3D poses of the corners in the image,detecting only 1 or 2 corners is sufficient to estimate the pose of the object,which makes the approach robust to occlusions. We finally rely on a final checkthat exploits the full 3D geometry of the objects, in case multiple objectshave the same corner spatial arrangement. The advantages of our approach makeit particularly attractive for industrial contexts, and we demonstrate ourapproach on the challenging T-LESS dataset.

CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining