  We address the task of open-world class-agnostic object detection, i.e.,
detecting every object in an image by learning from a limited number of base
object classes. State-of-the-art RGB-based models suffer from overfitting the
training classes and often fail at detecting novel-looking objects. This is
because RGB-based models primarily rely on appearance similarity to detect
novel objects and are also prone to overfitting short-cut cues such as textures
and discriminative parts. To address these shortcomings of RGB-based object
detectors, we propose incorporating geometric cues such as depth and normals,
predicted by general-purpose monocular estimators. Specifically, we use the
geometric cues to train an object proposal network for pseudo-labeling
unannotated novel objects in the training set. Our resulting Geometry-guided
Open-world Object Detector (GOOD) significantly improves detection recall for
novel object categories and already performs well with only a few training
classes. Using a single "person" class for training on the COCO dataset, GOOD
surpasses SOTA methods by 5.0% AR@100, a relative improvement of 24%.
