  Intelligent robots require object-level scene understanding to reason about
possible tasks and interactions with the environment. Moreover, many perception
tasks such as scene reconstruction, image retrieval, or place recognition can
benefit from reasoning on the level of objects. While keypoint-based matching
can yield strong results for finding correspondences for images with small to
medium view point changes, for large view point changes, matching semantically
on the object-level becomes advantageous. In this paper, we propose a
learning-based approach which combines local keypoints with novel object-level
features for matching object detections between RGB images. We train our
object-level matching features based on appearance and inter-frame and
cross-frame spatial relations between objects in an associative graph neural
network. We demonstrate our approach in a large variety of views on
realistically rendered synthetic images. Our approach compares favorably to
previous state-of-the-art object-level matching approaches and achieves
improved performance over a pure keypoint-based approach for large view-point
changes.
