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Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
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. Show more
Publication status
publishedExternal links
Book title
2023 IEEE International Conference on Robotics and Automation (ICRA)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
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ETH Bibliography
yes
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