Cycle-Correspondence Loss: Learning Dense View-Invariant Visual Features from Unlabeled and Unordered RGB Images

• URL: http://arxiv.org/abs/2406.12441v1
• Date: Tue, 18 Jun 2024 09:44:56 GMT
• Title: Cycle-Correspondence Loss: Learning Dense View-Invariant Visual Features from Unlabeled and Unordered RGB Images
• Authors: David B. Adrian, Andras Gabor Kupcsik, Markus Spies, Heiko Neumann,
• Abstract summary: We introduce Cycle-Correspondence Loss (CCL) for view-invariant dense descriptor learning. The key idea is to autonomously detect valid pixel correspondences by attempting to use a prediction over a new image. Our evaluation shows that we outperform other self-supervised RGB-only methods, and approach performance of supervised methods.
• Score: 8.789674502390378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robot manipulation relying on learned object-centric descriptors became popular in recent years. Visual descriptors can easily describe manipulation task objectives, they can be learned efficiently using self-supervision, and they can encode actuated and even non-rigid objects. However, learning robust, view-invariant keypoints in a self-supervised approach requires a meticulous data collection approach involving precise calibration and expert supervision. In this paper we introduce Cycle-Correspondence Loss (CCL) for view-invariant dense descriptor learning, which adopts the concept of cycle-consistency, enabling a simple data collection pipeline and training on unpaired RGB camera views. The key idea is to autonomously detect valid pixel correspondences by attempting to use a prediction over a new image to predict the original pixel in the original image, while scaling error terms based on the estimated confidence. Our evaluation shows that we outperform other self-supervised RGB-only methods, and approach performance of supervised methods, both with respect to keypoint tracking as well as for a robot grasping downstream task.

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