Keypoint Matching via Random Network Consensus

Siyan Dong, Shuzhe Wang, Daniel Barath, Juho Kannala, Marc Pollefeys, Baoquan Chen

Keywords: Computer Vision, Keypoint Matching, Randomized Networks, Visual Descriptor, Detector

Abstract: Visual description, detection, and matching of keypoints in images are fundamental components of many computer vision problems, such as camera tracking and (re)localization. Recently, learning-based feature extractors on top of convolutional neural networks (CNNs) have achieved state-of-the-art performance. In this paper, we further explore the usage of CNN and present a new approach that ensembles randomly initialized CNNs without any training. Our observation is that the CNN architecture inherently extracts features with certain extents of robustness to viewpoint/illumination changes and thus, it can be regarded as visual descriptors. Consequently, randomized CNNs serve as descriptor extractors and a subsequent consensus mechanism detects keypoints using them. Such description and detection pipeline can be used to match keypoints in images and achieves higher generalization ability than the state-of-the-art methods in our experiments.

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TL;DR: A new approach that uses convolutional neural networks (CNNs) for keypoint description, detection, and matching, without requiring the deep networks to be trained.