Rotation-Equivariant Keypoint Detection
We show how to train a rotation-equivariant representation to extract local keypoints for image matching. Existing learning-based methods focused on extracting translation-equivariant keypoints using conventional convolutional neural networks (CNNs), but rotation-equivariant keypoint detectors have not been studied extensively. Therefore, we propose a rotation-invariant keypoint detection method using rotation-equivariant CNNs. Our rotation-equivariant representation enables us to estimate local orientations to image keypoints accurately. We propose a dense histogram alignment loss to assign an orientation to keypoints more consistently. We validate the effectiveness compared to existing keypoint detection methods. Furthermore, we check the transferability of our method on public image matching benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
Keypoint DetectionTranslationSimilar Papers 제목 키워드 기반
Self-Supervised Equivariant Learning for Oriented Keypoint Detection
Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching. Exist…
Camera Pose EstimationKeypoint DetectionPose EstimationSelf-Supervised Learning+1RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy
Unlike in natural images, in endoscopy there is no clear notion of an up-right camera orientation. Endoscopic videos therefore often contain large rotational motions, which require keypoint detection and description algo…
Keypoint DetectionPose EstimationSelf-Supervised LearningS-TREK: Sequential Translation and Rotation Equivariant Keypoints for local feature extraction
In this work we introduce S-TREK, a novel local feature extractor that combines a deep keypoint detector, which is both translation and rotation equivariant by design, with a lightweight deep descriptor extractor. We tra…
On the Role of Rotation Equivariance in Monocular 2D-to-3D Human Pose Lifting
Estimating 3D from 2D is one of the central tasks in computer vision. In this work, we consider the monocular setting, i.e. single-view input, for 3D human pose estimation (HPE), where the goal is to predict a 3D point s…
3D Human Pose EstimationKeypoint DetectionData AugmentationLearning Rotation-Equivariant Features for Visual Correspondence
Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to ext…
Camera Pose EstimationPose EstimationSelf-Supervised Learning