68 citations · 89 across the 10 of their papers we have counts for
11 papers
MV-RoMa: From Pairwise Matching into Multi-View Track Reconstruction
Jongmin Lee, Seungyeop Kang, Sungjoo Yoo
Establishing consistent correspondences across images is essential for 3D vision tasks such as structure-from-motion (SfM), yet most existing matchers operate in a pairwise manner,…
3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction
Jongmin Lee, Minsu Cho
Determining the 3D orientations of an object in an image, known as single-image pose estimation, is a crucial task in 3D vision applications. Existing methods typically learn 3D ro…
SACReg: Scene-Agnostic Coordinate Regression for Visual Localization
Jerome Revaud, Yohann Cabon, Romain Brégier +2
Scene coordinates regression (SCR), i.e., predicting 3D coordinates for every pixel of a given image, has recently shown promising potential. However, existing methods remain limit…
Learning Rotation-Equivariant Features for Visual Correspondence
Jongmin Lee, Byungjin Kim, Seungwook Kim +1
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 s…
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
Dongwoo Park, Jong-Min Lee
Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. Existing meta-learning methods insufficiently exploit features between support and quer…
Self-Supervised Learning of Image Scale and Orientation
Jongmin Lee, Yoonwoo Jeong, Minsu Cho
We study the problem of learning to assign a characteristic pose, i.e., scale and orientation, for an image region of interest. Despite its apparent simplicity, the problem is non-…