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arXiv 2603.27969cs.CV
Hg-I2P：通过异构图实现通用的图像到点云配准
Hg-I2P: Bridging Modalities for Generalizable Image-to-Point-Cloud Registration via Heterogeneous Graphs
Huazhong University of Science and Technology（华中科技大学）；Northwestern Polytechnical University（西北工业大学）；Huazhong Agricultural University（华中农业大学）；Delft University of Technology（代尔夫特理工大学）
Pei An, Junfeng Ding, Jiaqi Yang, Yulong Wang, Jie Ma, Liangliang Nan
Image-to-point-cloud (I2P) registration aims to align 2D images with 3D point clouds by establishing reliable 2D-3D correspondences. The drastic modality gap between images and point clouds makes it challenging to learn features that are both discriminative and generalizable, leading to severe performance drops in unseen scenarios.
We address this challenge by introducing a heterogeneous graph that enables refining both cross-modal features and correspondences within a unified architecture. The proposed graph represents a mapping between segmented 2D and 3D regions, which enhances cross-modal feature interaction and thus improves feature discriminability. In addition, modeling the consistency among vertices and edges within the graph enables pruning of unreliable correspondences. Building on these insights, we propose a heterogeneous graph embedded I2P registration method, termed Hg-I2P. It learns a heterogeneous graph by mining multi-path feature relationships, adapts features under the guidance of heterogeneous edges, and prunes correspondences using graph-based projection consistency. Experiments on six indoor and outdoor benchmarks under cross-domain setups demonstrate that Hg-I2P significantly outperforms existing methods in both generalization and accuracy. Code is released on https://github.com/anpei96/hg-i2p-demo.
Surrogate Supervision for Robust and Generalizable Deformable Image Registration
Department of Electrical and Computer Engineering, Vanderbilt University（维斯尼尔大学电气与计算机工程系）；Department of Radiology and Radiological Science, Johns Hopkins Medical School（约翰霍普金斯医学学校放射学与放射科学系）；Image Analysis and Communications Laboratory in the Department of Electrical and Computer Engineering, Johns Hopkins University（约翰霍普金斯大学电气与计算机工程系图像分析与通信实验室）；Center for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center（维斯尼尔大学医学中心认知医学中心）；University of Pittsburgh, School of Medicine（匹兹堡大学医学院）
Bridging Severe Cross-Modal Misalignment: End-to-End Visible-Infrared Object Detection via Explicit Feature-Domain Affine Registration
Beijing University of Technology（北京工业大学）；Central South University（中南大学）；Beijing Electronics Science & Technology Institute（北京电子科学技术研究所）；China Aerospace Science & Industry Corporation（中国航天科技集团）；Beijing Institute of Technology（北京理工大学）；Information Support Force Engineering University（信息支援部队工程大学）；Trunk Technology (Beijing) Co., Ltd.（trunk技术（北京）有限公司）
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；King’s College London（伦敦国王学院）
FMIR, a foundation model-based Image Registration Framework for Robust Image Registration
School of Artificial Intelligence and Robotics（人工智能与机器人学院）；Hunan University（湖南大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Cornell University（康奈尔大学）
SG-Reg: Generalizable and Efficient Scene Graph Registration
Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；School of Information Engineering, Chang’an University（信息工程学院，长安大学）
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, United States Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Germany Department of Imaging Physics, Delft University of Technology, Netherlands TUM School of Computation, Information；Technology, Technische Universität München, Germany Department of Surgery, Taipei Veterans General Hospital, Taiwan
