Two-view correspondence pruning aims to identify reliable correspondences for camera pose estimation, serving as a fundamental step in many 3D vision tasks. Existing methods rely on geometric consistency to seek true correspondences (inliers) from numerous false correspondences (outliers). In this learning paradigm, outliers severely affect the representation learning of inliers, resulting in models that are neither robust nor generalizable. To address this issue, we propose a geometry-consistent pre-training paradigm that sculpts scalable and generalizable representations free from outlier interference. The paradigm features two appealing properties. 1) Implementation of geometry-consistent pre-training. We introduce masked inlier reconstruction as a pretext task and develop a simple yet effective pre-training framework based on a masked autoencoder. Specifically, due to the irregular and unordered nature of correspondences, which lack explicit positional information, we adopt a dual-branch structure that separately reconstructs the keypoints of two images. This enables indirect reconstruction of 4D correspondences, where keypoints from the paired image provide positional prompts. 2) Unified correspondence encoder. We propose a simple dual-stream encoder with built-in consensus interaction, providing a unified, extensible architecture that enhances representation learning. Extensive experiments demonstrate that our method, GeneralPruner, consistently outperforms state-of-the-art approaches in terms of robustness and generalization across various downstream tasks. Specifically, our method achieves 10.76%, 11.84%, and 8.65% performance gains in camera pose estimation, visual localization, and 3D registration, respectively.
H3R: Hybrid Multi-view Correspondence for Generalizable 3D Reconstruction
ReLER Lab, CCAI, Zhejiang University（浙江大学ReLER实验室，中国计算机协会，浙江大学）；The State Key Lab of Brain-Machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）；Singapore University of Technology and Design（新加坡科技设计大学）
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）
EndoMatcher: Generalizable Endoscopic Image Matcher via Multi-Domain Pre-training for Robot-Assisted Surgery
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院呼吸与危重症医学科）；Hong Kong Institute of Science & Innovation（香港创新科技署）；Centre of AI and Robotics, Hong Kong Institute of Science & Innovation（人工智能与机器人中心，香港创新科技署）
MAC-Splat: Multi-Attribute Consistency for High-Fidelity Sparse-View Reconstruction
Xidian University（西安电子科技大学）；Harvard University（哈佛大学）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Xi’an Jiaotong University（西安交通大学）
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Zhejiang University（浙江大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；RPTU Kaiserslautern-Landau（凯撒斯劳滕-兰道工业大学）；Technische Universität München（慕尼黑工业大学）；Google（谷歌公司）
