GMatch: Geometry-Constrained Feature Matching for RGB-D Object Pose Estimation

Abstract: We present GMatch, a learning-free feature matcher designed for robust 6DoF object pose estimation, addressing common local ambiguities in sparse feature matching. Unlike traditional methods that rely solely on descriptor similarity, GMatch performs a guided, incremental search, enforcing SE(3)-invariant geometric consistency throughout the matching process. It leverages a provably complete set of geometric features that uniquely determine 3D keypoint configurations, ensuring globally consistent correspondences without the need for training or GPU support. When combined with classical descriptors such as SIFT, GMatch-SIFT forms a general-purpose pose estimation pipeline that offers strong interpretability and generalization across diverse objects and scenes. Experiments on the HOPE dataset show that GMatch outperforms both traditional and learning-based matchers, with GMatch-SIFT achieving or surpassing the performance of instance-level pose networks. On the YCB-Video dataset, GMatch-SIFT demonstrates high accuracy and low variance on texture-rich objects. These results not only validate the effectiveness of GMatch-SIFT for object pose estimation but also highlight the broader applicability of GMatch as a general-purpose feature matcher. Code will be released upon acceptance.