PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning
This work addresses the challenge of robust pose estimation for unseen objects in robotics and AR/VR applications, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the problem of 6D pose estimation for unseen objects by proposing PoseGAM, a geometry-aware multi-view framework that directly predicts object pose without explicit matching, achieving an average AR improvement of 5.1% over prior methods and up to 17.6% gains on individual datasets.
6D object pose estimation, which predicts the transformation of an object relative to the camera, remains challenging for unseen objects. Existing approaches typically rely on explicitly constructing feature correspondences between the query image and either the object model or template images. In this work, we propose PoseGAM, a geometry-aware multi-view framework that directly predicts object pose from a query image and multiple template images, eliminating the need for explicit matching. Built upon recent multi-view-based foundation model architectures, the method integrates object geometry information through two complementary mechanisms: explicit point-based geometry and learned features from geometry representation networks. In addition, we construct a large-scale synthetic dataset containing more than 190k objects under diverse environmental conditions to enhance robustness and generalization. Extensive evaluations across multiple benchmarks demonstrate our state-of-the-art performance, yielding an average AR improvement of 5.1% over prior methods and achieving up to 17.6% gains on individual datasets, indicating strong generalization to unseen objects. Project page: https://windvchen.github.io/PoseGAM/ .