GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence
This addresses the problem of data scarcity in pose estimation for computer vision and robotics applications, offering an incremental improvement over prior methods.
The paper tackles category-level object pose estimation by using both geometric and semantic features from a pre-trained foundation model, reducing the need for large datasets and achieving improved performance with less data.
Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics. Recently, deep-learning-based approaches have made great progress, but are typically hindered by the need for large datasets of either pose-labelled real images or carefully tuned photorealistic simulators. This can be avoided by using only geometry inputs such as depth images to reduce the domain-gap but these approaches suffer from a lack of semantic information, which can be vital in the pose estimation problem. To resolve this conflict, we propose to utilize both geometric and semantic features obtained from a pre-trained foundation model.Our approach projects 2D features from this foundation model into 3D for a single object model per category, and then performs matching against this for new single view observations of unseen object instances with a trained matching network. This requires significantly less data to train than prior methods since the semantic features are robust to object texture and appearance. We demonstrate this with a rich evaluation, showing improved performance over prior methods with a fraction of the data required.