The current approach may face limitations when handling images with significant perspective distortion, as it relies on matching the template feature field with the real image feature map using mean square error (MSE). To improve the method's performance in such challenging scenarios, several strategies can be implemented:
Geometry-aware Matching: Introduce a geometry-aware matching mechanism that considers the geometric transformations between the template and real images. This can involve incorporating geometric constraints or transformations to align the features more accurately, accounting for perspective distortions.
Feature Fusion: Explore techniques for feature fusion that combine semantic features with geometric features to enhance the matching process. By integrating both types of features, the method can better capture the underlying 3D structure of objects and improve pose estimation accuracy in the presence of perspective distortion.
Adaptive Matching: Implement adaptive matching algorithms that dynamically adjust the matching criteria based on the level of perspective distortion in the images. This can involve incorporating adaptive weighting schemes or loss functions to prioritize certain features or regions in the matching process.