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Image matching is a fundamental and critical task in various visualapplications, such as Simultaneous Localization and Mapping (SLAM) and imageretrieval, which require accurate pose estimation. However, most existingmethods ignore the occlusion relations between objects caused by camera motionand scene structure. In this paper, we propose Occ$^2$Net, a novel imagematching method that models occlusion relations using 3D occupancy and infersmatching points in occluded regions. Thanks to the inductive bias encoded inthe Occupancy Estimation (OE) module, it greatly simplifies bootstrapping of amulti-view consistent 3D representation that can then integrate informationfrom multiple views. Together with an Occlusion-Aware (OA) module, itincorporates attention layers and rotation alignment to enable matching betweenoccluded and visible points. We evaluate our method on both real-world andsimulated datasets and demonstrate its superior performance overstate-of-the-art methods on several metrics, especially in occlusion scenarios.

Occ$^2$Net: Robust Image Matching Based on 3D Occupancy Estimation for Occluded Regions