Occ$^2$Net: Robust Image Matching Based on 3D Occupancy Estimation for
  Occluded Regions
Miao Fan Mingrui Chen Chen Hu Shuchang Zhou
Image matching is a fundamental and critical task in various visual
applications, such as Simultaneous Localization and Mapping (SLAM) and image
retrieval, which require accurate pose estimation. However, most existing
methods ignore the occlusion relations between objects caused by camera motion
and scene structure. In this paper, we propose Occ$^2$Net, a novel image
matching method that models occlusion relations using 3D occupancy and infers
matching points in occluded regions. Thanks to the inductive bias encoded in
the Occupancy Estimation (OE) module, it greatly simplifies bootstrapping of a
multi-view consistent 3D representation that can then integrate information
from multiple views. Together with an Occlusion-Aware (OA) module, it
incorporates attention layers and rotation alignment to enable matching between
occluded and visible points. We evaluate our method on both real-world and
simulated datasets and demonstrate its superior performance over
state-of-the-art methods on several metrics, especially in occlusion scenarios.