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• The GLASS network proposes two novel modules: the Local Geometry Enhancement (LGE) module and the Graph Distribution Consistency (GDC) module.method 95%
• The Local Geometry Enhancement (LGE) module injects geometric structure into image features by enhancing both image and point cloud features with normal vectors.method 95%
• The Graph Distribution Consistency (GDC) module constructs a graph from matched points to update features and explicitly constrain similarity distributions.method 95%
• GLASS achieves state-of-the-art performance in image-to-point cloud registration on the RGB-D Scenes v2 and 7-Scenes benchmarks.result 90%
• The proposed method adopts a detector-free registration paradigm.method 90%
• Surface normals are estimated from point clouds using a Point Normal Estimation Unit based on k-nearest neighbors.method 95%
• Image surface normals are predicted using an Image Normal Prediction Head, trained with pseudo normal labels generated by Depth Anything v2.method 95%
• The Graph Distribution Consistency (GDC) module explicitly constrains the similarity distributions of features derived from matched points.method 95%
• The GLASS network incorporates a Local Geometry Enhancement (LGE) module to inject geometric structure into image features by fusing normal vectors, aiming to reduce mismatches.method 95%
• The GLASS network includes a Graph Distribution Consistency (GDC) module that constructs a graph from matched points to update features and explicitly constrain similarity distributions.method 95%
• The proposed GLASS approach achieves state-of-the-art performance in image-to-point cloud registration on the RGB-D Scenes v2 and 7-Scenes benchmarks.result 90%
• The Local Geometry Enhancement (LGE) module fuses surface normal information into image and point cloud features to improve structural awareness and reduce mismatches.method 95%

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