Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration
Recent detection-free approaches have shown significant efficacy in image-to-point cloud (I2P) registration by employing a coarse-to-fine matching pipeline. In the coarse stage, down-sampled image features and voxelized point cloud features are typically fused to establish initial coarse correspondences for subsequent refinement. However, existing methods largely overlook the critical role of point cloud density, which fundamentally dictates the quality of coarse correspondences and the final registration results. Specifically, excessively sparse point clouds lead to an insufficient number of
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “Cross-Coordinate Correspondence Pruning for Image-to-Point C” ≈ “Tongyi-MAI/Z-Image-Turbo””
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Muxin Liu →
“Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration”
- LinkedLinked via arxiv author · 85%Rong Qin →
“Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration”
- LinkedLinked via arxiv author · 85%Huipeng Lin →
“Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration”
- LinkedLinked via arxiv author · 85%Leizhi Shu →
“Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration”