A learned end-to-end differentiable method for hierarchical scene graph matching outperforms combinatorial baselines in F1 score and speed for BIM-assisted robot localization, with zero-shot generalization from floor-plan training to real LiDAR data.
Concerning nonnegative matrices and doubly stochastic matrices
2 Pith papers cite this work. Polarity classification is still indexing.
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A new listwise learning-to-rank method uses smooth rank approximation and boosting to optimize without depending on a single metric.
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Learning-Based Hierarchical Scene Graph Matching for Robot Localization Leveraging Prior Maps
A learned end-to-end differentiable method for hierarchical scene graph matching outperforms combinatorial baselines in F1 score and speed for BIM-assisted robot localization, with zero-shot generalization from floor-plan training to real LiDAR data.
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Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
A new listwise learning-to-rank method uses smooth rank approximation and boosting to optimize without depending on a single metric.