Multi-view instance matching with learned geometric soft-constraints

HIGHLIGHTS

  • who: Ahmed Samy Nassar and collaborators from the (UNIVERSITY) have published the research work: Multi-View Instance Matching with Learned Geometric Soft-Constraints, in the Journal: (JOURNAL)
  • what: The authors propose to turn object instance matching into a learning task where image-appearance and geometric relationships between views fruitfully interact. To image features the authors propose utilizing location information about the camera and the object to support image evidence via soft geometric constraints. The authors explore Siamese networks to jointly learn robust appearance-based and geometric features and improve instance matching across multiple views. After . . .

     

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