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We address the problem of populating object category detection datasets with dense , per-object 3D reconstructions , bootstrapped from class labels , ground truth figure-ground segmentations and a small set of keypoint annotations .
 used for: object category detection datasets, per-object 3D reconstructions; used for: ground truth figure-ground segmentations, per-object 3D reconstructions; conjunction: ground truth figure-ground segmentations, keypoint annotations; used for: keypoint annotations, per-object 3D reconstructions
Given options, please tell me the relationships of all the listed entity pairs.Output format is "relation1: word1, word2; relation2: word3, word4". 
Option: conjunction, feature of, hyponym of, used for, part of, compare, evaluate for 
Text: {0} 
 Entity Pairs:  object category detection datasets, per-object 3D reconstructions; ground truth figure-ground segmentations, per-object 3D reconstructions; ground truth figure-ground segmentations, keypoint annotations; keypoint annotations, per-object 3D reconstructions 
Answer:
 used for: object category detection datasets, per-object 3D reconstructions; used for: ground truth figure-ground segmentations, per-object 3D reconstructions; conjunction: ground truth figure-ground segmentations, keypoint annotations; used for: keypoint annotations, per-object 3D reconstructions