With complex scenes and rich annotations, the PADv2 dataset can be used as a test bed to benchmark affordance detection methods and may also facilitate downstream vision tasks, such as scene understanding, action recognition, and robot manipulation.
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A large-scale multi-view RGBD visual affordance learning dataset, a benchmark of 47210 RGBD images from 37 object categories, annotated with 15 visual affordance categories and 35 cluttered/complex scenes with different objects and multiple affordances. To the best of our knowledge, this is the first ever and the largest multi-view RGBD visual affordance learning dataset.