3D AffordanceNet
3D AffordanceNet is a dataset of 23k shapes for visual affordance. It consists of 56,307 well-defined affordance information annotations for 22,949 shapes covering 18 affordance classes and 23 semantic object categories.
Papers with Code 아카이브
Affordance Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation.
Image source: Object-Based Affordances Detection with Convolutional Neural Networks and Dense Conditional Random Fields
Unlike other visual or physical properties that mainly describe the object alone, affordances indicate functional interactions of object parts with humans.
데이터셋
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PADv2
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. It contains 30k diverse images covering 39 affordance categories as well as 103 object categories from different scenes.
Visual Affordance Learning
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.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.