Affordance Detection
13 papers with code • 4 benchmarks • 3 datasets
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.
Benchmarks
These leaderboards are used to track progress in Affordance Detection
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Libraries
Use these libraries to find Affordance Detection models and implementations
3D AffordanceNet
PADv2
Visual Affordance Learning
Most implemented papers
Affordance detection with Dynamic-Tree Capsule Networks
In the experimental evaluation we will show that our algorithm is superior to current affordance detection methods when faced with grasping previously unseen objects thanks to our Capsule Network enforcing a parts-to-whole representation.
Open-Vocabulary Affordance Detection in 3D Point Clouds
Fsoft-AIC/Open-Vocabulary-Affordance-Detection-in-3D-Point-Clouds
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In this paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method, which is capable of detecting an unbounded number of affordances in 3D point clouds.
Multi-label affordance mapping from egocentric vision
lmur98/epic_kitchens_affordances
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ICCV 2023
We use this method to build the largest and most complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff, which provides interaction-grounded, multi-label, metric and spatial affordance annotations.
