SOTAVerified

Affordance Detection

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.

Papers

Showing 1120 of 23 papers

TitleStatusHype
Egocentric affordance detection with the one-shot geometry-driven Interaction Tensor0
Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes0
Multi-Modal Trip Hazard Affordance Detection On Construction Sites0
Scene Understanding for Autonomous Manipulation with Deep Learning0
Visual Affordance and Function Understanding: A Survey0
What can you do with a rock? Affordance extraction via word embeddings0
3D-AffordanceLLM: Harnessing Large Language Models for Open-Vocabulary Affordance Detection in 3D Worlds0
Weakly Supervised Affordance DetectionCode0
What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detectionCode0
Recognizing Object Affordances to Support Scene Reasoning for Manipulation TasksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.18Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.14Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.13Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.16Unverified