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 11–20 of 23 papers

TitleStatusHype
Egocentric affordance detection with the one-shot geometry-driven Interaction Tensor—0
Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes—0
Multi-Modal Trip Hazard Affordance Detection On Construction Sites—0
Scene Understanding for Autonomous Manipulation with Deep Learning—0
Visual Affordance and Function Understanding: A Survey—0
What can you do with a rock? Affordance extraction via word embeddings—0
3D-AffordanceLLM: Harnessing Large Language Models for Open-Vocabulary Affordance Detection in 3D Worlds—0
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
1DGCNNAIOU0.18—Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.14—Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.13—Unverified
#ModelMetricClaimedVerifiedStatus
1DGCNNAIOU0.16—Unverified