Understanding and Exploiting Object Interaction Landscapes
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Abstract (arXiv)
Interactions play a key role in understanding objects and scenes, for both virtual and real world agents. We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved. The representation is based on tracking particles on one of the participating objects and then observing them with sensors appropriately placed in the interaction volume or on the interaction surfaces. We show how to factorize these interaction descriptors and project them into a particular participating object so as to obtain a new functional descriptor for that object, its interaction landscape, capturing its observed use in a spatio-temporal framework. Interaction landscapes are independent of the particular interaction and capture subtle dynamic effects in how objects move and behave when in functional use. Our method relates objects based on their function, establishes correspondences between shapes based on functional key points and regions, and retrieves peer and partner objects with respect to an interaction.
Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). First posted on arXiv 27 Sep 2016.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 2.
8 papers.
- 2021GOAL: Generating 4D Whole-Body Motion for Hand-Object Graspingcited by 143
- 2024ShapeLLM: Universal 3D Object Understanding for Embodied Interactioncited by 135
- 2023GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistencycited by 26
- 2022Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interactioncited by 23
- 2021BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Imagecited by 20
- 2022NIFT: Neural Interaction Field and Template for Object Manipulationcited by 4
- 2024Spatial and Surface Correspondence Field for Interaction Transfercited by 3
- 2022Fixing Malfunctional Objects With Learned Physical Simulation and Functional Predictioncited by 2
- 2024Spatial and Surface Correspondence Field for Interaction Transfer6 May 2024
- 2024ShapeLLM: Universal 3D Object Understanding for Embodied Interaction27 Feb 2024
- 2023GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency22 Aug 2023
- 2022NIFT: Neural Interaction Field and Template for Object Manipulation20 Oct 2022
- 2022Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction5 May 2022
- 2022Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction3 Apr 2022
- 2021GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping21 Dec 2021
- 2021BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image20 May 2021