Grounding 3D Object Affordance from 2D Interactions in Images (2303.10437v2)

Abstract: Grounding 3D object affordance seeks to locate objects' ''action possibilities'' regions in the 3D space, which serves as a link between perception and operation for embodied agents. Existing studies primarily focus on connecting visual affordances with geometry structures, e.g. relying on annotations to declare interactive regions of interest on the object and establishing a mapping between the regions and affordances. However, the essence of learning object affordance is to understand how to use it, and the manner that detaches interactions is limited in generalization. Normally, humans possess the ability to perceive object affordances in the physical world through demonstration images or videos. Motivated by this, we introduce a novel task setting: grounding 3D object affordance from 2D interactions in images, which faces the challenge of anticipating affordance through interactions of different sources. To address this problem, we devise a novel Interaction-driven 3D Affordance Grounding Network (IAG), which aligns the region feature of objects from different sources and models the interactive contexts for 3D object affordance grounding. Besides, we collect a Point-Image Affordance Dataset (PIAD) to support the proposed task. Comprehensive experiments on PIAD demonstrate the reliability of the proposed task and the superiority of our method. The project is available at https://github.com/yyvhang/IAGNet.

Grounding 3D Object Affordance from 2D Interactions in Images

The paper "Grounding 3D Object Affordance from 2D Interactions in Images" proposes a novel approach in the domain of computer vision and robotics by leveraging the concept of object affordances. Traditionally, affordance learning within 3D spaces has relied on static geometric cues, with systems attempting to map specific structures to predetermined affordances. This approach can suffer from generalization issues when encountering novel or dynamically complex environments. The authors propose an alternative methodology that integrates 2D interaction cues within images to predict affordances in 3D space, advancing the ability of systems to understand and predict interactive potential in more sophisticated and non-static contexts.

Method Overview

The authors introduce the Interaction-driven 3D Affordance Grounding Network (IAG) that grounds affordance by aligning 2D image features that showcase object interactions with 3D geometric features of point clouds. The IAG is constructed with two crucial components:

1. Joint Region Alignment Module (JRA): This module resolves alignment ambiguities between 2D images and 3D point clouds without using spatial priors (like camera parameters or depth information). The cross-similarity calculation across features aids in identifying analogous regions, and a learned feature-space mapping enhances region alignment.
2. Affordance Revealed Module (ARM): ARM models the interaction contexts between the object and affordance-related components (like the subject and scene) using cross-attention mechanisms. This module enhances affordance representation by integrating interactions and contextual cues, allowing the model to deduce potential affordances dynamically.

The model's architecture facilitates a complementary interaction between perception and reasoning. By employing 2D interaction clues effectively, the IAG network improves the robustness of affordance predictions across diverse and dynamic scenarios.

Dataset and Evaluation

To support their methodology, the authors introduce the Point-Image Affordance Dataset (PIAD), a comprehensive dataset containing paired 2D images and 3D point clouds annotated with affordance labels. Various metrics such as AUC, aIOU, SIM, and MAE exhibit the versatility and accuracy of the proposed method compared to baseline models in both seen and unseen environments.

Numerical Results and Claims

The numerical analysis presented in the paper substantiates the IAG's improved performance over existing techniques. The proposed network achieves significant gains in affordance prediction accuracy, outperforming baseline models by margins reflecting improvements in handling complex, real-world interactions. The results indicate that the model can comprehend and generalize even for unseen objects or affordances with minimal degradation in performance, highlighted by the superior metrics in both seen and unseen dataset partitions.

Implications and Future Directions

The proposed methodology sets a precedent for advancing affordance prediction frameworks by illustrating the practicality of integrating 2D interaction data with 3D semantic understanding. This approach bridges the gap between perception and action, yielding potential applications in autonomous systems, robotics, and augmented reality domains.

Moreover, the research outlines possible future directions, including enhancing feature extraction methodologies, increasing datasets for diverse interaction modeling, and applying the model within integrated agent systems to observe real-world operations. Further development could explore the extension to learn affordances in continuous environments or across varying interaction complexities, pushing the boundaries of autonomy in robotic interactions. This work serves as a stepping stone towards more adaptive, interaction-aware systems that mimic human-like understanding of the physical world's affordances.