Object Affordance Recognition and Grounding via Multi-scale Cross-modal Representation Learning

• URL: http://arxiv.org/abs/2508.01184v1
• Date: Sat, 02 Aug 2025 04:14:18 GMT
• Title: Object Affordance Recognition and Grounding via Multi-scale Cross-modal Representation Learning
• Authors: Xinhang Wan, Dongqiang Gou, Xinwang Liu, En Zhu, Xuming He,
• Abstract summary: A core problem of Embodied AI is to learn object manipulation from observation, as humans do.<n>We propose a novel approach that learns an affordance-aware 3D representation and employs a stage-wise inference strategy.<n> Experiments demonstrate the effectiveness of our method, showing improved performance in both affordance grounding and classification.
• Score: 64.32618490065117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A core problem of Embodied AI is to learn object manipulation from observation, as humans do. To achieve this, it is important to localize 3D object affordance areas through observation such as images (3D affordance grounding) and understand their functionalities (affordance classification). Previous attempts usually tackle these two tasks separately, leading to inconsistent predictions due to lacking proper modeling of their dependency. In addition, these methods typically only ground the incomplete affordance areas depicted in images, failing to predict the full potential affordance areas, and operate at a fixed scale, resulting in difficulty in coping with affordances significantly varying in scale with respect to the whole object. To address these issues, we propose a novel approach that learns an affordance-aware 3D representation and employs a stage-wise inference strategy leveraging the dependency between grounding and classification tasks. Specifically, we first develop a cross-modal 3D representation through efficient fusion and multi-scale geometric feature propagation, enabling inference of full potential affordance areas at a suitable regional scale. Moreover, we adopt a simple two-stage prediction mechanism, effectively coupling grounding and classification for better affordance understanding. Experiments demonstrate the effectiveness of our method, showing improved performance in both affordance grounding and classification.

Related papers

• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models [8.933560282929726]
  We introduce a novel affordance representation, named Comprehensive Affordance (ComA) Given a 3D object mesh, ComA models the distribution of relative orientation and proximity of vertices in interacting human meshes. We demonstrate that ComA outperforms competitors that rely on human annotations in modeling contact-based affordance.
  arXiv  Detail & Related papers  (2024-01-23T18:59:59Z)
• 2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation [92.17700318483745]
  We propose an image-guidance network (IGNet) which builds upon the idea of distilling high level feature information from a domain adapted synthetically trained 2D semantic segmentation network. IGNet achieves state-of-the-art results for weakly-supervised LiDAR semantic segmentation on ScribbleKITTI, boasting up to 98% relative performance to fully supervised training with only 8% labeled points.
  arXiv  Detail & Related papers  (2023-11-27T07:57:29Z)
• GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence [5.500735640045456]
  Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics. We propose to utilize both geometric and semantic features obtained from a pre-trained foundation model. This requires significantly less data to train than prior methods since the semantic features are robust to object texture and appearance.
  arXiv  Detail & Related papers  (2023-11-23T02:35:38Z)
• Grounding 3D Object Affordance from 2D Interactions in Images [128.6316708679246]
  Grounding 3D object affordance seeks to locate objects' ''action possibilities'' regions in the 3D space. Humans possess the ability to perceive object affordances in the physical world through demonstration images or videos. We devise an Interaction-driven 3D Affordance Grounding Network (IAG), which aligns the region feature of objects from different sources.
  arXiv  Detail & Related papers  (2023-03-18T15:37:35Z)
• On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation [57.766049538913926]
  Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. Much of the recent attention has shifted towards semi and (or) weakly supervised learning. We propose to impose multi-view geometrical constraints by means of a differentiable triangulation and to use it as form of self-supervision during training when no labels are available.
  arXiv  Detail & Related papers  (2022-03-29T19:11:54Z)
• Sim2Real Object-Centric Keypoint Detection and Description [40.58367357980036]
  Keypoint detection and description play a central role in computer vision. We propose the object-centric formulation, which requires further identifying which object each interest point belongs to. We develop a sim2real contrastive learning mechanism that can generalize the model trained in simulation to real-world applications.
  arXiv  Detail & Related papers  (2022-02-01T15:00:20Z)
• Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings [22.889059874754242]
  Generation of stroke-based non-photorealistic imagery is an important problem in the computer vision community. Previous methods have been limited to datasets with little variation in position, scale and saliency of the foreground object. We propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time.
  arXiv  Detail & Related papers  (2020-11-25T09:00:04Z)
• Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation [52.94078950641959]
  We present a deployment friendly, fast bottom-up framework for multi-person 3D human pose estimation. We adopt a novel neural representation of multi-person 3D pose which unifies the position of person instances with their corresponding 3D pose representation. We propose a practical deployment paradigm where paired 2D or 3D pose annotations are unavailable.
  arXiv  Detail & Related papers  (2020-08-04T07:54:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.