OP-Align: Object-level and Part-level Alignment for Self-supervised Category-level Articulated Object Pose Estimation

• URL: http://arxiv.org/abs/2408.16547v1
• Date: Thu, 29 Aug 2024 14:10:14 GMT
• Title: OP-Align: Object-level and Part-level Alignment for Self-supervised Category-level Articulated Object Pose Estimation
• Authors: Yuchen Che, Ryo Furukawa, Asako Kanezaki,
• Abstract summary: Category-level articulated object pose estimation focuses on the pose estimation of unknown articulated objects within known categories. We propose a novel self-supervised approach that leverages a single-frame point cloud to solve this task. Our model consistently generates reconstruction with a canonical pose and joint state for the entire input object.
• Score: 7.022004731560844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Category-level articulated object pose estimation focuses on the pose estimation of unknown articulated objects within known categories. Despite its significance, this task remains challenging due to the varying shapes and poses of objects, expensive dataset annotation costs, and complex real-world environments. In this paper, we propose a novel self-supervised approach that leverages a single-frame point cloud to solve this task. Our model consistently generates reconstruction with a canonical pose and joint state for the entire input object, and it estimates object-level poses that reduce overall pose variance and part-level poses that align each part of the input with its corresponding part of the reconstruction. Experimental results demonstrate that our approach significantly outperforms previous self-supervised methods and is comparable to the state-of-the-art supervised methods. To assess the performance of our model in real-world scenarios, we also introduce a new real-world articulated object benchmark dataset.

Related papers

• Generalizable Single-view Object Pose Estimation by Two-side Generating and Matching [19.730504197461144]
  We present a novel generalizable object pose estimation method to determine the object pose using only one RGB image. Our method offers generalization to unseen objects without extensive training, operates with a single reference image of the object, and eliminates the need for 3D object models or multiple views of the object.
  arXiv  Detail & Related papers  (2024-11-24T14:31:50Z)
• Zero-Shot Object-Centric Representation Learning [72.43369950684057]
  We study current object-centric methods through the lens of zero-shot generalization. We introduce a benchmark comprising eight different synthetic and real-world datasets. We find that training on diverse real-world images improves transferability to unseen scenarios.
  arXiv  Detail & Related papers  (2024-08-17T10:37:07Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• GenPose: Generative Category-level Object Pose Estimation via Diffusion Models [5.1998359768382905]
  We propose a novel solution by reframing categorylevel object pose estimation as conditional generative modeling. Our approach achieves state-of-the-art performance on the REAL275 dataset, surpassing 50% and 60% on strict 5d2cm and 5d5cm metrics.
  arXiv  Detail & Related papers  (2023-06-18T11:45:42Z)
• Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement [75.9289887536165]
  We present a hierarchical abstraction approach to uncover underlying entities. We show how to learn a correspondence between intervening on states of entities in the agent's model and acting on objects in the environment. We use this correspondence to develop a method for control that generalizes to different numbers and configurations of objects.
  arXiv  Detail & Related papers  (2023-03-20T18:19:36Z)
• Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance [33.10167928198986]
  Category-level articulated object pose estimation aims to estimate a hierarchy of articulation-aware object poses of an unseen articulated object from a known category. We present a novel self-supervised strategy that solves this problem without any human labels.
  arXiv  Detail & Related papers  (2023-02-28T03:02:11Z)
• Discovering Objects that Can Move [55.743225595012966]
  We study the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. We choose to focus on dynamic objects -- entities that can move independently in the world.
  arXiv  Detail & Related papers  (2022-03-18T21:13:56Z)
• Towards Self-Supervised Category-Level Object Pose and Size Estimation [121.28537953301951]
  This work presents a self-supervised framework for category-level object pose and size estimation from a single depth image. We leverage the geometric consistency residing in point clouds of the same shape for self-supervision.
  arXiv  Detail & Related papers  (2022-03-06T06:02:30Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)

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.