Parts-Based Articulated Object Localization in Clutter Using Belief Propagation

• URL: http://arxiv.org/abs/2008.02881v1
• Date: Thu, 6 Aug 2020 21:34:52 GMT
• Title: Parts-Based Articulated Object Localization in Clutter Using Belief Propagation
• Authors: Jana Pavlasek, Stanley Lewis, Karthik Desingh, Odest Chadwicke Jenkins
• Abstract summary: We present a generative-discriminative parts-based recognition and localization method for articulated objects in clutter. We demonstrate the efficacy of our methods in a tabletop environment for recognizing and localizing hand tools in uncluttered and cluttered configurations.
• Score: 6.813222130986094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robots working in human environments must be able to perceive and act on challenging objects with articulations, such as a pile of tools. Articulated objects increase the dimensionality of the pose estimation problem, and partial observations under clutter create additional challenges. To address this problem, we present a generative-discriminative parts-based recognition and localization method for articulated objects in clutter. We formulate the problem of articulated object pose estimation as a Markov Random Field (MRF). Hidden nodes in this MRF express the pose of the object parts, and edges express the articulation constraints between parts. Localization is performed within the MRF using an efficient belief propagation method. The method is informed by both part segmentation heatmaps over the observation, generated by a neural network, and the articulation constraints between object parts. Our generative-discriminative approach allows the proposed method to function in cluttered environments by inferring the pose of occluded parts using hypotheses from the visible parts. We demonstrate the efficacy of our methods in a tabletop environment for recognizing and localizing hand tools in uncluttered and cluttered configurations.

Related papers

• Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification [32.95715593278961]
  Unsupervised person re-identification (re-ID) methods achieve high performance by leveraging fine-grained local context. Part-based methods obtain local contexts through horizontal division, which suffer from misalignment due to various human poses. We introduce the Spatial Cascaded Clustering and Weighted Memory (SCWM) method to address these challenges. SCWM aims to parse and align more accurate local contexts for different human body parts while allowing the memory module to balance hard example mining and noise suppression.
  arXiv  Detail & Related papers  (2024-03-01T03:52:29Z)
• Local Region-to-Region Mapping-based Approach to Classify Articulated Objects [2.3848738964230023]
  This paper presents a registration-based local region-to-region mapping approach to classify an object as either articulated or rigid. It is observed that the proposed method can classify articulated and rigid objects with good accuracy.
  arXiv  Detail & Related papers  (2023-05-10T18:08:04Z)
• Spatial Reasoning for Few-Shot Object Detection [21.3564383157159]
  We propose a spatial reasoning framework that detects novel objects with only a few training examples in a context. We employ a graph convolutional network as the RoIs and their relatedness are defined as nodes and edges, respectively. We demonstrate that the proposed method significantly outperforms the state-of-the-art methods and verify its efficacy through extensive ablation studies.
  arXiv  Detail & Related papers  (2022-11-02T12:38:08Z)
• Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging [117.73967303377381]
  We propose a novel self-supervised Video Object (VOS) approach that strives to achieve better object-background discriminability. Our approach is based on a discriminative learning loss formulation that takes into account both object and background information. Our proposed approach, CT-VOS, achieves state-of-the-art results on two challenging benchmarks: DAVIS-2017 and Youtube-VOS.
  arXiv  Detail & Related papers  (2022-04-22T17:53:27Z)
• Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition [111.87412719773889]
  We propose a joint learning framework for "interacted object localization" and "human action recognition" based on skeleton data. Our method achieves the best or competitive performance with the state-of-the-art methods for human action recognition.
  arXiv  Detail & Related papers  (2021-10-28T10:09:34Z)
• Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects [68.73163598790255]
  We propose PPD (unsupervised Pose-aware Part Decomposition) to address a novel setting that explicitly targets man-made articulated objects with mechanical joints. We show that category-common prior learning for both part shapes and poses facilitates the unsupervised learning of (1) part decomposition with non-primitive-based implicit representation, and (2) part pose as joint parameters under single-frame shape supervision.
  arXiv  Detail & Related papers  (2021-10-08T23:53:56Z)
• RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks [53.15260967235835]
  We propose a novel framework that refines the output of such methods by utilizing a graph-based representation of instance masks. We train deep networks capable of sampling smart perturbations to the segmentations, and a graph neural network, which can encode relations between objects, to evaluate the segmentations. We demonstrate an application that uses uncertainty estimates generated by our method to guide a manipulator, leading to efficient understanding of cluttered scenes.
  arXiv  Detail & Related papers  (2021-06-29T20:29:29Z)
• Unsupervised Part Segmentation through Disentangling Appearance and Shape [37.206922180245265]
  We study the problem of unsupervised discovery and segmentation of object parts. Recent unsupervised methods have greatly relaxed the dependency on annotated data. We develop a novel approach by disentangling the appearance and shape representations of object parts.
  arXiv  Detail & Related papers  (2021-05-26T08:59:31Z)
• Self-supervised Segmentation via Background Inpainting [96.10971980098196]
  We introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving camera. We exploit a self-supervised loss function that we exploit to train a proposal-based segmentation network. We apply our method to human detection and segmentation in images that visually depart from those of standard benchmarks and outperform existing self-supervised methods.
  arXiv  Detail & Related papers  (2020-11-11T08:34:40Z)
• Learning to Manipulate Individual Objects in an Image [71.55005356240761]
  We describe a method to train a generative model with latent factors that are independent and localized. This means that perturbing the latent variables affects only local regions of the synthesized image, corresponding to objects. Unlike other unsupervised generative models, ours enables object-centric manipulation, without requiring object-level annotations.
  arXiv  Detail & Related papers  (2020-04-11T21:50:20Z)

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.