Survey on Modeling of Human-made Articulated Objects

• URL: http://arxiv.org/abs/2403.14937v2
• Date: Fri, 18 Oct 2024 23:59:09 GMT
• Title: Survey on Modeling of Human-made Articulated Objects
• Authors: Jiayi Liu, Manolis Savva, Ali Mahdavi-Amiri,
• Abstract summary: 3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics. This survey provides a comprehensive overview of the current state-of-the-art in 3D modeling of articulated objects.
• Score: 15.296495542958496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics. Its objective is to understand the shape and motion of the articulated components, represent the geometry and mobility of object parts, and create realistic models that reflect articulated objects in the real world. This survey provides a comprehensive overview of the current state-of-the-art in 3D modeling of articulated objects, with a specific focus on the task of articulated part perception and articulated object creation (reconstruction and generation). We systematically review and discuss the relevant literature from two perspectives: geometry modeling (i.e., structure and shape of articulated parts) and articulation modeling (i.e., dynamics and motion of parts). Through this survey, we highlight the substantial progress made in these areas, outline the ongoing challenges, and identify gaps for future research. Our survey aims to serve as a foundational reference for researchers and practitioners in computer vision and graphics, offering insights into the complexities of articulated object modeling.

Related papers

• 3D Shape Generation: A Survey [0.6445605125467574]
  Recent advances in deep learning have transformed the field of 3D shape generation.<n>This survey organizes the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols.<n>We identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation.
  arXiv  Detail & Related papers  (2025-06-27T23:06:06Z)
• SIGHT: Synthesizing Image-Text Conditioned and Geometry-Guided 3D Hand-Object Trajectories [124.24041272390954]
  Modeling hand-object interaction priors holds significant potential to advance robotic and embodied AI systems.<n>We introduce SIGHT, a novel task focused on generating realistic and physically plausible 3D hand-object interaction trajectories from a single image.<n>We propose SIGHT-Fusion, a novel diffusion-based image-text conditioned generative model that tackles this task by retrieving the most similar 3D object mesh from a database.
  arXiv  Detail & Related papers  (2025-03-28T20:53:20Z)
• Aether: Geometric-Aware Unified World Modeling [49.33579903601599]
  Aether is a unified framework that enables geometry-aware reasoning in world models.<n>Our framework achieves zero-shot generalization in both action following and reconstruction tasks.<n>We hope our work inspires the community to explore new frontiers in physically-reasonable world modeling.
  arXiv  Detail & Related papers  (2025-03-24T17:59:51Z)
• Generative Physical AI in Vision: A Survey [78.07014292304373]
  Gene Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content. As generative models evolve to increasingly integrate physical realism and dynamic simulation, their potential to function as "world simulators" expands.
  arXiv  Detail & Related papers  (2025-01-19T03:19:47Z)
• Collaborative Learning for 3D Hand-Object Reconstruction and Compositional Action Recognition from Egocentric RGB Videos Using Superquadrics [31.819336585007104]
  We propose to leverage superquadrics as an alternative 3D object representation to bounding boxes. We demonstrate their effectiveness on both template-free object reconstruction and action recognition tasks. We also study the compositionality of actions by considering a more challenging task where the training combinations of verbs and nouns do not overlap with the testing split.
  arXiv  Detail & Related papers  (2025-01-13T07:26:05Z)
• PhysPart: Physically Plausible Part Completion for Interactable Objects [28.91080122885566]
  We tackle the problem of physically plausible part completion for interactable objects. We propose a diffusion-based part generation model that utilizes geometric conditioning. We also demonstrate our applications in 3D printing, robot manipulation, and sequential part generation.
  arXiv  Detail & Related papers  (2024-08-25T04:56:09Z)
• Unsupervised Dynamics Prediction with Object-Centric Kinematics [22.119612406160073]
  We propose Object-Centric Kinematics (OCK), a framework for dynamics prediction leveraging object-centric representations. OCK consists of low-level structured states of objects' position, velocity, and acceleration. Our model demonstrates superior performance when handling objects and backgrounds in complex scenes characterized by a wide range of object attributes and dynamic movements.
  arXiv  Detail & Related papers  (2024-04-29T04:47:23Z)
• REACTO: Reconstructing Articulated Objects from a Single Video [64.89760223391573]
  We propose a novel deformation model that enhances the rigidity of each part while maintaining flexible deformation of the joints. Our method outperforms previous works in producing higher-fidelity 3D reconstructions of general articulated objects.
  arXiv  Detail & Related papers  (2024-04-17T08:01:55Z)
• Scalable Scene Modeling from Perspective Imaging: Physics-based Appearance and Geometry Inference [3.2229099973277076]
  dissertation presents a fraction of contributions that advances 3D scene modeling to its state of the art. In contrast to the prevailing deep learning methods, as a core contribution, this thesis aims to develop algorithms that follow first principles.
  arXiv  Detail & Related papers  (2024-04-01T17:09:40Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• 3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review [5.85206759397617]
  Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions. A significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation and reconstruction.
  arXiv  Detail & Related papers  (2023-06-09T15:45:23Z)
• Full-Body Articulated Human-Object Interaction [61.01135739641217]
  CHAIRS is a large-scale motion-captured f-AHOI dataset consisting of 16.2 hours of versatile interactions. CHAIRS provides 3D meshes of both humans and articulated objects during the entire interactive process. By learning the geometrical relationships in HOI, we devise the very first model that leverage human pose estimation.
  arXiv  Detail & Related papers  (2022-12-20T19:50:54Z)
• MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare [84.80956484848505]
  MegaPose is a method to estimate the 6D pose of novel objects, that is, objects unseen during training. We present a 6D pose refiner based on a render&compare strategy which can be applied to novel objects. Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner.
  arXiv  Detail & Related papers  (2022-12-13T19:30:03Z)
• State of the Art in Dense Monocular Non-Rigid 3D Reconstruction [100.9586977875698]
  3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics. This survey focuses on state-of-the-art methods for dense non-rigid 3D reconstruction of various deformable objects and composite scenes from monocular videos or sets of monocular views.
  arXiv  Detail & Related papers  (2022-10-27T17:59:53Z)
• Finding Fallen Objects Via Asynchronous Audio-Visual Integration [89.75296559813437]
  This paper introduces a setting in which to study multi-modal object localization in 3D virtual environments. An embodied robot agent, equipped with a camera and microphone, must determine what object has been dropped -- and where -- by combining audio and visual signals with knowledge of the underlying physics. The dataset uses the ThreeDWorld platform which can simulate physics-based impact sounds and complex physical interactions between objects in a photorealistic setting.
  arXiv  Detail & Related papers  (2022-07-07T17:59:59Z)

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.